Draft - University of Toronto T-Space€¦ · Kruger, Herculina; North West University Faculty of ... College of Agriculture and Environmental Sciences UNISA, c/o Christiaan de Wet

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Association of 25-hydroxyvitamin D and parathyroid

hormone with the metabolic syndrome in black South

African women

Journal: Applied Physiology, Nutrition, and Metabolism

Manuscript ID apnm-2016-0257.R1

Manuscript Type: Article

Date Submitted by the Author: 25-Nov-2016

Complete List of Authors: Sotunde, Olusola; North West University Faculty of Health Sciences, Centre

of Excellence for Nutrition; University of South Africa, Life and Consumer Science Kruger, Herculina; North West University Faculty of Health Sciences, Centre of Excellence for Nutrition; North West University Faculty of Health Sciences, Medical Research Council Hypertension and Cardiovascular Disease Research Unit Wright, Hattie; North West University Faculty of Health Sciences, Centre of Excellence for Nutrition; University of the Sunshine Coast, School of Health and Sports sciences Havemann-Nel , Lize; North West University Faculty of Health Sciences, Centre of Excellence for Nutrition Mels, Carina; North West University Faculty of Health Sciences,

Hypertension in Africa Research Team Ravyse, Chrisna; North West University Faculty of Health Sciences, Centre of Excellence for Nutrition; North West University, Physical activity, Sport and Recreation Research Focus Area Pieters, Marlien; North West University Faculty of Health Sciences, Centre of Excellence for Nutrition

Keyword: 25(OH)D, PTH, metabolic syndrome, adiposity, obesity

https://mc06.manuscriptcentral.com/apnm-pubs

Applied Physiology, Nutrition, and Metabolism

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Association of 25-hydroxyvitamin D and parathyroid hormone with the metabolic syndrome

in black South African women

Authors: Olusola Funmilayo Sotunde, Herculina Salome Kruger, Hattie H Wright, Lize

Havemann-Nel, Carina MC Mels, Chrisna Ravyse, Marlien Pieters.

Corresponding Author: Olusola Funmilayo Sotunde, Department of Life and Consumer

Sciences, College of Agriculture and Environmental Sciences UNISA, c/o Christiaan de Wet

& Pioneer Ave, Florida, Private Bag X6, UNISA Florida, 1710, South Africa.

E mail: [email protected]

Affiliations and address of all authors:

Olusola Funmilayo Sotunde1 Centre of Excellence for Nutrition, North-West University,

Potchefstroom South Africa. [email protected]

Herculina Salome Kruger, Centre of Excellence for Nutrition, North-West University,

Potchefstroom and Medical Research Council Hypertension and Cardiovascular Disease

Research Unit, North-West University, Potchefstroom Campus, Potchefstroom, 2520, South

Africa. [email protected]

Hattie H Wright,2 Centre of Excellence for Nutrition, North-West University, Potchefstroom

South Africa. [email protected]

Lize Havemann-Nel, Centre of Excellence for Nutrition, North-West University,

Potchefstroom South Africa. [email protected]

Carina MC Mels, Hypertension in Africa Research Team, Faculty of Health Sciences, North-

West University, Potchefstroom, South Africa. [email protected]

1 Department of Life and Consumer Science, University of South Africa. [email protected] 2 School of Health and Sports sciences, University of the Sunshine Coast, Maroochydore,

Queensland, Australia. [email protected]

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Chrisna Ravyse, Centre of Excellence for Nutrition, North-West University, Potchefstroom

and Physical activity, Sport and Recreation Research Focus Area, North-West University,

Potchefstroom, 2520, South Africa. [email protected]

Marlien Pieters, Centre of Excellence for Nutrition, North-West University, Potchefstroom

South Africa. [email protected]

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ABSTRACT

The relationship between 25 hydroxyvitamin D (25(OH)D), parathyroid hormone (PTH) and

metabolic traits appear to differ among ethnicities and may be influenced by obesity. The aim

of the study was to examine the association of serum 25(OH)D and PTH, respectively, with

the metabolic syndrome (MetS) while controlling for adiposity in black women. Using a

cross-sectional study design, 209 urban black women aged ≥ 43 years from the North West

Province, South Africa, were included. Multiple regression models were used to explore the

relationship between 25(OH)D, PTH and body composition. To explore the association

between 25(OH)D, PTH and MetS, a separate variable was created including at least three of

the MetS criteria, but excluding elevated waist circumference as a diagnostic criterion in a

logistic regression model. The majority of the women (69.9%) were overweight or obese and

65.5% of the women had excessive adiposity using the age specific cut-off points for body fat

percentage. All body composition variables were positively associated with PTH, while BMI

and waist circumference, but not body fat % had negative associations with 25(OH)D, also

after adjusting for confounders. Before and after adjusting for age, body fat, habitual physical

activity, tobacco use, season of data collection and estimated glomerular filtration rate,

neither 25(OH)D nor PTH showed significant associations with the MetS. Although PTH was

positively and 25(OH)D was negatively associated with adiposity in black women, there was

no association between either 25(OH)D or PTH and the MetS in this study population, nor

did adiposity influence these relationships.

