Obesity and cardiovascular disease risk among Africans residing in Europe and Africa: the RODAM study

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Original Article

Obesity and cardiovascular disease risk among Africans residing in Europe and Africa Y. Commodore-Mensah a,b,∗ , C. Agyemang c , J.A. Aboagye d , J.B. Echouffo-Tcheugui e , E. Beune c , L. Smeeth f , K. Klipstein-Grobush g,h , I. Danquah i,j , M. Schulze i,k , D. Boateng g , K.A.C. Meeks c,l , S. Bahendeka m , R.S. Ahima a,b,e a

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States Johns Hopkins School of Nursing, MD, United States c Department of Public Health, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, The Netherlands d Department of Surgery, Howard University, Washington, District of Columbia, United States e Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States f Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, United Kingdom g Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands h Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa i Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany j Charité – Universitaetsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute for Social Medicine, Epidemiology and Health Economics, Berlin, Germany k Institute of Nutritional Sciences, University of Potsdam, Nuthetal, Germany l Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States m MKPGMS-Uganda Martyrs University, Kampala, Uganda b

a r t i c l e

i n f o

Article history: Received 7 November 2019 Received in revised form 28 December 2019 Accepted 24 January 2020 Keywords: Cardiovascular diseases Emigrants and immigrants Obesity Risk factors Adiposity Ethnic groups Sub-Saharan Africa

a b s t r a c t Background: The association between anthropometric variables and CVD risk among Africans is unclear. We examined the discriminative ability of anthropometric variables and estimate cutoffs for predicting CVD risk among Africans. Methods: The Research on Obesity and Diabetes among African Migrants RODAM study was a multisite cross-sectional study of Africans in Ghana and Europe. We calculated AHA/ACC Pooled Cohort Equations (PCE) scores for 3661 participants to ascertain CVD risk, and compared a body shape index (ABSI), body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), Relative Fat Mass (RFM), and Waist to Height Ratio (WHtR). Logistic regression and receiver operating curve analyses were performed to derive cutoffs for identifying high predicted CVD risk (PCE score ≥7.5%). Results: Among men, WC (adjusted Odds Ratio (aOR): 2.25, 95% CI; 1:50–3:37) was strongly associated with CVD risk. Among women, WC (aOR: 1.69, 95% CI: 1:33–2:14) also displayed the strongest association with CVD risk in the BMI-adjusted model but WHR displayed the strongest fit. All variables were superior discriminators of high CVD risk in men (c-statistic range: 0.887–0.891) than women (c-statistic range: 0.677–0.707). The optimal WC cutoff for identifying participants at high CVD risk was 89 cm among men and identified the most cases (64%). Among women, the recommended WC cutoff of 94 cm or WHR cutoff of 0.90 identified the most cases (92%). Conclusions: Anthropometric variables were stronger discriminators of high CVD risk in African men than women. Greater WC was associated with high CVD risk in men while WHR and WC were associated with high CVD risk in women. © 2020 Asia Oceania Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.

Introduction

∗ Corresponding author at: Johns Hopkins School of Nursing, 525 N. Wolfe Street, Room #419, Baltimore, MD 21205, United States. E-mail address: [email protected] (Y. Commodore-Mensah).

African-descent populations are disproportionately affected by cardiovascular disease (CVD) [1], with a gradient of risk observed between those residing in sub-Saharan Africa(SSA) compared to those in high-income countries [2]. Epidemiological studies have

https://doi.org/10.1016/j.orcp.2020.01.007 1871-403X/© 2020 Asia Oceania Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.

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shown that obesity and central adiposity are major CVD risk factors [3]. Although genetic factors account for increased susceptibility toward obesity, increases in obesity prevalence globally have been attributed to an unhealthy diet, sedentary lifestyle, stress exposure and socioeconomic factors [3–5]. For example, Ghanaians in the Netherlands are three to four times more likely to be overweight/obese than ethnically-matched Ghanaians residing in rural or urban Ghana [6], due to acculturation and migration-related lifestyle changes [7,8]. Furthermore, Toselli et al.’s review [9] has highlighted significant gaps in knowledge on body image perception of African immigrants in Europe and demonstrated the differential effect of environmental context on body preferences of African immgrants. Body mass index (BMI) is routinely used to identify persons at high CVD risk [10,11]. However, BMI does not reflect fat distribution and is unreliable in evaluating body fat in non-Europeans [12]. Other surrogate measures of body fat distribution and abdominal obesity are better discriminators of CVD risk than BMI [13], although it is unclear which of the anthropometric variables best predicts CVD risk. Waist circumference (WC) and waist-to-hip ratio (WHR), surrogate markers of abdominal fat, are associated with CVD risk independent of blood pressure, glucose and lipoproteins [12,14,15]. In the INTERHEART study, WHR was strongly associated with myocardial infarction after adjustment for BMI globally, while WC was the strongest predictor of myocardial infarction among Africans [10]. Waist-to-Height ratio (WHtR) [15,16] is proposed as a global indicator of cardiometabolic risk which obviates the need for ethnic, age and sex-specific cutoffs. It outperforms BMI and WC in longitudinal studies predicting mortality and morbidity [16]. Recently, a Body Shape Index (ABSI) [17], has been proposed as a complementary anthropometric index to assess CVD risk because it predicts mortality independent of BMI with ethnic differences noted in this association [17]. Another newly developed index Relative fat mass (RFM), has been proposed as a more accurate estimation of whole body fat percentage than BMI and reduces obesity misclassification among persons of African, Mexican or European ancestry [18]. Prior studies have not compared the associations between several anthropometric measures and predicted CVD risk in an ethnically homogenous population of Africans across different continents. Thus, our goal was to explore the performance of six anthropometric variables (BMI, WC, WHR, ABSI, RFM, WHtR) in estimating the predicted 10-year CVD risk among Africans residing in SSA and Europe, and to identify the critical threshold for adiposity measures to discriminate between Africans with and without high predicted CVD risk.

