Mechanisms associated with cognitive impairment and mood ... · supportive and inspiring work environment. I would like to thank my family and friends for their support throughout

Mechanisms associated with cognitive impairment and

mood in elderly heart failure patients.

Doctor of Philosophy

Christina Elizabeth Kure

Centre for Human Psychopharmacology

Faculty of Life and Social Sciences

Swinburne University of Technology

Melbourne, Australia

2013

Abstract

i

ABSTRACT

Cognitive impairment is a prevalent comorbidity seen in elderly heart failure (HF) patients

and is linked to poor quality of life, reduced self-care abilities and increased hospital

readmissions, possibly due to a reduced capacity to understand and follow complex treatment

protocols. In addition to cognitive impairments, HF patients also endure depressed mood and

increased anxiety.

In order to establish the most effective treatment for improving or ameliorating cognitive

impairment and mood in older HF patients, a better understanding of the basic physiological

mechanisms is necessary. Previous research suggests that mechanisms underlying cognitive

impairments in these patients include reduced cerebral blood flow but little is known about

the influence of other processes on their cognitive impairments.

The aim of the present thesis was to examine whether inflammatory, antioxidant, oxidative

stress and arterial stiffness explain cognitive deficits and depressed mood and anxiety in

elderly HF. A further aim was to explore additional cognitive domains that may be impaired

in HF using a well-validated computerised neuropsychological assessment battery.

In this thesis, 36 patients with HF (NYHA class II, III or IV) aged 60 years and above were

compared to 40 age- and sex- matched controls on tests of cognitive function, mood and

several biological mechanisms including cerebral blood flow, arterial stiffness, inflammation,

oxidative stress and antioxidant markers.

The results indicated that Power of Attention is an additional cognitive domain impaired in

elderly HF patients. Determinable reactive oxygen metabolites (DROMs) are significantly

elevated in HF compared to controls. Furthermore, the results indicated that reduced common

carotid arterial blood flow velocity, arterial stiffness and reduced coenzyme Q10 levels were

related to poor attention and psychomotor abilities in elderly HF patients. Common carotid

arterial blood flow velocity and reduced circulating coenzyme Q10 were associated with

reduced executive function in HF. Although inflammation and oxidative stress were

significantly elevated in the HF group, there was no indication these biomarkers were related

to cognitive function. Finally, the results indicated that cerebral blood flow, arterial stiffness,

antioxidants and inflammatory makers did not relate to depression or anxiety levels in HF.

In conclusion, this thesis confirmed the existence of cognitive impairments in HF, and

suggested that Power of Attention may be a further, previously undescribed impairment.

These findings also indicate that DROMs are a useful measure of oxidative stress in older HF

patients, and those interventions that reduce central pulse pressure, increase cerebral blood

Abstract

ii

flow and elevate coenzyme Q10 levels may improve attention and executive function in

elderly HF patients.

Acknowledgements

iii

ACKNOWLEDGEMENTS

I would like to gratefully acknowledge the cardiologists and Heart Failure nurses at the

Alfred Hospital Heart Centre for their assistance and support with patient recruitment for this

investigation. In particular, I would like to acknowledge Professor David Kaye and Dr Peter

Bergin for their enthusiasm, advice and valuable contribution to the project. I would also like

to offer a special thanks to the participants who volunteered their time to take part in the

study.

I would like to thank the researchers at the Centre for Human Psychopharmacology,

Swinburne University who assisted with collecting the control data and who created a fun,

supportive and inspiring work environment. I would like to thank my family and friends for

their support throughout the duration of my candidature. In particular, my mum Julijana Kure

for her love, support and encouragement to help me overcome obstacles and reach my goals.

I also wish to thank Matthew Hughes in particular for his love, support, encouragement,

patience, understanding, assistance with generating appealing scatter plots, proof reading,

making me laugh and for ‘lifting the load’ during trying times.

Most importantly, I would like to thank my supervisors for their continuing support and

invaluable contribution to the thesis:

Professor Con Stough for his support, providing solutions to multiple issues, being a mentor,

for reading drafts and providing guidance in becoming a confident researcher.

Professor Franklin Rosenfeldt who provided invaluable medical and cardiovascular input into

the project, for reading drafts, being a mentor and for teaching me the importance of

perseverance and persistence.

Professor Andrew Scholey for his ongoing support, offering advice on methodology and

statistical analysis, his inspiration and for being a positive mentor.

Dr Andrew Pipingas for his guidance, support, advice on methodology and for reading drafts.

Professor Stephen Myers for his guidance, enthusiasm, passion and advice.

I would also like to acknowledge Professor Denny Myers for her advice on statistical

analyses, Professor Kevin Croft from the University of Western Australia for his contribution

to the project design, Professor Keith Wesnes who provided in-kind contribution for the use

of the Cognitive Drug Research® computerised test battery and Nestec™ for their support with

collecting control data.

Signed declaration

iv

SIGNED DECLARATION

I declare that this thesis does not contain material which has been accepted for the award of

any other degree or diploma; and to the best of my knowledge this thesis contains no material

previously published or written by another person except where due reference is made in the

text. I further declare that where the work in this thesis is based on joint research or

publications, relative contributions of the respective workers or authors are disclosed.

Name: Christina Elizabeth Kure

Signed:

Date:

Table of contents

v

TABLE OF CONTENTS

TITLE PAGE

ABSTRACT i

ACKNOWLEDGMENTS iii

SIGNED DECLARATION iv

TABLE OF CONTENTS v

LIST OF FIGURES xv

LIST OF TABLES xvi

LIST OF ABBREVIATIONS xx

CHAPTER 1 OVERVIEW 1

CHAPTER 2 BACKGROUND AND OVERVIEW OF HEART FAILURE 2

2.1 Chapter overview 2

2.2 Definition of heart failure 3

2.3 Heart failure diagnosis, classifications, signs and symptoms 4

2.4 Heart failure pathophysiology 6

2.5 Heart failure treatments 6

2.6 Economic costs of heart failure 7

2.7 Prevalence and incidence of heart failure 8

2.8 Prevalence and incidence of cognitive impairment in heart failure 9

2.9 Impact of cognitive impairment in heart failure 11

2.9.1 Heart failure prognosis and mortality 11

2.9.2 Hospital readmissions 12

2.9.3 Treatment compliance 13

2.9.4 Quality of life 14

2.9.5 Daily living and self-care abilities 15

2.9.6 Effects of poor sleep on cognitive function 16

2.10 Summary 17

CHAPTER 3 COGNITIVE IMPAIRMENT AND MOOD IN HEART FAILURE 18

3.1 Introduction 18

3.2 Global cognitive function 18

3.3 Neuropsychological function, specific cognitive domains 19

3.3.1 Memory 19

3.3.2 Attention 20

3.3.3 Executive functioning 20

3.4 Studies using comprehensive neuropsychological test batteries 21

3.4.1 Longitudinal studies 21

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3.4.2 Comparison studies: cognitive function in heart failure

compared with healthy controls 23


compared with other diseases 25


compared with normative data 27

3.5 Effects of disease severity on cognitive function in heart failure 28

3.6 Psychological parameters and mood disturbances in heart failure 29

3.6.1 Mood disturbances and cognitive function 30

3.7 Factors that improve or worsen cognitive dysfunction in heart failure 32

3.7.1 Pharmaceuticals 32

3.7.2 Exercise programs 33

3.7.3 Educational programs 34

3.8 Summary 35

CHAPTER 4 MECHANISMS ASSOCIATED WITH COGNITIVE IMPAIRMENT

AND MOOD IN OLDER HEART FAILURE PATIENTS 36

4.1 Introduction 36

4.2 Vascular mechanisms 37

4.3 Cerebral haemodynamic factors 38

4.3.1 Transcranial Doppler 39

4.4 Cerebral circulation and cognitive function 40

4.4.1 Cerebral blood flow and mood 42

4.4.2 Brain imaging studies 43

4.4.3 Summary 44

4.5 Arterial stiffness 44

4.5.1 Arterial stiffness and heart failure 45

4.5.2 Arterial stiffness and cognitive function 47

4.5.3 Arterial stiffness and mood 49

4.5.4 Arterial stiffness and cerebral circulation 50

4.5.5 Summary 51

4.6 Oxidative stress in heart failure and in cognitive impairment 51

4.6.1 The oxidative stress pathway 51

4.6.2 The role of antioxidants in the body 53

4.6.3 Oxidative stress in heart failure 53

4.6.4 Oxidative stress in cognitive impairment and mood 55

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4.7 Inflammation in heart failure and in cognitive impairment 58

4.7.1 The inflammatory pathway 58

4.7.2 Inflammation and heart failure 59

4.7.3 Inflammation and cognitive impairment 60

4.7.4 Inflammation and mood 60

4.8 Summary 62

CHAPTER 5 RATIONALE, RESEARCH QUESTIONS AND HYPOTHESES 63

5.1 Study Aims 63

5.2 Rationale 63

5.3 Methodological issues 64

5.3.1 Patient selection 64

5.3.2 Research environment 65

5.3.3 Neuropsychological test batteries 65

5.3.4 Practice effects 66

5.4 Summary of biological mechanisms 66

5.4.1 Vascular 66

5.4.2 Oxidative stress and antioxidants 67

5.4.3 Inflammation and omega-3 dietary intake 68

5.5 Mechanisms for changes in mood 68

5.6 Hypotheses and research questions 69

CHAPTER 6 METHODS 72

6.1 Introduction 72

6.2 Participants 72

6.2.1 Heart failure patients 72

6.2.2 Healthy control volunteers 73

6.3 Power analysis 73

6.4 Materials 74

6.4.1 Case Report Form (CRF) 74

6.4.2 Cognitive measures 74

6.4.2.1 Cognitive Drug Research 74

6.4.2.2 Stroop word task 79

6.4.2.3 Trail Making Test (TMT) 80

6.4.3 Screening Measures 81

6.4.3.1 Mini Mental State Examination (MMSE) 81

6.4.3.2 Wechsler Abbreviated Scale of Intelligence

Scales (WASI) 82

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6.4.4 Mood and quality of life measures 82

6.4.4.1 Profile of Mood States (POMS) 82

6.4.4.2 Short Form-36 Item (SF-36) 83

6.4.4.3 Chalder fatigue scale 84

6.4.4.4 General Health Questionnaire 84

6.4.4.5 Speilberger's State Trait Anxiety Inventory 84

6.4.5 Oxidative stress, antioxidant, inflammatory and omega-3

samples 85

6.4.5.1 F2-isoprostanes 85

6.4.5.2 Determinable reactive oxygen metabolites

(DROMs) 86

6.4.5.3 Coenzyme Q10 86

6.4.5.4 Glutathione peroxidase 86

6.4.5.5 High-sensitive C-reactive protein (hs-CRP) 87

6.4.5.6 Polyunsaturated fatty acid questionnaire 88

6.4.6 Cardiovascular Measures 88

6.4.6.1 Endothelin-1 analysis 88

6.4.6.2 Transcranial Doppler (TCD)

Ultrasconography 89

6.4.6.3 SphygmoCor® Px: pulse pressure and

augmentation index 90

6.4.7 Study design 93

6.4.8 Experimental Design 96

6.4.8.1 Testing environment 96

6.4.8.2 Data safety and monitoring 96

6.4.8.3 Equipment 96

CHAPTER 7 RESULTS: DEMOGRAPHIC CHARACTERISTICS 98

7.1 Introduction 98

7.2 Data screening 98

7.3 Demographic variables 99

7.4 Quality of Life 102

7.5 Summary 103

CHAPTER 8 RESULTS: GROUP DIFFERENCES BETWEEN COGNITIVE

MEASURES, MOOD AND BIOMARKERS 105

8.1 Introduction 105

8.2 Cognitive tasks 105

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8.2.1 Introduction 105

8.2.2 Data Screening 105

8.2.3 Selecting covariates 107

8.3 Results 111


8.3.2 Attention domains 111

8.3.3 Psychomotor function 113

8.3.4 Cognitive Drug Research task subsets 113

8.3.5 Summary for attention and psychomotor function 115

8.4 Memory tasks 115


8.4.2 Results 115

8.4.3 Summary for memory function 116

8.5 Executive function domains 117


8.5.2 Results 117

8.5.3 Summary for executive function 118

8.6 Mood measures 118


8.6.2 Results 118

8.6.3 Summary of mood measures 119

8.7 Vascular variables 120


8.7.2 Data Screening 120

8.7.3 Results 120

8.7.4 Summary 122

8.8 Oxidative stress, antioxidant and inflammatory biomarkers 122


8.8.2 Results 123

8.9 Relationships between the vascular, oxidative stress, antioxidant and

inflammatory biomarkers 124


8.9.2 Oxidative stress measures and vascular, antioxidant and

inflammatory markers 124

8.9.3 Antioxidant and vascular measures 125

8.9.4 Antioxidant and inflammatory measures 125

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8.9.5 Inflammatory and vascular measures 125

8.9.6 Summary 128

CHAPTER 9 RESULTS: RELATIONSHIPS BETWEEN COGNITIVE MEASURES

AND BIOMARKERS 129


9.2 Examination of the relationships between cognitive and vascular

measures 129


9.3 Relationships between common carotid and middle cerebral arterial blood

flow and cognitive function 130

9.3.1 Global cognition 130

9.3.2 Attention 130

9.3.3 Memory 134

9.3.4 Executive function 135

9.3.5 Summary 137

9.4 Relationships between arterial stiffness and cognitive performance 137



9.4.3 Attention 137

9.4.4 Memory 139


9.4.6 Summary 141

9.5 Relationships between cognitive performance and oxidative stress,

antioxidant and inflammatory markers 142


9.5.1.1 Global cognition in heart failure 142

9.5.2 Relationship between oxidative stress and cognitive

function 144

9.5.2.1 Attention 144

9.5.2.2 Memory 144

9.5.2.3 Executive function 144

9.5.2.4 Summary 144

9.5.3 Relationship between antioxidants and cognitive

function 144


9.5.3.2 Memory 146

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xi


9.5.3.4 Summary 147

9.5.4 Relationship between inflammation and cognitive

function 147


9.5.4.2 Memory 148


9.5.4.4 Summary 148

9.6 Multiple regression analysis examining the effect of vascular, oxidative

stress and inflammatory predictors on cognitive function 148


9.6.2 Attention tasks 149

9.6.2.1 Hierarchical multiple regression analysis

examining vascular and antioxidants

predictors on congruent Stroop

performance 149


examining the effect of vascular predictors

on Power of Attention 152

9.6.2.3 Summary 153



examining the effect of vascular and

antioxidant predictors on incongruent

Stroop 154

9.6.3.2 Summary 157

CHAPTER 10 RESULTS: RELATIONSHIPS BETWEEN MOOD AND

BIOMARKERS 158


10.2 Relationships between mood and vascular function 158


10.2.2 Results 159

10.2.3 Summary 159

10.3 Relationships between mood and oxidative stress 160


10.3.2 Results 160

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10.3.3 Summary 161

10.4 Relationships between mood and antioxidants 161


10.4.2 Results 161

10.4.3 Summary 162

10.5 Relationships between mood, inflammation and dietary omega-3

intake 162


10.5.2 Results 162

10.5.3 Summary 162

CHAPTER 11 DISCUSSION 163


11.2 Summary of the main findings 163

11.3 Demographics and clinical characteristics 170

11.4 Summary of cognitive measures 170

11.4.1 Global cognition and screening for dementia 170

11.4.2 Attention and psychomotor speed 171

11.4.3 Quality of Episodic Memory, Quality of Working Memory

and Speed of Memory 173

11.4.4 Executive Function 175

11.5 Summary of vascular measures 176

11.5.1 Cerebral blood flow 176

11.5.2 Arterial stiffness 177

11.5.3 Vasoconstriction: endothelin-1 177

11.6 Summary of oxidative stress measures 178

11.7 Summary of antioxidant measures 178

11.8 Summary of inflammation and dietary omega-3 fatty acid 179

11.9 Summary of the relationships between vascular, oxidative stress and

inflammatory biomarkers 180

11.9.1 Vascular measures and oxidative stress, inflammation and

antioxidant biomarkers 180

11.9.2 Oxidative stress, inflammation and antioxidants 181

11.10 Relationship between cerebral blood flow and cognitive function 182


11.10.2 Attention 183

11.10.3 Memory 184

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11.11 Relationship between arterial stiffness and cognitive function 185


11.11.2 Attention and psychomotor function 186

11.11.3 Memory 186


11.12 Relationship between oxidative stress and cognitive function 188



11.12.3 Memory 189


11.12.5 Summary 190

11.13 Relationship between antioxidant measures and cognitive function 191



11.13.3 Memory 191


11.13.5 Summary 192

11.14 Relationship between inflammatory measures, dietary omega-3

and cognitive function 192



11.14.3 Memory 193


11.15 Relationship between vascular, oxidative stress, antioxidant and

inflammatory measures and cognitive function 194

11.16 Summary of the relationships between cognitive function and

physiological measures 197

11.17 Mood measures 198

11.18 Relationship between vascular measures, oxidative stress,

antioxidant and inflammatory measures on depression and

anxiety 198

11.18.1 The relationship between cerebral blood flow and

arterial stiffness with depression and anxiety 198

11.18.2 The relationship between oxidative stress and

antioxidant measures with depression and anxiety 199

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11.18.3 The relationship between inflammatory measures

and depression and anxiety 201

11.19 Possible mechanisms for changes in mood in heart failure 202

CHAPTER 12 STUDY LIMITATION, STRENTHS AND FUTURE

DIRECTIONS 203

12.1 Study Limitations 203

12.1.1 Patient recruitment and sample size 203

12.1.2 Methodological issues 204

12.1.3 Biological markers 205

12.2 Study strengths 206

12.3 Directions for future research 208

REFERENCES 212

LIST OF APPENDICES 234

List of figures

xv

List of Figures

Figure 1 Generation of reactive oxygen species 52

Figure 2 Antioxidant defence mechanism 54

Figure 3 Formula for calculating mean blood flow velocity. MV = mean velocity;

PV= peak systolic blood flow velocity; EDV = end diastolic blood flow

velocity 89

Figure 4 Frontal view of the ultrasound probe (Transcranial Doppler; TCD

transducer) placed at the zygomatic arch/temporal window, directed

toward the middle cerebral artery 90

Figure 5 Formula for calculating central pulse pressure defined by subtracting the

central diastolic blood pressure (CDBP) from the central systolic blood

pressure (CSBP) 91

Figure 6 Augmentation index (AIx), defined as a percentage of augmentation

pressure (AP) and pulse pressure (PP) 91

Figure 7 A central aortic pressure waveform 91

Figure 8 Scatter plots of Power of Attention and common carotid arterial blood

flow velocity in the HF and control groups 132

Figure 9 Scatter plots of common carotid blood flow velocity and incongruent

Stroop in the HF and control groups 135

Figure 10 Scatter plots of coenzyme Q10 and congruent Stroop in the HF and

control groups 145

Figure 11 Scatter plots of coenzyme Q10 and incongruent Stroop in the HF and

control groups 146

List of tables

xvi

List of Tables

Table 1 New York Heart Association and American College of

Cardiology/American Heart Association (ACC/AHA) classifications for

heart failure 5

Table 2 Description of subsets from the Cognitive Drug Research (CDR) test

battery and the order in which the tasks were presented 75

Table 3 Composite scores for the five cognitive domains 78

Table 4 Summary of neuropsychological tests 81

Table 5 Summary of vascular, oxidative stress, antioxidant and inflammatory

measures 92

Table 6 Testing protocol timeline 95

Table 7 Demographic characteristics (age, gender and education), dementia

screening, general health questionnaire-12 item, premorbid IQ and

vitals for HF and control participants. 100

Table 8 Clinical characteristics for HF group - NYHA class, aetiology, common

comorbidities. 101

Table 9 Group differences for SF-36 subscales and Chalder fatigue scale 103

Table 10 Transformations selected for analysis for non-normally disturbed

cognitive variables 106

Table 11 Pearson’s correlation coefficients between neuropsychological and

mood variables for the HF Group 109

Table 12 Pearson’s correlation coefficients between neuropsychological and

mood variables for the control group 110

Table 13 Means and standard deviations of attention, memory and executive

function tasks for HF and controls after adjusting for premorbid IQ and

mood covariates 112

List of tables

xvii

Table 14 Group differences for Cognitive Drug Research subtests for HF and

control group 114

Table 15 Analysis of variance, means and standard deviations for each

experimental group for Profile of Mood States subscales and state trait

anxiety inventory 119

Table 16 Means and standard deviations of the vascular measures for HF and

control groups 121

Table 17 Pearson’s correlations between endothelin-1, high-sensitive C-reactive

protein and each of the each of the arterial stiffness measures

(augmentation index, central pulse pressure) in the heart failure group. 122

Table 18 Means and standard deviations of the oxidative stress, antioxidant and

inflammatory measures for HF and control groups 123

Table 19 Correlations between oxidative stress, antioxidant, inflammatory and

vascular measures for the HF group 126

Table 20 Correlations between oxidative stress, antioxidant, inflammatory and

vascular measures for the control group 127

Table 21 Correlation matrix for neuropsychological domains and blood flow

velocities, arterial stiffness and vascular function 131

Table 22 The effect of group on congruent Stroop reaction time after adjusting

for premorbid IQ and common carotid blood flow velocity 133

Table 23 The effect of group on Power of Attention domain after adjusting for

premorbid IQ and common carotid blood flow velocity 134

Table 24 The effect of group on incongruent Stroop reaction time adjusting for

premorbid IQ and common carotid blood flow velocity 136

Table 25 The effect of group on congruent Stroop reaction time after adjusting

for premorbid IQ and central pulse pressure 138

Table 26 The effect of group on Power of Attention domain after adjusting for

premorbid IQ and central pulse pressure 139

List of tables

xviii

Table 27 The effect of group on Trail Making-B after adjusting for premorbid IQ

and central pulse pressure 140

Table 28 The effect of group on incongruent Stroop reaction time after adjusting

for premorbid IQ and central pulse pressure 141

Table 29 Correlation coefficients between oxidative stress, antioxidant and

inflammatory markers with cognitive measures in each experimental

group 143

Table 30 The effect of group on congruent Stroop after adjusting for premorbid

IQ and CoQ10 145

Table 31 The effect of group on incongruent Stroop after adjusting for premorbid

IQ and CoQ10 147

Table 32 Hierarchical multiple regression analysis summary predicting congruent

Stroop reaction time from common carotid blood flow velocity and

central pulse pressure after controlling for premorbid IQ in the HF

group 150

Table 33 Hierarchical multiple regression analysis summary predicting congruent

Stroop reaction time from common carotid blood flow velocity and

CoQ10 after controlling for premorbid IQ in the HF group 151

Table 34 Hierarchical multiple regression analysis summary predicting Power of

Attention from common carotid blood flow velocity and central pulse

pressure after controlling for premorbid IQ in the HF group 153

Table 35 Hierarchical multiple regression analysis summary predicting

incongruent Stroop from common carotid blood flow velocity and

central pulse pressure after controlling for premorbid IQ in the HF

group 155

Table 36 Hierarchical multiple regression analysis summary predicting

incongruent Stroop from common carotid blood flow velocity and

CoQ10 after controlling for premorbid IQ in the HF group 156

Table 37 Correlation matrix for mood measures and blood flow velocities,

arterial stiffness and vascular function 159

List of tables

xix

Table 38 Correlation matrix for mood measures and oxidative stress, antioxidant

and inflammatory biomarkers 161

Table 39 Summary of the cognitive performance in the heart failure group

compared with healthy controls 164

Table 40 Summary of the biomarker values in the heart failure group compared

with healthy controls 165

Table 41 Summary of the relationships between cognitive and mood measures

with biomarkers in the heart failure group. 167

Abbreviations

xx

List of Abbreviations

ACE Angiotensin converting enzyme

AD Alzheimer’s disease

AIx Augmentation index

AMT Abbreviated Mental Test

ANCOVA Analysis of covariance

BDI Beck depression inventory

BP Blood pressure

CABG Coronary artery bypass grafting

CAMCOG Cambridge cognitive capacity scale

CARR Carratelli Units

CCA-BFV Common carotid arterial blood flow velocity

CDR Cognitive Drug Research

CFS Chalder fatigue scale

CHD Coronary heart disease

CI Cognitive impairment

CoQ10 Coenzyme Q10

CPP Central pulse pressure

CVLT Californian Verbal Learning Test

DHA Docosahexaenoic acid

DROM Determinable reactive oxygen metabolites

EDS Excessive daytime sleepiness

EPA Eicosapentaenoic acid

ET-1 Endothelin-1

Abbreviations

xxi

GDS Geriatric Depression Scale

GHQ General Health questionnaire

GPx Glutathione peroxidase

HADS Hospital Anxiety and Depression Scale

HF Heart failure

hs-CRP High-sensitive C-reactive protein

IL Interleukin

IQ Intelligence quotient

LVEF Left ventricular ejection fraction

MCA Middle cerebral artery

MDA Malondialdehyde

MDD Major depressive disorder

MMSE Mini Mental State Examination

MLWHF Minnesota Living With Heart Failure

MoCA Montreal Cognitive Assessment battery

NYHA New York Heart Association

PICF Participant Information and Consent Form

POMS Profile of Mood States questionnaire

PUFA Polyunsaturated fatty acid

PWA Pulse wave analysis

PWV Pulse wave velocity

QOL Quality of life

RAVLT Rey’s Auditory Verbal Learning Test

RBANS Repeatable Battery for the Assessment of Neuropsychological States

Abbreviations

xxii

ROM Reactive oxygen metabolite

SF-36 Short Form questionnaire - 36 item

SOD Superoxide dismutase

SPECT Single-photon emission computed tomography

STAI Speilberger State-Trait Anxiety Inventory

TCD Transcranial Doppler

TMT Trail making task

TNF-α Tumour necrosis factor alpha

WASI Wechsler Abbreviated Scale of Intelligence

WASI Vocabulary Wechsler Abbreviated Scale of Intelligence Vocabulary subset

Chapter 1: Overview

1

CHAPTER 1 OVERVIEW

This thesis begins with an overview of heart failure (HF) in elderly individuals in Chapter 2.

Chapter 2 provides a definition of HF, an outline of the classifications for HF and the

pathophysiology, aetiology, prevalence and incidence of this condition. Furthermore, the

prevalence and incidence of cognitive impairment in HF are outlined and a discussion of the

negative impact of cognitive impairments on elderly patient’s quality of life, self-care

abilities and treatment compliance is provided. This leads onto Chapter 3, which discusses

the literature pertaining to cognitive function in elderly HF patients’ global cognitive function

and specific cognitive domains including attention, episodic memory, working memory,

visuospatial abilities, psychomotor function and executive function. Included are previous

studies that have evaluated cognitive functions among HF patients in comparison to other

groups such as healthy controls and patients with diseases other than HF such as

cardiovascular disease. Chapter 3 also outlines the few studies that have explored possible

treatments for improving cognitive function in HF patients. Chapter 4 discusses possible

physiological mechanisms associated with cognitive impairments in elderly HF patients.

Specifically, known vascular mechanisms including cerebral blood flow using Transcranial

Doppler measurement and brain imaging studies are outlined. Chapter 4 also discusses

possible biological mechanisms associated with cognitive impairment including arterial

stiffness, oxidative stress, antioxidant and inflammatory markers. Here linkages are made

between studies that have investigated imbalances in these biological markers in conditions

associated with cognitive impairments and in HF. The rationale and aims of this thesis and

hypotheses and research questions asked are presented in Chapter 5. The selection of

participants, power analysis and sample size calculation, materials, procedures, and the

experimental design utilised in this thesis are outlined in Chapter 6. Demographic and quality

of life results between experimental groups are presented in Chapter 7. Results from the

statistical analyses for group differences between cognitive, mood and biomarker variables

are presented in Chapter 8. Results from the statistical analyses examining the relationships

between cognitive measures and biomarkers are presented in Chapter 9 and mood measures

and biomarkers are presented in chapter 10. The final chapters provide a discussion of the

results (Chapter 11), limitations and strengths of the thesis, directions for future research and

concluding comments (Chapter 12).

Chapter 2: Background and overview of heart failure

2

CHAPTER 2 BACKGROUND AND OVERVIEW OF HEART FAILURE

2.1 Chapter overview

Heart Failure (HF) is one of the leading causes of hospitalization and mortality in the

Western world especially in the elderly population (Schwarz, 2007; Tendera, 2004; Vogels,

Scheltens, Schroeder-Tanka, & Weinstein, 2007). HF is a common condition principally seen

in the elderly. This chapter will provide a brief overview of the prevalence, incidence and

economic burden of HF with a focus on a definition of the disease, the pathophysiology and

treatments commonly used to improve symptoms and treat the condition. Cognitive

impairment is commonly seen in older HF patients and the negative consequences of poor

cognitive function on factors such as hospital readmission rates, mortality rates and

compliance with treatment regimens will be discussed.

Despite effective treatments for improving HF symptoms, there are no effective treatments

for cognitive impairments in these patients. Life expectancy is increasing, with 10 per cent of

the population in the year 2000 aged 60 years or older and this percentage is expected to

double, reaching 21 per cent by the year 2050 (World Health Organisation [WHO], 2003).

With improvements in living conditions and an aging population, together with

improvements in medical treatments and diagnosis for HF, the number of older individuals

diagnosed with conditions such as HF is expected to increase (Thomas & Rich, 2007).

The ageing process not only involves changes in the cardiovascular system but also other

systems and organs (Rengo et al., 1996). Patients often seek medical attention for other

conditions and with further examination heart failure (HF) is detected. In a large study

(n=122,630) examining chronic HF patients aged 65 years and over, it was revealed that 40%

of these patients had five or more non-cardiac comorbidities. Elderly patients are therefore

more likely to be suffering from co-morbidities that may imitate or mask HF making

diagnosis difficult. Elderly patients are most likely to suffer from hypertension (55%),

diabetes mellitus (31%), coronary obstructive pulmonary disease, bronchiectasis (26%) and

ocular disorders (24%). Other common comorbidities seen in elderly HF inpatients (65-98

years) include chest disease (30%), incontinence (29%), cerebrovascular disease (26%) and

musculoskeletal problems (41%; Lien, Gillespie, Struthers, & McMurdo, 2002). Other less

common comorbidities include hypercholesterolemia (21%), atherosclerosis (16%),

osteoarthritis (16%), other chronic respiratory disorders (14%), thyroid conditions (14%),

asthma (5%) and osteoporosis (5%; Braunstein et al., 2003). Furthermore, mental changes


3

including cognitive impairment (e.g. Alzheimer’s disease), depression and affective disorders

(8%) also exist with HF (Braunstein et al., 2003).

Of these comorbidities, mental disorders such as cognitive impairment are of particular

interest in the current investigation as cognitive function in HF patients is often overlooked.

Cognitive abilities are important in order for patients to effectively plan and remember to take

their medications and to understand and comprehend treatment regimens (Wolfe, Worrall-

Carter, Foister, Keks, & Howe, 2006). Since HF is predominantly seen in the elderly and

cognitive function is known to decline with age (Riddle & Schindler, 2007) assessment of

cognitive abilities in these patients is an important area of research. Despite effective

diagnostic techniques and treatments for HF, cognitive impairments in these patients are

often unnoticed. This chapter will discuss evidence suggesting that cognitive impairments in

HF influence prognosis, self-care and treatment compliance.

The following chapters will review the literature on specific cognitive domains impaired in

elderly HF patients and possible mechanisms known to be related to these impairments. The

outcome of this review will lead on to the current investigation which will expand on the

current literature by examining possible mechanisms for cognitive decline using instruments

which can be easily administered in a clinical setting.

2.2 Definition of heart failure

Numerous definitions are used to describe heart failure (HF) with various classifications to

categorise the severity of the disease. Heart failure, which has previously been referred to as

“congestive HF”, is commonly defined as a complex clinical syndrome resulting in a

structural or functional cardiac disorder that impairs the ability of the left ventricle to fill with

or eject blood (Tendera, 2004). According to the European Society of Cardiology (ESC)

guidelines, HF is defined as:

“….a syndrome in which the patient should have the following features: symptoms of

HF, typically shortness of breath at rest or during exertion, and/or fatigue; signs of

fluid retention such as pulmonary congestion or ankle swelling; and objective evidence

of an abnormality of the structure of function of the heart at rest.” (Dickstein et al.,

2008).

and


4

“HF is a complex clinical syndrome that can result in any structural or

functional cardiac disorder that impairs the ability of the ventricle to fill with

or eject blood.” (Hunt et al., 2009)

As a result of a failing heart, cardiac output (amount of blood being pumped out of the heart)

is impaired, and the amount of blood reaching organs is insufficient to meet the body’s

metabolic requirements. Due to a poor blood supply, other organs weaken and become

compromised leading to presenting symptoms.

2.3 Heart failure diagnosis, classifications, signs and symptoms

Heart Failure (HF) classifications are used to describe the severity of the disease, which assist

with administering the appropriate treatment protocol. The New York Heart Association

(NYHA) criterion is a commonly used diagnostic tool to classify the disease into four

categories based on the severity of symptoms (Dickstein et al., 2008). Classifications range

from no HF (NYHA class I) to severe HF (NYHA class IV). Patients diagnosed with NYHA

class I are asymptomatic and have no physical activity limitations. Patients classified with

mild HF (or NYHA class II) have slight limitation of physical activity causing fatigue,

palpitations or dyspnoea. Patients with moderate HF severity (NYHA class III) have

noticeable physical limitations causing fatigue, palpitations or dyspnoea. Finally, patients

with severe HF (NYHA class IV) suffer breathlessness even at rest, are unable to carry on

with physical activity and may be hospitalised (Dickstein et al., 2008: Table 1).

Another classification for HF has been proposed by the American College of

Cardiology/American Heart Association (ACC/AHA) where stages of HF are determined by

structural changes and damage to the heart muscle (Hunt et al., 2009). The ACC/AHA

classification ranges across four phases from Stage A (high risk) to Stage D (an advanced

stage). Patients at an early stage or Stage A have a high risk for HF such as hypertension,

diabetes, obesity, metabolic syndrome but do not have any clinical symptoms of HF. Patients

who have some structural heart disease associated with the development of HF (e.g. previous

myocardial infarction, low EF) but do not have any signs or symptoms associated with HF

are classified as Stage B. The next level of disease severity is Stage C where patients are

symptomatic and HF is related to underlying structural heart disease. The final stage is where

advanced structural heart disease and symptoms of HF (e.g. symptoms at rest) is present,

requiring hospitalisation and intervention (Stage D; Hunt et al., 2009: Table 1). It is thought

that inconsistencies in research findings examining factors such as cognitive function in HF

patients between studies are due to various definitions of the disease and physicians using


5

different classifications to categorise the disease. The NYHA criterion is the classification for

HF predominantly used by Australian cardiologists.

Table 1 New York Heart Association and American College of Cardiology/American Heart

Association (ACC/AHA) classifications for heart failure (Hunt et al., 2009).

New York Heart Association

classification

American College of Cardiology/American

Heart Association (ACC/AHA)

Class Description Stage Description

NYHA I asymptomatic, no physical

activity limitations and

symptoms are well

controlled

Stage A having a high risk for HF without

any clinical symptoms of HF

NYHA II slight limitation of physical

activity causing fatigue,

palpitations or dyspnoea

Stage B some structural heart disease

associated with the development

of HF (e.g. previous myocardial

infarction, low EF) but no signs

or symptoms

NYHA III noticeable physical

limitations causing fatigue,

palpitations or dyspnoea

Stage C symptomatic HF related to

underlying structural heart

disease

NYHA IV breathlessness upon rest, an

inability to carry on with

physical activity and

possible hospitalisation

Stage D advanced structural heart disease

and symptoms of HF (e.g.

symptoms at rest), requiring

hospitalisation and intervention

HF classifications are objective measures based on presenting signs and symptoms and

although they provide a sound assessment of the disease severity and required treatments, a

complete evaluation using a combination of laboratory assessments and non-invasive

neuroimaging tests (e.g. echocardiography) is required for a specific diagnosis.

Heart failure (HF) can be described as systolic or diastolic, acute or chronic, compensative

and low output or high output. During HF ventricular remodelling (dilation) occurs to

compensate for the reduction in blood volume ejected from the heart. In diastolic HF, the left


6

ventricular wall becomes stiffer blood and filling is impaired. Conversely, during systolic HF,

ejection is reduced. A measurement used to differentiate systolic from diastolic HF is ejection

fraction or the stroke volume divided by the end-diastolic volume for that heart chamber.

Patients with diastolic HF for example, may present with signs and symptoms of HF although

the echocardiograph will indicate normal left ventricular ejection fraction (LVEF) at rest

(Dickstein et al., 2008; Swedberg et al., 2005). Acute HF refers to a new or sudden onset of

the condition, or new symptoms or signs with or without previously known cardiac

dysfunction. Acute HF can also refer to sudden deterioration or worsening of stable/chronic

HF, oftentimes requiring hospitalisation and urgent treatment (Nieminen et al., 2005). In the

patient with chronic HF, the condition can be compensated where it is persistent and stable,

or decompensated where there is deterioration and an increase in symptoms such as

breathlessness (Dickstein et al., 2008; Millane, Jackson, Gibbs, & Lip, 2000).

2.4 Heart failure pathophysiology

The pathophysiology of heart failure (HF) is complex and has been explained by models that

attempt to explain the mechanisms behind the poor functional ability of the heart. HF is a

progressive disorder, which over time involves destruction of heart myocytes and a resultant

reduction in the heart’s functional capacity to contract normally (Mann, 2011). As a result of

the reduced ability of the heart to pump, cardiac output is reduced and an insufficient supply

of blood is transported to tissues and organs. Because of poor blood supply, the tissues

become deficient in essential oxygen and nutrients and over time, bodily organs fail to work

properly. With diminished cardiac output, the brain in turn receives less energy, oxygen and

nutrients required for adequate functioning, possibly resulting in pathologies such as poor

cognitive function. Early stages of HF may remain asymptomatic and it is not until a chronic

lack of heart function, resulting in a reduced supply of oxygen and nutrients, that clinical

symptoms start to occur. Lack of cardiac output also causes organ dysfunction and patients

may experience symptoms that warrant them seeking medical attention and even

hospitalisation in severe cases.

2.5 Heart failure treatments

Given that HF is a multifaceted condition affecting various systems in the body it is no

surprise that the treatment protocol for HF is complex. Treatment of HF requires a team of

medical professionals and comprehensive treatment plans to improve symptoms. Tailored

individual treatment involves a combination of medication, dietary interventions (such as

fluid restriction, low sodium diet, diet consisting of adequate nutrients and limiting alcohol),


7

monitored physical activity, risk factor monitoring, patient education and symptom

management (Dickstein et al., 2008; Krum & Abraham, 2009). HF medication is vital in

order to treat this complex syndrome and in one report almost 90% of HF patients who were

admitted to hospital had been taking four or more medications (Lien et al., 2002).

Medications commonly prescribed include: i) angiotensin converting enzyme (ACE)

inhibitors which enhance survival in heart failure (HF), reduce the rate of hospitalisation,

improve neurohormonal levels and reduce the incidence of recurrent angina and myocardial

infarction in coronary artery disease; ii) diuretics, in particular loop diuretics that promote

dieresis and have been found to improve ventricular function and symptoms associated with

fluid overload in left ventricular systolic dysfunction and both decrease dyspnoea and

increase exercise tolerance (Swedberg et al., 2005); iii) digoxin (digitalis) a weak inotrope

that increases myocardial contractility, improves left ventricular function and renal blood

flow; and iv) beta-blockers (e.g. bisoprolol and succinate) which inhibit the sympathetic

activity of the nervous system and block adrenergic receptors thus reducing heart rate, blood

pressure and directly affect the myocardium (Jessup & Brozena, 2003). Additionally, beta-

blockers reduce hospitalisation, improve functional class and minimise worsening of HF

(Swedberg et al., 2005). Anticoagulant medicines including aspirin and warfarin are used to

prevent the risk of developing thromboembolism.

2.6 Economic costs of heart failure

As a large proportion of the elderly population are diagnosed with HF, the health care costs

of this disease pose a great burden on the economy. Requiring ongoing costs such as regular

outpatient visits, care and education from nurses and hospitalisations, HF is the most

expensive cardiovascular disorder in the USA (Thomas & Rich, 2007). Although the exact

health care costs associated with HF are unknown it is estimated that in the western world the

cost is between 1% and 2% of annual national healthcare expenditure (Liao, Allen, &

Whellan, 2008). Estimated annual health care costs associated with HF are as high as 1

billion dollars in Australia (Clark, McLennan, Dawson, Wilkinson, & Stewart, 2004) and

37.2 billion dollars in the USA (Lloyd-Jones et al., 2009). The bulk of costs of HF are related

to hospitalizations including inpatient, outpatient and emergency departments. In Australia it

is estimated that two thirds of the total expenditure for HF is attributed to hospitalisations

(Krum et al., 2006), and just over half ($20 billion) of the total HF costs in the USA (Lloyd-

Jones et al., 2009). Furthermore, in Australia, $4.5 billion (12%) of the total costs are related

to nursing home expenses and $2.4 billion (6.4%) to physicians and other professionals.


8

Approximately 80% of heart failure (HF) related hospitalisations in Australia involve elderly

patients aged 65 years and over (Clark et al., 2004). Not only is there a greater number of

elderly HF patients requiring hospital care compared to younger patients, the cost per

hospitalisation is greater for elderly patients. Demonstrating this, a large retrospective study

showed that in American hospitals the average cost per hospitalisation for older HF patients

(≥ 60 years; mean age = 72.7 years) was $18,086 compared to the overall average of $15,293

per patient (Titler et al., 2008). The additional costs required by elderly patients were related

to increased costs of medical procedures, medications and extra nursing interventions. The

authors suggest that these costs may be underestimated since some patients have HF as a

secondary not a primary diagnosis and other comorbidities may therefore further increase the

expenses (Wang, Zhang, Ayala, Wall, & Fang, 2010).

2.7 Prevalence and incidence of heart failure

The prevalence of HF in developed and developing countries is increasing with increases in

the number of older people in society (Abhayaratna, Smith, Becker, Marwick, Jeffery, &

McGill, 2006; Dickstein et al., 2008; Ho, Pinsky, Kannel, & Levy, 1993; Krum & Abraham,

2009; Tendera, 2004). The overall prevalence in Australia, United States of America and the

European countries is estimated to be between 2% to 3% (Abhayaratna et al., 2006; Dickstein

et al., 2008; Ho et al., 1993). In the year 2000 an estimated 565,000 Australians had HF of

whom 325,000 had symptomatic and 240,000 had asymptomatic HF (Clark et al., 2004). At

least 300,000 Australians over the age of 45 years are estimated to have chronic HF and

approximately 30,000 new cases are reported each year (Australian Institute of Health and

Welfare [AIHW], 2003). Studies in Western populations have revealed that there is a 4.4 fold

increase in people suffering the condition aged between 60 and 64 to the 80-86 age group

(Abhayaratna et al., 2006). In particular, the Framingham study, which examined the long-

term trend of HF from the years 1950 to 1999, showed that the prevalence of HF rises with

each decade (Bleumink et al., 2004). The occurrence of HF was 0.9% in individuals aged

between 55 and 64 years, 4% in those aged between 65 and 74 years, 9.7% aged between 75

and 84 years, and 17.4% aged 85 years and over (Bleumink et al., 2004).

The incidence of heart failure (HF) has been shown to increase with age in both men and

women. The rising incidence of HF with age was shown in a large study by Cowie et al.

(1999) that included a sample of 150,582 members from a London district who visited a

general practice. In their study, Cowie et al. (1999) found the incidence of new HF is

0.02/1000 in those aged 25-34 years and 11.6/1000 in those aged 85 years and over.

Examining the elderly population annually 10.6/1,000 persons aged 65-69 years and


9

42.5/1,000 for those aged ≥ 80 years are diagnosed with the condition (Gottdiener et al.,

2000). Furthermore, the overall incidence of HF is approximately two times higher in men

than women (Bleumink et al., 2004; Gottdiener et al., 2000). Ho et al. (1993) demonstrated in

an older sample that the annual incidence of HF increased from 3/1000 men and 2/1000

women aged 50-59 years to 27/1000 men and 22/1000 women aged 80-89 years of age (Ho et

al., 1993).

It is difficult to establish an accurate account of the incidence and prevalence of HF and it is

possibly underestimated with many undiagnosed individuals living in society (Abhayaratna et

al., 2006). Furthermore, inconsistent diagnostic criteria are used among researchers and

practitioners, different age groups and cohorts are examined, and different research designs

are implemented (e.g. population based and patients visiting a general practitioner; Krum &

Abraham, 2009). Although currently there is limited data on the incidence and prevalence of

HF, the incidence is expected to increase in the future. A projected future increase in the

incidence and prevalence of HF is due to multiple factors including expected increases in the

aging population, survival rates due to improvements in medical interventions of those with

coronary artery disease and acute cardiac disease such as myocardial infarctions and

improvements in diagnostic techniques (Krum et al., 2006; McMurray & Stewart, 2000;

Thomas & Rich, 2007).

2.8 Prevalence and incidence of cognitive impairment in heart failure

Studies examining global and specific cognitive abilities in HF have demonstrated that

impairments exist in this patient group, although few researchers have assessed the

prevalence and incidence of cognitive impairments. In an early study Cacciatore and

collegues (1998) examined the relationship between global cognitive impairment as measured

by an Italian version of the Mini Mental State Examination (MMSE; score of < 24 out of 30)

in a large population study in southern Italy. In this trial older individuals (73.9±6.2 years)

randomly selected from the electoral roles, were contacted at their home or institution and

interviewed. Of the 1075 individauls included in the trial the prevalence of HF based on the

NYHA guidelines was 8.2% (n=88), with hypertension as the most common eitiology (69%).

MMSE scores of < 24 were more prevalent in HF patients compared to the non-HF sample

(20.2% vs 4.6%). This study showed that HF patients have 1.96 times the risk of cognitive

impairments compared to individuals without HF with 56.8% of those with HF having

cognitive impairments compared to 20% of those without HF (Cacciatore et al., 1998).

Moreover, studies have revealed a prevalence of cognitive impairments (MMSE < 24) in

elderly HF patients to be as low as 13% (Di Carlo et al., 2000) to just over half (53%) in


10

patients with NYHA II and III (Zuccalà et al., 1997). However, no age matched control

groups were included in these studies. Furthermore, since the researchers employed a cut off

score for dementia only, they failed to detect and include patients with mild cognitive

impairments (MCI). When Debette and colleagues (2007) increased the Mini Mental State

Examination (MMSE) cut off score to include probable mild cognitive impairment, the

proportion of patients initially considered to be cognitively impaired increased from 31%

(MMSE < 24) to 61% (MMSE ≤ 28), when education levels were higher than eight years and

≤ 26 otherwise (Debette et al., 2007). Debette et al. (2007) examined adult patients (17-98

years), however, they failed to compare cognitive scores between various age groups to

explore whether MMSE scores change across the age groups. However, it demonstrates that a

simple screening tool for dementia may not be appropriate and tools that are more rigorous

are required to detect dementia in these patients. Since cognitive function changes over the

lifespan, reporting group differences between young, middle aged and older participants in

the Debette et al. (2007) study would have provided useful information regarding whether

evident cognitive impairments (MMSE < 24) and mild cognitive impairments are different

across age groups. Debette et al. (2007) did however use regression modelling to see if

MMSE scores of < 24 but not mild cognitive impairments are associated with age. However,

their study demonstrates that due to discrepancies in cut off scores, for cognitive impairment

across studies, it is difficult to establish an accurate prevalence for cognitive impairments in

HF. One study by Incalzi et al. (2003) for example revealed that MMSE had poor sensitivity

and specificity compared to verbal memory abilities suggesting that screening tests may not

be the best tools to detect declines in cognition (Incalzi et al., 2003). Although many studies

have assessed global cognitive function, these tests do not adequately detect cognitive

domains impaired in HF patients.

An assessment of global cognitive measures and dementia screening tools provide an

overview of cognitive abilities although do not differentiate between specific cognitive

domains. Riegel et al. (2002) demonstrated in a small sample (n=42) that 2.4% of HF patients

(NYHA class I, II, III and IV) had global cognitive impairments (MMSE < 24). Although

when using the standardized T scores of cognitive screening tests (draw a clock, commands

subset, MMSE and complex ideational material subtest), 28.6% of HF patients were

considered to have cognitive impairments (Riegel et al., 2002). This study demonstrates that

utilizing a global assessment tool as a single measure of cognitive impairment may

underestimate the prevalence of impairments and/or fail to detect impairments in research or

clinical settings. This study may have underestimated the number of patients with cognitive

impairments as they included asymptomatic or NYHA class I patients who may not present

with factors contributing to CI seen in HF. In a large Italian study, Trojano et al. (2003)


11

established that the prevalence of CI, as reflected by abnormal scores on 2 or more cognitive

tasks, was higher in patients with severe HF (NYHA class III or IV) followed by patients

with mild HF (NYHA class II) and no HF (57.9%, 43% and 34.3%, respectively).

Furthermore, employing an overall cognitive score (comprising memory, executive function,

visuospatial, language and memory speed and attention domains), Vogels et al. (2007),

showed cognitive impairment in 25% of HF patients (NYHA class III to IV; EF < 40%), 15%

in individuals with a history of ischemic cardiac disease but no symptoms of HF, and 4% of

the healthy controls. It is clear that cognitive deficits exist in HF and that the prevalence is

greater compared to healthy controls. The impact of cognitive impairments in patients is a

research imperative as it impacts negatively on the patients as a whole.

2.9 Impact of cognitive impairment in heart failure

2.9.1 Heart failure prognosis and mortality

Over the years, the numbers of hospitalisations, hospital stay durations and mortality rates

due to heart failure (HF) have decreased. The average hospital stay duration due to HF has

decreased from 21.1 days in 1980 to 12.9 days in 1999 and the in-hospital mortality has

declined from 18.6% to 13.5% during this period (Mosterd, Reitsma, & Grobbee, 2002). This

decline in hospital stay duration and mortality is likely due to advancements in diagnostic

techniques and improvements in medications and treatments. Despite a decline in mortality

rates, the number of patients dying from HF has increased possibly due to the increasing

incidence of the condition and the increasing elderly population in whom this condition is

frequently seen. Mortality rates have been investigated in various countries and there is

variability in the mortality rate statistics possibly due to inconsistent diagnostic methods used

between studies. For a general view of mortality, compared to older individuals without heart

failure (HF), older patients with HF (≥ 57 years) have a significantly worse 1-year (74% vs

97%), 2-year (65% vs 94%), 5-year (45% vs 80%) and 7-year (32% vs 70%) survival rates

(Van Jaarsveld et al., 2006). Physiological risk factors including dementia and

cerebrovascular disease are associated with increased mortality rates 30 days and 1 year

following discharge due to acute HF (D. S. Lee et al., 2003).

Various factors have been shown to influence mortality rates and prognosis in elderly HF

patients including depression (Jiang et al., 2004; Testa et al., 2011), and cognitive

impairments (Zuccalà et al., 2003). Assessments of cognitive function in hospitalised HF

patients have provided insight to the effects of cognitive impairments on mortality rates post

discharge. Overall, 6 and 12 months post-discharge mortality rates in elderly HF patients with

global cognitive impairments are higher than in patients without cognitive impairments (6


12

months: 35.6% vs 19% NYHA class III or IV; 12 months: 27% vs 15%; NYHA class IV;

Rozzini, Sabatini, Trabucchi, Zuccalà, & Bernabei, 2004; Zuccalà et al., 2003). Furthermore,

HF patients with global cognitive impairments have a greater 6-month mortality rate

compared to patients without HF but with cognitive impairments (35.6% versus 31%).

Finally, McLennan, Pearson, Cameron, and Stewart (2006) showed during a 5 year follow up

that compared to HF patients who were cognitively intact using a higher cut off (MMSE

>26), those with cognitive impairments (MMSE 19–26) had a 1.4-fold increased risk of being

admitted to hospital or dying. Furthermore, cognitively impaired patients at baseline had a

significantly higher mortality rate than those who were cognitively intact (96.3% vs 68.2%;

McLennan et al., 2006).

Examinations of specific cognitive domains have shown to relate to poor prognosis and

mortality. For example, baseline performance on global cognition, working memory, delayed

memory recall (verbal learning-Hopkins verbal learning test delayed recall), psychomotor

speed (Digit symbol scale and Trail Making-A), visual spatial ability and executive function

(as measured by the Trail Making-B), predicted 12 month mortality in outpatients with

chronic systolic HF (left ventricular ejection fraction < 40%; n=145, mean age 65.2±13.4;

Pressler, Kim, Riley, Ronis, & Gradus-Pizlo, 2010). Cardiovascular measures predicting 12-

month mortality were lower left ventricular ejection fraction mean, lower systolic blood

pressure, longer duration of HF and pacemaker implantation. Although no group differences

were observed in depressive symptoms, quality of life, NYHA class and age, patients who

died had poorer functional capacity (as measured by the Duke Activity Status Index) at

baseline compared to patients who survived (Pressler, Kim, et al., 2010). Patients with severe

HF (≥ 64 years; NYHA class IV) who failed to improve in memory, attention and

concentration from 4 days before to 6 weeks after discharge, also had worse 2-year prognosis

(24.7±2.9 months; Ochiai et al., 2004).

2.9.2 Hospital readmissions

Overall, in the year 2000 there were 100,000 admissions in Australian hospitals with

conditions associated with HF, for approximately 1.4 million days in total and costing more

than 1 billion dollars (Clark et al., 2004). Of the HF patients who are hospitalised about 80%

involve patients aged 65 years or older and this older age group stays in hospital longer than

younger age groups (89% of hospital stay time; Clark et al., 2004). Aspects such as

worsening of symptoms due to medication non-compliance can lead to increased

hospitalisation in HF patients. Studies in HF have shown that sodium retention and not

complying with medication and diet were the main factors contributing to increased hospital

admissions (Bennett et al., 1998; Happ, Naylor, & Roe-Prior, 1997). It is possible that poor


13

cognitive abilities together with other factors such as not recognising worsening symptoms

contribute to poor self-management and in turn hospital readmissions (Pressler, 2008). A

recent study demonstrated that older HF patients with reduced attention, executive function

and language abilities were more likely to adhere poorly to medical regimes, diet and exercise

(Alosco, Spitznagel, Van Dulmen, et al., 2012). Although cognitive impairments are related

to poor treatment adherence, impairments were not associated with length of hospital stay in

elderly patients (Zuccalà et al., 2003). However, when examining specific cognitive tasks,

poor executive function, memory, and processing speed in decompensated and compensated

patients related to increased hospital readmissions (14±2 months; Kindermann et al., 2012).

Furthermore vascular (LVEF) and inflammatory factors (C-reactive protein) predicted

hospital readmissions in patients. Hospitalisation not only causes great misery to patients and

a burden on their families and carers, it also poses a great burden on the economy.

With the elderly population projected to increase and a relative increase in the number of HF

patients and hospitalisations especially in this age group, the economic burden of HF is

expected to rise (W. C. Lee, Chavez, Baker, & Luce, 2004). Preventing hospital readmissions

will provide economic relief and potentially save an exorbitant amount of money per

admission. In conclusion, strategies to reduce hospital readmissions in elderly HF patients

such as improving cognitive function needs to be considered in order to help ease the

economic burden especially in elderly HF patients.

2.9.3 Treatment compliance

As outlined previously, medical regimes for the treatment of HF are complex as HF is a

multifaceted condition, although not all patients are compliant with these treatment regimes.

Elderly patients oftentimes misinterpret or forget medical advice, have difficulties adhering to

medical regimes, dietary advice and daily monitoring of weight (e.g. Schwarz, 2007). Poor

compliance with drug treatment and diets are leading factors for contributing to acute

decompensation in older patients with chronic HF and without dementia (75.4±9.9 years;

NYHA II: 8.9%; III: 33% and IV 58.1%; Michalsen, König, & Thimme, 1998). When asked

to recall information about the treatments patients received at discharge, more elderly patients

(NYHA class 2.5±0.9; n=22; 79±6 years) remembered verbal (91%) than written (23%)

information at the one-month post discharge follow up (Cline, Björck-Linné, Israelsson,

Willenheimer, & Erhardt, 1999). Interestingly, half of the patients were unable to name their

treatments and approximately two thirds (64%) were unable to state at what time of the day

and when in relation to meals they were required to take their treatments (Cline et al., 1999).

This was a small study, with ischemic heart disease as the main aetiology, therefore a larger

study with a broader representation of HF aetiology would help substantiate these findings.


14

During a longer post discharge follow up of 12 weeks, a large multicentre trial by Lainščak et

al., (2007) revealed that only 46% of older patients (68±12 years) correctly recalled advice

given to them in hospital and 67% correctly followed this advice (Lainščak et al., 2007). Of

the comprehensive treatment regime provided, patients were more likely to recall dietary and

exercise advice compared to factors such as daily weighing, reducing salt intake and avoiding

non-steroidal anti-inflammatory drugs (Lainščak et al., 2007). Older patients (aged ≥ 65

years) however were less likely to comply with exercise and dietary advice compared to

younger patients (aged < 65 years). This is possibly due to older patients finding compliance

to these treatments more challenging (Evangelista et al., 2003). Given that elderly HF patients

in these studies were more likely to have ischemic HF (Lainščak et al., 2007) it is possible

that the cerebral ischemia contributed to treatment non-compliance rather than the patient’s

age. Interestingly, patients who recalled fewer items of health advice (≤ 4) were significantly

older (70±12 years) than those who correctly recalled more items (> 4; 64±11 years; Lainščak

et al., 2007). Although elderly patients tend to be more compliant with taking medicine

compared to younger patients (Evangelista et al., 2003; Lainščak et al., 2007), non-

compliance in older patients still exists and is believed to be a major contributor to worsening

of their condition and leading to increased hospital readmissions. In contrast other researchers

suggest that non-compliance is greater in older HF patients and this may be due to

comorbidities including excessive daytime sleepiness (Riegel et al., 2011) and poor cognitive

functioning which impairs understanding and recall of treatments regimes (Wolfe et al.,

2006). It is important that patients have intact cognitive abilities to understand and follow

their treatment regimes.