Key words: 25(OH)D, PTH, metabolic syndrome, adiposity, obesity.

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Introduction

The metabolic syndrome (MetS) is a global epidemic, which increases the risk for

development of type 2 diabetes mellitus and cardiovascular morbidity and mortality (Wang et

al. 2007). MetS is a cluster of metabolic disorders that includes at least three of the following

five criteria: elevated fasting blood glucose, hypertension, abdominal obesity, elevated

triglycerides and low high density lipoprotein cholesterol (HDL-C) (Alberti et al. 2009).

A number of studies indicated metabolic roles for 25 hydroxy-vitamin D (25(OH)D) and

parathyroid hormone (PTH) (Reis et al. 2008, Lee et al. 2009), demonstrating a

comparatively consistent inverse relationship between serum vitamin D and MetS and a

positive association of PTH and the MetS (Hypponen et al. 2008, Reis et al. 2008, Lee et al.

2009, Yin et al. 2012). However, the relationship between low serum vitamin D and

metabolic traits appear to differ among ethnicities. In the USA, NHANES III data showed an

inverse association between vitamin D status and insulin resistance in non-Hispanic whites

and Mexican Americans, with no relationship in black Americans (Scragg et al. 2004).

South Africa is in the nutrition-related non-communicable disease phase of the nutrition

transition (Vorster et al. 2011) associated with an obesity epidemic. The MetS prevalence is

high among blacks and Asian-Indian South Africans (Motala et al. 2011, George et al. 2013).

To the best of our knowledge, only one study in South Africa has examined the association

between 25(OH)D, PTH and the MetS in Africans (George et al. 2013). The study found a

positive association between PTH and the MetS, but no association between 25(OH)D and

the MetS (George et al. 2013). We postulate that the association between 25(OH)D, PTH and

the MetS may be mediated by adiposity due to several metabolic functions of adipose tissue

(Wortsman et al. 2000, Lips 2001, Bischof et al. 2006, Greenberg and Obin 2006, Reinehr et

al. 2007, Reis et al. 2007).

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As individuals become obese their adipocytes enlarge and adipose tissue undergoes

molecular and cellular changes affecting systemic metabolism (Greenberg and Obin 2006).

Adipocytes are active endocrine organs that play multiple roles in the body in addition to

their role as a storage reservoir of fat (Greenberg and Obin 2006). Vitamin D deficiency was

associated with excess body weight in some studies (Bischof et al. 2006, Greenberg and Obin

2006, Reinehr et al. 2007), which could be due to fat-soluble vitamin D getting trapped in

excess body fat, thereby reducing its bioavailability (Wortsman et al. 2000). Low serum

vitamin D is also associated with elevated PTH secretion (Lips 2001) which has been linked

to obesity (Reis et al. 2007) and the MetS (Reis et al. 2007, Ahlström et al. 2009).

Consequently, the aim of the study was to examine the association of serum 25(OH)D and

PTH, respectively, with the MetS while controlling for adiposity in black women of the North

West Province, South Africa.

Methods

Study design and subjects

The South African North West Province arm of the Prospective Urban and Rural

Epidemiology (PURE-SA-NWP) study commenced with baseline data collection in 2005 on

randomly selected apparently healthy adults older than 35 years (Teo et al. 2009, Kruger et al.

2011). Using a cross-sectional study design, urban black women measured at 7 years follow-

up of the PURE-SA-NWP study were included in this sub-study. The measurements for this

sub-study took place between September 2012 and June 2013. Only participants who had

undergone dual energy X-ray absorptiometry (DXA) measurements and provided blood

samples at follow-up and who were HIV negative were eligible for inclusion in this study

(n=209). The seasons of data collection were defined as September to early December 2012

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for spring (season 0, reference) and April to early June 2013 for autumn (season 1). The study

was approved by the Ethics committee of the North-West University (NWU), Potchefstroom

campus (NWU-00016-10-A1). All participants provided written informed consent and all

procedures followed were in accordance with the ethical standards of the Helsinki

Declaration.