drawn from the Amsterdam Municipal register, which contains data on country of birth of citizens and their parents. In the UK, no population register exists for migrants so Ghanaian communitybased organizations served as the sampling frame. In Germany, a combined list of Ghanaians from the registration office and community-based organizations served as a sampling frame. In Ghana, two cities (Kumasi and Obuasi) were selected as the urban sites while 15 villages were chosen in the Ashanti region as the rural sites. For this analysis, all participants aged 40–79 years, free of clinical CVD and with complete data on anthropometric variables, biochemical and sociodemographic variables necessary to calculate absolute CVD risk were included. Ethics approval was obtained for all respective study sites. Informed written consent was also obtained from participants. Measurements Data collection consisted of a structured questionnaire, physical examination, and biological samples. Standardized data collection was performed by trained research assistants and questionnaires were tailored to the local circumstances in Europe and Ghana where necessary (e.g. educational level). After the informed consent process, physical measurements were made and biological samples (fasting blood and urine samples) were obtained. A description of all instruments used in this study is published elsewhere [19]. Anthropometric variables Measurements of body weight, height, waist, and hip circumference were performed using a standardized protocol across sites. Height was measured with the SECA 217© stadiometer. Weight was measured with the SECA 877 scale. WC was measured in centimeters (cm) at the point midway between the iliac crest and the costal margin (lower rib) using a tension tape to the nearest 0.1 cm. Hip circumference was measured over the hip bone (trochanter major) with a tension tape to the nearest 0.1 cm. BMI was defined as body weight (kg)/height (m) [2]. RFM was calculated as 64 − (20 × height/waist circumference) + (12 × sex [0-men,1-women]) [18]. The ABSI was calculated as WC (m)/[BMI0.66 × height (m) 0.5] [17]. WHR was defined as WC (cm)/hip circumference (cm). The cutoffs for WC and WHR were identified using the World Health Organization (WHO) guidelines [23]. WHtR was calculated as WC (cm)/height (cm) [15]. Estimation of the 10-year CVD risk

Methods Study design and population The Research on Obesity & Diabetes among African Migrants (RODAM) [19] study was a cross-sectional community-based study to understand the reasons for the high prevalence of obesity and type 2 diabetes (T2D) among Africans. This study provides a unique opportunity to address the performance of anthropometric variables in discriminating CVD risk and ethnic-specific thresholds among Africans because a highly standardized protocol was used. The study design has been previously published [19]. Briefly, adult Ghanaians (aged ≥ 25 years) were recruited from rural and urban Ghana, and in three European cities [Amsterdam, the Netherlands, Berlin, Germany, and London, United Kingdom (UK)]. Ghanaian migrants are one of the largest SSA migrant groups in Europe [20–22]. In the Netherlands, participants were randomly

The outcome was 10-year predicted CVD risk, henceforth CVD risk, calculated from the Pooled Cohort Equations (PCE) [24]. This risk equation estimates the 10-year absolute risk of ASCVD, defined as coronary death or nonfatal myocardial infarction or fatal or nonfatal stroke, in people free of CVD. The PCE permits the derivation of sex-and race-specific estimates of the 10-year risk for atherosclerotic cardiovascular disease (ASCVD) for African Americans and White Americans ages 40–79 years [24]. We have previously [25] shown a significant difference in the distribution of PCE scores across sites and higher CVD risk among Ghanaian men than women. We therefore performed sex- specific analyses. Variables in the PCE score include sex, age, high-density lipoprotein cholesterol, total cholesterol, diabetes status, systolic blood pressure, treatment for hypertension, and smoking status. A participant had “high CVD risk” if the PCE score was ≥7.5% [24]. The distribution of PCE scores across the RODAM study sites has been published [25,26].

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Table 1 Characteristics of study participants. Mean(±SD),

Men (N = 1430, 39%)

Women (N = 2231, 61%)

Total (N = 3661)

Age (years), mean(±SD)* ≥Intermediate vocational/ high school education n (%)* Employed (full/part time) n (%)* Married n (%)* Site n (%) Rural Ghana Urban Ghana Berlin, Germany Amsterdam, Netherlands London, United Kingdom SBP (mmHg), mean(±SD)* DBP (mmHg), mean(±SD)* Hypertension n (%)* Diabetes n (%)* BMI categories n (%)* Normal(18.5–24.9 kg/m2 ) Overweight(25–29.9 kg/m2 ) Obese(≥ 30 kg/m2 ) Current smoking n (%)* Physical inactivity n (%) Fasting blood glucose(mg/dl), mean(±SD) HDL cholesterol(mg/dl), mean(±SD) Total cholesterol(mg/dl), mean(±SD) PCE score, mean(±SD) PCE score ≥ 7.5% n (%)* Anthropometric indices ABSI, mean(±SD) BMI (kg/m2 ), mean(±SD)* WC (cm), mean(±SD)* WHR, mean(±SD) * RFM, mean(±SD)* WHtR, mean(±SD)*

