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Neurobiology of Aging 53 (2017) 1e10

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Neurobiology of Aging

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Association between CSF biomarkers, hippocampal volume andcognitive function in patients with amnestic mild cognitiveimpairment (MCI)

Pradeep J. Nathan a,b,*,1, Yen Ying Lim c,**,1, Rosemary Abbott d, Samantha Galluzzi e,Moira Marizzoni e, Claudio Babiloni f,g, Diego Albani h, David Bartres-Faz i, Mira Didic j,k,Lucia Farotti l, Lucilla Parnetti l, Nicola Salvadori l, Bernhard W. Müllerm,Gianluigi Forloni h, Nicola Girtler n, Tilman Hensch o, Jorge Jovicich p, Annebet Leeuwis q,Camillo Marra r, José Luis Molinuevo s, Flavio Nobili n, Jeremie Pariente t, Pierre Payoux t,Jean-Philippe Ranjeva j,k, Elena Rolandi e, Paolo Maria Rossini r, Peter Schönknecht o,Andrea Soricelli u, Magda Tsolaki v, Pieter Jelle Visser q, Jens Wiltfangm,w,Jill C. Richardson x, Régis Bordet y, Olivier Blin z, Giovanni B. Frisoni e,aa, on behalf of thePharmaCog ConsortiumaHeptares Therapeutics Ltd, Cambridge, UKbBrain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UKc The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, AustraliadCambridge Cognition, Cambridge, UKe Lab Alzheimer’s Neuroimaging & Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, ItalyfDepartment of Physiology and Pharmacology, University of Rome “La Sapienza”, Rome, Italyg IRCCS San Raffaele Pisana of Rome, ItalyhDepartment of Neuroscience, Mario Negri Institute for Pharmacological Research, Milano, ItalyiDepartment of Psychiatry and Clinical Psychobiology, Faculty of Medicine, University of Barcelona and Institut d’Investigacions Biomèdiques August Pi iSunyer (IDIBAPS), Barcelona, Catalunya, SpainjAix-Marseille Université, INSERM, INS UMR_S 1106, Marseille, Francek Service de Neurologie et Neuropsychologie, APHM Hôpital Timone Adultes, Marseille, FrancelClinica Neurologica, Centro Disturbi della Memoria, Università di Perugia, Ospedale Santa Maria della Misericordia, Perugia, ItalymDepartment of Psychiatry and Psychotherapy, LVR-Hospital Essen, Faculty of Medicine, University of Duisburg-Essen, Essen, GermanynDepartment of Neuroscience (DINOGMI), Clinical Neurology, University of Genoa, Genoa, ItalyoDepartment of Psychiatry and Psychotherapy, University of Leipzig, Leipzig, GermanypCenter for Mind/Brain Sciences, University of Trento, Rovereto, Trento, ItalyqDepartment of Neurology, Alzheimer Centre, VU Medical Centre, Amsterdam, the NetherlandsrDepartment of Gerontology, Neurosciences and Orthopedics, Institute of Neurology, Catholic University, Policlinic A. Gemelli Foundation, Rome, ItalysAlzheimer’s Disease Unit and Other Cognitive Disorders Unit, Hospital Clínic de Barcelona, and Institut d’Investigacions Biomèdiques August Pi i Sunyer(IDIBAPS), Barcelona, Catalunya, Spaint INSERM, Imagerie cérébrale et handicaps neurologiques UMR 825, Toulouse, Franceu SDN Istituto di Ricerca Diagnostica e Nucleare, Napoli, Italyv 3rd Neurologic Clinic, Medical School, G. Papanikolaou Hospital, Aristotle University of Thessaloniki, Thessaloniki, GreecewDepartment of Psychiatry and Psychotherapy, University Medical Center (UMG), Georg-August-University, Goettingen, GermanyxNeurosciences Therapeutic Area, GlaxoSmithKline R&D, Stevenage, UKyUniversity of Lille, Inserm, CHU Lille, U1171 - Degenerative and Vascular Cognitive Disorders, Lille, FrancezMediterranean Institute of Cognitive Neurosciences (INCM), UMR-CNRS (6193), Aix Marseille University, Marseille, FranceaaMemory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland

* Corresponding author at: Heptares Therapeutics BioPark, Broadwater Road,Welwyn Garden City, Hertfordshire, AL7 3AX, UK. Tel.: þ44 (0) 1707 448037.** Corresponding author at: 155 Oak Street, Parkville, Victoria 3051, Australia.Tel.: þ61 3 9389 2909; fax: þ61 3 9035 8642.

E-mail addresses: pradeep.nathan@heptares.com (P.J. Nathan), yen.lim@florey.edu.au (Y.Y. Lim).