Body composition measurements

Height (cm), weight (kg) and waist circumference (cm) were measured to the nearest decimal

according to the International Society for the Advancement of Kinanthropometry (ISAK)

criteria (Marfell-Jones et al. 2012). Body fat percentage was measured by a registered

radiographer using dual energy x-ray absorptiometry (Hologic Discovery W, APEX system

software version 12.7.3.1). Body mass index (BMI) was calculated (weight in kilograms

divided by height in meter squared). Abdominal obesity was defined as waist circumference

≥ 80 cm (Alberti et al. 2009). Age-specific cut-offs for high body fat percentage were defined

as ≥35.8% for women aged 43-49 years and ≥37.7% for women aged 50 years and above to

indicate adiposity (Heo et al. 2012).

Biochemical analyses and blood pressure measurement

Blood samples were collected from the ante-brachial vein following an overnight fast. Serum

samples were prepared and stored at -80ºC. All samples were analysed at the same time with

reagents from the same lot. Serum cholesterol, triglycerides (TG), HDL-C, high sensitivity C

reactive protein (CRP), creatinine and plasma glucose were analysed on the Cobas Integra

400 Plus (Roche, Basel, Switzerland). Insulin, 25(OH)D concentrations and PTH were

determined with an electrochemiluminescence immunoassay on the Elecsys 2010 (Roche,

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Basel, Switzerland). All inter- and intra assay coefficients of variation (CV) were less than

10%. We defined vitamin D deficiency as serum 25(OH)D concentrations < 20 ng/mL, and

vitamin D insufficiency as 25(OH)D between 21-29 ng/mL (Holick et al. 2011). Elevated

PTH was defined as PTH concentration > 65 pg/mL (Heil and Ehrhardt 2008). The

homeostasis model assessment (HOMA) technique was used to calculate insulin resistance

(HOMA-IR) (Matthews et al. 1985) and estimated glomerular filtration rate (eGFR) was

calculated with the modification of diet in renal disease (MDRD) formula (Levey et al. 2006).

After ten minutes’ rest, systolic and diastolic blood pressures were measured with an

OMRON HEM-757 instrument (Omron Healthcare, Kyoto, Japan), using appropriate sized

cuffs. The measurements were carried out in duplicate (5 minutes apart) on the right upper

arm, while the participants were seated with the right arm supported at heart level.

Questionnaires and physical activity

Structured questionnaires adapted for the PURE study were used by trained fieldworkers to

collect socio-demographic and lifestyle information, which included tobacco use (smoking

and/or snuff), in the participants’ language of choice (Teo et al. 2009). Habitual physical

activity was measured with a modified Baecke questionnaire (Kruger et al. 2000) and

physical activity scores were obtained (Kruger et al. 2011). A physical activity index value

from 1 to 3.3 was categorised as low, 3.34 to 6.67 was moderate and greater that 6.67 was

categorised as high physical activity (Kruger et al. 2011). Habitual activity energy

expenditure was also measured using an accelerometer with a combined heart rate monitor

(ActiHeart®, Camtech, UK) for 7 days (Brage et al. 2005). The time spent per individual in

each physical activity intensity category was recorded and activity energy expenditure (AEE)

was determined. Duration and intensity of activity were categorized as 1.1 – 2.9 METS as

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light-intensity activity, 3.0 – 5.9 METS as moderate-intensity activity and vigorous activity

as ≥ 6 METS (Donnelly et al. 2009). Total energy expenditure was also calculated (Kcal).

The reliability and validity of the ActiHeart® to evaluate physical activity in Sub-Saharan

Africans has been previously assessed (Assah et al. 2011).

Diagnosis of the MetS

Using the harmonized definition, participants with at least three of the following criteria were

diagnosed to have the MetS (Alberti et al. 2009): elevated waist circumference (≥ 80cm);

hypertension (diagnosed hypertensive subjects on blood pressure medications or subjects

with systolic blood pressure ≥ 130 mmHg and/or diastolic blood pressure ≥ 85 mmHg);

elevated serum triglycerides (≥ 1.7 mmol/L); reduced serum HDL-C (<1.3 mmol/L) and

subjects on oral hypoglycemic medication or with elevated fasting blood glucose (≥ 5.6

mmol/L). None of the subjects used hypolipidemic drugs. A separate MetS variable was

created, excluding elevated waist circumference as a diagnostic criterion, due to the strong

collinear relationship between body fat percentage and waist circumference. Hence, this

variable was defined as the presence of 3 out of 4 MetS criteria (Alberti et al. 2009) and used

for the logistic regression analysis, with presence of the modified MetS as dependent variable

and body fat percentage as a covariate.