52.71 (0.21) 502 (35) 1112 (78) 861 (61)

51. 48 (0.17) 449 (20) 1622 (73. 63) 968 (44)

51.96 (0.13) 951 (26) 2734 (76) 1829 (51)

262 (18) 272 (19) 212 (15) 434 (30) 250 (18) 136.46 (19.77) 86.14 (12.11) 843 (59) 206 (14)

424 (19) 651 (29) 169 (8) 562 (25) 425 (19) 132 (19.52) 81.57 (11.25) 1226 (55) 243 (11)

686 (19) 923 (25) 381 (10) 996 (27) 675 (18) 134.23 (19.69) 83.36 (11.80) 2069 (57) 449 (12)

644 (45) 594 (42) 192 (13) 92 (6) 335 (27) 102.24 (42.05) 49.67 (13.99) 193.11 (46.08) 10.09 (7.34) 745 (52)

579 (26) 777 (35) 875 (39) 15 (1) 677 (34) 98.41 (33.41) 53.22 (13.91) 201.36 (42.35) 6.01 (6.38) 537 (24)

1223 (33) 1371 (37) 1067 (29) 107 (3) 1012 (31) 99.91 (37.06) 51.83 (14.05) 198.14 (44.02) 7.61 (7.06) 1282 (35)

0.815 (0.005) 25.49 (4.49) 89.85 (12.04) 0.932 (0.06) 25.48 (4.96) 0.528 (0.069)

0.815 (0.005) 28.68 (5.71) 93.67 (12.42) 0.904 (0.07) 41.40 (4.50) 0.588 (0.076)

0.0815 (0.005) 27.43 (5.49) 92.18 (12.41) 0.915 (0.07) 35.18 (9.07) 0.564 (0.079)

Data are mean (±SD) or number (percentage), *-p < 0.05. ABSI-a body shape index; BMI-body mass index ; WC-waist circumference ; WHR-waist-to-hip ratio ; RFM-relative fat mass; WHtR-waist to height ratio; PCE-pooled cohort equations, SBP-systolic blood pressure, DBP-diastolic blood pressure, HDL-high density lipoprotein, cm-centimeters.

Statistical analyses The characteristics of participants are presented by sex, using means (standard deviations [SD]) or numbers (percentages). Chisquare tests and t-tests were used to assess differences in categorical and continuous variables, respectively. To permit direct comparability of the anthropometric measures, we used z-scores, calculated using the following equation: Z-score = (individual anthropometric value – group anthropometric value)/group standard deviation. Pearson’s correlation coefficients were examined for the associations between the anthropometric variables, height, and weight. Univariate and multivariate logistic regression analyses were performed to determine the associations between each of the measures and CVD risk, accounting for known confounders including age, physical activity, education, and site. Multivariate models were fitted where the variables were BMI-adjusted (except RFM) to establish the independent association between these variables and CVD risk. The ability of each of the measures to discriminate CVD risk was ascertained by deriving and comparing Receiver Operator Characteristic (ROC) curves. Model fit was assessed by comparing Akaike Information Criteria (AIC) and visually comparing the 95% CIs of the ROC c-statistic) to determine if they overlapped. The ROC curves were created by plotting the true positive rate against the false positive rate for all multivariate models by sex. ROC analysis were also performed to obtain sex-specific sensitivity and specificity cutoffs for BMI, WC and WHR per WHO guidelines. The Youden Index was used to identify the optimal cutoffs for anthropometric measures to distinguish between persons with high CVD risk (PCE ≥ 7.5%) and those with low to moderate CVD risk (PCE < 7.5%). A two-tailed p < 0.05 was considered sta-

tistically significant. All analyses were performed with Stata MP, Version 14.0 (StataCorp, College Station, TX, USA). Results Characteristics of participants The sex-specific characteristics of participants (n = 3661), mean age: 52 (±0.13) years, 61% women) are presented in Table 1. Men were more likely to have higher education and report being employed and married than women. Also, 19%, 25%, and 56% resided in rural Ghana, urban Ghana, and Europe, respectively. The mean systolic and diastolic blood pressures were 134 and 83 mm Hg respectively. More than half (57%) had hypertension, and 12% had diabetes. Only 3% reported current smoking while 76% were overweight/obese. Also, about a third of them were considered physically inactive. Men were more like than women to have higher blood pressure, fasting blood glucose, total cholesterol, and PCE scores and smoke. (All p < 0.05). In contrast, women were more likely to be overweight/obese and physically inactive than men. Correlation between anthropometric variables The linear associations between the anthropometric variables are summarized in the Supplemental table. Among men, BMI was strongly and positively associated with WC (r = 0.91), WHtR (r = 0.90), and RFM (r = 0.89) and moderately associated with WHR (r = 0.56); but there was no association between BMI and ABSI. Among women, BMI was strongly and positively associated with WC (0.88),

