Introduction
Excess body fat and its metabolic consequences are recognised global epidemics strongly linked to cardiovascular disease (CVD) morbidity and mortality [1] with 80% of the 18.6 million global CVD-related deaths recorded in 2019 occurring in low- and middle-income countries [2]. CVDs have been increasing in sub-Saharan Africa (SSA) while developed countries have witnessed a steady decline [3,4]. The burden and risk factors of CVDs in SSA differ between countries reflecting the different stages of the epidemiological health transition [5–8].
Central to the epidemiological transition is rising obesity levels, which the Non-Communicable Disease (NCD) Risk Factor Collaboration suggest is driven by rural communities [9,10]. The possible reasons for the rise in rural obesity include increasing incomes, better infrastructure, more mechanized agriculture and increased car use, all of which lead to lower energy expenditure and greater access to energy dense foods [9,10]. The principal metabolic comorbidity associated with excessive body fat is dyslipidaemia. Typically, obesity is strongly associated with atherogenic dyslipidaemia, characterized by high triglyceride (TGs) and low high-density lipoprotein cholesterol (HDL-C) levels [1]. Low-density lipoprotein cholesterol (LDL-C) is an independent predictor of CVD and is the primary target of treatment with non-HDL-C being regarded as a secondary treatment target in the control of dyslipidaemia for the prevention of CVD [1]. While general obesity measured by BMI is a suitable index for evaluating overall adiposity it is a poor indicator of body fat distribution. Central (such as waist circumference) as compared to peripheral body (measured as hip circumference) fat is a major risk factor for coronary artery disease, myocardial infarction and peripheral vascular disease (because it is a metabolically active fat depot and is associated with a higher risk of CVD, according to both epidemiological and Mendelian randomization studies [11,12], which offer both population level observed risk and a genetic-linked risk. Collectively, obesity and central adipose tissue depots contribute to unfavorable levels of dyslipidemia and further promote the development of atherosclerosis and established CVD.
The prevalence of dyslipidaemia including elevated total cholesterol (TC), LDL-C and TGs and low levels of HDL-C [13–15] is increasing in SSA but vary between countries [8,16]. Although ethnic differences in serum lipid levels have been reported [17,18], adiposity in addition to unhealthy diet, low physical activity and poor access to healthcare, especially in low- and middle-income countries are the main drivers of dyslipidaemia [19]. There is the need to create awareness and subsidize screening for lipid abnormalities so as to identify individuals at high risk for developing CVD in the context of rising obesity levels in SSA.
Large-scale population based studies with harmonized data collection of serum lipid levels in Africa are lacking with few studies small scale and localized regional studies [20–22]. To fill these critical gaps, the H3Africa Collaborative Centre, referred to as the Africa Wits-INDEPTH (International Network for the Demographic Evaluation of Populations and Their Health in low- and middle-income countries) partnership for genomics studies (AWI-Gen) generated a cohort of over 10,700 participants from across SSA [23,24]. This study reports burden of abnormal lipid fraction, dyslipidemia and their associations with obesity phenotypes and number needing dyslipidemia screening in middle-aged women and men in four SSA countries in different stages of epidemiological transition.
Methods
The H3Afric AWI-Gen study received ethical approval from University of Witwatersrand Ethics Committee in South Africa (M121029; M170880) and from each of the participating sites. Written and signed or thump printed informed consent was obtained from all participants before recruitment. This paper follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines for observational studies. Recruitment of participants across the six sites involved in the study are as follows: Agincourt (13/11/2014 to 30/11/2015); DIMAMO/Digkale (02/11/2014 to 19/08/2016); Nairobi (04/11/2014 to 08/11/2015); Nanoro (20/01/2015 to 25/07/2016); Navrongo (02/02/2015 to 06/10/2015) and Soweto (18/08/2011 to 08/12/2014).
Study design, setting, population and sampling
A cross-sectional study was conducted in which participants were recruited between 2013 and 2016 in five INDEPTH-Network Health and Demographic Surveillance Sites i.e., Agincourt (rural), Dikgale (rural) in South Africa, Navrongo (rural) in Ghana, Nanoro (rural) in Burkina Faso and Nairobi (urban) in Kenya. The sixth site was the urban Soweto cohort located within the MRC/Wits Developmental Pathways for Health Research Unit (DPHRU) also in South Africa [23–25]. The population was made up of women and men 40-60years, randomly selected from within the study sites. Pregnant women were excluded as well as participants who could not complete the prescribed study procedures. Participants from Nanoro, Nairobi and Navrongo HDSS were selected by simple random sampling using existing sampling frames with an equal number of females and males. In Agincourt the population census was used as the sampling frame in which convenience sampling was used to recruit participants between the ages of 40 and 60 years. In Soweto, men were recruited through simple random sampling from the Soweto community while women were samples from care givers of the “Birth-to-twenty cohort” [26]. Data from 10700 participants was used in this study.
Sample size determination
Using a pooled prevalence of dyslipidaemia of 16.5% [27] taken from a systematic review of studies in Africa with a margin of error of 5% and a standard deviation of 1.96, a minimum sample size of 1311 was determined using Cochran formula [28]. Therefore, a sample size of 10700 participants used in this study is sufficient to determine the levels of lipid fractions and the prevalence of dyslipidaemia and associated factors in these African populations.
Data collection
Trained field staff used a standard structured AWI-Gen questionnaire with modifications to suit each country context to collect data. Data were entered or imported into the RedCap electronic database.
Measures of obesity phenotypes
BMI in Kg/m2 (an indicator of general obesity).
Standing height was measured using a Harpenden digital stadiometer (Holtain, Crymych, Wales) while weight was measured using digital Physician Large Dial 200kg capacity scales (Kendon Medical, UK). BMI was computed as weight over height in meters squared and classified into four categories; underweight = BMI < 18.5 kg/m2, normal weight = BMI 18.5 kg/m2 to 24.9 kg/m2, overweight weight BMI 25 kg/m2 to 29.9 kg/m2 and obese BMI ≥ 30 kg/m2 [29].
Waist and hip circumference.
Waist circumference was measured in light clothes using a stretch-resistant tape measure (SECA, Hamburg, Germany). Abnormal WC, an indicator of central obesity was defined as waist circumference ≥80 cm for women and ≥94 cm for men [30]. The hip circumference, as gluteofemoral region, was measured by placing the tape around the most protruding part of the buttocks, ensuring that the zero mark was to the participant’s side. The measurement was done to the nearest 0.1cm.
Waist-to-hip ratio (WTHR).
The ratio of waist and hip circumference was subsequently computed. Abnormal WTHR ratio that can cause substantial risk of cardiometabolic diseases was defined as ≥0.90cm for men and ≥0.85cm for women.