Statistical analysis

IBM SPSS version 22 (IBM Company, Armonk, NY, USA) was used for all analyses.

Normally distributed data are presented as means ± standard deviation. Non-normally

distributed data was logarithmically transformed for statistical purposes and presented as

medians and interquartile range. Categorical data were analysed using frequency tables and

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prevalence of specific conditions was expressed as percentages. Independent t-tests were used

to compare normally distributed variables and Mann-Whitney U-tests to compare non-

normally distributed variables between groups with MetS (modified MetS classification, 3

out of 4 criteria) and without MetS. Analysis of covariance (ANCOVA) was used to adjust

for body fat percentage while comparing means of 25(OH)D and PTH between groups with

and without MetS. To explore the relationship between 25(OH)D, PTH and MetS variables

the correlations between 25(OH)D, PTH and individual components of the metabolic

syndrome were assessed. Stepwise multiple regressions were then used to explore the

relationship between 25(OH)D, PTH and body composition variables while adjusting for age,

physical activity score, tobacco use, season of data collection, CRP and eGFR as possible

confounders, based on known relationships observed in the literature (Brossard et al. 2000,

Fröhlich et al. 2000, Reis et al. 2008, Young et al. 2008, Muntner et al. 2009, Jungert et al.

2012). CRP may be increased in individuals with acute infection, but chronic low-grade

inflammation with mildly elevated serum CRP is often reported in obese individuals (Yudkin

et al. 1999, Fröhlich et al. 2000). We performed a sensitivity analysis for possible

confounding effects of CRP in individuals with CRP<10mg/L (n=161), but CRP did not

contribute significantly to the models and we therefore excluded CRP from the final models.

Only the variables that entered the stepwise models are presented in the tables in order to

provide the best fit models. Multivariate ORs for the modified MetS with 25(OH)D or PTH

were calculated, adjusting for age, tobacco use, physical activity, body fat percentage, eGFR

and season of data collection in logistic regression. Statistical significance was set at p <

0.05.

Results

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Demographic, body composition and metabolic characteristics of the participants are

presented in Table 1. Using the World Health Organisation (WHO) BMI classification,

69.9% of the women were overweight or obese and 65.5% of the women had excessive

adiposity using the age specific cut-off points for body fat percentage (Heo et al. 2012).

Actiheart® data for 184 (88.04%) women were available for analysis. Habitual physical

activity measured by Actiheart® indicated that the women spent on average 13.4% of their

time (3.21 hours) engaging in light-intensity activity, 2% in moderate-intensity activity (0.49

hours) and only 0.005% of total time (0.07 hours) in activities representing ≥ 6 METS (i.e.

vigorous activity). Results of the Baecke physical activity questionnaire also showed a low

mean physical activity score, within the inactive range from 1 - 3.3 (Kruger et al. 2011).

Vitamin D status of women measured in autumn was significantly higher than those

measured in spring (p<0.001), with mean serum 25(OH)D concentrations of 36.5 (±7.30) and

27.5 (±9.23) ng/mL, respectively. Similarly more women were vitamin D insufficient in

spring compared to autumn (55.4% vs. 16.9% respectively, p<0.001).

Women who used tobacco had significantly lower measures of adiposity compared to women

who did not use tobacco (body fat percentage 38.6% vs 42.4%, p <0.001, BMI 28.0 vs 31.3

kg/m2, p <0.01 and waist circumference 87.4 cm vs 92.6 cm, p = 0.01). Univariate

correlations between 25(OH)D, PTH and individual components of the metabolic syndrome

showed no significant correlations, except for the expected significant correlations with

waist circumference and an unexpected positive correlation between 25(OH)D and diastolic

blood pressure (Table 2).

Using the harmonized definition, the MetS was diagnosed in 43.1% of the women, with

hypertension (85.6%) being the most common and elevated triglycerides (27.8%) being the

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least common component of MetS (Table 1). Women with the MetS had significantly higher

body fat percentage, BMI, waist circumference, HOMA-IR, plasma glucose, insulin,

triglycerides and CRP, while the eGFR and HDL-C of women with MetS were significantly

lower. There were no differences between the mean serum 25(OH)D, PTH and age of women

with and without the MetS, even after adjusting for body fat. There were no differences in the

prevalence of insufficient serum vitamin D or elevated PTH levels between women with and

without the MetS.