2.9.4 Quality of life

The negative effects of the HF syndrome on the psychology and body of patients influence

patients’ overall quality of life. Compared to asymptomatic patients (NYHA class I), those

with mild and moderate HF (NYHA class II or III) have greater levels of physical limitations,

emotional distress and poor quality of life (QOL) as a result of their disease (Gorkin et al.,

1993). Furthermore patients with mild and moderate HF (NYHA class I, II and III) scored

lower on the physical health subset of the 36 item Short Form (SF-36) QOL questionnaire

and the anxiety subscale of the Hospital Anxiety and Depression Scale (HADS-A) compared

to patients with coronary heart disease (Almeida, Beer, et al., 2012). Furthermore, increased

disease severity has been related to increased disruption to daily living (Pressler,

Subramanian, et al., 2010a). However, few studies have examined whether a relationship

between cognition and QOL in elderly patients exists. Although Gorkin et al., (1993) found

that patients with mild and moderate HF performed significantly slower on psychomotor


15

(Trail Making-A), working memory and attention (digit span), but not on executive

functioning (Trail Making-B) tasks, the authors failed to report whether a relationship existed

between cognition and QOL. Pressler and colleagues (2010a) on the other hand demonstrated

that worse executive function (Trail Making-B) performance weakly related to increased

disease disruption to QOL as measured by the Minnesota Living with Heart Failure

(MLWHF) Questionnaire. In this study, the variance on the MLWHF questionnaire was

explained by disease severity, age and depressive symptoms. However, memory (as measured

by Hopkins Verbal Learning Tests total recall) was only a minor contributor to MLWHF and

did not contribute to the variance in disease severity and quality of life (Pressler,

Subramanian, et al., 2010a). This study enrolled patients aged 20 years and over and did not

compare older and younger patients. It is important to compare the difference in QOL

between younger and older patients as it has been reported that younger (≤ 64 years) HF

patients have greater emotional and physical disruptions to life as measured by MLWHF than

older patients do (> 64 years; Gottlieb et al., 2004). Additionally, younger patients exhibit

worse mental functioning and bodily pain as measured by the SF-36.

2.9.5 Daily living and self-care abilities

Researchers have emphasised the effects of cognitive function on quality of life and self-care

abilities of HF patients. Patients are provided with a large amount of important information

concerning how to manage their condition and recognize and act on worsening symptoms

(e.g. shortness of breath and weight gain) to prevent potential hospital readmission. It is

thought that patients poorly comprehend, retain, recall and utilize the information provided

by their cardiologists, GP, nurses and other health care workers (Wolfe et al., 2006). Older

HF patients (NYHA class II, III and IV) with greater verbal memory impairments appear to

score worse on daily living tasks (Incalzi et al., 2003). A large retrospective study found that

decreased cognitive function as measured by MMSE scores and psychomotor function (Trail

Making-A) performance predicted the ability of older patients (68±9.43; NYHA class II and

III) to independently drive a car and accurately take medications (Alosco, Spitznagel, Raz, et

al., 2012). Sufficient self-care maintenance, which is important for patients to make

approprate decisions about their symptoms, requires adeqate cognitive capacitites. When only

global cognitive function measures were assessed, mild cognitive impairment (MCI; MMSE

< 27) did not predict poor self-care maintenance 6±5 days post hospital admission in older

HF patients (NYHA class III or IV; 73±11 years; Cameron, Worrall-Carter, Riegel, Lo, &

Stewart, 2009). Although, when combining two global cognitive function measures (MMSE

and Montreal Cognitive Assessment; MoCA), MCI explained 20% of the variance of self-

care management (Cameron et al., 2009). However, cognitive function as measured by


16

MMSE was not found to be a significant predictor of self-care maintenance or management

in 50 hospitalised elderly chronic HF patients (mean 73 years; 6 days±5 days; Cameron et al.,

2009). This indicates that some tests for cognition may fail to detect impairments when they

are actually present. During a 6 month post-hospitalisation follow up, patients (> 65 years)

who did not participate in a nurse-directed structured treatment program had lower MMSE

scores at baseline (25.5±2.2) compared to those who did participate (26.3±4.8) in the program

(Ekman, Fagerberg, & Skoog, 2001). The structured program comprised of self-care

management factors known to prevent hospital readmissions including education about HF,

treatment, weight control advice, awareness of symptom deterioration and adherence to

medication. Patients with impaired cognitive functions have shown to perform worse on daily

living tasks and ability to take medications and self-care capacities. Treatments for improving

cognition in these patients will help improve self-care abilities and daily living.

2.9.6 Effects of poor sleep on cognitive function

The detrimental effects of disturbed sleep on cognitive function in adults are well

documented, and recent studies have investigated the relationship between these variables in

heart failure (HF). Riegel and Weaver (2009) proposed a conceptual model linking sleep

deprivation with cognitive impairments and poor self–care. This model illustrates that poor

sleep, due to the effects of HF medication and sleep disordered breathing, may stimulate a

cycle whereby the resulting excessive daytime sleepiness (EDS) leads to cognitive

impairment. Based on the conceptual model, the resultant cognitive impairment in turn leads

to poor self-care capacities and long-term clinical outcomes. Depression in this model relates

independently to these aforementioned variables (Riegel & Weaver, 2009). Poor sleep, is

common in older HF patients (50-85 years) and has shown to relate to reduced executive

function and attention but not language abilities in this patient group (Garcia et al., 2012).

Interestingly, compared to patients that do not have EDS, patients with EDS are less likely to

adhere to medications (Riegel et al., 2011). Furthermore, of the HF patients with EDS, those

with MCI were slightly less likely to comply with treatments compared to those without MCI

(2.5 verses 2 times more likely). More recently, Riegel et al. (2012) demonstrated that

cognition influenced HF patients’ quality of life only when considered together with

excessive daytime sleepiness. The authors suggest that the impact on quality of life is

therefore due to HF symptoms instead of changes in cognitive function. However, this notion

is novel and further studies are required to substantiate this hypothesis.


17

2.10 Summary

It is evident that cognitive impairments are prevalent in HF and to a large degree impact

negatively on the patient’s treatment compliance, quality of life, prognosis, mortality and

hospital readmissions. Adequate cognitive processes are necessary for patients to understand

and remember their treatment regime, plan when to take medications in relation to meal times

(Cline et al., 1999; Wolfe et al., 2006) and decide what to do when symptoms worsen

(Pressler, 2008; Wolfe et al., 2006). In order to determine the best treatment approach for

physicians to facilitate improvements in patient’s cognitive function, it is important to

establish which of these cognitive facets are impaired.

Chapter 3: Cognitive impairment and mood in heart failure

18

CHAPTER 3 COGNITIVE IMPAIRMENT AND MOOD IN HEART FAILURE

3.1 Introduction

Cognitive impairments have a detrimental effect on the prognosis, ability to follow treatment

regimes, quality of life and increase hospital readmission in HF patients. Numerous

researchers have concluded it is essential to acquire a solid understanding of the physiological

mechanisms associated with the relationship between HF and cognition in order to ascertain

appropriate memory enhancing and preventative interventions (e.g. Alosco, Spitznagel, Raz,

et al., 2012; Incalzi et al., 2003; Lavery et al., 2007). However, prior to establishing the

mechanisms for cognitive impairments a clear understanding of the cognitive factors, which

are compromised is required to appropriately target treatment regimes.

Studies investigating this topic to date have explored global cognitive function exclusively or

have employed a more advanced approach to clarify which specific cognitive factors are

impaired. Accurately detecting cognitive impairments and associated risk factors in patients

is necessary to help establish the best treatment protocol to improve these cognitive factors.

Improving cognitive performance in patients may improve mortality rates and decrease

hospital readmissions especially in the elderly patient cohort, where cognitive impairments

and hospitalisations are highest. This chapter will review the cognitive factors found to be

impaired and preserved in these patients especially in the older population. Additionally, an

assessment of the cognitive screening tools used in these studies will determine whether

appropriate tools are being used to detect these impairments or whether impairments are

missed due to the use of inappropriate tools.

3.2 Global cognitive function

Global cognitive function is a measure commonly used to assess overall mental processes.

Various brief assessment tools are preferred in clinical or research settings to obtain a quick

overview of individual’s cognitive status. These tools help determine whether an individual

has signs of dementia, including mild cognitive impairment, and whether further referrals are

necessary (Lezak, 2012). The Mini Mental State Examination (MMSE) has been widely used

as a dementia screening tool for HF patients in clinical settings and research trials. The

MMSE is a brief screening tool used worldwide to provide a global cognitive score by means

of summing performance on short-term memory, orientation, concentration, and visuospatial

tasks (Folstein, Folstein, & McHugh, 1975). Additionally, the Hodkinson Abbreviated


19

Mental Test (AMT; e.g. Zuccalà et al., 2005) and more recently the Montreal Cognitive

Assessment battery (MoCA; e.g. Athilingam et al., 2012; Cameron et al., 2009) provide an

assessment of global cognitive function in HF patients. With an overall cognitive score and

cut off values indicating possible dementia and mild cognitive impairments, these

aforementioned measures provide useful information about the patient’s general level of

functioning.

Although HF patients show impairments on global cognitive function such as the MMSE

compared to age matched controls, this measure is an assessment of global cognitive function

and does not evaluate performance on specific cognitive domains. Despite global cognitive

measures providing useful information about cognitive abilities, more sensitive and inclusive

neuropsychological assessment batteries are necessary to provide a better understanding of

which cognitive domains are impaired (Cameron et al., 2010; McKee, Castelli, McNamara, &

Kannel, 1971). Using various testing methods, an assessment of the various cognitive

functions in a HF patient can be determined. A thorough assessment of the various classes of

cognitive functions impaired in HF will assist in devising targeted treatments aimed to

improve cognition in these patients. Cognitive function refers to a broad range of brain

processes including memory, attention, and executive functions (Lezak, 2012). These

functions, known to deteriorate with ageing are pertinent to an elderly patient’s ability to

successfully perform daily tasks and activities (Alosco, Spitznagel, Cohen, et al., 2012;

Incalzi et al., 2003). This next section will provide definitions of mental processing in various

domains that fit under this umberella term. Additionally, neuropsychological measures used

to test these factors will be outlined.

3.3 Neuropsychological function, specific cognitive domains

3.3.1 Memory

Memory is a cognitive process where information, is stored, processed and retrieved. The

term ‘memory’ is divided into ‘working (or short-term) memory” and “episodic (or long-

term) memory”. Using short spurts of attention, information in working memory is

temporarily stored over a period of seconds by means of manipulation through rehearsal or

repeating information (Baddeley, 2000). Long-term (episodic) memory, on the other hand

involves storing information over minutes, days and years and this information can be

retrieved after a long period of time. Storage of information in long-term memory requires

effortful or focused activity (Lezak, 2012). During ageing, attention and memory decline

(Riddle & Schindler, 2007). Tests commonly used in trials to assess memory as in immediate


20

and delayed recall in HF patients include Rey’s Auditory Verbal Learning Test (RAVLT),

California verbal learning test (CVLT), digit span and Hopkins verbal learning test. In these

tests, patients are presented with a list of words or numbers and are asked to recall as many

items as they can remember from the list immediately after the words are presented, or after

some delay with or without a distracting task. Many elderly HF patients have deficits in

short-term, long-term and immediate memory (Almeida, Garrido, et al., 2012; Hjelm et al.,

2011; Kindermann et al., 2012). Such deficits are believed to contribute to the patient

forgetting to take their medications or remembering when in relation to meal times they are

supposed to take their medication (Cline et al., 1999).

3.3.2 Attention

Attentional processes are required for storing information into working memory. Attention

refers to an individual’s ability to disengage and alter their focus and to be responsive to

surrounding stimuli (Lezak, 2012; Paarasaman, 1998 in Lezak, 2012, p. 36). Various

functions defining attention include vigilance, which is the process of sustained attention, and

information processing. Studies exploring attention in HF have typically employed the digit

span test, Rey’s Auditory Verbal Learning Test (RAVLT), Visual Scanning Test, Repeatable

Battery for the Assessment of Neuropsychological States (RBANS) subtest, Trail Making-A

and Trail Making-B and Stroop word naming task.

3.3.3 Executive functioning

Executive functions are higher order cognitive processes involved in planning, attentional

control, and response inhibition. These functions are required for an individual to live

independently, be productive, carry out goal orientated and self-motivated behaviours, and

undergo successful social interactions (Lezak, 2012). Impairments in executive functions

have been observed in HF patients (e.g. Hoth, Poppas, Moser, Paul, & Cohen, 2008; Lavery,

Vander Bilt, Chang, Saxton, & Ganguli, 2007; Pressler, Subramanian, et al., 2010b).

Measures generally used to assess executive function in HF include the Trail Making-B task

and Stroop interference test as measures of response inhibition. Additionally, verbal fluency

(Lavery et al., 2007; Stanek et al., 2009) and digit symbol coding (Almeida & Tamai, 2001a;

Pressler, Kim, et al., 2010; Pressler, Subramanian, et al., 2010a) are commonly used as

measurements of executive function. It is evident that all or some of these cognitive

functions are necessary for a HF patient, especially an elderly patient, to remember and

adequately comply with treatments. The following section will focus on research that has

examined specific cognitive domains in elderly HF patients.


21

3.4 Studies using comprehensive neuropsychological test batteries

A growing number of researchers have administered comprehensive neuropsychological test

batteries in order to provide an enhanced assessment of cognitive function in HF patients.

These tests are superior to global cognitive assessments, which as mentioned in the previous

section, only provide a general cognitive assessment. Neuropsychological assessment

batteries however determine which cognitive domains are impaired and which remain intact.

An understanding of the status of these cognitive abilities will in turn provide insight as to the

appropriate treatment approach required to improve impaired cognitive domains in these

patients. The next section will critically review studies that have employed

neuropsychological test batteries to assess cognitive function in HF patients. The review will

outline the evidence in both outpatients and inpatients from prospective, retrospective and

case control studies. The structure of this section will review the literature on cognitive

impairments in elderly HF patients based on longitudinal, prospective and baseline studies

that have included or omitted control groups.

3.4.1 Longitudinal studies

Few researchers have conducted longitudinal studies to examine whether cognitive function

in older heart failure (HF) patients changes over time. Longitudinal studies using a

comprehensive neuropsychological assessment battery will provide valuable information on

whether cognitive function is intermittent or stable over time (Riegel et al., 2002). Hjelm and

colleagues (2011) investigated cognitive changes in octogenarians every 2 years over a 10-

year period. At baseline, participants diagnosed with HF exhibited worse performance on

visual spatial ability (block design), short-term memory (digit span backward and forward)

and episodic memory tasks (Prose Recall test, Thurstone’s picture and memory-in Reality)

compared to non-HF in the same age group (Hjelm et al., 2011). Interestingly, compared to

baseline measures HF patients improved on the semantic and episodic memory tasks during

the 4th (years 7 & 8) and 5th (years 9 & 10) testing periods. The authors suggested that these

cognitive improvements could be due to better health and more enhanced cognitive abilities

from surviving patients (Hjelm et al., 2011). Over the 10 year observatory period, HF patients

performed significantly worse than non-HF on the visual spatial task (Block design) when

dementia was both included and excluded in the model. Furthermore, episodic memory and

short-term memory performance was worse in HF patients compared to non-HF when

dementia cases were included in the model, even when adjusting for other variables (sex, age,

educational levels and diabetes with and without including arterial hypertension and

smoking). HF episodic memory performance showed a longitudinal decline when all these


22

variables were adjusted for in the model and when the analysis did not include patients with

dementia (Hjelm et al., 2011). Overall, compared to non-HF individuals, significantly more

HF patients were taking Furosemide and diuretics, had myocardial infarctions, strokes and

vascular dementia, and lower systolic blood pressure (BP). Therefore, it is possible that HF

medications and comorbidities influenced cognitive decline in these patients. The authors did

not report the number of patients with mild, moderate or severe HF and hence the relationship

between HF severity and cognition is therefore unknown.

Almeida, Beer, et al. (2012) examined cognitive function over a 2 year period in an older

sample (45-86 years) of patients with HF (NYHA class I to III; EF < 40%), a clinical history

of coronary heart disease (CHD; EF > 60%) and a control group (EF > 60%) that did not have

a history of CHD. At baseline assessment, patients with HF performed worse than healthy

controls on total scores and immediate and delayed recall (Cambridge cognitive capacity

scale; CAMCOG total score, immediate recall, short and long delayed recall of the

Californian Verbal Learning Test; CVLT). At the two-year follow up assessment, HF patients

scored significantly worse than healthy controls on the total CAMCOG measure. After

correcting for age, gender, IQ and mood HF patients CAMCOG scores declined over time

compared to controls. However, scores on the word recall, digit coding and digit copying

however did not significantly change at the two-year follow up. The authors did not find any

interactions between cognition and age. However since cognitive impairment increases with

age, a comparison between older (e.g. > 60 years) and younger adults (e.g. 45-59 years)

would help differentiate cognitive functions and severity of decline over time between the

two groups. In addition, since no biological measures were obtained, the researchers were

unable to establish likely physiological mechanisms associated with eventual cognitive

decline seen in their HF cohort (Almeida, Beer, et al., 2012).

Stanek et al. (2009) examined cognitive changes employing the dementia rating scale in

patients over a 12-month period. Compared to a well-matched cardiovascular disease control

group whose cognitive performance remained unchanged, dementia rating scale scores in HF

patients (NYHA class II or III; 69.08±8.74) improved 12 months following baseline

assessment. Additionally, over a 12-month period HF patients significantly improved on

attention, working memory, verbal fluency and reasoning. However, scores on memory and

construction subscales of the dementia rating scale did not change over time in HF patients.

Furthermore, the finding that higher baseline diastolic blood pressure in HF patients was

related to better dementia rating scale scores, suggests that controlling blood pressure may

improve cognitive performance in these patients (Stanek et al., 2009). This study screened for

dementia and excluded patients who scored MMSE < 24, however the authors did not report


23

information on medication, thus the effects of drug treatment on cognitive performance were

not established. Researchers have suggested that longer observational studies are required to

assess cognitive changes over time. This will help determine factors that predict decline in

HF and establish severity of the decline (Beer et al., 2009).

These longitudinal studies have shown that specific cognitive deficits including visual spatial

abilities, episodic and short-term memory are seen in HF patients. Deficiencies in specific

cognitive domains may impair a patient’s ability to understand and comply with treatment

regimens and few studies have directly examined whether these cognitive domains are a

reliable measure for predicting mortality in these patients.

3.4.2 Comparison studies: cognitive function in heart failure compared with healthy

controls

A large number of studies comparing HF performance with age matched control groups have

helped ascertain whether cognitive impairments seen in HF are due to the normal cognitive

ageing or due to the HF itself. Sauvé, Lewis, Blankenbiller, Rickabaugh, and Pressler (2009)

reported cognitive impairments in patients who had stable HF for greater than 6 months

(n=50; age > 30 years; 63±14 years; NYHA II-IV; 4.8±5.1 years) compared to healthy gender

and age (not clinically significant) matched community dwelling controls (n=50; 62.5±14

years). Notably patients displayed significantly impaired performance on attention and

immediate and delayed word recall tasks as measured by Rey’s Auditory Verbal Learning

Test (RAVLT) including the distracter item. Error rates on attention (Visual Scanning Test)

and immediate recall (Memory Scanning Test) were significantly higher in patients.

Supporting this, Beer et al. (2009) found that HF patients (n=31; 54.3±10.6 years; NHYA

class II; EF ≤ 40%) performed significantly worse than healthy controls (n=21; 56.1±8.2

years; EF ≥ 40%) on short and long delayed verbal learning (Californian Verbal Learning

test), visual memory (Brief Visuospatial Memory) as well as on general intelligence (overall

CAMCOG score). These findings represent slower mental processes in patients when

responding to difficult processes requiring concentration and the ability to store or retrieve

information (Sauvé et al., 2009). However, another study did not observe differences between

an elderly group of patients (≥ 65years) with (n=68; 78.8±7.2 years) and without HF (n=286;

77.2±6.5 years) on verbal learning and recall (Hopkins) or delayed recall, although worse

performance on visual immediate recall (clock drawing) after adjusting for age, sex, race and

education were observed (Lavery et al., 2007).

Additionally, Almeida and Tamai (2001a) compared global cognitive function upon hospital

admission and six weeks following standard treatment in patients with severe HF to that of


24

geriatric controls. Compared to geriatric controls, significant impairments upon hospital

admission in older inpatients (> 60 years) with severe HF (NYHA class IV; ejection fraction

< 45%) were seen on attentional scores, global cognitive function (MMSE), CAMCOG, digit

span and symbol, letter cancellation and Trail Making-B (but not Trail Making-A). However,

following six weeks of standard medical treatment that was targeted to the individual patient,

significant improvements were seen on visuo-motor function (digit symbol) and visual

scanning (letter cancellation) and patient functional class as measured by the 6-minute walk

test following treatment (Almeida & Tamai, 2001a). No significant group differences were

seen in post treatment cognitive scores. Based on the findings the authors suggested that

cognitive impairment in HF is reversible and that their cognitive function may equate to that

of geriatric patients in the same age group.

Examining motor abilities, HF patient’s performance on tapping rate variability as

determined by performance on the attention and immediate recall (Visual and Memory

Scanning Tests) was worse than healthy controls (Sauvé et al., 2009). In the elderly patient

cohort however, HF patients showed no significant impairments on psychomotor functions

(Trail Making-A) compared to patients without HF (Lavery et al., 2007). Nevertheless, HF

patients performed worse than controls on visuo-spatial abilities (Block design) and executive

function as measured by Trail Making-B and Verbal fluency (Lavery et al., 2007). However

Lavery et al. (2007) conducted neuropsychological testing at the patient’s home and the

uncontrolled testing environment may have confounded the results.

Studies examining HF patients following standardised treatment such as cardiac

resynchronisation in patients who do not respond to treatments and examining the effects of

treatments on decompensated patients suggest that cognitive dysfunction in HF is reversible.

Recently Kindermann et al. (2012) provided insight on how cognition in decompensated

patients changes following cardiac compensation. In their study, Kindermann et al. (2012)

tested cognitive performance in decompensated patients who had moderate or severe HF

(NYHA class III or IV) for 6 months prior to testing. Assessments of patients’ cognitive

performance were conducted within 48 hours after hospital admission and again when

compensation was apparent following intravenous diuretics and/or vasodilators and/or

inotropes (14±7 days). Cardiac compensation was shown to significantly improve patients’

short-term verbal (digit span forward) and episodic memory, speed of information processing

and executive control but not verbal working memory (digit span backward), quality of life or

depression levels. Decompensated patients performed significantly worse than stable HF

patients and healthy controls on short-term memory, working memory, speed of information

processing and executive control (Stroop interference test). However, patients with stable HF


25

performed similarly to healthy controls on the Stroop interference. Additionally, patients with

stable HF performed worse than healthy controls on working memory, episodic memory and

speed of information processing. The two control groups who were recruited from a HF

outpatient clinic and a psychology clinic participated in two testing sessions, which were on

average the same number of days apart as the experimental group. In their study, although the

healthy controls performed significantly better in the post-test working and episodic memory,

the authors fail to suggest these improvements were due to practice effects (Kindermann et

al., 2012).

3.4.3 Comparison studies: cognitive function in heart failure compared with other diseases

Researchers have also compared cognitive functions with those of patient groups that have

similar risk factors to that of HF patients. Hoth et al. (2008) for instance compared cognitive

performance of patients (aged > 55 years) with HF (NYHA class II, III or IV; 69.1±8.5 years;

LVEF < 40%) to those with cardiovascular disease but no HF (68.9±8.5 years), matched for

age, sex and years of education. When exploring raw data it was revealed that HF patients

performed significantly worse than cardiovascular disease controls on attention and

psychomotor speed (Trail Making-A), executive function (Trail Making-B) and response

inhibition (Stroop interference) tasks (Hoth et al., 2008). However, no group differences were

seen on immediate or delayed memory recall, visuospatial, language or attention indexes (as

measured by the RBANS).

In a study by Trojano et al. (2003), cognitive data was compared from elderly hospitalised

patients (≥ 65 years) with heart failure (HF) to that of patients with cardiovascular disease but

no HF. The authors aim was to assertain cognitive performance in HF patients when

controlling for possible confounding factors such as neurological disease and organ damage.

A greater percentage of patients with severe HF showed abnormal performance on 2 or more

cognitive tasks followed by patients with mild HF and no HF (57.9%, 43% and 34.3%,

respectively; Trojano et al., 2003). Patients with mild HF (NYHA II) scored significantly

worse than controls with cardiovascular disease on verbal fluency (verbal attainment,

indicating frontal lobe function). Patients with moderate and severe HF (NYHA III-IV) on

the other hand scored significantly worse than cardiovascular controls on global cognitive

function as measured by MMSE, attention matrices (sustained attention), immediate and

delayed word recall tasks. In this sample the mean global cognitive scores (MMSE) were

generally low, with the mean for each group below 25 (no CHF 24.7±4; mild HF 23.7±4.2

and severe HF = 22.4±5.6) and significant differences seen only between the no HF and

severe HF groups. These low MMSE scores could have been due to the severity of the HF


26

since the patients were inpatients. An additional cognitive assessment during a follow up

period when the HF condition was more stable would have provided a better long-term

assessment of cognitive function in these patients when their HF improved (Trojano et al.,

2003). Since HF symptoms improve post discharge and cognitive performance is correlated

with HF severity, it is more appropriate to assess cognitive performance at follow up

outpatient visits when the HF condition is stable.

Recently, Almeida, Garrido, et al. (2012) found that HF patients scored lower on immediate

and long-term memory and slower psychomotor speed (as measured by the CVLT) compared

to healthy controls. However, these observations were not seen between patients with HF and

ischemic heart disease. HF patients however also had significantly lower CAMCOG and digit

code scores to those of healthy controls only when controlling for possible confounding

variables.

Cognition in hospitalised patients also differs from that of controls and HF outpatients.

Almeida and Tamai (2001b) explored cognitive performance differences between elderly

patients (≥ 60 years) with severe heart failure (HF; NYHA class III and IV) who were

admitted to hospital. Compared to patients attending a geriatric outpatient clinic (n=30;

ejection fraction > 60%), HF inpatients (n=50; ejection fraction < 45%) performed

significantly worse on overall CAMCOG scores and 5 of the 7 subscales (orientation,

language, memory, praxis and abstraction; Almeida & Tamai, 2001b). Overall, almost 75%

of the HF patients and 30% of the older controls demonstrated cognitive impairments as

described by a CAMCOG score of less than 80. There were no variations in performance on

attention tasks of the CAMCOG neuropsychological test battery.

Almeida and Tamai (2001b) assessed inpatients within 72 hours following admission,

therefore factors commonly seen in hospitalised patients and not in geriatric outpatients may

have contributed to poor cognitive performance in the HF group. Furthermore, factors known

to influence cognition were present in a greater number of HF patients than controls. For

instance more HF patients than controls had a history of stroke (14% versus 0%), were heavy

smokers (52% versus 23%) and regularly consumed alcohol (28% versus 0%). Finally, these

studies did not investigate whether certain medications were possible confoundering factors

on cognitive performance.

Case controlled studies using greater than one control group have provided insight into

whether cognitive deficiencies in heart failure (HF) are due to the HF itself, normal ageing or

result from comorbidities commonly seen in HF (Pressler, Subramanian, et al., 2010b).

Pressler, Subramanian, et al. (2010b) compared cognitive function of adults predominantly


27

with mild HF (n=249, NYHA class I – IV), a healthy control group living independently in

the community (n=63) and a group of patients diagnosed with a chronic condition other than

HF (n=102). In this study, the groups were matched for intellectual function (IQ) and global

memory scores (MMSE) but not age as the healthy control group was significantly younger

(53.3±17.2 years) than the HF (62.9±14.6 years) and clinical patient groups (63.0±11.9

years). Compared to the two control groups, HF patients had inferior scores on total verbal

memory recall (as measured by Hopkins Verbal learning test), psychomotor speed (as

measured by digit symbol substitution and Trail Making-A) and executive function (as

measured by Trail Making-B). These observations were seen between the HF and non-HF

patient groups when exploring Z scores. Additionally compared to non-HF patients those

with HF recalled fewer words on the delayed memory recall task (Hopkins Verbal learning

test). However, no group differences were seen in global cognition (MMSE) or visuospatial

and working memory tasks (digit span). This study included asymptomatic HF patients

(NYHA class I) in the analyses, hence it is unknown whether significant group differences

were present in patients with mild, moderate and severe HF. Of this participant cohort, almost

one-fifth HF patients exhibited impairments in total verbal memory recall (23%) and

executive function (19%) as opposed to approximately 10% of the healthy and non-HF

medical groups. Likewise, almost a quarter (24%) of HF patients showed impairments on

greater than three cognitive domains followed by the healthy (14%) and medical non-HF

controls (12%; Pressler, Subramanian, et al., 2010b).

In a small study, Grubb, Simpson, and Fox (2000) compared neuropsychological functioning

in patients aged 53-75years who had a previous episode of myocardial infarction with

(NYHA class II or IV; LVEF < 40%) and without (LVEF > 50%) stable HF. No significant

group differences were observed on any of the cognitive measures (Rivermead and digit span

tests). Although compared to myocardial infarction controls, the HF patient group scored

significantly higher on depression and anxiety, and scored lower on intelligence quotient (as

measured by the NART). It is unreasonable for the authors to conclude that patients with

stable heart failure and a history of myocardial infarction do not have memory problems,

since the control group consisted of patients with a history of myocardial infarctions, which

are associated with cognitive impairments. A control group without factors known to effect

cognition are preferred when examining cognitive abilities and any deficits in HF patients.

3.4.4 Comparison studies: cognitive function in heart failure compared with normative data

Other researchers have compared cognitive results of heart failure (HF) patients to normative

data rather than a control group. In one study HF patient’s (72±12 years) performance on


28

immediate and delayed memory (RBANS), language, attention, executive function and

psychomotor speed (Trail Making-A) was shown to be significantly lower than normative

means on respective tests (Bauer et al., 2011). Executive function, as measured by Trail

Making-B and visual constructional measures, were not different to those of normative data.

In a small study, Wolfe et al. (2006) found that compared to means of age matched normative

data, older patients HF patients (n=38; 76% male; 64±7.62 years) showed impairments on

immediate and delayed memory and reduced total RBANS index score. However, the authors

did not stipulate whether impairments in memory scores were visual or auditory.

Furthermore, HF patients’ executive function scores, as measured by the Winston card-

sorting test, were significantly higher than those of the age expected norms. Although

significant observations were not found on premorbid intelligence (IQ), language or

attentional abilities, patients showed a trend towards impaired performance on attention and

visuospatial/constructional index scores on the RBANS test battery compared to normative

data (Wolfe et al., 2006). A thorough assessment of the HF disease severity such as NYHA

classifications in the Wolfe et al. (2006) study would have provided a more accurate

diagnosis of HF and enabled better comparison of cognitive performance with findings from

other studies. In addition, this study did not assess depression and carer support that may

influence coping styles and patient performance on cognitive tests. Finally, comparing HF

patient results from age adjusted normative data rather than an age and IQ matched control

group further decreases the reliability of the findings from this study.

3.5 Effects of disease severity on cognitive function in heart failure

The influence of disease severity on cognitive impairment in heart failure (HF) has been

broadly studied. Based on studies that have pooled patients with various degrees of HF in

their analysis, there is clear evidence that cognitive impairments exist in this patient group.

However, few studies have compared cognitive performance amongst patients with mild,

moderate and severe HF in order to ascertian whether disease severity has an impact on

cognitive ability. Disease severity has been shown to predict of poor cognitive performance

in patients with chronic HF and cardiac controls (Vogels, Oosterman, et al., 2007). Studies

have shown that reduced attention, immediate recall (memory scan and RAVLT; Sauvé et al.,

2009), memory, visuospatial ability, psychomotor speed, and executive function (Pressler,

Subramanian, et al., 2010b) is associated with a higher NYHA functional class in older

patients. Even after controlling for variables known to affect cognition (e.g. age, atrial

fibrillation, diabetes mellitus, hypertension, and depression), Incalzi et al. (2003)

demonstrated that global cognition as measured by the MMSE reduced significantly with


29

increasing NYHA class in older patients with stable HF. Supporting this, Bauer et al. (2011)

showed that poorer performance on psychomotor speed (Trail Making-A), executive function

(Trail Making-B), attention (RBNS) and global cognition (RBNS total scores) were related to

increased disease severity. Furthermore, Trojano et al. (2003) showed that mild HF patients

recalled significantly more words on the Rey’s immediate recall task compared to patients

with severe HF (NYHA class III-IV), however no other group differences on cognitive tasks

were observed. Interestingly, Sauvé et al. (2009) failed to find a relationship between HF

severity and cognitive impairments in older patients with chronic HF (> 6 months; NYHA

class II – III).

Heart failure (HF) patients impaired in global cognitive scores are more likely to be older and

less educated compared to controls, suggesting that education has a protective role in global

cognitive function (Cacciatore et al., 1998; McLennan et al., 2006). Interestingly, disease

severity in HF is also associated with increasing age (Trojano et al., 2003). Compared to

patients who do not have severe cognitive impairment (MMSE score of ≥ 24), older HF

patients with severe cognitive impairment or probable dementia (MMSE score of < 24) have

been shown to be older, less educated and present with higher depression scores (Geriatric

Depression Scale; Cacciatore et al., 1998). Addtionally, HF patients with severe cognitive

impairment (MMSE score of ≤ 24) are less likely to have an education level of more than 8

years (Debette et al., 2007). However, other smaller studies which included asymptomatic

patients and no control group have failed to find an association between age, education,

NYHA class or hypotenstion with cognitive impairment (Riegel et al., 2002). Significant

differences in years of education between severe (NYHA class III or IV) and mild (NYHA

class II) HF groups was not observed in a large Italian study, however signficiant differences

were found with severe HF patients who had lower numbers of school years compared to

patients without HF (Trojano et al., 2003).

3.6 Psychological parameters and mood disturbances in heart failure

Few researchers have examined the relationship between depression and cognitive functions

in HF. Psychological factors known to effect cognitive function such as depression, anxiety

and fatigue, are greater in older HF patients (e.g. Stephen, 2008). Some authors have shown

no significant differences in depression among patients with mild HF (NYHA class II; EF ≤

40%; 54.3±10.6 years) and healthy controls (EF > 40%; 56.1±8.2 years) using a brief mood

scale (Beer et al., 2009). Whereas other authors have reported significantly greater levels of

depression (Patient Health Questionnaire; PHQ-8) in patients with mild, moderate or severe

HF (NYHA class I, II, III and IV) compared to healthy controls and a group of patients


30

without HF (Pressler, Subramanian, et al., 2010b). In addition, a recent study by Almeida,

Beer, et al. (2012) reported increased levels of depression and anxiety in HF patients (NYHA

I to III; EF < 40%) who also exhibited cognitive decline over a two year period. Although the

authors found changes in negative affective states and cognition over time, they failed to

explore whether a relationship exists between depression or anxiety and cognitive function

(Almeida, Beer, et al., 2012). Given the association between mood and cognitive function, it

is crucial to examine how these psychological states might affect patients’ cognitive function.

3.6.1 Mood disturbances and cognitive function

Some researchers have reported that depression is independently related to cognition, sleep

issues (poor sleep and excessive daytime sleepiness), impaired daily living capabilities and

poor quality of life (Garcia et al., 2012; Riegel & Weaver, 2009). Based on their findings

authors suggest that there is no association between cognitive impairment and depression in

HF. In an early study Cacciatore and coworkers found that although HF patients had

significantly higher levels of depression compared to patients with no HF, that global

cognitive impairment (measured by the MMSE) was independent of depression (and gender,

age, educational level, diabetes, hypertension, alcohol consumption, smoking, atrial

fibrillation, BP, heart rate; Cacciatore et al., 2008). Other authors have reported that

depression and anxiety does not explain performance on cognitive measures (Pressler,

Subramanian, et al., 2010b; Sauvé et al., 2009). Interestingly with Sauvé et al. (2009) found

that psychologically distressed patients were significantly younger, had greater difficulties

performing their physical roles, and had less social support (as measured by the SF-36). This

suggests that younger patients may be more aware of their physical limitations than older

patients are and react with anxiety and depression. However, given that the controls were

aged 55 years and older with 90% of the controls aged 65 years and HF patients aged > 30

years, caution must be taken interpreting these results. Despite evidence suggesting that

cognitive deficits seen in HF occurs independently of common comorbidities such as mood,

cognitive impairments may therefore be due to secondary factors (Almeida & Flicker, 2001;

Cacciatore et al., 1998; Pressler, Subramanian, et al., 2010b).

In addition to depression, other mood states including fatigue and anxiety increase in severity

in older HF patients. Fatigue has been related to depression, emotional health and poor

physical health in HF (Evangelista et al., 2008) with levels increasing with increasing disease

severity (Fink et al., 2012). The fatigue subset of the Profile of Mood States questionnaire

(POMS) has been used to demonstrate that fatigue is prevalent in elderly patients (> 65 years)

with systolic dysfunction (LVEF ≤ 40%; NYHA class II or III) or compensated HF patients


31

scoring on average 11.5 (range 5 – 25; Stephen, 2008). However, without a control group in

the study by Stephen (2008), it is unknown whether these observations are unique to HF or

related to the age of this older group. In another study Fink et al. (2012) found that HF

patients with reduced ejection fraction (57±1.7 years) were more fatigued (POMS) than

controls, however this result was not observed when depressive symptoms was included as a

covariate. Furthermore, in addition to increased depression and anxiety scores, reduced levels

of vigour using the POMS questionnaire was reported in female HF patients (Riedinger,

Dracup, & Brecht, 2002).

There is some evidence suggesting a relationship between depressed mood and cognition in

HF. Incalzi et al. (2003) showed that mood, as measured by the Geriatric Depression Scale

(GDS), was associated with a decrease in the number of words recalled by elderly HF

patients (NYHA II, III and IV) in the delayed recall task. Recently, Garcia et al. (2011) found

that when adjusting for sex, hypertension and cardiac fitness (2MWT), depression (as

measured by the Beck depression inventory; BDI-II) predicted cognition in HF (NYHA class

II and III; aged 68.53±9.3 years; range 50-85 years; 36.5% female). Specifically, higher

depression scores predicted scores on global cognition, attention and executive function

(frontal assessment battery, Trail Making-A, Trail Making-B and digit substitution), memory

(CVLT), language (Boston and Animal naming tests) and motor (grooved pegboard test)

composite scores. Furthermore, increased depression was related to poorer performance on

attention (Trail Making-A), executive functioning, (Trail Making-B, digit symbol coding),

motor abilities (grooved pegboard test) and language tasks (animal naming task) but not

memory (CVLT), cardiac fitness (2MWT) or anterior and middle cerebral blood flow

velocity (Garcia et al., 2011). Although patients were clinically impaired on the depression

scores and cognitive tasks, the authors did not report whether these impairments were

statistically significant. These findings suggest that depression relates to a decrease in

performance on global and specific cognitive function measures in older HF patients (Garcia

et al., 2011).

Like cognitive function, higher levels of depression in HF has been related to reduced levels

of quality of life (Pressler, Subramanian, et al., 2010a), and predicts treatment compliance

(Glazer, Emery, Frid, & Banyasz, 2002). It is therefore necessary to examine the influence of

depression when assessing mechanisms for cognitive impairment in HF to ascertain whether

these factors are linked.


32

3.7 Factors that improve or worsen cognitive dysfunction in heart failure

3.7.1 Pharmaceuticals

A limited number of studies have explored treatments for improving cognitive function in

HF. It is possible that medications taken at different stages of the disease may impact on

patients cognitive capacities. There is evidence that heart failure (HF) medication does not

influence cognitive function in older patients (65-95 years) with low (MMSE < 24; mean age

76.7±6.9) or high (> 24; mean age 72.6±5.5) global cognitive function (Cacciatore et al.,

1998). In contrast, a more recent study by Hoth et al. (2008) showed that patients taking

angiotensin converting enzyme (ACE) inhibitors performed worse on psychomotor speed

(Trail Making-A) compared to those who were not taking this drug, although no differences

were seen on the executive function (Trail Making-B or Stroop interference task).

Interestingly, the opposite findings were observed with patients taking non-steroidal anti-

inflammatory drugs who performed better on executive function tasks, however no

differences were observed with psychomotor speed (Hoth et al., 2008). These findings

suggest that ACE inhibitors may negatively influence performance on psychomotor speed

and nonsteroidal anti-inflammatory drugs positively influence performance on executive

function tasks in patients with HF and cardiovascular disease.

In contrast, a large Italian study revealed that cognitive function during hospitalisation, as

measured by the Hodkinson Abbreviated Mental Test (AMT) was higher in HF patients

treated with angiotensin converting enzyme (ACE) inhibitors compared to patients not treated

with this drug (Zuccalà et al., 2005). However, the relationship between cognition and ACE

inhibitors was not observed with inpatients that did not have HF. In this cohort, cognitive

impairment was more prevalent in patients with lower blood pressure. However, patients with

higher systolic blood pressure were more likely to be prescribed higher dosages of ACE

inhibitors, suggesting that that high systolic blood pressure influenced cognition rather than

the treatment itself (Zuccalà et al., 2005). Moreover, HF patients who started taking digoxin

in hospital showed improvement in cognitive function as measured by the Hodkinson AMT

compared to HF patients (≥ 65 years) who did not take this drug at discharge (23% vs 17%;

Laudisio et al., 2009). Finally, compared to normative data based on age and education.

Incalzi et al. (2003) found that patients (NYHA class II, III and IV) taking digoxin (68.8

verses 54.9%) and diuretics (68.2 verses 55.3%) were more likely to perform abnormally on

immediate word recall (Rey’s immediate recall) compared to patients not taking these drugs

(Incalzi et al., 2003). Interestingly, in this study diuretics and digoxin but not ACE inhibitor


33

use increased with increased disease severity, therefore it is possible that worse cognitive

performance was related to disease severity and not the medication.

To date there is mixed evidence for heart failure (HF) medications in particular angiotensin

converting enzyme (ACE) inhibitors, digoxin and diuretics to selectively improve cognitive

performance in older hospitalised HF patients (Hoth et al., 2008; Laudisio et al., 2009;

Zuccalà et al., 2005), although only a handful of studies have examined the effects of drugs

on patients with stable HF. It is therefore premature to conclude whether or not

pharmaceuticals contribute to cognitive impairments commonly seen in HF patients and

further trials need to examine this relationship. Interestingly, in a large study, Callahan,

Hendrie, and Tierney (1995) investigated the rate of cognitive impairment as measured using

a short Mental Status Questionnaire during routine oupatient visits in an eldely sample

requiring primary care. Patients with cognitive impairment were more likely to have

malnutrition, been prescribed aspirin and less likely to have taken anti-inflammatories two

years before the screening date. It is possible that the pharmcological mechanisms of these

drugs may prevent cognitive decline in patients. Only a few studies have explored the effects

of pharmaceutical interventions on cognition. To date there is some evidence indicating that

ACE inhibitors and digoxin to selectively improve cognitive performance in older

hospitalized HF patients (Laudisio et al., 2009; Zuccalà et al., 2005). Even with successful

conventional treatments for improving HF, there are no specific remedies designed to repair

or prevent cognitive deficits prevalent in HF patients.

3.7.2 Exercise programs

In a small study, Tanne and colleagues (2005) assessed the effects of an 18-week daily

aerobic exercise program (twice a week) on cognitive function in patients with stable HF

(NYHA class III). The exercise program consisted of a 15-minute warm up followed by 35

minutes on a treadmill, stair machine and bicycle (at 60-70% maximal heart rate). Five

patients who were unable to complete the exercise program were the controls. The aerobic

exercise program significantly improved patients (n=18; NYHA class III; mean age 63 years;

39-81 years; mean EF < 26%) performance times on the executive functioning tasks (Trail

Making-A and Trail Making-B, congruent Stroop word naming task). Although these effects

were possibly due to learning effects, the authors did not find similar improvements in the

control group (n=5; mean age 66 years; 58-77 years; mean EF < 23%). However, no changes

were seen on other cognitive measures (MMSE scores, verbal fluency). Additionally, no

significant changes due to exercise were observed in middle cerebral arterial (MCA) blood

flow velocity, which has been implicated in poor cognition (Tanne et al., 2005). Furthermore,


34

HF patients (aged 54.3±10.6 years; NHYA class II) who were able to walk long distances on

the 6-minute walk test had better cognitive performance (as measured by the CAMCOG;

Beer et al., 2009). Although the benefits of exercise are promising, elderly patient’s ability to

exercise may be limited due to concomitant diseases such as arthritis, poor vision and

hearing, which will affect patients’ ability to exercise (Schwarz, 2007).

3.7.3 Educational programs

Educational programs tailored to HF patients have proven to be promising for improving

patients understanding of treatments and in turn improving compliance. Educational program

guided by a nurse who outlined and explained treatment protocols to outpatients after

discharge, was effective in improving patients understanding of their treatments and

enhanced treatment compliance, however cognitive impairments still existed in these patients

(Pressler et al., 2011). Given that nurses and pharmacists do not usually provide ongoing

regular educational support and follow ups, educational programs utilizing computers at

home may also be promising. Pressler and colleagues (2011) showed that the effects of a

computerised educational program (Brain Fitness Program; PositScience) designed to

enhance learning through auditory and visual exercises, in addition to Health Education

Interventions involving regularly reading a magazine (Heart Insight), significantly improved

the number of words patients learned and recalled following a 12 week intervention (Pressler

et al., 2011). The computerised intervention but not the Health Education Intervention also

improved delayed memory performance following the 12-week intervention. It is possible

that due to poor cognitive capacities, patients did not understand or assimilate the information

provided to them during the education intervention (McLennan et al., 2006).

Despite promising findings for improving patient’s ability to learn and recall treatments, there

is no established universal treatment for detecting or improving cognitive dysfunctions in HF.

Authors have proposed that interventions are required to prevent and delay cognitive

impairments in HF (Pressler, Kim, et al., 2010). This may be achieved using a

multidisciplinary team to help detect early signs of memory loss and methods to help care for

these patients. Treatments that directly target mechanisms of cognitive impairment in these

patients need to be established and tested. Although there is no universal treatment for

improving cognitive function in HF patients, there is limited evidence suggesting that

exercise, educational programs and pharmaceutical treatment may assist. However, in order

to establish the most effective treatment for improving cognition in HF, a clear understanding

of which cognitive domains are impaired is imperative. In addition, targeting physiological


35

mechanisms associated with these impairments is vital when devising an appropriate

treatment intervention.

3.8 Summary

In summary, longitudinal studies have shown that overall cognitive scores improve (Almeida,

Beer, et al., 2012; Stanek 2009) and episodic memory declines (Hjelm et al., 2011) in older

heart failure (HF) patients without dementia. It seems that HF treatment improves patients’

short-term memory, working memory, speed of information processing and executive

function. Although comparing performance with those of age-matched controls, older HF

patients still exhibit impairments in attention, immediate and delayed memory recall,

visuospatial abilities and executive function. Possible interventions for improving cognitive

function in HF may include nurse led and computerised educational programs, exercise

programs and HF medications, however there are no established universal treatments for

improving cognitive function in HF patients. An understanding of the mechanisms for these

specific cognitive impairments HF is necessary in order to help devise a safe adjunctive

intervention to reduce or ameliorate further decline. Since HF is a complex condition,

addressing every potential mechanism is not feasible, therefore this thesis will focus on

vascular, inflammatory, oxidative stress and antioxidant mechanisms that may be associated

with cognitive impairments as assessed by non-invasive measurements and those that can be

easily used in a clinical setting.

Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure

36

CHAPTER 4 MECHANISMS ASSOCIATED WITH COGNITIVE IMPAIRMENT

AND MOOD IN HEART FAILURE

4.1 Introduction

Although cognitive impairment is prevalent in older HF patients, there are no proven

effective treatments to improve cognitive function in these patients. As outlined in the

previous chapter, it is clear that HF patients have significantly greater impairments in

cognitive processes compared to healthy controls (e.g. Beer et al., 2009; Sauvé et al., 2009;

Vogels, Oosterman, et al., 2007) and patients with other forms of cardiovascular disease

(Vogels et al., 2008). Cognitive domains primarily shown to be impaired include memory,

mental speed, attention, language and global cognitive scores. A relationship has been

purported between cognitive deficits and factors such as disease severity or medications,

however supportive evidence is inconclusive.

It is difficult to obtain an accurate account of the prevalence of cognitive impairments and the

domains effected. This is due to inconsistencies in trials, in particular with different studies

using different cognitive assessment batteries and cut off scores for cognitive impairments.

There is a poor understanding of the physiological mechanisms associated with cognitive

dysfunction in HF patients. Interventions addressing these mechanisms may help prevent or

ameliorate cognitive dysfunction in elderly HF patients.

This chapter will provide an overview of current theories related to possible mechanisms for

cognitive impairment in elderly HF. Firstly, the evidence concerning changes to vascular

function thought to relate to cognitive impairment in HF will be reviewed. Furthermore,

possible additional mechanisms including oxidative stress and inflammation, which are seen

in both HF and cognitive decline, will also be discussed. To provide an overview of the

vascular factors contributing to cognitive impairment in HF, the first section will address the

physiology of blood flow from the heart to the brain. Vascular factors described in this first

section will include the hearts functional abilities, haemodynamics, biomarkers and changes

in cerebral blood flow.


37

4.2 Vascular mechanisms

The function of the heart is to pump enough blood to provide tissues and organs in particular

the brain with a sufficient supply of nutrients, glucose and oxygen for adequate functioning.

The pathophysiological pathways involved in the progression of HF encompass a complex

model that involves the activation of neurohumoral mechanisms. It is thought that an “index

event” which can be sudden (e.g. myocardial infarction) or long standing pathology (e.g.

haemodynamic pressure and genetic cardiomyopathies) results in a loss of myocardial

functional force. Because of a decreased functional force, the heart is unable to pump

adequately and the resultant decrease in cardiac output leads to poor blood supply to organs

and tissues. With an aim to reach a level of homeostasis, the body has inbuilt protective

mechanisms, which work towards increasing cardiac output to a normal level (Libby, Bonow,

Mann, & Zipes, 2008) and in turn maintain perfusion to vital organs, and myocardial

hypertrophy to normalise heart wall stress (Rundqvist, Elam, Bergmann-Sverrisdottir,

Eisenhofer, & Friberg, 1997). During early stages of the disease, certain haemodynamic

compensatory mechanisms are activated in order to initially maintain a normal level of

cardiac output.

Vascular risk factors reducing carotid arterial and cardiac blood flow to the brain are believed

to relate to cognitive impairments (e.g. de la Torre, 2000). A theory presented by de la Torre

(2000) describes the role of critically attained threshold of cerebral hypoperfusion (CATCH)

in the development of AD. CATCH arises when additional vascular risk factors further

reduce cerebral blood flow over and above that seen in normal ageing resulting in a cascade

of events, including reduced energy metabolism and increased oxidative stress, leading to the

production of amyloid-β proteins, neurodegeneration and finally symptoms of dementia (de la

Torre, 2000).

Various authors studying this topic accept that vascular factors such as poor cerebral blood

flow, in particular to brain regions fundamental to cognitive processing and microemboli in

the brain are chief mechanisms involved in poor cognitive function in HF (Jesus et al., 2006).

Researchers have proposed that insufficient vascular function leads to reduced metabolism

and a deprived supply of vital nutrients and oxygen to the brain.

Researchers have proposed that low left ventricular ejection fraction (LVEF) results in

impaired cerebrovascular function and in turn poor cognitive function. LVEF is the

percentage of blood pumped out of the left ventricle. An early study by Zuccalà and

colleagues (1997) showed a non-linear correlation between global cognitive function

(MMSE) scores and ejection fraction in HF patients. In their study, Zuccalà and colleagues


38

(1997) demonstrated that greater decreases in visuospatial ability (Ravens matrix), overall

MMSE scores, and the attention subset of the MMSE related to ejection fraction values of

less than 30%. Ejection fraction has shown to not only predict global cognitive function but

also specific cognitive functions including motor speed and visuo-constructural performance

independent of beta-blockers (Gottesman et al., 2009), working memory (digit span),

attention, and executive function (Trail Making-B; Almeida & Tamai, 2001b).

Supporting these findings, other trials showed that low ejection fraction is related to poor

short-term, working and episodic memory, attention and executive function in patients with

decompensated and stable HF (Kindermann et al., 2012). Other authors however have failed

to find any correlations between ejection fraction and MMSE scores (Jesus et al., 2006),

memory or executive function (Wolfe et al., 2006). However, the sample size in the latter

study was small and echocardiography was conducted within 18-months prior to testing,

during which time the HF patient’s condition may have improved or worsened. There is some

evidence that structural changes in the heart as seen in left ventricular dysfunction causing

changes in ejection fraction may be a possible mechanism for worse performance on memory

in older HF patients, however more research is needed to confirm these suggestions.

Nutrient, glucose and oxygen supply to the brain is so important that during conditions where

blood flow is compromised, the brain has an in built mechanism to modify cerebral

microvascular circulation (or autoregulate) to achieve consistent blood flow in the brain

(Newell & Aaslid, 1992; Paulson, 2002). This dynamic process ensures that blood volume in

the brain remains consistent when there are changes in perfusion pressures (Newell & Aaslid,

1992). Researchers have suggested that cognitive impairments arise from functional and

pathological change in the entire cardiovascular system seen in older HF patients, including

changes to the heart, the central and peripheral vascular systems and cerebral vascular

changes (Vogels, Scheltens, et al., 2007). There is increasing evidence suggesting that

vascular factors contributing to ischemia and reduced cerebral blood flow are implicated in

cognitive impairments seen in HF patients (Georgiadis et al., 2000; Jesus et al., 2006; Vogels

et al., 2008).

4.3 Cerebral haemodynamic factors

There is evidence suggesting that cerebral hypoperfusion may be the mechanism underlying

cognitive impairments in heart failure (HF) patients. This is due to poor cerebral blood flow

in brain regions involved in cognitive function in patients with HF (Georgiadis et al., 2000).

Heart failure (HF) patients who have undergone heart transplants have shown significantly


39

improved cerebral blood flow following transplant (Deshields, McDonough, Mannen, &

Miller, 1996; Gruhn et al., 2001). Interestingly patients with heart transplants (Bornstein,

Starling, Myerowitz, & Haas, 1995), ventricular assist devices and other surgical devices (e.g.

pacemakers) have shown to improve cognitive function following procedure. These

aforementioned studies indicate that improvements in cognitive function are possible or that

cognitive impairments are reversible in HF patients (Taylor & Stott, 2002). Three months

following cardiac resynchronisation therapy through implantation of a biventricular device

(implantable cardioverter defibrillator), patients with moderate and severe HF (NYHA class

III and IV) were shown to improve performance on task measuring working memory,

attention and speed of information processing (Dixit et al., 2010). Additionally, quality of life

specific to cardiac symptoms improved in patients three months following implantation of the

device.

In recent years, an increasing number of researchers have provided further insight into the

cerebrovascular mechanisms associated with cognitive impairment in HF using advanced

imaging technologies. These studies have utilized advancements in Doppler and imaging

techniques to assess whether cerebral perfusion changes with cognitive decline. Exploring

changes in blood pressure, blood flow velocity and biomarkers known to influence the

vasculature can determine cerebrovascular function. Furthermore, with improvements in

imaging techniques, researchers have applied advanced techniques to assess whether blood

flow supplied to the periphery and in specific brain regions where cognitive processing

occurs are sufficient. Such useful imaging techniques include Transcranial Doppler (TCD)

and Magnetic Resonance Imaging (MRI) techniques.

4.3.1 Transcranial Doppler

There is growing interest in the relationship between changes in cerebral blood flow velocity

and the influence cognitive function in HF patients. Cerebral blood flow velocity in basal

cerebral arteries can be measured by means of a Transcranial Doppler (TCD) device. The

TCD is a non-invasive, hand held device used to measure velocity of blood flow using the

Doppler Effect. Blood vessels supplying blood to the brain include the common carotid

arteries, which branch off into the internal and external carotid arteries. The internal carotid

artery enters the skull via the carotid canal and the temporal bone. Further along its path, the

internal carotid artery penetrates the dura mater and branches into three branches, the anterior

cerebral artery (ACA), the posterior cerebral artery (PCA) and the middle cerebral artery

(MCA; Babikian & Wechsler, 1993). The ACA supplies blood to the anterior, superior and

medial sections of the frontal lobes and the medial area of the cerebral hemispheres. The PCA


40

supplies blood to the midbrain, thalamus and the inferior temporal lobe and medial section of

the occipital lobe. The MCA supplies blood to the basal ganglia and then splits into two or

three additional arteries, which collectively supply blood to the lateral hemisphere (superior

branch), temporal and inferior parietal lobes (inferior branch; Babikian & Wechsler, 1993).

The MCA is of particular interest in this study as it supplies almost 80% of blood to the

hemispheres and regions involved in cognitive processing. The temporal lobes are involved

in memory processes, in particular learning and retention (Newell & Aaslid, 1992), which

have shown to be impaired in HF patients. Additionally, the parietal lobes are important for

processing short-term memory and attention, which are cognitive factors impaired in elderly

HF patients. TCD has been utilised to assess whether associations exist between cognitive

function and blood flow velocities in common carotid and basal cerebral arteries providing

relevant brain regions with sufficient blood. Although carotid arterial blood flow in heart

failure patients has not been widely studied, cardiovascular risk factors general

cardiovascular disease risk scores have shown to predict reduced pulsatile blood flow

velocity in the common carotid arteries of healthy elderly individuals (Pase, Grima, Stough,

Scholey, & Pipingas, 2012).