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WHtR (0.86), and RFM (0.84) and minimally and positively associated with WHR (0.18). Also, there was a minimal and negative association between BMI and ABSI. Associations between anthropometric discriminators and CVD risk by sex The estimates of the associations between anthropometric variables and CVD risk by sex are presented in Table 2. The adjusted odds ratios for CVD risk for high CVD risk for each 1 standard deviation increase in each measure by sex are shown in Table 2. The sex-specific ROC curves for each anthropometric measure by sex are shown in Fig. 1. We observed considerable sex-differences in the ability of each anthropometric measure in discriminating CVD risk with poorer fit in women than men. Among men, WC was most strongly associated with CVD risk (adjusted odds ratio [aOR]: 2.10, 95% CI: 1.71–2.59). The BMI-adjusted multivariate model showed a stronger association between WC and CVD risk (aOR: 2.25, 95% CI: 1.50–3.37). WC displayed the best fit for men as evidenced by the lowest AIC (1041.21) and highest ROC C-statistic (0.891). WC did not differ significantly from the other five variables in discriminating CVD risk among men across sites (Table 2 and Fig. 1). Among women, WHR (aOR: 1.55, 95% CI: 1.38–1.74) and RFM (aOR: 1.55, 95% CI: 1.37–1.75) displayed the strongest association with CVD risk. However, in the BMI-adjusted multivariate models, the association between WHR and CVD risk was slightly attenuated (aOR: 1.46: 1.29–1.64) but was slightly strengthened for WC (aOR: 1.69, 95% CI: 1.33–2.14) and RFM (aOR: 1.64, 95% CI: 1.30–2.06). WHR displayed the best fit for women as evidenced by the lowest AIC (2015.11) and highest ROC-statistic (0.707). Sensitivity and specificity of cutoffs of anthropometric measures for identifying high CVD risk The case identifiers cutoffs associated with high CVD risk for each anthropometric measure are shown in Table 3. Among men, the case identifier value for WC (i.e. cutoff) was 89.45 cm, which is considerably lower than the WHO-recommended cutoff (94 cm). Among women, the WC cutoff was 96.2 cm, which is much higher than the WHO-recommended cutoff (80 cm). The WHR cutoff of 0.947 was higher than the WHO-recommended cutoff of 0.85 among men. The WHR cutoff of 0.919 among women was slightly higher than the WHO-recommended cutoff of 0.90. Among men, the BMI cutoff was 26.6 kg/m2 , which is slightly higher than the WHO-recommended cutoff of 25 kg/m2 . Among women, the BMI cutoff was almost 30 kg/m2 , which was again higher than WHOrecommended cutoff of 25 kg/m2 . The sensitivity and specificity of cutoffs of BMI, WC, and WHR for identifying high CVD risk are described in Table 3. Since there are no WHO-recommended cutoffs for ABSI, RFM, WHtR, those were omitted. Using the BMI cutoff of ≥25 kg/m2 , resulted in higher sensitivity among women (80%) than men (63%). The optimal BMI cutoffs in this study did not identify high CVD risk well among men and women (sensitivity <52%). Among men, the optimal WC cutoff in this study (89 cm) was higher than WHO-recommended cutoff and had the highest sensitivity (64%). Among women, using the WHO-recommended WC cutoff of 94 cm or WHR cutoff of 0.90 identified the most cases of high CVD risk (92%). Discussion This study examined the associations between different anthropometric variables and predicted CVD risk, among Africans residing in rural, urban Ghana and three European cities. There were sex differences in the discriminative ability of the six measures with

regards to predicted CVD risk, with a better discriminative ability in men compared to women for all six variables. Among men, WC had the strongest association with predicted CVD risk but did not differ significantly from the other five variables. Although BMI is widely used methods to assess general adiposity, WC is superior to BMI in predicting CVD risk [13,27,28] because of the stronger association with intra-peritoneal adipose tissue. Some studies have also shown similar associations between BMI and WC with CVD risk [29,30]. Among urban Nigerian men and women, WC was a stronger predictor of glucose intolerance and high blood pressure than WHR and higher income was associated with a higher prevalence of central obesity [31]. Among Kenyans [32], WC better predicts hypertension and hyperglycemia while WHtR is a superior predictor of hypercholesterolemia. In our investigation, WHR demonstrated a stronger association with CVD risk among women, even after adjusting for BMI, but did not differ significantly from the other five measures. Some studies have demonstrated that the association between adiposity and CVD is strongest for WHR in comparison to WC and BMI [10,29,33,34]. In the Women’s Health Study [33], among primarily white women, WHR did not provide significantly better prediction of CVD mortality than WC, which is similar to our results. In the INTERHEART case-control study [10], WHR has the strongest association with risk of myocardial infarction globally. Analyses from the UK Biobank [35] showed sex differences in the association between central adiposity measures and myocardial infarction, with a stronger association observed between WHR and myocardial infarction than BMI among women. Although both WC and WHR are clinically meaningful, WHR may be a superior discriminator of CVD risk among women because they have larger hip circumferences and greater subcutaneous fat rather than visceral fat, which are protective against dyslipidemia and CVD [35–38]. A study of Nigerians found a stronger association between WHR and hypertension but not diabetes and impaired glucose tolerance [31]. WHtR has been proposed to be superior to BMI in discriminating CVD. In our study, WHtR did not differ significantly from the other five variables in discriminating CVD risk although sex differences were observed. Most men (72%) and women (57%) were considered to have high “early health risk” based on the recommended cutoff of 0.5 [15]. In contrast, a recent study among Nigerians [32], found that 34% of men and 67.9% of women had an increased WHtR using the same cutoff. Our findings could be explained by the fact that we aggregated data on Ghanaians residing in different geographical regions. The newly proposed RFM was also considered as a measure of whole-body fatness because it is proposed to improve body fatdefined obesity misclassification among African Americans in the National Health and Nutrition Examination Survey [18]. We found that the RFM was similar to the other variables, but had superior discrimination of CVD risk among men than women. No cutoff values have been proposed for RFM to diagnose obesity due to the cross-sectional nature of the validation study [18]. Hence, we cannot comment on how the cutoffs identified in our study compare with prior studies. The ABSI, which is based on WC, adjusted for height and weight, performed similarly in comparison to the other variables although it had the lowest association with CVD in men and women. Furthermore, ABSI showed no correlation with height, weight or BMI, which is consistent with findings by Krakauer and colleagues [17]. Our results suggest considerable sex differences in CVD risk factors [25,26,39] and performance of anthropometric variables, which complements previous studies. For each measure, the discrimination of CVD risk and the strength of the associations were more pronounced in men than in women. The observed sex differences reflect an inherent heterogeneity in the pool of determinants of CVD risk among RODAM participants. A previous meta-analysis