Unadjusted multiple regression models showed all adiposity variables to be inversely

associated with 25(OH)D and positively associated with PTH (Table 3, model 1). These

associations remained when adjusting for possible confounders, except for the association

between body fat % and 25(OH)D, which became non-significant. Apart from the adiposity

variables, age, season of data collection and eGFR remained as significant contributors to

PTH in the stepwise regression models, while eGFR did not contribute to 25(OH)D models

(Table 3, model 2)

In the logistic regression analysis using the modified MetS variable, neither 25(OH)D nor

PTH was significantly associated with the MetS (Table 4). In the adjusted models, only the

use of tobacco was significantly associated with the MetS (model 2) with women who use

tobacco being less likely to have the MetS.

Discussion

Studies showed inconsistent relationships between 25(OH)D and PTH, respectively and the

MetS and to our knowledge this study is the first study to investigate the influence of

adiposity on these relationships. The findings of this study indicate a general lack of

significant associations between 25(OH)D and PTH respectively, with the MetS, before and

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after adjusting for adiposity in black South African women. However, positive associations

between adiposity variables and PTH and negative associations with 25(OH)D were found.

Some studies show inverse associations between 25(OH)D and the MetS (Hypponen et al.

2008, Yin et al. 2012) and positive associations between PTH and the MetS (Reis et al. 2007,

Ahlström et al. 2009). Other studies have reported no associations between 25(OH)D and/or

PTH and the MetS (Reis et al. 2007, Rueda et al. 2008). The lack of a negative relationship

between the MetS and 25(OH)D, despite the high prevalence (43%) of the MetS observed in

this present study could be explained in part by the low prevalence of vitamin D deficiency

(15.9%). In contradiction to this study, higher prevalence of vitamin D deficiency were found

in studies that showed inverse associations between 25(OH)D and the MetS (Hypponen et al.

2008, Yin et al. 2012). Another possible contributing factor may be that black individuals are

not sensitive to the metabolic effects of vitamin D (Scragg et al. 2004). A similar study

among black and Asian-Indian South Africans also found no association between 25(OH)D

and the MetS (George et al. 2013). The same study (George et al. 2013), however, found a

positive association between PTH and the MetS, which is in contrast to the findings of this

present study. Although results of this study showed a tendency for women with the MetS to

have a higher PTH compared to women without MetS (p = 0.09), this difference was

attenuated after adjustment for body fat % in the ANCOVA (Table 1). Univariate correlations

between 25(OH)D, PTH and individual components of the metabolic syndrome showed no

correlations, except for the expected significant correlations with waist circumference, as

well as an unexpected positive correlation between 25(OH)D and diastolic blood pressure

(Table 2). This positive correlation is difficult to explain, given that a systematic review and

meta-analysis concluded that there was weak evidence for an inverse, but not for a positive

association between vitamin D and blood pressure, although most included studies were

performed in hypertensive patients (Witham et al. 2009).

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It has previously been postulated that low 25(OH)D and resulting increases in PTH were both

consequences of obesity (Wortsman et al. 2000, Bischof et al. 2006, Reinehr et al. 2007).

Some studies on the other hand have suggested that elevated PTH promotes the accumulation

of adipose tissue, thereby suggesting the possibility that elevated PTH may play a role in the

development of obesity (McCarty and Thomas 2003, Valiña-Tóth et al. 2010). The results of

this study are consistent with previous reports on positive associations between PTH and

measures of adiposity (Wortsman et al. 2000, McCarty and Thomas 2003, Snijder et al. 2005,

Bischof et al. 2006, Reinehr et al. 2007, Valiña-Tóth et al. 2010). Adipose tissue also acts as

a reservoir for vitamin D in the body (Wortsman et al. 2000) and in addition to this,

abdominal adipose tissue releases inflammatory cytokines which further decrease the amount

of circulating 25(OH)D (Blum et al. 2008). In agreement with this evidence, adiposity

variables (except body fat percentage) retained their inverse associations with 25(OH)D after

adjusting for confounders in these African women. The relationship observed between

adiposity variables, 25(OH)D and PTH respectively in this study further corroborates the

results of earlier studies carried out in different study populations (Wortsman et al. 2000,

McCarty and Thomas 2003, Snijder et al. 2005, Bischof et al. 2006, Reinehr et al. 2007,

Valiña-Tóth et al. 2010, Jungert et al. 2012).

Age and season of data collection consistently contributed to 25(OH)D and PTH

concentrations respectively, while eGFR had consistent inverse associations with PTH

concentrations (Table 2, models 2). The observation that the vitamin D status of women

measured in autumn was significantly higher than those measured in spring and the inverse

relationship with PTH in this study sample, is consistent with results of other studies (Bischof

et al. 2006, Valiña-Tóth et al. 2010). Higher vitamin D status in autumn compared to spring is

assumed to be the result of greater sunlight exposure over the long South African summer

compared to less sunlight during winter. This could lead to individuals having a higher

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vitamin D store going into autumn compared to the vitamin D available in spring after winter.