4.4 Cerebral circulation and cognitive function

Few studies have explored the relationship between common carotid blood flow velocity and

cognitive function. Reduced common carotid arterial blood flow is associated with poor

global cognitive function in patients with mild-moderate carotid arterial disease (Fu, Miao,

Yan, & Zhong, 2012). The present study will expand on this preliminary evidence to explore

whether reduced blood flow in the common carotid artery is a possible mechanism for

cognitive impairment in HF patients. Research into the function of the cerebral arterial blood

flow velocity rather than common carotid blood flow and cognitive function has received

greater interest. This is possibly due to the anatomical position of the middle cerebral artery

being closer to regions involved in cognitive functioning. Jesus et al. (2006) recorded

cerebral blood flow velocity in younger adults with congestive HF (n=83; mean age 55±12

years). Reduced blood flow velocity in the right middle cerebral arterial was shown to relate

to poorer global cognitive scores as measured by the Mini Mental State Examination

(MMSE; mean = 23; range 7–30). However, since the researchers included patients with

stroke the middle cerebral blood flow changes observed may have been due to factors aside

from HF. Moreover, when corrected for stroke increased blood flow velocity in the right

anterior cerebral artery correlated with better global cognitive function scores (MMSE; Jesus

et al., 2006). These important findings indicate that increased cerebral blood flow is


41

associated with improved global cognition however, given that other cognitive domains are

also impaired in HF the effects of cerebral blood flow on these cognitive domains is also of

interest.

Expanding on the effects of cerebral blood flow velocities on global cognitive measures,

researchers conducted a series of experiments to ascertain whether similar blood flow

observations are seen with specific cognitive functions. In one study, Vogels et al. (2008)

explored such relationships in older age matched (≥ 50 years) outpatients with mild and

moderate HF (n=43; LVEF < 45%; NYHA class II and III; age 68.0±8.9 years),

cardiovascular disease but without evidence of HF (n = 33; age 67.8±9.7 years) and healthy

controls (n=22; LVEF > 55%; age 64.1±8.3years). Overall, group differences were observed

in memory, executive function, mental speed, attention and overall cognitive z scores. In this

trial, HF patients performed worse on these cognitive factors followed by the cardiovascular

disease group and healthy controls (Vogels et al., 2008). Compared with healthy controls

(56.1±10.9 cm/s), middle cerebral arterial blood flow velocity as measured by the TCD was

significantly slower in HF patients (47.3±10.7 cm/s) and cardiovascular disease patients

(49.8±11.2 cm/s; Vogels et al., 2008). No significant group differences were seen in mean

blood flow velocity in the middle cerebral artery between the HF and cardiovascular disease

patient groups. The authors therefore suggest that reduced cerebral blood flow in HF patients

may be due to risk factors shared by patients with cardiovascular disease, rather than due to

poor cardiac output seen in HF (Vogels et al., 2008). However, this theory needs to be further

explored. Despite group differences in cognitive function and middle cerebral arterial blood

flow velocities, unlike Jesus et al. (2006) who found relationships between cerebral blood

flow and global cognitive function, Vogels et al. (2008) did not find any relationship between

specific cognitive domains and cerebral blood flow velocity.

Since Vogels et al. (2008) recorded mean blood flow velocities from the right and left middle

cerebral arteries, any unilateral differences are therefore unknown. Additionally, total z

scores for the cognitive tests rather than individual test scores were used in the analysis.

Assessing relationships between cerebral blood flow velocities and specific

neuropsychological tests may have provided additional information that could have

potentially been lost when tests were combined as z scores. Furthermore, other possible

confounding variables such as medication, diagnosis and disease severity were not

considered. Finally, the study may have been underpowered by a small participant size.

The TCD system has also been used to measure cerebrovascular reactivity using a formula

incorporating middle cerebral arterial (MCA) blood flow velocity during normocapnea and


42

hypercapnea (with and without CO2 administration, respectively). In a small study Georgiadis

et al. (2000) compared cerebrovascular reactivity to CO2 (average between left and right

middle cerebral arteries) in HF patients with NYHA class II (59±11 years), III (61±11 years)

and IV (53±11 years) to that of age matched controls (57±9 years) recruited from a neurology

ward. It was revealed that controls had higher cerebrovascular reactivity compared to each of

the HF groups tested. Furthermore, patients with mild and moderate HF (NYHA functional

class II and III) showed higher cerebrovascular reactivity compared to patients with severe

HF (NYHA class IV). No differences in cerebrovascular reactivity were observed between

patients with mild and moderate heart failure. Furthermore, higher LVEF levels and lower

NYHA functional class, but not age related to greater cerebral vasoreactivity (Georgiadis et

al., 2000). Further, this study failed to find group differences in cerebrovascular reactivity in

patients receiving and not receiving beta-blockers, ACE inhibitors or nitrates indicating that

drugs do not influence cerebrovascular reactivity in HF patients.

Although improvements in executive function were seen in older patients with moderate HF

(NYHA class III) following an 18 week exercise program, no significant changes were

observed in middle cerebral arterial (MCA) blood flow velocity, which has been implicated

in poor cognition (Tanne et al., 2005). However, given that no changes were seen on global

cognition (MMSE scores), verbal fluency, and visual constructional abilities, visual

perceptual planning and short-term nonverbal memory (Rey-Osterrieth complex figure) it is

possible that poor middle cerebral arterial (MCA) blood flow velocity is not related to these

cognitive functions.

Studies using the TCD to measure cerebral blood flow have led authors to suggest that

mechanisms for cognitive impairment in HF are changes in haemodynamics, microemboli

(Jesus et al., 2006) and impaired cerebral autoregulation (Vogels et al., 2008). These authors

concluded that reduced cerebrovascular reserve capacity might contribute to poor cognitive

function in HF patients (Georgiadis et al., 2000).

4.4.1 Cerebral blood flow and mood

Few studies have examined physiological effects of mood in particular depressive symptoms

and anxiety in older heart failure patients. Using single-photon emission computed

tomography (SPECT), Alves et al. (2006) demonstrated slower cerebral blood flow in elderly

HF patients (NYHA II or III) with major depressive symptoms compared to patients that

were not depressed. In particular, in depressed HF patients, reduced blood flow was detected

in the medial temporal region (the left anterior parahippocampal gyrus and hippocampus) and


43

reduced blood flow was related to increased severity of depressive symptoms but not

cognitive dysfunction (Alves et al., 2006). There have been no studies that have explored the

associations between anxiety or depression with cerebral blood flow in the common carotid

and middle cerebral arteries using the TCD in elderly HF patients.

Blood flow measures in the common carotid arteries have revealed possible relationships

with cognitive functioning in healthy controls, however this thesis will expand on this new

evidence and explore whether any relationship exists in HF patients. The relationship

between MCA blood flow velocity and cognition in HF has been more widely researched but

with mixed results. Research described in this thesis will expand on previous studies and will

examine whether impairments in additional cognitive domains relate to reduced MCA blood

flow velocity in these patients.

4.4.2 Brain imaging studies

Since cognitive impairments may influence the patient’s ability to care for themselves, it has

been suggested that Magnetic Resonance Imaging (MRI) studies need to be undertaken to

obtain a better understanding of how areas of the brain involved in decision making are

affected in HF (Dickson, Tkacs, & Riegel, 2007). Utilizing single-photon emission computed

tomography (SPECT) analysis Alves, et al., (2005) revealed that compared to age matched

controls, elderly patients with mild and moderate HF (NYHA class II and III) displayed

bilateral reductions in regional cerebral blood flow of the posterior cortical brain regions. In

particular, poor blood flow in brain regions involved in memory processing including the

precuneus, cuneus, right lateral temporoparietal cortex and posterior cingulate gyrus was seen

in HF patients. Importantly, poor blood flow in the posterior cingulate cortex and precuneus,

which are brain regions related to cognitive functioning, suggests that poor cognitive function

in HF may be due to hypoperfusion in these brain regions (Alves et al., 2005). No significant

group differences in brain white matter hyperintensities as measured by MRI were seen.

Given that SPECT scanning in this study was conducted one week and two months following

cognitive assessments, the relationships found between cognitive tasks and brain imaging

may not accurately represent cognitive function at the time of assessment. Furthermore, a

lack of MRI findings may be due to small sample size and again the fact that the imaging

component of the study was conducted two months following cognitive assessments.

Cardiovascular symptoms as well as cognitive abilities in these patients may have changed

during this time and interpreting these findings should be done with caution.

Studies showing impairments in cognitive factors in HF patients have shown interesting

structural changes in these patients compared to controls, which may explain these


44

impairments. Almeida, Garrido, et al. (2012) for example, examined a cohort of HF patients

who scored significantly worse on psychomotor speed and immediate and delayed memory

tests compared to healthy controls. In this study, the HF group showed greater cerebral grey

matter loss in the frontal lobes, anterior cingulate, and temporal-parietal lobes compared to a

healthy control group and patients with ischemic heart disease (Almeida, Garrido, et al.,

2012). Supporting this, researchers suggest that regional grey matter loss seen in a younger

HF patient cohort may play a role in impaired cognitive function in these patients (Woo,

Macey, Fonarow, Hamilton, & Harper, 2001). On the contrary, another study failed to

demonstrate structural brain changes using MRI in HF patients (LVEF < 40%) who had

impairments in global cognition and memory, however failing to find relationships with

structural brain changes may have been due to methodological issues (Beer et al., 2009).

4.4.3 Summary

In summary, the mechanisms associated with cognitive impairment in heart failure (HF) are

poorly understood although it appears that cognitive impairment is underpinned by vascular

factors such as cerebral hypoperfusion. Additionally, mechanisms for cognitive impairment

in HF patients may include decreased ejection fraction due to impaired heart function.

Hypoperfusion due to emboli forming in the brain may reduce cognitive capacities. However,

smaller studies failed to find similar associations. Treatment with surgery or devices have

shown to improve working memory, attention, speed of information processing and quality of

life indicating that factors associated with poor heart function are implicated. Brain imaging

techniques have revealed that cerebral perfusion changes are related to cognitive impairments

in HF. Specifically, global cognitive function is related to reduced blood flow velocity in the

middle cerebral arteries (MCA). Although researchers have found impairments in memory,

executive function, attention and overall cognitive scores in HF patients, these cognitive

domains were not related to reduced MCA blood flow velocity.

4.5 Arterial stiffness

As mentioned, heart failure (HF) patients with cognitive impairments are more likely to have

low cerebrovascular reactivity, which may explain findings of increased white matter

hyperintensities and microemboli in these patients. Recent authors have proposed that these

brain changes leading to cognitive impairment relate to elevate arterial stiffness measures

(Hanon et al., 2005; Kearney-Schwartz et al., 2009; Mitchell et al., 2011). An understanding

of the circulatory system is required to understand how arterial stiffness is involved in the

pathogenesis of HF and cognitive dysfunction. The circulatory system needs to operate


45

optimally for sufficient blood flow to reach organs and tissues. This is to guarantee sufficient

nutrient and oxygen supply to the brain for adequate metabolic function. During healthy

conditions, the heart pumps with adequate force required to eject blood from the left ventricle

to circulate blood. The elastic smooth muscle walls of the arteries allow a continuous

pulsatile flow permitting a constant movement of blood throughout the circulatory system to

reach vital organs (Mann, 2008).

During long-term hardening of the arterial walls in the case of hypertension, arteriosclerosis

and ageing the arterial walls harden making blood flow through the arteries difficult. To

ensure that the body receives an adequate continuous flow of blood, the left ventricle pumps

with greater force to break through the increasing impedance in the arterial walls due to

stiffening. The pulsatile flow of blood pumped from the left ventricle creates an increase in

pulse pressure, which can cause injury to vessels and tissues. During normal vascular

functioning, compliant conduit vessels prevent damage from the increased pressure by

submitting a reflected wave back to the heart to augment diastolic pressure (Mitchell et al.,

2001). The reflective wave in turn minimises pressure on the left ventricle by augmenting

diastolic pressure and decreasing pulse pressure. Stiff arteries do not have the capacity to

minimise pulse pressure and as a result, there is an increase in left ventricular load (O'Rourke

& Safar, 2005)

Vessels in the brain however are vulnerable to the high-pressure fluctuations of an increased

pulse pressure. Due to high metabolic requirements and the need for a constant perfusion

through systole and diastole, arteries in the brain provide low resistance to blood flow

(O'Rourke & Safar, 2005). Furthermore, unlike other body tissues in which constricted

arteries protect downstream tissues, brain arteries remain dilated leading to high fluctuations

of pressure and flow, see O'Rourke and Safar (2005) for further reading. It has been

suggested that cerebral deterioration is due to microvascular damage caused by high pulse

pressure in vertebral and carotid arteries (O'Rourke & Safar, 2005). Microcirculatory

remodelling due to arterial stiffness is believed to cause microvascular ischemia in the brain

(Mitchell, 2008; Mitchell et al., 2001). Assessing the relationship between arterial stiffness

and cognitive function in heart failure is therefore important to gain an overview of the

mechanisms involved.

4.5.1 Arterial stiffness and heart failure

Added vascular pathologies seen in heart failure (HF) have also been associated with

cognitive impairments. Researchers have shown that arterial stiffness increases with age (e.g.

Elias et al., 2009), is increased in elderly patients with HF (Tartière, Logeart, Safar, & Cohen-


46

Solal, 2006) and is a risk factor for HF (Chae et al., 1999; Vaccarino, Holford, & Krumholz,

2000). Since a direct measure of arterial stiffness is in most cases not viable in research

settings, indirect measures including pulse wave velocity (PWV) and aortic pressure

augmentation (AIx) are studied using devices such as the SphygmoCor® Px. These

instruments use haemodynamic information through pulse waveforms to understand the

relationship between the heart and arterial system. Although PWV is the most robust

measurement of arterial stiffness measures, other non-invasive measures including

augmentation index (AIx) and pulse pressure (PP) are efficient measures that are suitable for

research and clinical settings. The SphygmoCor® Px uses a mathematical transfer function to

obtain the central (ascending) aortic pressure waveform from systolic and diastolic pressure

values of the brachial (as measured by a conventional cuff) and radial arteries. Augmentation

index (AIx) is an indirect measure of arterial stiffness defined by the difference between the

second (P2) and first (P1) systolic peaks of the central aortic waveform (augmentation

pressure), expressed as a percentage of the pulse pressure.

Elevated arterial stiffness is one of the factors known to be associated with the

pathophysiology of HF (Denardo, Nandyala, Freeman, Pierce, & Nichols, 2010). HF patients

(age > 40 years; 61±10), have greater central and peripheral pulse pressure and diastolic

pressures compared to patients without HF (age > 40 years; 57±10; Mitchell et al., 2001) and

greater brachial pulse pressures compared to patients with preserved LVEF (Tartière et al.,

2006). Based on their findings, Mitchell et al. (2001) explain that central conduits are

therefore likely to be more stiff and distal conduit vessels less stiff in HF patients. The

authors suggest that peripheral resistance has been a focus for vasodilator therapy, however

these results suggest that central changes in pressures are also important in HF patients.

Interestingly, drugs commonly taken by HF patients such as angiotensin converting enzyme

(ACE) inhibitors, angiotensin receptor blockers, calcium channel blockers and nitrates are

known to reduce arterial stiffness (O'Rourke & Safar, 2005). These drugs do this by

decreasing wave reflection leading to lowered central augmentation and pulse pressure

(O'Rourke & Safar, 2005). Heart failure (HF) patients with low LVEF (< 40%; NYHA class

2.6±0.9) are likely to be taking ACE inhibitors, angiotensin II receptor blockers and diuretics,

whereas those with preserved LVEF (≥ 40%; NYHA class 2.1±0.8) LVEF were more likely

to be taking calcium channel blockers (Tartière et al., 2006). Hypertensive medications

however do not seem to alter the relationship between pulse pressure and HF risk (Vaccarino

et al., 2000).


47

However, Scuteri et al. (2009) found that increased left ventricular mass index in older

individuals was associated with poor global cognitive function as measured by the MMSE,

independent of blood pressure or arterial stiffness as measured by pulse wave velocity

(Scuteri et al., 2009). Although the cohort tested did not have HF per-se, they did have

cardiovascular risk factors such as hypertension (and previous stroke, MI) and were taking

drugs commonly taken by HF patients such as anti-hypertensives, nitrates, statins, diuretics

and beta-blockers. In this study, however group differences were observed only in patients

taking anti-hypertensive drugs. This suggests that medications taken by HF patients may

influence arterial stiffness. In a cohort of patients attending a memory clinic, higher arterial

stiffness as measured by carotid-femoral pulse wave velocity was weakly related to poorer

global cognitive scores (as measured by the Mini Mental State Examination; MMSE) even

after controlling for nitrates, anti-hypertensive, demographics and cardiovascular risk factors

(Scuteri, Brancati, Gianni, Assisi, & Volpe, 2005).

Biomarkers related to vascular function are related to HF and arterial stiffness. During

neurohormonal activation, potent vasoconstrictors including endothelin-1 are released. The

role of endothelin-1 together with vasodilators (e.g. nitric oxide) is to maintain ideal vascular

tone and normal blood pressure. Increased levels of plasma endothelin-1 are associated with

poorer prognosis of HF patients (Pousset et al., 1997) and are seen predominantly in patients

with severe but not mild HF (Wei et al., 1994). Preliminary evidence proposes that an

additional function of endothelin-1 is to elevate pulse wave velocity and augmentation index

and decrease cardiac output (Vuurmans, Boer, & Koomans, 2003). Given that arterial

stiffness (see below), poor cardiac output and increased disease severity are perhaps related to

poor cognitive function, it is reasonable to investigate whether endothelin-1 also has a role in

cognitive dysfunction in HF patients.

4.5.2 Arterial stiffness and cognitive function

In recent years, a growing number of researchers have explored whether an association exists

between arterial stiffness and cognitive function. Evidence suggests there is a relationship

between vascular factors and cognitive dysfunction and dementia. Kearney-Schwartz et al.

(2009) reported that pulse wave velocity was associated with cognitive function in older men

(aged 60-85 years) but not women with hypertension and subjective memory impairment.

This suggests that the associations between arterial stiffness and cognition are strongly

influence by gender. Interestingly, compared to women, men performed significantly poorer

on overall memory scores and a greater number of men were taking angiotensin converting

enzyme (ACE) inhibitors (32% versus 15%) and anticoagulants (37% versus 19%). This may


48

suggest that the cohort tested overall showing gender differences may also be related to more

males taking drugs known to have vascular effects.

Researchers have observed a relationship between pulse wave characteristics and specific

cognitive domains. Pulse wave velocity (PWV) was shown to be significantly increased in

elderly participants (> 60years) with vascular dementia, Alzheimer’s dementia and in patients

with mild cognitive impairment (Hanon et al., 2005). Moreover, PWV was also related to

poorer global cognitive function (as measured by the MMSE) and overall cognitive scores

(Hanon et al., 2005). In community-dwelling older population (≥ 70 years) without

cardiovascular disease, higher brachial-ankle pulse wave velocities is linked to worse global

cognitive performance using the MMSE assessment tool (Fujiwara et al., 2005). Examining

baseline measures, older age and PWV combination related to worse performance on global

cognitive function, visual spatial organisation and memory, verbal episodic memory, and

scanning and tracking (Elias et al., 2009).

Longitudinal studies however have shown mixed results. Based on data from the large

Rotterdam study, Poels et al. (2007) showed that arterial stiffness as measured by carotid-

femoral PWV and carotid distensibility was significantly related to reduced global cognitive

scores (MMSE), poor performance on executive function as measured by the incongruent

Stroop test but not letter digit substitution. However, when adjusting for cardiovascular

factors, only small associations were found between PWV and poor executive function as

determined by the incongruent Stroop test (incongruent). These data suggest that in elderly

individuals, arterial stiffness was unrelated to cognitive decline over time or the risk of

developing dementia (Poels et al., 2007). Supporting this, Elias et al. (2009) failed to find

associations between PWV and working memory. This may have possibly been due to the

memory task being too simple to complete. In contrast, carotid-femoral pulse wave velocity

has shown to be the best predictor of longitudinal reductions (median =12 months) on MMSE

scores in older patients (79±6 years) with memory complaints, even after controlling for

demographics and cardiovascular risk factors (Scuteri et al., 2007).

Increased arterial stiffness (brachial pulse pressure; n=1749; 57.1±17.2 years and carotid-

femoral pulse wave velocity; n=582; 54.3±17.1 years) at baseline resulted in worse

performance on learning and cognitive screening tools over time (Waldstein et al., 2008) after

accounting for possible confounders including inflammation or psychological factors for

example anxiety which may have affect cognitive performance. However, attention,

psychomotor speed, executive function or language was not influenced by arterial stiffness

(Waldstein et al., 2008). On the contrary, in a middle age sample of healthy participants,


49

increased pulse pressure was weakly related to decreased performance on Quality of Episodic

Secondary Memory domain (Pase et al., 2010). Furthermore, in this study, elevated

augmentation index was related to slower memory retrieval times. Additionally pulse

pressure was an independent predictor of memory processes predicting 7% and 6% of the

variance in Quality of Episodic Memory and Speed of Memory performance, respectively.

Finally, augmentation index independently predicted 7% of the variation in Speed of Memory

(Pase et al., 2010). Ellins et al. (2008) demonstrated that carotid arterial stiffness 3 years post

baseline testing was related to high levels of systemic inflammation as measured by C-

reactive protein (CRP) in a cohort of middle-aged participants aged 45-59 years.

Interestingly, baseline levels of inflammatory markers interleukin-6 and tumour necrosis

factor alpha were not related to arterial stiffness (Ellins et al., 2008).

Recently, Mitchell et al. (2011) conducted a large study on elderly community dwelling

individuals (69-93 years) and revealed that arterial stiffness as measured by carotid-femoral

pulse wave velocity and carotid pulse pressure, were associated with lower memory and

executive function scores. Increased white matter hyperintensities and cerebral infarctions

possibly accounted for the variance between memory and carotid-femoral PWV.

Augmentation index however was not associated with any of the cognitive scores measured.

Mitchell et al. (2011) findings suggest that increased aortic stiffness is related to increases in

microvascular brain lesions and poor cognitive function via extreme flow pulsatility in the

brain. There has been great interest in the link between the arterial system and cognitive

impairment associated with ageing and in particular dementia of the vascular type. Arterial

stiffness is associated with and cognitive impairment, in particular, global cognitive function,

visual spatial and working memory. To date no study has investigated whether arterial

stiffness is a possible mechanism for cognitive impairments in HF.

An investigation of the association between arterial stiffness and cognitive impairment in HF,

in particular, global cognitive function, visual spatial and working memory may provide

insight into the possible mechanisms for cognitive decline in these patients.

4.5.3 Arterial stiffness and mood

The influence of arterial stiffness in mood has been explored in individuals with depression

and anxiety. In particular, a large study demonstrated that elevated depressive symptoms and

anxiety were related to early wave reflection due to arterial stiffness as measured by

augmentation index (Seldenrijk et al., 2011). In addition, increased augmentation index was

higher in individuals who had experienced longer periods of depressive symptoms and

anxiety. Data from the large Rotterdam population study indicated that elderly individuals


50

who are depressed are more likely to have increased arterial stiffness, as measured by

common carotid distensibility or carotid-femoral pulse wave velocity (Tiemeier, Breteler,

Van Popele, Hofman, & Witteman, 2003). Additionally, in a small study, patients with

general anxiety disorder as well as cardiovascular disease had greater levels of arterial

stiffness as measured by pulse wave velocity and augmentation index than controls

(Yeragani, Kumar, Bar, Chokka, & Tancer, 2007). It has been suggested that augmentation

index may therefore be a mechanism by which depression is linked to cardiovascular disease

risk (Seldenrijk et al., 2011) and vascular factors (Tiemeier et al., 2003). For this reason it is

also possible that arterial stiffness may be related to depressed mood and increased anxiety

levels in HF patient. However, no study has examined the association between depressive

symptoms and anxiety in HF patients.

4.5.4 Arterial stiffness and cerebral circulation

Recent evidence shows a link between arterial stiffness and cerebral blood flow. Kwater,

Gsowski, Gryglewska, Wizner, and Grodzicki (2009) found a relationship between peripheral

arterial stiffness and haemodynamic changes in the middle cerebral artery in individuals at

risk of cardiovascular disease. In particular, arterial stiffening (as measured by carotid-

femoral PWV and brachial pulse pressure) and age were positively related with pulsatility

and resistance indices of the middle cerebral artery flow, even when accounting for

covariates. No significant correlations were found between middle cerebral arterial indices,

blood pressures (systolic and diastolic) or mean arterial pressures. The authors however

suggested that aortic or carotid pulse pressure measurements would have provided more

accurate representations of how arterial stiffness is related to cerebral haemodynamics. More

recently, findings by Xu et al. (2012) suggest that increased systemic arterial stiffness effects

middle cerebral arterial blood flow in patients who were referred to a hypertension clinic and

not taking hypertensive medication. The authors found that this change in middle cerebral

arterial blood flow was due to elevated pulse pressure. Furthermore, white matter

hyperintensities as measured by magnetic resonance imaging was related to an increase in

carotid stiffens as measured by the augmentation index (Kearney-Schwartz et al., 2009).

These authors suggest that measurement of middle cerebral arterial blood flow will help

understand the effects of peripheral haemodynamic changes on central vascular changes.

Furthermore, in a cohort of patients with memory deficits, pulse wave velocity was

significantly higher in patients who also had cortical (brain) atrophy compared to those who

also had subcortical microvascular lesions (lacunes or white matter lesions) as detected by

computerised tomography (CT) scans (Scuteri et al., 2005).


51

4.5.5 Summary

In summary previous studies have demonstrated that indirect measures of arterial stiffness are

elevated in HF and in the elderly. Additionally arterial stiffness is related to poor global

cognition, learning, executive function and visuospatial abilities in elderly individuals.

Finally, impairments in speed of information processing and episodic memory in middle-aged

healthy cohort are related to increased arterial stiffness. The association between arterial

stiffness on cognitive function in elderly heart failure patients has not been examined.

4.6 Oxidative stress in heart failure and in cognitive impairment

In recent years, there has been growing interest in the role of oxidative stress and

inflammatory pathways on the natural aging process and the development of disease states.

Biological oxidative stress pathways were first purported by Harman in the 1950’s with his

“free radial theory of ageing” and the actual term “oxidative stress” was first defined by

Helmus Sies in 1985 (Sies, 1985). This theory proposes that highly reactive biomarkers

known as “free radicals” accumulate in the body over time as a result of normal metabolic

processes. This build-up of free radicals in turn causes cellular damage and cell death. This

next section will provide an overview of the various biomarkers involved in the physiological

processes related to the endogenous oxidative stress and inflammatory pathways, and how

they are implicated in natural healthy and diseased bodily functions in particular HF and

cognitive impairment.

4.6.1 The oxidative stress pathway

Mitochondria play a significant role in normal metabolic function by providing energy for

adequate cellular activity (Fernández-Checa et al., 1998; Valko et al., 2007). During normal

metabolism, numerous redox reactions take place whereby electrons are removed from

(oxidation) or added to (reduction) molecules to ensure cellular homeostasis. For example,

molecular oxygen is used to produce adenosine triphosphate (ATP) from adenosine

diphosphate (ADP) in a process known as oxidative phosphorylation. Molecules with

unpaired electrons are also known as oxidants or free radicals (Mak & Newton, 2001; Sies,

1997). As a result of these reactions and the reduction of oxygen, reactive oxygen species

(ROS) or “free radicals” including superoxide anions (O2-), hydrogen peroxide (H2O2), nitric

oxide (NO•) and hydroxyl radicals are formed (Thannickal & Fanburg, 2000; Figure 1). In

low concentrations, these ROS have protective physiological roles. For instance, superoxide

anions, hydroxyl radicals and hydrogen peroxide, respond to harmful processes such as

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53

4.6.2 The role of antioxidants in the body

With the aim to reach cellular homeostasis, the body has inbuilt antioxidant protective

mechanisms to counteract the deleterious effects of oxidants. These defence mechanisms

encompass enzymatic and non-enzymatic molecules and pathways to protect and repair cells

damaged by free radicals (Sies, 1997; Valko et al., 2007). In one enzymatic antioxidant

pathway, superoxide dismutase initially speeds up the conversion of superoxide to hydrogen

peroxide (H2O2). Catalase and glutathione peroxidises then convert hydrogen peroxide to

water (Finkel & Holbrook, 2000). Through these pathways, the antioxidant enzymes prevent

oxidative damage by keeping reactive oxygen species levels to a minimum. Non-enzymatic

antioxidants including phenolic compounds, vitamin C (ascorbic acid), vitamin E (α-

tocopherol) and glutathione also play an important protective role. To prevent oxidants from

causing cellular damage, these non-enzymatic molecules convert them into non-radical end

products (Sies, 1997). The glutathione redox cycle for example intercepts the chain of

reactions that form reactive species by reducing hydrogen peroxide levels and in turn

reducing hydroxyl radical formation (Fernández-Checa et al., 1998). Alternatively, non-

enzymatic antioxidants relocate radicals to regions where their detrimental effects are less

harmful such as from a hydrophobic to aqueous phase (Sies, 1997). Coenzyme Q10 (CoQ10)

is a vitamin like substance found principally in the mitochondrial membranes and is involved

in metabolic functions including manufacturing adenosine triphosphate, protecting the

stability of cell membranes, regulating genes and enhancing the immune system (Boreková,

Hojerová, Koprda, & Bauerová, 2008). As an antioxidant CoQ10 has a vital role in protecting

DNA from free radical induced oxidative damage, and recycling and regenerating other non-

enzymatic antioxidants, ascorbic acid and α-tocopherol (Boreková et al., 2008). Rosenfeldt,

Hilton, Pepe, and Krum (2003) propose that CoQ10 decreases blood pressure by protecting

nitric oxide within the endothelium. ROS produced in the vasculature can reduce the amount

of nitric oxide available. Coenzyme Q10 ensures protection of nitric oxide indirectly by

scavenging reactive oxygen species, resulting in nitric oxide induced vasodilatation.

4.6.3 Oxidative stress in heart failure

Oxidative stress is known to play an important role in the pathogenesis of heart failure.

Oxidative stress causes impairments to myocardial function due to generalised and cardiac-

specific actions (Mak & Newton, 2001) causing oxidative damage to cellular proteins and

membranes leading to apoptosis (Grieve & Shah, 2003) and damage to DNA. Together these

actions all contribute to reduced cardiac contractility, malfunction of iron transporters and

calcium cycling (Ng, 2009). Additionally the production of free radicals such as superoxide

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55

4.6.4 Oxidative stress in cognitive impairment and mood

Interestingly elevated concentrations of oxidative stress markers and deficiencies in

antioxidants seen in HF patients are also observed in individuals with cognitive impairment.

Researchers have proposed that oxidative stress in the brain has a role in the development of

cognitive deficits in older individuals. Compared to other organs, the brain has a higher

metabolic rate and elevated oxygen consumption. Together these factors make the brain

highly susceptible to increased oxidative stress and lipid peroxidation (Gironi et al., 2011;

Mariani, Polidori, Cherubini, & Mecocci, 2005). In the normally aging brain, amyloid-β

plaques may play important roles in anti-oxidant defences and prevent neuronal dysfunction.

However, in AD and oxidative stress seen in ageing, the presence of additional sources of

oxidative stress increases the production of these amyloid-β plaques and in turn

neurodegeneration and dementia (Smith et al., 2002). Given that the oxidative stress

pathways are complex and involve various oxidative stress promoters, by-products and anti-

oxidants researchers suggest the need to explore multiple oxidative stress related biomarkers

in HF patients (e.g. Gironi et al., 2011).

Few researchers have examined the oxidative stress profile of elderly individuals with

memory impairments. Some authors demonstrated that older patients with mild cognitive

impairment have elevated concentrations of lipid peroxidation as measured by mild cognitive

impairment (Torres et al., 2011) and F2-isoprostanes (Praticò, Clark, Liun, Lee, &

Trojanowski, 2002) compared with healthy elderly controls. Interestingly a reduction in lipid

peroxidation and improvements in Quality of Working Memory was demonstrated following a

three month daily intervention of an antioxidant Pycnogenol®; Ryan et al., 2008).

Furthermore, Torres et al. (2011) demonstrated higher malondialdehyde (MDA) levels in

Alzheimer’s disease patients (66-90 years) compared to patients with mild cognitive

impairment patients (61-89 years). Additionally, patients with Alzheimer’s disease had

significantly higher antioxidant activity as measured by catalase and glutathione peroxidase

activity, compared with controls (62-83 years) and patients with mild cognitive impairment.

However, antioxidant activity does not appear to differ between patients with mild cognitive

impairment and healthy controls. In the study by Torres et al. (2011), controls had superior

global cognitive abilities as measured by the MMSE followed by patients with mild cognitive

impairment and Alzheimer’s disease. High levels of lipid peroxidation (MDA) and low

enzymatic antioxidant defences (as measured by glutathione reductase/glutathione peroxidase

ratio) were associated with poorer global cognitive function in Alzheimer’s disease patients

but not mild cognitive impairment patients or healthy controls. These findings indicate that

oxidative stress occurs during early stages of cognitive impairment. Moreover, an imbalance


56

exists between oxidative stress and antioxidant defences in patients with Alzheimer’s disease

and mild cognitive impairment (Torres et al., 2011). The authors suggest that in healthy

conditions and during early stages of cognitive impairment, the body adequately adapts to

oxidative stress changes. However, in severe cognitive decline as in Alzheimer’s disease, the

body progresses into a destructive phase, which in turn contributes to disease.

Contrary to previous findings (Praticò et al., 2002; Torres et al., 2011), Gironi et al. (2011)

found that elderly patients with memory deficits (65 – 90 years) defined as having either mild

cognitive impairment or dementia (Alzheimer’s disease, Parkinson’s disease with dementia,

dementia with Lewy bodies) showed lower concentrations of lipid peroxidation compared to

controls (Gironi et al., 2011). Patients with memory deficiencies had significantly lower

levels of the oxidative stress markers (malondialdehyde, glutathione, reduced glutathione)

and lower serum antioxidant power compared to controls. There were no significant

differences in Coenzyme Q10 (CoQ10) or reactive oxygen species as measured by

determinable reactive oxygen metabolites (DROMs). Additionally, oxidative stress and

antioxidant biomarkers however did not influence MMSE scores. Age and antioxidant power

predicted the risk of developing a memory deficit. Oxidative stress on the other hand as

measured by reduced glutathione and MDA predicted a reduced risk of developing memory

impairment. The authors suggest that reduced levels of oxidative stress markers may be a

result of over activation of the antioxidant system. In response to augmented oxidative stress,

there is an up regulation of the antioxidant system. As a result, oxidants and radical products

are cleared to prevent tissue damage (Gironi et al., 2011). Preliminary data revealed that

DROM levels are significantly increased in healthy ageing (Rosenfeldt et al., 2013). Older

healthy adults (> 60 years) have significantly higher levels of DROM compared to younger

healthy younger adults (18-30 years). Additionally, older healthy adults have lower plasma

levels of DROM compared to HF patients in the same age group. The current research will

expand on this data and explore whether DROM levels are associated with cognitive function

in healthy older HF patients.

The role of oxidative stress and antioxidants in cognitive functioning however is unclear.

Some authors theorise that with less antioxidant capacity compared to other organs, that the

brain is unable to combat the damaging effects of the oxidants. Neurodegenerative disease,

mild cognitive impairment and Alzheimer’s disease may occur because of persistent

oxidative damage and lack of antioxidant support (Mariani et al., 2005). However, others

suggest that during early stages, oxidants and radical products cleared by antioxidants to

prevent cognitive decline (e.g. Gironi et al., 2011). Although since an imbalance between

oxidative stress and antioxidant defences exist in HF and in some cases of cognitive decline,


57

it is reasonable to propose that similar associations may exist between oxidative stress and

cognitive impairments seen in HF patients.

It is well established that lower levels of the antioxidant and energy producer, coenzyme Q10

(CoQ10) are seen in the myocardium and plasma of HF patients and that blood levels

increase following supplementation (Folkers, Langsjoen, & Langsjoen, 1992; Keogh et al.,

2003). Numerous trials have examined the effects of CoQ10 supplementation on symptoms,

exercise capacity, recovery rates and biomedical status in HF. For example Keogh et al.

(2003) found that compared to controls taking placebo (n=17; 61±9years), patients with

dilated cardiomyopathy (n=18; 62±7years; not taking beta-blockers) improved NYHA

functional class by -0.5 class, exercise capacity following a three month 150mg/day CoQ10

intervention (Keogh et al., 2003). Additionally, an 8 week CoQ10 (300mg/day) intake was

positively correlated with improved vascular endothelial function as measured by flow

mediated dilatation in patients with ischaemic left ventricular systolic dysfunction (Dai et al.,

2011). Larger, long-term randomised studies have revealed that high daily intake of CoQ10

(2g/day) for 12 months reduced hospital stay in patients with moderate to severe HF (NYHA

class III and IV) by 33% (Morisco, Trimarco, & Condorelli, 1993). Additionally, reduced

complications associated with HF including pulmonary edema and cardiac asthma and

decreases in number of hospital readmissions due to worsening of the HF (23% vs 37%) were

seen in patients taking CoQ10. A meta-analysis examining eleven trials indicated that CoQ10

improved cardiac output and ejection fraction (3.7% absolute: relative improvement of

approximately 10%) and quality of life in HF patients suggesting that benefits from this

supplement is not only limited to cardiovascular related symptoms but extends to improve

feelings of wellbeing (Sander, Coleman, Patel, Kluger, & Michael White, 2006).

In the only study that examined the relationship between depression and oxidative stress in

HF patients, Kupper, Gidron, Winter, and Denollet (2009) failed to find an association

between serum levels of oxidative stress marker or an imbalance between oxidative stress and

antioxidant markers expressed as a ratio and depression in patients with chronic HF (aged <

80 years; LVEF ≤ 40%). Contrary to these findings, Michalakeas et al. (2011) demonstrated

that compared to non-depressed HF patients (≥ 18 years) those with depressive

symptomatology (Beck Depression Index > 10) had significantly higher levels of lipid

peroxidation as measured by malondialdehyde indicating higher oxidative stress levels.

Interestingly, following treatment with the SSRI sertraline, malondialdehyde levels decreased

in patients with depressive symptomatology. This reduction in oxidative stress in depressed

HF patients is possibly due to the antioxidant effects of sertraline (Michalakeas et al., 2011).


58

These trials only examined measures of oxidative stress and not inflammatory measures and

understanding the interaction between these are important when interpreting results from

studies that have examined depression in heart failure. Due to limited research it is unclear

whether oxidative stress is related to depressive symptoms especially in elderly patients with

heart failure, therefore more research is needed to clarify this relationship.

Recent evidence indicates that coenzyme Q10 (CoQ10) plasma levels are significantly lower

in patients with clinical depression compared to healthy controls (Maes et al., 2009). Here

just over half of depressed patients were deficient in CoQ10 levels. Despite a deficiency of

CoQ10 levels in depressed patients, CoQ10 levels were not associated with depressive

symptoms as measured by the Hamilton Depression Rating Scale. These observations

highlight the prospect of correcting deficient CoQ10 levels with an appropriate

supplementation to help reduce depressive symptoms by targeting oxidative stress and

inflammation that is seen in these patients (Maes et al., 2009). Additionally, CoQ10 was

shown to have neuroprotective effects related to cognition on Alzheimer’s disease model rats

and increased acetylcholinesterase (AChE) activity in the hippocampus and cerebral cortex

and decreased oxidative stress activity in the hippocampus (Ishrat et al., 2006). In addition,

some authors have examined the efficacy of CoQ10 in combination with other antioxidants,

anti-inflammatories and cofactors.

When Alzheimer’s disease patients with depression were encouraged to take CoQ10,

multivitamins, vitamin E, alpha-lipoic acid (ALA) and omega-3 PUFAs as adjuncts to their

regular treatments, improvements were seen on attention, delayed memory correct recall and

a decrease in delayed memory errors following 24 months treatment (Bragin et al., 2005).

Additionally depressive symptoms resolved following 6 months treatment, however with the

absence of a control group, no compliance monitoring and the addition of lifestyle

suggestions including dietary recommendations and physical exercises it is difficult to

ascertain which treatment/s influenced these positive results. These findings suggest that

reduced CoQ10 plasma levels may be related to decreased quality of life, mood and cognition

in HF. Whether there is a relationship between reduced CoQ10 on mood and cognition in

elderly HF patients, which are commonly seen in these patients, has not been explored.

4.7 Inflammation in heart failure and in cognitive impairment

4.7.1 The inflammatory pathway

There is widespread evidence indicating that inflammatory pathways and associated

biomarkers have a major role in the pathogenesis of chronic conditions such as HF,


59

depression and cognitive decline such as Alzheimer’s disease. The involvement of the

inflammatory response in the progression and pathogenesis of HF and cognitive impairment

has also received great interest. With the purpose of restoring a healthy equilibrium,

inflammatory and anti-inflammatory agents work with oxidants and antioxidants, to destroy

destructive agents in the body (Khaper et al., 2010). During any initial inflammatory

response, immune modulators such as Interleukin-6 (IL-6) and tumor necrosis factor–alpha

(TNF-α) trigger the formation of oxidants including superoxide anions and nitric oxide.

Furthermore, oxidative stress mechanisms play a role to assist with healing of damaged

tissue. For example, ROS triggers the release of inflammatory signalling molecules including

C-reactive protein (CRP) during the acute phase and IL-1, TNF-α during chronic phases of

injury (Khaper et al., 2010; Mann, 2008; Valko et al., 2007). Given the dynamic integrative

role between oxidative stress and inflammation in disease processes, we cannot explore these

pathways in isolation.

4.7.2 Inflammation and heart failure

HF is associated with increased pro inflammatory cytokines, activation of the complement

system, production of autoantibodies, over expression of histocompatibility complex class II

molecules and activation of vascular cellular adhesion molecules that perpetuate the

inflammatory state (Blum, 2009). In addition to the vasoconstrictors endothelins (e.g.

endothelin-1; ET-1), prostaglandins, nitric oxide, inflammatory mediators are also elevated in

heart failure (HF; Jackson, Gibbs, Davies, & Lip, 2000). Cytokines including TNF and IL-1

are pro-inflammatory mediators released from the myocardium in response to cardiac

damage. C-reactive protein (CRP), a measure of systemic inflammation is significantly

higher in patients with HF and is an indicator of disease severity (Xue, Feng, Wo, & Li,

2006). Anti-inflammatory cytokines levels (e.g. IL-10) are shown to be reduced in HF and

are inversely associated with the severity of the disease. It is the imbalance between the pro-

inflammatory and anti-inflammatory levels that are thought to be involved in the

pathogenesis of HF (Libby et al., 2008). The inflammatory response and resultant biomarkers

are believed to be a result of increased oxidative stress. Neurohormonal activation (increase

in AII, aldosterone, endothelin-1) and cytokines (TNF, IL-1) stimulate oxidative stress in the

heart, adding to the complexity of HF pathophysiology (Libby et al., 2008). Furthermore,

there is increasing evidence suggesting that vascular inflammation contributes to clinical

deterioration of clinical HF (Blum, 2009).


60

4.7.3 Inflammation and cognitive impairment

Interestingly a number of molecules associated with inflammation seen in HF are also

believed to be involved in the pathogenesis of Alzheimer’s disease (AD; e.g. Interleukin-1),

depression (e.g. high-sensitive C-reactive protein; hs-CRP) and impaired cognitive

performance in older adults (e.g. hs-CRP; Braunwald, 2008; Su, Huang, Chiu, & Shen, 2003;

Teunissen et al., 2003). This suggests that inflammatory factors may be related to

neuropsychological deficits seen in HF patients. Only two small studies to date have

examined the effects of inflammation on cognitive function in HF patients. In an early study

Said, Fouad, and Alvan (2007) observed that inflammatory markers TNF-α and IL-6 were

significantly higher in patients with moderate or severe HF (NYHA class III and IV)

compared to patients with no symptoms or mild HF (NYHA class I and II). When assessing

the entire HF population in this study, poor cognitive function evaluated by the Hodkinson

Abbreviated Mental Test (AMT) was strongly associated with high inflammatory markers

(TNF-α and IL-6). Additionally, IL-6 was the only variable shown to predict AMT scores.

Another recent study showed that in HF patients aged > 65 years (NYHA class I, III and III)

higher CRP and IL-6 levels were associated with lower global cognitive scores as measures

by the Montreal Cognitive Assessment battery (MoCA; Athilingam et al., 2012). However,

no associations were observed between tumour necrosis factor alpha and global cognitive

scores (Athilingam et al., 2012). Additionally, these studies measured global cognitive

function and a more detailed assessment would provide a better understanding of how

cognitive impairments are related to inflammatory markers (Athilingam et al., 2012).

However, in the absence of a control group, it is unknown whether the relationship between

cognition and inflammation was due to ageing. More recently, Kindermann et al. (2012)

assessed variance in cognitive function between decompensated HF patients with stable HF

and healthy controls. Using ANCOVA it was revealed that inflammation as measured by C-

reactive protein was significantly related to impaired memory, speed of information

processing and executive function. Another recent study showed that in HF patients aged >

65 years, higher CRP and IL-6 levels were associated with lower global cognitive scores as

measured by the MoCA (Athilingam et al., 2012). However, without a healthy control group

it is uncertain whether these relationships were due to the HF itself or aging.

4.7.4 Inflammation and mood

Few researchers have examined the link between these biomarkers on mood in heart failure

(HF). The prevalence of depression and anxiety among HF patients is high and these

conditions were shown to be independently associated with cognitive deterioration in HF


61

(Havranek, Ware, & Lowes, 1999). Interestingly mechanisms shown to be associated with

depression in HF may be similar to those associated with cognitive impairment in this patient

cohort. For instance, Andrei et al. (2007) found that high-sensitive C-reactive protein (hs-

CRP) levels were higher in elderly HF patients (LVEF < 0.40; NYHA class II or III) with

major depressive disorder (MDD) compared to patients without this psychiatric disorder.

Given that group differences were not observed with pro-inflammatory cytokine levels (TNF-

α or IL-6) the findings that systemic inflammation (hs-CRP) is an important marker for

depression in HF patients. However, in a small study the pro-inflammatory cytokines TNF-α,

but not IL-6 or IL-1, were higher in HF outpatients aged 18 years and over (age = 56±10

years; ejection fraction = 26±10%) with elevated levels of depression as determined by the

beck depression index (BDI; ≥ 10), compared to patients who scored low on the depression

scale (Ferketich, Ferguson, & Binkley, 2005). Supporting this, higher depression scores as

measured by the Hamilton depression scale related to the circulating inflammatory marker

IL-6 in elderly patients with decompensated HF (Guinjoan et al., 2009). Moreover,

significantly reduced systemic levels of IL-6 and TNF-α and a trend towards reduced CRP

levels were associated with higher levels of positive affect in HF patients as measured by the

Hospital Anxiety and Depression Scale (HADS; Brouwers et al., 2013). Given that

depression scores are related to circulating serum inflammation in HF patients with a

diagnosis of depression and in decompensated patients, an examination of whether

inflammatory markers are also related to depressed mood in elderly HF patients without

diagnosis of depression has not been examined.

Factors that reduce inflammation in heart failure (HF) patients may prove to be effective in

improving mood in these patients. Following 12 months omega-3 polyunsaturated fatty acid

(omega-3 PUFA) supplementation (month 1: 5g/day; 5 x EPA 850–882 mg and DHA as ethyl

esters in the average ratio of 0.9:1.5; months 2-12: 2g/day of omega-3 PUFAs), inflammatory

markers as measured by serum TNF-α, IL-1 and IL-6 levels had decreased in the treatment

group and increased in the placebo group. These findings indicated that omega-3 PUFA

showed anti-inflammatory effects in HF patients (Nodari et al., 2011). In addition, HF

patients taking omega-3 PUFA supplementation showed an improvement in left ventricular

ejection fraction, exercise capacity and NYHA functional class (from 1.88 to 0.33) following

12 month intervention, whereas the placebo group showed a decline in these factors (Nodari

et al., 2011). In addition, compared to controls, 1g daily omega-3 PUFA (EPA 850–882

mg/day DHA as ethyl esters in the average ratio of 1:1·2) mildly reduced all-cause mortality

(27% vs 29%) and admissions to hospital (NYHA class II to IV; 67±11 years for each group),

as a result of reduced cardiovascular events (57% vs 59%) in older HF patients (Tavazzi, et


62

al., 2008). In addition to improving cardiac symptoms associated with HF, omega-3 PUFA

levels may be linked with other concomitant conditions involving a heightened inflammatory

state seen in this syndrome.

There is strong evidence for the effective use of omega-3 polyunsaturated fatty acids (PUFA)

dietary intake for depression and some evidence supporting its utilisation in cognitive

functioning. Dietary PUFA intake in the form of fatty fish is inversely related to the risk of

cognitive impairment and Alzheimer’s disease (Kalmijn et al., 2004; Morris et al., 2003),

however supplementation studies have shown mixed results. Although evidence regarding

cognitive function is mixed, data from small studies have demonstrated the use of omega-3

PUFA as an effective treatment in depression. For example Su et al. (2003) showed that

compared to controls taking placebo (n=14), patients with major depression taking omega-3

PUFA supplement (EPA 4.40g/d; DHA 2.20g/d; n=14) for four weeks scored significantly

better on the Hamilton depression rating scale. A study using a lower EPA dose for 12 weeks

however (8g fish oil/day: EPA-0.6g & DHA-2.4g) did not support these results (Silvers,

Woolley, Hamilton, Watts, & Watson, 2005). Since the prevalence of depression and anxiety

among HF patients is high and changes in mood and quality of life have been correlated with

cognitive dysfunction in HF (Havranek et al., 1999), dietary omega-3 PUFA intake may also

be associated with mood factors in these patients. It is therefore possible that low omega-3

PUFA dietary intake will directly influence mood and cognition in HF by increasing

inflammation and indirectly through reducing cardiovascular symptoms associated with the

HF syndrome.

4.8 Summary

Heart failure, cognitive decline and mood disorders are associated with increased oxidative

stress, decrease antioxidant capacity and increased inflammation. However, the effects of

oxidative stress on cognitive function in HF have not been investigated. New evidence is

emerging suggesting that elevated levels of systemic inflammation may contribute to

cognitive impairment and may relate to depressed mood in HF patients.

Chapter 5: Rationale, research questions and hypotheses

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CHAPTER 5 RATIONALE, RESEARCH QUESTIONS AND HYPOTHESES

5.1 Study Aims

The primary aim of the current thesis was to investigate the physiological mechanisms for

cognitive impairment in older heart failure (HF) patients. Disturbances in cognitive function

are prevalent in HF patients and contribute to negative health consequences in these patients.

In order to establish effective interventions that help preserve or improve cognitive function,

a clear understanding of the basic biological mechanisms associated with cognitive

impairment (CI) in particular attention, working memory, executive function and visual

spatial abilities using well-validated neuropsychological tests is required. This thesis will

focus on vascular, oxidative stress, antioxidant and inflammatory mechanisms implicated in

both HF and cognitive decline and to explore whether these mechanisms are associated with

cognitive function in HF. Findings from this study will provide a foundation for devising an

appropriate intervention which targets appropriate mechanisms which have been identified as

dysfunctional in these patients.

There are three major aims of this thesis. The first is to investigate additional cognitive

domains that may be impaired in HF, using a well-validated neuropsychological test battery

and after accounting for possible practice effects. Another aim is to examine whether

cognitive deficits are related to changes in mood. If cognitive deficits occur the second aim is

to investigate whether deficits in HF can be explained by decreased cerebral blood flow,

increased oxidative stress, reduced antioxidant capacity and increase in inflammation. The

third aim is to investigate whether depressive and anxiety symptoms in HF can be explained

by increased oxidative stress, reduced antioxidant capacity and increase in inflammation

compared to a healthy age matched control group.

5.2 Rationale

This thesis will specifically investigate whether impairments in attention, executive function,

working memory, episodic memory and visual spatial abilities exist in older patients with

mild, moderate and severe HF as diagnosed by the NYHA classifications after accounting for

practice effects. Moreover, this thesis will explore whether additional cognitive domains

including Speed of Memory, Power of Attention, Continuity of Attention, Quality of Episodic

Memory and Quality of Working Memory as measured by the Cognitive Drug Research® test

battery are impaired in older HF patients without dementia compared to an age matched

control group. Additionally, this thesis will select well-validated methods that can be easily


64

implemented in a clinical setting to detect biomarkers that may be related to cognitive

impairment.

Based on previous research, it is expected that older HF patients in this study will show

significantly poorer performance on attention, working memory, episodic memory tasks and

executive functioning (Beer, et al., 2009; Sauvé et al, 2009) and psychomotor function

(Almeida, Garrido, et al., 2012; Pressler, Subramanian, et al., 2010a) compared to age

matched healthy controls.

Performance on cognitive tests is often associated with depression and anxiety. This

investigation will therefore control for these mood parameters by excluding patients and

controls diagnosed with clinical depression and anxiety.

5.3 Methodological issues

5.3.1 Patient selection

Confounding factors are commonly seen in patients and the elderly, which may affect

participant scores on neuropsychological assessments. Factors such as premorbid IQ, age and

years of education and mood are known to influence cognitive performance in adults and not

all trials have controlled for these measures. Alosco, et al. (2012) showed that a higher score

on premorbid IQ as measured by the American National Adult Reading Test (AMNART)

was positively related to average composite scores on attention, executive function, memory

cognitive domains and language. This thesis will aim to compare cognitive functioning in HF

with those of a healthy control group matched for age and premorbid IQ functioning.

Concomitant conditions, such as fatigue and arthritis, which may negatively influence a

patient’s ability to complete tasks, need to also be considered. Administration time for some

of the cognitive test batteries in previous studies have lasted as long as 6 hours (Incalzi et al.,

2003; Vogels, Oosterman, van Harten, et al., 2007), which is less than optimal for patients

with HF who commonly experience fatigue. It has been previously reported that a

neuropsychological test battery taking on average 40 or 45 minutes to complete is well

tolerated and does not cause fatigue in patients with HF (Bauer & Pozehl, 2011; Bauer et al.,

2011). With the aim to obtain an accurate account of patients’ cognitive performance without

the influence of fatigue, this study will therefore incorporate a neuropsychological test battery

that takes approximately 45 minutes to complete.


65

5.3.2 Research environment

Research environments have not been consistent between studies that have explored cognitive

impairments in HF. Firstly, various professionals including nurses, neuropsychologists and

neurologists have administered cognitive tests. Additionally, testing environments between

studies have been inconsistent between studies with some groups being tested in the patients’

home, (Lavery et al., 2007) in a hospital bed as inpatients, others in controlled testing

environments located at medical centres and hospitals, whilst other patient have been tested

in their homes or in the researcher’s office (e.g. Suave, 2007). Neuropsychological testing

requires a suitable, consistent and controlled environment in order to minimise confounding

variables such as surrounding comfort, noise, temperature and lighting. With various testing

environments used between studies it is therefore difficult to ascertain whether the testing

environments have influenced patient’s mood or task performance. This thesis will conduct

neuropsychological testing in purpose built, environmentally controlled testing rooms.

5.3.3 Neuropsychological test batteries

Few studies have examined the validity and reliability of these cognitive assessments

specifically in a HF population (Bauer & Pozehl, 2011). Researchers have set out to

investigate the validity and reliability of a cognitive assessment battery in chronic HF patients

(Bauer & Pozehl, 2011; Bauer et al., 2011). A cognitive assessment battery entailing Trail

Making-A and Trail Making-B, RBANS and letter fluency was found to be a reliable and

valid test battery with good test-retest reliability for this patient group (Bauer et al., 2011).

Although adults aged 21 years and above were enrolled in this validity study the majority of

patients were elderly (95% aged > 50 years).

Since there is no universally exepted method for assessing cognitive function in HF research,

studies have used different screening tools to assess cognitive performance which vary in

reliability, validity and measure different outcomes. Some screening tools utilizing global

cognitive function (e.g. MMSE) use different cut off scores. For instance cut off scores for

cognitive impairment using the MMSE has varied between studies from 19-26 (McLennan et

al., 2006), < 24 (Cacciatore et al., 1998) and ≤ 24 (Debette et al., 2007). McLennan showed a

prevalence of CI as measured by MMSE score of ≤ 24 in 3.5% (out of n=200) of HF patients

at discharge (NYHA class II, III and IV) and when including probable MCI score of MMSE

≤ 26 this percentage raised to 13.5% (McLennan et al., 2006). Other measures however are

more comprehensive and assess various cognitive domains and therefore provide a better

assessement of which cognitive facets are effected. Additionally, when assessing cognitive

function in HF most studies have administered traditional cognitive assessments batteries


66

including paper pencil, computerised, verbal and non-verbal tests. These individual test

provide

Only a few studies however have provided a thorough cognitive assessment to provide an

evaluation of patient’s performance on each cognitive domain.

5.3.4 Practice effects

Another motivation for this thesis was to expand on the cognitive domains that may be

affected in these patients using a well-validated test battery. This study will use a

comprehensive computerised test battery to find examine whether additional domains are

impaired. Furthermore, no study to date has accounted for possible practice effects commonly

seen in neuropsychological testing. This is a possible confounding factor especially for

studies without a control group. It is not known in longitudinal studies whether improvements

in cognitive factors over time or whether it is the learning or practice effects. A training or

practice session on a day separate to the testing days would assist patients with task

familiarisation to minimise practice effects and patient anxiety, which has shown to influence

performance on cognitive tasks.

This thesis aimed to confirm whether these cognitive domains are still impaired when using a

research paradigm that controls for possible confounding factors including practice effects by

incorporating a training session on a day separate to baseline testing in order for participants

to become familiar with the neuropsychological tasks.

5.4 Summary of biological mechanisms

If the results show that patients perform significantly worse on these cognitive measures after

accounting for practice effects, this thesis will also investigate whether vascular, oxidative

stress, antioxidant and inflammatory biomarkers will be related to group differences in these

cognitive measures. Exploring the relationships between these biomarkers and cognitive

function may provide insight into possible mechanisms associated with cognitive

impairments in elderly heart failure patients.