1 Joint first authors.

0197-4580/$ e see front matter � 2017 Elsevier Inc. All rights reserved.http://dx.doi.org/10.1016/j.neurobiolaging.2017.01.013

P.J. Nathan et al. / Neurobiology of Aging 53 (2017) 1e102

a r t i c l e i n f o

Article history:Received 15 August 2016Received in revised form 8 January 2017Accepted 10 January 2017Available online 18 January 2017

Keywords:Mild cognitive impairmentProdromalAlzheimer’s diseaseCognitive assessmentCANTABCSFAmyloidTauHippocampal volume

a b s t r a c t

Few studies have examined the relationship between CSF and structural biomarkers, and cognitivefunction in MCI. We examined the relationship between cognitive function, hippocampal volume andcerebrospinal fluid (CSF) Ab42 and tau in 145 patients with MCI. Patients were assessed on cognitive tasksfrom the Cambridge Neuropsychological Test Automated Battery (CANTAB), the Geriatric DepressionScale and the Functional Activities Questionnaire. Hippocampal volume was measured using magneticresonance imaging (MRI), and CSF markers of Ab42, tau and p-tau181 were also measured. Worse per-formance on a wide range of memory and sustained attention tasks were associated with reducedhippocampal volume, higher CSF tau and p-tau181 and increased tau/Ab42 ratio. Memory tasks were alsoassociated with lower ability to conduct functional activities of daily living, providing a link between ADbiomarkers, memory performance and functional outcome. These results suggest that biomarkers of Aband tau are strongly related to cognitive performance as assessed by the CANTAB, and have implicationsfor the early detection and characterization of incipient AD.

� 2017 Elsevier Inc. All rights reserved.

1. Introduction

There is now general consensus that Alzheimer’s disease (AD)has a long prodromal phase, initially characterized by synapticdysfunction due to amyloid precursors (dimers, oligomers) then bythe gradual accumulation of beta-amyloid (Ab) plaques andneurofibrillary tangles which lead to neuronal death and theemergence of impairment in cognitive function and functionalactivities of daily living (Jack and Holtzman, 2013; McKhann et al.,2011). Efforts to characterize this prodromal phase have resultedin the clinical entity of amnestic mild cognitive impairment (MCI),which is defined by the presence of a subjectivememory complaint,an objective memory impairment that has been matched for age(e.g., performance i.e., 1.5 standard deviations less than normativecontrols) and the relative preservation of activities of daily living(Petersen, 2004; Winblad et al., 2004). However, MCI remains alargely heterogeneous condition with a range of different under-lying neurodegenerative mechanisms that result in different formsof dementia, of which AD is only one.

Recent advances in neuroimaging and fluid biomarkers haveallowed for the detection and early characterization of AD patho-logical markers in vivo, and may help in the etiologic definition ofMCI. Neuroimaging studies conducted in awide range of laboratorieshave now consistently shown that themajority of patients whomeetclinical classification of dementia of the Alzheimer’s type haveabnormal accumulation of Ab (Abþ) in the brain (Jack et al., 2008;Rowe et al., 2010). Similarly, studies examining cerebrospinal fluidassays of patients with AD have shown significantly decreased levelsof Ab42. These studies suggest that Ab biomarkers have a highsensitivity for identifying individuals with AD. However, the sensi-tivity of Ab for the identification of the prodromal, orMCI stage of thedisease is less clear, particularly as only 50%e60% of older adults whomet clinical criteria for amnestic MCI were also Abþ (Fleisher et al.,2011; Johnson et al., 2013; Ong et al., 2013). An important limita-tion of these studies, however, has been the dichotomization of Ablevels into positive and negative (e.g., standardized uptake valueratio scores that are higher or lower than the 1.5 threshold on apositron emission tomography scan using the Pittsburgh compoundB radiotracer). As only very subtle differences in cognitive perfor-mance are likely to be observed when samples are divided into Abþand Ab�, one approach has been to understand the relationshipbetween cognitive performance and AD biomarkers instead. A sec-ond issue is that these studies have only focused on the relationshipbetween Ab and cognitive performance. As neurodegeneration andmarkers of neuronal injury such as tau, phosphorylated tau (p-tau)and hippocampal volume may be more closely related to cognitive

function, particularly episodic memory (Bennett et al., 2004; Hanet al., 2012), it is necessary to examine the relationships betweencognitive function and other markers of AD beyond that of Ab,particularly in the prodromal or MCI stage of the disease (Morminoet al., 2008).

The overarching aim of this study was to examine the rela-tionship between performance on a well-established computerizedcognitive test battery (Cambridge Neuropsychological Test Auto-mated Battery [CANTAB]) and AD-related biomarkers includinghippocampal volume and cerebrospinal fluid (CSF) biomarkers ofAb and tau in a group of well characterized patients with amnesticMCI. Our first hypothesis was that performance on measures ofmemory would be associated with reduced hippocampal volume,increased levels of CSF tau and p-tau, particularly given the earlydistribution of tau in the temporal cortex. As previous studies havereported relationships between Ab and cognitive function, partic-ularlymemory and executive function (Pike et al., 2007), our secondhypothesis was that lower performance on measures of memoryand executive functionwould be associated with lower levels of CSFAb42.