This is an indication of the important effect of exposure to sunlight on vitamin D status

(Rucker et al. 2002). In addition we found a decline of 25(OH)D with advancing age which

corroborates results of previous studies (Rucker et al. 2002, Bischof et al. 2006). Although

aging itself does not necessarily cause a decrease in 25(OH)D, age-related impaired renal

function may interfere with vitamin D metabolism (Lips 2001). The increase in PTH with

advancing age observed in this present study is consistent with what has been previously

established and this too has been generally attributed to age-related decline in renal function

(Lips 2001, Lee et al. 2009). The consistent negative association between eGFR and PTH in

this study indicates a specific role for GFR on PTH concentration. This is in line with the

hypothesis that GFR influences the concentration of PTH which may be as a result of

decreased renal clearance of PTH as GFR decreases (Brossard et al. 2000, Muntner et al.

2009).

While 25(OH)D and PTH were not main contributors to the presence of the MetS in black

women in this study in neither the adjusted nor unadjusted model, tobacco use was found to

make a significant contribution. We found an unexpected negative association between

tobacco use on having the MetS. We propose that the relationship between tobacco use and

the MetS may, at least in part, be through the effect of smoking on adiposity (Klesges et al.

1989). This result contradicts the result of a previous study in which the relationship between

smoking and the MetS was found to be dose responsive with heavy smokers having a higher

risk of developing MetS in comparison to light or non-smokers (Slagter et al. 2014).

Despite significant differences in eGFR and body fat % between the MetS and non-MetS

groups, these variables did not enter the logistic regression model as significant contributors

to having the MetS. The non-significant contribution of eGFR to the MetS in this study could

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be due to the fact that the eGFR values of the majority of the study population including the

women with MetS, were within the normal range (Table 1). Only 9.5% of the women had

eGFR < 90 ml/min/1.73m2, indicating possible impaired kidney function (data not shown)

(Levey et al. 2006). We propose that the non-significant contribution of total body fat % to

the MetS in this present study could be explained by the phenomenon that most black South

African women have a larger percentage of their body fat distributed subcutaneously around

their hips than around their waists (Fox et al. 2007). This is supported by literature

demonstrating a stronger association between an adverse metabolic risk profile with visceral

compared to subcutaneous adipose tissue (Fox et al. 2007). A sub-analysis excluding

participants with acute inflammation (CRP > 10mg/L) was carried out to investigate the

contribution of CRP to the MetS and showed that CRP had no significant contribution to the

MetS and was not associated with serum 25(OH)D nor PTH in this study population.

Limitations of this study include its cross-sectional design, thus we cannot draw conclusions

regarding causality. This study was performed in black urban women and the results may not

be generalizable to the greater black South African population. Furthermore we could not get

accurate records from the self-report of the quantity of tobacco used, in order to separate light

and heavy tobacco users. Despite the limitations, this study has further highlighted the

differences between the risk factors for the MetS in Africans compared to other ethnic

groups.

Conclusions

In conclusion, PTH was positively and 25(OH)D negatively associated with measures of

adiposity in black South African women. There were, however, no association between PTH

nor 25(OH)D and the MetS in black South African women, nor were these associations

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influenced by obesity. In view of the high prevalence of overweight and obesity with the

associations observed between PTH, 25 (OH)D and measures of adiposity respectively,

intervention programs aimed at healthier lifestyles should be promoted to prevent and combat

obesity and its associated negative health effects.

Conflict of interests

The authors declare that they have no conflict of interests.

Acknowledgments

1. The authors would like to thank all supporting staff and the participants of the PURE

study and in particular: PURE-South Africa: The PURE-NWP-SA research team, field

workers and office staff in the Africa Unit for Transdisciplinary Health Research

(AUTHeR), Faculty of Health Sciences, North-West University, Potchefstroom, South

Africa. PURE International: Dr S Yusuf and the PURE project office staff at the

Population Health Research Institute (PHRI), Hamilton Health Sciences and McMaster

University. ON, Canada. Funders: South African Medical Research Council, SANPAD

(South Africa - Netherlands Research Programme on Alternatives in Development),

South African National Research Foundation (NRF GUN numbers 2069139 and

FA2006040700010), North-West University and PHRI. Any opinion, findings, and

conclusions or recommendations expressed in this material are those of the authors and

therefore the National Research Foundation does not accept any liability in regard thereto.