5.4.1 Vascular

Only a few studies have explored the relationship between cerebral blood flow and cognitive

function in HF (Alves et al., 2005; Jesus et al., 2006; Vogels et al.,2008). There is evidence

that cognitive impairment in HF is underpinned by reduced cerebral blood flow (Alves et al.,

2005; Jesus et al., 2006). However there is limited evidence suppoting the role of cerebral


67

blood flow on performance on specific cognitive domains (Vogels et al.,2008). Therefore this

thesis will expand on previous studies and will explore whether cerebral blood flow measured

from the middle cerebral and common carotid arteries using the Transcranial Doppler (TCD)

is related cognitive impairment in HF patients compared to a control group. It is anticipated

that reduced cerebral blood flow will be related to decreased cognitive function in HF

patients.

An additional aim was to explore whether arterial stiffness is an additional vascular factor

associated with cognitive function (episodic memory, Speed of Memory, visuospatial ability

and executive function) in HF. Studies have shown relationships between reduced cerebral

blood flow velocities and global cognitive impairment in HF (Jesus et al., 2006; Vogels et al.,

2008) however, no study has explored whether arterial stiffness is related to patients impaired

cognitive performance. Additionally, another aim is to examine whether oxidative stress and

inflammation is related to arterial stiffness and cognitive function in HF.

Arterial stiffness is associated with aging (Elias et al., 2009) and is elevated in heart failure

(HF; Tartière et al., 2006) and cognitive impairments (Hanon et al., 2005; Kearney-Schwartz

et al., 2009; Scuteri et al., 2005). No study to date has assessed whether cognitive decline in

elderly HF patients is associated with arterial stiffness. Indirect measures of increased arterial

stiffness are associated with executive function and word fluency in elderly individuals, Poels

et al. (2007) although the association in HF patients has not been explored. Given that

pharmaceuticals commonly taken by HF patients and known to reduce arterial stiffness

(O'Rourke & Safar, 2005), it is therefore reasonable to explore whether there is a relationship

between arterial stiffness and cognitive function in HF patients. It is anticipated that arterial

stiffness will be related to cognitive function in elderly HF patients.

5.4.2 Oxidative stress and antioxidants

To date no study has investigated the effects of oxidative stress and antioxidant biomarkers

on working memory, episodic memory, executive function, attention and mood in older HF

patients compared to an age matched control group. Exploring whether a relationship exists

between oxidative stress, antioxidants and cognitive function may provide further insight into

the possible mechanisms for cognitive impairment in heart failure and in turn help devise

appropriate treatments to improve cognition in these patients. Given that oxidative stress

markers elevated in HF are also increased in memory impairments (Praticò et al., 2002;

Torres et al., 2011) an assessment of whether these biomarkers are related to cognitive

impairment in HF may provide an indication into suitable treatments for improving cognitive

function. Furthermore, the aim of this thesis is to explore whether reduced enzymatic


68

antioxidant (glutathione peroxidase) activity seen in older HF patients and in cognitive

impairment is a mechanism for cognitive impairment in elderly heart failure (Polidori et al.,

2004; Torres et al., 2011). Moreover, since coenzyme Q10 is known to be reduced in HF and

improvements in HF severity is observed following supplementation (Keogh et al., 2003),

examining whether a relationship exists between reduced plasma levels of coenzyme Q10 and

poor cognitive function in HF is another aim of the present thesis.

Given the complexity and the interrelatedness of the oxidative stress and antioxidant

pathways, the present study will examine multiple biomarkers rather than an individual

measure (Gironi et al., 2011). Examining a battery of oxidative stress and antioxidant

biomarkers will provide a better indication of how these biomarkers are related to each other

and to cognitive function. It is anticipated that oxidative stress and antioxidant measures will

be related to cognitive function in elderly HF patients.

5.4.3 Inflammation and omega-3 dietary intake

This thesis explored whether there is an association between systemic inflammation as

measured by high-sensitive C-reactive protein (hs-CRP) is in HF patients. Two studies to

date have explored the effects of inflammation on cognitive function in HF (Kindermann et

al., 2012; Said et al., 2007). Although, one study however failed to include a control group

(Said et al., 2007). This study will expand on these projects to examine whether cognitive

function is related to systemic inflammation in an elderly HF patient sample compared with a

control group matched for age and gender and a comprehensive cognitive test battery. It is

anticipated that cognitive function will be to inflammation as measured by hs-CRP in older

HF patients.

5.5 Mechanisms for changes in mood

Few studies have examined psychological factors including depression, anxiety, fatigue and

quality of life that could affect cognitive functioning in HF patients. Mood disturbances

including depression and anxiety are prevalent in HF patients (Cacciatore et al., 2008;

Stephens, 2008), however the mechanisms associated with these mood disturbances have not

been widely examined. Exploring the mechanisms for mood disturbances in elderly HF

patients will assist with formulating suitable interventions for improving mood in these

patients. Studies examining cerebral blood flow or arterial stiffness have not accounted for

confounders including cardiovascular disease risk factors such as inflammation or

psychological factors (e.g. depression or anxiety) which are known to influence cognitive

performance (Waldstein et al., 2008). It is unknown whether mood is realted to vascular,


69

oxidative stress or anitoxidant measures. However, there is some evidence to suggest that

inflammatory factors are involved with depressive symptoms in HF (Andrei, et al., 2007).

This thesis therefore aimed to assess whether oxidative stress, antioxidants, arterial stiffness

and cerebral blood flow are related to depression and anxiety in HF. Given that this is the first

study to explore whether arterial stiffness, cerebral blood flow, oxidative stress, antioxidant

capacity are related to mood disturbances in HF patients, this observation is purely

exploratory in nature and this study will explore whether a relationship between these

variables exist. Therefore a specific hypothesis regarding the direction of this relationship has

therefore not been made. It is anticipated that higher levels of systemic inflammation will be

related to depression in HF patients.

5.6 Hypotheses and research questions

Attention domains:

H1 HF patients will perform significantly worse on attention tasks as measured by

congruent Stroop task, Power of Attention and Continuity of Attention compared to the

control group.

H2 HF patients will perform significantly worse than controls on psychomotor function

(Trail Making-A).

Memory domains:

H3 HF patients will perform significantly worse than controls on Quality of Working

Memory, Quality of Episodic Memory and Speed of Memory tasks.

Executive function domains:

H4 HF patients will perform significantly worse on executive function as measured by

Trail Making-B, incongruent Stroop and Stroop effect tasks compared to the control group.

Mood:

H5 HF patients will score higher on depression, anxiety and fatigue measures and lower

on vigour as measured by the Profile of Mood States questionnaire than controls.

Vascular:

H6 HF patient group will have significantly lower cerebral blood flow velocity as

measured by common carotid and middle cerebral blood flow velocity compared to the

control group.


70

H7 HF patients will have increased arterial stiffness as measured by augmentation index

and central pulse pressures compared to the control group.

H8 HF patients will have higher plasma levels of the vasoconstrictor endothelin-1

compared to controls.

Oxidative stress and antioxidant biomarkers:

H9 HF patients will have significantly higher levels of oxidative stress as measure by

determinable reactive oxygen metabolites (DROMs) and lipid peroxides (F2-isoprostanes)

compared to the control group.

H10 HF patients will have significantly lower levels of plasma antioxidants as measured

by coenzyme Q10 and glutathione peroxidise compared to the control group.

H11 Patients will have significantly higher levels of inflammation as measured by hs-CRP

and omega-3 dietary PUFA intake compared to the healthy control group.

Relationships between cognitive measures and vascular markers:

H12 Reduced cerebral blood flow velocity as measured by common carotid and middle

cerebral arterial blood flow will be related to poorer performance on cognitive tests

measuring global cognition, attention, psychomotor speed, working memory, episodic

memory and executive function in HF patients.

H13 Arterial stiffness as measured by augmentation index and pulse pressure will be

related to cognitive tests measuring global cognition, attention, psychomotor speed, working

memory, episodic memory and executive function.

Relationships between cognitive measures, biomarkers and omega-3 dietary intake:

H14 Oxidative stress, antioxidants, inflammation and omega-3 dietary intake will be

related to cognitive tests measuring global cognition, attention, psychomotor speed, working

memory, episodic memory and executive function.

Relationships between mood and vascular measures:

Research question (R1) No specific hypotheses were made with relation to whether cerebral

blood flow or arterial stiffness have an effect on depression and anxiety in HF patients.

Therefore, the current investigation explored the research question “is there a relationship

between cerebral blood flow or arterial stiffness and depressive symptoms and anxiety “.


71

Relationships between mood and biomarkers:

Research question (R2) No specific hypotheses were made with relation to whether

oxidative stress, antioxidant measures or omega-3 dietary intake will have an effect on

depressive symptoms and anxiety in HF patients. Therefore the research question “is there a

relationship between oxidative stress, antioxidant measures and depressive symptoms and

anxiety” was explored in the present investigation.

Research question (R3) No specific hypotheses were made with relation to whether

inflammatory measures have an effect on anxiety in HF patients. Therefore the research

question “is there a relationship between inflammatory measures and anxiety in HF patients”

was explored in the present investigation.

H15 It was hypothesised that inflammation as measured by high-sensitive C-reactive

protein will be related to depression scores in HF patients.

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72

CHAPTER 6 METHODS

6.1 Introduction

This trial was an observational study designed to assess the mechanisms of cognitive deficit

in heart failure (HF) patients and in particular, whether, compared to a control group matched

on age and IQ, vascular, inflammatory and/or oxidative mechanisms are associated with

cognitive decline in HF. The trial received approval from the Alfred Hospital and Swinburne

University of Technology Human Research Ethics Committees (HREC; Appendix A). All

conditions concerning the ethics clearance were met and annual and final reports have been

submitted to the HRECs. After fulfilling the screening criteria, subjects took part in

neuropsychological testing to assess cognitive function, mood and quality of life as well as

biochemical tests to compare HF patients with a group of healthy controls matched on age

and IQ. This chapter will outline the materials used and recruitment process for the first

study.

6.2 Participants

The target population were males and females aged 60 years and above with an MMSE score

of ≥ 24 and estimated IQ of > 70 as measured by the Wechsler Abbreviated Scale of

Intelligence-Vocabulary subset (WASI-vocabulary). All participants were screened during an

interview by the researcher to ensure they had no existing or pre-existing neurological

conditions such as dementia, no history of psychiatric conditions (e.g. depression and

anxiety), no endocrine, gastrointestinal or bleeding disorders, no hearing impairments, not

taking psychoactive medication, no history of substance abuse, non-smoker and fluent in the

English language.

6.2.1 Heart failure patients

The patient group comprised of Heart Failure patients (n=46; NYHA class II, III and IV)

aged 60 years and over who were recruited from the Heart Centre, Heart Failure Clinic,

Alfred hospital, Prahran Melbourne. Prior to the patients regular outpatient appointment with

their cardiologist, the heart failure nurse and cardiologist initially assessed patient’s

suitability to participate in the study based on information recorded on the patients file such

as age and severity of disease. At their outpatient visit, the heart failure nurse or research

student asked potentially suitable patients if they were interested in taking part in the study.

The student investigator provided information regarding the study procedures, aims and

requirements to interested patients and in a quiet, private setting (consulting room in the

Chapter 6: Methods

73

Heart Centre, Heart Failure Clinic or in the waiting room) and conducted a screening

interview to assess patients’ eligibility to participant in the study. Eligible participants were

provided with a Participant Information and Consent Form (PICF; Appendix B) which further

explained the study. An appointment was then made for the initial testing session. Patients

who wanted more time to read the information sheet were followed-up with a phone call one

week later and if they were still interested in taking part in the study, an appointment was

made for the first testing session. Additional selection criteria for Heart Failure patients

included a current diagnosis of NYHA class II, III or IV, no heart failure due to thyroid

disease, and no stroke in the 6 months prior to enrolment or unstable angina.

6.2.2 Healthy control volunteers

Healthy participants were recruited and tested at the Centre for Human Psychopharmacology,

Swinburne University, Melbourne. Healthy volunteers were from the general public who

responded to local newspaper advertisements, word of mouth, emails and official flyers.

Individuals who responded to advertisements by way of phone call or email, were phoned by

one of the researchers who explained the study in detail, conducted screening interviews and

posted a copy of the information sheet so that participants could read the information sheet

before the first testing session. Additional selection criteria for the control sample included

unmedicated hypertension, no history of cardiovascular disease, no heart failure and

considered to be generally healthy.

6.3 Power analysis

The primary outcome was performance on the pencil-and-paper Trail Making-B task.

Previous work in this field using this outcome have found effect sizes consistent with a ‘large

effect' - defined by Cohen (1992) as at least .80 of a standard deviation. For example a

systematic review of cognitive impairment in heart failure by Vogels, Scheltens, et al. (2007)

reported effect sizes of up to 1.098 in the best controlled trials. According to Cohen (1992),

there is an 80% chance of detecting such an effect at the p < .05 level using a sample size of

26 per group in a between-subject study. Therefore, the sample size per group for this study

was at least 26 per group. Additionally, previous studies in the field have successfully

captured the effects of HF on Trail Making-A task with sample sizes between 40 and 140.

Using this information to calculate sample size and using a statistical power of 80% with an

alpha level of .05, to detect a 48% change in Trail Making-A a sample size of 12 per group

was required, and to detect a 30% change 24 per group would be required. The aim was

therefore to complete testing on at least 30 participants in each group. To account for a

Chapter 6: Methods

74

twenty per-cent participant dropout rate, the aim was to recruit at least 40 HF patients and 40

healthy male and female volunteers aged 60 years and above.

6.4 Materials

6.4.1 Case Report Form (CRF)

Participants completed a demographic questionnaire where data including age, sex, highest

level of school completion, ethnicity, five year medical history and current medications were

recorded on the Clinical reference form (CRF).

6.4.2 Cognitive measures

6.4.2.1 Cognitive Drug Research

Cognitive testing comprised of a battery of computerised and traditional paper-pencil tests.

There is evidence indicating that older HF patients show impairment in specific cognitive

domains including immediate and delayed recall (Almeida, Beer, et al., 2012), attention

(Sauvé et al., 2009) and visuospatial abilities (Lavery et al., 2007). Majority of these studies

have used age matched controls to compare cognitive scores with those of heart failure (HF)

patients (Beer et al., 2009; Sauvé et al., 2009) yet others compared results to normative data

(Bauer et al., 2011; Wolfe et al., 2006). The Cognitive Drug Research® (CDR) standardised

computerised test battery was employed in this study to measure participant’s performance on

memory and attention tasks. Previous studies have shown that scores on the well-validated

CDR test battery are correlated strongly with MMSE scores in older patients with dementia

(Simpson, Surmon, Wesnes, & Wilcock, 1991). The CDR test battery has been extensively

used to assess the effects of older age (Simpson et al., 1991), hypertension (Harrington,

Saxby, McKeith, Wesnes, & Ford, 2000), coronary artery bypass grafting (CABG; van den

Goor, Saxby, Tijssen, Wesnes, de Mol, & Nieuwland, 2008) and cardiac enterectomy on

cognitive function (Fearn et al., 2003). This is the first time CDR was administered to a HF

outpatient group. The CDR is has been shown to have good test retest reliability in an elderly

population (Simpson et al., 1991). The CDR test battery is comprised of ten individual task

variables outlined in Table 2. The CDR cognitive test battery was chosen for this study as

computerised tests provide a more accurate reaction time scores compared to paper pencil and

verbally administered tasks. Furthermore, the CDR test battery incorporates a training session

separate to the actual testing sessions to minimise practice effects. As no prior computer

skills were necessary, the training session provided an opportunity for participants to become

familiar with the two-button box response box, computer screen on which the tasks were

presented and to practice the reaction time tests using the button box (van den Goor et al.,

Chapter 6: Methods

75

2008). To date no study has eliminated practice effects when assessing cognitive function in a

HF cohort.

Table 2 Description of subsets from the Cognitive Drug Research (CDR) test battery and the

order in which the tasks were presented

CDR Task Task Description and assessment Measure

Immediate Word

Recall

A series of fifteen words are presented on the screen

one every 2 seconds. The participant is required to

remember word list and write down as many words

they can remember in 60 seconds

Number of

words

correctly

recalled.

Picture

Presentation

A series of twenty pictures are presented on the

screen at an interval of one every three seconds. The

participant is required to remember each picture in

detail.

No data

recorded

Simple reaction

time

The word “YES” appears on the screen for a period

of 200 msec at varying intervals ranging between 1

and 3.5 seconds. The participant then presses the

“YES” button as soon as they see the stimuli appear.

Mean RT

(msec)

Digit Vigilance

Task

A random digit appears on the right hand side of the

computer screen for the duration of the task. A series

of digits appear in the middle of the screen (150 per

minute) and each time the two digits match, the

participant presses the “YES” button a swiftly as

possible. Intensive vigilance, sustained

concentration, and ability to ignore distractions.

% accuracy,

RT (msec)

and number

of false

alarms.

Choice reaction

time

Either the words “YES” or “NO” appear randomly

on the screen. The participant is required to press the

corresponding button as soon as they see the word.

Speed and accuracy of stimulus discrimination

RT (msec)

and %

accuracy

Note: Reaction times (RT) were measured in milliseconds (msec; van den Goor et al., 2008).

Chapter 6: Methods

76

Table 2 cont’d... Description of subsets from the Cognitive Drug Research (CDR) test battery and the order in which the tasks were presented


Spatial Working

Memory

A picture or a house with 9 windows initially appears

on the screen. Four of the windows are lit or

coloured white, the other windows are black, and the

participant is asked to remember the position of the

lit windows. The original house disappears and the

house then reappears 36 times with one of the nine

windows lit. The participant responds as quickly as

possible by pressing the “YES” and “NO” buttons if

the window was lit or was not lit in the original

house presentation, respectively.

Mean RT in

msec, and %

accuracy of

responses to

both original

and novel

(distractor)

stimuli.

Numeric Working

Memory

A sequence of five digits (0-9) appears on the screen

one at a time for the participant to remember. A

subsequent series of 30 random single digits appear

on the screen and the participant is required to

respond to each word by pressing the “YES” and

“NO” button as quickly as possible if that word was

presented or not presented in the original series,

respectively.

Mean RT

(msec), and

% accuracy

of responses

to both

original and

novel

(distractor)

stimuli.

Delayed Word

Recall

The participant is asked to write down as many

words that they can recall from the series of 15

words presented at the beginning of the test session.

Number of

words

correctly

recalled.


Chapter 6: Methods

77

Table 2 cont’d... Description of subsets from the Cognitive Drug Research (CDR) test battery

and the order in which the tasks were presented


Delayed Word

Recognition

The 15 words from the Word recall task and 15

distractor words appear randomly on the screen

one at a time. For each word the participant

responds as quickly as possible by pressing the

YES” and “NO” button if the word was in the

original list or if it was not, respectively.

Ability to discriminate novel from previously

presented words and long-term verbal learning

capacity.

Mean RT (msec),

and % accuracy

of responses to

both original and

novel (distractor)

stimuli.

Delayed Picture

Recognition

As per the Delayed Word Recognition tasks with

pictures presented instead.

Ability to discriminate novel from previously

presented pictures.

Mean RT (msec),

and % accuracy

of responses to

both original and

novel (distractor)

stimuli


The scores on these individual task variables were later used to calculate the following five

cognitive domains or composite scores outlined in Table 3: Power of Attention, Continuity of

Attention, Speed of Memory, Quality of Working Memory and quality of episodic secondary

memory.

Chapter 6: Methods

78

Table 3 Composite scores for the five cognitive domains (van den Goor et al., 2008)

Domain Definition CDR subtest

Power of Attention* ability to focus ones attention

during a short period of time

when extreme concentration is

required.

sum of participant’s

response speed (msec) to

stimuli on simple and choice

reaction time and digit

vigilance tasks

Continuity of Attention the ability of focus attention

over a period of time when there

is a distraction and without

mistakes/error

average percentage accuracy

on choice reaction time and

digit vigilance accuracy

scores minus false alarms for

digit vigilance task.

Speed of Memory*

assessment of the time it takes to

decide whether information is

held in memory or information

retrieval time.

summation of speed of

responses (msec) on delayed

picture and word recognition

tasks, numeric working

memory and spatial memory

tasks.

Quality of Working Memory an assessment of the ability to

store, hold and manipulate

information

the mean percentage

accuracy scores from the

spatial and numeric working

memory tasks

Quality of Episodic Secondary

Memory

an assessment of the ability to

code and retrieve information

from episodic memory

combines performance

accuracy scores as a

percentage from delayed

word reconition, delayed

picture recognition,

immediate word recall and

delayed word recall tasks.

Note: *=higher scores on these cognitive domains indicate worse performance (van den Goor et al., 2008); msec=milliseconds.

Elderly individuals have been shown to adequately carry out computerised testing using the

CDR test battery (Simpson et al., 1991). To prevent learning effects parallel forms of the

tasks were presented at each session.

Computerised tasks were presented on a laptop and participants were asked to respond to

visual stimuli presented on the laptop screen by pressing either a “yes” or “no” button on a

Chapter 6: Methods

79

response box; except for the immediate and delayed word recall tasks where participants were

asked to write down words remembered from the word presentation task. Practice effects can

influence performance on cognitive tests (Lezak, 2012). To minimise practice effects and

anxiety, participants underwent a training day where they became familiar with the

equipment and computerised cognitive tasks. During the training sessions, the administrator

presented standardised instructions verbally to explain the CDR test battery and instructed

participants on how to use the response button box (Appendix C). Prior to each task the

administrator read out instructions on how to perform the task and if the participant did not

completely understand what was required of them, the instructions were repeated until the

participant clearly understood the task.

For the control group, the administrator read out task instructions once at the first training

session. During subsequent testing sessions, participants read abbreviated task instructions

displayed on the laptop screen and proceeded when ready. For the patient group, the

administrator read out instructions prior to every testing session during training and baseline

testing days.

Each task was initiated by the participant pressing the ‘yes’ button in the Control group and

by the administrator pressing the <Enter> key in the Patient group. A written log was kept

for each testing session recording problems occurred during the testing session that may

explain outliers and missing data. During the training sessions, patients and healthy

participants went through the cognitive test batteries twice and four times, respectively as per

the CDR training protocol.

Participants were seated at a desk and asked to position the button box in front of them in

such a way that it would be comfortable for them to rest their fingers gently on the response

buttons so that the speed of response was accurately measured. Participants were asked to use

their right hand to press the right “yes” button and their left hand to press the “no” response

button. Participants were informed that there would be a series of tasks (each a few minutes

long) and that it would take approximately 25 – 30 minutes to complete the whole session.

Technical support was received from CDR Ltd throughout the study.

6.4.2.2 Stroop word task

There is evidence in the literature for impairments on tests of selective attention in HF. The

Stroop word task (Stroop, 1935) is a commonly used measure of selective attention, cognitive

flexibility and response inhibition. As a measure of cognitive control, the Stroop task assesses

the ability to keep a goal in mind whilst suppressing a habitual response (Strauss, Sherman, &

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80

Spreen, 2006). Impaired performance on the Stroop task is associated with aging and is

shown to be influenced by years of education (Van Der Elst, Van Boxtel, Van Breukelen, &

Jolles, 2006). Elderly patients with HF have shown impairments in congruent, incongruent

(Vogels, Oosterman, et al., 2007) and Stroop interference scores compared to controls (Hoth

et al., 2008; Vogels, Oosterman, et al., 2007). In the current study a computerised version of

the Stroop task was employed where one of four stimulus words (BLUE, RED,

GREEN,YELLOW) were randomly presented on the computer screen on a black background

for 1.7 seconds with an inter stimulus interval (ISI) of 0.5 seconds (Pipingas et al., 2010). In

the congruent task the font colour of the stimulus word was the same as the stimulus word (i.e

the word BLUE was presented in the colour ‘blue’) and in the incongruent task the stimulus

word was presented in one of the other three colours (i.e. the word BLUE was presented in

the colour ‘yellow’). Participants responded by pressing the corresponding coloured button

on the button box in front of them for the colour of the word irrespective of the word

presented (Pipingas et al., 2010). The response scores were measured by percentage accuracy

and reaction time (ms). The Stroop interference score, a measure of executive function and

response inhibition, was determined by subtracting the mean congruent reaction time scores

from that of the incongruent mean reaction time. Instructions read out by the administrator

for the Stroop task are presented in Appendix D.

6.4.2.3 Trail Making Test (TMT)

The Trail Making Test (TMT; Reitan, 1958) has been frequently used to measure information

processing speed in older HF patients. The Trail Making Test is a reliable measure of

attention, visual perceptual and psychomotor speed, and mental flexibility (Giovagnoli et al.,

1996; Lezak, 2012; Strauss et al., 2006). The TMT is a paper pencil test consisting of two

parts. Part A (Trail Making-A) consists of 13 encircled numbers (1-13) randomly positioned

on a sheet of paper and the participant consecutively connects the numbers with a continuous

line, as quickly as possible. Similar to Trail Making-A, part B (Trail Making-B) involves 13

numbers (1-13) and 12 letters (A-K) encircled on a sheet of paper and participants join the

digits and letter by alternating between consecutive numbers and letters (i.e. 1 to A, 2 to B

etc). The time taken to complete each task is recorded in milliseconds as a measure of the

participants’ performance. Without stopping the stopwatch, the researcher stopped the

participant and notified them if they had made an error and instructed them to continue the

task from the previous number or letter correctly joined. Trail Making-B also assesses

executive function by measuring the ability to switch between stimuli. The standard

administration requires Trail Making-A to always be administered before Trail Making-B and

a short practice test prior to each part. The TMT takes 5-10 minutes to complete.

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81

Studies investigating speed of information processing using the Trail Making Test in HF

patients has yielded mixed results. There has been conflicting evidence for HF patient

performance on TMT. There is some evidence indicating no significant differences for

performance on Trail Making-A, however HF patients perform slower on Trail Making-B

(Almeida & Tamai, 2001b; Hoth et al., 2008). Table 4 provides a summary of the cognitive

domains measured by the Cognitive Drug Research® assessment battery and

neuropsychological tests that have previously shown to be sensitive to heart failure.

Table 4 Summary of neuropsychological tests

Cognitive Domain Task Source

Attention Congruent Stroop

Trail Making-A (psychomotor speed)

Power of Attention

Continuity of Attention

SNP

SNP

CDR

CDR

Working Memory Quality of Working Memory CDR

Episodic Memory Quality of Episodic Memory CDR

Executive function Trail Making-B SNP

Incongruent Stroop reaction time SNP

Stroop effect SNP

Note: CDR=Cognitive Drug Research test battery; SNP=neuropsychological measures sensitive to heart failure.

6.4.3 Screening Measures

6.4.3.1 Mini Mental State Examination (MMSE)

The Mini Mental State Examination (MMSE; Folstein et al., 1975) is a test widely utilised to

screen for mild to severe dementia based on an assessment of an individual’s orientation,

attention, immediate recall, short-term recall, language and the ability to follow simple verbal

and written commands. Previous studies employed the MMSE to assess cognitive impairment

in heart failure (HF) patients (Akomolafe, et al., 2005; Cacciatore et al., 1998; Cameron et al.,

2009; McLennan et al., 2006). Although HF patients show impairments on the MMSE

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82

compared to age matched controls, this measure is an assessment of global cognitive function

and does not evaluate performance on specific cognitive domains. In the present study, the

MMSE was used as a screening tool for dementia rather than an assessment of cognitive

function. The 30-item test involved verbally answering the examiners questions relating to

familiar items such as current location (i.e. building, floor), current season, and repeating and

later recalling words said by the examiner. The individual was asked to spell the word

“WORLD” backwards, write a random sentence on a piece of paper and copy a geometric

figure. The total number of correct responses is 30 with higher scores representing improved

global cognitive function. Scores of below 24 are suggestive of dementia, therefore only

participants who scored 24 or above were enrolled in the study. The MMSE took

approximately 10 minutes to complete.

6.4.3.2 Wechsler Abbreviated Scale of Intelligence Scales (WASI) Vocabulary subset

Factors such as intelligence are known to influence cognitive performance in adults.

Intelligence in HF patients has been shown to influence cognitive performance including

attention, executive function, memory cognitive domains and language (Alosco, Spitznagel,

Raz, et al., 2012). In order obtain an estimate of general premorbid intelligence (IQ) for the

experimental groups, participants completed the vocabulary subset of the Wechsler

Abbreviated Scale of Intelligence (WASI; Wechsler, 1999). The WASI Vocabulary subtest is

a well-accepted, short and reliable measure of estimated premorbid IQ in research and

clinical settings. This test is independent of confounding factors such as state of health. The

vocabulary subtest is a 42-item task individually administered and takes approximately 10

minutes to complete. The participant was asked to provide meanings of words that were

presented orally and visually. The administrator wrote down the participant’s answers and

queried the participant using the prompts in the WASI booklet if the answer was vague. The

task was discontinued if the participant had five consecutive scores of zero. The average

reliability coefficients calculated with an adult sample is 0.96 for the VIQ.

6.4.4 Mood and quality of life measures

6.4.4.1 Profile of Mood States (POMS)

It has been reported in the literature that HF patients experience increased levels of

depression, anxiety and fatigue (Almeida, Beer, et al., 2012; Evangelista et al., 2008; Fink et

al., 2012; Pressler, Subramanian, et al., 2010b; Stephen, 2008). The Profile of Mood States

(POMS; McNair, Lorr, & Droppleman, 1992) is a self-reported questionnaire designed to

measure six facets of mood: tension-anxiety; depression-dejection; anger-hostility; vigour-

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83

activity; fatigue-inertia and confusion-bewilderment (Appendix E). The POMS consists of a

list of 65 adjectives depicting mood and feelings and using a Likert-type scale ranging from

“not at all” to “extremely” the participant rates their mood responses in the preceding week

including the present day. The Total mood disturbance score is determined by adding the five

factor scores of Tension, Depression, Anxiety, Fatigue and Confusion and subtracting Vigour

from these scores. The internal consistency for each factor is highly satisfactory. The reported

Cronbach alpha values for males and females combined are between K-R20 = .86 - .95 for

each factor. Although HF patients have higher levels of depression and anxiety scores

compared to controls (Grubb et al., 2000), the severity of cognitive impairment in HF has not

shown to be effected by depression (Sauvé et al., 2009). Depression is known to correlate

with cognitive outcomes in elderly HF patients (Garcia et al., 2011; Incalzi et al., 2003;

Trojano et al., 2003). Yet other authors have shown depression to be independently realted to

patients cogntive function (Cacciatore et al., 1998; Garcia et al., 2012; Riegel & Weaver,

2009). The POMS questionnaire was therefore administered to control for mood, in particular

depression. Additionally, since inflammatory markers are higher in patients with depression

and heart failure patients with major depressive disorder (Andrei et al., 2007), relationships

between inflammation as measured by C-reactive protein (hs-CRP) and mood will be

explored examined in this study.

6.4.4.2 Short Form-36 Item (SF-36)

The influence of quality of life (QOL) on depression, disease severity has been investigated

in HF patients (Gottlieb et al., 2004). The Short Form-36 item (SF-36; McCallum, 1995) is a

widely used self-administered health survey containing 36 items measuring overall

wellbeing, and mental and physical health perception (Appendix F). The SF-36 measures

eight health dimensions: physical and social functioning, role limitations due to physical and

emotional problems, mental health energy/fatigue, bodily pain and general health perception.

The SF-36 has acceptable internal consistency and reliability with Cronbach’s alpha > 0.85

and reliability coefficients > 0.75 for each health dimension, except social functioning

(α=0.73, reliability=0.74; Brazier et al., 1992). In an elderly population, Cronbach’s alpha

was between .81-.94 and .8-.93 for each health dimension and summary scores in the 65-74

and 75-84 year age group, respectively (Gandek, Sinclair, Kosinski, & Ware, 2004). Internal

consistencies decline with increasing age and additional chronic illness.

Chapter 6: Methods

84

6.4.4.3 Chalder fatigue scale

The Chalder fatigue scale (CFS; Chalder et al., 1993) is a widely used to measure the severity

of mental and physical fatigue. The CFS consists of 14 items each rated on a 4-point Likert

scale ranging from ‘better than usual’ to ‘much worse than usual’ and the items provide

mental and fatigue scores and a combined total fatigue score which is determined by adding

up all the items (Appendix G). The CFS has high level of internal reliability with a

Cronbach’s alpha for each item ranging between 0.88-0.90 (Chalder et al., 1993).

6.4.4.4 General Health Questionnaire

An evaluation of participant’s current mental health was established using the General Health

Questionnaire (GHQ-12; Goldberg, 1992). The GHQ-12 is a standard screening tool for

identifying psychological distress or minor (non-psychotic) psychiatric disorder in primary

medical care settings or among general medical outpatients (Goldberg, 1992) and elderly

heart failure patients (Johansson, Broström, Dahlström, & Alehagen, 2008). The respondents

answer questions on a 4-point Likert scale regarding the degree of happiness, depression and

anxiety symptoms, and sleep disturbance they experienced over the last four weeks

(Appendix H). The test has reported high internal consistency (Cronbach alpha = -.9), validity

(ROC curves = .88), overall sensitivity of 83.7% and specificity of 79.0% (Goldberg et al.,

1997).

6.4.4.5 Speilberger's State Trait Anxiety Inventory

Heart failure patients are known to have high levels of anxiety, which increases over time

(Almeida, Beer, et al., 2012). However, there is evidence that anxiety does not explain

performance on cognitive measures in HF patients (Pressler, Subramanian, et al., 2010b;

Sauvé et al., 2009). Yet it is always important to assess whether anxiety affects cognitive

performance. The Speilberger State-Trait Anxiety Inventory (STAI) was used to measure

participant’s state and trait anxiety symptoms (Speilberger, Gorsuch, & Lushene, 1970). The

STAI comprises two self-reported questionnaires used to measure and differentiate between

State (STAI-S) and Trait anxiety (STAI-T). The STAI-S consists of 20 statements reflecting

temporary anxiety at the time of the assessment. STAI-S Statements are rated on a 4-point

intensity scale ranging from not at all to very much so (Appendix I). STAI-T consists of 20

statements reflecting a person’s general anxiety symptoms seen as stable personality trait and

statements are rated on a 4-point intensity ranging from almost never to almost always. The

internal consistency for both STAI questionnaires is reasonable ranging from .83 to .92 in

males and females (Speilberger et al., 1970).

Chapter 6: Methods

85

6.4.5 Oxidative stress, antioxidant, inflammatory and omega-3 samples

Blood samples were collected from participants by a registered nurse during baseline testing

sessions. 26ml of whole blood was obtained using a syringe by venipuncture from the

antecubital vein. High-sensitive C-reactive protein samples were collected in a 6 ml yellow

cap serum tube, stored at 4oC until transported to the pathology laboratory for analysis.

Remaining blood samples collected in 1 x 10ml heparin (for DROM, glutathione peroxidase

and CoQ10 assays) and 1 x 10 ml EDTA tube (for F2-isoprostane and endothelin-1 assays)

were kept on ice and centrifuged at 3000 g force for 10 minutes at 4oC within one hour of

blood collection. Plasma was extracted from centrifuged samples and stored in 2ml and 0.5ml

aliquot tubes at -80 oC and later shipped via courier on dry ice to respective pathology

laboratories for analysis. Prior to storage additional preparation was required for F2-

isoprostane and CoQ10 assays. The stored plasma intended for F2-isoprostane assays was

protected from oxidation with butylated hydroxy toluene (100µml/1ml). Since CoQ10 is

sensitive to light the vacutainer intended for CoQ10 analyses was covered with aluminium

foil to protect the sample from light exposure. Additional preparation was not required prior

to storing plasma for the other biological assays.

6.4.5.1 F2-isoprostanes

F2- isoprostanes are a series of chemically stable prostaglandin F2–like compounds that are

produced during the peroxidation of unsaturated fatty acids, mainly prostaglandins in

phospholipid membranes. Increases in F2-isoprostanes are measured not only in heart failure

(Polidori et al., 2004) but also in diseases associated with cognitive impairment including

Alzheimer’s dementia (Mariani et al., 2005). In vivo F2-isoprostanes can be measured

efficiently in plasma providing a reliable measure of lipid peroxidation and oxidative stress.

Plasma F2-isoprostane concentrations were quantified using a methodology employing a

combination of a) negative ionisation mass spectrometry (GC-ECNI-MS), the most reliable,

sensitive and specific method for measuring F2-isoprostanes, b) silica and c) reverse-phase

cartridges, and high-performance lipid chromatography (HPLC; Mori et al., 1999). This latest

and improved method begins with adding 8-iso- PGF2a-d4 (2 ng, internal standard) and 1 M

potassium hydroxide (KOH) in methanol (1 ml) to a glass tube containing 2 ml plasma. The

tube and its contents are subsequently flushed with N2, heated to 40oC for 30 minutes and

then cooled down. In order to precipitate or separate the proteins, the mixture was diluted

with methanol (1 ml) and centrifuged at 1500 g force, at 4oC for a further 10 minutes. The

supernatant was then diluted with 8ml of .1M phosphate buffer, which had a pH of four. The

acidity was then adjusted to a pH of three by using 2M hydrochloric acid and to remove

Chapter 6: Methods

86

protein precipitate, the mixture was centrifuged once more at 1500 g force, at 4oC for 10

minutes. The hydrolysate was then applied to a C18 Sep-Pak Cartridge and chromatographed

(Mori et al., 1999).

6.4.5.2 Determinable reactive oxygen metabolites (DROMs)

Hydroperoxides were quantified by measuring plasma determinable reactive oxygen

metabolite (DROM) levels. DROMs were measured in plasma using the free radical

analytical system (FRAS; Kanaoka, Inagaki, Hamanaka, Masaki, & Tanemoto, 2010). This

procedure involves mixing a small amount of plasma (10 µl) with an acidic buffer solution of

pH 4.8 in order to stabilise the hydrogen ion concentration. In this acidic medium, iron

released from the protein component of the plasma, operates as a catalyst to break down

hydroperoxides (ROOH) in the blood into hydroxyperoxyl (ROO+) and alkoxyl (RO+)

radicals. The solution was then transferred into a cuvette containing a colourless chromogen.

Here the radicals oxidize the colourless chromogen in the solution to become a pink-coloured

radical cation. The intensity of colour is directly proportional to the concentration of the

reactive oxygen metabolites (ROMs) which is expressed as Carratelli Units (1 CARR U =

.08mg hydrogen peroxide/dl; Pasquini, Luchetti, Marchetti, Cardini, & Iorio, 2008).

6.4.5.3 Coenzyme Q10

Coenzyme Q10 (CoQ10) is an antioxidant, cellular energiser, gene regulator and is involved

in the generation of ATP (Boreková et al., 2008). CoQ10 has shown to be neuroprotective in

animal models (Ishrat et al., 2006). HF patient have lower myocardial and blood CoQ10

levels and blood levels increase following supplementation (Folkers et al., 1992; Keogh et al.,

2003). Plasma CoQ10 levels will be correlated with cognitive measures. Total plasma CoQ10

was determined by Chromsystems Level I and level II (Cat. Number 0091). This technique

utilised a solid phase extraction method followed by ultraviolet detection on reverse-phase

high-performance liquid chromatography method (R P-HPLC; Barshop & Gangoiti, 2007).

Standards, internal standard and reagents were obtained from Chromsystems (GMBH).

6.4.5.4 Glutathione peroxidase

Levels of the enzyme antioxidant glutathione peroxidase have been shown to be reduced in

heart failure (Keith et al., 1998; Polidori et al., 2004). Glutathione peroxidase works jointly

with the other enzyme antioxidants superoxide dismutase (SOD) and catalase to reduce levels

of harmful reactive oxygen species (ROS). The Cayman Chemical Assay (Ann Harbor, MI)

kit was used to indirectly measure glutathione peroxidase activity by a coupled reaction with

glutathione reductase (Baud et al., 2004). The Cayman assay protocol involves stimulating

Chapter 6: Methods

87

reactions in the sample with Cumene hydroperoxide (20 µl). Oxidised glutathione, which is

produced as a result of the reduction of hydroperoxide by glutathione reductase, was recycled

to its reduced form by the oxidation of NADPH to NADP+. The decrease in the absorbance

(at 340 nm) is directly proportional to the GPx activity in the sample (Baud et al., 2004).

Glutathione reductase activity was quantified by the amount of enzyme triggering the

oxidation of one nmol of NADPH per minute and per milligram of protein in the sample

(Baud et al., 2004). The glutathione reductase activity was analysed by the laboratory team at

the Oxidative Stress Laboratory, Diabetic Complications Division, Baker IDI Heart and

Diabetes Institute, Melbourne, Australia.

6.4.5.5 High-sensitive C-reactive protein (hs-CRP)

Inflammatory markers observed in heart failure (HF) are also believed to be involved in the

pathogenesis of Alzheimer’s disease (AD; e.g. Interleukin-1), depression (e.g. high-sensitive

C-reactive protein; hs-CRP) and impaired cognitive performance in older adults (e.g. hs-

CRP; Braunwald, 2008; Su et al., 2003; Teunissen et al., 2003). Recent evidence suggests

that inflammation is related to memory, speed of information processing and executive

functioning (CRP; Kindermann et al., 2012). Preliminary evidence also suggests that

inflammation (TNF-α and IL-6) is related to global cognition with IL-6 predicting cognitive

scores (Said et al., 2007). In order to assess whether inflammation is related to cognitive

functioning in HF patients, serum high-sensitive C-reactive protein (hs-CRP) was assessed.

In order to quantify participant’s hs-CRP the Roche Diagnostics Tina-quant ® and Siemens

automated clinical chemistry analysers were used to assay patient samples at the Alfred

Hospital and Healthscope Pathology laboratories, respectively. This assay uses a particle-

enhanced immunotubdidmetric providing the desired high analytical sensitivity, reproducible,

accurate and valid results (Eda, Kaufmann, Roos, & Pohl, 1998; Price, Trull, Berry, &

Gorman, 1987). In short, an anti-CRP antibody-latex was added to the sample to trigger the

reaction. Anti-CRP antibodies that are coupled to the latex microparticles react with the

antigen in the sample to form an antigen/antibody complex. Once the red cells were clumped

together by the antibodies (agglutination) the sample was then measured turbidimetrically

(An instrument for measuring the loss in intensity of a light beam through a solution that

contains suspended particulate matter). Buffers and other materials required for this assay

were supplied by Roche diagnostics and Siemens.

Chapter 6: Methods

88

6.4.5.6 Polyunsaturated fatty acid questionnaire

The effects of omega-3 polyunsaturated fatty acid (omega-3 PUFA) dietary intake and

supplementation have been widely researched for the prevention and treatment of

cardiovascular disease and more recently in HF. Chronic administration of omega-3 PUFA in

the form of fish oils have shown to improve mortality, decrease hospital admissions (Tavazzi

et al., 2008), improve left ventricular ejection fraction (LVEF), NYHA class and exercise

capacity, and decrease serum inflammatory markers (TNF-α, IL-1 and IL-6) in HF patients

(Nodari et al., 2011). There is strong evidence for the effective use of omega-3 PUFA dietary

intake for concomitant conditions including depression and some evidence supporting its

utilization in cognition functioning. Dietary PUFA intake in the form of fatty fish has been

shown to be inversely related to the risk of cognitive impairment and Alzheimer’s disease

(Kalmijn et al., 2004; Morris et al., 2003), however supplementation studies have shown

mixed results. An assessment of participant’s dietary polyunsaturated fatty acid (EPA +

DHA) intake was determined by a short dietary questionnaire, the PolyUnsaturated Fatty

Acids Questionnaire (PUFAQ; Appendix J) devised by Rogers et al. (2008). This

questionnaire evaluates frequency of dietary fat intake over the last three months, including

dietary fat, including white fish and oily fish, and dietary supplements including fish oils.

Responses were coded whereby a score of one is equivalent to eating one serve of fish per

week.

6.4.6 Cardiovascular Measures

6.4.6.1 Endothelin-1 analysis

The effect of the vasoconstrictor endothelin-1 on cognitive function was assessed. An

increase in the vasoconstrictor endothelin-1 is seen in HF (Jackson et al., 2000) and is

associated with disease severity, and mortality in these patients (Wei et al., 1994).

Stimulation of the neurohormonal response activates endothelin-1 release and oxidative stress

in the heart (Pousset et al., 1997). Additionally, endothelin-1 activates inflammation (Sharma,

Coats, & Anker, 2000) and causes increase arterial stiffness (Vuurmans et al., 2003). Plasma

endothelin-1 samples were determined using the Enzo Life Science endothelin-1 Enzyme

Immunoassay (EIA) kit from Assay Designs (cat # ADI-900-020a). The extraction method of

the sample using this assay kit is comparable to that described in Rolinski, Sadri, Bogner, and

Goebel (1994). The endothelin-1 assay was conducted according to the manufacturer’s

protocol. Typical plasma levels of endothelin-1 in HF patients is approximately 33 pg/mL

(Kiowski et al., 1995).

Chapter 6: Methods

89

6.4.6.2 Transcranial Doppler (TCD) Ultrasonography

The middle cerebral artery supplies blood to the temporal and inferior parietal lobes, which

are brain regions related to memory. HF patients have slower blood flow velocities in the

middle cerebral artery compared to controls (Jesus et al., 2006). However, the relationship

between middle cerebral artery and cognitive function has shown mixed results. Common

carotid arterial and cerebral blood flow velocities (BFV) were determined by means of a

Transcranial Doppler (TCD; Compumedics DWL® Germany GmbH). The common carotid

and left middle cerebral arterial blood flow velocities were measured using a 4 and 2 MHz

DWL® transducer (probe) respectively. A small amount of ultrasound gel was applied to each

transducer to minimise noise and increase signal detection. Once blood flow through an

artery is detected, the ultrasound beam omitted from the transducer is reflected by the cells in

that blood vessel. Mean blood flow velocity is calculated by the following formula presented

in Figure 3 (Babikian & Wechsler, 1993).

MVPV EDV 2

3

Figure 3 Formula for calculating mean blood flow velocity. MV = mean velocity; PV= peak systolic blood flow velocity; EDV = end diastolic blood flow velocity

To obtain the common carotid blood flow velocity, the artery was first found by palpating the

neck with thumb or second fingers just above the clavicle and between the trachea and

sternocleidomastoid muscle. Once the artery was clearly detected the 4 MHz transducer was

applied to neck. Common carotid signals in healthy elderly individuals (≥ 60 years) have

normal velocities ranging from 19-21cm/sec (Hamada, Takita, Kawano, Noh-Tomi, &

Okayama, 1993; Scheel, Ruge, & Schöning, 2000). The left middle cerebral arterial blood

flow was detected via ‘acoustical windows’ or areas of the skull thin enough for the

transducer beam to pass through. These ‘windows’ are located in the temporal

region/zygomatic arch (Figure 4).

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Chapter 6: Methods

91

collected and recorded straight on a laptop. The SphygmoCor® Px uses a mathematical

transfer function to obtain the central (ascending) aortic pressure waveform from systolic and

diastolic pressure values of the brachial (as measured by a conventional cuff) and radial

arteries.

Central pulse pressure (PP) is calculated by subtracting the central diastolic blood pressure

(DBP) from the central systolic (SBP) blood pressure (Figure 5).

CPP CSPB–CDBP

Figure 5 Formula for calculating central pulse pressure defined by subtracting the central diastolic blood pressure (CDBP) from the central systolic blood pressure (CSBP).

Augmentation index (AIx) is an indirect measure of arterial stiffness and was defined by the

difference between the second (P2) and first (P1) systolic peaks of the central aortic waveform

(augmentation pressure), expressed as a percentage of the PP (Figure 6 and Figure 7).

AIx AP x100PP

Figure 6 Augmentation index (AIx), defined as a percentage of augmentation pressure (AP) and pulse pressure (PP).

To ensure that the recordings were acceptable, recordings with an operator index of 80 and

above were used for analysis. The SphygmoCor® has been well-validated (Asmar et al., 1995;

Chen et al., 1997).

Figure 7 A central aortic pressure waveform. Augmentation index (AIx) as defined by the difference between the second (P2) and first (P1) systolic peaks of the central aortic waveform (augmentation pressure), and is expressed as a percentage of the pulse pressure (PP). TR=time to reflected wave. Adapted from Wilkinson et al., (2000).

Chapter 6: Methods

92

A summary of the vascular, oxidative stress, antioxidant and inflammatory and omega-3

dietary measures is presented in Table 5.

Table 5 Summary of vascular, oxidative stress, antioxidant and inflammatory measures

Mechanism Measure

Vascular

Cerebral Blood flow velocity

Common Carotid blood flow velocity Transcranial Doppler

Middle cerebral arterial blood flow

velocity

Transcranial Doppler

Arterial stiffness

Augmentation Index SphygmoCor®

Central Pulse Pressure SphygmoCor®

Oxidative Stress

Hydroperoxides DROM

Lipid peroxidation F2-isoprostanes

Antioxidant

Non-enzymatic antioxidant Coenzyme Q10

Lipophilic enzymatic antioxidant Glutathione peroxidase

Inflammation and omega-3 intake

Systemic inflammation High-sensitive C-reactive protein

Omega-3 dietary intake PUFA questionnaire

Chapter 6: Methods

93

6.4.7 Study design

Participants were asked to attend two testing sessions. The first was a practice session to

become familiar with the cognitive tests, as well as gather information on their demographics,

medical history and provide informed consent.

The control group were asked to return to Swinburne University’s Centre for Human

Psychopharmacology within one month and patients would return during their next medical

appointment or another convenient day at The Alfred Hospital for their actual testing session.

The second testing session consisted of cognitive tests, cardiovascular measures including

blood flow and arterial stiffness, and a blood sample to measure inflammatory markers,

antioxidant status and oxidative stress.

Testing Session 1 – Screening, enrolment and practice session:

Participants were asked to attend two testing sessions. At the first testing session, participants

signed the Participant Information and Consent Form (PICF; Appendix B) after the student

researcher addressed any queries the participant had about the study. Additionally, a face-to-

face screening interview, including administration of the MMSE, brief medical history and

collection of demographic data was conducted to confirm volunteer’s eligibility.

During visit 1, participants completed the full battery of computerised cognitive tasks to

reduce practice effects. Following the Cognitive Drug Research (CDR) protocol, patients and

controls went through two and four training sessions, respectively. During the first training

session and prior to each CDR task the researcher verbally explained the task to the

participant and once the participant was confident on what was required of them the

researcher began the task. For the subsequent practice sessions, healthy controls read

instructions on the screen prior to each task and the researcher again verbally explained the

task to patients. Furthermore, the same procedure was administered to both groups for the

Stroop task whereby instructions were read out to participants prior to practice and once

participants were confident with the task, the researcher began the real test.

Chapter 6: Methods

94

Testing Session 2 – Baseline data collection:

Participants were asked to return to the testing site for a second testing session, which was the

actual data collection session for the study. A brief medical update was obtained to ensure

participants were still eligible to take part in the study.

During the baseline visit, participants completed the following measures:

Mood Scales and quality of life measures

- Profile of Mood States (POMS)

- Chalder fatigue Scale

- Quality of life SF-36

- General health questionnaire (GHQ-12)

Cognitive Measures

- The CDR cognitive test battery

- Computerised Stroop task,

- Trail Making Tests A and B

Physiological measures

- Blood pressure

- Pulse rate

- Transcranial Doppler - carotid and middle cerebral arterial blood flow

- SphygmoCor® arterial stiffness.

Blood sample

- 10ml whole blood using a heparin vacutainer

- 10ml whole blood using a EDTA vacutainer

- 6-8ml whole blood using a serum tube

At the end of the study, a nurse took a blood sample. Within one hour of taking the blood, the

blood samples were centrifuged and plasma samples stored in a -80oC freezer. Additionally,

samples intended for hs-CRP analysis were collected by a courier and delivered to the

pathology laboratory for analysis on the same day. An outline of the timeline for each testing

day is presented in Table 6.

Chapter 6: Methods

95

Table 6 Testing protocol timeline

Time elapsed (min)

Event

Training Day

0 5 15 20 (40*) 45 (90*) 55 (100*) 105 (150*) 115 (160*)

Participant Consent (5mins) Screening questions, medical and demographic questions (10mins) Mini Mental State Examination (5mins) CDR Cognitive testing - practice session test 1 (and 2 for controls) Premorbid IQ – WASI Vocabulary subset CDR cognitive testing - practice session test 2 (and 4 for controls) Stroop practice test Testing session complete

Baseline

0 5 15 60 70 75 85 95 105 115

Medical overview POMS, SF-36 CDR Cognitive testing Stroop task (congruent and incongruent) Trail Making-A and Trail Making-B STAI-S/T, Chalder fatigue scale, GHQ-12, PUFA questionnaires SphygmoCor® (arterial stiffness) Transcranial Doppler (middle cerebral and common carotid arterial blood flow velocity) Blood Samples Testing session complete

Note: *Time elapsed for control group; CDR=Cognitive Drug Research; WASI=Wechsler Abbreviated Scale of Intelligence; POMS= Profile of Mood States; STAI-S/T= Speilberger State-Trait Anxiety Inventory-state/trait; GHQ=General Health questionnaire; PUFA= Polyunsaturated fatty acid.

Chapter 6: Methods

96

6.4.8 Experimental Design

6.4.8.1 Testing environment

Cognitive and cardiovascular assessments on the Patient group were performed at the Alfred

Hospital Heart Centre, Heart Failure Clinic in any one of three testing rooms available in the

Research Centre depending on availability. Healthy controls were tested at Swinburne

University’s Centre for Human Psychopharmacology in purpose built testing rooms.

Participants were examined by a trained researcher in a controlled environment in a quiet,

well-ventilated and well-lit room, free from distractions. Participants were seated at a table

with the CDR laptop screen placed in front of the participant in position that was at easy

reach and allowed ample space for the button box, arm movement, writing space and a

viewing distance of approximately 50 centimetres. Efforts were made to keep the testing

room conditions consistent with adequate lighting, comfortable room temperature and

minimal background noise.

6.4.8.2 Data safety and monitoring

Researcher was training on administering the questionnaires, cognitive and vascular measures

(BP, SphygmoCor® Px and TCS). The Doppler-Box™ is certified by CE and meets the

requirements of the 93/42/EEC – Annex II.3 Medical Device Directive (Clas IIb according to

rule 10. The Doppler-Box™ is designed to the standard EN60601-1, EN60601-1-1,

EN60601-1-2, EN^0601-1-4, ICE61157 and IEC60601-2-37 and complies with the

guidelines issued by the German Association of Medical Insurance Companies.

The SphygmoCor® is classified as Class IIa (Annex IX Rule 10) and is in conformity with the

Annex I essential requirements and provisions of the Council Directive 93/42/EEC Annex II.

CE 0120. The SphygmoCor® system is designed, tested and approved to the following

standards: IEC60601-1: EN60601-1: As/NZS 3200.1.0 Medical electrical equipment with

Amendments 1 & 2 Part 1: General requirements for safety (the international Electro-Medical

Safety Standard for Medical Equipment).

6.4.8.3 Equipment

The Cognitive Drug Research® (CDR) test battery was displayed on a Viglen Dossier CDP

laptop with a 12.1” TFT colour screen. Participants responded to stimuli presented on the

laptop screen by pressing ‘YES’ and ‘NO’ buttons on a two-button response box using the

index finger or thumb from each hand. The Stroop colour word task, Trail-Making Tests and

Chapter 6: Methods

97

WASI Vocabulary subset tests were administered according to the recommended standard

task instructions. The Stroop colour word task was presented on a 17-inch colour desktop

monitor using a DOS-based software package and participants responded by pressing one of

four coloured buttons on a button box using their fingers. Participants completed the Trail-

Making Test in pencil on photocopies of the task on an A4 sheet of paper and the

administrator using a stopwatch recorded the time taken to complete the tasks. The

administrator on standard response sheets recorded participant response to the WASI

Vocabulary subset verbatim. Mood and QOL questionnaires were designed using the

Teleform scanner software and presented on A4 sheets of paper and participants completed

the questionnaires using a ballpoint pen. Additionally, the Doppler-Box™ medical ultrasound

device (Compumedics DWL® Germany GmbH) and transducers (4MHz and 2MHz) were

used to measure MCA and CC blood flow velocities. SphygmoCor® Px (AtCor Medical,

Sydney, Australia) electronics module and tonometer was used to measure arterial stiffness

parameters. The SphygmoCor® and QL software were installed on separate laptops.

Chapter 7: Results – demographic characteristics

98

CHAPTER 7 RESULTS - DEMOGRAPHIC CHARACTERISTICS FOR

EXPERIMENTAL GROUPS

7.1 Introduction

In this chapter, results are initially examined with respect to demographics (age, gender, and

estimated IQ). This will be followed by an overview of the patient group clinical

characteristics. Data was analysed using SPSS statistical package software (SPSS Version 20,

SPSS Inc, Chicago, IL). For all analyses, group differences were considered statistically

different if the p value was less than 0.05. Two tailed tests were used to determine whether

group differences were statistically different.

To determine whether the heart failure (HF) group demographic variables (age, premorbid IQ

and gender) differed from those of controls, differences were examined using the appropriate

parametric and non-parametric statistics. Independent or paired samples Student’s t-test was

conducted to examine group differences between normally distributed (parametric) numeric

and continuous variables. Non-parametric tests, e.g. Mann-Whitney U test were conducted

for continuous variables that were non- normally distributed. Chi squared tests were used to

examine group differences for categorical variables. Assumptions of normality were explored

examining skewness, kurtosis, normality plots in order to ascertain whether the distribution of

data for each variable was normally distributed.

Patients who failed to fulfil the selection criteria during the study period were excluded from

the analysis. From the time of enrolling in the study, five patients improved in their HF

symptoms and were classified as having NYHA class I at the time of the baseline data

collection period. These patients were therefore excluded in the statistical analysis.

