Obesity phenotypes and dyslipidemia in adults from four African countries: An H3Africa AWI-Gen study

Engelbert A Nonterah; Godfred Agongo; Nigel J Crowther; Shukri F Mohamed; Lisa K Micklesfield; Palwendé Romuald Boua; Alisha N Wade; Solomon S R Choma; Hermann Sorgho; Isaac Kissiangani; Gershim Asiki; Patrick Ansah; Abraham R Oduro; Shane A Norris; Stephen M Tollman; Frederick J Raal; Marianne Alberts; Michele Ramsay; as members of AWI-Gen and the H3Africa Consortium

doi:10.1371/journal.pone.0316527

. 2025 Jan 30;20(1):e0316527. doi: 10.1371/journal.pone.0316527

Obesity phenotypes and dyslipidemia in adults from four African countries: An H3Africa AWI-Gen study

Engelbert A Nonterah ^1,^2,^3,^*, Godfred Agongo ^1,⁴, Nigel J Crowther ⁵, Shukri F Mohamed ⁶, Lisa K Micklesfield ⁷, Palwendé Romuald Boua ^8,⁹, Alisha N Wade ¹⁰, Solomon S R Choma ¹¹, Hermann Sorgho ⁸, Isaac Kissiangani ⁶, Gershim Asiki ⁶, Patrick Ansah ¹, Abraham R Oduro ¹, Shane A Norris ⁷, Stephen M Tollman ¹⁰, Frederick J Raal ¹², Marianne Alberts ¹¹, Michele Ramsay ^9,^*; as members of AWI-Gen and the H3Africa Consortium

Editor: Neftali Eduardo Antonio-Villa¹³

¹Navrongo Health Research Centre, Ghana Health Service, Navrongo, Ghana

²Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands

³Department of Epidemiology, School of Public Health, C.K. Tedam University of Technology and Applied Science, Navrongo, Ghana

⁴Department of Biochemistry and Forensic Science, School of Chemical and Biochemical Sciences, CK Tedam University of Technology and Applied Science, Navrongo, Ghana

⁵Faculty of Health Sciences, Department of Chemical Pathology, National Health Laboratory Service, University of the Witwatersrand, Johannesburg, South Africa

⁶African Population and Health Research Center, Nairobi, Kenya

⁷Faculty of Health Sciences, MRC/Wits Developmental Pathways for Health Research Unit, University of the Witwatersrand, Johannesburg, South Africa

⁸Institut de Recherché en Sciences de la Santé, Clinical Research Unit of Nanoro, Clinical Research Unit of Nanoro, Burkina Faso

⁹Faculty of Health Sciences, Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, Johannesburg

¹⁰Faculty 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

¹¹Faculty of Health Sciences, Department of Pathology and Medical Science, DIMAMO, School of Health Care Sciences, University of Limpopo, Polokwane, South Africa

¹²Faculty of Health Sciences, Department of Medicine, Division of Endocrinology & Metabolism, Carbohydrate & Lipid Metabolism Research Unit, Johannesburg Hospital, University of the Witwatersrand, Johannesburg, South Africa

¹³Instituto Nacional de Cardiologia Ignacio Chavez, MEXICO

Competing Interests: The authors have declared that no competing interests exist.

^✉

* E-mail: drenanonterah@gmail.com (EAN); michele.ramsay@wits.ac.za (MR)

Roles

Engelbert A Nonterah: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Godfred Agongo: Conceptualization, Data curation, Investigation, Validation, Visualization, Writing – review & editing

Nigel J Crowther: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Validation, Visualization, Writing – review & editing

Shukri F Mohamed: Conceptualization, Data curation, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing

Lisa K Micklesfield: Conceptualization, Validation, Visualization, Writing – review & editing

Palwendé Romuald Boua: Conceptualization, Data curation, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing

Alisha N Wade: Investigation, Methodology, Validation, Visualization, Writing – review & editing

Solomon S R Choma: Data curation, Investigation, Methodology, Project administration, Validation, Visualization, Writing – review & editing

Hermann Sorgho: Data curation, Investigation, Methodology, Project administration, Validation, Visualization, Writing – review & editing

Isaac Kissiangani: Data curation, Investigation, Validation, Visualization, Writing – review & editing

Gershim Asiki: Investigation, Methodology, Project administration, Validation, Visualization, Writing – review & editing

Patrick Ansah: Validation, Visualization, Writing – review & editing

Abraham R Oduro: Project administration, Validation, Visualization, Writing – review & editing

Shane A Norris: Conceptualization, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – review & editing

Stephen M Tollman: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing

Frederick J Raal: Conceptualization, Investigation, Methodology, Supervision, Validation, Visualization, Writing – review & editing

Marianne Alberts: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Michele Ramsay: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing

Neftali Eduardo Antonio-Villa: Editor

PMCID: PMC11781721 PMID: 39883633

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.