Serum lipid fractions and dyslipidemia
Fasting venous blood samples were analyzed at a central laboratory using a Randox Daytona Plus (Randox Laboratories Ltd, UK) autoanalyser. The TC, TG and HDL-C were measured by enzymatic colorimetric methods while LDL-C was calculated using the Friedewald equation [31] and non-HDL-C was computed by subtracting HDL-C from TC [32]. For all assays the coefficient of variation of the technician was low (<2%) and the laboratory performance was acceptable using the Randox International Quality Assessment Scheme. Abnormal lipid levels were defined as follows: TC ≥5.0mmol/l (hypercholesterolaemia), LDL-C ≥3.0 mmol/l, TG ≥1.7 mmol/l, and non-HDL-C >3.4mmol/l and low HDL-C <1.0mmol/l for men and <1.3mmol/l for women [32,33]. Dyslipidaemia was defined as having at least one abnormal level of either of the five lipid fractions or being told by a health professional that they have a high cholesterol level or on treatment foe dyslipidaemia. Treatment for dyslipidaemia was a self-report of the use of lipid-lowering therapy among those who were aware of their condition.
Other risk factors of CVD
Other variables included risk factors of CVD included demographic (such as, age, sex, education, household socioeconomic status); self-reported behavioral risk (smoking, alcohol use, physical activity, fruit and vegetable intake as self-reported daily number of servings of fruits and vegetables); metabolic risk (menopause status for women, diabetes and hypertension). Details of the definitions of these variables have been published by Ali et al, 2018 [24].
Statistical analysis
Characteristics of the study participants were summarized using counts and proportions for categorical data and means and standard deviations (±SD) for continuous data due to approximate normal distribution. Age-standardised prevalence rates of abnormal lipid levels and dylipidaemia were calculated using the age distribution of the total AWI-Gen study population and this method has previously been used [34,35]. A mixed effect linear regression with a random effect of mean serum lipid levels predicted by socio-demographic factors, the behavioral risk factors of CVD, and metabolic and anthropometric indices was computed. Post regression estimated adjusted means with standard errors generated through the delta method.
Regional differences in prevalence of dyslipidaemia were computed using Poisson regression analyses with a variance-covariance method to obtain robust standard errors. The effects are presented as prevalence ratios (PRs) with 95% confidence intervals. The PRs indicate the ratio between the prevalence of an outcome in the most transitioned sites versus the least transitioned sites. The populations in West Africa served as the reference group. A random mixed effects logistic regression meta-analysis for the combined AWI-Gen sample to determine association of obesity phenotypes and dyslipidemia as well with each of the lipid fractions. Multiplicative interaction terms between sex and the adiposity measures into each the models were used to determine sex differences.
Finally, the number of participants needing screening for dyslipidemia was computed using World Health Organization package of essential non-communicable disease interventions for primary healthcare in low resource settings (WHO PEN) [36]. This is defined as those exhibiting at least one of the following risk factors: smoking; elevated glucose; high blood pressure; waist circumference ≥90cm in males; waist circumference ≥100cm in females. In the original recommendation age >40 years was recommended but since the minimum age for our study was 40 years, we omitted age from our computation. Statistical significance for all the inferential statistics was set at a p-value of less than 0.05. All analyses were carried out using STATA version 14.2 SE.
Results
Participant characteristics
A total of 10,700 participants (55.5% women), average age 50 ± 5 years (women) and 50 ± 6 years (men) from six sites in four SSA countries were studied (Table 1). Current smoking levels were higher in men than women at all sites and were highest in Soweto in both sexes. A high proportion of women and men across all sites were deemed physically active. In both men and women, general, central and peripheral obesity was higher in the South African and East African sites compared to the West African sites and the proportions were higher for women across all sites except in Burkina Faso where the prevalence of obesity was higher in men than women (Table 1).
[Figure omitted. See PDF.]
Mean serum lipid levels
The adjusted mean levels of the various lipid fractions are presented in Fig 1A for women and 1B for men. The adjusted mean lipid levels differed by study site and sex. There was a general trend of low means lipid levels among women and men from Nanoro and Navrongo (West African sites) except for mean LDL-C levels where men from Nanoro, Burkina Faso and Nairobi, Kenya presented with higher mean levels compared to other sites. South African men had the highest levels of each of the 4 lipid fractions and the lowest HDL-C levels.
[Figure omitted. See PDF.]
Adjusted mean levels of the various lipid fractions in women (A) and men (B) from the AWI-Gen study. Adjusted for age, educational status, household socioeconomic status, smoking, alcohol use, physical activity, fruit and vegetable intake, and use of lipid lowering medication.
Age-standardised prevalence of abnormal lipid levels
The age-standardised prevalence of abnormal lipid levels for each of the lipid fractions and dyslipidaemia in the men and women at the various sites are presented in Table 2. In the combined sample, women were reported a higher prevalence of elevated total cholesterol, triglycerides and non-HDL-C while men had higher prevalence of low HDL-C and elevated LDL-C compared to women. Similarly, women and men from West Africa had the lowest prevalence of elevated TC, LDL-C, TG and non-HDL-C and the lowest prevalence of low HDL-C. Men in Nanoro, Burkina Faso were likely to present with elevated LDL-C compared to Navrongo, Ghana and other sites in South Africa and East Africa. The combined prevalence of dyslipidaemia among women was 58.4% while that among men was 75.4%.
[Figure omitted. See PDF.]
Obesity measures and dyslipidaemia
There were varied associations of BMI, WC and WTHR with dyslipidemia (Table 3). BMI was associated with a greater risk (adjusted odds ratio, AOR [95% confidence interval]) of elevated TC (1.89 [1.57, 2.28]), LDL-C (1.32 [1.06, 1.62]), TG (1.68 [1.32, 2.13]) and non-HDL-C (2.18 [1.84, 2.58]) and a greater low HDL-C (2.18 [1.84, 2.58]) in men and women. In the sex stratified analyses, men had a greater risk of dyslipidaemia than women. Waist circumference was associated with elevated TC (1.64 [1.25, 2.14]), LDL-C (1.26 [1.03, 1.54]), TG (1.83 [1.30, 2.57]) and non-HDLC (1.67 [1.32, 2.11]) and a greater low HDL-C (1.12 [1.08, 1.18]). There was a significant sex interaction with all five lipid fractions (p<0.05). Women were more at risk of all abnormal lipid fractions but LDL-C where men had a greater risk than women. Waist-to-hip ratio was associated with a greater odds of elevated TC (1.47 [1.25, 1.75]), LDL-C (1.77 [1.46, 2.15]), Triglycerides (2.21 [1.75, 2.79]) and non-HDLC (1.63 [1.39, 1.91]) and a greater odds of low HDL-C (1.16 [1.04, 1.20]) differences between women and men except for Triglycerides.
[Figure omitted. See PDF.]
Regional difference
We first reported association of each phenotype with the lipid fractions for the sub-regional blocks (S1 Table) and further examined regional differences in prevalence ratio of the abnormal fractions with West Africa as a reference (S2 Table). There was an observed gradient regarding the prevalence ratio of all five lipid fractions with East Africa and South Africa having the higher prevalence ratio compared to West Africa. Similarly, the association of obesity phenotypes with dyslipidemia showed higher odds in Southern and east Africa compared to West Africa.
Number recommended for screening
The number of participants needing dyslipidemia screening, 72.2% of men and 38.7% of women in the total AWI-Gen population would benefit from lipid screening. Sites in South Africa had a higher need for screening compared to East and West African sites (Fig 2).
[Figure omitted. See PDF.]