7.2 Data screening

Prior to analyses, demographic variables (age, premorbid IQ, gender, vitals) were explored

for accuracy of data entry and missing values and fit between their distributions and the

assumptions of parametric and non-paramedic analysis. There were no missing values for

demographic, clinical characteristics and screening variables. There were no univariate

outliers. A full description of the data screening procedure is outlined in Appendix K. There

were no missing data, no cases were excluded due to violation of assumptions. There were 36

cases in the HF group (NYHA class II-IV) and 40 cases in the Control group.


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7.3 Demographic variables

To verify that the HF group did not differ from controls on demographic variables Mann-

Whitney U test, Student’s t-test and Chi squared test were conducted for age, premorbid IQ

and sex respectively. Demographic characteristics (age, gender and education), dementia

screening, premorbid IQ and vitals for HF and Control participants are presented in Table 7.

HF patients and controls were well matched for age (Z = -.480, p = .63). Although there were

more males (69%) than females (31%) in the HF group compared to controls (50% males)

this group difference was not significant (χ2 (1) = 2.97, p = .11; Table 7).

HF patients had significantly lower, premorbid IQ scores (t(74) = -2.15, p < .05) and MMSE

scores (Z = -3.10, p < .01) compared to controls. There was a significant group difference for

education with years of education significantly higher in controls than the HF group (χ2 (6) =

16.3, p = .02). No group differences were observed for general health questionnaire.

Examining the vital signs, the HF group had significantly lower systolic blood pressure (SBP;

p < .001) and diastolic blood pressure (DBP, p < .001) than controls.


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Table 7 Demographic characteristics (age, gender and education), dementia screening, general health questionnaire-12 item, premorbid IQ and vitals for HF and control participants. Data shown are mean and (SD) or sample size and percentages.

Heart failure Healthy control n=36 n=40

Characteristic n (%) M (SD) n (%) M (SD) Z/ χ2/t p

Age, years 68 (7) 67 (5) Z = -.48 .631

Gender χ2 (1) = 2.97 .105

Male 25 (69) 20 (50)

Female 11 (31) 20 (50) Education 4 (2) 5.8 (2) χ2 (6) = 16.3 .017

Year 7 3 (8) 1 (3) Year 8 6 (17) 2 (5) Year 9 5 (14) 0 (0) Year 10 4 (11) 7 (18) Year 11 4 (11) 1 (3) Year 12 6 (17) 9 (23) Year 12+ 8 (22) 20 (50)

Vital signs SBP mmHg 115 (17) 139 (16) Z = -5.39 <.001 DBP mmHg 66 (12) 80 (8) Z = -5.02 <.001 BMI 28 (4) 27 (4) t(73) = 1.40 .164 Heart Rate 66 (12) 65 (78) t(74) = .46 .647

Screening MMSE 28 (1) 29 (1) Z = -3.10 .002 WASI 60 (8) 64 (8) t(74) = -2.20 .035 GHQ-12 21 (3) 21 (2) t(66) = -.39 .697

Note: SBP=systolic blood pressure; DBP=diastolic blood pressure; BMI=body mass index; MMSE=Mini Mental State Examination; WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset; GHQ-12= general health questionnaire (12 item); mmHg=millimetres of mercury.

Clinical characteristics of the HF group are presented in Table 8. Since these clinical

characteristics are specific to the HF patient group and exclusions for the control group,

exploring statistical group differences was therefore not applicable.


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Table 8 Clinical characteristics for HF group - NYHA class, aetiology, common

comorbidities.

Clinical characteristic n (%)

NYHA Class

NYHA class II 30 (83)

NYHA class III 5 (14)

NYHA class IV 1 (3)

Cause of Heart Failure

Dilated cardiomyopathy 17 (47)

Ischemic cardiomyopathy 10 (28)

Diastolic Heart Failure 3 (8)

Hypertrophic cardiomyopathy 1 (3)

Pulmonary hypertension 1 (3)

Alcoholic cardiomyopathy 1 (3)

Amyloid 1 (3)

Valvular Heart Disease 1 (3)

Hypertrophic cardiomyopathy 1 (3)

Arrhythmias management of heart failure 1 (3)

Comorbidities

Atrial fibrillation 8 (22)

CABG 8 (22)

Pacemaker 7 (19)

Defibrillator 2 (5.6)

Note: NYHA=New York Heart Association; CABG=coronary artery bypass graft.

As displayed in Table 8 majority of patients were diagnosed with mild HF (NYHA class II;

83%) followed by moderate (NYHA class II; 14%) and severe HF (NYHA class IV; 3%).

The aetiology for HF was mainly dilated cardiomyopathy (47%), followed by Ischemic

cardiomyopathy (28%) and diastolic heart failure (8%). As expected, the main comorbidities

observed in the HF group were atrial fibrillation (22%) followed by coronary artery bypass

graft (CABG; 22%) and diabetes (11%). Furthermore, 19% (n=7) of patients had a

defibrillator and 5.6% (n=2) a pacemaker.

A list of the common medications taken by each experimental group is presented in Appendix

L. Majority of patients were taking diuretics (89%; n=32) and beta-blockers (81%; n=29). As

expected, more HF patients than controls were taking statins (64% vs 5%) and mild

anticoagulants (e.g. aspirin; 44% vs 10%). The aim of this investigation is not to explore


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whether drugs or comorbidities are related to cognitive impairment or mood in HF patients

and will therefore not be included as covariates in the ANCOVA models when exploring

group differences.

A detailed overview of comorbidities seen in experimental groups is displayed in Appendix

M. Additionally, a list of common pharmaceuticals including over the counter medicines and

natural supplements are displayed in Appendix N and Appendix O, respectively.

7.4 Quality of Life

Continuous Quality of Life (QOL) measures using the 36 Short Form (SF-36) measure were

examined in each experimental group to ascertain whether they met assumptions of

normality. Mann-Whitney U test was used to examine group differences on continuous QOL

variables that were non-normally distributed. The Student’s t-test was conducted to examine

group differences between normally distributed continuous variables.

Means and standard deviations for the quality of life (QOL) variables are presented in Table

9. Heart failure patients scored significantly lower on each of the SF-36 subtests except SF-

36-Health Transition, which did not show significant group differences (p > .05).

Experimental groups scored equally on Chalder fatigue scale-mental symptoms subscale and

a trend towards worse scores on the Chalder fatigue scale-physical symptoms subscale (p =

.057). Overall, HF patients had scored higher than controls on the Total Fatigue Scale as

measured by the sum of the Physical and Mental Fatigue subscales (p < .05).


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Table 9 Group differences for SF-36 subscales and Chalder fatigue scale

Heart Failure Controls

n = 36 n = 40

Quality of Life Variable n M SD n M SD z/t p

SF-36 subscale

Physical functioning 36 49.12 21.90 40 85.85 14.97 -6.24 <.001

Role limitn physical 36 53.24 25.22 40 87.86 16.51 -5.66 <.001

Role limitn emotional 36 76.62 26.94 40 93.96 10.33 -3.33 .001

Vitality 36 46.82 23.80 40 73.23 15.07 -4.89 <.001

Mental health 36 76.15 17.46 40 85.54 9.35 -2.69 .007

Social functioning 36 68.75 23.62 40 93.75 11.67 -4.91 <.001

Bodily pain 36 67.71 25.64 40 83.00 18.57 -2.63 .009

General health 36 43.13 19.95 40 75.95 14.39 -8.29 <.001

Health transition 36 49.31 22.75 40 45.63 17.80 0.79 .432

SS: Physical 36 36.37 13.43 40 62.41 8.68 -6.72 <.001

SS: Mental 36 67.08 19.92 40 86.62 8.13 -4.67 <.001

Chalder fatigue scale

Physical symptoms 35 9.37 3.08 40 7.97 2.33 -1.90 .057

Mental symptoms 35 6.35 1.91 40 5.95 0.96 -1.70 .089

Total Score 35 15.72 4.28 40 13.92 2.97 -2.12 .034

Note: SF-36=Short Form 36 item, higher score on SF-36 is better functioning; limitn=limitation; SS=summary score.

7.5 Summary

In the current thesis, the two experimental groups were well-matched for age and gender.

Group differences however were observed for premorbid IQ. The aim of this thesis was to

control for IQ as this variable can influence scores on cognitive measures. Since HF patients

scored significantly lower on premorbid IQ, this variable will be controlled for in subsequent

analyses exploring whether the two experimental groups differ significantly on cognitive

outcome measures. As expected groups differed on the Mini Mental State Examination

(MMSE) screening tool and although this tool was utilised for screening purposes to exclude

individuals with probable dementia, these findings suggest that the HF group had lower

global cognitive scores compared to controls, which is in line with previous research.

Subsequent analyses will explore whether biomarkers are associated with global cognitive

function.

The majority of patients were diagnosed with mild HF (NYHA class II; 83%) followed by

moderate (NYHA class II; 14%) and severe HF (NYHA class IV; 3%), therefore results from


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this thesis may apply mainly to patients with mild heart failure. Due to the unequal sample

size among NYHA classifications, an assessment of whether cognitive impairments found

differ across disease severity cannot be made. However, if warranted an exploratory analysis

was done between HF patients with mild (NYHA class II) and severe (NYHA class III and

IV) disease severity.

Chapter 8: Results – group differences between cognitive measures, mood and biomarkers

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CHAPTER 8 RESULTS – GROUP DIFFERENCES BETWEEN

COGNITIVE MEASURES, MOOD AND BIOMARKERS

8.1 Introduction

This chapter will provide an examination of whether the experimental groups differed on any

of the cognitive, mood and biomarker variables. For all analyses, group differences were

considered statistically different if the p value was less than 0.05. Two tailed tests were used

to determine whether group differences were statistically different. Due to the small samples

size of the current investigation and to prevent decreasing statistical power, Bonferroni

corrections were not applied to the analyses to ensure that moderate effect sizes were detected

and to account for possible type II error. The focus of this study was limited to cognitive

measures of secondary and episodic memory, attention, psychomotor speed and executive

function. Secondary measures included mood, quality of life, oxidative stress, antioxidant

levels, systemic inflammation, arterial stiffness and cerebral blood flow.

8.2 Cognitive tasks

8.2.1 Introduction

To test the hypotheses regarding whether groups would differ on attention, psychomotor

speed, secondary memory, episodic memory and executive function, a series of one way

analysis of covariance statistics were conducted to examine whether differences existed on

the cognitive measures after adjusting for premorbid IQ and other possible confounding

variables. In addition to premorbid IQ, mood variables that correlated significantly with the

cognitive dependent variables were also included in the ANCOVA model when assessing

whether group differences existed on the cognitive outcome measures.

8.2.2 Data Screening

To examine whether groups differed on the cognitive measures, data was initially explored

for accuracy of data entry and missing values, best model of fit and assumptions of normality

as required to conduct ANOVA statistics. Variables that violated the assumptions for

normality were transformed to create normally distributed variables. The transformed

variables were incorporated into the ANOVA analysis to explore the effects of cognitive and

mood variables between groups.


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Continuous Cognitive Drug Research® (CDR) individual task variables were examined in

each experimental group to ascertain whether they met assumptions of normality. The Mann-

Whitney U test was used to examine group differences on continuous CDR variables that

were non-normally distributed. The Student’s t-test was conducted to examine group

differences between normally distributed continuous variables. For a complete overview of

the data screening procedure, refer to Appendix P.

Non-normally distributed variables were transformed prior to running ANOVA analysis.

Outliers were removed following transformations except for Continuity of Attention and the

Mann Whitney U test was therefore conducted to explore group differences. Additionally,

due to major skewness of congruent Stroop percentage accuracy, incongruent Stroop

percentage accuracy and Continuity of Attention non-parametric analysis was conducted to

compare group differences for these variables.

For positively skewed variables Log transformations were chosen for congruent Stroop

reaction time, incongruent Stroop reaction time, Trail Making-A, Trail Making-B and Speed

of Memory variables. The square root transformation was selected for the Stroop effect

variable. For negatively skewed variables, cubed transformation was chosen for quality

working memory. Table 10 presents a summary of the transformations selected for non-

normally disturbed variables.

Table 10 Transformations selected for analysis for non-normally disturbed cognitive variables

Variable Chosen transformation

Congruent Stroop RT log10

Incongruent Stroop RT log10

Stroop effect square root

Trail Making-A log10

Trail Making-B log10

Quality of Working Memory cubed

Speed of Memory log10

All other outliers were removed following variable transformation except for Continuity of

Attention therefore non-parametric analysis was conducted to explore whether group

differences exist for this variable. Tests for homogeneity of variance revealed that group


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variance did exist thereby rejecting the null hypothesis and the assumptions for ANOVA

were therefore met.

The first analysis explored main effects of group differences and after adjusting for

premorbid IQ as measured by the WASI Vocabulary subset. To examine whether groups

differed on cognitive measures, the main analysis initially explored group differences without

covariates and the second analysis was conducted including covariates.

8.2.3 Selecting covariates

With the attempt to minimise extraneous variables effecting cognitive performance, the

experimental design excluded participants with mood disorders including depression and

anxiety, participants with a premorbid intelligence quotient (IQ) score of less than 80 as

measured by the WASI Vocabulary subset and those with dementia. To adjust for extraneous

variables known to affect cognitive function, the main analysis explored whether differences

in cognitive function existed between the experimental groups after adjusting for additional

potential cofounding variables. Given that the control group scored significantly higher on

the premorbid IQ measure, WASI Vocabulary subset, compared to the HF group, group

differences for cognitive function were assessed after adjusting for the WASI Vocabulary

subset.

In order to reduce error variance, additional factors known to affect cognitive function were

included as covariates in the ANCOVA model. Due to limited research on the effects of

possible confounding factors on cognitive function, additional potential covariates were

selected based on the present data. Obtaining a large sample size on the patient group studied

in this investigation was difficult as patients were recruited from only one centre. Due to the

small sample size, additional potential covariates were chosen based on significant

relationships seen with outcome measures from the present data.

Univariate correlations between each of the cognitive measures and mood subscales were

conducted for the HF and control groups. Pearson’s correlation coefficients between

neuropsychological and mood variables for the HF and control groups are displayed in Table

11 and Table 12, respectively. Mood variables that correlated significantly with the cognitive

dependent variable were included as a covariate.

In the HF group Quality of Episodic Memory was significantly negatively correlated with

POMS-tension/anxiety, POMS-fatigue/inertia, POMS-confusion/bewilderment and POMS-

Total mood disturbance with the strongest correlation appeared with POMS-fatigue/inertia.

Given that each of the POMS variables were significantly correlated with each other, POMS-


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Total mood disturbance which represents a combined score of the mood variables was used

as a covariate when exploring group differences between Quality of Episodic Memory.

Additionally, POMS-vigour/activity was an additional covariate when exploring group

differences in Trail Making-A, as significant correlations were observed between these

variables. In the control group, there were no significant correlations between Quality of

Working Memory, Continuity of Attention and the mood variables.

Furthermore, multivariate regression analysis revealed no significant relationships between

the cognitive measures and POMS subscales in each experimental group. The only finding

was a trend for Power of Attention and POMS-fatigue/inertia in the HF group (p = .06) and

the control group (p = .07). However, since these relationships did not reach significance

POMS-fatigue/inertia was therefore not included as a covariate. Failing to find significant

relationships between cognitive and mood measures using multivariate analyses suggests that

no additional variables were suitable covariates for subsequent analyses.


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Table 11 Pearson’s correlation coefficients between neuropsychological and mood variables for the HF Group

ICS SE TM-A TM-B CDR- QESM

CDR- QWM

CDR- PA

CDR- SM

CDR- CA

POMS- T/A

POMS- D/D

POMS-A/H

POMS-V/A

POMS- F/I

POMS- C/B

POMS-TMD

CS .704*** .449** .283 .431* -.501** -.307 .677*** .571*** -.269 .180 .160 .009 .022 .105 .123 .066

ICS .945*** .394* .398* -.373* -.067 .495** .428* -.240 -.014 -.072 -.191 .197 .003 -.078 -.122

SE .386* .310 -.263 .065 .358* .311 -.182 -.076 -.134 -.233 .233 -.044 -.154 -.170

TM-A .504** -.482** -.031 .538** .216 .101 .239 .169 .280 .136 .270 .248 .162

TM-B -.475** -.094 .439** .347* -.120 .224 .101 .063 .002 .319 .116 .156

CDR-QESM .269 -.510** -.330* .023 -.410* -.279 -.238 .113 -.421* -.402* -.352*

CDR-QWM -.197 -.517** 409* .051 .221 .106 -.105 .192 .048 .152

CDR-PA .521** -.004 .168 .108 .137 .163 .053 -.002 .005

CDR-SM -.409* -.036 -.097 -.058 .206 -.267 -.205 -.179

CDR-CA -.028 .046 .194 -.262 .156 .132 .149

POMS-T/A .887*** .660*** -.630*** .730*** .827*** .899***

POMS-D/D .734*** -.647*** .740*** .740*** .922***

POMS-A/H -.469** .685*** .509** .765***

POMS-V/A -.611 -.629*** -.795

POMS-F/I .652*** .855***

POMS-C/B .824***

Note: CS=congruent Stroop; ICS=incongruent Stroop; SE=Stroop effect; TM-A=Trail Making-A; TM-B=Trail Making-B; CDR-QESM=Quality of Episodic Memory; CDR-QWM=Quality of Working Memory; CDR-PA=Power of Attention; CDR-SM=Speed of Memory; CDR-CA=Continuity of Attention; POMS-T/A=tension/anxiety; POMS-D/D=depression/dejection; POMS-A/H=anger/hostility; POMS-V/A=vigour/activity; POMS-F/I=fatigue/inertia; POMS-C/B=confusion/bewilderment; correlations significant at: * p < .05; ** p < .01; *** p < .001.


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Table 12 Pearson’s correlation coefficients between neuropsychological and mood variables for the control group

ICS SE TM-A TM-B QESM CDR- QWM

CDR- PA

CDR- SM

CDR- CA

POMS- T/A

POMS- D/D

POMS- A/H

POMS- V/A

POMS- F/I

POMS- C/B

POMS- TMD

CS .726*** .273 .391* .469** -.348* -.446** .674*** .616*** -.221 .016 -.055 -.089 -.093 -.161 .254 .050

ICS .856*** .392* .372* -.311 -.346* .561*** .564*** -.238 .093 .182 .163 -.108 .046 .049 .127

SE .274 .192 -.190 -.162 .276 .341* -.186 .131 .295 .302 -.075 .197 -.125 .138

TM-A .569** -.059 -.313* .269 .290 -.035 .059 -.055 -.018 -.465** .020 .023 .200

TM-B -.384* -729*** .336* .286 -.200 .101 -.007 -.036 -.307 .014 .244 .200

CDR-QESM

.494** -.220 -.315* .207 .089 .105 -.089 .019 .121 -.210 -.009

CDR-QWM -.288 -.191 .114 .033 .052 -.051 .161 .121 -.251 -.116

CDR-PA .678*** -.133 -.004 .045 -.088 -.098 -.133 .158 .054

CDR-SM -.005 -.020 -.015 .039 -.101 -.108 .031 .017

CDR-CA .088 -.089 -.093 -.147 -.043 -.233 -.028

POMS-T/A .673*** .364* -.425** .631*** .542*** .779***

POMS-D/D ¤ .527*** -.319* .683*** .461** .729***

POMS-A/H .001 .365* .178 .441**

POMS-V/A -.567 -.543*** -.764***

POMS-F/I .485** .830***

POMS-C/B .760

Note: CS=congruent Stroop; ICS=incongruent Stroop; SE=Stroop effect; TM-A=Trail Making-A; TM-B=Trail Making-B; CDR-QESM=Quality of Episodic Memory; CDR-QWM=Quality of Working Memory; CDR-PA=Power of Attention; CDR-SM=Speed of Memory; CDR-CA=Continuity of Attention; POMS-T/A=tension/anxiety; POMS-D/D=depression/dejection; POMS-A/H=anger/hostility; POMS-V/A=vigour/activity; POMS-F/I=fatigue/inertia; POMS-C/B=confusion/bewilderment; correlations significant at: * p < .05; ** p <.01; *** p < .001.


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8.3 Results

8.3.1 Introduction

Analysis of covariance (ANCOVA) statistics was used to explore whether experimental

groups differed on cognitive measures after adjusting for WASI Vocabulary subset and other

possible confounding mood variables. The Mann Whitney U test was conducted for non-

normally distributed variables.

8.3.2 Attention domains

To test the hypothesis (H1) that heart failure (HF) patients will perform significantly worse on

attention tasks as measured by congruent Stroop task, Power of Attention and Continuity of

Attention compared to the control group, analysis of covariance and the Mann Whitney U

tests were conducted for normally and non-normally distributed variables, respectively.

Contrary to what was predicted, Mann Whitney U tests revealed that HF patients performed

as accurately as controls on the congruent Stroop (p = .670) and on the Continuity of

Attention cognitive domain (F(1,73) = 0.43, p >.05; Table 13).

Evaluating assumptions of normality of sampling distributions, linearity, homogeneity of

variance, homogeneity of regression and reliability of covariates were shown to be

acceptable. Without controlling for premorbid IQ (WASI Vocabulary), HF patients

performed significantly worse than controls on congruent Stroop (F(1,73) = 8.38, p =.01),

Power of Attention (F(1,73) = 10.46, p =.002) but similarly on Continuity of Attention

(F(1,73) = 0.43, p =.515.

As predicted, there was a significant weak effect of group on congruent Stroop task

performance, after controlling for premorbid IQ, F(1,26) = 6.7, p =.01, partial η2=.086.

Additionally, as predicted, after adjusting for premorbid IQ, there was a significant weak

main effect of group on Power of Attention cognitive domain (F(1,72) = 9.33, p < .01, partial

η2=.115; Table 13).


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Table 13 Means and standard deviations of attention, memory and executive function tasks for HF and controls after adjusting for premorbid IQ and mood covariates


Variables n M SD n M SD df F/Z p partial η2

Attention Tasks Congruent Stroop RT (ms)*

35 908.00 157.66 40 813.73 138.00 (1, 72) 6.79 .011 .086

Congruent Stroop %Acc

35 97.87 3.54 40 98.56 2.11 -.43 .670

Attention Domains Power of Attention (ms)*

36 1270.31 121.77 39 1191.52 87.63 (1, 72) 9.33 .003 .115

Continuity of Attention (ms)*

36 90.78 4.11 39 91.36 3.27 -.45 .651

Psychomotor Task Trail Making-A (ms)*

36 37.48 9.78 40 33.90 9.83 (1, 72) .49 .486 .007

Memory Domains Quality of Episodic Memory (ms)*

36 171.30 54.56 40 183.79 50.81 (1, 72) .43 .513 .016

Quality of Working Memory (ms)*

36 1.82 .19 40 1.78 .37 (1, 73) .20 .659 .003

Speed of Memory (ms)*

36 4353.22 889.69 40 4077.92 617.23 (1, 73) 1.67 .200 .022

Executive Function Incongruent Stroop RT (ms)*

35 1319.39 503.68 40 1057.75 291.25 (1, 72) 6.03 .016 .077

Incongruent Stroop %Acc

35 94.00 10.04 40 98.44 2.70

-1.98 .047

Stroop effect (ms)*

35 411.29 416.00 40 244.01 223.48 (1, 72) 2.74 .102 .037

Trail Making-B (ms)*

36 106.51 40.02 40 89.10 46.79 (1, 73) 3.94 .051 .051

Note: Group differences were obtained after adjusting for covariates (CV: WASI) except – Trail Making-A (CV: WASI, POMS-vigour/activity; CDR-Quality of Episodic Memory (CV: WASI, POMS-Total mood disturbance); RT=reaction time; %Acc=percentage accuracy; ms=milliseconds; *=higher scores indicate worse performance.


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8.3.3 Psychomotor function

To test the hypothesis (H2) that HF patients will perform significantly worse than controls on

psychomotor function (Trail Making-A) a between subjects (HF, controls) univariate analysis

of covariance (CV: WASI, POMS-V/A) was conducted. Without controlling for covariates

(IQ and POMS-vigour/activity), there were no significant differences between HF patients

and controls on psychomotor function as measured by Trail Making-A, F(1,74) = 3.57, p

=.06.

Contrary to expectation after controlling for premorbid IQ and POMS-vigour/activity, the

main effect of group on Trail Making-A task was not significant, F(1,72) = .491, p =.49,

partial η2=.007 (Table 13). The covariate POMS-vigour/activity had a significant weak effect

on Trail Making-A, F(1,72) = 4.25, p < .05, partial η2=.056.

8.3.4 Cognitive Drug Research task subsets

Mann-Whitney U test was carried out to examine group differences on continuous CDR

individual task variables that were non- normally distributed. The Student’s t-test was

conducted to examine group differences between normally distributed continuous variables.

Means and standard deviations for the CDR subtests for each experimental group are

presented in Table 14.

Heart failure (HF) patients recalled significantly fewer words in the immediate word recall

task compared to controls (Z = -2.15, p < .05). Furthermore, compared to controls, the HF

group’s reaction times were significantly slower for the Simple (t(73) = 3.70, p < .001) and

Digit Vigilance (t(73) = 2.09, p < .05) CDR individuals tasks. Reaction times during the

Word Recognition and Word Recognition New Stimuli tasks were significantly longer in the

HF group compared to controls, t(74)= 2.09, p < .05 and Z = -3.10, p < .01, respectively.

Almost significant findings were observed on Choice Reaction Time (Z = -1.93, p = .054)

and Word Recognition New stimuli percentage accuracy (Z = -1.93, p = .053).

Examining the individual cognitive tasks from which the CDR cognitive domains are

derived, these group differences in reaction times and digit vigilance tasks explains the Group

differences seen in the Power of Attention cognitive domain.


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Table 14 Group differences for Cognitive Drug Research subtests for HF and control group

Heart Failure Controls Cognitive Drug

Research Variable n M SD n M SD z/t p

IWR # correct 36 4.50 1.84 40 5.50 1.83 -2.15 .031 IWR % Acc 36 30.00 12.29 40 36.67 12.17 -2.15 .031 IWR # Errors 36 .39 .55 40 .40 .63 -.16 .871 SRT (ms) 36 315.06 42.33 39 283.93 29.92 3.70 <.001 DV RT (ms) 36 443.84 38.34 39 425.64 37.23 2.09 .041 DV %Acc 36 97.41 5.16 39 97.95 3.32 -.52 .602 DV # False Alarms 36 1.67 1.59 39 1.62 1.97 -.55 .583 CRT (ms) 36 511.10 65.88 39 481.95 45.13 -1.93 .054 CRT % Acc 36 97.22 2.84 39 97.79 2.14 -.67 .503 SWM RT (ms) 36 1050.56 318.22 40 974.18 159.33 -.56 .574 SWM Original RT (ms)

36 986.59 322.94 40 912.36 139.69 -.23 .819

SWM New RT (ms) 36 1098.02 325.18 40 1028.60 208.29 -.58 .560 SWM Original % Acc 36 92.88 10.89 40 94.06 12.01 -.78 .435 SWM New % Acc 36 94.58 7.11 40 93.38 14.38 -1.39 .166 NWM RT (ms) 36 887.81 227.43 40 874.16 210.16 -.28 .779 NWM Original RT (ms)

36 836.73 195.91 40 824.05 184.76 -.44 .662

NWM Original % Acc

36 95.00 6.83 40 93.50 8.78 -1.44 .150

NWM New RT (ms) 36 937.60 268.47 40 921.54 251.57 -.14 .892 NWM New % Acc 36 98.15 3.42 40 96.50 7.76 -.92 .356 DWR # correct 36 3.19 1.94 40 3.55 2.05 -.48 .632 DWR % Acc 36 21.30 12.93 40 23.67 13.67 -.48 .632 DWR # Errors 36 .64 .99 40 .53 .72 -.28 .777 WR RT (ms) 36 1094.58 224.07 40 977.00 227.37 2.27 .026 WR Original RT (ms) 36 1025.82 230.57 40 969.73 250.95 -1.31 .190 WR New RT (ms) 36 1159.52 272.82 40 990.59 245.52 -3.10 .002 WR Original % Acc 36 69.07 15.05 40 64.50 15.05 1.32 .190 WR New % Acc 36 84.44 12.75 40 90.00 9.90 -1.93 .053 PR New RT (ms) 36 1367.43 439.02 40 1301.93 284.19 -.10 .917 PR New % Acc 36 82.92 17.09 40 85.50 11.20 -.13 .895 PR Original RT (ms) 36 1288.12 489.78 40 1207.02 361.80 -.38 .700 PR Original % Acc 36 90.42 9.88 40 89.63 9.16 -.49 .627 PR RT (ms) 36 1320.28 439.87 40 1252.59 295.01 -.18 .860

Note: IWR=immediate word recall; SRT=simple reaction time; DV=digit vigilance; CRT=choice reaction time; SWM=spatial working memory; NWM=numeric working memory; DWR=delayed word recall; WR=word recognition; PR= picture recognition; # correct=number correct; % Acc=percentage accuracy; ms=milliseconds; RT=reaction time; items in bold represent variables that constitute the Power of Attention domain.


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8.3.5 Summary for attention and psychomotor function

In summary, the hypotheses that that patients would perform worse than controls on attention

tasks (H1) supported. However, contrary to expectation patients did not perform significantly

worse than controls on psychomotor function as measured by Trail Making-A (H2). As

predicted HF patients’ mean reaction time on the congruent Stroop RT task was significantly

slower than healthy controls after adjusting for WASI Vocabulary subset scores. However,

after adjusting for premorbid IQ and POMS-vigour/activity experimental groups did not

perform differently on the psychomotor task as measured by the Trail Making-A.

Furthermore, HF patients’ overall performance on the Power of Attention cognitive domain

was worse than controls after controlling for premorbid IQ (1270 ms versus 1192 ms).

Examining the individual CDR tasks defining the Power of Attention cognitive domain HF

patients displayed reduced simple (315ms versus 284ms), digit vigilance (444ms versus

426ms) and choice (511ms versus 482ms) reaction times. This indicates that HF patients have

an impaired ability to focus attention during a short period requiring extreme concentration.

Furthermore, it was hypothesised that patients will perform worse than controls on the

Continuity of Attention cognitive domains. After adjusting for WASI Vocabulary scores, this

investigation did not observe differences between experimental groups on the Continuity of

Attention cognitive domain performance. These results suggest that HF patients are impaired

on attention tasks however, they make a similar number of errors compared to controls when

focussing on a task over a prolonged period. Additionally, the results of the present study

suggest that HF patients are not impaired on their psychomotor abilities.

8.4 Memory tasks

8.4.1 Introduction

A series of one-way between-subject analysis of covariance statistics were performed to test

the hypothesis (H3) that HF patients will perform significantly worse than controls on Quality

of Working Memory, Quality of Episodic Memory, and Speed of Memory tasks. Evaluating

assumptions of normality of sampling distributions, linearity, homogeneity of variance,

homogeneity of regression and reliability of covariates were shown to be acceptable.

8.4.2 Results

Without adjusting for premorbid IQ and other covariates Quality of Working Memory

(F(1,74) = .003, p > .05), Quality of Episodic Memory (F(1,74) = 1.07, p > .05), Speed of

Memory (F(1,74) = 2.22, p > .05).


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Experimental groups did not differ on the Quality of Working Memory cognitive domain after

adjusting for premorbid IQ and other confounding factors (p > .05; Table 13). Although

patients recalled significantly fewer words in the immediate word recall task compared to

controls (p =.031), groups did not differ on the spatial working memory or numeric working

memory tasks (Table 14).

After adjusting for premorbid IQ and confounding factors, experimental groups did not differ

on the Quality of Episodic Memory (p > .05) or Speed of Memory cognitive domains (p > .05;

Table 13). Furthermore, there were no significant group differences in the number of words

recalled during the delayed word recall or number or images recognised during the picture

recognition task (Table 14).

This indicates that HF patients are not impaired compared with age matched controls on

working memory, episodic memory or in the time it takes to recall information from memory.

Additionally, these results suggest that HF patients may be impaired in their immediate

verbal memory recall ability although may not in spatial working memory or numeric

working memory (Table 14).

8.4.3 Summary for memory function

The hypothesis (H3) that HF patients will perform worse on the Quality of Working Memory

domain was not supported as this investigation found that after adjusting for IQ, the HF group

and controls performed equally on the Quality of Working Memory cognitive domain.

Examining the individual tasks from which this domain is derived, this investigation found

that HF patients and controls performed equally on spatial working memory tasks with

relation to reaction time, performance accuracy and error rates. This indicates that patients’

ability to store spatial visual information in working memory and the percentage accuracy is

the same as controls. Additionally, these findings suggest that patients’ ability to store

numeric information in working memory and the accuracy for this information is the same as

controls tasks with relation to reaction time, performance accuracy and error rates. Taken

together these findings suggest that when compared to age matched controls, HF patients are

not impaired in their visuo-spatial working memory and numeric working memory abilities.

After adjusting for possible confounding factors (premorbid IQ scores, POMS-Total mood

disturbance), this investigation did not observe differences between groups on the Quality of

Episodic Memory domain. These findings suggest that HF patients are not impaired in their

ability to recall verbal information correctly from short-term and episodic memory and visual


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information from episodic memory. Additionally, this suggests that patients are not impaired

in their ability to recall verbal and visual information correctly from episodic memory.

Examining the individual CDR tasks that reflect performance on Quality of Episodic

Memory, the hypothesis that HF patients would perform more poorly than controls on

episodic memory was supported. HF patients recalled fewer words than controls on the

immediate word recall task (M=5 verses M=6 out of a total of 15 words). However,

experimental groups did not differ in the number of words recalled in the delayed word recall

task.

8.5 Executive function domains

8.5.1 Introduction

To test the hypothesis (H4) that HF patients will perform significantly worse than controls on

executive function group differences on the Trail Making-B, incongruent Stroop and Stroop

effect tasks were explored using a one way ANOVA. This was followed by a between

subjects (HF, controls) univariate analysis of covariance (CV: WASI) to examine whether

experimental groups differed on measures of executive function after adjusting for IQ and

other possible confounding variables.

8.5.2 Results

The Mann Whitney U test indicated that HF patients were less accurate in their performance

on the incongruent Stroop task compared to controls (Z = -1.98, p < .05; Table 13). As

expected, a between subjects (HF, controls) univariate analysis of covariance (CV: WASI)

revealed that the covariate WASI Vocabulary subset was not significantly related to

incongruent Stroop, (F(1,72) = 2.77, p =.10). After controlling for IQ, there was a significant

weak effect of group on incongruent Stroop, F(1,72) = 6.03, p =.016, partial η2=.077.

Without adjusting for premorbid IQ HF patients performed significantly worse than controls

on Stroop effect (F(1,73) = 4.51, p =.037) and Trail Making-B (F(1,74) = 6.02, p =.016).

In the ANCOVA model the covariate WASI was not significantly related to Stroop effect

(F(1,72) = 3.172, p =.08). However, after controlling for IQ, there was no significant main

effect of group on Stroop effect F(1,72) = 2.74, p =.10, partial η2=.037.

In the ANCOVA model the covariate WASI was not significantly related to Trail Making-B,

F(1,73) =3.14, p =.08). Although, after controlling for WASI, there was an almost significant

group effect on Trail Making-B task performance, F(1,73) = 3.94, p =.051, partial η2=.051.


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8.5.3 Summary for executive function

Supporting the hypothesis HF patients demonstrated slower performance than controls on

executive function as measured by the Trail Making-B (107ms verses 89ms) task and

incongruent Stroop RT task (M=132 sec vs M=106 sec) even after controlling for premorbid

IQ. Additionally, HF patient’s accuracy on executive function performance as measured by

incongruent Stroop task was worse than that of controls (94% versus 98%). On the contrary,

after adjusting for WASI Vocabulary scores, there were no significant main effects for group

on executive function as measured by Stroop effect. This indicates that experimental groups

performed equally on Stroop effect measure.

8.6 Mood measures

8.6.1 Introduction

To explore the hypothesis (H5) that the HF group will score higher on the

depression/dejection, tension/anxiety, confusion/bewilderment, anger/hostility, fatigue/inertia

and lower on vigour/activity compared to controls, group differences were determined using a

one way ANOVA. Prior to analyses the mood variables were explored for accuracy of data

entry, missing values and best model of fit. The Profile of mood states (POMS) subscale and

Speilberger’s State Trait Anxiety Inventory (STAI) questionnaires were examined to

determine whether they met assumptions of normality for ANOVA.

These variables were also examined to determine whether they met assumptions of normality

for ANOVA. Appendix Q provides a detailed outline for the data exploration methods for

mood variables. The STAI-S, STAI-T and POMS subscales, except the vigour/activity subset,

were positively skewed. The log 10 transformation was used for all POMS subscales except

POMS-Total mood disturbance where the square root transformation was the best formula to

normalise the variable. All outliers were removed following variable transformation.

8.6.2 Results

Table 15 presents mean scores on the individual POMS subtests, STAI-State and STAI-Trait.

As expected an ANOVA revealed that HF patients scored significantly higher than controls

on the POMS-tension/anxiety, POMS-depression/dejection, POMS-anger/hostility, POMS-

fatigue/inertia, POMS-confusion/bewilderment subtests of the POMS questionnaire and

POMS-Total mood disturbance. Additionally, HF patients scored significantly lower than

controls on the POMS-vigour/activity subset. HF patients scored significantly higher than


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controls on the STAI-State (p < .01), however the results indicate that experimental groups

displayed equivalent scores on the STAI-Trait scale (Table 15).

Table 15 Analysis of variance, means and standard deviations for each experimental group for Profile of Mood States subscales and state trait anxiety inventory


Mood Variable n M SD n M SD df F p

POMS

Tension/anxiety 36 7.44 6.24 40 3.75 3.39 (1, 74) 9.69 .003 Depression/ dejection 36 7.14 9.37 40 2.05 2.70 (1, 74) 11.56 .001

Anger/hostility 36 5.06 4.97 40 2.48 2.95 (1, 74) 5.22 .025

Vigour/activity 36 15.50 5.67 40 20.38 6.20 (1, 74) 12.71 .001

Fatigue/inertia 36 9.42 7.18 40 4.15 4.06 (1, 74) 15.17 <.001 Confusion/ bewilderment 36 7.17 4.70 40 3.95 2.96 (1, 74) 12.11 .001 Total mood disturbance 36 20.72 32.84 40 -4.00 16.66 (1, 74) 18.04 <.001

STAI

State score 36 32.40 7.19 39 27.08 6.26 (1, 73) 13.12 .001

Trait score 36 32.90 8.43 39 30.56 7.13 (1, 73) 1.49 .226

Note: POMS=Profile of Mood States; STAI=State Trait Anxiety Inventory.

8.6.3 Summary of mood variables

The hypothesis that HF patients will display greater mood disturbances than controls was

supported. The findings from this study demonstrated that compared to controls, HF patients

had higher levels of tension/anxiety (7.44±6.24 vs 3.75±3.39), anger/hostility (5.06±4.95 vs

2.48±2.95), fatigue/inertia (M = 9.42±7.18 vs 4.15±4.06) and confusion/bewilderment

(7.17±4.70 vs 3.95±2.96) than controls. Additionally, HF patients scored higher on

depression/dejection (7.14±9.37 vs 2.05±2.70) and had less vigour/activity compared to

controls (15.50±5.67 vs 20.38±6.20). Finally, HF patients (20±32.84) had greater Total mood

disturbance compared to controls (-4.00±16.66).


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8.7 Vascular variables

8.7.1 Introduction

A series of one way ANOVA analyses were conducted to test the hypotheses related to

whether experimental groups differed on the vascular markers (H6 – H8).

8.7.2 Data Screening

To ensure the assumptions for ANOVA the Levene’s statistic confirmed no violation of

assumptions of homogeneity for each vascular variables except for endothelin-1 which had an

extreme outlier which was not removed following transformation, therefore this case was

removed for this variable before analysis. Additionally skewness and kurtosis statistics,

normality plots and bivariate scatter plots were examined for signs of violations of normality

and linearity assumptions. Apart from common carotid arterial blood flow velocity (BFV),

middle cerebral BFV and augmentation index which were normally distributed all other

vascular biomarkers were non-normally distributed and log transformations were used to

correct positive skewness were used in the ANOVA analyses. For a complete overview of the

data screening procedure refer to Appendix R.

8.7.3 Results

A one way ANOVA was conducted to test the hypothesis (H6) that HF patient group will

have significantly lower cerebral blood flow velocity as measured by common carotid and

middle cerebral blood flow velocity compared to the control group. Means and standard

deviations for vascular measures are displayed in Table 16. As expected, HF patient’s

common carotid (F(1,64) = 18, p < .01) and left middle cerebral arterial (F(1,53) = 5.07, p <

.05) blood flow velocities were significantly slower compared to controls (Table 16).

Additionally to test the hypothesis (H7) that HF patients will have increased arterial stiffness

as measured by augmentation index and central pulse pressures compared to controls, a one-

way ANOVA was conducted to examine group differences on the augmentation index and

central pulse pressure variables. In contrast to what was expected, experimental groups did

not differ on arterial stiffness as measured by augmentation index (F(1,57) = 1.20, p = .28).

Additionally, HF patients had significantly lower levels of arterial stiffness as measure by

central pulse pressure compared to controls (F(1,59) = 4.76, p < .05).


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Table 16 Means and standard deviations of the vascular measures for HF and control groups


Vascular variable n M SD n M SD df F p

Arterial Blood Flow Velocity (cm/s)

Common carotid 33 17.11 4.92 33 21.69 3.86 (1,64) 17.66 <.001

Middle cerebral 23 50.44 6.70 32 56.21 10.88 (1,53) 5.07 .028 Central Pressures (mmHg)

Central systolic BP 21 108.52 17.9 40 128.75 15.30 (1,59) 25.05 <.001

Central diastolic BP 21 68.24 12.91 40 81.12 7.53 (1,59) 21.01 <.001 Arterial Stiffness (mmHg)

Augmentation index*

19 21.79 11.15 40 24.60 8.16 (1,57) 1.20 .278

Central pulse pressure

21 40.29 13.33 40 47.63 14.91 (1,59) 4.76 .033

Vascular function Endothelin-1 (pg/mL)

34 24.94 11.39 22 27.77 6.56 (1, 54) 1.35 .235

Note: cm/s=centimetres per second; BP=blood pressure; mmHg=millimetres of mercury; pg/mL=pictograms per millilitre; *=corrected for heart rate.

The hypothesis (H8) that HF patients will have higher plasma levels of the vasoconstrictor

endothelin-1 compare to controls was not supported. An ANOVA showed that the

experimental groups did not differ on plasma endothelin-1 levels, (F(1,54) = 1.35, p > .05;

Table 16).

To explore whether elevated endothelin-1 levels in HF patients will be related to increased

inflammation, Pearson’s correlations were carried out between endothelin-1 and high-

sensitive C-reactive protein and each of the each of the arterial stiffness measures

(augmentation index, central pulse pressure). Pearson’s correlation coefficients for the HF

group are presented in Table 17.

Contrary to what was expected, as displayed in Table 17, endothelin-1 levels were not

significantly related to inflammation as measured by high-sensitive C-reactive protein or

arterial stiffness as measured by augmentation index, peripheral pulse pressure and central

pulse pressure.


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Table 17 Pearson’s correlations between endothelin-1, high-sensitive C-reactive protein and

each of the each of the arterial stiffness measures (augmentation index, central pulse

pressure) in the heart failure group.

AIx PPP CPP ET-1

hs-CRP .239 .429* .486* .275

AIx -439 .482 .193

PPP .950** .233

CPP .274

Note: hs-CRP=high-sensitive C-reactive protein; AIx=augmentation index; PPP= peripheral pulse pressure; CPP=central pulse pressure, ET-1=endothelin-1; correlations significant at: * p < .05; ** p < .01.

8.7.4 Summary

As predicted mean blood flow velocity in the left common carotid artery and left middle

cerebral artery was slower in patients compared to controls. However, the hypothesis that

patients will have increased arterial stiffness compared to controls, was partially supported

from the current findings. Only one of the two measures of arterial stiffness demonstrated

that patients had elevated arterial stiffness compared to controls. Central pulse pressure,

which is an indirect measure of arterial stiffness, was reduced in HF patients however there

were no differences observed on measures of augmentation index between experimental

groups.

8.8 Oxidative stress, antioxidant and inflammatory biomarkers

8.8.1 Introduction

A series of one way ANOVA analyses were conducted to test the hypotheses related to

whether experimental groups differed on the oxidative stress (DROM, F2-isoprostanes),

antioxidant (glutathione peroxidase, coenzyme Q10), inflammatory (hs-CRP) and omega-3

(PUFA; H9 - H11) measures. To ensure the assumptions for ANOVA the Levene’s statistic

confirmed no violation of assumptions of homogeneity for the oxidative stress, antioxidant or

inflammatory variables. Additionally skewness and kurtosis statistics, normality plots and

bivariate scatter plots were examined for signs of violations of normality and linearity

assumptions. The variables PUFA, F2-Isoprosane levels, glutathione peroxidase and

coenzyme Q10 were all normally distributed. To ensure the assumptions for ANOVA were


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met for hs-CRP, DROM and endothelin-1, which were non-normally distributed, the log

transformation of these variables were utilised in the analysis.

8.8.2 Results

Means and standard deviations for oxidative stress, antioxidant and inflammatory measures

are presented Table 18. A one way ANOVA was conducted to test the hypothesis that HF

patients will have significantly higher levels of oxidative stress as measure by determinable

reactive oxygen metabolites (DROMs) and lipid peroxides (F2-isoprostanes) compared to the

control group (H9). As predicted HF patients had significantly higher plasma levels of the

hydroperoxide measure, DROM compared to the control group, F(1, 55) = 28, p < .001.

However contrary to what was expected, the experimental groups had similar levels of lipid

peroxidation as measured by plasma F2-isoprostane levels, F(1,70) = 0.55, p = .46 (Table

18).

Table 18 Means and standard deviations of the oxidative stress, antioxidant and inflammatory measures for HF and control groups


Biomarker Variable n M SD n M SD df F p Oxidative Stress

DROM (Ucarr) 28 469.64 94.05 29 352.66 83.90 (1,55) 27.60 <.001

F2-isoprostanes (pmol/L)

36 1782.97 407.94 36 1712.31 403.68 (1,70) 0.55 .463

Antioxidant Glutathione peroxidase (nmol/min/ml

34 102.34 25.52 27 108.73 29.23 (1,59) 0.83 .367

CoQ10 (nmol/L) 35 795.17 376.87 37 1069.81 296.18 (1,70) 11.89 .001Inflammation and omega-3

hs-CRP (mg/L) 35 4.97 6.27 23 1.21 1.31 (1,56) 21.31 <.001

PUFA (units/day) 34 7.09 4.66 21 10.00 5.38 (1,53) 4.51 .038

Note: DROM=determinable reactive oxygen metabolites; Ucarr=Carratelli Units; pmol/L=picomole per litre; nmol/min/ml=nanomole per minute per mililitre; CoQ10=coenzyme Q10; nmol/L=nanomole per litre; hs-CRP=high-sensitive C-reactive protein; mg/L=milligrams per litre; PUFA=polyunsaturated fatty acid.


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It was hypothesised that HF patients will have significantly lower levels of plasma

antioxidants as measured by coenzyme Q10 (CoQ10) and glutathione peroxidise compared to

the control group (H10). A one way ANOVA revealed that this hypothesis (H10) was partially

supported. As predicted HF patients’ plasma CoQ10 levels were significantly lower than

controls, F(1,70) = 12, p < .01. However, contrary to what was anticipated, plasma levels of

the lipophilic antioxidant biomarker glutathione peroxidase was not significantly different

between experimental groups, F(1,59) = 0.83, p = .37 (Table 18).

It was predicted that HF patients will have significantly higher levels of inflammation as

measured by hs-CRP and dietary omega-3 PUFA intake compared to the healthy control

group (H11). As expected, HF patients had significantly higher levels of systemic

inflammation as measured by hs-CRP compared to controls F(1,56) = 21, p < .01.

Additionally, the hypothesis was further supported by the observation that HF patients

consumed significantly less dietary omega-3 polyunsaturated fatty acid units in the 3 months

prior to baseline testing compared to controls, F(1,53) = 4.51, p < .05. This suggests a lower

dietary intake of omega-3 polyunsaturated fatty acids in the heart failure group.

8.9 Relationships between the vascular, oxidative stress, antioxidant and

inflammatory biomarkers

8.9.1 Introduction

Correlation coefficients were examined to explore the interplay between the vascular (blood

flow velocity, arterial stiffness), oxidative stress (DROM, F2-isoprostanes), antioxidants

(CoQ10, GPx) and inflammatory biomarkers in each experimental group. Pearson’s

correlations were carried out between each of the oxidative stress, antioxidant, inflammatory

and vascular measures for each experimental group. Non-normally distributed variables were

transformed in order to meet the Pearson’s correlation assumptions of normality. Table 19

displays the correlations between the biomarker variables for the HF group. Additionally,

Table 20 displays the correlations between the biomarker variables for controls.

8.9.2 Oxidative stress measures and vascular, antioxidant and inflammatory markers

No significant correlations were observed between the oxidative stress measures and any of

the vascular, antioxidant or inflammatory measures in either experimental group. This

suggests that in the cohort tested in this investigation lipid hydroperoxides and F2-

isoprostanes are possibly not related to measures of arterial stiffness, cerebral blood flow or

systemic inflammation.


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8.9.3 Antioxidant and vascular measures

Furthermore, in the HF group, higher GPx activity was significantly moderately related to

reduced central pulse pressures, r(17) = -.517, p < .05. These observations were not seen in

controls. These results suggest that in the HF group, reduced antioxidant status as measured

by lower glutathione peroxidase activity relates to elevated arterial stiffness as measured by

central pulse pressure.

In the HF group, there was a significant, moderate, relationship between the antioxidant

CoQ10 and arterial stiffness as measured by central pulse pressure, r(18) = -.576, p < .01.

These results indicate that reduced antioxidant levels as measured by CoQ10 is associated

with elevated arterial stiffness as measured by central pulse pressure. In addition unlike the

control group, in the HF group, there was a significant, moderate, negative relationship

between the antioxidant CoQ10 and central systolic BP, r(18) = -.524, p < .05 these findings

indicate higher central systolic blood pressure is related to reduced antioxidant levels as

measured by CoQ10.

8.9.4 Antioxidant and inflammatory measures

The antioxidant glutathione peroxidase activity was significantly negatively correlated with

hs-CRP plasma levels, r(31) = -.534, p < .01 in the heart failure (HF) group (Table 19).

Glutathione peroxidase activity however was not significantly related to systemic

inflammation in controls (Table 20). These findings suggest that in the HF group, reduced

antioxidant status as measured by reduced glutathione peroxidase activity relates to raised

systemic inflammation as measured by hs-CRP. Additionally, there was a significant negative

trend between CoQ10 and hs-CRP in the HF group (r(34) = -.319, p = .066) but not in

controls (r(20) = -.074, p = .744).

8.9.5 Inflammatory and vascular measures

In the HF group, the inflammatory maker hs-CRP was significantly correlated with central

systolic BP, r(18) = .509, p < .05, this relationship was not significant in controls (p > .05).

Additionally, hs-CRP was significantly positively related to central pulse pressure in HF

patients, r(18) = .485, p < .05,but not in controls (p > .05). Taken together these finding

suggest an increase in systemic inflammation in HF patients relates to an increase in central

systolic blood pressure and arterial stiffness as determined by central pulse pressure.


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Note: DROM=determinable reactive oxygen metabolites; F2 Iso=F2-isoprostanes; GPx=glutathione peroxidase; CoQ10=coenzyme Q10; hsCRP=high-sensitive C-reactive protein; PUFA=polyunsaturated fatty acid questionnaire; CC=common carotid arterial blood flow velocity; MCA=middle cerebral arterial blood flow velocity; PSBP=peripheral systolic blood pressure; PDBP=peripheral diastolic blood pressure; CSBP=central systolic blood pressure; CDBP=central diastolic blood pressure; AIx=augmentation index; PPP=peripheral pulse pressure; CPP=central pulse pressure; significant at: * p < .05; ** p <.01; *** p < .001; ¥=Trend.

Table 19 Correlations between oxidative stress, antioxidant, inflammatory and vascular measures for the HF group

Variable F2 Iso GPx CoQ10 hsCRP PUFA CC MCA PSBP PDBP CSBP CDBP AIx PPP CPP ET-1

DROM -.001 -.203 .001 .275 -.283 -.023 .153 .113 -.213 .198 -.241 .418 .326 .396 .155 F2 Iso -.290 .077 .148 .151 .184 .280 .250 .155 .405 .261 -.068 .140 .331 -.211 GPx .038 -.534** .076 .265 .233 -.184 .048 -.375 .038 -.249 -.286 -.517* -.013CoQ10 -.319¥ -.026 .213 -.276 -.299 -.128 -.524* -.233 -.019 -.277 -.576** -.246 hsCRP .003 -.306 -.115 .422* .103 .509* .139 .235 .428* .485* .239 PUFA .021 -.269 .258 .328 .056 .261 -.143 .058 -.096 -.191 CC .109 -.193 .009 -.215 -.098 .057 -.289 -.247 -.209 MCA .113 -.132 .066 -.247 -.038 .259 .491 -.039 PSBP .562*** .957*** .586** .305 .729*** .781*** -.008 PDBP .684** .993*** -.058 -.145 -.036 -.270 CSBP .721*** .470 .563** .686** .045 CDBP .094 -.103 .038 -.270 AIx .413 .464 .193 PPP .946*** .213CPP .274


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Table 20 Correlations between oxidative stress, antioxidant, inflammatory and vascular measures for the control group

Variable F2 Iso GPx CoQ10 hsCRP PUFA CC MCA PSBP PDBP CSBP PDBP AIx PPP CPP ET-1

DROM -.163 .100 -.114 .262 -.169 -.198 .276 -.067 .084 -.140 .101 -.087 -.105 -.184 -.069 F2 Iso .076 .186 -.062 -.219 -.044 .115 .007 -.080 -.104 -.058 -.225 .063 -.075 -.200GPx .278 .291 .113 -.253 .024 -.084 .138 -.182 .174 -.125 -.146 -.293 .218 CoQ10 -.074 .505* -.195 -.048 .028 .289 .110 .280 .033 -.104 -.053 .045 hsCRP -.227 .395 .006 -.260 -.171 -.246 -.179 .202 -.126 -.103 .482 PUFA -.560* -.402 -.392 .087 -.269 .091 .168 -.422 -.287 -.407CC -.022 -.022 -.113 -.025 -.140 -.139 .022 .081 .339MCA -.341 -.220 -.330 -.212 .007 -.264 -.275 .350PSBP .272 .943*** .282 .193 .871*** .806*** -.102PDBP .318* .994*** .076 -.226 -.225 -.244 CSBP .318* .420** .797*** .843*** -.047PDBP .073 -.207 -.224 -.260AIx .176 .415** .232 PPP .926*** .023

CPP .085

Note: DROM=determinable reactive oxygen metabolites; F2 Iso=F2-isoprostanes; GPx=glutathione peroxidase; CoQ10=coenzyme Q10; hsCRP=high-sensitive C-reactive protein; PUFA=polyunsaturated fatty acid questionnaire; CC=common carotid arterial blood flow velocity; MCA=middle cerebral arterial blood flow velocity; PSBP=peripheral systolic blood pressure; PDBP=peripheral diastolic blood pressure; CSBP=central systolic blood pressure; CDBP=central diastolic blood pressure; AIx=augmentation index; PPP=peripheral pulse pressure; CPP=central pulse pressure; significant at: * p < .05; ** p <.01; *** p < .001.


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8.9.6 Summary

Taken together these findings suggest that patients have elevated levels of inflammation

compared to controls. Additionally, HF patients have higher levels of hydroperoxides as

measured by DROMs but equivalent levels of lipid peroxides as measured by F2-

isoprostanes. As anticipated, HF patient have lower levels of the antioxidant CoQ10 although

do not differ on levels of the lipophilic antioxidant biomarkers GPx compared to age matched

controls.

Chapter 9: Results – relationships between cognitive measures and biomarkers

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CHAPTER 9 RELATIONSHIPS BETWEEN COGNITIVE MEASURES

AND BIOMARKERS

9.1 Introduction

This chapter will provide an examination of how the hypotheses (H12 - H13) pertaining to the

relationships between cognitive measures and vascular markers are examined. Specifically

results exploring the relationships between cognitive measures and blood flow velocities in

the common carotid and middle cerebral arteries (H12) and arterial stiffness as measured by

central pulse pressures and augmentation index (H13) will be presented. In order to test these

hypotheses and research questions, simple regression analyses were initially conducted for

each experimental group to explore whether biomarkers were related to cognitive

performance. These analyses were followed by one-way, between-subjects analysis of

covariance (ANCOVA) to examine how much of the variance between cognitive outcome

measures were accounted for by the physiological measures. Next, a series of multiple

regression analyses were performed to explore to what extent vascular measures and

biomarkers predicted performance on cognition in the HF group. For all analyses,

relationships between variables were considered to be statistically different if the p value was

less than 0.05 using two tailed tests. For all analyses relationships between variables were

considered to be statistically different if the p value was less than 0.05 using two tailed tests.

9.2 Examination of the relationships between cognitive and vascular measures

9.2.1 Introduction

To investigate whether blood flow velocity as measured by common carotid and middle

cerebral arterial blood flow and arterial stiffness were related to measures of attention,

memory or executive function in HF patients simple regression analyses were initially

conducted. The effects of the vascular biomarkers on cognitive outcome measures that were

significantly different between groups in the ANCOVA models presented in Chapter 8 will

be reported.

Pearson’s correlation coefficients between cognitive measures and vascular markers for each

experimental group are displayed in Table 21. MMSE scores were positively skewed and

none of the transformation variables adequately transformed the data, therefore Spearman’s

Rho statistic was used to correlate MMSE with biomarker variables.