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Table 2 Univariate and multivariate associations between adiposity measuresa and CVD risk by sex (N = 3661). aOR (95%CI) Men (n = 1430) Univariate Bivariate Age-adjusted only BMI adjusted Multivariate Multivariate + BMI Model AIC ROC C-statistic Women (n = 2231) Univariate Bivariate Age-adjusted BMI adjusted Multivariate Multivariate + BMI Model AIC ROC C-statistic

ABSI

BMI

WC

WHR

RFM

WHtR

2.22 (1.93–2.54)

1.48 (1.32–1.65)

1.82(1.62–2.05)

2.04(1.80–2.31)

1.88 (1.67–2.12)

1.94 (1.72–2.19)

1.45 (1.23–1.70) 1.38 (1.18–1.62) 1.37 (1.15–1.65) 1.35 (1.13–1.62) 1044.31 0.891 (0.873–0.908)

2.24 (1.91–2.63) – 1.85 (1.51–2.26) – 1055.61 0.888 (0.870–0.905)

2.50 (2.12–2.96) 2.58 (1.79–3.7) 2.10 (1.71–2.59) 2.25 (1.50–3.37) 1041.21 0.891 (0.874–0.909)

1.91 (1.64–2.22) 1.38 (1.16–1.64) 1.63 (1.38–1.93) 1.37 (1.13–1.66) 1047.59 0.890 (0.873–0.909)

2.32 (1.98–2.72) – 1.92 (1.58–2.34) – 1049.51 0.889 (0.871–0.907)

2.31 (1.96–2.71) 1.77 (1.24–2.52) 1.95 (1.60–2.38) 1.77 (1.20–2.63) 1047.89 0.889 (0.872–0.907)

1.42 (1.28–1.57)

1.31 (1.19–1.45)

1.53 (1.38–1.69)

1.72 (1.54–1.91)

1.61 (1.44–1.79)

1.57 (1.42–1.74)

1.22 (1.10–1.36) 1.41 (1.26–1.59) 1.23 (1.10–1.39) 1.35 (1.19–1.52) 2032.30 0.693 (0.667–0.721)

1.44 (1.31–1.60) – 1.42 (1.26–1.59) 1.42 (1.26–1.59) 2053.22 0.677 (0.649–0.704)

1.59 (1.43–1.76) 1.89 (1.51–2.37) 1.53 (1.36–1.72) 1.69 (1.33–2.14) 2035.43 0.693 (0.666–0.719)

1.61 (1.44–1.79) 1.52 (1.36–1.70) 1.55 (1.38–1.74) 1.46 (1.29–1.64) 2015.11 0.707 (0.681–0.733)

1.62 (1.45–1.81) 1.78 (1.43–2.21) 1.55 (1.37–1.75) – 2036.23 0.692 (0.665–0.719)

1.58 (1.42–1.75) 1.79 (1.44–2.23) 1.51 (1.35–1.69) 1.65 (1.31–2.08) 2034.38 0.691 (0.664–0.718)

aOR: adjusted odds ratio; models were constructed using logistic regression, with PCE ≥ 7.5% as a response to each anthropometric index (i.e. a body shape index [ABSI], body mass index [BMI], waist circumference, waist: hip ratio [WHR], waist to height ratio [WHtR], relative fat mass [RFM]]) adjusted for age, physical activity, education and site. ROC C-statistics and Model AICs are from the multivariate model. a Each anthropometric measure was standardized into z-scores by sex except the case identifiers.

Fig. 1. Receiver operating characteristic (ROC) curves for anthropometric indices by sex; ABSI-a body shape index; BMI-body mass index, WC-waist circumference, WHRwaist: waist to hip ratio, RFM-relative fat mass, WHtR-waist to height ratio.