2. Methods

2.1. Participants

A total of 145 older adults withMCI were recruited from the IMIPharmacog’s WP5 (European ADNI) study, a prospective studywith multiple centers in Europe. The process of recruitment andenrollment has been described in detail previously (Galluzzi et al.,2016). Briefly, the inclusion criteria included: (1) age between55e90 years; (2) presence of a subjective memory complaint thathas been verified by a family relative; (3) at least 1 standard de-viation deficit in ameasure of episodic memory (Logical Memory IIsubscaleedelayed paragraph recall); (4) a MinieMental StateExamination score between 24 and 30; (5) a Clinical DementiaRating (CDR) scale score of 0.5 (with a score of 0.5 for the memorysubscale); (6) a clinical diagnosis of amnestic MCI, but preserva-tion of general cognitive and functional performance sufficient tonot meet clinical criteria for AD; (7) Geriatric Depression Scale(GDS) scale score <6; and (8) Hachinski Modified Ischemic Scalescore �4. Participants were excluded if there was a history ofsignificant neurological or psychiatric illness, ferromagnetic im-plants, and/or devices that may preclude them from magneticresonance imaging (MRI), brain malformations or other conditionsthat may complicate lumbar punctures, and use of the followingmedications: antidepressants with anticholinergic properties,

P.J. Nathan et al. / Neurobiology of Aging 53 (2017) 1e10 3

regular use of narcotic analgesics, use of neuroleptics with anti-cholinergic properties, and use of antiParkinsonian medications.The demographic and clinical characteristics of participants aresummarized in Table 1.

All participants provided written informed consent prior toparticipating in the study. The study was reviewed and approved bythe Ethics Committee of the coordinating site (Comitato Etico delleIstituzioni Ospedaliere Cattoliche) and then by Ethics Committeesof all other sites (Galluzzi et al., 2016).

2.2. Assessments

2.2.1. CANTAB cognitive batteryAll participants underwent cognitive evaluation using the

CANTAB battery of computerized cognitive tests (Egerházi et al.,2007). The tests in this battery included the motor control task(practice task only), reaction time (RTI) task, delayed matching tosample (DMS) task, paired associate learning (PAL) task, spatialworking memory (SWM) task, rapid visual information processing(RVP) task, pattern recognition memory (PRM) task (immediateand delayed), and spatial recognition memory (SRM) task. The keyoutcome measures selected for the analysis were; RTI (medianreaction time), DMS (% correct [all delays]), PAL (total errorsadjusted), SWM (between errors and strategy score), RVP (A0),PRM (% correct immediate and delayed), and SRM (% correct).These outcome variables were selected as they have been shownto be sensitive in detecting cognitive impairment in Alzheimer’sdisease (Swainson et al., 2001).

2.2.2. Neuropsychiatric and functional measuresAll participants completed the GDS. Participants’ caregivers

completed the Functional Activities Questionnaire (FAQ), whereinformants rated the participants’ ability to conduct a range ofactivities of daily living. Participants’ caregivers also participated ina detailed interview with a trained clinician to complete theNeuropsychiatric Inventory Questionnaire (NPIQ).

2.2.3. Biochemical analysisCSF from each participant was preprocessed, frozen, and stored at

each site according to a standardized protocol developed by MarioNegri Institute of Milan (Italy) which is in line with the Alzheimer’sAssociation’s quality control program for CSF biomarkers. At thecentral site in Milan, dedicated single parameter colorimetric ELISAkits (Innogenetics, Belgium) were used to quantify the amyloid-bpeptide 1e42 (Ab42). Levels of tau and p-tau at residue 181 (p-tau181)in the CSF were also quantified using ELISA kits. Each sample was

Table 1Demographic and clinical characteristics

Outcomevariable

Overall (n ¼ 145),Mean (SD)

Abþ (n ¼ 55),Mean (SD)

Ab� (n ¼ 90),Mean (SD)

Range p

Age (y) 68.2 (7.3) 69.8 (6.7) 68.8 (7.7) 50e84 0.40GDS 2.6 (1.9) 2.7 (1.9) 2.6 (1.9) 0e10 0.69NPIQ 8.8 (10.3) 9.6 (11.1) 8.3 (9.7) 0e48 0.48FAQ 2.5 (2.3) 2.6 (2.5) 2.5 (2.3) 0e11 0.67MMSE 26.6 (1.9) 26.0 (1.8) 27.0 (1.8) 23e30 0.01

Percentage (%) Percentage (%) Percentage (%)

APOE (% ε4) 37.3 52.7 27.8 0.00Sex (% Male) 42.8 42.2 43.6 0.87Education

(% �10 y)53.1 45.5 56.8 0.15

Bold values are significant at the p < 0.05 level.Key: APOE, apolipoprotein E (gene); FAQ, Functional Activities Questionnaire; GDS,Geriatric Depression Scale; MMSE, MinieMental Status Examination; NPIQ,Neuropsychiatric Inventory Questionnaire.

assessed in duplicate. A sigmoidal standard curve was plotted toallow quantitative expression (picograms/milliliter) of measuredlight absorbance. Of note, CSF samples of 2 patients were excludedfrom all statistical analyses as the value of measured proteinsresulted below the limit of detection. While this report consideredlevels of CSF biomarkers as a continuous variable, we have alsodichotomized participants into groups Abþ (<550 pg/mL) and Ab�(>550 pg/mL), consistent with previous reports (Fagan et al., 2007).