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9.3 Relationships between common carotid and middle cerebral arterial blood flow

and cognitive function

9.3.1 Global cognition

It was hypothesised that reduced cerebral blood flow velocity as measured by common

carotid and middle cerebral arterial blood flow will be related to cognitive function in HF

patients (H12). Contrary to what was predicted, global cognitive scores, as measured by the

Mini Mental State Examination, was not significantly related to common carotid or middle

cerebral arterial blood flow velocities in either experimental group (Table 20).

9.3.2 Attention

Simple regressions were initially observed to examine the hypothesis that cerebral blood flow

velocity as measured by common carotid and middle cerebral blood flow will be related to

poorer performance on cognitive tests measuring attention, memory or executive function in

HF patients (H12).


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Table 21 Correlation matrix for neuropsychological domains and blood flow velocities,

arterial stiffness and vascular function

Variable Blood flow

Velocity Arterial Stiffness Vascular function

Heart Failure Group CC MCA AIx CPP ET-1

Mini Mental State Examination¥ .135 .260 .410 .116 -.128 Attention

Congruent Stroop -.414* .065 .108 .506* .253

Trail Making-A -.101 -.214 .171 .271 .081

Power of Attention -.379* .219 .130 .669** -.097

Continuity of Attention .146 -.074 -.471* -.287 -.026 Memory

Quality of Episodic Memory .377* .195 .114 -.214 .172

Quality of Working Memory -.073 .112 -.082 -.242 -.022

Speed of Memory -.362* -.018 .269 .401 -.078 Executive Function

Incongruent Stroop -.409* .188 .351 .502* -.151

Stroop effect -.353* .238 .471* .419 .090

Trail making-B -.084 -.055 .497* .552** -.255

Control Group CC MCA AIx CPP ET-1

Mini Mental State Examination¥ .126 .106 -.017 .097 -.374 Attention

Congruent Stroop -.097 -.072 -.069 .056 .091

Trail making-A .188 .818 .121 .175 .363

Power of Attention .057 -.244 -.057 .162 -.142

Continuity of Attention .040 .237 -.119 .020 .160 Memory

Quality of Episodic Memory .017 .186 .055 .190 -.176

Quality of Working Memory -.202 .113 -.072 -.048 -.310

Speed of Memory .087 -.309 .053 .274 .149 Executive Function

Incongruent Stroop -.231 -.163 .038 .367* .170

Stroop effect -.248 -.228 .091 .470** .171

Trail making-B .042 -.185 .256 .104 .212 Note: CC=common carotid blood flow velocity; MCA=middle cerebral arterial blood flow velocity; AIx=augmentation index; CPP = central pulse pressure; ET-1=endothelin-1; ¥=Spearman’s correlations using untransformed data; * p < .05; ** p < .01.


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As presented in Table 21, in the HF group common carotid blood flow velocity was

negatively related to reduced performance on congruent Stroop task (r(31) = -.414, p < .05)

and Power of Attention cognitive domain (r(31) = -.379, p < .05). These observations were

not seen in controls. Scatterplots for the relationship between common carotid blood flow

velocity and Power of Attention for each group are presented in Figure 8. This result suggests

that poor attention abilities in HF patients may be related to reduced blood flow velocity in

the common carotid artery. Interestingly, middle cerebral arterial blood flow velocity was not

related to the attention measures in the HF group.

Figure 8 Scatter plots of Power of Attention and common carotid arterial blood flow velocity in the HF and control groups

Analysis of covariance statistics were conducted to explore the extent to which common

carotid blood flow velocity accounted for the group variance on congruent Stroop reaction

time and Power of Attention domain after adjusting for premorbid IQ.

In the first model, an ANCOVA [between-subjects factor: Group (HF, controls); CV: WASI

Vocabulary subset, common carotid blood flow velocity] was conducted to assess whether HF

patients performed significantly worse on congruent Stroop reaction time after controlling for

common carotid blood flow velocity and premorbid IQ. The Levene’s test for homogeneity of

variance, normality of sampling distributions, homogeneity of regression and reliability of

covariates were satisfactory. Since there were no group by covariate interactions observed

therefore the interaction terms were removed from the model to obtain greater power to

detect the main effects. As displayed in Table 22 the premorbid IQ measure, WASI

Vocabulary subset, was not significantly related to congruent Stroop reaction time, F(1,62) =

r = -.379, p = .030 r = .057, p = .757


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.405, p > .05. However, the covariate common carotid blood flow velocity had a significant

weak effect across groups on the congruent Stroop task performance, F(1,62) = 5.1, p < .05,

partial η2=.076. There was no significant effect of group on congruent Stroop after

controlling for premorbid IQ and common carotid blood flow velocity (p = .093).

Table 22 The effect of group on congruent Stroop reaction time after adjusting for premorbid

IQ and common carotid blood flow velocity

Source SS df MS F P partial η2 Group .01 1 .01 2.91 .093 .045 WASI .00 1 .00 .41 .527 .006 CCA-BFV .02 1 .02 5.10 .027 .076 Error .29 62 .01 Total 564.99 66

Note: CCA-BFV=common carotid arterial blood flow velocity; WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset.

In the second model, a between-subjects (group: HF, controls) ANCOVA (CV: WASI,

common carotid blood flow velocity) was conducted to examine whether common carotid

blood flow velocity accounted for some of the group variance seen in the Power of Attention

cognitive domain (Table 23). The Levene’s test for homogeneity of variance, normality of

sampling distributions, homogeneity of regression and reliability of covariates were

satisfactory. There were no significant interactions observed between the covariates and

group. The covariate WASI was not significantly related to Power of Attention, F(1, 61) =

.099, p > .05. There was a trend for a relationship between the covariate common carotid

blood flow velocity and Power of Attention, F(1, 61) = 3.43, p = .069, partial η2 = .053. There

was no significant main effect of group on Power of Attention after adjusting for premorbid

IQ and common carotid blood flow velocity (p = .06).


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Table 23 The effect of group on Power of Attention domain after adjusting for premorbid IQ

and common carotid blood flow velocity

Source SS df MS F p partial η2

Group 42029 1 42030 3.73 .058 .058

WASI 1112 1 1112 .10 .754 .002

CCA-BFV 38657 1 38658 3.43 .069 .053

Error 687317 61 11268

Total 98895723 65 Note: CCA-BFV=common carotid arterial blood flow velocity; WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset.

These results suggests that the group variance seen in attention as measured by congruent

Stroop and Power of Attention cognitive domains can be explained by reduced blood flow

speed in the common carotid artery. Middle cerebral blood flow velocity does not appear to

be related to attentional abilities in HF patients.

9.3.3 Memory

Although HF patients did not perform significantly different to controls on Quality of

Episodic Memory, Quality of Working Memory and Speed of Memory cognitive domains, the

hypothesis that cerebral blood flow velocity as measured by common carotid and middle

cerebral arterial blood flow will be related to attention, memory or executive function in HF

patients was explored (H12). Correlation coefficients were observed to explore whether

cerebral blood flow velocity measures related to Quality of Episodic Memory, Quality of

Working Memory and Speed of Memory domains (Table 21). In the HF group as expected

better scores on Quality of Episodic Memory cognitive domain was related to faster common

carotid blood flow velocity (r(31) = .377, p < .05). Additionally, in the HF group better

scores on the Speed of Memory cognitive domain was related to faster common carotid blood

flow velocity (r(31) = -.362, p < .05). These relationships were not significantly associated in

the control group. Middle cerebral arterial blood flow velocity was not related to Quality of

Episodic Memory, Quality of Working Memory or Speed of Memory domains in either

experimental group (p > .05).

There were no significant group differences observed between the HF and control group on

quality of episodic memory, Quality of Working Memory or Speed of Memory and cognitive

domains. Therefore, multiple regression analysis was not performed to explore the

relationship between these cognitive domains and vascular measures.


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9.3.4 Executive function

The hypothesis that cerebral blood flow velocity as measured by common carotid and middle

cerebral arterial blood flow will be related to reduced attention, memory or executive

function in HF patients was explored further (H12). Here, it was hypothesised that in the HF

group a relationship would be apparent between executive function and cerebral blood flow.

Correlations between incongruent Stroop reaction time and Trail Making-B executive

function measures, which were shown to be significantly different between groups and

cerebral blood flow were examined. As expected, in the HF group, reduced blood flow

velocity in the common carotid artery was related to worse performance on executive

function as measured by incongruent Stroop reaction time (r(31) = -.409, p < .05; Table 21).

These observations were not observed in controls (p > .05). Scatterplots for the relationship

between common carotid blood flow velocity and incongruent Stroop reaction time for each

group are presented in Figure 9. Contrary to expectation, no significant relationships were

observed between middle cerebral blood flow velocity and incongruent Stroop reaction time

in the experimental groups. Additionally, performances on Trail Making-B was not related to

blood flow velocity in the common carotid or middle cerebral arteries.

Figure 9 Scatter plots of common carotid blood flow velocity and incongruent Stroop in the HF and control groups

r = -.231, p = .196r = -.409, p = .018


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An analysis of covariance statistics were conducted to explore the extent to which common

carotid blood flow velocity explained the group differences seen in the executive function

measure incongruent Stroop reaction time task. Incongruent Stroop reaction time task was

the executive function variable which displayed significant group differences in which HF

patients performed significantly worse than control. Common carotid blood flow velocity was

significantly moderately negatively correlated with incongruent Stroop reaction time scores

in the HF group indicating that it is a suitable covariate to explore in the ANCOVA model.

A between-subjects (group: HF, controls) ANCOVA (CV: WASI, common carotid blood

flow velocity) was conducted to examine the effect of common carotid blood flow velocity on

the incongruent Stroop reaction time task performance between groups. The Levene’s test for

homogeneity of variance, normality of sampling distributions, homogeneity of regression and

reliability of covariates were satisfactory. Since there were no group by covariate interactions

observed the interaction terms were removed from the model to obtain greater power to

detect the main effects.

The ANCOVA model exploring the effect of group on incongruent Stroop reaction time

adjusting for premorbid IQ and common carotid blood flow velocity is presented in (Table

24). As presented in Table 24, the premorbid IQ covariate (WASI Vocabulary) was not

significantly related to incongruent Stroop reaction time, F(1,62) = 2.25, p > .05 However,

the covariate common carotid blood flow velocity has a significant weak effect on

incongruent Stroop, F(1,62) = 8.33, p < .01, partial η2=.118. After controlling for WASI

Vocabulary and common carotid blood flow velocity, the effect of group on incongruent

Stroop reaction time was no longer significant (p > .05).

Table 24 The effect of group on incongruent Stroop reaction time adjusting for premorbid IQ

and common carotid blood flow velocity

Source SS df MS F p partial η2 Group .04 1 .04 2.96 .091 .046 WASI .03 1 .03 2.25 .138 .035 CCA-BFV .11 1 .11 8.33 .005 .118 Error .81 62 .01 Total 611.90 66

Note: WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset; CCA-BFV=common carotid arterial blood flow velocity.


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9.3.5 Summary

In summary faster blood flow velocity in the common carotid artery was related to better

performance on Quality of Episodic Memory and Speed of Memory cognitive domains in HF

patients. These results suggest that worse performance on the Quality of Episodic Memory

and Speed of Memory cognitive domains in HF patients relates to reduced cerebral blood flow

velocity in the common carotid artery but not in the middle cerebral artery. Furthermore, the

prediction that executive function will be related to cerebral blood flow was partially

supported. Improved performance on Incongruent Stroop but not Trail Making-B was related

to a reduced common carotid blood flow velocity. However, none of the executive function

measures were related to middle cerebral blood flow velocity in either experimental group.

9.4 Relationships between arterial stiffness and cognitive performance

9.4.1 Introduction

The next section will test the hypothesis that arterial stiffness as measured by augmentation

index and pulse pressure will be related to cognitive tests measuring attention, psychomotor

speed, working memory, episodic memory and executive function (H13). Correlation

coefficients for the relationship between cognitive and arterial stiffness measures are

presented in Table 21.


Global cognitive scores as measured but the MMSE was not significantly related to arterial

stiffness as measured by augmentation index or central pulse pressure in either experimental

group.

9.4.3 Attention

Correlation coefficients for the relationship between attention and arterial stiffness measures

are presented in Table 21. Arterial stiffness as measured by augmentation index was not

related to performance on congruent Stroop reaction time or Power of Attention cognitive

domain in either experimental group. Interestingly, arterial stiffness as measured by central

pulse pressure was positively, moderately related with congruent Stroop (r(18) = .506, p <

.05) and Power of Attention (r(18) = .669, p < .01) in the HF group. No relationships were

observed between arterial stiffness and measures of attention in the control group. This

suggests that reduced attention abilities in HF patients may be related to an increase in arterial

stiffness as measured by central pulse pressure.


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A series of between-subjects (group: HF, controls) ANCOVA (CV: WASI, central pulse

pressure) analyses were conducted to explore the extent to which central pulse pressure

explained the group differences seen in congruent Stroop reaction time and Power of

Attention cognitive domain. In the first model an ANCOVA [between-subjects factor: Group

(HF, controls); CV: WASI, CPP] examined the effect of IQ and central pulse pressure on

congruent Stroop.

The Levene’s test for homogeneity of variance, normality of sampling distributions and

reliability of covariates were satisfactory. A significant interaction was observed between

group and WASI (p = .042, partial η2 = .077), indicating that the direction of the regression

slope for WASI and congruent Stroop reaction time is different for each group. As presented

in Table 25, the ANCOVA model revealed that the covariates premorbid IQ and central pulse

pressure were not significantly related to congruent Stroop reaction time (p > .05). After

controlling for WASI and central pulse pressure, there was a significant main effect of group

on congruent Stroop reaction time (p = .013, partial η2 = .104).

Table 25 The effect of group on congruent Stroop reaction time after adjusting for premorbid IQ and central pulse pressure


Group .04 1 .04 6.53 .013 .104 WASI .00 1 .00 .20 .659 .004 CPP .02 1 .02 3.10 .084 .052 Error .33 56 .01 Total 512.43 60

Note: WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset; CPP=common central pulse pressure.

In the second model an ANCOVA [between-subjects factor: Group (HF, controls); CV:

WASI, CPP] examined the effect of premorbid IQ and central pulse pressure on Power of

Attention cognitive domain. The Levene’s test for homogeneity of variance, normality of

sampling distributions, homogeneity of regression and reliability of covariates were

satisfactory. Since there were no group by covariate interactions observed the interaction

terms were removed from the model to obtain greater power to detect the main effects. As

presented in Table 26, the ANCOVA model revealed that the covariate WASI Vocabulary

subset was not significantly related to Power of Attention (p > .05). However, central pulse

pressure was significantly weakly related to Power of Attention across groups (p < .01,


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partial η2 = .157). After controlling for WASI and central pulse pressure, there was still a

significant main effect of group on Power of Attention (p < .01, partial η2 = .193).

Table 26 The effect of group on Power of Attention domain after adjusting for premorbid IQ and central pulse pressure


Group 143576 1 143576 13.38 .001 .193 WASI 265 1 265 .03 .876 .000 CPP 112273 1 112273 10.46 .002 .157 Error 601066 56 10733 Total 90228500 60

Note: WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset; CPP=central pulse pressure.

9.4.4 Memory

To explore whether arterial stiffness was related to Quality of Episodic Memory, Quality of

Working Memory and Speed of Memory cognitive domains in the HF group Pearson’s

correlation coefficients were examined. Correlation coefficients for the relationship between

memory domains and arterial stiffness measures are presented in Table 21. As presented in

Table 21, arterial stiffness as measured by augmentation index and central pulse pressure,

was not significantly correlated with Quality of Episodic Memory, Quality of Working

Memory or Speed of Memory in either experimental group. No relationships were observed

between memory domains and arterial stiffness in the control group. This suggests that

arterial stiffness has not effect on Quality of Working Memory, Quality of Episodic Memory

or Speed of Memory in HF patients.


To explore whether arterial stiffness was related to incongruent Stroop and Trail Making-B

tasks which were the executive measures shown to differ significantly between experimental

groups, Pearson’s correlation coefficients were examined. Correlation coefficients for the

relationship between incongruent Stroop, Trail Making-B and arterial stiffness measures are

presented in Table 21. As displayed in Table 21, worse performance on Trail Making-B in the

HF group was associated with increased arterial stiffness as measured by augmentation index

(r(17) = .497, p < .05) and central pulse pressure (r(19) = .552, p < .01). However, increased


140

arterial stiffness as measured by central pulse pressure, which was significantly different

between groups was significantly correlated with slower incongruent Stroop reaction times in

both the HF (r(18) = .502, p < .05) and control (r(38) = .367, p < .05) groups.

These findings suggest that increased arterial stiffness as measured by central pulse pressure

is related to poorer executive function in HF.

An analysis of covariance statistics were conducted to further explore the extent to which

arterial stiffness as measured by central pulse pressure effects incongruent Stroop reaction

time after adjusting for IQ.

An ANCOVA [between-subjects factor: Group (HF, controls); CV: WASI, CPP] revealed

that the covariates WASI and central pulse pressure were not significantly related to Trail

Making-B performance (Table 27). The effect of group on Trail Making-B remained

significantly different even after controlling for IQ and central pulse pressure, (p = .004,

partial η2 = .138). The Levene’s test for homogeneity of variance, normality of sampling

distributions, homogeneity of regression and reliability of covariates were satisfactory.

Table 27 The effect of group on Trail Making-B after adjusting for premorbid IQ and central pulse pressure


Group .29 1 .29 9.16 .004 .138 WASI .04 1 .04 1.38 .244 .024 CPP .11 1 .11 3.38 .071 .056 Error 1.78 57 .03 Total 235.44 61


To further explore the effects of central pulse pressure on incongruent Stroop reaction time a

second ANCOVA [between-subjects factor: Group (HF, controls); CV: WASI, CPP] was

conducted. The Levene’s test for homogeneity of variance, normality of sampling

distributions, homogeneity of regression and reliability of covariates were satisfactory. There

were no group by covariate interactions observed. As presented in Table 28 the ANCOVA

model revealed that the covariate IQ was not significantly related to incongruent Stroop

reaction time (p > .05). The covariate central pulse pressure however was significantly


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related to incongruent Stroop reaction time. After controlling for WASI and central pulse

pressure, there was a significant effect of group on incongruent Stroop reaction time (p =

.002, partial η2 = .165).

Table 28 The effect of group on incongruent Stroop reaction time after adjusting for

premorbid IQ and central pulse pressure


Group .14 1 .14 11.03 .002 .165 WASI .02 1 .02 1.18 .281 .021 CPP .14 1 .14 11.37 .001 .169 Error .69 56 .01 Total 555.24 60


9.4.6 Summary

Addressing the hypothesis that a relationship exists between arterial stiffness and cognitive

function it was revealed that arterial stiffness is related to executive function but not attention,

psychomotor speed, working memory or episodic memory. Increased augmentation index

related to slower performance on executive function as measured by Trail Making-B task in

the HF group but not controls. Furthermore, arterial stiffness as measured by central pulse

pressure was moderately related to worse performance on Trail Making-B in HF patients but

not in controls. Interestingly, higher central pulse pressure was associated with worse

performance on the incongruent Stroop reaction time task in both experimental groups.


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9.5 Relationships between cognitive performance and oxidative stress, antioxidant and

inflammatory markers

9.5.1 Introduction

The following section will test the hypothesis that oxidative stress, antioxidants,

inflammation and omega-3 dietary intake will be related to cognitive tests measuring

attention, psychomotor speed, working memory, episodic memory and executive function

(H14). Simple regressions using Pearson’s correlations for normally distributed and

transformed variables and spearman’s correlations for non-normally distributed variables

were conducted to initially explore whether relationships exist between cognitive measures

and biomarkers. Correlation coefficients between oxidative stress, antioxidants and

inflammation and cognitive outcome measures are presented in Table 29. Only relationships

between cognitive measure and biomarkers that displayed significant differences between

experimental groups are reported.

9.5.1.1 Global cognition in heart failure

Spearman’s correlation coefficient was used to detect whether relationships exist between

MMSE and oxidative stress. As displayed in Table 29, global cognition as measured by

MMSE was not significantly correlated with measures of oxidative stress (DROM, F2-

isoprostanes), antioxidants (glutathione peroxidase, CoQ10) or inflammation (hs-CRP) or

omega-3 dietary intake (PUFA). However, there was a trend for higher levels of

hydoperoxides as measured by DROM to be related to better scores on the MMSE in HF

(r(26) = .348, p = .069).


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Table 29 Correlation coefficients between oxidative stress, antioxidant and inflammatory markers with cognitive measures in each experimental group

Variable Oxidative Stress Antioxidant Inflammation and omega-3

Heart Failure Group DROM F2 GPx CoQ10 hsCRP PUFA

MMSE¥ .348 .195 .093 .058 -.066 .104

Attention Congruent Stroop .109 -.045 -.308 -.401* -.014 .063 Trail making-A -.044 -.136 .186 -.346* -.152 -.021 Power of Attention -.022 -.050 -.221 -.259 .012 .085

Continuity of Attention -.472* .060 .287 .214 -.035 .244

Memory Quality of Episodic Memory

-.025 .239 .083 .157 -.009 .203

Quality of Working Memory

-.146 .014 .104 .080 -.008 .019

Speed of Memory .282 -.067 -.205 .022 -.173 -.104 Executive Function

Incongruent Stroop .209 -.075 -.273 -.425* -.056 .047

Stroop effect .190 -.090 -.198 -.357* -.080 .057

Trail making-B .241 -.166 .139 -.305 -.193 -.097

Control Group DROM F2 GPx CoQ10 hsCRP PUFA

MMSE¥ .184 .131 -.282 -.147 .223 -.061

Attention Congruent Stroop -.235 -.249 .014 -.183 -.161 .024 Trail making-A -.013 .020 -.213 -.079 -.032 -.090 Power of Attention .031 -.238 -.195 -.158 .005 -.053

Continuity of Attention .432* .204 -.017 .000 .088 -.299

Memory Quality of Episodic Memory

.076 .196 -.169 .036 .125 -.307


.031 .287 .146 -.001 .233 -.031

Speed of Memory .001 -.132 -.176 -.086 .173 -.287 Executive Function

Incongruent Stroop -.063 -.322 .055 -.304 -.112 -.084 Stroop effect .067 -.252 .019 -.284 -.054 -.137 Trail making-B -.135 -.180 -.391* -.096 -.433* .279

Note: MMSE=Mini Mental State Examination; DROM=determinable reactive oxygen metabolites; F2=F2-isoprostanes; GPx=glutathione peroxidase; CoQ10=coenzymeQ10; hsCRP=high-sensitive C-reactive protein; PUFA=polyunsaturated fatty acid; * p < .05; ** p <.01; *** p < .001.


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9.5.2 Relationship between oxidative stress and cognitive function

9.5.2.1 Attention

As revealed in the previous chapter, patients had significantly higher levels of

hydroperoxides as measured by plasma DROM levels. To explore whether relationships exist

between DROMs and attention as measured by congruent Stroop reaction time and Power of

Attention cognitive domain in the HF group Pearson’s correlation coefficients were observed.

As presented in Table 29, no significant correlations were observed between DROMs and

congruent Stroop reaction time or Power of Attention cognitive domain in either

experimental group.

9.5.2.2 Memory

There were no significant relationships between Quality of Episodic Memory, Quality of

Working Memory or Speed of Memory and oxidative stress in either experimental group

(Table 29).

9.5.2.3 Executive function

Although HF patients had significantly higher levels of hydroperoxides as measured by

determinable reactive oxygen metabolites (DROMs), oxidative stress was not significantly

related to incongruent Stroop or Trail Making-B tasks of executive function in either

experimental group (Table 29).

9.5.2.4 Summary

In summary these findings suggests that oxidative stress is not related to reduced global

cognition, poor attentional abilities, impairments in episodic or working memory or executive

function in HF.

9.5.3 Relationship between antioxidants and cognitive function

9.5.3.1 Attention

As shown in the previous chapter, patients had significantly lower plasma levels of the

antioxidant CoQ10 compared to controls. To explore whether CoQ10 is related to attention as

measured by congruent Stroop reaction time and Power of Attention cognitive domain in the

HF group Pearson’s correlation coefficients were observed. As presented in Table 29, lower

CoQ10 levels in the HF group was associated with slower reaction times on the congruent

Stroop task, r(32) = -.401, p < .05, however this relationship was not observed in controls,


145

r(35) = -.183, p = .28. Scatterplots for the relationship between CoQ10 and incongruent

Stroop for each group are presented in Figure 10. No significant correlations were observed

between CoQ10 and Power of Attention cognitive domain in either experimental group. This

suggests that reduced antioxidant levels as measured by CoQ10 are related to poor reaction

time in HF.

Figure 10 Scatter plots of coenzyme Q10 and congruent Stroop in the HF and control groups

Since CoQ10 was significantly correlated with congruent Stroop reaction time in HF, an

ANCOVA [between-subjects factor: Group (HF, controls); CV: WASI, CoQ10] was

conducted to examine the effect of CoQ10 on congruent Stroop reaction time. The Levene’s

test for homogeneity of variance, normality of sampling distributions, homogeneity of

regression and reliability of covariates were satisfactory. As presented in Table 30, the

covariate WASI was not significantly related to congruent Stroop, F(1,67) = .014, p > .05,

partial η2 <.0001. The covariate CoQ10 was significantly related to congruent Stroop and had

a similar effect across groups, F(1,67) = 6.45, p < .05, partial η2 = .088. After adjusting for

WASI and CoQ10, the effect of group on congruent Stroop remained significant (p = .03).

Table 30 The effect of group on congruent Stroop after adjusting for premorbid IQ and CoQ10


Group .02 1 .02 4.95 .030 .069 WASI .00 1 .00 .01 .905 .000 CoQ10 .03 1 .03 6.45 .013 .088 Error .31 67 .00 Total 607.67 71

Note: WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset; CoQ10=coenzyme Q10.

r = -.401, p = .019 r = -.183, p = .278


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9.5.3.2 Memory

No significant relationships were observed between the cognitive domains Quality of

Episodic Memory, Quality of Working Memory or Speed of Memory and any of the

antioxidant biomarkers in either experimental group (Table 29).


Pearson’s correlation coefficients were observed to explore whether CoQ10 was related to

executive function as measured by incongruent Stroop reaction time and Trail Making-B in

HF group (Table 29). Performance on incongruent Stroop reaction time task was significantly

related to lower plasma levels of the antioxidant CoQ10 in the HF group (r(32) = -.425, p <

.05) but not in controls (r(35) = -.304, p = .07). Scatterplots for the relationship between

CoQ10 and incongruent Stroop for each group are presented in Figure 11. CoQ10 was not

significantly related to Trail Making-B task performance in either experimental group.

Figure 11 Scatter plots of coenzyme Q10 and incongruent Stroop in the HF and control groups

Since CoQ10 was correlated with incongruent Stroop reaction time, CoQ10 was included as a

covariate in the model to examine whether it accounted for some of the variance in the IV.

An ANCOVA [between-subjects factor: Group (HF, controls); CV: WASI, CoQ10] was

conducted to examine whether CoQ10 accounted for some of the group variance seen in the

IV. The Levene’s test for homogeneity of variance, normality of sampling distributions,

homogeneity of regression and reliability of covariates were satisfactory. As presented in

Table 31, the covariate WASI was not significantly related to incongruent Stroop, F(1,67) =

r = -.304, p = .068r = -.425, p = .012


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.259, p > .05. However, CoQ10 had a significant similar effect across groups on incongruent

Stroop task performance (F(1,67) = 9.86, p < .01, partial η2 = .128). After adjusting for

CoQ10 and WASI, there was no effect of group on incongruent Stroop, F(1,67) = 2.66, p =

.11, partial η2=.038. This suggests that CoQ10 may influence performance on executive

function as measured by incongruent Stroop task.

Table 31 The effect of group on incongruent Stroop after adjusting for premorbid IQ and CoQ10

Source SS df MS F p partial η2 Group .04 1 .04 2.66 .107 .038 WASI .00 1 .00 .26 .613 .004 CoQ10 .14 1 .14 9.86 .003 .128 Error .94 67 .01 Total 660.15 71 Note: WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset; CoQ10=coenzyme Q10.

9.5.3.4 Summary

In this investigation MMSE scores did not relate to any of the antioxidant biomarkers in

either of the experimental groups. Worse performance on attention and psychomotor function

as measured by congruent Stroop and Trail Making-A were related to reduced plasma CoQ10

levels in HF patients but not in controls. This suggests that lower antioxidants in the form of

CoQ10, which are deficit in HF, are related to poor performance on attention tasks and

psychomotor speed. In addition, these findings suggest that plasma CoQ10 levels are not

related to attention and psychomotor speed in older healthy populations. There were no

significant correlations observed between Power of Attention and antioxidant markers.

9.5.4 Relationship between inflammation and cognitive function

9.5.4.1 Attention

Although HF patients had significantly higher levels of inflammation as measured by hs-CRP

and had greater units of daily dietary polyunsaturated fatty acid consumption, were not

significantly related to specific attention tasks as measured by congruent Stroop reaction time

or Power of Attention cognitive domain in either experimental group Table 29.


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9.5.4.2 Memory

No significant relationships were observed between Quality of Episodic Memory, Quality of

Working Memory or Speed of Memory and inflammatory measures or omega-3 dietary intake

in either experimental group (Table 29).


Although HF patients had significantly higher levels of inflammation as measured by hs-CRP

and dietary polyunsaturated fatty acid consumption, these inflammatory markers were not

significantly related to executive function as measured by incongruent Stroop and Trail

Making-B in the HF group (Table 29). Interestingly, in controls higher levels of inflammation

as measured by hs-CRP was related to better performance on Trail Making-B task r(21) =

-.433, p < .05.

9.5.4.4 Summary

In this investigation, systemic inflammation as measured and dietary polyunsaturated fatty

acid consumption did not relate significantly with measures of attention, Quality of Episodic

Memory, Quality of Working Memory, Speed of Memory or executive function in the heart

failure group.

9.6 Multiple regression analysis examining the effect of vascular, oxidative stress and

inflammatory predictors on cognitive function

9.6.1 Introduction

In the following section, multiple regression analyses were performed to further explore the

hypotheses (H13 H14) that vascular measures (H13), oxidative stress, antioxidants,

inflammation and omega-3 dietary intake (H14) will be related to cognitive tests measuring

attention, psychomotor speed, working memory, episodic memory and executive function.

Here multiple regressions were performed to explore the extent to which the vascular

measures and biomarkers predicted HF patients performance on cognitive measures, found to

be significant different between the two experimental groups. Cerebral blood flow, arterial

stiffness, oxidative stress, antioxidant and inflammatory measures were found to be

significantly related to measures of attention (congruent Stroop and Power of Attention) and

executive function (incongruent Stroop) were included as possible predictors in the multiple

regression models. Additionally, according to Cohen (1992) in order to achieve a large effect

size of .80 with two and three independent variables, a sample size of 30 and 34 in each


149

group is required, respectively. Although the total sample size in each experimental group

was sufficient for multiple regression analysis involving a maximum of three independent

variables, there was some data missing for the biomarkers. Consequently, the sample size in

this investigation is insufficient for performing multiple regression analyses. Therefore,

interpretation of multiple regression results is only exploratory and need to be made with

caution.

9.6.2 Attention tasks

9.6.2.1 Hierarchical multiple regression analysis examining vascular and antioxidants

predictors on congruent Stroop performance

To investigate how well the common carotid blood flow velocity, central pulse pressure and

CoQ10 variables predict congruent Stroop reaction time in the HF group, after controlling for

IQ, a hierarchical linear regression was computed. As CoQ10 and central pulse pressures

were significantly related and in order to meet the assumptions for multiple regression, two

separate multiple regression analyses were conducted to explore these variables separately to

explore CoQ10 and central pulse pressure separately as possible predictors.

In the first multiple regression analysis, the extent to which the common carotid blood flow

velocity and central pulse pressure variables predict congruent Stroop reaction time in the HF

group, after controlling for IQ was computed. The assumptions of linearity, normally

distributed errors and uncorrelated errors were checked and the model was considered to be a

good fit. Examination of multivariate outliers using Mahalanobis distance, based on a critical

χ2 at α = .001 for three degrees of freedom is 16.266, indicated there were no apparent

outliers in the data (Tabachnick & Fidel, 2007).

The hierarchical multiple regression summary is presented in Table 32. In model 1, when IQ

was entered alone, it did not significantly predict performance on congruent Stroop reaction

time, F(1,17) = .02, p > .05. When common carotid blood flow velocity was entered into the

model it significantly improved the prediction of congruent Stroop reaction time, R2 change =

.22, F(1, 16) = 4.64, p < .05, accounting for 22.5% of the variance of congruent Stroop (R2 =

.225).

When the arterial stiffness measure central pulse pressure was added to the model, the entire

group of variables showed significant improvement in predicting congruent Stroop reaction

time, R2 change = .421, F(1,15) = 5.07, p < .05, accounting for 42.1% of the variance of

congruent Stroop (R2 = .421). The beta weights and significance values, presented in Table


150

32, reveal that central pulse pressure contributes significantly to predicting performance on

congruent Stroop task.

Table 32 Hierarchical multiple regression analysis summary predicting congruent Stroop reaction time from common carotid blood flow velocity and central pulse pressure after controlling for premorbid IQ in the HF group

Model summary

Predictors R R2 ∆ R2 ∆ F df p

Model 1 .03 .00 .00 .02 1, 17 .896

Model 2 .48 .23 .22 4.64 1, 16 .047

Model 3 .65 .42 .20 5.07 1, 15 .040 Coefficients

B SE Β t p Model 1

Constant 2.94 .15 20.17 .000 WASI .00 .00 .03 .13 .896

Model 2 Constant 3.13 .16 19.64 .000 WASI -.00 .00 -.05 -0.24 .811 CCA-BFV -.01 .00 -.48 -2.15 .047

Model 3 Constant 2.53 .30 8.33 .000 WASI 33 .00 .03 .16 .876 CC-BFV -.01 .00 -.36 -1.69 .112 CPP .33 .15 .47 2.25 .040

Note: WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset; CCA-BFV=common carotid arterial blood flow velocity; CPP=central pulse pressure.

In the second multiple regression analysis, the extent to which the common carotid blood

flow velocity and coenzyme Q10 variables predict congruent Stroop reaction time in the HF

group, after controlling for IQ was computed. The assumptions of linearity, normally



χ2 at α = .001 for three degrees of freedom is 16.266, there were no apparent outliers in the

data (Tabachnick & Fidel, 2007).

The hierarchical multiple regression summary is presented in Table 33. In model 1, when IQ

was entered alone, it did not significantly predict performance on congruent Stroop reaction


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time, F(1,30) = .04, p > .05. When common carotid blood flow velocity was entered into the

model it significantly improved the prediction of congruent Stroop reaction time, R2 change =

.17, F(1, 29) = 6.08, p = .020, accounting for 17.5% of the variance of congruent Stroop (R2

= .175).

When the antioxidant CoQ10 was added to the model, the entire group of variables showed

an almost significant improvement in predicting congruent Stroop reaction time, R2 change =

.101, F(1, 28) = 3.90, p = .058, accounting for 27.5% of the variance of congruent Stroop (R2

= .275). The beta weights and significance values, presented in Table 33, reveal that carotid

blood flow velocity contributes significantly and there is a trend for CoQ10 to contribute to

predicting performance on congruent Stroop task.

Table 33 Hierarchical multiple regression analysis summary predicting congruent Stroop reaction time from common carotid blood flow velocity and CoQ10 after controlling for premorbid IQ in the HF group

Model summary


Model 1 .04 .00 .00 .04 1, 30 .834

Model 2 .42 .18 .17 6.08 1, 29 .020

Model 3 .53 .28 .10 3.90 1, 28 .058

Coefficients

B SE Β t p

Model 1

Constant 2.98 .10 29.43 .000

WASI -.00 .00 -.04 -.21 .834

Model 2

Constant 3.08 .10 29.82 .000

WASI -.00 .00 -.047 -0.28 .785

CCA-BFV -.01 .00 -.416 -2.47 .020

Model 3

Constant 3.06 .10 30.92 .000

WASI .00 .00 .05 0.29 .778

CC-BFV -.01 .00 -.34 -2.07 .048 CoQ10 -6.37E-005 .00 -.34 -1.97 .058

Note: WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset; CCA-BFV=common carotid arterial blood flow velocity; CoQ10=coenzyme Q10.


152

9.6.2.2 Hierarchical multiple regression analysis examining the effect of vascular predictors

on Power of Attention

To investigate how well common carotid blood flow and arterial stiffness as measure by

central pulse pressure predict Power of Attention in the HF group, after controlling for IQ, a

hierarchical linear regression was computed. The assumptions of linearity, normally



χ2 at α = .001 for three degrees of freedom is 16.266, revealed no apparent outliers in the data

(Tabachnick & Fidel, 2007). The hierarchical multiple regression analysis summary is

presented in Table 34. In model 1, when IQ was entered alone, it did not significantly predict

performance on Power of Attention cognitive domain, F(1,17) = .03, p > .05. When common

carotid blood flow velocity was entered into the model it did not significantly improve the

prediction of Power of Attention, R2 change = .18, F(1, 16) = 3.74, p > .05.

When the arterial stiffness measure central pulse pressure was added to the model, the entire

group of variables showed a significant improvement in predicting Power of Attention

performance, R2 change = .414, F(1, 15) = 16.82, p < .01, accounting for 63.1% of the

variance of Power of Attention performance (R2 = .631). The beta weights and significance

values, presented in Table 34, reveal that central pulse pressure significantly contributes to

predicting performance on Power of Attention cognitive domain.


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Table 34 Hierarchical multiple regression analysis summary predicting Power of Attention

from common carotid blood flow velocity and central pulse pressure after controlling for

premorbid IQ in the HF group

Model summary


Model 1 .18 .03 -.03 .03 1, 17 .455

Model 2 .47 .22 .18 3.74 1, 16 .071

Model 3 .79 .63 .41 16.82 1, 15 .001

Coefficients

B SE β t p

Model 1

Constant 1096.03 235.25 4.66 .000

WASI 3.01 3.93 .18 .77 .455

Model 2

Constant 1380.18 236.15 5.25 .000

WASI 1.72 3.71 .10 .46 .650

CCA-BFV -12.53 6.48 -.44 -1.93 .071

Model 3

Constant -59.56 397.52 -.15 .883

WASI 3.80 2.68 .23 1.42 .176

CCA-BFV -7.06 4.78 -.25 -1.48 .161 CPP 780.88 190.38 .68 4.11 .001


9.6.2.3 Summary

In summary, common carotid blood flow velocity significantly predicted performance and

coenzyme Q10 showed a trend towards predicting performance on congruent Stroop task in

the HF group. The arterial stiffness measure central pulse pressure significantly improved the

prediction of Power of Attention performance when premorbid IQ and common carotid blood

flow velocity were included in the model. These results suggest that common carotid blood

flow velocity, central pulse pressure and possibly coenzyme Q10 significantly predict

attentional abilities in HF patients.


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9.6.3.1 Hierarchical multiple regression analysis examining the effect of vascular and

antioxidant predictors on incongruent Stroop

To investigate how well the common carotid blood flow velocity, central pulse pressure and

CoQ10 variables predict incongruent Stroop reaction time in the HF group, after controlling

for IQ, a hierarchical linear regression was computed. Since CoQ10 and central pulse

pressures were significantly related to each other and in order to meet the assumptions for

multiple regression, two separate multiple regression analyses were conducted to explore if

these variables separately to explore CoQ10 and central pulse pressure separately as possible

predictors.

In the first multiple regression analysis, the extent to which the common carotid blood flow

velocity and central pulse pressure variables predict incongruent Stroop reaction time in the

HF group, after controlling for premorbid IQ was computed. The assumptions of linearity,

normally distributed errors and uncorrelated errors were checked and the model was

considered to be a good fit. Examination of multivariate outliers using Mahalanobis distance,

based on a critical χ2 at α = .001 for three degrees of freedom is 16.266, indicated there were

no apparent outliers in the data (Tabachnick & Fidel, 2007).

The hierarchical multiple regression analysis summary is presented in Table 35. In model 1,

when IQ was entered alone, it did not significantly predict performance on executive function

as measured by incongruent Stroop, F(1,17) = .12, p > .05. When common carotid blood flow

velocity was entered into the model it significantly improved the prediction of Incongruent

Stroop reaction time, R2 change = .33, F(1, 16) = 7.99, p < .05.

When the central pulse pressure was added to the model, the entire group of variables

showed significant improvement in predicting incongruent Stroop, R2 change = .18, F(1, 15)

= 5.74, p <.05, accounting for 52.1% of the variance of incongruent Stroop performance (R2 =

.521). The beta weights and significance values, presented in Table 35, reveal that carotid

blood flow velocity and central pulse pressure significantly contributes towards predicting

performance on incongruent Stroop performance in the HF group.


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Table 35 Hierarchical multiple regression analysis summary predicting incongruent Stroop from common carotid blood flow velocity and central pulse pressure after controlling for premorbid IQ in the HF group

Model summary


Model 1 .09 .01 -.01 .12 1, 17 .731

Model 2 .58 .34 .33 7.99 1, 16 .012

Model 3 .72 .52 .18 5.74 1, 15 .030

Coefficients

B SE β t p

Model 1

Constant 3.18 .23 14.00 .000

WASI .00 .00 -.09 -.35 .731

Model 2

Constant 3.55 .23 15.37 .000

WASI .00 .00 -.10 -.91 .373

CCA-BFV -.02 .01 -.58 -2.83 .012

Model 3

Constant 2.63 .43 6.10 .000

WASI .00 .00 -.11 -.57 .577

CC-BFV -.01 .01 -.46 -2.42 .029

CPP .50 .21 .45 2.40 .030


In the second multiple regression analysis, the extent to which the common carotid blood

flow velocity and CoQ10 variables predict incongruent Stroop reaction time in the HF group,

after controlling for IQ was computed. The assumptions of linearity, normally distributed

errors and uncorrelated errors were checked and the model was considered to be a good fit.

Examination of multivariate outliers using Mahalanobis distance, based on a critical χ2 at α =

.001 for three degrees of freedom is 16.266, indicated there were no apparent outliers in the

data (Tabachnick & Fidel, 2007).

The hierarchical multiple regression analysis summary is presented in Table 36. In model 1,

when IQ was entered alone, it did not significantly predict performance on executive function

as measured by incongruent Stroop, F(1,30) = .63, p > .05. When common carotid blood flow


156

velocity was entered into the model it significantly improved the prediction of incongruent

Stroop reaction time, R2 change = .17, F(1, 29) = 6.14, p < .05.

When the CoQ10 was added to the model, the entire group of variables showed an almost

significant improvement in predicting incongruent Stroop, R2 change = .093, F(1, 28) = 3.64,

p = .067, with a trend accounting for 28.5% of the variance of incongruent Stroop

performance (R2 = .285). The beta weights and significance values, presented in Table 36,

reveal that carotid blood flow velocity significantly contributes towards and there is a trend

for CoQ10 (p = .067) to significantly contribute towards predicting performance on

incongruent Stroop task in the HF group.

Table 36 Hierarchical multiple regression analysis summary predicting incongruent Stroop from common carotid blood flow velocity and CoQ10 after controlling for premorbid IQ in the HF group

Model summary


Model 1 .14a .02 -.02 .63 1, 30 .432

Model 2 .44b .19 .17 6.14 1, 29 .019

Model 3 .53c .29 .09 3.64 1, 28 .067

Coefficients

B SE β t p

Model 1

Constant 3.25 .20 16.16 .000

WASI .00 .00 .14 -.80 .432

Model 2

Constant 3.47 .21 16.87 .000

WASI .00 .00 -.152 -.91 .371

CCA-BFV -.01 .01 -.414 -2.48 .019

Model 3

Constant 3.43 .20 17.35 .000

WASI .00 .00 -.06 -.37 .717

CC-BFV -.01 .01 -.34 -2.09 .046 CoQ10 .00 .00 -.33 -1.91 .067

Note: WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset; CCA-BFV=common carotid arterial blood flow velocity; CoQ10=coenzyme Q10.


157

9.6.3.2 Summary

In summary, common carotid arterial blood flow velocity and central pulse pressure

significantly predict HF patient’s performance on executive function tasks as measured by

incongruent Stroop. Additionally, when included in a model with premorbid IQ and common

carotid arterial blood flow velocity, there is a trend towards coenzyme Q10 to predict

performance on incongruent Stroop performance in the HF group.

Chapter 10: Results – relationships between mood and biomarkers

158

CHAPTER 10 RELATIONSHIPS BETWEEN MOOD AND BIOMARKERS

10.1 Introduction

This chapter will explore whether relationships between the Profile of Mood States (POMS)

subsets (tension/anxiety, depression/dejection) and biomarkers (vascular, oxidative stress,

antioxidants and inflammation) exist in heart failure (HF). Simple regression analyses were

initially conducted for each experimental group to explore whether biomarkers were related

to the Profile of Mood States subtests. Significant relationships were further explored by one-

way between-subjects analysis of covariance (ANCOVA) to examine how much of the

variance between the mood outcome measures were accounted for by the physiological

measure.

If warranted, a series of multiple regression analyses were then performed to explore to what

extent possible biomarkers predicted mood in the HF group. In order to have a large effect

size in a multiple regression analysis with two independent variables, a sample size of 30 is

required per group (Cohen, 1992). Additionally, in order to achieve a large effect size of .80

with three independent variables, a sample size of 34 in each group is required. Although the

total sample size in each group was sufficient for multiple regression analysis involving a

maximum of three independent variables, there was some data missing for the biomarkers.

Consequently, the sample size in this investigation is insufficient for performing multiple

regression analyses. Interpretation of results is therefore exploratory and need to be made

with caution. For all analyses, relationships between variables were considered to be

statistically different if the p value was less than 0.05 using two tailed tests.

10.2 Relationships between mood and vascular function

10.2.1 Introduction

There were no specific hypotheses generated with relation to whether cerebral blood flow or

arterial stiffness has an effect on depression and anxiety in HF patients. Therefore the current

investigation explored the research question “is there a relationship between cerebral blood

flow or arterial stiffness and depressive symptoms and anxiety (R1)“. To investigate whether

blood flow velocity (common carotid and middle cerebral arterial) and arterial stiffness

(central pulse pressure and augmentation index) were related to depression/dejection and

tension/anxiety measures in HF patients simple regression analyses were initially conducted


159

on normally distributed and transformed variables. Pearson’s correlation coefficients for each

experimental group are displayed in Table 37.

Table 37 Correlation matrix for mood measures and blood flow velocities, arterial stiffness and vascular function

Variable Blood flow Velocity Arterial Stiffness Vascular function

Heart Failure Group CC MCA AIx CPP ET-1

Tension/Anxiety -.088 -.252 .306 -.057 -.357

Depression/Dejection -.197 -.227 .128 -.027 -.372

Control Group CC MCA AIx CPP ET-1

Tension/Anxiety .012 -.257 -.009 -.141 -.378

Depression/Dejection -.102 -.182 .080 .086 -.159

Note: CC=common carotid blood flow velocity; MCA=middle cerebral arterial blood flow velocity; AIx=augmentation index; CPP=central pulse pressure; ET-1=endothelin-1.

10.2.2 Results

As indicated in Table 37, none of the POMS mood subsets were significantly related to

carotid blood flow blood flow velocity in either experimental group.

No significant correlations were observed between measures of arterial stiffness

(augmentation index and central pulse pressure) or vascular function (endothelin-1) and

tension/anxiety or depression/dejection measures in either of the experimental groups.

10.2.3 Summary

Decreased cerebral blood flow as common carotid or middle cerebral arterial blood flow

velocity does not appear to be related to the tension/anxiety or depression/dejection POMS

subscales in HF patients or age matched controls. In addition, arterial stiffness as measured

by augmentation index and central pulse pressure does not appear to be associated

tension/anxiety or depression/dejection measures in either of the experimental groups.


160

10.3 Relationships between mood and oxidative stress

10.3.1 Introduction

There were no specific hypotheses generated with relation to whether oxidative stress

(DROM, F2-isoprostanes) or antioxidant measures (CoQ10, glutathione peroxidase) have an

effect depressive symptoms and anxiety in HF patients. Therefore the research question “is

there a relationship between oxidative stress or antioxidant measures and depressive

symptoms and anxiety (R2)?” was explored in the present investigation. Biomarkers shown

to be significantly different between groups will be reported.

To investigate the whether the oxidative stress marker DROM was related to mood measures

in HF patients simple regression analyses were initially conducted on normally distributed

and transformed variables. Pearson’s correlation coefficients for each experimental group are

displayed in Table 38.

10.3.2 Results

The POMS tension/anxiety or depression/dejection subscales were not related to DROM in

the HF group. Although no significant group differences were seen in lipid peroxides as

measured by F2-isoprostanes, interestingly higher plasma F2-Isoprostane levels were

significantly associated with decreased scores on POMS-tension/anxiety (r(34) = -.355, p <

.05) and POMS- depression/dejection (r(34) = -.332, p < .05) in the HF group. These

observations were not observed in controls (p > .05).


161

Table 38 Correlation matrix for mood measures and oxidative stress, antioxidant and


Variable Oxidative Stress Antioxidant Inflammation and

omega-3

Heart Failure DROM F2 GPx CoQ10 hsCRP PUFA

Tension/Anxiety .147 -.355* .051 .062 .051 -.160

Depression/Dejection .108 -.332* -.003 .031 .166 -.125

Controls

Tension/Anxiety .136 -.129 -.040 -.334* .168 .306

Depression/Dejection .227 -.298 .167 -.169 .152 .308

Note: DROM=determinable reactive oxygen metabolites; F2=F2-isoprostanes; GPx=glutathione peroxidase; CoQ10=coenzyme Q10; hs-CRP=high-sensitive C-reactive protein; PUFA=polyunsaturated fatty acid dietary consumption; * p < .05.

10.3.3 Summary

In the HF group higher scores on the tension/anxiety and depression/dejection subscales of

the POMS were moderately related to lower levels of lipid peroxidation as measure by F2-

isoprostanes in the HF group but not in controls.

10.4 Relationships between mood and antioxidants

10.4.1 Introduction

There were no specific hypotheses generated with relation to whether oxidative stress

(DROM, F2-isoprostanes) or antioxidant measures (CoQ10, glutathione peroxidase) are

associated with mood, in particular depressive symptoms and anxiety in HF patients.

Therefore the research question “is there a relationship between oxidative stress or

antioxidant measures and depressive symptoms and anxiety (R2)” was explored in the present

investigation were further explored. Biomarkers shown to be significantly different between

groups will be reported.

10.4.2 Results

To investigate the whether the antioxidant CoQ10 were related to mood measures in HF

patients simple regression analyses were initially conducted on normally distributed and

transformed variables. Pearson’s correlation coefficients for each experimental group are

displayed in Table 38. CoQ10 was not related to any of the mood measures in the HF group.


162

Interestingly, higher plasma CoQ10 levels were related to reduced scores on the POMS-

Tension/Anxiety subscale in controls (r(35) = -.334, p < .05).

10.4.3 Summary

Antioxidant levels were not related to mood measures in HF. However higher levels of the

antioxidant CoQ10 was related to lower levels of Tension/Anxiety in controls.

10.5 Relationships between mood, inflammation and dietary omega-3 intake

10.5.1 Introduction

There were no specific hypotheses generated with relation to whether inflammation (hs-CRP)

or dietary omega-3 PUFA intake is associated with mood measures in HF patient’s except

for depression where it was hypothesised that higher levels of inflammation as measured by

hs-CRP will be related to higher depression scores in HF.

10.5.2 Results

To explore whether inflammatory measures are related to mood in HF, Pearson’s correlation

coefficients were observed. Since no specific hypotheses were made with relation to whether

inflammatory measures have an effect on anxiety/tension HF patients, the research question

“is there a relationship between inflammatory measures and anxiety/tension, HF patients

(R3)” was explored in the present investigation. There were no significant relationships

between anxiety/tension or antioxidant markers as measured by hs-CRP and dietary PUFA

intake.

Additionally, it was hypothesised that inflammation as measured by high-sensitive C-reactive

protein (hs-CRP) will be related to depression scores in HF patients (H15). The hypothesis

that higher levels of inflammation as measured by hs-CRP will be related to higher

depression scores in HF was rejected as no significant relationships were observed, r(32) =

.166, p > .05.

10.5.3 Summary

Systemic inflammation as measured by circulating high-sensitive C-reactive protein levels

and increased dietary PUFA intake was not related to measures of tension/anxiety or

depression/dejection.

Chapter11: Discussion

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CHAPTER 11 DISCUSSION

11.1 Introduction

This chapter will first provide a summary of the main findings of this thesis. This will be

followed by a summary of the comparison between the ways in which the different groups

performed on each individual cognitive domain tested, followed by results from the mood

and quality of life measures. The chapter will then provide a summary of the group

differences observed on oxidative stress, antioxidant, inflammatory and vascular measures

followed by a summary of which biomarkers relate to the cognitive domains tested in this

investigation. Following this a summary of the biomarkers found to relate to mood measures.

This chapter will then outline the limitations and strengths of this thesis followed by

suggestions for future research. The chapter concludes with limitations and strengths of this

study and possible future research to stem from this thesis.

11.2 Summary of the main findings

This thesis investigated whether there are additional cognitive functions impaired in HF other

than those already described. The additional cognitive domains examined were Speed of

Memory, Power of Attention, Continuity of Attention, quality of episodic working and Quality

of Working Memory and these were tested using the well-validated Cognitive Drug Research®

(CDR) computerised test battery.

Table 39 presents a summary of the cognitive performance in the heart failure group

compared with controls. Participants completed cognitive tests known to be sensitive in heart

failure including the Stroop word-naming task (congruent Stroop and incongruent Stroop)

and paper pencil tests (Trail Making-A and Trail Making-B). In addition, participants

completed the Cognitive Drug Research® computerised test battery to assess cognitive

domains that may be impaired in HF. As expected, HF patients showed impairments on tasks

measuring selective attention and executive functioning, cognitive flexibility and response

inhibition. However, the two groups displayed similar scores on Stroop effect. In addition,

patients were not impaired on traditional measures of psychomotor abilities (Trail Making-A),

however, they were impaired on traditional executive function measures (Trail Making-B).

The results also indicated that impairments in cognitive domains seen in HF patients are

restricted to the Power of Attention, as determined by the sum of reaction time scores on

CDR individual tasks measuring sustained attention from the simple, digit vigilance and

choice reaction times. This suggests that Power of Attention is an additional cognitive domain

impaired in heart failure.


164

Table 39 Summary of the cognitive performance in the heart failure group compared with healthy controls

Variable Heart failure group

performance compared to controls

Cognitive measures

Attention Tasks

Congruent Stroop RT (ms) ↓ Congruent Stroop %Acc NS

Attention Domains

Power of Attention (ms) ↓ Continuity of Attention (ms) NS

Psychomotor Task

Trail Making-A (ms) NS Memory Domains

Quality of Episodic Memory (ms) NS Quality of Working Memory (ms) NS Speed of Memory (ms) NS

Executive Function

Incongruent Stroop RT (ms) ↓ Incongruent Stroop %Acc ↓ Stroop effect (ms) NS Trail Making-B (ms) ↓

Note: ↓=significantly reduced function compared to the control group; NS=no significant difference between the heart failure and control group; RT=reaction time; %Acc=percentage accuracy; ms=milliseconds.

Another aim was to examine whether reduced cognitive function in HF patients related to

decreased cerebral blood flow (common carotid arterial blood flow velocity, middle cerebral

arterial blood flow velocity) and elevated arterial stiffness (augmentation index, central pulse

pressures). A Transcranial Doppler instrument was used to record blood flow velocities from

participant’s common carotid and middle cerebral arteries. The results demonstrated that

compared to controls, blood flow velocity was reduced in the common carotid and middle

cerebral arteries of HF patients. The SphygmoCor® instrument was utilised to record indirect

measurements of arterial stiffness, augmentation index and central pulse pressures. The

results indicate that arterial stiffness as measured by augmentation index was not different

between experimental groups. However, central pulse pressure, an indirect measure of

arterial stiffness, was significantly lower in HF patients compared to controls.


165

This investigation also explored other physiological mechanisms related to cognitive

impairment in older HF patients. In particular, the aim was to explore whether an increase in

oxidative stress (DROM, F2-isoprostanes), reduced antioxidants (CoQ10, glutathione

peroxidase) and an increase in systemic inflammation (hs-CRP) can explain cognitive deficits

seen in HF. As expected, patients had higher levels of oxidative stress than controls as

measured determinable reactive oxygen metabolites (DROM). However, experimental groups

had equivalent levels of lipid peroxidation as measured by F2-isoprostanes. Additionally,

compared to controls, HF patients had lower levels of the antioxidant and cellular energiser

CoQ10. Although plasma levels of the enzyme antioxidant glutathione peroxidase was

similar for the two experimental groups. A summary of the direction of the biomarker results

in the heart failure compared with the control group is presented in Table 40.

Table 40 Summary of the biomarker values in the heart failure group compared with healthy controls

Variable Heart failure group values compared to

controls

Blood Flow Velocity (cm/s)

Common carotid ↓

Middle cerebral ↓

Central Pressures (mmHg)

Augmentation index NS Central pulse pressure ↓

Vascular function

Endothelin-1 (pg/mL) NS Oxidative Stress

DROM (Ucarr) ↑ F2-isoprostanes (pmol/L) NS

Antioxidants

Glutathione peroxidase (nmol/min/ml) NS CoQ10 (nmol/L) ↓

Inflammation and omega-3

hs-CRP (mg/L) ↑ PUFA (units/day) ↓

Note: ↑=significantly higher levels in heart failure patients compared to controls; ↓=significantly lower levels in heart failure patients compared to controls; NS=no significant difference between the heart failure and control group; DROM=determinable reactive oxygen metabolites; Ucarr=Carratelli Units; pmol/L=picomole per litre; nmol/min/ml=nanomole per minute per mililitre; CoQ10=coenzyme Q10; nmol/L=nanomole per litre; hs-CRP=high-sensitive C-reactive protein; mg/L=milligrams per litre; PUFA=polyunsaturated fatty acid.