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).

Table 1. Basic characteristics of AWI-Gen participants in six sites from sub-Saharan Africa stratified by sex.

African region	South Africa			East Africa	West Africa
Variable	Agincourt (n = 1465)	Dikgale (n = 1212)	Soweto (n = 2030)	Nairobi (n = 1951)	Nanoro (n = 2092)	Navrongo (n = 2014)	All (N = 10700)
Women
N (%)	892 (60.9)	845 (69.7)	1,003 (49.4)	1,059 (54.3)	1,040 (49.7)	1,091 (54.2)	5,930 (55.1)
Age in years	50.9±5.8	50.5±6.0	49.1±5.6	48.3±5.3	49.8±5.6	51.6±5.7	50.0±5.8
Formal educational	612 (68.6)	737 (90.8)	784 (7.2)	1,795 (92.4)	70 (6.74)	243 (22.3)	3,389 (57.5)
Household SES	7 (4–9)	10 (8–12)	7 (6–9)	10 (8–13)	11 (8–13)	8 (6–11)	9 (6–11)
Current smoking	10 (1.12)	54 (6.65)	100 (9.98)	81 (7.67)	2 (0.19)	37 (3.39)	284 (4.82)
Current alcohol use	161 (18.1)	248 (30.5)	-	298 (28.2)	858 (78.6)	759 (70.1)	2,324 (39.4)
Fruit/vegetable intake	343 (38.4)	327 (40.3)	-	740 (70.1)	918 (88.4)	785 (71.9)	4,115 (69.8)
Physically active	697 (78.6)	781 (96.3)	588 (58.7)	950 (90.2)	898 (86.4)	882 (81.1)	4,796 (81.6)
BMI in kg/m²	29.2±6.6	30.8±8.0	33.2±7.2	27.6±6.1	20.2±3.2	22.13.8	26.8±7.5
General obesity n, %	346 (41.8)	408 (51.2)	601 (66.3)	338 (32.2)	13 (1.3)	41 (3.8)	1747 (30.7)
WC in cm	94.8±15.1	94.0±16.3	98.8±14.3	90.6±14.1	75.9±8.1	76.4±9.3	87.9±15.9
WC≥80cm n, %	693 (82.9)	622 (78.0)	818 (90.3)	804 (76.5)	254 (24.7)	324 (30.3)	818 (90.3)
Waist-to-hip ratio	625 (70.1)	439 (54.1)	456 (45.5)	684 (64.8)	547 (52.7)	600 (55.0)	3,351 (56.9)
Post menopause	537 (36.7)	558 (47.8)	668 (33.0)	599 (30.8)	717 (34.4)	718 (35.7)	3797 (35.5)
Men
N (%)	551 (39.7)	353 (30.7)	991 (52.2)	880 (45.6)	1027 (49.9)	909 (45.9)	4711 (45.3)
Age in years	50.8±5.8	50.0±6.0	49.5±6.0	48.8±5.6	49.8±6.0	50.5±5.7	49.8±5.9
Formal educational	449 (78.5)	334 (93.8)	1,017 (99.2)	852 (96.2)	283 (27.2)	351 (38.1)	3,286 (68.4)
Household SES	6 (4–8)	9 (7–12)	12 (10–14)	11 (9–14)	11 (9–14)	9 (7–13)	10 (8–13)
Current smoking	280 (49.0)	54 (15.4)	712 (69.6)	418 (47.2)	264 (25.3)	332 (35.9)	2,615 (54.5)
Current alcohol use	386 (67.4)	301 (84.6)	726 (100)	629 (71.0)	768 (73.8)	850 (92.3)	3,660 (81.3)
Fruit/vegetable intake	217 (37.9)	179 (50.3)	-	584 (65.9)	854 (81.7)	672 (72.8)	3,531 (73.4)
Physically active	444 (77.9)	340 (96.3)	840 (81.9)	846 (95.5)	782 (74.8)	814 (89.9)	4,066 (84.9)
BMI in kg/m²	24.0±5.2	21.7±4.0	24.9±5.7	22.8±3.9	21.6±3.5	20.9±3.3	22.7±4.6
General obesity	65 (11.9)	10 (2.8)	176 (17.8)	45 (5.1)	20 (1.9)	11 (1.2)	327 (6.9)
WC in cm	87.0±13.2	80.3±11.3	89.2±15.0	83.4±10.7	81.4±9.8	73.2±7.4	82.4±12.6
WC≥94cm n, %	151 (27.4)	46 (13.0)	361 (36.4)	146 (16.6)	114 (11.1)	17 (1.9)	835 (17.7)
Waist-to-hip ratio	295 (51.5)	151 (42.4)	528 (51.5)	353 (39.8)	460 (42.0)	246 (26.7)	2,033 (42.3)