Discussion
In this large multi-county SSA study we observed that the mean lipid levels were higher in Southern (Agincourt, Dikgale and Soweto) and Eastern Africa (Nairobi, Kenya) compared to the two sites in West Africa (Nanoro, Burkina Faso and Navrongo, Ghana). We also observed that the age-standardized prevalence of dyslipidaemia followed a similar pattern. We further observed that all forms of obesity were associated with dyslipidaemia in the full cohort and in the regional blocks. These associations had a similar direction of but the magnitude of association was higher in South Africa then East compared to West Africa. Finally, a greater proportion of the AWI-Gen participants needed screening for dyslipidemia with more men than women needing screening.
The observed rural-urban gradient in the burden of dyslipidaemia in our study has been previously reported. The Research on Obesity and Diabetes among African Migrants (RODAM) study previously reported Ghanaian men living in rural Ghana had a lower burden of high LDL-C, high TC levels and elevated TGs compared to those in urban Ghana and Ghanaian migrants in Europe [34,35]. Other studies within the African continent have also reported on rural-urban difference in dyslipidaemia [28,36–38] with a systematic review and meta-analysis, reporting that East and Southern African countries were likely to present with a higher prevalence of elevated LDL-C compared to West African countries [39]. Epidemiological data on morbidity and mortality due to CVD and data on CMD risk factors from the current and a previous study [40,41] suggest that the West African sites are the least transitioned with East Africa and South African sites being further along the transition pathway. In addition, multiple studies conducted at each site have allowed in-depth characterisation of the prevailing sociodemographic features and demonstrate that both West African sites are rural with residents reliant on subsistence farming, whilst the site in Nairobi is an urban shanty town, and the sites in South Africa are a mix of peri-urban (Dikgale and Agincourt) and urban (Soweto). Based on these characteristics and the findings, we suggest the influence of epidemiological transition on dyslipidaemia with the two West African populations depicting early stage of epidemiological health transition compared to Nairobi (East Africa) and South African populations.
Despite this, some variations were observed in the prevalence of abnormal levels of LDL-C and HDL-C. For instance, men the West African site of Nanoro (68%) had a higher prevalence of low HDL-C than Navrongo, Ghana (64%) but similar to East Africa (Nairobi, 68%). The RODAM study previously made similar observations (23). Africans in general present with more favorable lipid profiles compared to European and other race-ethnic groups [42,43] but evidence from African American populations demonstrate that this does not translate into lower cardiovascular events [44]. Recent genome-wide association (GWAS) meta-analysis of the AWI-Gen cohort with four other African cohorts suggest genetic and environmental contribution to variation in lipid levels across SSA and between African and non-African population [45], but further studies are required to confirm these observations as well to investigate the contribution of low HDL-C to CVD risk.
Women were likely to present with a high prevalence of all lipid abnormalities except elevated LDL-C. There are inconsistencies in the literature regarding this finding with some studies reporting similar data to our study [42] while other studies demonstrated a higher burden of dyslipidaemia in men [43,44]. Differences in lipid metabolism between men and women have been reported with women of child bearing age likely to have high atheroprotective lipids (HDL-C) compared to men with the reverse observed during postmenopausal stage [44]. Further studies establishing the causal link between genetic, metabolic (including sex hormones) and lifestyle-associated predisposition to elevated LDL-C will be useful in explaining the observed sex differences.
In a systematic review, independent predictors of dyslipidemia in different African settings included high BMI and waist circumference [45]. We observed that high BMI, waist circumference and WTHR, were associated with a higher odd of dyslipidaemia. The adipose tissue that contributes to central obesity (of which waist circumference and WTHR are proxy indicators) includes visceral and subcutaneous fat. The visceral fat component of waist circumference is a metabolically active endocrine and immune organ and correlates more strongly with CMD risk factors than does subcutaneous fat due to its higher output of cytokines [46] and its release of FFAs into the portal circulation A previous study using the AWI-Gen cohort had demonstrated that adiposity phenotypes such as general obesity (BMI), central obesity (waist circumference and visceral fat) were associated with subclinical atherosclerosis in African populations [47].
Consistent with our findings, a previous study had observed a high unmet need for the identification and treatment of hypercholesterolemia in 35 low- and middle-income countries [48] while another study also observed low awareness and poor control of dyslipidaemia in Africa [49]. There is a greater need for systematic screening to detect dyslipidaemia at the early stages, education and prompt management within the population and through primary health care strengthening such as provision of point-of-care testing methods. With effective screening, minor issues could be identified in a timely manner to enable implementation of preventive measures that will prevent complications or overt clinical conditions. This could further enable the health system to assess the effectiveness of interventions and monitor treatment outcomes.
Strengths and limitations
The availability of data from three different geographical regions in SSA is a unique strength of the AWI-Gen study. It provided us the opportunity to report data from countries representing different stages of the epidemiological transition using three sub-regional blocks in SSA. Another major strength of this study is the use of highly standardised procedures in data collection across all study sites as well as centralised analyses of serum lipid levels minimising measurement variability. The absence of wide confidence intervals further confirms that major within and across sites variations were minimized. Given the cross-sectional nature of this study, causality cannot be demonstrated and residual confounding from other unmeasured variables cannot be ruled out.
Conclusion
Obesity in all forms may drive a dyslipidaemia epidemic in sub-Saharan Africa with men and transitioned societies at higher risk. Interventions should aim at reducing the burden of obesity in SSA countries with attention paid to dietary intake, physical activity, improved access to screening services at the primary healthcare level. Additional research is needed to establish the true contribution of low HDL-C to CVD risk in African populations.
Supporting information
S1 Checklist. STROBE statement—checklist of items that should be included in reports of observational studies.
https://doi.org/10.1371/journal.pone.0316527.s001
(DOC)
S1 Table. Regional differences in the association of the various adiposity phenotypes with abnormal lipid fractions in the combined AWI-Gen cohort.
Elevated cholesterol is defined as TC ≥ 5 mmol/L; Elevated LDL-C is LDL-C ≥3 mmol/L; Low HDL is HDL-C <1.0 mmol/L in men and <1.3 mmol/L in women; elevated TGs is TGs ≥1.7 mmol/L and elevated non-HDL-C is non-HDL-C >3.4 mmol/L; results are presented as odds ratios with corresponding 95% confidence intervals; the models are adjusted for age, educational status, household socioeconomic status, smoking, alcohol intake, physical inactivity, fruits and vegetable intake and use of lipid lowering medication; *The BMI model had only WTHR it them will BMI was included in WC and WTHR model.
https://doi.org/10.1371/journal.pone.0316527.s002
(DOCX)
S2 Table. Prevalence ratio of abnormal lipid levels sub-regional blocks with West Africa as the reference.
Data presented as prevalence ratios with West Africa as the reference group. West Africa includes Nanoro, Burkina Faso and Navrongo, Ghana sites; East Africa includes Nairobi, Kenya; and South Africa, Agincourt, Dikgale and Soweto sites; LDL-C, low density lipoprotein cholesterol; HDL-C, high density lipoprotein cholesterol and non-HDL-C, non-high-density lipoprotein cholesterol.
https://doi.org/10.1371/journal.pone.0316527.s003
(DOCX)
S3 Table. STROBE statement—checklist of items that should be included in reports of observational studies.
https://doi.org/10.1371/journal.pone.0316527.s004
(DOC)
S1 File.
https://doi.org/10.1371/journal.pone.0316527.s005
(DOCX)
Acknowledgments
This paper is dedicated to the memory of Professor Marianne Alberts formerly head of the Dikgale (later renamed DIMAMO) health and demographic surveillance site in Limpopo, South Africa. She sadly passed away before submission of this manuscript. This study would not have been possible without the generosity of the participants who spent many hours responding to questionnaires, being measured, and having samples taken. We wish to acknowledge the sterling contributions of our field workers, phlebotomists, laboratory scientists, administrators, data personnel and other staff who contributed to the data and sample collections, processing, storage, and shipping. We would like to acknowledge investigators from the various sites for the significant contributions made to this research.