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The relationships between cognitive measures, mood and biomarker variables that were

significantly different between the two experimental groups is summarised for the heart

failure group in Table 41. Global cognitive function as determined by MMSE scores was not

associated with augmentation index or central pulse pressure in either experimental group. In

addition, MMSE scores did not relate to cerebral blood flow, arterial stiffness, lipid

peroxidation (F2-isoprostanes), antioxidants (glutathione peroxidase, CoQ10), inflammation

(hs-CRP) or dietary PUFA intake.

Slower reaction times on attention tasks and Power of Attention were related to reduced blood

flow velocity in the common carotid artery in HF patients. These associations however were

not significant in controls. An increase in central pulse pressure, another indirect measure for

arterial stiffness, related moderately to poorer reaction times and Power of Attention in the

HF group. These findings suggest that reduced attention abilities in HF patients relate to a

decrease in common carotid arterial blood flow velocity and an increase in arterial stiffness

as measured by central pulse pressure. Additionally, reduced performance on measures of

attention and psychomotor speed were significantly related to lower plasma antioxidant

CoQ10 levels in HF patients but not in controls.

In addition, common carotid arterial blood flow velocity and CoQ10 accounted for some of

the between group variance on tasks measuring reaction time on attention tasks. Furthermore,

blood flow velocity in the common carotid artery and arterial stiffness as measured by

central pulse pressure accounted for the variance between groups on the Power of Attention

cognitive domain.

These findings suggest that reduced blood flow velocity on the common carotid artery and

plasma CoQ10 levels are a possible oxidative stress marker related to attention and

psychomotor speed. Inflammatory markers did not relate to tasks measuring attention in HF

or controls.

Better scores on Quality of Episodic Memory and Speed of Memory cognitive domains related

to faster common carotid arterial blood flow velocities in HF patients. There were no

significant relationships between tasks measuring Quality of Episodic Memory or Speed of

Memory and oxidative stress, antioxidants or inflammation in either experimental group.

Worse performance on traditional measures of executive function (incongruent Stroop,

Stroop effect) was related to slower blood flow velocity in the common carotid artery and

increased arterial stiffness in the HF group but not in controls. Furthermore, blood flow

velocity in the common carotid artery and antioxidant levels (CoQ10) accounted for the

variance between groups on tasks measuring executive function and response inhibition.


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Table 41 Summary of the relationships between cognitive and mood measures with biomarkers in the heart failure group.

Cerebral blood flow

velocity

Arterial stiffness

Oxid stress

Anti-oxidant

Infla omega-

3

Variable CC MCA AIx CPP DROM CoQ10 hsCRP PUFA

Mini Mental State Examination

- - - - - - - -

Attention

Congruent Stroop -ve - - +ve - -ve - -

Trail Making-A - - - - - -ve - -

Power of Attention -ve - - +ve - - - - Continuity of Attention

- - -ve - -ve - - -

Memory

Quality of Episodic Memory

+ve - - - - - - -


- - - - - - - -

Speed of Memory -ve - - - - - - -

Executive Function

Incongruent Stroop -ve - - +ve - -ve - -

Stroop effect -ve - +ve - - -ve - -

Trail making-B - - +ve +ve - - - -

Mood POMS-depression/dejection

- - - - - - - -

POMS-tension/anxiety - - - - - - - - Note: +ve=significant positive correlation between variables; -ve= significant negative correlation between variables; Oxid=oxidative; Infla=inflammation; CC=common carotid arterial blood flow velocity MCA=middle cerebral arterial blood flow velocity; AIx=augmentation index; CPP=central pulse pressure; DROM=determinable reactive oxygen metabolites; CoQ10=coenzyme Q10; hsCRP=high-sensitive C-reactive protein; PUFA=polyunsaturated fatty acid questionnaire.

There was no evidence indicating an association between middle cerebral arterial blood flow

velocity speed and performance on attention, memory or executive function domains in HF.

Furthermore, higher levels of the oxidative stress measures in this investigation were not

related to global cognition, attention abilities, Quality of Episodic Memory or Quality of

Working Memory or executive function in HF.

A final aim was to explore whether vascular function, oxidative stress, reduced antioxidant

capacity or an increase in systemic inflammation relate to mood measures. Participants


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completed the Profile of Mood States (POMS) a self-report Questionnaire that measures six

facets of mood tension-anxiety; depression-dejection; anger-hostility; vigour-activity;

fatigue-inertia; and confusion-bewilderment. As expected, HF patients scored significantly

higher than controls on each of these mood dimensions except for vigour-activity where HF

patients scored significantly higher than controls.

Arterial stiffness does not appear to be associated with mood in HF patients or controls.

Additionally, greater lipid peroxidation levels in the body were related to lower levels of

tension/anxiety and depression/dejection in HF patients.

Summary of main findings:

Cognitive measures

1. HF patients are impaired on tasks measuring selective attention and executive

functioning, cognitive flexibility and response inhibition compared to age and sex-

matched controls

2. HF patients and controls displayed similar scores on Stroop effect.

3. HF patients were not impaired on executive function measures (Trail Making-B) but

not impaired on psychomotor abilities (Trail Making-A).

4. HF patients performed significantly worse than controls on sustained attention as

determined by the Power of Attention cognitive domain.

Vascular measures

5. Blood flow velocity in the common carotid and middle cerebral arteries was

significantly reduced in HF patients compared to controls.

6. Arterial stiffness as measured by augmentation index was not different between the

HF and control groups. However, central pulse pressure, an indirect measure of

arterial stiffness, was significantly lower in HF patients compared to controls.

Oxidative stress, antioxidant and inflammatory measures

7. HF patients had higher levels of oxidative stress than controls as measured

determinable reactive oxygen metabolites and lower levels of the antioxidant and

cellular energiser CoQ10. Although plasma levels of the enzyme antioxidant

glutathione peroxidase and lipid peroxidation as measured by F2-isoprostanes was

similar for experimental groups.


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Associations between cognitive measures and physiological makers

8. Global cognitive function as determined by MMSE scores was not associated with

arterial stiffness (augmentation index, central pulse pressure), cerebral blood flow

(common carotid and middle cerebral arterial blood flow velocity), oxidative stress

(DROMs, F2-isoprostanes), antioxidants (glutathione peroxidase, CoQ10),

inflammation (hs-CRP) or dietary PUFA intake in either experimental group.

9. In heart failure (HF) patient’s slower reaction times on attention tasks and Power of

Attention cognitive domain related to reduced blood flow velocity in the common

carotid artery.

10. An increase in central pulse pressure, an indirect measure for arterial stiffness, was

moderately related to poorer reaction times and Power of Attention in the HF group.

11. Reduced performance on measures of attention and psychomotor speed was

significantly correlated with lower plasma antioxidant CoQ10 levels in HF patients

but not in controls.

12. In the HF group, common carotid blood flow velocity and CoQ10 accounted for

some of the between group variance on tasks measuring reaction time on attention

tasks. Furthermore, blood flow velocity in the common carotid artery and arterial

stiffness as measured by central pulse pressure accounted for some of the variance

between groups on the Power of Attention cognitive domain.

13. Better scores on Quality of Episodic Memory and Speed of Memory cognitive

domains related to faster common carotid arterial blood flow velocities in the HF.

Associations between depression, anxiety and physiological makers

14. No significant relationships between tasks measuring Quality of Episodic Memory or

Speed of Memory and oxidative stress, antioxidants or inflammation were observed in

either experimental group.

15. Worse performance executive function related to slower blood flow velocity in the

common carotid artery and increased arterial stiffness in the HF group but not in

controls.

16. Blood flow velocity in the common carotid artery and antioxidant levels (CoQ10)

accounted for the variance between groups on tasks measuring executive function

and response inhibition.

17. Greater lipid peroxidation levels in the body related to lower levels of tension/anxiety

and depression/dejection in HF patients.


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11.3 Demographics and clinical characteristics

The experimental groups were well matched on demographic variables. In particular, the two

groups did not differ significantly on the demographic variables of age and sex. Patients

however scored significantly lower on the premorbid IQ test, Wechsler Abbreviated Scale of

Intelligence vocabulary subset, and had significantly less years of education compared to

controls. Therefore group difference on cognitive outcome measures after adjusting for

premorbid IQ were compared with controls in order to account for this possible confounding

factor. Premorbid IQ did not make a significant difference to the attention, psychomotor and

memory measures. The HF group demonstrated significantly worse performance compared to

controls on Stroop effect and Trail Making-B, however these group differences on the

executive function tasks disappeared after adjusting for premorbid IQ. In addition to

premorbid IQ, multivariate analyses for cognitive variables adjusted for mood variables that

significantly related to the cognitive outcome measure in the current data.

11.4 Summary of cognitive measures

The following sections discuss whether global cognitive function and scores on attention,

memory and executive function were significantly different between heart failure and control

groups. Additionally an examination of whether the HF and control groups differed on

overall scores on the Power of Attention, Continuity of Attention, Speed of Memory, Quality

of Episodic Memory and Quality of Working Memory cognitive domains will be discussed.

An outline of the overall summary of the cognitive results will be included in this section.

11.4.1 Global cognition and screening for dementia

As a screening tool, the MMSE was utilised as a quick measure to exclude individuals with

possible signs of dementia. Although all participants in each the HF and control groups

scored 25 or above on the MMSE, HF patients scored significantly lower on the global

cognitive measure than controls. These findings are in line with earlier studies that reported

impairment in MMSE in elderly patients with severe HF (Almeida & Tamai, 2001b)

compared to controls with no HF (Trojano et al., 2003), and those with mild and moderate

HF (Incalzi et al., 2003). However, the findings from this thesis did not support the results of

other studies, which failed to find significant group differences between HF patients

(62.9±14.6 years; NYHA class I – IV) and younger controls (53.3±17.2 years; Pressler et al

2010b). A decline in MMSE scores is possibly associated with increasing disease severity in

elderly patients (Incalzi., 2003). Some researchers have suggested that other tools are more

sensitive to changes in global cognitive function. The application of the Montreal Cognitive


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Assessment battery (MoCA) as a screening tool for cognitive dysfunction and overall

assessment of global cognitive function in HF has increased in recent years. The MoCA has

been shown to be better over the MMSE in detecting cognitive impairments in HF (e.g.

Cameron et al., 2009) and although the aim of this study was to utilise the MMSE purely as a

screening tool for dementia, future studies may also consider the MoCA.

Since global cognitive function provides an overall assessment of cognitive function, it does

not provide an assessment of which cognitive domains are impaired. Therefore, the aim of

this thesis was to examine the relationships between specific cognitive domains and

physiological mechanisms for cognitive decline in HF. In this investigation, MMSE scores

did not relate to any of the oxidative stress, antioxidant, inflammatory or vascular biomarkers

in either of the different groups. If significant relationships between specific cognitive facets

and biomarkers existed and the measure for cognitive function in this study was exclusively

global cognition, then this would have resulted in a Type I error.

Additionally, this investigation explored whether attention, Quality of Working Memory,

Quality of Episodic Memory and executive function are related to oxidative stress,

antioxidant capacity, inflammation and vascular measures. Moreover, the current

investigation explored whether additional cognitive domains that included Power of

Attention, Speed of Memory, Quality of Working Memory, Quality of Episodic Memory and

Continuity of Attention are impaired in HF patients and if so whether they are related to the

biomarkers tested.

11.4.2 Attention and psychomotor speed

The results from this investigation support the hypothesis that HF patients would perform

worse than controls on attention tasks as measured by congruent Stroop task, Power of

Attention and Continuity of Attention compared to controls (H1) was partially supported. As

predicted, after adjusting for WASI-vocabulary scores, HF patients’ mean reaction time on

the congruent Stroop task was significantly slower than healthy controls. However, the HF

patient’s (98%) were as accurate as controls (98%) on the congruent Stroop task

performance. These findings support previous studies, which indicated that HF patients

display reduced performance on attention tasks compared to healthy controls (Sauvé et al,

2009). However, unlike Sauvé et al (2009), who observed HF patients to have higher error

rates on attention tasks compared to controls, this study observed similar error rates on

congruent Stroop tasks between the two experimental groups. Power of Attention derived by

the average reaction times on the simple reaction time, digit vigilance and choice reaction

time individual CDR tasks was different between different groups. HF patients overall


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reaction time performance on the Power of Attention cognitive domain was 6% longer than

controls even after controlling for Premorbid IQ (1270 ms versus 1192 ms). Examining the

individual CDR tasks that define the Power of Attention domain, HF patients had

significantly longer reaction times than controls on the simple 9.8% longer (315ms versus

284ms), digit vigilance 4.5% longer (444ms versus 426ms) and choice 5.7% longer (511ms

versus 482ms) reaction time tasks. This indicates that HF patients have an impaired ability to

focus attention during a short period requiring extreme concentration. These findings support

previous research that found reduced ability to sustain attention in HF patients compared to

cardiovascular controls using the attention matrices tasks (Trojano et al., 2003). However,

summing multiple reaction time tasks enables a better representation of patients’ performance

on this cognitive domain. Adequate levels of attention and focus is required for information

to transfer into short and long-term memory.

The cognitive domain Continuity of Attention is determined by calculating the average

accuracy (%) scores on the choice reaction time and digit vigilance CDR tasks, subtracted by

the number of false alarms for digit vigilance task. This indicates that heart failure (HF)

patients and healthy controls make a similar number of errors when focussing on a task that

requires the ability to sustain attention over a prolonged period. As mentioned earlier, the HF

group and controls also had the same error rates for the congruent Stroop task. These findings

fail to support previous research that observed higher error rates on attention tasks in HF

patients compared to controls (Sauvé et al., 2009). Few studies however, have examined error

rates on measures of attention and vigilance in HF. Patients are required to have intense

concentration when listening and interpreting treatment advice given to them by their

physician and nurses. It seems likely that psychomotor control of elderly HF patients is intact.

However, these results suggest that patients may have an impaired ability to focus their

attention on tasks such as treatment advice given to them, which may in turn affect their

ability to remember these treatments.

The hypothesis that HF patients will perform significantly worse than controls on

psychomotor function (Trail Making-A; H2) was not supported. The results of this study

revealed that after adjusting for confounding variables premorbid IQ and POMS-

vigour/activity, the two groups performed equally on the on the psychomotor tasks (p = .49).

These findings support previous studies who also failed to find significant impairments on

psychomotor speed in elderly HF patients compared to patients without HF (Lavery et al.,

2007). However contrary to the present findings, other authors found impaired psychomotor

function in HF patients compared to healthy controls (Almeida, Garrido, et al., 2012;

Pressler, Subramanian, et al., 2010b), normative data (Bauer et al., 2011) and patients with a


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chronic condition other than HF (Pressler, Subramanian, et al., 2010b). These previous

studies differed to the present investigation in that they did not include a healthy age matched

control group (Bauer et al., 2011; Pressler, Subramanian, et al., 2010b) and included middle

aged as well as elderly participates (≥ 45 years; Almeida, Garrido, et al., 2012). Worse

performance on attention tasks and psychomotor speed increases with disease severity as

measured by NYHA class Bauer et al., (2011). Since NYHA class II was the predominant

disease classification for patients in this study, results suggest that elderly HF patients with

mild HF are impaired on attention domain but not psychomotor speed.

11.4.3 Quality of Episodic Memory, Quality of Working Memory and Speed of Memory

The results from the present study failed to support the hypothesis that HF patients will

perform significantly worse than controls on the Quality of Episodic Memory, Quality of

Working Memory and Speed of Memory cognitive domains (H3).

Quality of Episodic Memory:

Unexpectedly, after adjusting for possible confounding factors (premorbid IQ, POMS-Total

mood disturbance), this investigation did not observe differences between groups on the

Quality of Episodic Memory. These findings fail to support previous research demonstrating

worse performance on short-term, episodic (Hjelm et al., 2011) and visual memory (Brief

Visuospatial Memory; Sauvé et al., 2009) compared to controls without HF. However, in

support of previous trials, when examining the individual CDR tasks that reflect performance

on Quality of Episodic Memory, HF patients in the current study recalled less words than

controls on the immediate word recall task (M=5 verses M=6 out of a total of 15 words).

However, exploring additional individual CDR tasks that reflect performance on Quality of

Episodic Memory, HF patients in the current study recalled similar number of words as

controls on the delayed word recognition, picture recognition and immediate and delayed

word recall tasks. Supporting this, Lavery et al. (2007) did not show differences between an

elderly group of patients (≥ 65years) with (n=68; 78.8±7.2 years) and without HF (n=286;

77.2±6.5 years) on delayed recall, although similar to the current study worse performance on

visual immediate recall was reported in HF patients. Taken together, the results from the

present investigation suggest that patient’s resemble controls in terms of their ability to recall

verbal and pictorial information from episodic memory.


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Quality of Working Memory:

Surprisingly, after adjusting for possible confounding factors (premorbid IQ, POMS-Total

mood disturbance), this investigation did not observe differences between the HF and control

groups on the Quality of Episodic Memory. Examining the working memory tasks from

which the Quality of Working Memory cognitive domain is determined, revealed no

significant differences observed between the different groups on the spatial working memory

and numeric working memory CDR tasks. Findings from the present study fail support

previous researchers who found worse performance on working memory tasks compared to

age matched controls (Almeida, Garrido, et al., 2012; Beer et al., 2009; Hjelm et al., 2011;

Kindermann et al., 2012; Pressler, Subramanian, et al., 2010b; Sauvé et al., 2009). Previous

studies demonstrating impairments on working memory included HF patients with impaired

left ventricular ejection fraction (LVEF; e.g. Beer et al., 2009; Kindermann et al., 2012;

Sauvé et al., 2009). Additionally, the patient cohort in previous studies had greater HF

severity (e.g. Kindermann et al., 2012) and were older (> 80 years; Hjelm et al., 2011) than in

the present study. Since the current investigation did not record participants LVEF and were

classified predominantly as mild HF (NYHA class II), it is unknown how LVEF in the

current patient cohort compare to that of controls or participants in previous studies. Future

studies that replicate the present study may benefit from recording LVEF and sample a

greater number of patients with moderate and severe HF in order to examine whether disease

severity or LVEF are related to working memory abilities in HF.

Speed of Memory:

Refuting the hypothesis, patients did not exhibit worse performance in the Speed of Memory

domain compared to controls. Speed of Memory reflects the time it takes to recall information

from memory. Even after adjusting for possible confounding factors (premorbid IQ scores,

POMS-Total mood disturbance) no difference was observed between the groups on the Speed

of Memory. Calculating Speed of Memory involves averaging the speed of responses (msec)

on delayed picture and word recognition tasks, numeric working memory and spatial memory

tasks. When the individual CDR tasks that form the Speed of Memory cognitive domain, were

explored HF patients and controls performed similarly on the spatial, numeric and picture

recognition tasks.

However, patients required significantly longer to recognise words in the CDR delayed word

recognition task than controls, suggesting that patients are impaired in the time it takes to

recognise verbal information accurately from episodic memory, or to manipulate information

in working memory. HF patients and controls did not differ significantly in the CDR picture


175

recognition task. This suggests that HF patients may be impaired in their ability to recognise

verbal information but not pictorial information stored in episodic memory. Comparing

results from the present study with other research is not possible since this is the first study to

measure Speed of Memory in heart failure patients. Taken together, findings from the current

investigation suggest that HF patients are similar to healthy age-matched controls in terms of

the Speed of Memory cognitive domain, although examining individual cognitive tasks their

ability to recall verbal information from episodic memory is impaired.

11.4.4 Executive Function

It was hypothesised that patients will perform worse than controls on tasks of executive

functioning as measured by incongruent Stroop, Stroop effect and Trail Making-B.

Supporting the hypothesis, HF patient were 16.8% slower at completing the Trail Making-B

task compared to controls (107 ms verses 89 ms). Additionally, HF patients’ mean reaction

time was 19.7% slower on the incongruent Stroop task (M=132 sec vs M=106 sec) even after

controlling for premorbid IQ. These results are consistent with previous findings

demonstrating that HF patients perform worse than healthy controls on the Trail Making-B

task (Lavery et al., 2007; Pressler, Subramanian, et al., 2010b), in patients diagnosed with a

condition other than HF (Pressler, Subramanian, et al., 2010b), and in patients with

cardiovascular disease (Hoth et al., 2008). However, the current results do not support

previous studies that showed no impairment in Trail Making-B task performance in HF

patients compared to normative data (Bauer et al., 2011). More recently, Bauer et al., (2011)

showed that increased disease severity as measured by NYHA functional class was

significantly related to poorer executive function performance (Trail Making-B). However,

due to the small sample size and unequal number of participants with mild, moderate and

severe HF classifications in the present investigation, the existence of any relationship

between executive function and disease severity could not be determined.

Few studies have reported data regarding HF on the Stroop effect (or Stroop interference).

The results of the current investigation indicate that after controlling for premorbid IQ, the

groups exhibited a similar Stroop effect. These findings support those of Kindermann et al.

(2012) who also did not observe group Stroop effect differences between patients with stable

HF and healthy controls. Similarly, studies using normative data also did not find

impairments in executive function in HF patients (Bauer et al., 2011). However, one study

that compared HF patients to a healthy control group did find impairments in the patient

group (Lavery et al., 2007). It is possible that in the current study, the computerised

congruent Stroop and incongruent Stroop tasks were not demanding enough to detect


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impairment. Taken together these findings suggest that a measure suitable for detecting

executive function impairments in HF is Trail Making-B task.

The next section discusses findings from the present investigation related to whether the heart

failure (HF) and control groups differed on cerebral blood flow, arterial stiffness, oxidative

stress, antioxidant and inflammatory biomarkers. Additionally, this section will cover an

overall summary of the relationships between each of the vascular, oxidative stress,

antioxidant and inflammatory variables.

11.5 Summary of vascular measures

11.5.1 Cerebral blood flow

The hypothesis that HF patient group would exhibit lower cerebral blood flow velocity as

measured by common carotid and middle cerebral arterial blood flow velocity compared to

the control group (H6 ) was supported. Mean blood flow velocity in the left common carotid

artery was significantly slower in patients (17±4.9 cm/s), compared to healthy controls

(22±3.9 cm/s). Furthermore, mean blood flow velocity in the left middle cerebral artery was

slower in the HF group (50±6.7 cm/s), compared to healthy controls (56±10.9 cm/s). These

findings corroborate those of Vogels et al. (2008), who demonstrated a similar reduction in

middle cerebral artery blood flow velocity as measured by Transcranial Doppler in HF

patients (47.3±10.7 cm/s) compared to healthy controls (56±10.9 cm/s). Carotid arterial

blood flow velocities in HF patients on the other hand have not been widely studied.

Cardiovascular risk factors have been shown to predict reduced pulsatile blood flow velocity

in both the common carotid and middle cerebral arteries of healthy elderly individuals (Pase

et al., 2012). Vogels and colleagues have suggested that reduced cerebral blood flow in HF

patients may be due to risk factors shared by patients with cardiovascular disease, rather than

poor cardiac output (Vogels et al., 2008). Given that HF patients display cardiovascular risk

factors, it is possible that slower blood flow in the common carotid and middle cerebral

arteries in HF is due to risk factors associated with cardiovascular disease. Results of the

current study may indicate that although both elderly and HF patients may possess

cardiovascular disease risk factors, greater cardiovascular disease risk factors in HF patients

may be related to reduced common carotid and middle cerebral arterial blood flow velocity

in HF patients.


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11.5.2 Arterial stiffness

The current investigation partially supported the hypothesis that HF patients would exhibit

increased arterial stiffness as measured by the augmentation index and central pulse

pressures compared to the control group (H7). Compared to controls, HF patients had reduced

arterial stiffness measures as determined by central pulse pressure but not on the

augmentation index. A reduction in central pulse pressure in the heart failure group is likely

due to lower cardiac contractility in heart failure patients. These findings do not support

previous observations demonstrating that HF patients have elevated central and peripheral

pulse pressures compared to patients without HF (Mitchell et al., 2001). However, these

findings do support recent trials that observed lower arterial stiffness measures in HF patients

with left ventricular systolic dysfunction compared with healthy controls (Denardo et al.,

2010) and those with preserved left ventricular ejection fraction (Tartière et al., 2006). In

these studies however, arterial stiffness was determined by different means, either indirectly

by recording augmentation index from the common carotid artery (Tartière et al., 2006) or

via carotid-femoral pulse wave velocity (Denardo et al., 2010). Also the central aortic

pressure wave has been determined from recordings of the radial artery in patients with

severe left ventricular dysfunction (Denardo et al., 2010). Although augmentation index is a

suitable measure in a clinical setting, based on findings from the current investigation it may

not be the optimal measure to use for detecting arterial stiffness in HF patients with mild

disease severity. Central pulse pressure may be a more sensitive measure to use in clinical

settings to detect arterial stiffness in HF patients. Interestingly, significant relationships were

not observed between cerebral blood flow and arterial stiffness measures. The present

findings suggest that HF patients in the present study may have elevated arterial stiffness as

an indication of increased peripheral resistance, however given that the current sample size

larger trials examining the validity of using augmentation index measurements with carotid-

femoral pulse wave velocity in heart failure patients are required.

11.5.3 Vasoconstriction: endothelin-1

In contrast to the prediction that HF patients will have higher plasma levels of the

vasoconstrictor endothelin-1 compared to controls (H8), heart failure (HF) patients and

controls had similar plasma endothelin-1 concentrations. These results are in contrast to

previous findings demonstrating elevated levels of the vasoconstrictor endothelin-1 in HF

compared to controls (Jackson et al., 2000). The findings from this study suggest that

vasoconstriction in HF patients was not significantly different to that of healthy age-matched

controls. Given that raised endothelin-1 levels are principally seen in patients with severe and

not mild HF (Wei et al., 1994), the present findings which involved predominantly mild heart


178

failure patients, are therefore not surprising. Due to the low sample size and uneven

participant numbers classified with mild, moderate and severe HF there was inadequate

power to explore the difference in endothelin-1 across disease severity. Previous studies have

revealed that endothelin-1 relates to increased pulse wave velocities, augmentation index and

reduced cardiac output (Vuurmans et al., 2003). This was not observed in the present study

suggesting that increased arterial stiffness does not relate to endothelin-1 in patients with

mild heart failure.

11.6 Summary of oxidative stress measures

The current investigation partially supports the hypothesis that patients would exhibit

significantly higher levels of oxidative stress, as measured by determinable reactive oxygen

metabolites (DROMs) and lipid peroxides (F2-isoprostanes), compared to the control group

(H9). As expected, compared to controls HF patients had higher plasma levels of DROMs,

indicating higher levels of hydroperoxides in the patient group. These findings support

preliminary data indicating that higher plasma DROM concentrations are present in elderly

HF patients compared to healthy age matched controls, and that DROMs levels increased

with increasing age (Rosenfeldt et al., 2013). These results indicate that DROMs are an

additional oxidative stress marker impaired in HF.

Surprisingly, there were no group differences for lipid peroxidation as measured by plasma

F2-isoprostanes concentrations. These findings do not support earlier observations by

Polidori et al. (2004) who showed elevated F2-isoprostanes levels in older HF patients with

moderate to severe HF compared to age matched controls (Polidori et al., 2004). The absence

of significant group differences for F2-isoprostanes in the present study may be due to the

patient cohort diagnosed predominantly with mild heart failure and increased lipid peroxides

may be present in patients with greater disease severity. Results from the present

investigation suggest that patients with predominantly mild heart failure have high levels of

oxidative stress in the form of hydroperoxides but not lipid peroxides.

11.7 Summary of antioxidant measures

It was hypothesised that HF patients would have significantly lower levels of plasma

antioxidants as measured by Coenzyme Q10 (CoQ10) and glutathione peroxidise compared

to the control group (H10). As anticipated, plasma CoQ10 levels were significantly lower in

HF patients compared to age-matched controls, supporting previous investigations (Folkers et

al., 1992; Keogh et al., 2003). CoQ10 has multiple roles in the body including production of

energy in the form of adenosine triphosphate, enhancing the immune system and recycling


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other antioxidants including ascorbic acid and α-tocopherol (Boreková et al., 2008).

Furthermore, randomised trials have demonstrated that supplementation with CoQ10 results

in elevating previously deficient levels of CoQ10 in patients with HF (Folkers et al., 1992;

Keogh et al., 2003). Additionally, improvements in exercise capacity, decreases in disease

severity (Keogh et al., 2003) and reductions in hospital stay duration (Morisco et al., 1993)

have been observed in HF patients taking CoQ10 supplements.

Surprisingly, heart failure (HF) patients and healthy controls did not differ on observed levels

of the lipophilic antioxidant biomarker glutathione peroxidase. These findings are

inconsistent with previous studies that demonstrated reduced glutathione peroxidase activity

in HF patients compared to healthy controls (Keith et al., 1998; Polidori et al., 2004).

Previous studies have also shown that higher levels of the lipid peroxidation measure F2-

isoprostanes were associated with reduced circulating antioxidant biomarkers including

glutathione peroxidation in HF patients compared to controls (Polidori et al., 2004). Since the

present study also did not find that HF patients and controls differed on F2-isoprostanes, it is

possible that patients did not have high levels of oxidative stress and in turn reduction in

glutathione peroxidase activity. The HF cohort of the current study included only 5 patients

with moderate (NYHA class III) and one patient with severe (NYHA class IV) disease

severity with the remaining 30 patients (83%) classified mild HH (NYHA class II).

Therefore, due the HF group in the present study predominantly classified as mild HF, any

significant differences between oxidative stress and antioxidant biomarkers were not

detected. It is probable that higher levels of oxidative stress and reduced antioxidant

biomarkers would be observed in a sample with greater disease severity.

11.8 Summary of inflammation and dietary omega-3

As expected, HF patient’s systemic inflammation as measured by high-sensitive C-reactive

protein levels and dietary polyunsaturated fatty acid (PUFA) intake were significantly

elevated in HF patients compared to controls (H11). These findings support the well-

established evidence that CRP levels are elevated in HF patients and are a reliable biomarker

used in clinical practice as a reflection of disease severity (Xue et al., 2006). Moreover, HF

patients consumed less dietary omega-3 foods than controls three months prior to baseline

testing supporting the prediction that patients will have consumed higher dietary PUFA

compared to controls. These findings support previous work indicating that omega-3 PUFA

supplementation is related to decreased inflammatory markers in HF patients (Nodari et al.,

2011). Supplementing with omega-3 PUFAs in the form of fish oils has been shown to

decrease serum inflammatory markers (TNF-α, IL-1 and IL-6) in HF patients (Nodari et al.,


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2011), improve mortality, decrease hospital admissions (Tavazzi L et al., 2008), improve

NYHA class and improve exercise capacity in HF patients. Taken together these findings

suggest that HF patients have higher levels of inflammation compared to controls.

11.9 Summary of the relationships between vascular, oxidative stress and


The following section discusses how the results from each of the vascular, oxidative stress,

antioxidant and inflammatory biomarkers examined relate to each other in HF patients and

controls. Considering how these biomarkers relate to each other in the current investigation

may help understand mechanisms underlying cognition impaired and mood in the following

sections.

11.9.1 Vascular measures and oxidative stress, inflammation and antioxidant biomarkers

The results of the current study suggest that cerebral blood flow velocity might not relate to

markers of oxidative stress, antioxidant and inflammatory markers in HF patients or healthy

controls, and similarly endothelin-1 did not relate to arterial stiffness or cerebral blood flow

measures. Previous researchers propose that a function of endothelin-1 is to elevate arterial

stiffness as measured by augmentation index and pulse wave velocity (Vuurmans et al.,

2003). Given that the different groups in the present study did not differ on the

vasoconstrictor endothelin-1 and augmentation index, it is possible that endothelin-1 levels in

the HF group did not influence elevation of arterial stiffness. Additionally, previous studies

have demonstrated that elevated endothelin-1 levels are associated with a poorer prognosis

for HF patients (Pousset et al., 1997). It is possible that elevated vasoconstriction as measured

by endothelin-1 level was not observed in the current patient cohort as majority of patients

did not have severe HF and perhaps their overall prognosis may have been more positive than

previous studies. Longitudinal studies are recommended to further explore this hypothesis.

Additionally, elevated endothelin-1 levels are seen predominantly in patients with severe but

not mild HF (Wei et al., 1994). Given that 80% of the patients in the current study were

NYHA class II it is possible that the present cohort were not severe enough to present with

vasoconstriction.

In the present study, augmentation index did not relate to antioxidant or oxidative stress

markers. However higher arterial stiffness as measured by central pulse pressure was

moderately related to lower antioxidant levels in HF patients (glutathione peroxidase and

coenzyme Q10). Additionally, the findings from the present study revealed that in HF

patients, elevated inflammation and reduced antioxidants were related to increased arterial


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stiffness as measured by central pulse pressure. Moreover, the level of high-sensitive C-

reactive protein (hs-CRP) was related to increased central pulse pressure in HF patients.

These findings are in line with previous studies which demonstrated that carotid arterial

stiffness was related to higher levels of systemic inflammation as measured by C-reactive

protein (CRP) but not the inflammatory markers Interkeukin-6 (IL-6) and tumor necrosis

factor-alpha (TNF-α) in a cohort of middle aged participants aged 45-59 years (Ellins et al.,

2008). The findings from the present study suggest that glutathione peroxidase activity and

coenzyme Q10 levels are associated with elevated arterial stiffness in HF patients.

11.9.2 Oxidative stress, inflammation and antioxidants

The current findings suggest that measures of oxidative stress (DROM; F2-isoprostanes) may

not be related to any of the vascular, antioxidant or inflammatory measures in either

experimental group. This is in contrast to an earlier study, which found that markers of

inflammation (tumour necrosis factor alpha), lipid peroxidation and decreased antioxidant

reserves are observed with increasing severity of oxidative stress (Keith et al., 1998). In the

present study, however levels of hydroperoxides as measured by F2-isoprostanes were not

elevated in HF patients and lipophilic antioxidants as determined by glutathione peroxidase

levels were not reduced in patients. This is in contrast to findings by Polidori et al. (2004)

who reported a negative relationship between F2-isoprostanes lipophilic antioxidant

biomarkers (e.g. glutathione peroxidase and super oxide dismutase). The current

investigation did not replicate this relationship between F2-isoprostanes and glutathione

peroxidase. In addition, levels of lipid peroxidation were higher in patients with greater

disease severity than those with less severe HF (Keith et al., 1998; Polidori et al., 2004). It is

therefore probable that high levels of lipid peroxidation were not observed in the current

investigation since the majority of patients had mild HF (Keith et al., 1998; Polidori et al.,

2004). Additionally this is possibly why there were no group differences on plasma

glutathione peroxidase activity. Since only five HF patients in the current were classified

with moderate HF (NHYA class III) and only one HF patient with severe HF (NYHA class

IV), there was insufficient power to assess whether biomarker levels varied as a function of

disease severity.

In the present study, enzymatic antioxidant levels as measured by glutathione peroxidise

activity were negatively related to hs c-reactive protein in HF patients. Additionally, in the

present study a trend towards an association between lower coenzyme CQ10 levels associated

and higher hs c-reactive protein in HF patients was observed. This suggests that reduced

antioxidant levels may be associated with elevated systemic inflammatory measures in older

HF patients. It is reasonable to suggest that reactive oxygen species, as measured by DROMs


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in the current study, trigger the release of inflammatory signalling molecules including c-

reactive protein, IL-1 and TNF-α, (Khaper et al., 2010; Mann, 2008; Valko et al., 2007).

However, given that no associations existed between the oxidative stress measures DROM

and F2-isoprostanes and any of the antioxidant biomarkers in either of the experimental

groups in the present investigation, further trials are required with larger sample sizes to

clarify these relationships. In particular, more elaborate statistical methods such as structural

equation modelling is recommended to better understand how each of these biomarkers are

interrelated in patients with HF.

11.10 Relationship between cerebral blood flow and cognitive function


The hypothesis that reduced cerebral blood flow velocity as measured by common carotid

and middle cerebral arterial blood flow will be related to poorer performance on cognitive

tests measuring global cognition, attention, psychomotor speed, working memory, episodic

memory and executive function in HF patients (H12) was partially supported. The outcomes

from the present study revealed that cerebral blood flow as measured by blood flow velocity

in the common carotid artery did not correlate with global cognitive scores. This finding

failed to support previous research that found that reduced common carotid arterial blood

flow was related to poorer global cognitive function in patients with mild-moderate carotid

arterial disease (Fu et al., 2012). The findings from the current investigation suggest that the

speed of blood flowing though the common carotid artery does not predict global cognitive

function in HF patients.

Further along the vascular path supplying blood to the cerebrum is the middle cerebral

artery. The hypothesis that higher MCA blood flow velocities will relate to better global

cognitive function was refuted by the results of this study. The findings support those of

Vogels et al. (2008) who also did not find a relationship between specific cognitive domains

and mean middle cerebral arterial blood flow velocity from both left and right sides. On the

contrary, findings from the present study fail to support those by Jesus et al. (2006) who

found that poor global cognition was related to decreased right middle cerebral arterial blood

flow in younger patients with HF. In the study by Jesus et al., (2006), HF patients with a

history of stroke, poor education and probable dementia (MMSE scores ranged from 3 to 30)

were recruited in the study. Pathological changes to the cerebrovascular system due to stroke

or dementia may have contributed to poor cerebral blood flow that study and not the HF

itself. Vogels et al. (2008) however failed to find a relationship between global cognitive

function and middle cerebral arterial blood flow velocity, however those authors included a


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more cognitively intact patient cohort with a mean MMSE score of 27.6 and excluded

patients with a history of stroke or prior diagnosis of dementia. A reason why the results from

the current investigation are more in line with Vogels et al. (2008) may be due to the adoption

of similar exclusion criteria. The current findings suggest that reduced blood supply to

temporal lobes and parietal lobes as measured by middle cerebral arterial blood flow

velocity does not affect global cognitive function in HF patients.

11.10.2 Attention

The prediction that cerebral blood flow would be related to attentional abilities and

psychomotor speed in HF patients (H12), was partially supported. In the present HF patient

cohort, slower reaction times on attention tasks (congruent Stroop reaction time) were

moderately associated with reduced blood flow velocity in the common carotid artery. These

findings suggest that slower cerebral blood flow is associated with lower attentional abilities

in elderly patients with HF. Contrary to expectation, psychomotor speed did not relate to

common carotid arterial blood flow velocity in either experimental group. These findings

suggest that attentional ability indicated by longer reaction times but not psychomotor speed

is associated with slower blood flow in the common carotid artery. Moreover, the present

investigation found that slower overall reaction times on the Power of Attention cognitive

domain were moderately associated with reduced common carotid arterial blood flow

velocity in HF patients but not in controls. These findings suggest that reduced ability to

sustain attention over a long period of time in HF patients may be explained by reduced

cerebral blood flow speed.

The prediction that middle cerebral arterial blood flow velocity would relate to attentional

abilities and psychomotor speed in HF patients (H12), was not supported. Contrary to

expectation, attentional abilities, psychomotor speed and Power of Attention domain were

unrelated to blood flow velocity the middle cerebral artery in both the HF and control

groups. These results suggest middle cerebral arterial blood flow velocity does not appear to

be related to attentional abilities in HF patients and controls. These findings support those of

Tanne et al. (2005) who found that improvements in attention (Stroop A, congruent Stroop)

and psychomotor speed (Trail Making-A) seen after an exercise program, were not due to

enhanced vasodilator reserve or middle cerebral arterial blood flow velocity in patients with

moderate HF. These findings did not support previous research where an association between

common carotid arterial blood flow velocity and global cognitive scores was observed (Fu et

al., 2012).


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Conversely, the present results support Vogels et al. (2008) who also failed to find an

association between mental speed and attention with mean cerebral blood flow velocities.

This is the first time that common carotid arterial blood flow velocity has been shown to

correlate with longer reaction times on attention tasks and a decreased ability to focus

attention over a long period in HF patients.

Multivariate analysis revealed that common carotid arterial blood flow velocity had a weak

effect across groups on attention as measured by congruent Stroop. Additionally, after

adjusting for premorbid IQ and common carotid arterial blood flow velocity, the main effect

of group on congruent Stroop was no longer significant. This suggests that after adjusting for

premorbid IQ, common carotid arterial blood flow velocity explained the difference between

groups on the attention task. Furthermore, after adjusting for premorbid IQ, there was a trend

between common carotid arterial blood flow velocities. After adjusting for premorbid IQ and

common carotid arterial blood flow, the main effects of group on Power of Attention

disappeared, suggesting that common carotid arterial blood flow velocity accounted for the

between group variance in the Power of Attention cognitive domain. These findings suggest

that the between group variance computed on attention might be explained by reduced

common carotid arterial blood flow velocity. Clinical applications may involve interventions

that can accelerate cerebral blood flow velocity to enhance HF patients’ ability to focus their

attention on information provided to them.

11.10.3 Memory

Cerebral blood flow velocity as measured by common carotid and middle cerebral arterial

blood flow was not related to Quality of Episodic Memory, Quality of Working Memory or

Speed of Memory domains in HF patients. While this investigation did not find that HF

patients were impaired on Quality of Episodic Memory, Quality of Working Memory or Speed

of Memory cognitive domains compared to healthy age matched controls, interpreting

relationships observed between these measures and vascular measures requires caution. The

results from the present study indicate that in HF patients, worse performance on Quality of

Episodic Memory was related to slower cerebral blood flow in the common carotid artery but

not the middle cerebral artery. Moreover, the current study revealed that in HF patients’

slower performance on Speed of Memory cognitive domain was related to reduced cerebral

blood flow in the left common carotid artery but not the left middle cerebral arteries. The

relationships observed between cerebral blood flow and Quality of Episodic Memory and

Speed of Memory domains were not present in controls. The present study failed to observe

relationships between cerebral blood flow and Quality of Working Memory domain in either


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experimental group. These results suggest that in HF patients, longer information retrieval

times from working memory and reduced accuracy in the ability to retrieve information from

episodic memory were related to reduced cerebral blood flow.

In the current study, relationships between middle cerebral arterial blood flow and cognitive

domains of Quality of Episodic Memory, Quality of Working Memory or Speed of Memory in

did not reach statistical significance in either HF patients or controls. These findings support

those of Vogels et al. (2008) who despite finding significant differences in cerebral blood

flow measures in HF patients and controls, there were no associations observed between

middle cerebral arterial blood flow speed and memory domains in HF patients (Vogels et al.,

2008).


The prediction that executive function will be related to reduced cerebral blood flow (H12)

was partially supported. Improved performance on the incongruent Stroop but not the Trail

Making-B tasks, measuring executive function, was related to reduced common carotid

arterial blood flow velocity. However, none of the executive function measures were related

to middle cerebral arterial blood flow velocity in either group. These findings support those

of Vogels et al. (2008) who also failed to demonstrate a relationship between executive

function and MCA blood flow velocities in HF patients. Furthermore, supporting the current

findings, Tanne et al. (2005), did not to find significant changes in middle cerebral arterial

blood flow velocity in older patients with moderate HF (NYHA class III) who underwent an

exercise program, despite improvements seen in executive function following. The findings

from the present study indicate that reduced inhibitory control and divided attention in older

HF patients is related to slower cerebral blood flow velocities.

11.11 Relationship between arterial stiffness and cognitive function

The current investigation explored the hypothesis that arterial stiffness will be related to

cognitive tests measuring attention, psychomotor speed, working memory, episodic memory

and executive function (H13). Since previous studies have not explored the relationship

between arterial stiffness and cognitive function in HF patients, it is difficult to interpret the

findings of the current study.


In the present study, global cognitive function was not associated with measures of arterial

stiffness (augmentation index, central pulse pressure) in either of the experimental groups.


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These findings fail to support previous studies that have shown that arterial stiffness as

measured by pulse wave velocity is related to poorer MMSE scores in older individuals

without cardiovascular disease (Fujiwara et al., 2005), older patients with cognitive

impairments (Alzheimer’s Disease, Mild Cognitive Impairment; Hanon et al., 2005), patients

with cardiovascular disease risk factors (Scuteri et al., 2005) and elderly individuals free from

dementia (Poels et al., 2007). The findings from the current investigation suggest that

although HF patients without a history of dementia have significantly reduced global

cognitive scores compared to healthy controls, reduced cognitive function may not be due to

microvascular ischemia in the brain because of arterial stiffness (Mitchell et al., 2001). Future

studies utilizing larger samples sizes are required to substantiate these findings.

11.11.2 Attention and psychomotor function

Slower reaction times on the attention tasks (congruent Stroop and Power of Attention)

correlated strongly with increased central pulse pressures, an indirect measure of arterial

stiffness, in the HF group but not controls. However, there were no associations apparent

between augmentation index and attention or psychomotor speed in either of the experimental

groups, suggesting that less precise measures of arterial stiffness via pulse pressures rather

than augmentation index possibly are related to attention. This suggests that reduced attention

abilities in HF patients may be related to an increase in arterial stiffness as measured by

central pulse pressure, supporting previous studies that did not find that attention and

psychomotor speed is subjected to changes in arterial stiffness (Waldstein et al., 2008). On

the contrary, these findings do not support previous studies that found arterial stiffness as

measured by carotid-femoral pulse wave velocity is related to worse performance on

scanning and tracking tasks (Elias et al., 2009). Future studies using sophisticated and more

reliable measures of arterial stiffness, such as carotid-femoral pulse wave velocities, may

afford better insight into whether arterial stiffness in HF is related to the ability to focus

attention.

11.11.3 Memory

Refuting the hypothesis (H13), arterial stiffness measures (augmentation index, central pulse

pressures) were unrelated to Quality of Episodic Memory, Quality of Working Memory and

Speed of Memory in both experimental groups. Since there are no studies that have examined

arterial stiffness in HF patients, the results from this study cannot be compared directly with

results from previous trials. Although indirectly supporting the current results, longitudinal

trials also failed to find associations between arterial stiffness (PWV) and working memory

(Elias et al., 2009), cognitive decline or dementia risk (Poels et al., 2007) in elderly


187

individuals. Conversely, it has been suggested that increased arterial stiffness is related to

lower memory scores in the elderly (Mitchell et al., 2011) and middle aged individuals (Pase

et al., 2010). Equally, the current findings indicate that reduced Quality of Episodic Memory,

Quality of Working Memory and Speed of Memory domains may not relate to arterial stiffness

and microvascular brain lesions caused by extreme pulsatile flow in the brain (Mitchell et al.,

2011) may not be a mechanism for memory impairment in HF patients. Additionally these

results suggest that since patients did not have elevated arterial stiffness, peripheral resistance

might not be a mechanism related to cognitive impairment in HF.


Supporting the hypothesis (H13), the present investigation revealed that increased

augmentation index, an indirect assessment of arterial stiffness, was correlated with slower

performance on executive function as measured by Trail Making-B task in the HF group but

not controls. Furthermore, arterial stiffness as assessed by central pulse pressure moderately

related to worse performance on Trail Making-B in HF patients but not in controls.

Interestingly, elevated central pulse pressure was associated with worse performance on

incongruent Stroop in both experimental groups. These findings support previous work

showing that arterial stiffness measurements (common carotid-femoral-pulse wave velocity

and carotid pulse wave velocity) correlate with incongruent Stroop in elderly community

based individuals without dementia (Poels et al., 2007). These results also support previous

findings in which a significant association was observed between elevated pulse wave

velocity and worse performance on executive function as measured by the Stroop test in

elderly community based individuals without dementia (Poels et al., 2007). Similarly, other

studies found an association between executive function and arterial stiffness (CF-PWV,

carotid PP, PWV) in elderly individuals (Elias et al., 2009; Mitchell et al., 2011). On the

contrary, other researchers did not demonstrate a relationship between executive function and

arterial stiffness (Waldstein et al., 2008). The current findings suggest that elevated measures

of arterial stiffness are associated with reduced cognitive flexibility, divided attention ability,

and slower inhibitory responses in HF patients. Since these observations were also observed

in healthy controls, it is possible that the negative effects of arterial stiffness on executive

function are due to the natural ageing process and not unique to heart failure. It is possible

that microvascular lesions due to extreme pulsatile flow in the brain regions (Mitchell et al.,

2011) are a possible mechanism related to executive function impairments in HF patients.


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11.12 Relationship between oxidative stress and cognitive function

The hypothesis that oxidative stress, antioxidants, inflammation and omega-3 dietary intake

will be related to cognitive tests measuring global cognition, attention, psychomotor speed,

working memory, episodic memory and executive function (H14) was partially supported.


Using simple regression models, it was found that global cognition as measured by the

MMSE was not related to antioxidant capacity, oxidative stress, inflammation or omega-3

dietary intake in either the heart failure (HF) or control groups. These findings suggest that a

relationship does not exist between global cognitive function and the biomarkers. This

finding supports previous research reporting no association between oxidative stress and

antioxidant biomarkers on MMSE scores, in elderly patients with cognitive impairments such

as mild cognitive impairment or dementia (Gironi et al., 2011). The findings from the present

study support those of Torres et al. (2011) who demonstrated that higher levels of lipid

peroxidation (MDA) and low enzymatic antioxidant defences (as measured by glutathione

reductase/glutathione peroxidase ratio) are not related to poorer global cognitive function in

patients with MCI patients or healthy controls. The results from the present study suggest that

global cognitive function may not be related to increased oxidative stress markers and a result

of increased free radical production.

11.12.2 Attention

Despite significant increases in hydroperoxides as determined by increased DROM levels in

HF patients compared to controls, DROM concentrations were unrelated to measures of

attention and psychomotor function in either group. Furthermore, DROMs were not related to

Power of Attention, however a significant relationship was seen between DROM and

Continuity of Attention, although HF patients did not perform differently to controls on this

cognitive domain, suggesting that this relationship may be due to ageing. Additionally, F2-

isoprostanes, a measure of lipid peroxidation generated during the peroxidation of

unsaturated fatty acids (arachidonic acid) in phospholipid membranes (Mori et al., 1999;

Schwedhelm et al., 2008), were not related to measures of attention, psychomotor speed or

the domains of Power of Attention or Continuity of Attention in either experimental group.

The present results suggest that HF patient’s ability to attend to stimuli and perform tasks

requiring sustained and focussed attention, in addition to psychomotor speed may not be

related to increased oxidative stress markers because of increased free radical production.


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11.12.3 Memory

The findings from the present study did not reveal a relationship between Quality of Episodic

Memory, Quality of Working Memory or Speed of Memory and oxidative stress (DROMs and

F2-isoprostanes) in either experimental group. Although there were no experimental group

differences in the memory domains (Quality of Episodic Memory, Quality of Working

Memory and Speed of Memory), HF patients did indicate worse memory than controls as

measured by the delayed word recall task. Since patients who had reduced memory

performance compared to controls also had elevated oxidative stress markers, these findings

support studies that have shown lower levels of oxidative stress markers (MDA, glutathione,

reduced glutathione) in patients with memory deficits (Gironi et al., 2011). Few studies have

explored the association between oxidative stress and memory domains. Previous studies

have reported that older individuals with memory impairments exhibit elevated levels of lipid

peroxidation as measured by MDA (Gironi et al., 2011; Torres et al., 2011) and F2-

isoprostanes (Praticò et al., 2002) compared to healthy controls. Moreover, researchers have

revealed that older patients with memory impairments have a greater oxidative stress profile

(Praticò et al., 2002; Torres et al., 2011). For example, elevated lipid peroxidation levels as

determined by MDA are higher in patients with MCI compared to healthy elderly controls

(Praticò et al., 2002) and in AD patients compared to MCI patients (Torres et al., 2011).

Additionally, antioxidant administration in healthy older individuals improved memory and

reduced F2-isoprostanes following 3-month intervention suggesting a link between oxidative

stress and memory impairments (Ryan et al., 2008).

Since previous findings suggest that oxidative stress maybe related to memory impairments, a

failure to find a reduction in F2-isoprostane levels in the patient group is expected since the

HF patient group in did not exhibit memory impairments relative to the control group. The

results of the present study therefore suggest that HF patient’s ability to store information in

episodic memory and working memory, and psychomotor functions may not relate to

increased oxidative stress markers because of increased free radical production. A possible

future study may examine whether lipid peroxidation is elevated in patients with mild HF and

mild cognitive impairment. Alternatively, future studies may explore whether lipid

peroxidation in patients with moderate to severe HF are related to cognitive impairment, in

particular attention, Power of Attention and executive function.


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The results from the present study partially supported the hypothesis that oxidative stress,

antioxidants, inflammation and omega-3 dietary intake will be related to cognitive tests

measuring global cognition, attention, psychomotor speed, working memory, episodic

memory and executive function (H14). The results from the present study failed to reveal

relationships between oxidative stress measures (DROMs and F2-isoprostanes) and

executive function tests in either the HF or control group. Few studies have explored the

association between oxidative stress and executive function. The results from the present

study suggest that HF patient’s ability to plan and capacity for executive control, may not be

affected by increased free radical production.

11.12.5 Summary

The results from the present investigation suggest that an increase in oxidative stress in the

form of lipid peroxidation and hydrogen peroxides commonly seen in HF may not affect HF

patients’ attentional abilities, memory domains or executive functions. Despite significantly

elevated levels of lipid hydroperoxides as measured by DROMs in the HF group compared to

healthy controls, hydroperoxides were not associated with cognitive impairments in HF

patients. Since the brain is highly susceptible to increased oxidative stress, lipid peroxidation

and potentially damaging end products (Gironi et al., 2011; Mariani et al., 2005) it is possible

that the brains of patients with predominantly mild HF observed in the present study are

protected from oxidative damage. Some authors suggest that during the early stages of

neurodegenerative disease, oxidants and radical products are cleared by antioxidants

preventing cognitive decline (e.g. Gironi et al., 2011). Since majority of patients in the

current study had mild HF, it is possible that in mild HF free radicals are removed by

antioxidants, protecting patients from cognitive decline. However, this hypothesis is based on

circulating oxidative stress markers, which do not represent oxidative stress in the brain.

Although the present study cannot establish the true extent of oxidative damage to the

myocardium and overall free radical damage in the HF patient group, the results suggest that

circulating oxidative stress measures F2-isoprostanes and DROMs do not relate to cognitive

function in mild HF patients. Since greater levels of oxidative stress are seen in patients with

severe heart failure compared to mild or moderate forms of the condition (Keith et al., 1998;

Polidori et al., 2004), future studies examining the effects of cognitive function in HF patients

with greater disease severity are required.


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11.13 Relationship between antioxidant measures and cognitive function


Further exploring the hypothesis that oxidative stress, antioxidants and inflammation are

related to cognitive function in HF (H14), global cognitive function, as measured by MMSE

scores were not associated with antioxidant biomarkers in the heart failure (HF) or control

groups. Since there have been no studies to date that have examined the effects of

antioxidants and global cognitive function in HF it is difficult to discuss the present results.

These findings suggest that global cognitive function may not relate to overall global

cognitive function in HF patients with mild disease severity.

11.13.2 Attention

Further exploring the hypothesis that there will be a relationship between oxidative stress,

antioxidants and inflammation and cognitive tests measuring attention, psychomotor speed

(H14), the present study did find an association between attention tasks and plasma

antioxidant concentrations. In particular, supporting the hypothesis, worse performance on

tasks measuring attention as measured by congruent Stroop and psychomotor function as

measured by the Trail Making-A task, were related to elevated plasma levels of the

antioxidant coenzyme Q10 (CoQ10) in HF patients. When incorporating premorbid IQ and

CoQ10 as covariates in the analysis of covariance model, CoQ10 explained approximately

9% of the variance on congruent Stroop performance. These observations were not observed

in age-matched controls. Moreover, no significant correlations between Power of Attention

and antioxidant markers were observed, suggesting that the attention domain in HF may not

be influenced by antioxidant status. Results from the present study suggest that worse simple

attentional but not sustained attentional processing in these patients may be due to reduced

CoQ10.

11.13.3 Memory

No significant relationships were observed between domains of Quality of Episodic Memory,

Quality of Working Memory or Speed of Memory and antioxidant measures in either

experimental group. Previous studies have shown that patients with memory impairments do

not present with lower coenzyme Q10 (CoQ10) levels compared to healthy controls (Gironi et

al., 2011). The results suggest that reduced antioxidant status does not have an effect on

memory domains assessing working memory, episodic memory or retrieval times to access

information from memory. Future studies examining whether domains of Quality of Episodic


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Memory, Quality of Working Memory or Speed of Memory in patients with moderate and

severe HF are required.


In support of the hypothesis that there will be a relationship between antioxidant markers and

cognitive function (H14), in the present study, performance on two of the three executive

function measures correlated with antioxidant plasma levels. In particular, poorer

performance on executive function as measured by incongruent Stroop and Stroop effect was

moderately related to reduced plasma CoQ10 levels in HF patients but not in controls. In

addition, CoQ10 accounted for almost 13% of the variance across groups on executive

function as measured by incongruent Stroop.

Although no previous trial has examined the relationship between CoQ10 and executive

function, animal studies have demonstrated neuroprotective effects of CoQ10 on Alzheimer’s

disease model rats (Ishrat et al., 2006). It is reasonable to suggest that due to inadequate

CoQ10 levels removing free radicals in the brain, a build-up of oxidative stress that damages

the cerebral cortex may impact on HF patient’s executive functions. Findings from the

present investigation suggest that a reduction in plasma CoQ10 levels may be related to HF

patients’ reduced ability to perform higher cortical functions.

11.13.5 Summary

These results suggest that a reduction in coenzyme Q10 levels may relate to reduced

attentional abilities and poor executive function in older HF patients.