2.2.4. Neuroimaging analysisAll MRI scans were performed on 3.0 Tesla machines. Details of

scanners used at each site have been detailed previously (Galluzziet al., 2016; Jovicich et al., 2013; Marizzoni et al., 2015). All partic-ipants underwent MRI scans during the screening phase of thestudy, between January 2012 and July 2013. All analyses wereperformed at the central study site (IRCCS Institute of San Giovannidi Dio, Brescia) as previously described (Jovicich et al., 2013;Marizzoni et al., 2015). Briefly, structural T1 images were visuallyinspected for quality assurance prior to analyses to ensure thatthere were no gross partial brain coverage errors and no majorvisible artefacts, including motion, wrap around, radio frequencyinterference, and signal intensity or contrast inhomogeneities.Within session T1 images were averaged and were processed usingthe FreeSurfer v5.1.0 (Dale et al., 1999; Fischl et al., 2002; Fischlet al., 2004; Reuter et al., 2012) on the neuGRID platform.

2.3. Data analysis

To determine differences in cognitive performance between Abþand Ab� groups, a series of t tests were conducted. To determine therelationship between cognitive performance and biomarkers of AD, aseries of linear mixed models using SAS software (v9.4) were con-ducted, with the relevant biomarker as a fixed effect, and site as arandom effect. All statistical models were adjusted for age, years ofeducation, testing site, and evaluated for outliers. An adjustment formultiple comparisons was not conducted because (1) we had priorhypotheses pertaining to the relations between AD biomarkers andthe cognitive outcomes reported here; (2) the cognitive variableswere likely to be related; and (3) effect size statistics (e.g., r2 andCohen’s d) were used to quantify themagnitude of relations betweenoutcomemeasures, thus lowering the likelihood of over-interpretingour results. Finally, with multiple tests for each biomarker with 9cognitive variables, we would also expect less than 1 case per anal-ysis to be significant by chance at p ¼ 0.05.

3. Results

3.1. Clinical and demographic differences

The clinical and demographic data for the overall group and theAbþ and Ab� groups are shown in Table 1. The Abþ group hadlower MinieMental State Examination scores than the Ab� groupat baseline, and the Abþ group had a significant higher proportionof apolipoprotein E (APOE) ε4 carriers than the Ab� group. No otherclinical or demographic characteristic was statistically significantbetween Abþ and Ab� groups.

3.2. Relationships between cognitive performance and markers ofAb, tau, and brain volume

Higher CSF levels of tau and p-tau181 were associated with worseperformance on “visuospatial” memory tests including associativememoryandcuedrecall (PAL), visual episodicmemory(PRMdelayed),SWM, and SRM (Table 2, Figs. 1 and 2). Higher CSF levels of tau werealso associatedwithworse performance on sustained attention (RVP)

P.J. Nathan et al. / Neurobiology of Aging 53 (2017) 1e104

(Table2). Lower levels ofAb42wasassociatedwithworseperformanceon visual episodic memory (PRM delayed), working memory (SWMand DMS) and recognition memory (SRM) (Table 2). Interestingly,higher tau/Ab42 ratio was associated with worse performance on alltests of memory, including episodic memory (PAL and PRM delayed),working memory (SWM and DMS), recognition memory (SRM andPRM immediate), and sustained attention (RVP).

Associations were also found between reduced hippocampalvolume and memory including episodic memory (PAL and PRMdelayed), working memory (SWM), recognition memory (PRMimmediate), and sustained attention (RVP) (Table 2).

Table 2Relationship between each cognitive task and each biomarker

Outcome variable Memory tasks

Episodic memory,PAL errors

Working memory Recognition m

SWM errors DMS SRM PRim

Baseline modelRandomeffects (site)ICC 0.15 0.03 0.00 0.03

Taut 2.51 2.21 �0.84 �2.58R2 0.06 0.05 0.01 0.06p 0.014 0.030 0.402 0.011ICC 0.15 0.01 0.00 0.02

P-Taut 2.00 2.12 �0.92 �2.06R2 0.04 0.05 0.01 0.04p 0.048 0.037 0.370 0.042ICC 0.16 0.02 0.00 0.03

Ab42t �1.50 �2.56 3.35 2.75R2 0.02 0.07 0.09 0.07p 0.138 0.012 0.001 0.007ICC 0.14 0.02 0.00 0.04

Tau/Ab42t 2.96 2.77 �2.17 �3.77R2 0.08 0.08 0.04 0.11p 0.004 0.007 0.037 <0.001ICC 0.17 0.01 0.00 0.03

Left HVt �4.29 �2.23 0.84 1.47R2 0.18 0.07 0.40 0.03p <0.001 0.029 0.402 0.147ICC 0.10 0.00 0.02 0.02

Right HVt �4.89 �2.00 1.58 1.24R2 0.22 0.05 0.03 0.02p <0.001 0.049 0.118 0.220ICC 0.10 0.00 0.01 0.02 d