AWI-Gen and the H3Africa Consortium: Lucas Amenga-Etego1, Cornelius Debpuur1, Eric Fato1, Immaculate Anati1 (1Navrongo Health Research Centre, Ghana Health Service, Navrongo, Ghana); Christopher Khayeka–Wandabwa6, Tilahun Nigatu Haregu6, Stella Muthuri6 (African Population Health Research Center, Nairobi, Kenya); Nomses Baloyi7, Juliana Kagrana7, Richard Munthali7, Yusuf Guman7; Toussaint Rouamba8, Seydou Diallo-Nakanabo8 (8Clinical Research Unit of Nanoro, Institut de Recherché en Sciences de la Santé, Clinical Research Unit of Nanoro, Burkina Faso); Freedom Mukomana9, Ananyo Choudary9, Zane Lombard9, Scot Hezelhurst9 (9Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg); Francesc Xavier Gomez-Olive Casas10, Kathleen Kahn10 (10MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 2193, South Africa); Given Mashaba11, Felistas Mashinya11 and Sam Ntuli11 (11DIMAMO, Department of Pathology and Medical Science, School of Health Care Sciences, Faculty of Health Sciences, University of Limpopo, Polokwane, South Africa).
References
1. 1. Bays HE, Toth PP, Kris-Etherton PM, Abate N, Aronne LJ, Brown WV, et al. Obesity, adiposity, and dyslipidemia: a consensus statement from the National Lipid Association. J Clin Lipidol. 2013;7(4):304–83. pmid:23890517
* View Article
* PubMed/NCBI
* Google Scholar
2. 2. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update From the GBD 2019 Study. J Am Coll Cardiol. 2020;76(25):2982–3021. pmid:33309175
* View Article
* PubMed/NCBI
* Google Scholar
3. 3. Collaborators GRF. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet (London, England). 2016;388(10053):1659–724. pmid:27733284
* View Article
* PubMed/NCBI
* Google Scholar
4. 4. O’Flaherty M, Buchan I, Capewell S. Contributions of treatment and lifestyle to declining CVD mortality: why have CVD mortality rates declined so much since the 1960s? Heart. 2013;99(3):159–62. pmid:22962283
* View Article
* PubMed/NCBI
* Google Scholar
5. 5. Santosa A, Byass P. Diverse Empirical Evidence on Epidemiological Transition in Low- and Middle-Income Countries: Population-Based Findings from INDEPTH Network Data. PLoS One. 2016;11(5):e0155753. pmid:27187781
* View Article
* PubMed/NCBI
* Google Scholar
6. 6. Streatfield PK, Khan WA, Bhuiya A, Hanifi SM, Alam N, Bagagnan CH, et al. Adult non-communicable disease mortality in Africa and Asia: evidence from INDEPTH Health and Demographic Surveillance System sites. Glob Health Action. 2014;7:25365. pmid:25377326
* View Article
* PubMed/NCBI
* Google Scholar
7. 7. Bawah A, Houle B, Alam N, Razzaque A, Streatfield PK, Debpuur C, et al. The Evolving Demographic and Health Transition in Four Low- and Middle-Income Countries: Evidence from Four Sites in the INDEPTH Network of Longitudinal Health and Demographic Surveillance Systems. PLoS One. 2016;11(6):e0157281. pmid:27304429
* View Article
* PubMed/NCBI
* Google Scholar
8. 8. Dalal S, Beunza JJ, Volmink J, Adebamowo C, Bajunirwe F, Njelekela M, et al. Non-communicable diseases in sub-Saharan Africa: what we know now. International Journal of Epidemiology. 2011;40:885–901. pmid:21527446
* View Article
* PubMed/NCBI
* Google Scholar
9. 9. NCD Risk Factor Collaboration (NCD-RisC)–Africa Working Group. Trends in obesity and diabetes across Africa from 1980 to 2014: an analysis of pooled population-based studies. Int J Epidemiol. 2017;46(5):1421–32. pmid:28582528
* View Article
* PubMed/NCBI
* Google Scholar
10. 10. NCD Risk Factor Collaboration (NCD-RisC). Rising rural body-mass index is the main driver of the global obesity epidemic in adults. Nature 2019;569(7755):260–4. pmid:31068725
* View Article
* PubMed/NCBI
* Google Scholar
11. 11. Huang Y, Liu Y, Ma Y, Tu T, Liu N, Bai F, et al. Associations of Visceral Adipose Tissue, Circulating Protein Biomarkers, and Risk of Cardiovascular Diseases: A Mendelian Randomization Analysis. Front Cell Dev Biol. 2022;10:840866. pmid:35186940
* View Article
* PubMed/NCBI
* Google Scholar
12. 12. Abraham TM, Pedley A, Massaro JM, Hoffmann U, Fox CS. Association between visceral and subcutaneous adipose depots and incident cardiovascular disease risk factors. Circulation. 2015;132(17):1639–47. pmid:26294660
* View Article
* PubMed/NCBI
* Google Scholar
13. 13. Jacobson TA, Ito MK, Maki KC, Orringer CE, Bays HE, Jones PH, et al. National lipid association recommendations for patient-centered management of dyslipidemia: part 1—full report. J Clin Lipidol. 2015;9(2):129–69. pmid:25911072
* View Article
* PubMed/NCBI
* Google Scholar
14. 14. Steyn K, Sliwa K Fau—Hawken S, Hawken S Fau—Commerford P, Commerford P Fau—Onen C, Onen C Fau—Damasceno A, Damasceno A Fau—Ounpuu S, et al. Risk factors associated with myocardial infarction in Africa: the INTERHEART Africa study. Circulation. 2005;112(23):3554–61. pmid:16330696
* View Article
* PubMed/NCBI
* Google Scholar
15. 15. Grundy SM. Small LDL, atherogenic dyslipidemia, and the metabolic syndrome. Circulation. 1997;95(1):1–4. pmid:8994405
* View Article
* PubMed/NCBI
* Google Scholar
16. 16. Dugas LR, Forrester TE, Plange-Rhule J, Bovet P, Lambert EV, Durazo-Arvizu RA, et al. Cardiovascular risk status of Afro-origin populations across the spectrum of economic development: findings from the Modeling the Epidemiologic Transition Study. BMC Public Health. 2017;17(1):438. pmid:28499375
* View Article
* PubMed/NCBI
* Google Scholar
17. 17. Willey JZ, Rodriguez CJ, Carlino RF, Moon YP, Paik MC, Boden-Albala B, et al. Race-ethnic differences in the relationship between lipid profile components and risk of myocardial infarction: the Northern Manhattan Study. American heart journal. 2011;161(5):886–92.
