H3Africa AWI-Gen Collaborative Centre: a resource to study the interplay between genomic and environmental risk factors for cardiometabolic diseases in four sub-Saharan African countries

Background: There is a high and increasing prevalence of overweight and obesity in South Africans of all ages. Risk factors associated with overweight and obesity must be identified to provide targets for intervention. Objective: To identify the demographic, socio-economic and behavioural factors associated with body mass index (BMI) in middle-aged black South African men and women. Methods: Data on demographic and socio-economic factors were collected via questionnaire on 1027 men and 1008 women from Soweto Johannesburg, South Africa. Weight and height were measured and BMI was determined. Behavioural factors included tobacco use and consumption of alcohol, and physical activity data were collected using the Global Physical Activity Questionnaire. Menopausal status was determined for the women, and HIV status was available for 93.6% of the men and 39.9% of the women. Results: Significantly more women were overweight or obese than men (87.9 vs. 44.9%). Smoking prevalence (current or former) and minutes spent in moderate to vigorous intensity physical activity was significantly different between the sexes (both p < 0.0001). In the final hierarchical model, marital status (+ married/cohabiting), household asset score (+), current smoking (-), moderate to vigorous physical activity (-) and HIV status (- HIV infected) significantly contributed to 26% of the variance in BMI in the men. In the women, home language (Tswana-speaking compared to Zulu-speaking), marital status (+ unmarried/cohabiting), education (-), current smoking (-) and HIV status (- HIV infected) significantly contributed to 14% of the variance in BMI. Conclusions: The sex difference in BMI and the prevalence of overweight and obesity between black South African men and women from Soweto, as well as the sex-specific associations with various demographic, socio-economic and behavioural factors, highlight the need for more tailored interventions to slow down the obesity epidemic.

Section: Methodsmentioning

confidence: 99%

Demographic, socio-economic and behavioural correlates of BMI in middle-aged black men and women from urban Johannesburg, South Africa

Micklesfield

Kagura

Munthali

et al. 2018

“…The communities within the study area were randomly sampled and the participants randomly selected using the NHDSS existing sampling frame, aiming for a sex and ethnic balance. This study was nested within the AWI-Gen study, which is a large pan-African investigation that aims to assess the genetic, physiological and environmental factors that contribute to BMI, body fat distribution and cardiometabolic disease risk factors in older (40–60 years old) African subjects in both rural and urban environments [29]. …”

Section: Methodsmentioning

confidence: 99%

“…The Global Physical Activity Questionnaire (GPAQ) [32] and the CAGE questionnaire [33] were incorporated into the main AWI-Gen questionnaire and used to assess physical activity and alcohol consumption respectively (see Supplementary material Table S3). Details of all data collection methods and measurements carried out in the study are described elsewhere [29] and are also presented as supplementary material (Table S3).…”

Section: Methodsmentioning

confidence: 99%

“…In this study, nested in the H3Africa (Human Heredity and Health in Africa) AWI-Gen (Africa Wits-INDEPTH [International Network for the Demographic Evaluation of Populations and Their Health] partnership for Genomic studies) project [29], we report the prevalence of BMI categories ranging from underweight to obesity, and the influence of socio-demographic and behavioural factors on BMI among adults in the Kassena-Nankana districts in rural Northern Ghana. The age group selected for this study were 40–60 years old as the prevalence of obesity and associated cardiometabolic diseases rises during this period [15–17].…”

Section: Introductionmentioning

confidence: 99%

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Socio-demographic and behavioural determinants of body mass index among an adult population in rural Northern Ghana: the AWI-Gen study

Nonterah

Debpuur

Agongo

et al. 2018

Background: Obesity and associated cardiometabolic diseases are increasing in urban sub-Saharan Africa due to a complex epidemiological and nutritional transition. Related data on rural communities is scarce. Objectives: The study characterized the socio-demographic and behavioural factors influencing body mass index (BMI) among adults in rural Northern Ghana Methods: A population-based cross-sectional study involving adults aged 40–60 years residing in the Kassena-Nankana districts was undertaken. Demographic, socio-economic and behavioural data were collected along with measures of anthropometry. We determined factors associated with BMI among women and men. Results: A total of 2014 adults were studied. The median age was 51 (IQR 45–57) years and 54% were women. The prevalence of overweight/obesity was higher among women than men (18.4% vs. 7.2%; p < 0.001), whilst underweight was more prevalent in men (18.3% vs. 13.1%; p = 0.001). Participants with the highest level of education and a high household socio-economic status had higher BMIs than those in the lowest strata in both men (β = 0.074, p = 0.028 and β = 0.072, p < 0.001, respectively) and women (β = 0.174, p = 0.001 and β = 0.109, p < 0.001, respectively). Men (β = −0.050; p < 0.001) and women (β = −0.073; p < 0.001) of the Nankana ethnic group had a lower BMI than the Kassena ethnic group. Among men, alcohol consumption (β = −0.021; p = 0.001) and smoking (β = −0.216; p < 0.001) were associated with lower BMI. Smokeless tobacco was associated with lower BMI among women. Pesticide exposure was associated with higher BMI (β = 0.022; p = 0.022) among men. Conclusion: Age, sex, ethno-linguistic group and prevailing socio-demographic and behavioural factors within this rural community in Northern Ghana influence BMI. The observed positive association between pesticide use and BMI warrants further investigation.

“…The data presented in this paper were obtained from the Agincourt Health and Socio-Demographic Surveillance System (HDSS), one of the rural South African sites [14–16]. The Agincourt HDSS comprises 450 km 2 and 31 research villages and is located 500 km northeast of Johannesburg in rural Mpumalanga.…”

Section: Methodsmentioning

confidence: 99%

Sociodemographic, socioeconomic, clinical and behavioural predictors of body mass index vary by sex in rural South African adults-findings from the AWI-Gen study

Wagner

Crowther

Gómez‐Olivé

et al. 2018

Background: Despite increasing obesity in South African adults, data on the prevalence and determinants of body mass index (BMI) from rural communities, home to a significant proportion of the population, are scarce. Objectives: To investigate overall and sex-specific determinants of BMI in a rural adult South African population undergoing rapid social and epidemiological transitions. Methods: Baseline cross-sectional demographic, socioeconomic, anthropometric, clinical and behavioural data were collected between 2015 and 2016 from 1388 individuals aged 40–60 years and resident in the Agincourt sub-district of Mpumalanga province, a setting typical of rural northeast South Africa. A Health and Socio-Demographic Surveillance System (HDSS) underpins the sub-district and contributes to the Africa Wits-INDEPTH partnership for Genomic Studies (AWI-Gen). Linear regression was used to investigate univariate associations between log-transformed BMI and individual variables and multiple linear regression was used to investigate independent predictors of BMI overall and in sex-stratified analyses. Results: Median BMI was significantly higher in females (28.7 kg/m2[95% CI 24.2–33.2] vs 23.0 kg/m2[95% CI 20.3–26.8];p < 0.001) with male sex associated with 17% lower BMI. In sex-stratified multiple linear regression models, compared to those never married, BMI was 7% higher in currently married males and 6% in currently married females. Current smoking in men and former smoking in women were associated with reductions in BMI of 13% and 26% respectively, compared with non-smokers. Higher educational attainment in women and higher socioeconomic status in men were both associated with higher BMI, while being HIV-positive and alcohol consumption in women were associated lower BMI. Conclusions: Female sex strongly predicts higher BMI in this rural African population. While some predictors of higher BMI differ by sex, married individuals in both sexes had a higher BMI, suggesting that, in addition to developing sex-specific interventions to combat overweight and obesity, targeting married couples may result in reduction in population BMI.