Baseline modelBIC 1155.5 744.1 1053.0 989.2 10

TauBIC 1129.3 722.5 1022.3 961.6 10

P-TauBCC 1119.6 722.7 1022.4 963.8 10

Ab42BCC 1115.9 740.3 1044.8 984.5 10

Tau/Ab42BIC 1115.1 719.8 1011.1 947.7 9

Left HVBIC 904.9 583.9 834.4 786.0 8

Right HVBIC 900.4 584.8 832.9 786.4 8

t, R2, and p indicate fixed effects of biomarker.Bold values are significant at the p < 0.05 or p < 0.001 level; all values have been adjusKey: BIC, Bayesian information criteria; DMS, delayed matching to sample; FAQ, Funcvolume; ICC, intraclass correlation coefficient (random effect of site); NPIQ, Neuropsychiatmemory; p-tau, phosphorylated tau; RTI, reaction time; RVP, rapid visual processing; SR

For comparison, we also examined relationships betweenbiomarkers and cognition as dichotomous groups. When partic-ipants were divided into Abþ and Ab� groups, the Abþ groupshowed worse performance relative to the Ab� group on recog-nition and working memory tasks (i.e., SRM and DMS) (Table 3),with these differences, by convention, large in magnitude(i.e., Cohen’s d ¼ 0.61e0.67), with a moderate differenceidentified for SWM errors (d ¼ �0.30). Groups did not differ onany measure of attention (i.e., RTI) or PAL (PAL total errors),with any differences between groups small in magnitude(d < 0.30; Fig. 3).

Executive function,SWM strategy

Sustainedattention, RVP

Processingspeed, RTIemory

Mmediate

PRM delayed

0.00 0.12 0.00 0.16 0.06

�1.36 �2.07 �0.19 �2.03 �0.420.02 0.04 0.00 0.04 0.000.178 0.041 0.849 0.045 0.6760.00 0.15 0.01 0.16 0.06

�0.37 �0.95 �0.91 �1.63 0.190.00 0.01 0.01 0.03 0.000.713 0.346 0.367 0.106 0.8500.00 0.13 0.01 0.17 0.06

1.15 2.16 �0.41 1.80 �0.220.01 0.04 0.00 0.03 0.000.251 0.033 0.681 0.075 0.8260.00 0.15 0.01 0.15 0.06

�2.09 �2.39 0.14 �2.66 0.070.04 0.05 0.00 0.07 0.000.039 0.019 0.886 0.009 0.9480.00 0.16 0.01 0.16 0.06

1.22 2.40 �2.37 1.45 �0.090.02 0.06 0.07 0.03 0.000.225 0.019 0.021 0.151 0.9270.00 0.15 0.00 0.13 0.03

2.03 1.92 �1.78 2.54 �0.330.05 0.04 0.04 0.08 0.000.046 0.058 0.079 0.013 0.740

0.13 0.01 11.56 0.03

32.7 1055.7 485.9 �333.0 1367.7

09.0 1030.2 481.1 �342.2 1337.6

10.3 1033.7 480.4 �340.9 1337.6

34.0 1053.8 488.3 �333.8 1370.2

98.0 1021.4 481.1 �341.0 1326.6

27.3 839.2 384.8 �288.4 1073.9

24.7 841.1 389.2 �292.4 1073.8

ted for age, years of education, and testing site.tional Activities Questionnaire; GDS, Geriatric Depression Scale; HV, hippocampalric Inventory Questionnaire; PAL, paired associate learning; PRM, pattern recognitionM, spatial recognition memory; SWM, spatial working memory.

Fig. 1. Relationship between performance on the spatial working memory (SWM) task and hippocampal volume, CSF Ab42, and CSF tau levels. Model predicted scores adjusted forage and education.

P.J. Nathan et al. / Neurobiology of Aging 53 (2017) 1e10 5

3.3. Relationships between cognitive performance andneuropsychiatric and functional measures

There were significant associations between tasks of memorywith FAQ, but not the GDS or NPIQ (Table 4). Specifically, asso-ciations were observed between episodic memory (PAL, PRMdelayed), working memory (DMS), and ratings on the FAQ.There was also a small but significant association betweenGDS scores and performance on the task of sustained attention(RVP) (Table 4).

4. Discussion

This is the first study examining the relationship betweencognitive function as assessed by the CANTAB battery and bothstructural and CSF biomarkers relevant to prodromal AD. Our firsthypothesis, that performance on measures of memory would beassociated with reduced hippocampal volume, and increasedlevels of CSF tau and p-tau, was supported. Worse performanceon CANTAB tasks measuring “visuospatial memory” includingassociative memory and cued recall (e.g., PAL), episodic memory(e.g., PRM delayed recall), spatial recognition memory (e.g., SRM),

and spatial working memory (e.g., SWM) were significantly asso-ciated with higher CSF levels of tau and p-tau181 (Table 2). Thisrelationship between memory and tau is consistent with previousstudies (Bennett et al., 2004; Ingelsson et al., 2004), and is alsoconsistent with our observations that performance on these taskswere also significantly associated with lower hippocampal volume(Table 2), with PAL accounting for approximately 20% of the vari-ance. These results suggest that performance on tasks of memory,particularly PAL, is strongly associated with hippocampal volume,and suggests a role for the PAL task in characterizing hippocampal-dependent memory impairment in the early stages of AD. The latteris of great significance, given that reduction in hippocampal volumeis a recognized biomarker of AD (Frisoni et al., 2010), the rate ofhippocampal atrophy is among the most sensitive markers ofdisease progression in AD (Frisoni et al., 2010; Jack et al., 2004) andone of the core biomarkers in the revised National Institute onAging-Alzheimer’s Association (NIA-AA) diagnostic criteria for AD(Jack et al., 2012). Hippocampal atrophy has been qualified by theEuropean Medicines Agency as a biomarker for enrichment inregulatory clinical trials in the early stages of AD (CHMP, 2011) andis currently being used as a secondary outcome in several clinicaltrials with candidate disease modification drugs.