* View Article
* Google Scholar
18. 18. Goedecke JH, Utzschneider K, Faulenbach MV, Rizzo M, Berneis K, Spinas GA, et al. Ethnic differences in serum lipoproteins and their determinants in South African women. Metabolism. 2010;59(9):1341–50. pmid:20096899
* View Article
* PubMed/NCBI
* Google Scholar
19. 19. Hunter DJ, Reddy KS. Noncommunicable diseases. New England Journal of Medicine. 2013;369(14):1336–43.
* View Article
* Google Scholar
20. 20. Asiki G, Murphy GA, Baisley K, Nsubuga RN, Karabarinde A, Newton R, et al. Prevalence of dyslipidaemia and associated risk factors in a rural population in South-Western Uganda: a community based survey. PLoS One. 2015;10(5):e0126166. pmid:25974077
* View Article
* PubMed/NCBI
* Google Scholar
21. 21. Akintunde AA. Epidemiology of conventional cardiovascular risk factors among hypertensive subjects with normal and impaired fasting glucose. South African Medical Journal. 2010;100(9):594–7. pmid:20822649
* View Article
* PubMed/NCBI
* Google Scholar
22. 22. Koopman JJE, van Bodegom D, Jukema JW, Westendorp RGJ. Risk of Cardiovascular Disease in a Traditional African Population with a High Infectious Load: A Population-Based Study. PLOS ONE. 2012;7(10):e46855. pmid:23071653
* View Article
* PubMed/NCBI
* Google Scholar
23. 23. Ramsay M, Crowther NJ, Tambo E, Agongo G, Baloyi V, Dikotope S, et al. 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. Global Health, Epidemiology and Genomics. 2016;1(e20):1–13.
* View Article
* Google Scholar
24. 24. Ali SA, Soo C, Agongo G, Alberts M, Amenga-Etego L, Boua RP, et al. Genomic and environmental risk factors for cardiometabolic diseases in Africa: methods used for Phase 1 of the AWI-Gen population cross-sectional study. Glob Health Action. 2018;11(sup2):1507133. pmid:30259792
* View Article
* PubMed/NCBI
* Google Scholar
25. 25. Ramsay M, Sankoh O, as members of the AWIGen study and the H3Africa Consortium. African partnerships through the H3Africa Consortium bring a genomic dimension to longitudinal population studies on the continent. Int J Epidemiol. 2016;45(2):305–8. pmid:26659658
* View Article
* PubMed/NCBI
* Google Scholar
26. 26. Richter L, Norris SA, pettifor J, Yach D, Cameron N. Cohort Profile: Mandela’s children: the 1990 Birth to Twenty study in South Africa. Int J Epidemiol, 2007. 36(3): p. 504–511. pmid:17355979
* View Article
* PubMed/NCBI
* Google Scholar
27. 27. Obsa MS, Ataro G, Awoke N, Jemal B, Tilahun T, Ayalew N, et al. Determinants of Dyslipidemia in Africa: A Systematic Review and Meta-Analysis. Front Cardiovasc Med. 2022 Feb 23;8:778891. pmid:35284497
* View Article
* PubMed/NCBI
* Google Scholar
28. 28. Cochran W. G. (1977). Sampling techniques (3rd ed.). New York: John Wiley & Sons
29. 29. Ferro-Luzzi A, Garza C, Haas J, Habicht JP, Himes J, Pradilla A, et al. Physical Status: The use and interpretation of anthropometry. Geneva, Switzerland: World Health Organisation; 1995.
30. 30. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120(16):1640–5. pmid:19805654
* View Article
* PubMed/NCBI
* Google Scholar
31. 31. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18(6):499–502. pmid:4337382
* View Article
* PubMed/NCBI
* Google Scholar
32. 32. Virani SS. Non-HDL cholesterol as a metric of good quality of care: opportunities and challenges. Texas Heart Institute Journal. 2011;38(2):160–2. pmid:21494527
* View Article
* PubMed/NCBI
* Google Scholar
33. 33. Klug E, Raal FJ, Marais AD, Taskinen M-R, Dalby AJ, Schamroth C, et al. South African dyslipidaemia guideline consensus statement. S Afr Med J. 2012;102(3 Pt 2):178–87. pmid:22380916
* View Article
* PubMed/NCBI
* Google Scholar
34. 34. van der Linden E, Meeks K, Beune E, de-Graft Aikins A, Addo J, Owusu-Dabo E, et al. Dyslipidaemia among Ghanaian migrants in three European countries and their compatriots in rural and urban Ghana: The RODAM study. Atherosclerosis. 2019;284:83–91. pmid:30875497
* View Article
* PubMed/NCBI
* Google Scholar
35. 35. van der Linden EL, Meeks K, Beune E, de-Graft Aikins A, Addo J, Owusu-Dabo E, et al. The prevalence of metabolic syndrome among Ghanaian migrants and their homeland counterparts: the Research on Obesity and type 2 Diabetes among African Migrants (RODAM) study. European Journal of Public Health. 2019. pmid:31220248
* View Article
* PubMed/NCBI
* Google Scholar
36. 36. Package of essential noncommunicable (PEN) disease interventions for primary health care in low-resource settings. Geneva: World Health Organization; 2010. Available from: https://www.who.int/nmh/publications/essential_ncd_interventions_lr_settings.pdf.
37. 37. de Groot R, van den Hurk K, Schoonmade LJ, de Kort W, Brug J, Lakerveld J. Urban-rural differences in the association between blood lipids and characteristics of the built environment: a systematic review and meta-analysis. BMJ Glob Health. 2019;4(1):e001017. pmid:30740247
* View Article
* PubMed/NCBI
* Google Scholar
38. 38. Kavishe B, Vanobberghen F, Katende D, Kapiga S, Munderi P, Baisley K, et al. Dyslipidemias and cardiovascular risk scores in urban and rural populations in north-western Tanzania and southern Uganda. PLoS One. 2019;14(12):e0223189. pmid:31809516
* View Article
* PubMed/NCBI
* Google Scholar
39. 39. Omran AR. The Epidemiologic Transition: A Theory of the Epidemiology of Population Change. The Milbank Quarterly. 2005;18(4):731–57.