Sincere appreciation to Prof Edith Feskens of Wageningen University for critically

reviewing the first draft of this manuscript.

Statistical support: Dr Suria Ellis

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Table 1: Demographic, body composition, health and lifestyle measures

Variable Total group

(n=209)a

Women without

Metabolic

Syndrome

(n=119)a

Women with

Metabolic

Syndrome

(n=90)a

pb

Age(years) 59.6 ± 10.6 59.8 ±10.5 59.4 ± 10.9 0.78

Body fat % 40.4 ± 7.41 38.5 ± 8.11 42.9 ± 5.51 <0.0001

Body mass index (kg/m2) 29.5 ± 7.58 27.0 ± 7.64 32.8 ± 6.14 <0.0001

Waist circumference (cm) 89.8 ± 14.4 84.0 ± 14.8 97.3 ± 9.64 <0.0001

Serum 25(OH)D (ng/mL) 30.6 ± 9.52 31.3 ± 9.44 29.7 ± 9.61 0.24 (0.57c)

Serum PTH (pg/mL) 44.3 (34.2, 58.8) 41.9 (32.7, 54.3) 47.9 (35.1, 63.5) 0.09 (0.58c)

HOMA-IR 2.67 (1.43, 4.99) 2.14 (1.00, 4.29) 3.33 (1.91, 7.53) <0.0001

Plasma glucose (mmol/L) 4.74 (4.27, 5.37) 4.48 (4.10, 4.88) 5.35 (4.61, 6.37) <0.0001

Insulin (µU/ml) 12.58 (6.51, 21.58) 11.47 (5.67, 19.78) 14.43 (8.40, 24.18) 0.004

Triglycerides (mmol/L) 0.99 (0.74, 1.41) 0.91(0.70,1.22) 1.18 (0.81, 1.79) <0.0001

HDL-cholesterol (mmol/L) 1.19 (0.98, 1.56) 1.46 (1.14, 1.82) 1.05 (0.86, 1.17) <0.0001

Systolic BP(mmHg) 128.1 ± 22.8 125.0 ± 23.7 132.1 ± 21.1 0.03

Diastolic BP (mmHg) 81.2 ± 12.6 79.0± 13.5 84.0 ± 10.8 0.005

CRP (mg/L) 4.47 (1.99, 8.75) 3.89 (1.17, 8.18) 5.18 (3.24, 9.81) 0.01

eGFR (ml/min/1.73m2) 136.58 ± 38.78 142.1 ± 39.4 129.7 ± 37.2 0.03

AEE (Kcal/day) 884.0 (521.25, 1622.0) 737.0 (522.0,

1314.0)

1073.0 (494.5,

1912.0)

0.09

Light-intensity activity/

day(1.1-2.9 METs) (min)

192.36 ± 39.1 195.6 ± 38.3 187.84 ± 40.2 0.19

Moderate-intensity activity/ day

(3-5.9 METs) (min)

29.14 ± 37.3 26.1 ± 37.9 33.3 ± 36.3 0.20

Vigorous activity /day (>6

METs) (min)

0.65 ± 1.92 0.65 ± 2.2 0.65 ± 1.39 0.99

Physical activity score 2.08 (1.43, 2.64) 2.18 (1.61, 2.73) 1.90 (1.29, 2.60) 0.06

Tobacco users n (%) 100 (47.8) 66 (58.4) 34 (39.5) 0.01

Elevated fasting glucose n (%) 41 (19.6) 5 (4.7) 36 (44.4) <0.0001

Elevated triglycerides n (%) 34 (16.3) 9 (8.0) 25 (27.8) <0.0001

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Reduced HDL-C n (%) 118 (56.5) 37 (32.7) 81 (90.0) <0.0001

Hypertensive n (%) 151 (72.2) 74 (62.2) 77 (85.6) <0.0001

Abdominal obesity: WC ≥ 80cm

n (%)

150 (71.8) 62 (53.0) 88 (97.8) <0.0001

Abdominal obesity: WC ≥ 92cm

n (%)

104 (49.8) 40 (34.2) 64(71.1) <0.0001

Excess adiposity d n (%) 137 (65.6) 66 (55.5) 71 (78.9) <0.0001

Overweight/ obese n (%) 146 (69.9) 65 (55.1) 81 (90.0) <0.0001

Vitamin D deficiency (<20

ng/mL)

32 (15.9) 16 (13.4) 16 (17.8) 0.44

Vitamin D insufficiency (21-29

ng/mL)