Fig. 2. Relationship between performance on the paired associate learning (PAL) task and hippocampal volume, CSF Ab42, and CSF tau levels. Model predicted scores adjusted for ageand education.

P.J. Nathan et al. / Neurobiology of Aging 53 (2017) 1e106

The second hypothesis, that lower performance on measures ofmemory and executive function would be associated with lowerlevels of CSF Ab42, was partially supported. Worse performance ontasks of working (i.e., SWM, DMS) and recognition memory (i.e.,SRM, PRM) from the CANTAB battery were significantly associatedwith lower CSF levels of Ab42 (Table 2), and these were, byconvention, of a moderate magnitude; however, there were no

Table 3Differences between Abþ and Ab� groups on each cognitive outcome measure

Outcome variable Overall (n ¼ 145), Mean (SD)

PAL total errors (adjusted) (�ve) 55.3 (26.6)SWM between errors (Lve) 27.7 (8.3)DMS % correct (all delays) (Dve) 67.9 (16.3)SRM % correct (Dve) 63.7 (13.5)PRM immediate % correct (þve) 77.6 (15.2)PRM delayed % correct (þve) 65.3 (17.9)SWM strategy (�ve) 19.9 (2.4)RVP A0 (þve) 0.8 (0.1)RTI (ms) 417.9 (96.4)

þve indicates that higher score ¼ better performance; �ve indicates that higher score ¼Bold values are significant at the p < 0.05 level; all values have been adjusted for age anKey: DMS, delayed matching to sample; PAL, paired associate learning; PRM, pattern rrecognition memory; SWM, spatial working memory.

associations between executive function (i.e., as measured by theSWM task) and CSF Ab42 levels (Table 2). However, in this study,only 1 measure of executive function (SWM strategy) was included,and as the domain of executive function can be divided into severalsubdomains, such as planning, response, inhibition, and manipu-lation, it is possible that a relationship may exist only with execu-tive processes that also overlap with memory processes, such as

Abþ (n ¼ 55), Mean (SD) Ab� (n ¼ 90), Mean (SD) p

59.4 (25.3) 52.3 (27.2) 0.1429.6 (7.1) 26.3 (8.4) 0.0462.5 (16.6) 72.0 (14.9) <0.0158.7 (13.6) 67.3 (12.3) <0.0175.5 (14.5) 79.2 (15.6) 0.1763.5 (17.4) 66.6 (18.3) 0.3420.2 (2.0) 19.7 (2.7) 0.430.8 (0.1) 0.8 (0.1) 0.33

414.5 (97.7) 420.5 (96.0) 0.74

worse performance.d years of education.ecognition memory; RTI, reaction time; RVP, rapid visual processing; SRM, spatial

Fig. 3. Magnitude of difference between Ab� MCI and Abþ MCI groups on a series of cognitive measures from the CANTAB computerized test battery (“0” line represents Ab� MCIand error bars represent 95% confidence intervals). Abbreviations: CANTAB, Cambridge Neuropsychological Test Automated Battery; MCI, mild cognitive impairment.

P.J. Nathan et al. / Neurobiology of Aging 53 (2017) 1e10 7

working memory. In support, previous studies from the AustralianImaging, Biomarkers and Lifestyle study have observed that Abþolder adults with amnestic MCI performed worse than Ab� older

Table 4Relationship between each cognitive task and neuropsychiatric measure

Outcome variable Memory tasks

Episodic memory,PAL errors

Working memory Recognition m

SWM errors DMS SRM PRim

Baseline modelRandomeffects (site)ICC 0.15 0.03 0.00 0.03

GDSt 0.85 0.38 1.18 �1.11R2 0.01 0.00 0.01 0.00p 0.396 0.702 0.242 0.589ICC 0.14 0.02 0.00 0.03

FAQt 3.70 1.59 �2.20 �1.15R2 0.12 0.03 0.05 0.02p <0.001 0.115 0.030 0.253ICC 0.11 0.03 0.00 0.03

NPIQt 1.90 0.63 �1.15 �0.54R2 0.04 0.01 0.01 0.00p 0.060 0.529 0.255 0.589ICC 0.19 0.04 0.00 0.03

Baseline modelBIC 1155.5 744.1 1053.0 989.2 10

GDSBIC 1154.7 746.5 1054.2 990.5 10

FAQBIC 1031.3 671.0 938.2 842.9 9

NPIQBIC 1022.9 665.4 928.9 873.0 9

t, R2, and p indicate fixed effects of biomarker.Bold values are significant at the p < 0.05 or p < 0.001 level; all values have been adjusKey: BIC, Bayesian information criteria; DMS, delayedmatching to sample; FAQ, Functionacoefficient (random effect of site); NPIQ, Neuropsychiatric Inventory Questionnaire; PAL, prapid visual processing; SRM, spatial recognition memory; SWM, spatial working memo

adults with amnestic MCI on a computerized task of workingmemory from the Cogstate Brief Battery (Lim et al., 2013c) but noton neuropsychological pen and paper tasks such as the Rey