* View Article
* Google Scholar
40. 40. George JA, Brandenburg J-T, Fabian J, Crowther NJ, Agongo G, Alberts M, et al. Kidney damage and associated risk factors in rural and urban sub-Saharan Africa (AWI-Gen): a cross-sectional population study. The Lancet Global Health. 2019;7(12):e1632–e43. pmid:31708144
* View Article
* PubMed/NCBI
* Google Scholar
41. 41. Noubiap JJ, Bigna JJ, Nansseu JR, Nyaga UF, Balti EV, Echouffo-Tcheugui JB, et al. Prevalence of dyslipidaemia among adults in Africa: a systematic review and meta-analysis. The Lancet Global Health. 2018;6(9):e998–e1007. pmid:30103999
* View Article
* PubMed/NCBI
* Google Scholar
42. 42. Soares ALG, Banda L, Amberbir A, Jaffar S, Musicha C, Price AJ, et al. A comparison of the associations between adiposity and lipids in Malawi and the United Kingdom. BMC Medicine. 2020;18(1). pmid:32669098
* View Article
* PubMed/NCBI
* Google Scholar
43. 43. Robinson GA, Pineda-Torra I, Ciurtin C, Jury EC. Sex Differences in Lipid Metabolism: Implications for Systemic Lupus Erythematosus and Cardiovascular Disease Risk. Front Med (Lausanne). 2022;9:914016. pmid:35712086
* View Article
* PubMed/NCBI
* Google Scholar
44. 44. Bentley AR, Rotimi CN. Interethnic Differences in Serum Lipids and Implications for Cardiometabolic Disease Risk in African Ancestry Populations. Glob Heart. 2017;12(2):141–50. pmid:28528248
* View Article
* PubMed/NCBI
* Google Scholar
45. 45. Choudhury A, Brandenburg JT, Chikowore T, Sengupta D, Boua PR, Crowther NJ, et al. Meta-analysis of sub-Saharan African studies provides insights into genetic architecture of lipid traits. Nat Commun. 2022;13(1):2578. pmid:35546142
* View Article
* PubMed/NCBI
* Google Scholar
46. 46. Lee MJ, Wu Y, Fried SK. Adipose tissue heterogeneity: implication of depot differences in adipose tissue for obesity complications. Mol Aspects Med. 2013;34(1):1–11. pmid:23068073
* View Article
* PubMed/NCBI
* Google Scholar
47. 47. Nonterah EA, Bots ML, Oduro A, Agongo G, Soo CC, Micklesfield LK, et al. Adiposity Phenotypes and Subclinical Atherosclerosis in Adults from Sub–Saharan Africa: An H3Africa AWI–Gen Study. Global Heart. 2021;16(1). pmid:33833943
* View Article
* PubMed/NCBI
* Google Scholar
48. 48. Marcus ME, Ebert C, Geldsetzer P, Theilmann M, Bicaba BW, Andall-Brereton G, et al. Unmet need for hypercholesterolemia care in 35 low- and middle-income countries: A cross-sectional study of nationally representative surveys. PLoS Med. 2021;18(10):e1003841. pmid:34695124
* View Article
* PubMed/NCBI
* Google Scholar
49. 49. Reiger S, Jardim TV, Abrahams-Gessel S, Crowther NJ, Wade A, Gomez-Olive FX, et al. Awareness, treatment, and control of dyslipidemia in rural South Africa: The HAALSI (Health and Aging in Africa: A Longitudinal Study of an INDEPTH Community in South Africa) study. PLoS One. 2017;12(10):e0187347. pmid:29077762
* View Article
* PubMed/NCBI
* Google Scholar
Citation: Nonterah EA, Agongo G, Crowther NJ, Mohamed SF, Micklesfield LK, Boua PR, et al. (2025) Obesity phenotypes and dyslipidemia in adults from four African countries: An H3Africa AWI-Gen study. PLoS ONE 20(1): e0316527. https://doi.org/10.1371/journal.pone.0316527
About the Authors:
Engelbert A. Nonterah
Roles: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing
E-mail: [email protected] (EAN); [email protected] (MR)
Affiliations: Navrongo Health Research Centre, Ghana Health Service, Navrongo, Ghana, Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands, Department of Epidemiology, School of Public Health, C.K. Tedam University of Technology and Applied Science, Navrongo, Ghana
ORICD: https://orcid.org/0000-0001-8491-6478
Godfred Agongo
Roles: Conceptualization, Data curation, Investigation, Validation, Visualization, Writing – review & editing
Affiliations: Navrongo Health Research Centre, Ghana Health Service, Navrongo, Ghana, Department of Biochemistry and Forensic Science, School of Chemical and Biochemical Sciences, CK Tedam University of Technology and Applied Science, Navrongo, Ghana
Nigel J. Crowther
Roles: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Validation, Visualization, Writing – review & editing
Affiliation: Faculty of Health Sciences, Department of Chemical Pathology, National Health Laboratory Service, University of the Witwatersrand, Johannesburg, South Africa
Shukri F. Mohamed
Roles: Conceptualization, Data curation, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing
Affiliation: African Population and Health Research Center, Nairobi, Kenya
Lisa K. Micklesfield
Roles: Conceptualization, Validation, Visualization, Writing – review & editing
Affiliation: Faculty of Health Sciences, MRC/Wits Developmental Pathways for Health Research Unit, University of the Witwatersrand, Johannesburg, South Africa
Palwendé Romuald Boua
Roles: Conceptualization, Data curation, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing
Affiliations: Institut de Recherché en Sciences de la Santé, Clinical Research Unit of Nanoro, Clinical Research Unit of Nanoro, Burkina Faso, Faculty of Health Sciences, Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, Johannesburg
Alisha N. Wade
Roles: Investigation, Methodology, Validation, Visualization, Writing – review & editing
Affiliation: Faculty of Health Sciences, MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
Solomon S. R. Choma
Roles: Data curation, Investigation, Methodology, Project administration, Validation, Visualization, Writing – review & editing
Affiliation: Faculty of Health Sciences, Department of Pathology and Medical Science, DIMAMO, School of Health Care Sciences, University of Limpopo, Polokwane, South Africa
Hermann Sorgho
Roles: Data curation, Investigation, Methodology, Project administration, Validation, Visualization, Writing – review & editing
Affiliation: Institut de Recherché en Sciences de la Santé, Clinical Research Unit of Nanoro, Clinical Research Unit of Nanoro, Burkina Faso
Isaac Kissiangani
Roles: Data curation, Investigation, Validation, Visualization, Writing – review & editing
Affiliation: African Population and Health Research Center, Nairobi, Kenya
Gershim Asiki
Roles: Investigation, Methodology, Project administration, Validation, Visualization, Writing – review & editing
Affiliation: African Population and Health Research Center, Nairobi, Kenya
Patrick Ansah
Roles: Validation, Visualization, Writing – review & editing
Affiliation: Navrongo Health Research Centre, Ghana Health Service, Navrongo, Ghana
Abraham R. Oduro
Roles: Project administration, Validation, Visualization, Writing – review & editing
Affiliation: Navrongo Health Research Centre, Ghana Health Service, Navrongo, Ghana
Shane A. Norris
Roles: Conceptualization, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – review & editing
Affiliation: Faculty of Health Sciences, MRC/Wits Developmental Pathways for Health Research Unit, University of the Witwatersrand, Johannesburg, South Africa
ORICD: https://orcid.org/0000-0001-7124-3788
Stephen M. Tollman
Roles: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing
Affiliation: Faculty of Health Sciences, MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
Frederick J. Raal
Roles: Conceptualization, Investigation, Methodology, Supervision, Validation, Visualization, Writing – review & editing
Affiliation: Faculty of Health Sciences, Department of Medicine, Division of Endocrinology & Metabolism, Carbohydrate & Lipid Metabolism Research Unit, Johannesburg Hospital, University of the Witwatersrand, Johannesburg, South Africa
ORICD: https://orcid.org/0000-0002-9170-7938
Marianne Alberts
† Deceased.