Table 3 Sensitivity and specificity of high CVD risk (PCE ≥ 7.5) using sex-specific cutoffs and Youden Index for BMI, WC, and WHR. Variable

BMI (kg/m2 ) ≥ 25 ≥30 Optimal cutoff : 26.6 WC (cm) ≥80 ≥88 Optimal cutoff :89.4 ≥94 ≥102 WHR ≥ 0.85 ≥0.90 Optimal cutoff: 0.95

Men (n = 1430)

Women (n = 2231)

Sensitivity (%)

Specificity (%)

Sensitivity (%)

Specificity (%)

63 18 48

54 91 71

29.8

80 49 51

28 64 63

47 20 64

76 94 62

96.2

92 79 55

15 37 64

0.92

92 58

24 65

79

44

53

78

BMI = body mass index; WC-waist circumference; WHR-waist to hip ratio, optimal cutoffs were identified with the Youden index.

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[13] in Asian and European populations primarily has shown more precise discrimination of CVD risk factors by variables of visceral adiposity in women than in men, which conflicts with our findings. The only African-descent population (Jamaicans) [40] included in that meta-analysis had significant and stronger associations between BMI, WC, WHR, and diabetes among men only. In a pooled analysis of cross-sectional data on >24,000 Africans across eight countries [41], there were no sex differences in the accuracy of WC in predicting at least two components of metabolic syndrome. However, the cutoff for women (81 cm versus 80 cm) in that study was similar to WHO-recommended cutoff [23] but significantly lower for men (81 cm versus 94 cm). Another study among rural South Africans demonstrated that the optimal WC cutoff to predict two or more components of the metabolic syndrome was 86 cm for men and 92 cm for women [42]. In our study, the WC cutoff among men was lower than the WHO-recommended cutoff (94 cm). Among women, the WC cutoff was higher than the WHO-recommended cutoff (80 cm) for women. A case-control study [43] in urban Ghana identified WHR as the best discriminator of diabetes, with optimal cutoffs of 0.90 for men and 0.88 for women. In this study, the WHO WHR cutoffs of 0.90 and WC cutoff of 94 cm among women identified the most cases of high CVD risk. Among men, the optimal WC cutoff of 89 cm identified more cases than the WHO-recommended cutoffs. Although not definitive, our findings support the need to conduct prospective studies in sub-Saharan Africa to examine whether different cutoffs are more appropriate for Africans if scarce health care resources are to be efficiently utilized. Examining the sensitivity and specificity of the measures provides clinically meaningful information to identify those at highest CVD risk. We observed sex differences in the sensitivity and specificity of the measures. Since none had 100% sensitivity, our approach to establishing the best cutoff for each anthropometric measure was based on the highest sensitivity, at the expense of losing specificity. To this end, WC (among men) and WC or WHR (among women) identified the most cases. Our study has some limitations. The cross-sectional design precludes examination of a temporal association between the anthropometric variables and CVD risk. Also, we assessed predicted CVD risk and not observed CVD events or mortality, which limits our ability to validate the accuracy of the cutoffs identified. The PCE [24] has not been externally validated or recalibrated for Africans in SSA. Thus, there is a potential for bias in the estimation of the CVD risk. We chose the PCE [24] because of our knowledge; it is the only risk equation which includes a significant African-descent population. There is limited evidence on the performance of existing risk equations among Africans [26]. Given the rigorous process of the derivation of the PCE and validation, it is highly likely to reflect observed CVD events in Africans, but this needs to be confirmed in future studies. This study has numerous strengths. First, our study is the first of its kind to provide evidence on the cutoffs for six different measures of adiposity in identifying CVD risk among Africans residing in SSA and Europe, which could be considered in future longitudinal studies. Arguments have been made against universal cutoff value worldwide for measures of adiposity such as BMI [44–46]. This phenomenon contributed to the WHO’s recommendation to use a lower BMI cutoff for Asians populations [47]. However, to our knowledge, no cutoffs have explicitly been endorsed by the WHO for Africans. Secondly, we studied a relatively homogenous (at least genetically) population of Africans who were primarily from the same ethnic tribe(Akan) and resided in rural, urban Ghana and European settings, thus addressing concerns about differences in the presence of genetic admixture in African Americans. Lastly, we utilized highly standardized procedures to obtain the anthropometric variables across all the sites, as well as standardized values of these measures, to enhance comparability of the data obtained.

Conclusions Our findings underscore considerable sex differences in the associations between anthropometric variables and CVD risk in a homogenous African population. The anthropometric measures performed similarly in discriminating CVD risk across sexes. However, WC (in men) and WC and WHR (in women) consistently performed better than the other anthropometric variables and identified the most cases of high CVD risk. WC is the cheapest to determine and interpret and may be the most clinically useful measure of anthropometric adiposity. Prospective studies are needed to confirm these cutoffs and provide more robust evidence on the accuracy of the PCE in predicting CVD risk among Africans. Ethical standard statement Ethics approval was obtained for all respective study sites. Informed written consent was also obtained from participants. Funding The RODAM study was supported by the European Commissionunder the Framework Program Grant Number: 278901. YCM was supported by a career development grant awarded to the Johns Hopkins Institute for Clinical and Translational Research 5KL2TR001077–05. “KACM is supported by the Intramural Research Program of the National Institutes of Health in the Center for Research on Genomics and Global Health CRGGH. The CRGGH is supported by the National Human Genome Research Institute, the National Institute of Diabetes and Digestive and Kidney Diseases, the Center for Information Technology, and the Office of the Director at the National Institutes of Health1ZIAHG200362.” RSA is supported by a Bloomberg Distinguished Professorship at Johns Hopkins University. Availability of data and material The datasets created and/or analyzed during the current study are available from the corresponding author on reasonable request. Acknowledgments The authors are very grateful to the advisory board members for their valuable support in shaping the methods, to the research assistants, interviewers and other staff of the five research locations who have taken part in gathering the data and, most of all, to the Ghanaian volunteers participating in this project. We gratefully acknowledge Jan van Straalen from the Academic Medical Centre for his valuable support with standardization of the lab procedures. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:10.1016/j.orcp.2020.01.007. References [1] Benjamin EJ, Blaha MJ, Chiuve SE, Cushman M, Das SR, Deo R, et al. Heart disease and stroke statistics-2017 update: a report from the American Heart Association. Circulation 2017;135(10):e146–603. [2] Cooper R, Rotimi C, Ataman S, McGee D, Osotimehin B, Kadiri S, et al. The prevalence of hypertension in seven populations of west African origin. Am J Public Health 1997;87:160–8. [3] Eckel RH. Obesity and heart disease: a statement for healthcare professionals from the Nutrition Committee, American Heart Association. Circulation 1997;96:3248–50. [4] Reilly JJ, El-Hamdouchi A, Diouf A, Monyeki A, Somda SA. Determining the worldwide prevalence of obesity. Lancet 2018;391:1773–4.