11.14 Relationship between inflammatory measures and cognitive function


Further exploring the hypothesis that inflammatory markers and dietary omega-3 intake will

be related to cognitive tests measuring global cognition, attention, psychomotor speed,

working memory, episodic memory and executive function (H14), the present study found that

global cognitive function as measured by MMSE scores was not related to systemic

inflammation as measured by high-sensitive C-reactive protein (hs-CRP). These results fail to

support previous findings demonstrating a negative relationship between inflammatory

measures and global cognition in HF patients (Said et al., 2007) and elderly patients (> 65

years; Athilingam et al., 2012). In particular a previous study found that elevated levels of the

inflammatory markers tumour necrosis factor alpha (TNF-α) and interleukin-6 (IL-6) were

related to worse global cognitive scores in HF patients (Said et al., 2007). Additionally IL-6,


193

specifically predicted cognitive performance in HF patients (Said et al., 2007). Supporting

these previous findings, another study demonstrated that the pro-inflammatory cytokine IL-6

and systemic inflammation as measured by CRP were associated with global cognitive scores

as measures by the Montreal Cognitive Assessment battery (MoCA) in patients with NYHA

functional class I, II and III (Athilingam et al., 2012). It is possible that the reason why the

present study failed to find similar associations between global cognition and inflammation is

because the assessment tool used to measure cognitive function was different to those used in

previous studies. The present study used the MMSE whereas previous studies utilised the

MoCA (Athilingam et al., 2012) and the Hodkinson Abbreviated Mental Test (AMT; Said et

al., 2007). Additionally, studies that found an association between C-reactive protein and

global cognition included patients with moderate cognitive impairments (Athilingam et al.,

2012). The present study excluded patients with cognitive impairments, hence it is possible

that systemic inflammation may be related to cognition in patients with known cognitive

impairments. These aforementioned studies also failed to include a control group without HF.

Whether the relationships between inflammation and global cognitive function observed in

previous studies related to ageing or the heart failure itself is unknown.

11.14.2 Attention

Refuting the hypothesis (H14), the present study showed that inflammatory markers hs-CRP

did) not predict congruent Stroop mean reaction time, Trail Making-A task or Power of

Attention domain. These results contrast with the findings by Kindermann et al. (2012) who

demonstrated that systemic inflammation was related to poor performance on information

processing speed in patients with decompensated and stable heart failure and healthy controls.

Additionally, results of the present study did not reveal a relationship between dietary omega-

3 polyunsaturated fatty acid (PUFA) dietary intake and attention measures in HF patients.

Since previous studies have failed to examine the effects of dietary PUFA intake and

cognitive domains in HF include attentional abilities, the current results are difficult to

interpret. Earlier studies revealed that supplementing with omega-3 PUFA demonstrated anti-

inflammatory effects in HF patients by reducing TNF-α, IL-1 and IL-6 levels (Nodari et al.,

2011).

11.14.3 Memory

Refuting the hypothesis (H14), the present study indicated that the inflammatory marker hs-

CRP and dietary PUFA intake did not relate to domains of Quality of Episodic Memory,

Quality of Working Memory and Speed of Memory. These results fail to support findings by

Kindermann et al. (2012) who demonstrated that systemic inflammation was related to


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impaired memory in patients with decompensated and stable HF and healthy controls. One

explanation for this discrepancy is that the present investigation tested patients with stable HF

whereas Kindermann et al. (2012) included patients with decompensated HF patients who are

more likely to have memory impairments and increased inflammation. Additionally,

Kindermann et al. (2012) found that patients with stable heart failure are impaired in working

memory and episodic memory scores compared to controls. Since the present study failed to

find significant differences between domains of Quality of Episodic Memory, Quality of

Working Memory and Speed of Memory between the HF patients and controls it is possible

that inflammation may relate to memory in patients who are impaired in memory domains.

However, future studies are required to test this hypothesis. The results from the current

investigation suggest that inflammation may not be related to a reduction in memory

impairments in HF.


The results in the present study showed that inflammatory markers hs-CRP and dietary PUFA

intake did not relate to Trail Making-B, incongruent Stroop or Stroop effect. These results fail

to support the findings of Kindermann et al. (2012) who demonstrated that systemic

inflammation was related to Stroop interference (Stroop effect) in patients with

decompensated HF, compensated HF and controls. Similar to Kindermann et al. (2012), the

present results showed that stable HF patients and controls performed similarly on the Stroop

effect measure for executive function. Further to this, Kindermann et al. (2012) showed that

patients with decompensated HF performed worse on executive function compared to

patients with stable HF and controls. One explanation for the discrepant results between these

studies is that the current investigation tested patients with stable HF whereas Kindermann et

al. (2012) included patients with decompensated HF patients who are more likely to have

memory impairments and increased inflammation. Results from the present study suggest that

increased systemic inflammation may not relate to executive control in HF patients.

However, future studies using a larger sample size to explore the relationships between

systemic inflammation and executive function in patients with stable HF are required.

11.15 Relationship between vascular, oxidative stress, antioxidant and inflammatory

measures and cognitive function

The following section discusses analysis of covariance models, which examined the extent to

which vascular, oxidative stress, antioxidant and anti-inflammatory measures explain the

variance in the attention, memory and executive function measures in HF patients and

controls. Multiple regression was used to explore which biomarkers were the best predictors


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of the cognitive outcome measures in the HF group. However, due to the small sample size,

interpretation of these results are made with caution.

As mentioned previously HF patients displayed impairments in selective attention as

determined by the congruent Stroop. Slower reaction times on selective attention tasks were

related to slower cerebral blood flow velocity as detected by blood flow speed through the

left common carotid artery. In addition, poorer selective attention in HF related to increased

arterial stiffness as measured by elevated central pulse pressures and lower levels of the

antioxidant and energy synthesiser coenzyme Q10.

Examining multivariate models, unlike the covariate premorbid IQ, which did not

significantly account for the variance in congruent Stroop reaction time, common carotid

arterial blood flow velocity predicted performance on attention tasks in HF patients as

determined by congruent Stroop reaction times. In addition, there was a trend for CoQ10 to

account for some of the variance in congruent Stroop reaction times. In a second multivariate

analysis, central pulse pressure was a moderate predictor of congruent Stroop, and when

combined with premorbid IQ and common carotid arterial blood flow velocity, the model

predicted 63% of the variance in attention in HF patients. Due to the low statistical power

because of the small sample size, interpretation of these finding are exploratory and larger

studies are required to confirm these findings. Preliminary results from the present

investigation raise the possibility that interventions that increase blood flow velocities in the

common carotid artery, treating CoQ10 deficiencies or increasing central pulse pressures

may help increase attention abilities in HF patients.

As mentioned previously, HF patients displayed an impaired ability to focus their attention as

determined by the Power of Attention cognitive domain. In HF patients, a reduced ability to

sustain attention on simple reaction time and vigilance tasks for prolonged periods were

related to vascular measures. In particular, the results from the present study indicate that

reduced performance on the Power of Attention cognitive domain is related to slower blood

flow speed through the common carotid artery. This suggests that HF patients’ reduced

ability to sustain attention for prolonged periods and during tasks requiring selective

attention, is explained by a reduced cerebral blood flow rate. Additionally, reduced ability to

be vigilant and sustain attention over a prolonged period in HF patients was related to

increased arterial stiffness as measured by central pulse pressures. When these variables were

included in a prediction model, after adjusting for premorbid IQ, there was a trend for

common carotid arterial blood flow velocity to relate moderately to Power of Attention in HF

patients. When the arterial stiffness measure central pulse pressure was added to the model, it


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was a significantly greater predictor for the Power of Attention cognitive domain significantly

contributing to an additional 41% (R2 change = .41) of the predictive value.

The results from the current investigation suggest that HF patients reduced ability to sustain

attention for prolonged periods and during tasks requiring selective attention, relates to a

reduced cerebral blood flow rate. It is possible that reduced simple attention abilities and

sustained attention in heart failure is related to cerebral deterioration due to microvascular

damage is caused by increased pulse pressure downstream in the vertebral and carotid arteries

(O'Rourke & Safar, 2005). Additionally, based on the findings from the present study it is

conceivable that microcirculatory remodelling due to arterial stiffness may cause

microvascular ischemia in the brain; in this case, affecting the brain regions associated with

attentional processing (Mitchell, 2008). Larger studies using direct measures of arterial

stiffness and such as carotid-femoral pulse wave velocity are required to explore this

hypothesis and to substantiate findings from the current investigation.

As mentioned previously, HF patients displayed impairments in executive functioning

compared to healthy controls. In particular, after adjusting for premorbid IQ, HF patients

demonstrated significant impairment in selective attention and response inhibition

(incongruent Stroop reaction time) and the ability to switch between stimuli (Trail Making-

B). Reduced selective attention and response inhibition in HF was related to vascular and

antioxidant measures. In particular, executive function as measured by incongruent Stroop

was associated with reduced blood flow velocity in the common carotid artery and higher

arterial stiffness as measured by central pulse pressure with medium effects sizes.

Furthermore, slower reaction times on the incongruent Stroop tasks indicating reduced

selective attentional abilities were moderately related to decreased antioxidant status and

energy production as detected by a reduced coenzyme Q10 levels.

Examining multivariate models, unlike the covariate premorbid IQ, which did not

significantly account for the variance in congruent Stroop reaction time, common carotid

arterial blood flow velocity significantly predicted performance on executive function in HF

patients as determined by incongruent Stroop reaction times. Furthermore, when the

covariate CoQ10 was added to the model, there was a trend towards CoQ10 explaining

further variance in incongruent Stroop performance.

Recent evidence suggests there is a relationship between peripheral arterial stiffness and

haemodynamic changes in the middle cerebral artery in individuals with risk for developing

cardiovascular disease (Kwater et al., 2009). Although the present study failed to find

associations between middle cerebral arterial blood flow velocity and arterial stiffness, given


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that HF patients share similar risk factors for cardiovascular disease it might be fruitful to

examine the relationships between arterial stiffness and cerebral blood flow in future studies

with a larger sample size.

Heart failure patients are impaired on tasks measuring selective attention, sustained attention,

executive functioning, cognitive flexibility and response inhibition compared to age and sex-

matched controls. These impairments may be related to vascular and antioxidant

physiological dysfunctions that are seen in heart failure patients specifically and not seen in

normal ageing.

That some biomarkers could explain group differences on some domain sub-tests and not

others suggest that these biomarkers assay the integrity (if indirectly) of brain regions that

may be critically engaged for performance of those particular sub-tests, although these same

brain regions may not be critical for performance of all domain sub-tests. That is, specific

tasks within specific cognitive domains may be influenced by different mechanisms. For

example, reduced common carotid arterial blood flow velocity and increased arterial stiffness

(central pulse pressures) may be associated with the congruent Stroop and Power of

Attention domain but not psychomotor speed or Continuity of Attention. Whereas reduced

psychomotor speed (Trail Making-A) and performance on the congruent Stroop task are

possibly related to reduced antioxidant levels. Collectively, impaired attentional abilities may

be associated with reduced blood flow, increased arterial stiffness and reduced antioxidant

capacity

11.16 Summary of the relationships between cognitive function and physiological

measures

The results from the present investigation suggest that HF patients are impaired on the Power

of Attention cognitive domain. Furthermore, determinable reactive oxygen metabolite

(DROM) is an additional oxidative stress measure elevated in elderly HF patients. Taken

together, the results from this study suggest that mechanisms related to cognitive function in

elderly HF patients, in particular simple attention, Power of Attention cognitive domain and

executive function abilities may involve central pulse pressure, common carotid arterial

blood flow velocities and possibly the level of coenzyme Q10. Interventions that improve

central pulse pressures, increase cerebral blood flow and increase circulating coenzyme Q10

levels may improve attention and executive function in elderly HF patients. Larger studies are

required to confirm these mechanisms and appropriate interventions need to be devised to

help improve clinical outcomes in HF patients.


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11.17 Mood measures

The hypothesis (H5) that HF patients will score higher on depression, anxiety and fatigue

measures, and lower on vigour as measured by the Profile of Mood States questionnaire than

controls was supported. The findings from this study demonstrated that HF patients had

approximately twice the levels of tension/anxiety (50%), anger/hostility (51%), fatigue/inertia

(56%) and confusion/bewilderment (45%) than controls. Additionally, there was a trend

towards a greater level of fatigue based on physical (p = .06) and mental symptoms (p = .09)

in HF patients. HF patients were significantly more fatigued overall as measured by the sum

of the physical and mental fatigue measures of the Chalder fatigue scale. Additionally, HF

patients were 71% more depressed and had 31% less vigour compared to controls. Finally,

HF patients had greater Total mood disturbance compared to controls (M=20 versus M= -4).

The findings from the present investigation support previous findings indicating that higher

levels of depression and anxiety in elderly HF patients compared to controls (Almeida, Beer,

et al., 2012; Pressler, Subramanian, et al., 2010b). The present results support previous

reports of a high prevalence of fatigue in elderly HF patients with preserved ejection fraction

(Stephen, 2008). Also, the results from the present study support previous studies that have

also indicated lower of vigour using the POMS questionnaire in female HF patients

(Riedinger et al., 2002).

11.18 Relationship between vascular measures, oxidative stress, antioxidant and

inflammatory measures on depression and anxiety

11.18.1 The relationship between cerebral blood flow and arterial stiffness with depression

and anxiety

No specific hypotheses were made with relation to whether cerebral blood flow or arterial

stiffness has an effect on mood, in particular anxiety and depression in HF patients.

Therefore, the current investigation explored the research question “Is there a relationship

between cerebral blood flow or arterial stiffness and depressive symptoms and anxiety (R1)?”

Despite scoring significantly higher on the POMS depression subset, POMS anxiety subset

and reduced cerebral blood flow in HF patients compared to controls, the results of the

present investigation did not demonstrate a relationship between depression symptoms and

cerebral blood flow in HF patients as measured by common carotid and middle cerebral

arterial blood flow velocities. These findings fail to support those of Alves et al. (2006) who

demonstrated slower cerebral blood flow in HF patients with major depressive symptoms as

measured by the SPECT imaging technique. However, the current findings cannot be directly

compared with those of Alves et al. (2006) as these researchers used a different imaging


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technique to measure blood flow and examined patients with major depressive symptoms. It

is possible that slower blood flow in the medial temporal regions (Alves et al., 2006) but not

in the common carotid or middle cerebral arteries as measured by Transcranial Doppler is

related to depression in elderly HF patients. Nonetheless, further investigation is required to

elucidate the relationships between cerebral blood flow and depressive symptoms and anxiety

in elderly HF patients. The current results suggest that cerebral blood flow speed is not

associated with depression or anxiety in elderly HF patients.

In addition, neither depression scores nor anxiety related to arterial stiffness as measured by

augmentation index or central pulse pressures in either experimental group. These results fail

to support previous findings indicating that early wave reflection as determined by

augmentation index was related to elevated depressive symptoms and anxiety in individuals

with cardiovascular disease (Seldenrijk et al., 2011). Furthermore, although elderly depressed

individuals were found to be more likely to have increased pulse wave velocities in a

previous trial (Tiemeier et al., 2003), the current investigation failed to find an association in

elderly heart failure patients and age-matched controls. Although it has been suggested that

augmentation index is a possible mechanism for linking depression to cardiovascular disease

risk (Seldenrijk et al., 2011), the current study does not support this notion in patients with

stable HF.

11.18.2 The relationship between oxidative stress and antioxidant measures with depression

and anxiety

No specific hypotheses were made in relation to whether oxidative stress or antioxidant

measures have an effect on depressive symptoms and anxiety in HF patients. Therefore, the

research question “Is there a relationship between oxidative stress or antioxidant measures

and depressive symptoms and anxiety? (R2)” was explored in the present investigation.

Exploring this research question, lower levels of lipid peroxidation as measured by F2-

isoprostanes were related to higher scores on the POMS subscale depression/dejection in the

HF group but not in controls. Other measures of oxidative stress (determinable reactive

oxygen metabolites; DROMs), however, were not associated with depression in HF patients.

The current study also showed that higher levels of anxiety/tension over the seven days

preceding baseline testing session were moderately related to lower levels of lipid

peroxidation as measured by F2-isoprostanes in the HF group but not in the controls. Other

measures of oxidative stress (determinable reactive oxygen metabolites; DROMs) were not

associated with anxiety measures in HF patients.


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The finding that increased lipid peroxidation but not DROMs were related to depression

scores partially support findings from previous research. Kupper et al. (2009) also failed to

find an association between serum levels of oxidative stress markers and depressive

symptoms in patients with chronic HF. Other researchers have shown that depressed HF

patients had higher levels of lipid peroxidation (MDA) compared to non-depressed patients

(Michalakeas et al., 2011). Although HF patients in the current investigation did not differ

significantly to controls on F2-isoprostane measures, interpretation of these findings are

therefore made with caution. However since previous studies have found a decrease in

oxidative stress markers in depressed HF patients following treatment with SSRIs which also

have antioxidant actions, it is possible that treating patients with a safe antioxidant will help

improve depressive symptoms. Future randomized, double-blind, placebo-controlled studies

are required. Given that the current investigation excluded patients with clinical depression, a

comparison of oxidative stress markers between depressed and non-depressed patients could

not be made. Further studies using a larger sample size, comparing lipid peroxidation and

reactive oxygen metabolite levels in patients with and without clinical depression will clarify

the role of oxidative stress in HF patients.

The findings from this study support the notion that plasma CoQ10 levels are lower in

patients with depression (Maes et al., 2009), and corroborate the finding of Maes et al (2009)

who did not observe a relationship between CoQ10 and depressive symptoms in HF patients.

However, given that the current study excluded patients with depression, a comparison in

CoQ10 levels between depressed and non-depressed patients could not be made. Taken

together, the present findings suggest that although HF patients have a deficiency in CoQ10

levels, these deficiencies are not related to depression or anxiety measures in these patients.

Moreover, other measures of antioxidants (glutathione peroxidase) were not found to be

associated with depression or anxiety measures in HF patients. The findings from the present

study suggest that lipid peroxides as measured by F2-isoprostanes but not the antioxidant

coenzyme Q10 are possibly related to higher levels of tension/anxiety and

depression/dejection in older HF patients. It is reasonable to suggest that factors that reduce

lipid peroxidation such as antioxidant treatment may decrease anxiety and depression in

elderly patients with HF. Future studies exploring the effects of an antioxidant treatment

shown to be effective in reducing lipid peroxidation levels may are required to examine the

effects of these antioxidants on mood in elderly patients with heart failure.


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11.18.3 The relationship between inflammatory measures and depression and anxiety

No specific hypotheses were made in relation to whether inflammatory measures have an

effect on anxiety in HF patients. Therefore, the research question “Is there a relationship

between inflammatory measures and anxiety in HF patients? (R3)” was explored in the

present investigation. The current investigation failed to find significant associations between

inflammatory markers and tension/anxiety measures in the HF patient group.

It was hypothesised that inflammation as measured by high-sensitive C-reactive protein

(hs-CRP) would be related to depression scores in HF patients (H15). Contrary to expectation,

inflammation as measured by hs-CRP and dietary levels of omega 3 essential fatty acid intake

were not associated with depression scores in HF patients.

Outcomes from the present study fail to support those of Andrei et al. (2007) who found that

hs-CRP levels were higher in elderly HF patients with major depressive disorder (MDD)

compared to patients without MDD. Although the cohort in the present study were free from

clinical depression or other psychiatric disorders, their levels of depression during the 7 days

preceding the baseline testing session was higher than controls. A reason why the present

study failed to find an association between depression in HF patients is because of small the

sample size in the present study. Additionally, the results from the current investigation

support those of Fink et al. (2012) who, despite demonstrating that younger heart failure

patients had higher levels of fatigue and depression than controls, also found a significant

association between fatigue and depression and inflammatory markers (TNF-α, IL-6, CRP or

IL-10), this finding was not replicated in the present study.

Additionally the findings of the present study fail to support previous work observing

elevated inflammatory markers that were related to higher depressive symptoms in HF

patients (Ferketich et al., 2005; Guinjoan et al., 2009). Contrary to the findings of the present

study, previous studies have shown that circulating levels of pro-inflammatory cytokines

TNF-α, but not IL-6 or IL-1, were higher in HF outpatients with elevated levels of depression

(Ferketich et al., 2005). Moreover, higher depression scores in elderly HF patients were

related to higher circulating inflammatory marker IL-6 in these patients (Guinjoan et al.,

2009). Patient cohorts in previous studies examining the relationships between inflammation

and mood in HF were generally younger (56±10 years; Ferketich et al., 2005), had

decompensated heart failure (Guinjoan et al., 2009) and inflammatory measures tested were

pro-inflammatory cytokines. The current study however enrolled an older cohort with stable

HF and measured systemic inflammation not cytokines. Findings from the current


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investigation suggest that systemic inflammation as measured by hs-CRP is not a possible

mechanism associated with depression or anxiety in elderly patients with stable HF.

11.19 Possible mechanisms for changes in mood in heart failure

It is clear from the results of the current investigation that HF patients have higher levels of

fatigue, depression, anxiety and reduced vigour. However, it is unclear from the present

investigation if physiological mechanisms are related to elevated levels of depressive

symptoms or anxiety in elderly HF patients. Cerebral blood flow, arterial stiffness, oxidative

stress, antioxidants and inflammatory makers did not relate to depressive symptoms or

anxiety in patients. However, further studies using larger sample sizes comparing these

physiological mechanisms in patients with and without clinical depression will clarify the

role of these physiological mechanisms in mood in HF patients.

Chapter12: Study limitations, strengths and future directions

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CHAPTER 12 STUDY LIMITATIONS, STRENGTHS

AND FUTURE DIRECTIONS

12.1 Study Limitations

There is a range of factors that limit the generalizability of the study findings.

12.1.1 Patient recruitment and sample size

The sample size for this thesis was moderately small. A low sample size reduces the

statistical power of the results therefore limiting the generalizability of findings. It took

approximately 24 months to recruit and test the HF patients for the study. Reaching a sample

size adequate for statistical power was not achievable during this time. The low sample size is

primarily due to challenges with recruiting from this population and further reducing

recruitment rates due to exclusion criteria. In this study, the purpose for excluding patients

with depression and anxiety and those taking anti-depressants was to control for possible

confounding factors known to influence cognitive performance. Since depression is common

in HF, excluding these patients further reduced the ability to recruit patients in a time

efficient manner.

In the present study, the NYHA classification was used as the HF diagnosis, which is an

objective measure of heart failure. Other markers for HF such as plasma brain neurotropic

factor (BNP) levels and left ventricular ejection fraction would have provided a more

accurate HF diagnosis and measure of disease severity. Patients aged 60 years and above

enrolled in this study may not have been able or want to commit the time to travel long

distances if they live far away to attend testing sessions, and/or sit through a 1.5 hour testing

session. The total testing time for each visit took 1.5 – 2 hours (0.45 to 1 hour for cognitive

testing, 0.5 hours for mood and quality of life questionnaires, 0.5 hours for vascular measures

and blood test) despite efforts made to keep the testing session time to a minimum. Patients

who lived far away or had multiple medical appointments and were interested in taking part

in the study preferred that the testing sessions coincide with other appointments at the Alfred

Hospital. However, the day would have been too exhausting and fatiguing for the patient and

some patients had medical appointments at the Alfred 6 or 12 months apart, which was too

long between the training day and baseline-testing visit. Furthermore, HF patients are more

likely to have comorbidities and as a result additional medical appointments, which may

reduce their willingness to commit to taking part in the trial.


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12.1.2 Methodological issues

Due to the small sample size, there were constraints on statistical methods appropriate for

analysing the dataset. Analysis of covariance and regression were appropriate statistical

methods for the sample size. Although the sample size was not suitable to obtain high

statistical power, multiple regression used in this study was included for exploratory purposes

only. A more robust statistical analysis using structural equation modelling would have

provided a superior assessment to help ascertain whether a relationship exists between the

biomarkers, cognition, and mood, and if so, how these variables are linked and influence

other variables. A sample size of at least 100 in each experimental group is required for such

an analysis.

Due to the small sample size and unequal number of participants in each of the NYHA

classifications, there was insufficient statistical power to explore whether cognitive

dysfunction in this sample changed with increasing disease severity. Majority of the HF

patients had mild HF (NYHA class II), therefore the results of the current investigation may

not be applicable to patients with moderate or severe HF.

During the practice session, the CDR test battery was administered twice in patients and four

times in controls. Additionally, the researcher read out instructions to patients for each of the

two training session and the baseline session. The researcher read out task instructions only

once to controls at the first training session. During subsequent training sessions and the

baseline testing session control participants read instructions on the screen. Although there

was some variation in the administration of the CDR tasks, repeated measures analysis of

variance indicated that there were no group x time interactions for each of the CDR cognitive

domains assessed at the practice and baseline testing days.

Another limitation is the observational nature of the study design, which may contribute to

bias and inhibit generalisation of the research findings. However, the aim of the current study

was to acquire knowledge about the mechanisms associated with cognitive impairment in

heart failure and generate hypotheses for future experimental randomised controlled trials.

Although this study controlled for possible extraneous factors known to influence cognitive

function such as depression diagnosis, anxiety, psychiatric disorders, anti-depressant and

anti-psychotic medication and dementia, additional factors may have explained poorer

performance of cognitive function in HF patients. Poor sleep has been shown to effect

cognitive function and researchers have proposed that poor sleep negatively impacts on HF

patients’ cognitive function and self-care (Riegel & Weaver, 2009). A short sleep

questionnaire aimed to ascertain the quality of sleep the night before each testing session and


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a typical night sleep may be useful in providing insight into whether patients sleep quality

differs to those of controls and if so whether inferior performance on cognitive tasks may

have been due to lack of sleep. Given that poor sleep results in daytime fatigue, it is

reasonable to suggest that the fatigue measures (POMS-fatigue subscale and the Chalder

fatigue questionnaire) administered in this study would have captured fatigued caused by

sleepiness.

Certain risk factors for HF are shared risk factors for diseases of cognitive impairment. This

study did not explore shared risk factors such as hypertension, atherosclerosis and history of

smoking as factors possibly contributing to poor cognitive performance in HF. Additionally,

this study failed to explore how these risk factors may have contributed to group variations of

oxidative stress, antioxidant, and inflammatory markers. Other shared risk factors may have

contributed to vascular changes such as in pulse pressures, augmentation index and cerebral

blood flow. For example, diabetes is linked to impaired cognitive function and increased

pulse pressure and arterial stiffness. A comprehensive assessment of participants’ medical

history may have provided useful data regarding how these risk factors contributed to

cognitive decline.

Other confounding variables not accounted for in this study included medications taken by

HF patients and controls. Unlike the healthy control group, HF patients take medications such

as digoxin (2009) and ACE-inhibitors (Zuccalà et al., 2005), which are known to influence

cognitive function. These are standard medications taken by patients and it would have been

unreasonable to exclude patients taking these medications.

12.1.3 Biological markers

In regard to measuring cerebral blood flow velocity, carbon dioxide challenge is a preferred

method of measurement. Due to financial limitations, equipment to undertake carbon dioxide

challenge was not available for the present investigation. Additionally, to avoid using

different devices at different testing sites the researcher transported the TCD and

SphygmoCor® devices to the two testing sessions. Transportation of the CO2 tank was not

practical for this work and measuring resting TCD measurements was better than not taking

these measurements at all.

Three different researchers administered the TCD and SphygmoCor® for the controls and

only one for the HF group. This raises the possibility for inconsistent measurement error

between researchers. Additionally, patients tended to be more agitated and tired than controls,

and their agitation made it difficult for them to sit still which is required in order to obtain a

clear adequate signal especially for the SphygmoCor® measurement, resulting in some


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missing data. It is worthwhile considering testing patients in a supine rather than an upright

position in future studies when taking vascular measurements, as this would have made

patients more relaxed and prevented missing data. Additionally, it was difficult to obtain a

SphygmoCor® measurement in patients with atrial fibrillation and irregular heart rate leading

to additional missing data.

High-sensitive C-reactive protein (hs-CRP) blood samples were analysed at two different

pathology laboratories and this may have led to increased unreliability. Further, although hs-

CRP is a viable measure of systemic inflammation, an assessment of interleukins (IL-6 and

IL-10) would have provided a better assessment of inflammation. Furthermore, examining

fatty acid by means of omega-3 index in fasting blood samples would have been a preferred

assessment of omega-3 in the blood rather than a questionnaire to determine dietary intake.

Additionally, future studies exploring other measures of oxidative stress (e.g. urinary F2-

Isoprostanes, malondialdehyde, glutathione or reduced glutathione) and antioxidants (e.g.

superoxide dismutase, vitamins C, E and A, α-carotene and β-carotene) would enhance the

understanding of the oxidative stress and antioxidant profiles associated with cognitive

function in heart failure patients.

12.2 Study strengths

Despite the limitations discussed above, the present investigation had various strengths.

Firstly, the present study compared cognitive performance and mood measures in HF patients

to those of a control group matched for age and gender. The results from the present study

therefore reflect impairment of cognitive performance, in particular attentional processing

and executive function and mood deficiencies in HF patients themselves that are independent

of the cognitive decline seen in the normal aging process.

Secondly, including the CDR test battery enabled assessment of additional cognitive domains

that may be vulnerable in HF and other domains not detected in previous studies. This study

revealed that Power of Attention is an additional cognitive domain impaired in HF patients.

Although patient recruitment was challenging due to the reasons outlined above, involvement

of the cardiologists and nurses at the Alfred Heart Centre, Heart Failure Clinic, aided

recruitment. Cardiologists at the Alfred Heart centre, Heart Failure Clinic, would

occasionally introduce a potentially suitable patient to the researcher after their appointment.

Further, two cardiologists were co-investigators in the study. These factors may have


207

provided confidence in patients to communicate with the unfamiliar researcher and obtain

information about the study, thereby boosting recruitment.

Testing rooms were on the same level as the Alfred Heart Centre, Heart Failure Clinic where

patients have their regular cardiology appointments. The testing environment was therefore

familiar to the patients and the same receptionists who greet patients at their medical

appointments greeted them when they attended their testing sessions and paged the researcher

informing them that the patient had arrived and the researcher was able to meet them

immediately. It is reasonable to suggest that the familiar environment minimised patients’

anxiety levels and they felt comfortable during the testing session.

An additional strength of this study was the inclusion of multiple oxidative stress and

antioxidant biomarkers to explore their relationship with cognitive function. Given the

complexity of the oxidative stress and antioxidant biochemical pathways, examining multiple

rather than single biomarkers has provided an indication of which biomarkers are specifically

relevant. If this study only examined one biomarker, an incomplete view of the relationship

between these biomarkers and cognitive function would have been achieved. Owing to the

complexity and the interrelatedness of the oxidative stress and antioxidant pathways, some

authors have referred to the importance of examining multiple rather than single biomarkers

(e.g. Gironi et al., 2011). Examining a battery of oxidative stress and antioxidant biomarkers

will provide a superior overview of how these biomarkers are related to each other, and

moreover with cognitive function. Since no study to date has explored the effects of oxidative

stress and antioxidant biomarkers on cognitive function in HF this component of the study

was novel.

The present study incorporated a practice session. A practice session on a day separate to

baseline testing day enabled participants to become familiar with the cognitive tasks and

minimise factors including anxiety and training effects, shown to influence performance on

neuropsychological testing. The data collected at baseline therefore was a more accurate

representation of participant’s actual cognitive performance. The Cognitive Drug Research®

(CDR) test battery incorporates a training session separate to the actual testing sessions to

minimise practice effects.

The present study utilised the CDR, which is a comprehensive cognitive assessment battery

specific to detecting cognitive changes in ageing. The CDR was used to ascertain whether

additional cognitive domains are impaired in HF patients.

Furthermore, in the present study, patients classified as NYHA class I were excluded.

Patients with NYHA class I are asymptomatic and without any physical limitations; since


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their symptoms for HF are well controlled, they do not present with clinical symptoms of HF.

Previous studies that have examined cognitive function HF have included patients without

symptoms of HF (NYHA class I; e.g. Almeida, Beer, et al., 2012; Pressler, Subramanian, et

al., 2010b; Riegel et al., 2002). It is possible that cognitive dysfunction may not be apparent

in patients with NYHA class I and as a result, a true account of cognitive function from

patients with mild moderate or severe heart failure also included in the study may be masked.

However, although patients with NYHA class I are asymptomatic and their condition is well

controlled, they may be taking common HF medications which may affect cognitive function

in these patients. Future studies need to ascertain whether HF pharmaceuticals taken over

time have detrimental effects on patients cognitive function and mood and whether these

pharmaceuticals influence vascular measures such as arterial stiffness and cerebral blood

flow, as well as oxidative stress, antioxidant capacity and inflammation.

12.3 Directions for future research

A key finding in the present investigation was that older patients with heart failure have

impairments in the Power of Attention cognitive domain indicating that patients are deficient

in their ability to sustain their attention for a prolonged period. Additional studies utilizing a

larger sample size are required to confirm these findings.

This study also indicated that HF patients are impaired in attention and executive function

and that cerebral blood flow, arterial stiffness and antioxidant levels may be related to these

cognitive processes in HF patients. Future studies employing a larger sample size, preferably

in a multicentre trial, will help substantiate these findings. Furthermore, a larger sample size

of at least 100 HF patients will provide greater power to incorporate robust multiple

regression models and to confirm possible biomarker predictors for cognitive impairment. A

larger sample size will also permit application of other statistical methods such as structural

equation modelling, which will provide further information regarding how predictor variables

are related to each other and whether they directly or indirectly relate to cognitive function, in

particular attentional and executive functions.

Additionally, future studies incorporating a longitudinal study design may be able to profile

HF patients in relation to biochemical, psychosocial, mood, lifestyle and cardiovascular risk

factors that may help predict worsening of cognitive function over time. Establishing a profile

of HF patients who are at risk of cognitive decline, may provide information about which

preventive interventions may be suitable and then later tested. Longitudinal studies may also

provide an understanding of how cognitive function in HF changes in relation to disease


209

severity, and whether vascular and biochemical biomarkers are related to any changes in

cognitive function over time.

Future studies combining neuropsychological assessments, neuroimaging such as functional

magnetic resonance imaging, and the biomarkers examined in the present study, arterial

stiffness, oxidative stress, antioxidant capacities and inflammation, will provide a better

understanding of the neural mechanisms underpinning cognitive dysfunction and associated

mechanisms. Brain imaging studies will also help detect brain regions affected by blood flow,

oxidative stress and whether these brain regions are related to attentional and executive

function processes. The present study examined augmentation index and pulse pressure as

indirect measures of arterial stiffness. Future studies employing carotid-femoral PWV, a

direct measure of arterial stiffness, will provide additional information regarding how this

measure is related to cognitive function and whether it is related to other biomarkers

including oxidative stress, antioxidant capacity and inflammation.

Additionally, larger studies are required to explore how vascular and antioxidant mechanisms

are associated with cognitive impairment across disease severity. Studies with larger sample

size and with equivalent numbers of participant in NYHA class II, III and IV will help

understand how these cognitive domains, in particular Power of Attention, and related

biological markers change with increasing disease severity.

Other factors known to affect cognitive function including comorbidities, sleep and

pharmaceutical drugs commonly taken by HF patients, and how vascular, oxidative stress,

antioxidant and inflammatory measures are related to these factors can be tested in future

studies. For example, future studies may incorporate the comorbidity index to get an

overview of how comorbidities effect cognitive performance in patients and a sleep

questionnaire such as the Pittsburgh Sleep Quality Index for an overview of whether sleep in

an additional factor effecting cognitive function and whether biomarkers are related to poor

sleep quality.

The present study excluded individuals with dementia, depression, anxiety and other

psychological conditions to control for factors known to affect cognitive performance. Since

depression is prevalent in HF, future studies are required to examine the physiological

mechanisms, in particular cerebral blood flow, oxidative stress, antioxidants and

inflammatory markers, in HF patients with and without clinical depression. Additionally, an

examination of which biological mechanisms are related to cognitive function in patients with

mild cognitive impairment and dementia will provide a better understanding of cognitive

impairment in these patients.


210

It is still unknown whether medications commonly taken by HF patients such as ACE-

inhibitors, digoxin and diuretics influence cognitive function in HF patients. Therefore,

longitudinal studies aimed at monitoring cognitive function and modifications to

pharmaceutical treatments and dosages may help ascertain whether pharmaceutical drugs

affect cognitive function in HF patients.

Findings from the present study suggest that reduced cerebral blood flow, increased arterial

stiffness as measured by central pulse pressure and low plasma coenzymeQ10 levels are

related to attention and executive function in elderly HF patients. New evidence has emerged

demonstrating an association between cerebrovascular impairment and obstructive sleep

apnea syndrome in AD patients (Buratti et al., 2014). Future studies assessing whether a

relationship between cerebrovascular impairment and sleep dysfunction in heart failure

patients is a possible mechanisms for cognitive impairment are required.

Future studies examining interventions using safe and effective treatments aimed at

addressing each of the reported mechanisms (reduced blood flow, arterial stiffness and low

antioxidants) rather than a single pathophysiological pathway shown to be associated with

cognitive impairments in HF are required. For instance, cognitive enhancers known to

improve blood flow, reduce arterial stiffness and/or increase levels of coenzyme Q10 in heart

failure patients are likely interventions to improve cognitive function in HF patients.

Such interventions may include the Ayurvedic herb Bacopa monnieri, which has been shown

to improve attention and cognitive processing following 12 weeks daily intervention in older

individuals (Peth-Nui et al., 2012). Mechanisms by which Bacopa monnieri improves

cognitive function may be due to increasing antioxidant levels in the prefrontal cortex and

hippocampus (Stough et al., 2008), and reduce amyloid-β peptides as demonstrated in animal

studies (Holcomb et al., 2006). Additionally, polyphenols present in a flavonol-rich cocoa

drink (990mg/day) for example increase blood flow velocity in the middle cerebral artery and

decrease lipid peroxidation in adults (Desideri et al., 2012). Future randomised, double blind,

placebo-controlled studies exploring the effects of interventions such as Bacopa monnieri and

flavonols on cognitive function in HF are required.

In addition, coenzyme Q10 supplementation has been previously shown to have positive

effects on symptoms related to heart failure (Keogh et al., 2003) and animal studies have

revealed possible neuroprotective effects related to cognition (Ishrat et al., 2006).

Furthermore, CoQ10 supplementation in HF patients has been shown to improve plasma

levels of CoQ10 (Folkers et al., 1992; Keogh et al., 2003), improve NYHA functional class

by -.05 (Keogh et al., 2003), and improve ejection fraction, cardiac output and quality of life


211

(Sander et al., 2006). These aforementioned trials have shown that CoQ10 is tolerated well

and produces no known side effects in cardiac patients taking their regular pharmaceutical

treatments. Given that CoQ10 is beneficial for cardiac symptoms and wellbeing in HF, is an

antioxidant and has neuroprotective effects in areas of the brain related to memory, it is

reasonable to suggest that CoQ10 may be important in reducing cognitive deficits in HF

patients.

Even though some studies have examined the effects of oxidative stress and anti-

inflammatory treatments (e.g. CoQ10, n-3 PUFA) on cardiovascular factors and quality of

life in cardiac conditions, no study to date has tested whether these interventions positively

influence cognition, mood or patient self-care in HF. It is clear that multiple etiologies

contribute to cognitive impairment in HF, hence treatment of this condition must therefore

target more than one pathophysiological pathway aimed at improving vascular function,

cardiac symptoms, cerebral blood flow, and reducing inflammation and oxidative stress.

CoQ10 and n-3 PUFAs have been administered in cardiac patients without any side effects or

interactions with other medical treatments and have been well tolerated. In combination,

these products have anti-inflammatory, antioxidant and vascular benefits making them ideal

treatments for addressing each of the mechanisms also associated cognitive impairment in

these HF patients.

Future studies examining the effects of CoQ10 and omega-3 PUFAs employing a

randomized, parallel groups, multi-centre, double blind, placebo controlled design is

supported. The effects of these interventions following a minimum of three months and

ideally 12 months on neuropsychological measures (attention, executive function, episodic

and working memory), cardiovascular factors (e.g. NYHA class, LVEF), oxidative stress

(F2-isoprostanes, DROMs), antioxidant levels (CoQ10, glutathione peroxidase) and

inflammation (hs-CRP, TNF-α, IL-1 and IL-6) compared to baseline, will be an important

study aimed towards a possible intervention for improving cognitive function and mood in

HF patients.

References

212

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Appendices

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List of Appendices

Appendix A Human Research Ethics Committee approval i

Appendix B Participant Information and Consent Form for the Heart Failure Group iv

Appendix C Cognitive Drug Research task instructions xi

Appendix D Computerised congruent and incongruent Stroop task instructions xv

Appendix E The Profile of Mood States Questionnaire xvi

Appendix F The Short Form (36 Item) quality of Life Questionnaire xvii

Appendix G The Chalder Fatigue Scale xx

Appendix H The General Health Questionnaire (12 Item) xxi

Appendix I Speilberger’s State Trait Anxiety Inventory xxii

Appendix J Polyunsaturated Fatty Acid Questionnaire xxiv

Appendix K Data screening: demographic variables xxv

Appendix L Frequency for common medications taken by heart failure patients and

controls presented as n (%) xxvi

Appendix M Frequency table for comorbidities in HF and Control groups xxvii

Appendix N Common pharmaceuticals including over the counter medicines and

supplements taken by participants xxviii

Appendix O Common natural medicines and supplements taken by participants xxix

Appendix P Data screening for baseline Stroop word colour tasks, Trail making-A,

Trail making-B and the five cognitive drug research factors xxx

Appendix Q Data exploration for mood variables to test assumptions for analysis of

variance xxxi

Appendix R Data exploration for vascular, oxidative stress, antioxidant and

inflammatory variables to test assumptions for ANOVA xxxii

Appendix

x A Human RResearch Eth

hics Commit

ttee approvaal

AAppendices

i

Appendix

Appendix

x A cont’d…

x A cont’d…

Human Res

Human Res

search Ethic

search Ethic

cs Committee

cs Committee

e approval

e approval

AAppendices

ii

Appendix

x A cont’d… Human Res

search Ethic

cs Committee

e approval

AAppendices

iii

Appen

ndix B Particcipant Inform

mation and C

Consent For

rm for the H

A

Heart Failure

Appendices

iv

e Group

Appendix

x B Participaant Informat

tion and Con

nsent Form ffor the Hear

A

rt Failure Gr

Appendices

v

roup

AppendixGroup

x B cont’d…PParticipant IInformation

n and Consennt Form for

A

the Heart F

Appendices

vi

Failure

AppendixGroup

x B cont’d…PParticipant I

Information


A

the Heart F

Appendices

vii

Failure

AppendixGroup

AppendixGroup

x B…cont’d P

x B cont’d…P

Participant I

Participant I

Information

Information

n and Consen

n and Consen

nt Form for

nt Form for

A

the Heart F

the Heart F

Appendices

viii

Failure

Failure

AppendixGroup



A

the Heart F

Appendices

ix

Failure

AppendixGroup



A

the Heart F

Appendices

x

Failure

Appendix

x C Cognitivee Drug Rese

earch task in

structions

AAppendices

xi

Appendix

x C cont’d…Cognitive Drug Researc

ch task instruuctions

AAppendices

xii

Appendix



AAppendices

xiii

Appendix



AAppendices

xiv

Appendices

xv

Appendix D Computerised congruent and incongruent Stroop task instructions In this task you will need to respond to a series of coloured words.

A series of words will appear on the screen one at a time. These will be the words BLUE, RED, GREEN, and YELLOW. The words will be presented in matching colours, BLUE, RED, GREEN, and YELLOW.

You need to respond to each word by pressing the corresponding coloured button as quickly as possible.

Since you will need to use all four coloured buttons for this task, you might want to re-position the button box so you can press all four buttons easily.

We want you to make accuracy your priority but also try to be quick to respond.

We will do a short practice first. Any questions?

[Run practice task]

How was that? Any questions?

[Run full task]

How was that? Any problems?

Stroop Incongruent task

The next task will test your ability to ignore a distraction.

A series of words will appear on the screen one at a time in the same manner as in the previous task. Again, these will be the words BLUE, RED, GREEN, and YELLOW. The words will be presented in different colours, either BLUE, RED, GREEN, and YELLOW. However, this time the colour will not match the written word.

Your job is to ignore the written word and focus on the colour that it is presented in. Respond by pressing the corresponding button.

Again, we want you to make accuracy your priority but also try to be quick to respond.

We will do a short practice first. Any questions?

[Run practice task]

How was that? Any questions?

[Run full task]

How was that? Any problems?

Appendix

x E The Proffile of Mood

States Ques

stionnaire

AAppendices

xvi

Appendix

1.1

x F The Shorrt Form (36 Item) quality

ty of Life Quuestionnaire

AAppendices

xvii

Appendixx F cont’d…The Short FForm (36 Item

m) quality off Life Questi

A

ionnaire

Appendices

xviii

Appendixx F cont’d…The Short FForm (36 Item

m) quality off Life Questi

A

ionnaire

Appendices

xix

Appendix

x G The Chalder Fatigue

e Scale

AAppendices

xx

Appendix

x H The Genneral Health

Questionna

ire (12 Item))

AAppendices

xxi

Appendix

x I Speilbergger’s State Tr

rait Anxiety

Inventory

AAppendices

xxii

Appendix

x I cont’d Speilberger’s S

State Trait A

Anxiety Invenntory

AAppendices

xxiii

Appendix

x J Polyunsaaturated Fatty

ty Acid Ques

stionnaire

AAppendices

xxiv

Appendices

xxv

Appendix K Data screening: demographic variables

i) Missing Values and Univariate outliers

Missing values for demographic (age, sex, education, handedness and estimated IQ), clinical considerations (NHYA class, diagnosis or no heart failure) and screening criteria (MMSE < 24) were explored using Missing Value Analysis (MVA) in SPSS. There were no missing values for demographic, clinical characteristics and screening variables. To detect univariate outliers, standardised Z scores were calculated for each demographic measure for the two experimental groups. None of the standardised scores for age, MMSE and WASI Vocabulary subset (premorbid IQ) for each group exceeded the α = .001 criterion of 3.29 for two tailed test (Tabachnick & Fidel, 2007).

ii) Normality

Examining skewness and kurtosis, inspection of probability plots of residuals. Age and MMSE did not meet the assumptions of normality and non-parametric tests were therefore used to assess group differences for these variables. WASI Vocabulary subset (premorbid IQ). Basal metabolic index (BMI), and GHQ-12 and heart rate were normally distributed in each group therefore the Student’s t-test was conducted to explore group difference for these variables.

Appendices

xxvi

Appendix L Frequency for common medications taken by heart failure patients and controls presented as n (%)

Heart failure Healthy control

n = 36 n = 40

Diuretic 32 (89) 0

Aldostrone antagonist 14 (39) 1 (2.5)

B-blocker 29 (81) 1 (2.5)

Warfarin 19 (53) 0

Digoxin 13 (36) 0

Statins 23 (64) 2 (5)

Potassium chloride 11 (31) 0

ACEI (antihypertensive) angiotensin II blocker 21 (58) 4 (10) Angiotensin II receptor antagonist 15 (42) 1 (3) CARTIA, (anticoagulant) 2 (5.6) 0 other anticoagulant 1 (2.8) 0 Calcium channel blocker (anti-hypertensive) 4 (11) 0 Antiarrhythmic (e.g. amiodarone) 11 (31) 0 Statins (hypolipid Agents) 23 (64) 2 (5) Aspirin (anticoagulant) 16 (45) 4 (10) Alpha blockers 3 (8.3) 0 Slow K (KCl-potassium supplement) 11 (31) 0 Plavix - (platelet aggregation inhibitor) 2 (6) 0 Imidure/nitrate 1 (3) 0 Cardiotonic agent (e.g. Coloran) 1 (3) 0

Appendices

xxvii

Appendix M Frequency table for comorbidities in HF and Control groups

displayed as n (%)


n = 36 n = 40

High blood pressure - slight 5 (14) 2 (5)

High blood pressure - controlled 6 (17) 6 (15)

Cholesterol 11 (31) 0

Respiratory 3 (8.3) 4 (10)

stomach/intestinal 5 (13.9) 4 (10)

Liver problems 0 1 (2.5)

Kidney/urinary 1 (2.8) 2 (5)

Diabetes 4 (11.1) 0

Anaemia 0 0

Hemochromatosis 0 1 (2.5)

Epilepsy/fitting 1 (2.8) 0

Cancer/remission/past 3 (8.3) 4 (10)

Cancer current 1 (2.8) 0

Skin disorders 1 (2.8) 1 (2.5)

Anxiety/depression 0 2 (5)

Arthritis 10 (27.8) 1 (2.5)

Gout 6 (16.7) 0

Oedema 1 (2.8) 0

Shortness of Breath 2 (5.6) 0

Hyperthyroidism 2 (5.6) 0

Hypothyroidism 1 (2.8) 0

Depression, not diagnosed 1 (2.8) 0

Sleep apnoea 1 (2.8) 1 (2.5)

Poor Sleep 1 (2.8) 0

High alcohol use in past 1 (2.8) 0

Osteoporosis 2 (5.6) 0

Vertigo 1 (2.8) 0

Other 7 (19.4) 0

Appendices

xxviii

Appendix N Common pharmaceuticals including over the counter medicines and supplements taken by participants displayed as n (%)


n = 36 n = 40

Viagra 1 (2.8) 0 Vasodilator 3 (8.3) 0 Glucocorticoid 4 (11.1) 0 Corticosteroid 1 (2.8) 0 Gout treatment 5 (13.9) 0 Insulin 2 (5.6) 0 Cholesterol lowering 6 (16.7) 2 (5) Fluticasone/salmeterol inhaler/sibicord 2 (5.6) 0 COX 2 inhibitor (anti-inflammatory) 0 1 (2.5) Proton pump inhibitor (e.g. Somac) 7 (19) 2 (5) Thyroxine 3 (8.3) 0 Panadol/Paracetamol 1 (2.8) 1 (2.5) Analgesic 2 (5.6) 0 Hormone replacement therapy 1 (2.8) 0 Panadol - Osteo 3 (8.3) 1 (2.5) Alendronate 0 1 (2.5) Antibacterial 1 (2.8) 0 Imodium 0 1 (2.5) Anti-hypertensive 3 (8.3) 2 (5) Antiasthma 0 1 (2.5) Antacid 3 (8.3) 1 (2.5) Asthma puffer (as needed) 1 (2.8) 2 (5) Immune-suppressant 1 (2.8) 0 Antibiotic 1 (2.8) 1 (2.5) Anti-inflammatories 1 (2.8) 0 Cholesterol lowering 2 (5.6) 3 (7.5) Keppra (grand mal epilepsy) 1 (2.8) 0 Antidepressant (Tricyclic) 1 (2.8) 0 Antidepressant (SSRI) 1 (2.8) 0 Benzodiazapine (anti-anxiety) 1 (2.8) 0

Appendices

xxix

Appendix O Common natural medicines and supplements taken by participants displayed

as n (%)


n = 36 n = 40

Lipic Acid 0 1 (2.5) Folic Acid 0 1 (2.5) Arginine 0 1 (2.5) CoQ10 current 2 (5.6) 3 (7.5) Lysine 0 1 (2.5) Fosamax D 0 4 (10) Cranberry 0 1 (2.5) Selenium 0 1 (2.5) Tumeric powder 0 1 (2.5) Royal jelly 0 1 (2.5) Aloe Vera 0 1 (2.5) Zinc 0 1 (2.5) Saw Palmeto 0 0 Glucosamine 2 (5.6) 9 (22.5) Iron supplement 2 (5.6) 1 (2.5) Fish Oil 2 (5.6) 14 (35) Vitamin D 1 (2.8) 5 (12.5) Vitamin B complex 0 3 (7.5) Vitamin B12 shots (every 2 months) 1 (2.8) 2 (5) Multivitamin 1 (2.8) 5 (12.5) Vitamin E 1 (2.8) 2 (5) Spirulina 0 1 (2.5) Vitamin C 0 5 (12.5) Thiamine 1 (2.8) 0 Magnesium 5 (13.9) 4 (10) Calcium/Caltrate 4 (11.1) 10 (25) Garlic 0 1 (2.5)

Appendices

xxx

Appendix P Data screening for baseline Stroop word colour tasks, Trail making-A, Trail making-B and the five Cognitive Drug Research factors

i) Missing Values

The variables were assessed separately for each of the experimental groups. For the HF group, Missing value analysis (MVA) revealed one missing case for each of the Stroop variables (congruent Stroop, incongruent Stroop and Stroop effect) and no missing values for Trail Making-A, Trail Making-B or CDR tasks. The missing case for the Stroop variables was due to the button box not working for the participant on the data collection day and this case was therefore deleted from the analysis exploring Stroop colour task variables.

For the Control group, there were no missing values for the Stroop tasks, Trail Making-A or Trail Making-B. However, there were 13 missing CDR task variables and two for each of the CDR factor scores. Since these missing variables were all from the same case, this participant was included when analysing the other three CDR factor scores.

ii) Univariate outliers

Standardised Z scores for each demographic measure for the two experimental groups determined univariate outliers. The α = .001 criterion of 3.29 for two tailed test (Tabachnick & Fidel, 2007) was used to determine outliers for every cognitive variable in each experimental group. Standardised z scores detected an outlier for each of the following variables in the HF group: congruent Stroop percentage accuracy, incongruent Stroop percentage accuracy and Stroop effect variables. Additionally, in the control group one case was an outlier for the Stroop effect variable. Finally, there was one outlier in the HF group for the time it took to complete the Trail Making-A task. In the HF group, there was one outlier for the Continuity of Attention CDR domain. In the control group, there was one outlier for each the Quality of Working Memory and Continuity of Attention. Transforming these variables removed these outliers (see below).

iii) Normality

The distributions of the cognitive variables were examined for skewness and kurtosis and Q-Q and P-P plots for normality. Slight kurtosis and positive skewness was observed for congruent Stroop, incongruent Stroop, Stroop effect, Trail Making-A and Trail Making-B variables. These tests for normality indicated that Stroop, Trail Making-A and Trail Making-B variables violated the ANOVA assumptions of variable normality. These variables were therefore transformed prior to running ANCOVA analyses. Congruent Stroop percentage accuracy and Incongruent Stroop percentage accuracy scores were heavily negatively skewed and non-parametric analysis was therefore conducted to determine whether group differences existed for these variables. Quality of Episodic Memory and Power of Attention cognitive domains were normally distributed. Kurtosis and slight negative skewness was observed for Quality of Working Memory and Continuity of Attention. Kurtosis and slight positive skewness was observed for the Speed of Memory variable. Transformations were conducted for Quality of Working Memory and Power of Attention. None of the formulas suitable for negatively skewed distributed variables were successful in transforming Continuity of Attention and non-parametric tests were therefore conducted to examine group differences.

Appendices

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Appendix Q Data exploration for mood variables to test assumptions for analysis of variance

i) Missing Values

The variables were assessed separately for each of the experimental groups. Missing value analysis (MVA) revealed no missing cases for the Profile of Mood States (POMS) and Short Form-36 (SF-36) subscales and STAI-S and STAI-T variables in the HF group. There was one missing case for each the Chalder fatigue scale-physical symptoms, Chalder fatigue scale-mental symptoms symptoms and Chalder fatigue scale-total in the HF group. There were no missing cases for each of the POMS mood domains, SF-36 and Chalder fatigue scale variables in the control group. There was one missing case for each the STAI-S and STAI-S variables in the control group.


Standardized z scores based on untransformed data were calculated to examine univariate outliers in each cognitive variable for each experimental group. There was one outlier for Chalder fatigue scale-mental symptoms in the HF group. In the control group there was one outlier for each the POMS-fatigue/inertia, SF 36- physical functioning, SF-36 role limitations due to emotional problems and SF-36 vitality subsets and Chalder fatigue scale-total score.

iii) Normality

The STAI-S, STAI-T and POMS subscales, except POMS-vigour/activity subset, were positively skewed. The log 10 transformation was used for all variables expect POMS-Total mood disturbance where the square root transformation was the best formula to normalise the variable. All outliers were removed following variable transformation.

iv) Homogeneity of variance

Levene’s test for equality of variance on transformed POMS variable, STAI-S and STAI-T establishes no significant differences in the variance of the POMS subscales. These analyses indicate that POMS and STAI meet the assumption of ANOVA.

Appendices

xxxii

Appendix R Data exploration for vascular, oxidative stress, antioxidant and inflammatory variables to test assumptions for ANOVA

i) Missing Values

The variables were assessed separately for each of the experimental groups. For the HF group, Missing value analysis (MVA) revealed missing cases were reported for each of the common carotid arterial blood flow (n=4), middle cerebral arterial blood flow (n=13), central pulse pressure (n=17) and augmentation index (n=19). These missing cases were due to inability in obtaining suitable signals for recording. In the HF group there were no missing cases for F2-isoporstanes in the HF group, however missing cases were observed for determinable reactive oxygen metabolite (DROM; n=9), glutathione peroxidase activity (n=2) and coenzyme Q10 (n=1), PUFA questionnaire (n=4) and high-sensitive C-reactive protein (n=1). For the control group, there were missing cases for common carotid (n=7) and middle cerebral arterial (n=8) blood flow velocities, PUFA questionnaire (n=19), high-sensitive C-reactive protein (n=17), DROM (n=11), glutathione peroxidase activity (n=13) and coenzyme Q10 (n=3). Missing cases for blood measures in both the HF and control group were due to insufficient volumes of blood obtained as a result of difficult bleed. There were no missing values in the control group for augmentation index and central pulse pressure.


Standardised Z scores were calculated to examine outliers in each biomarker variable for each experimental group. Standardised z scores for one HF patient case for high-sensitive C-reactive protein exceeded the α = .001 criterion of 3.29 for two tailed test (Tabachnick & Fidel, 2007). For the control group there was one outlier for central pulse pressure.

iii) Normality

The common carotid and middle cerebral blood flow velocities, augmentation index, PUFA questionnaire, F2-isoprostanes, CoQ10 and glutathione peroxidase activity variables were normally distributed. Other variables were positively skewed and log transformations were chosen to normalise these variables (high-sensitive C-reactive protein, DROM, endothelin-1). There was one outlier for endothelin-1, which was removed before analyses. All other outliers were removed following variable transformation.

iv) Homogeneity of variance

Levene’s test for equality of variance on transformed variables, except endothelin-1 indicated that these variables met the assumption of ANOVA.

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