Roles: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing
Affiliation: Faculty of Health Sciences, Department of Pathology and Medical Science, DIMAMO, School of Health Care Sciences, University of Limpopo, Polokwane, South Africa
Michele Ramsay
Roles: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing
E-mail: [email protected] (EAN); [email protected] (MR)
Affiliation: Faculty of Health Sciences, Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, Johannesburg
as members of AWI-Gen and the H3Africa Consortium
[/RAW_REF_TEXT]
[/RAW_REF_TEXT]
[/RAW_REF_TEXT]
1. Bays HE, Toth PP, Kris-Etherton PM, Abate N, Aronne LJ, Brown WV, et al. Obesity, adiposity, and dyslipidemia: a consensus statement from the National Lipid Association. J Clin Lipidol. 2013;7(4):304–83. pmid:23890517
2. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update From the GBD 2019 Study. J Am Coll Cardiol. 2020;76(25):2982–3021. pmid:33309175
3. Collaborators GRF. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet (London, England). 2016;388(10053):1659–724. pmid:27733284
4. O’Flaherty M, Buchan I, Capewell S. Contributions of treatment and lifestyle to declining CVD mortality: why have CVD mortality rates declined so much since the 1960s? Heart. 2013;99(3):159–62. pmid:22962283
5. Santosa A, Byass P. Diverse Empirical Evidence on Epidemiological Transition in Low- and Middle-Income Countries: Population-Based Findings from INDEPTH Network Data. PLoS One. 2016;11(5):e0155753. pmid:27187781
6. Streatfield PK, Khan WA, Bhuiya A, Hanifi SM, Alam N, Bagagnan CH, et al. Adult non-communicable disease mortality in Africa and Asia: evidence from INDEPTH Health and Demographic Surveillance System sites. Glob Health Action. 2014;7:25365. pmid:25377326
7. Bawah A, Houle B, Alam N, Razzaque A, Streatfield PK, Debpuur C, et al. The Evolving Demographic and Health Transition in Four Low- and Middle-Income Countries: Evidence from Four Sites in the INDEPTH Network of Longitudinal Health and Demographic Surveillance Systems. PLoS One. 2016;11(6):e0157281. pmid:27304429
8. Dalal S, Beunza JJ, Volmink J, Adebamowo C, Bajunirwe F, Njelekela M, et al. Non-communicable diseases in sub-Saharan Africa: what we know now. International Journal of Epidemiology. 2011;40:885–901. pmid:21527446
9. NCD Risk Factor Collaboration (NCD-RisC)–Africa Working Group. Trends in obesity and diabetes across Africa from 1980 to 2014: an analysis of pooled population-based studies. Int J Epidemiol. 2017;46(5):1421–32. pmid:28582528
10. NCD Risk Factor Collaboration (NCD-RisC). Rising rural body-mass index is the main driver of the global obesity epidemic in adults. Nature 2019;569(7755):260–4. pmid:31068725
11. Huang Y, Liu Y, Ma Y, Tu T, Liu N, Bai F, et al. Associations of Visceral Adipose Tissue, Circulating Protein Biomarkers, and Risk of Cardiovascular Diseases: A Mendelian Randomization Analysis. Front Cell Dev Biol. 2022;10:840866. pmid:35186940
12. Abraham TM, Pedley A, Massaro JM, Hoffmann U, Fox CS. Association between visceral and subcutaneous adipose depots and incident cardiovascular disease risk factors. Circulation. 2015;132(17):1639–47. pmid:26294660
13. Jacobson TA, Ito MK, Maki KC, Orringer CE, Bays HE, Jones PH, et al. National lipid association recommendations for patient-centered management of dyslipidemia: part 1—full report. J Clin Lipidol. 2015;9(2):129–69. pmid:25911072
14. Steyn K, Sliwa K Fau—Hawken S, Hawken S Fau—Commerford P, Commerford P Fau—Onen C, Onen C Fau—Damasceno A, Damasceno A Fau—Ounpuu S, et al. Risk factors associated with myocardial infarction in Africa: the INTERHEART Africa study. Circulation. 2005;112(23):3554–61. pmid:16330696
15. Grundy SM. Small LDL, atherogenic dyslipidemia, and the metabolic syndrome. Circulation. 1997;95(1):1–4. pmid:8994405
16. Dugas LR, Forrester TE, Plange-Rhule J, Bovet P, Lambert EV, Durazo-Arvizu RA, et al. Cardiovascular risk status of Afro-origin populations across the spectrum of economic development: findings from the Modeling the Epidemiologic Transition Study. BMC Public Health. 2017;17(1):438. pmid:28499375
17. Willey JZ, Rodriguez CJ, Carlino RF, Moon YP, Paik MC, Boden-Albala B, et al. Race-ethnic differences in the relationship between lipid profile components and risk of myocardial infarction: the Northern Manhattan Study. American heart journal. 2011;161(5):886–92.
18. Goedecke JH, Utzschneider K, Faulenbach MV, Rizzo M, Berneis K, Spinas GA, et al. Ethnic differences in serum lipoproteins and their determinants in South African women. Metabolism. 2010;59(9):1341–50. pmid:20096899
19. Hunter DJ, Reddy KS. Noncommunicable diseases. New England Journal of Medicine. 2013;369(14):1336–43.
20. Asiki G, Murphy GA, Baisley K, Nsubuga RN, Karabarinde A, Newton R, et al. Prevalence of dyslipidaemia and associated risk factors in a rural population in South-Western Uganda: a community based survey. PLoS One. 2015;10(5):e0126166. pmid:25974077
21. Akintunde AA. Epidemiology of conventional cardiovascular risk factors among hypertensive subjects with normal and impaired fasting glucose. South African Medical Journal. 2010;100(9):594–7. pmid:20822649
22. Koopman JJE, van Bodegom D, Jukema JW, Westendorp RGJ. Risk of Cardiovascular Disease in a Traditional African Population with a High Infectious Load: A Population-Based Study. PLOS ONE. 2012;7(10):e46855. pmid:23071653
23. Ramsay M, Crowther NJ, Tambo E, Agongo G, Baloyi V, Dikotope S, et al. 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. Global Health, Epidemiology and Genomics. 2016;1(e20):1–13.
24. Ali SA, Soo C, Agongo G, Alberts M, Amenga-Etego L, Boua RP, et al. Genomic and environmental risk factors for cardiometabolic diseases in Africa: methods used for Phase 1 of the AWI-Gen population cross-sectional study. Glob Health Action. 2018;11(sup2):1507133. pmid:30259792
25. Ramsay M, Sankoh O, as members of the AWIGen study and the H3Africa Consortium. African partnerships through the H3Africa Consortium bring a genomic dimension to longitudinal population studies on the continent. Int J Epidemiol. 2016;45(2):305–8. pmid:26659658
26. Richter L, Norris SA, pettifor J, Yach D, Cameron N. Cohort Profile: Mandela’s children: the 1990 Birth to Twenty study in South Africa. Int J Epidemiol, 2007. 36(3): p. 504–511. pmid:17355979
27. Obsa MS, Ataro G, Awoke N, Jemal B, Tilahun T, Ayalew N, et al. Determinants of Dyslipidemia in Africa: A Systematic Review and Meta-Analysis. Front Cardiovasc Med. 2022 Feb 23;8:778891. pmid:35284497
28. Cochran W. G. (1977). Sampling techniques (3rd ed.). New York: John Wiley & Sons
29. Ferro-Luzzi A, Garza C, Haas J, Habicht JP, Himes J, Pradilla A, et al. Physical Status: The use and interpretation of anthropometry. Geneva, Switzerland: World Health Organisation; 1995.
30. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120(16):1640–5. pmid:19805654
31. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18(6):499–502. pmid:4337382
32. Virani SS. Non-HDL cholesterol as a metric of good quality of care: opportunities and challenges. Texas Heart Institute Journal. 2011;38(2):160–2. pmid:21494527
33. Klug E, Raal FJ, Marais AD, Taskinen M-R, Dalby AJ, Schamroth C, et al. South African dyslipidaemia guideline consensus statement. S Afr Med J. 2012;102(3 Pt 2):178–87. pmid:22380916
34. van der Linden E, Meeks K, Beune E, de-Graft Aikins A, Addo J, Owusu-Dabo E, et al. Dyslipidaemia among Ghanaian migrants in three European countries and their compatriots in rural and urban Ghana: The RODAM study. Atherosclerosis. 2019;284:83–91. pmid:30875497
35. van der Linden EL, Meeks K, Beune E, de-Graft Aikins A, Addo J, Owusu-Dabo E, et al. The prevalence of metabolic syndrome among Ghanaian migrants and their homeland counterparts: the Research on Obesity and type 2 Diabetes among African Migrants (RODAM) study. European Journal of Public Health. 2019. pmid:31220248
36. Package of essential noncommunicable (PEN) disease interventions for primary health care in low-resource settings. Geneva: World Health Organization; 2010. Available from: https://www.who.int/nmh/publications/essential_ncd_interventions_lr_settings.pdf.
37. de Groot R, van den Hurk K, Schoonmade LJ, de Kort W, Brug J, Lakerveld J. Urban-rural differences in the association between blood lipids and characteristics of the built environment: a systematic review and meta-analysis. BMJ Glob Health. 2019;4(1):e001017. pmid:30740247
38. Kavishe B, Vanobberghen F, Katende D, Kapiga S, Munderi P, Baisley K, et al. Dyslipidemias and cardiovascular risk scores in urban and rural populations in north-western Tanzania and southern Uganda. PLoS One. 2019;14(12):e0223189. pmid:31809516
39. Omran AR. The Epidemiologic Transition: A Theory of the Epidemiology of Population Change. The Milbank Quarterly. 2005;18(4):731–57.
40. George JA, Brandenburg J-T, Fabian J, Crowther NJ, Agongo G, Alberts M, et al. Kidney damage and associated risk factors in rural and urban sub-Saharan Africa (AWI-Gen): a cross-sectional population study. The Lancet Global Health. 2019;7(12):e1632–e43. pmid:31708144
41. Noubiap JJ, Bigna JJ, Nansseu JR, Nyaga UF, Balti EV, Echouffo-Tcheugui JB, et al. Prevalence of dyslipidaemia among adults in Africa: a systematic review and meta-analysis. The Lancet Global Health. 2018;6(9):e998–e1007. pmid:30103999
42. Soares ALG, Banda L, Amberbir A, Jaffar S, Musicha C, Price AJ, et al. A comparison of the associations between adiposity and lipids in Malawi and the United Kingdom. BMC Medicine. 2020;18(1). pmid:32669098
43. Robinson GA, Pineda-Torra I, Ciurtin C, Jury EC. Sex Differences in Lipid Metabolism: Implications for Systemic Lupus Erythematosus and Cardiovascular Disease Risk. Front Med (Lausanne). 2022;9:914016. pmid:35712086
44. Bentley AR, Rotimi CN. Interethnic Differences in Serum Lipids and Implications for Cardiometabolic Disease Risk in African Ancestry Populations. Glob Heart. 2017;12(2):141–50. pmid:28528248
45. Choudhury A, Brandenburg JT, Chikowore T, Sengupta D, Boua PR, Crowther NJ, et al. Meta-analysis of sub-Saharan African studies provides insights into genetic architecture of lipid traits. Nat Commun. 2022;13(1):2578. pmid:35546142
46. Lee MJ, Wu Y, Fried SK. Adipose tissue heterogeneity: implication of depot differences in adipose tissue for obesity complications. Mol Aspects Med. 2013;34(1):1–11. pmid:23068073
47. Nonterah EA, Bots ML, Oduro A, Agongo G, Soo CC, Micklesfield LK, et al. Adiposity Phenotypes and Subclinical Atherosclerosis in Adults from Sub–Saharan Africa: An H3Africa AWI–Gen Study. Global Heart. 2021;16(1). pmid:33833943
48. Marcus ME, Ebert C, Geldsetzer P, Theilmann M, Bicaba BW, Andall-Brereton G, et al. Unmet need for hypercholesterolemia care in 35 low- and middle-income countries: A cross-sectional study of nationally representative surveys. PLoS Med. 2021;18(10):e1003841. pmid:34695124
49. Reiger S, Jardim TV, Abrahams-Gessel S, Crowther NJ, Wade A, Gomez-Olive FX, et al. Awareness, treatment, and control of dyslipidemia in rural South Africa: The HAALSI (Health and Aging in Africa: A Longitudinal Study of an INDEPTH Community in South Africa) study. PLoS One. 2017;12(10):e0187347. pmid:29077762
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
© 2025 Nonterah et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Abstract
Introduction
The contribution of obesity phenotypes to dyslipidaemia in middle-aged adults from four sub-Saharan African (SSA) countries at different stages of the epidemiological transition has not been reported. We characterized lipid levels and investigated their relation with the growing burden of obesity in SSA countries.
Methods
A cross-sectional study was conducted in Burkina Faso, Ghana, Kenya and South Africa. Participants were middle aged adults, 40–60 years old residing in the study sites for the past 10 years. Age-standardized prevalence and adjusted mean cholesterol, LDL-C, HDL-C, triglycerides and non-HDL-C were estimated using Poisson regression analyses and association of body mass index (BMI), waist circumference (WC) and waist-to-hip ratio (WTHR) with abnormal lipid fractions modeled using a random effects meta-analysis. Obesity phenotypes are defined as BMI ≥ 30 kg/m2, increased WC and increased waist-to-hip ratio.
Results
A sample of 10,700 participants, with 54.7% being women was studied. Southern and Eastern African sites recorded higher age-standardized prevalence of five lipid fractions then West African sites. Men had higher LDL-C (19% vs 8%) and lower HDL-C (35% vs 24%) while women had higher total cholesterol (15% vs 19%), triglycerides (9% vs 10%) and non-HDL-cholesterol (20% vs 26%). All lipid fractions were significantly associated with three obesity phenotypes. Approximately 72% of participants in the sample needed screening for dyslipidaemia with more men than women requiring screening.
Conclusion
Obesity in all forms may drive a dyslipidaemia epidemic in SSA with men and transitioned societies at a higher risk. Targeted interventions to control the epidemic should focus on health promoting and improved access to screening services.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details
; Agongo, Godfred; Crowther, Nigel J; Mohamed, Shukri F; Micklesfield, Lisa K; Palwendé Romuald Boua; Wade, Alisha N; Choma, Solomon S R; Sorgho, Hermann; Kissiangani, Isaac; Asiki, Gershim; Ansah, Patrick; Oduro, Abraham R; Norris, Shane A
; Tollman, Stephen M; Raal, Frederick J
; Marianne Alberts †; † Deceased. Michele Ramsay; as members of AWI-Gen; the H3Africa Consortium