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[5] Murphy M, Robertson W, Oyebode O. Obesity in international migrant populations. Curr Obes Rep 2017;6:314–23. [6] Agyemang C, Owusu-Dabo E, de Jonge A, Martins D, Ogedegbe G, Stronks K. Overweight and obesity among Ghanaian residents in The Netherlands: how do they weigh against their urban and rural counterparts in Ghana? Public Health Nutr 2009;12:909–16. [7] Berry JW. Acculturation and adaptation: health consequences of culture contact among circumpolar peoples. Arct Med Res 1990;49:142–50. [8] Delavari M, Sonderlund AL, Swinburn B, Mellor D, Renzaho A. Acculturation and obesity among migrant populations in high income countries–a systematic review. BMC Public Health 2013;13, 458-2458-13-458. [9] Toselli S, Rinaldo N, Gualdi-Russo E. Body image perception of African immigrants in Europe. Glob Health 2016;12, 48-016-0184-6. [10] Yusuf S, Hawken S, Ounpuu S, Bautista L, Franzosi MG, Commerford P, et al. Obesity and the risk of myocardial infarction in 27,000 participants from 52 countries: a case-control study. Lancet 2005;366:1640–9. [11] Lauderdale DS, Rathouz PJ. Body mass index in a US national sample of Asian Americans: effects of nativity, years since immigration and socioeconomic status. Int J Obes Relat Metab Disord 2000;24:1188–94. [12] Klein S, Allison DB, Heymsfield SB, Kelley DE, Leibel RL, Nonas C, et al. Waist circumference and cardiometabolic risk: a consensus statement from shaping america’s health: association for weight management and obesity prevention; NAASO, the obesity society; the American society for nutrition; and the American diabetes association. Obesity 2007;15:1061–7. [13] Lee CM, Huxley RR, Wildman RP, Woodward M. Indices of abdominal obesity are better discriminators of cardiovascular risk factors than BMI: a metaanalysis. J Clin Epidemiol 2008;61:646–53. [14] Moore SC. Waist versus weight: which matters more for mortality? Am J Clin Nutr 2009;89:1003–4. [15] Ashwell M, Lejeune S, McPherson K. Ratio of waist circumference to height may be better indicator of need for weight management. BMJ 1996;312:377. [16] Ashwell M, Hsieh SD. Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. Int J Food Sci Nutr 2005;56:303–7. [17] Krakauer NY, Krakauer JC. A new body shape index predicts mortality hazard independently of body mass index. PLoS One 2012;7:e39504. [18] Woolcott OO, Bergman RN. Relative fat mass (RFM) as a new estimator of whole-body fat percentage horizontal line A cross-sectional study in American adult individuals. Sci Rep 2018;8, 10980-018-29362-1. [19] Agyemang C, Beune E, Meeks K, Owusu-Dabo E, Agyei-Baffour P, Aikins A, et al. Rationale and cross-sectional study design of the research on obesity and type 2 diabetes among African migrants: the RODAM study. BMJ Open 2014;4, e004877-2014-004877. [20] German Technical Cooperation(GTZ). The Ghanaian Diaspora in Germany Its Contribution to Development in Ghana. Federal Ministry for Economic Cooperation and Development; 2009 http://www.migration4development.org/sites/ m4d.emakina-eu.net/files/The Ghanaian diaspora in Germany.pdf. [21] Office for National Statistics. Estimated population resident in the United Kingdom, by foreign country of birth(Table 1.3). ONS 2009. [22] Central Bureau for Statistics. Statistical yearbook 2009. Statistics Netherlands Facilities Services; 2009. [23] World Health Organization (WHO). Waist circumference and waist-hip ratio: report of a WHO expert consultation. Available at: http://apps.who.int/ iris/bitstream/handle/10665/44583/9789241501491 eng.pdf?ua=1. (Accessed 3 July 2018). [24] Goff Jr DC, Lloyd-Jones DM, Bennett G, Coady S, D’Agostino RBS, Gibbons R, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 2013;129(25 (Suppl 2)):S49–73, http://dx.doi. org/10.1161/01.cir.0000437741.48606.98. [25] Boateng D, Agyemang C, Beune E, Meeks K, Smeeth L, Schulze M, et al. Migration and cardiovascular disease risk among Ghanaian populations in Europe: the RODAM study (Research on Obesity and Diabetes Among African Migrants). Circ Cardiovasc Qual Outcomes 2017;10, http://dx.doi.org/10.1161/ CIRCOUTCOMES.117.004013.