49 (24.4) 32 (28.3) 17 (19.3) 0.23

Elevated PTH (>65 pg/mL) 37 (17.7) 17(15.2) 20(22.2) 0.20

Note: 25(OH)D = serum 25 hydroxy vitamin D, PTH= parathyroid hormone, HOMA-IR= Homeostasis model assessment

Insulin resistance, HDL= high density lipoprotein, BP= Blood pressure, CRP = C- reactive protein, eGFR = estimated

glomerular filtration rate, AEE = activity energy expenditure, METs = metabolic equivalents, min = minutes.

a Sample size varies due to missing values.

b Difference between groups with and without the metabolic syndrome . t-test/Mann-Whitney test/chi-square test

c Difference between groups with and without the metabolic syndrome, adjusted for body fat percentage (ANCOVA)

d age specific cut off values for adiposity based on body fat percentage (≤ 35.8 - > 37.7 %).

Data presented as mean ± SD for normally distributed data and median (IQR) for non-normally distributed data

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Table 2: Correlation between 25(OH)D and PTH, respectively and individual MetS

components

25(OH)D PTH

MetS component r p R p

Waist circumference -0.19 0.008 0.18 0.01

Plasma glucose (mmol/L) -0.06 0.37 -0.05 0.49

Triglycerides (mmol/L) -0.009 0.90 0.08 0.26

HDL-cholesterol (mmol/L) 0.06 0.38 -0.02 0.75

Systolic BP(mmHg) 0.11 0.11 0.08 0.23

Diastolic BP (mmHg) 0.20 0.004 -0.03 0.71

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Table 3: Multiple regression analysis for 25(OH)D or PTH on body composition variables

25(OH)D PTH

β p Β p

Waist circumference

Model 1 Unadjusted model


Model 2 Model with best fit


Age -0.18 0.01 0.24 0.002

Season (0=Spring; 1=Autumn) 0.40 <0.001 -0.11 0.13

eGFR (ml/min/1.73m2) -- -- -0.17 0.03

Body mass index


Body mass index -0.20 0.004 0.22 0.002

Model 2 Model with best fit

Body mass index -0.18 0.01 0.19 0.01

Age -0.19 0.01 0.24 0.002

Season (0=Spring; 1=Autumn) 0.41 <0.001 0.11 0.12

eGFR (ml/min/1.73m2) -- -- -0.19 0.02

Body fat %


Body fat % -0.15 0.04 0.27 <0.001

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Model 2 Model with the best fit

Body fat % -0.12 0.09 0.18 0.01

Age -0.16 0.02 0.21 0.01

Season (0=Spring; 1=Autumn) 0.42 <0.001 -0.12 0.09

eGFR (ml/min/1.73m2) -- -- -0.19 0.01

Note: 25(OH)D = serum 25 hydroxy vitamin D, PTH= parathyroid hormone, eGFR = estimated glomerular filtration rate.

Model 1 is unadjusted . Models 2 are adjusted for age, physical activity, tobacco use, season and eGFR

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Table 4: Multiple logistic regression analysis for the association between 25(OH)D or PTH

and other covariates and the metabolic syndrome (modified definition)

Dependent variable MetS (modified definition, excluding

waist circumference)

MetS (modified definition excluding waist circumference)

Odds

ratios

95% CIs p Odds

ratios

95% CIs p

Model 1: Unadjusted model

25(OH)D 1.03 0.98, 1.07 0.24 Log PTH 1.22 0.17, 8.83 0.84

Model 2 25(OH)D (Model with best fit) Log PTH (Model with best fit)

25(OH)D 1.04 0.99, 1.10 0.14 Log PTH 0.45 0.03, 6.12 0.55

Age (years) 1.00 0.95, 1.04 0.88 Age (years) -- -- --

Body fat % 1.04 0.97, 1.12 0.26 Body fat % 1.04 0.97, 1.12 0.23

Physical activity

score

0.51 0.24, 1.11 0.09

Physical activity score

0.56 0.27, 1.14 0.11

Tobacco use (0=no;

1=yes)

0.35 0.13, 0.92 0.03

Tobacco use (0=no;

1=yes)

0.45 0.18, 1.14 0.09

Season (0=Spring;

1=Autumn)

1.36 0.47, 3.92 0.57 Season (0=Spring;

1=Autumn)

1.87 0.73, 4.81 0.19

eGFR

(ml/min/1.73m2)

1.00 0.99, 1.02 0.47

eGFR

(ml/min/1.73m2)

1.00 0.99, 1.02 0.54

Note: 25(OH)D = serum 25 hydroxy vitamin D, PTH= parathyroid hormone, eGFR = estimated glomerular filtration rate.

*MetS =Metabolic syndrome excluding the elevated waist circumference component.

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