Executive function,SWM strategy

Sustainedattention, RVP

Processingspeed, RTIemory

Mmediate

PRM delayed

0.00 0.12 0.00 0.16 0.06

�1.28 �0.33 �0.18 �2.02 �0.320.02 0.00 0.00 0.04 0.000.202 0.740 0.854 0.046 0.7490.00 0.12 0.00 0.08 0.05

�1.19 �2.41 0.45 �1.27 0.340.01 0.06 0.00 0.02 0.000.239 0.018 0.652 0.208 0.7340.00 0.11 0.01 0.12 0.08

0.51 �1.70 0.46 0.62 0.300.00 0.03 0.00 0.00 0.000.610 0.092 0.650 0.535 0.7680.01 0.14 0.02 0.13 0.08

32.7 1055.7 485.9 �333.0 1367.7

33.7 1058.2 488.4 �287.8 1370.2

25.5 950.5 422.1 �295.3 1229.0

11.9 939.6 433.1 �293.1 1206.2

ted for age, years of education, and testing site.l Activities Questionnaire; GDS, Geriatric Depression Scale; ICC, intraclass correlationaired associate learning; PRM, pattern recognition memory; RTI, reaction time; RVP,ry.

P.J. Nathan et al. / Neurobiology of Aging 53 (2017) 1e108

Complex Figure Test and or tests of verbal fluency (Ellis et al., 2013;Lim et al., 2014b). The hypothesis that only executive processes thatalso have amemory load are affected in the early AD process, can betested in future studies that have a wider range of executive func-tion measures in their cognitive battery.

Interestingly, worse performance on all tests of memoryincluding episodic memory (PAL), working memory (SWM, DMS),and recognitionmemory (SRM, PRM) were associated with a highertau/Ab42 ratio (Table 2). Previously, pathological studies and CSFstudies have shown that in AD, cognitive impairment and synapticloss are associated more strongly with the presence and number ofneurofibrillary tangles, rather than amyloid plaques (Bennett et al.,2004; Giannakopoulos et al., 2003; Ingelsson et al., 2004). However,neuroimaging studies of preclinical and prodromal AD have re-ported that higher cortical Ab load is associated with greater ratesof cognitive decline and progression to AD (Lim et al., 2014a; Roweet al., 2013). The results of this study suggest that while there areindependent associations between CSF Ab42 as well as tau andp-tau, and cognitive function in MCI, these associations becomestronger, affecting broader cognitive domains when levels of bothCSF tau and Ab42 are considered together.

Worse performance on the CANTAB task of sustained attention(i.e., RVP) was also similarly associated with higher CSF levels of tauand p-tau, and tau/Ab42 ratio, and lower hippocampal volume(Table 2). Previous studies have not reported any relationshipbetween tasks of complex or simple attention and any biomarker ofAD pathophysiology (e.g., Ab levels or hippocampal volume), withmost studies only noticing any deterioration in attentional functionwhen individuals progress to meet a clinical diagnosis of AD.One possible reason why the CANTAB sustained attention taskmay be sensitive to subtle impairments in this prodromal stage ofAD is because in addition to probing sustained attention, thetask involves aspects of working memory (e.g., remembering thesequences 3-5-7, 2-4-6, and 4-6-8 in order to respond accurately)(van der Wardt et al., 2015). Further, brain imaging studieshave shown that successful performance on the CANTAB task ofsustained attention is associatedwith activationwithin the classicalattentional network, including the frontal, parietal, and occipitalgyrus, as well as deactivation within the temporal and para-hippocampal gyrus regions (Coull et al., 1996), which are known tobe affected by both Ab and tau pathology (Brier et al., 2016). Asneurodegenerative mechanisms leading to cognitive declineseverely impact on brain connectivity (initially by synapticdysfunction), then also by degeneration of cortico-cortical con-nections, it is not surprising that highly specialized functionswhich involve large networks linking adjacent and remote neuronalassemblies “around” a given function, are precociously involved(D’Amelio and Rossini, 2012).

While the focus of this study was to examine the relationshipbetween AD-related biomarkers and cognitive function, forcompleteness, it is important to compare the findings examiningassociations with biomarkers as continuous variables withimpairment in cognition in dichotomized groups (i.e., Abþ vs Ab�).The latter findings have been previously reported in brief(Galluzzi et al., 2016), but we further discuss the significance ofthese findings here. In comparison to the Ab� MCI group, the AbþMCI group showed significantly worse performance, of a moderatemagnitude, on measures of working memory (SWM and DMS) andrecognition memory (SRM) (Fig. 3, Table 3). These findings areconsistent with the findings observed when examined as acontinuous variable with worse performance on SWM, DMS, andSRM associated with lower CSF levels of Ab42. The substantial def-icits (d z 0.6) observed in the Abþ MCI group on memory tasksincluding the DMS and SRM suggest that despite all participantsmeeting criteria for amnestic MCI (and showing impairment on

episodic memory), the Abþ MCI group also showed additionaldeficits in other types of memory probing a wider neural networkbeyond the hippocampus/temporal cortex. This is consistent withprevious reports from our group (Galluzzi et al., 2016), and theAustralian Imaging, Biomarkers and Lifestyle study that have alsoobserved that in older adults with amnestic MCI, Abþ groups per-formed worse than Ab� groups on tasks of language and workingmemory (Lim et al., 2013b). It is also important to note that nodifferences between Abþ MCI and Ab� MCI groups were observedon the hippocampal-dependent task of episodic memory (i.e., PAL).One hypothesis for this is that as all participants had met clinicalcriteria for amnestic MCI with objective and subjective episodicmemory deficits at screening, it is unlikely that any further deficitsin episodic memory would be evident.