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[26] Boateng D, Agyemang C, Beune E, Meeks K, Smeeth L, Schulze MB, et al. Cardiovascular disease risk prediction in sub-Saharan African populations—comparative analysis of risk algorithms in the RODAM study. Int J Cardiol 2018;254:310–5. [27] Welborn TA, Dhaliwal SS. Preferred clinical measures of central obesity for predicting mortality. Eur J Clin Nutr 2007;61:1373–9. [28] Balkau B, Deanfield JE, Despres JP, Bassand JP, Fox KA, Smith Jr SC, et al. International day for the evaluation of abdominal obesity (IDEA): a study of waist circumference, cardiovascular disease, and diabetes mellitus in 168,000 primary care patients in 63 countries. Circulation 2007;116:1942–51. [29] van Dis I, Kromhout D, Geleijnse JM, Boer JM, Verschuren WM. Body mass index and waist circumference predict both 10-year nonfatal and fatal cardiovascular disease risk: study conducted in 20,000 Dutch men and women aged 20-65 years. Eur J Cardiovasc Prev Rehabil 2009;16:729–34. [30] Freiberg MS, Pencina MJ, D’Agostino RB, Lanier K, Wilson PW, Vasan RS. BMI vs. waist circumference for identifying vascular risk. Obesity 2008;16:463–9. [31] Olatunbosun ST, Kaufman JS, Bella AF. Central obesity in Africans: anthropometric assessment of abdominal adiposity and its predictors in urban Nigerians. J Natl Med Assoc 2018;110(5):519–27. [32] Haregu TN, Oti S, Egondi T, Kyobutungi C. Measurement of overweight and obesity an urban slum setting in sub-Saharan Africa: a comparison of four anthropometric indices. BMC Obes 2016;3, 46-016-0126-0. eCollection 2016. [33] Zhang C, Rexrode KM, van Dam RM, Li TY, Hu FB. Abdominal obesity and the risk of all-cause, cardiovascular, and cancer mortality: sixteen years of follow-up in US women. Circulation 2008;117:1658–67. [34] Pischon T, Boeing H, Hoffmann K, Bergmann M, Schulze MB, Overvad K, et al. General and abdominal adiposity and risk of death in Europe. N Engl J Med 2008;359:2105–20. [35] Peters SAE, Bots SH, Woodward M. Sex differences in the association between measures of general and central adiposity and the risk of myocardial infarction: results from the UK biobank. J Am Heart Assoc 2018;7. [36] Lissner L, Bjorkelund C, Heitmann BL, Seidell JC, Bengtsson C. Larger hip circumference independently predicts health and longevity in a Swedish female cohort. Obes Res 2001;9:644–6. [37] Snijder MB, Dekker JM, Visser M, Yudkin JS, Stehouwer CD, Bouter LM, et al. Larger thigh and hip circumferences are associated with better glucose tolerance: the Hoorn study. Obes Res 2003;11:104–11. [38] Seidell JC, Han TS, Feskens EJ, Lean ME. Narrow hips and broad waist circumferences independently contribute to increased risk of non-insulin-dependent diabetes mellitus. J Intern Med 1997;242:401–6. [39] van Nieuwenhuizen B, Zafarmand MH, Beune E, Meeks K, Aikins AD, Addo J, et al. Ideal cardiovascular health among Ghanaian populations in three European countries and rural and urban Ghana: the RODAM study. Intern Emerg Med 2018;13:845–56. [40] Sargeant LA, Bennett FI, Forrester TE, Cooper RS, Wilks RJ. Predicting incident diabetes in Jamaica: the role of anthropometry. Obes Res 2002;10:792–8. [41] Ekoru K, Murphy GAV, Young EH, Delisle H, Jerome CS, Assah F, et al. Deriving an optimal threshold of waist circumference for detecting cardiometabolic risk in sub-Saharan Africa. Int J Obes 2018;42:487–94. [42] Motala AA, Esterhuizen T, Pirie FJ, Omar MA. The prevalence of metabolic syndrome and determination of the optimal waist circumference cutoff points in a rural South african community. Diabetes Care 2011;34:1032–7. [43] Frank LK, Heraclides A, Danquah I, Bedu-Addo G, Mockenhaupt FP, Schulze MB. Measures of general and central obesity and risk of type 2 diabetes in a Ghanaian population. Trop Med Int Health 2013;18:141–51. [44] Razak F, Anand SS, Shannon H, Vuksan V, Davis B, Jacobs R, et al. Defining obesity cut points in a multiethnic population. Circulation 2007;115:2111–8. [45] Evans EM, Rowe DA, Racette SB, Ross KM, McAuley E. Is the current BMI obesity classification appropriate for black and white postmenopausal women? Int J Obes 2006;30:837–43. [46] Heymsfield SB, Peterson CM, Thomas DM, Heo M, Schuna Jr JM. Why are there race/ethnic differences in adult body mass index-adiposity relationships? A quantitative critical review. Obes Rev 2016;17:262–75. [47] WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363:157–63.

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