Cognitive tasks that were associated with structural and CSFbiomarkers were also associated with functional outcome. Inparticular, significant associations between memory tasks andratings on the FAQ (Table 4) were observed. Specially, associationswere found between hippocampal (PAL, PRM) and frontal cortex(DMS)edependent memory tasks and FAQ. The worse performanceon these tasks that is associated with lower ability to conductactivities of daily living is consistent with the notion that thepreservation of memory function is highly involved in an in-dividual’s ability to conduct and manage such activities of dailyliving (Perneczky et al., 2006; Royall et al., 2005). These findings areof importance given the need to identify cognitive measures relatedto functional outcomes for use in clinical trials in prodromal ADand the requirements placed by regulatory bodies to demonstratethe benefit of new treatments on functional outcome.

When interpreting the results of this study, there are severalcaveats to consider. First, the clinicopathologic inferences that wehave made in this study between measures of cognitive functionand AD biomarkers were based on cross-sectional data. As AD isa neurodegenerative disease, the measurement of change incognitive function and AD biomarkers over time may improveour understanding of the relationship between AD biomarkers andcognitive function in this early stage of the disease. Second, we haveonly explored the relationship between AD biomarkers and cogni-tive function, without taking into consideration other potentialmoderating factors, such as the APOE ε4 allele. As previous studiesin cognitively normal older adults have suggested that some of theheterogeneity in the relationship between Ab and memory may beexplained by the APOE ε4 allele (Kantarci et al., 2012; Lim et al.,2013a), it will be important for future studies to extend theseinvestigations into MCI populations. Finally, this study did notrecruit cognitively normal older adults, and as such, a character-ization of the current MCI group to a matched healthy controlsample was not conducted. We were also unable to directly deter-mine whether relations between each AD biomarker and cognitivefunction as assessed by the CANTAB were similarly present incognitively normal older adults.

These caveats notwithstanding, the results of this study showthat the strong association between CSF markers of neuronalinjury such as tau and p-tau181 and memory processes that tapinto both frontal and hippocampal networks, accords withneuropathological studies that have demonstrated strongrelationships between neurofibrillary tangles and cognitive func-tion (Bennett et al., 2004). It also highlights that while cognitivefunction, particularly memory, is closely associated to markers ofneurodegeneration and neuronal injury (e.g., loss of hippocampalvolume), they are also highly associated with levels of Ab. This isconsistent with previous biomarker studies that have also shownstrong relationships between cortical amyloid levels and cognitivefunction in both cognitively normal older adults and in MCI pop-ulations. Finally, the findings also raise an important distinction in

P.J. Nathan et al. / Neurobiology of Aging 53 (2017) 1e10 9

the classification of patients with MCI due to AD, and individualswith MCI due to a non-AD pathophysiological process. Whileclinical subclassifications such as “amnestic” and “nonamnestic”MCI have been previously used to make this distinction (Petersen,2004), the results of this study have shown that even with carefulclinical classification of individuals, there remain a substantialproportion of Ab negative older adults who have been clinicallyclassified as amnestic MCI. As such, the presence of an abnormalbiomarker of Ab and an abnormal biomarker of tau, in conjunctionwith the presence of objective cognitive impairment, particularlyin memory and executive function, may provide the greatestconfidence in identifying individuals whomay be at highest risk ofprogressive cognitive decline (Albert et al., 2011; Dubois andAlbert, 2004; Shaw et al., 2009).

Disclosure statement

R. A. is an employee of Cambridge Cognition, the company thatprovides the CANTAB cognitive test battery. J. R. is an employee ofGlaxoSmithKline. F. N. has received fees from Eli Lilly & Co in2014e2015 for amyloid imaging reading courses. P. J. V. hasreceived grants from EU/EFPIA Innovative Medicines Initiative JointUndertaking, EU Joint ProgrammeeNeurodegenerative DiseaseResearch (JPND) and ZonMw, during the conduct of this study; healso received grants from Bristol-Myers Squibb, nonfinancialsupport from GE Healthcare and other support from RocheDiagnostics which is of no relevance to the submitted work. Allother authors have no conflict of interests to disclose.

Acknowledgements

The research leading to the current results has received fundingfrom the European Community’s Seventh Framework Programme(FP7/2007-2013) for the Innovative Medicine Initiative under grantagreement no 115009 (prediction of cognitive properties of newdrug candidates for neurodegenerative diseases in early clinicaldevelopment, PharmaCog).

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