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Data presented as absolute count and proportions (%) or mean ±standard deviation (SD) and median (interquartile range) for household socioeconomic status (SES); BMI, body mass index; general obesity is BMI ≥30 kg/m²; WC, waist circumference; central obesity is WC ≥ 80cm in women and WC ≥ 94cm for men; waist-to-hip ratio (WHR) ≥ 0.90 in men and ≥ 0.85; Physically active if they reported a moderate-to-vigorous physical activity of >150 minutes per week.

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.

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%.

Table 2. Age-standardised prevalence rates (with 95% confidence intervals) of elevated lipid fractions among women and men in the six sites of the AWI-Gen study.

	Elevated total cholesterol	Elevated LDL-C	Low HDL-C	Elevated TG	Elevated non-HDL-C	Dyslipidaemia
Women
Agincourt	23.4 (20.6–26.1)	5.27 (3.79–6.75)	34.1 (30.9–37.2)	11.3 (9.17–13.3)	32.7 (29.6–35.7)	39.5 (32.5–41.5)
Dikgale	22.1 (19.3–24.9)	8.61 (6.69–10.5)	29.9 (26.9–33.1)	13.5 (11.2–15.8)	32.9 (29.8–36.1)	34.6 (29.8–37.6)
Soweto	37.3 (34.3–40.3)	17.5 (15.1–19.1)	46.1 (42.9–49.2)	20.1 (17.5–22.6)	46.3 (43.3–49.4)	61.2 (60.8–71.4)
Nairobi	27.1 (24.2–30.0)	10.0 (8.12–11.9)	37.7 (34.6–40.8)	12.4 (13.3–14.6)	38.2 (35.1–41.2)	26.4 (23.7–37.4)
Nanoro	3.81 (2.6–4.9)	3.13 (2.08–4.2)	18.9 (16.5–21.3)	2.58 (1.60–3.56)	4.91 (3.6–6.2)	28.6 (27.1–29.2)
Navrongo	4.53 (3.3–5.8)	1.80 (1.0–2.6)	27.3 (24.6–30.1)	2.97 (1.86–4.1)	5.77 (4.4–7.1)	33.5 (31.4–34.8)
Combined	18.8 (17.8–19.8)	7.7 (7.0–8.4)	24.3 (20.8–29.6)	9.93 (9.7–10.7)	25.7 (24.6–26.8)	58.4 (48.4–63.4)
Men
Agincourt	15.0 (12.0–17.9)	18.1 (14.8–21.3)	67.2 (63.4–71.1)	11.0 (8.43–13.7)	19.3 (16.0–22.5)	78.7 (66.3–78.9)
Dikgale	14.6 (10.9–18.3)	18.8 (14.8–22.9)	70.3 (65.6–75.1)	12.9 (9.48–16.4)	22.0 (17.7–26.3)	80.3 (76.5–90.5)
Soweto	20.1 (17.7–22.6)	15.3 (13.1–17.5)	67.7 (64.8–70.5)	12.5 (10.4–14.5)	27.3 (24.6–30.1)	77.8 (69.2–78.4)
Nairobi	22.6 (19.8–25.4)	31.9 (28.7–35.0)	68.7 (65.5–71.8)	12.2 (10.0–14.4)	29.5 (26.4–32.5)	86.0 (84.5–89.5)
Nanoro	12.0 (10.1–14.0)	25.3 (22.7–27.9)	68.2 (65.4–71.0)	7.37 (5.78–8.95)	17.1 (14.8–19.4)	78.7 (76.7–80.3)
Navrongo	3.76 (2.51–5.0)	2.77 (1.71–3.8)	64.8 (61.7–67.9)	2.15 (1.21–3.1)	5.40 (3.92–6.9)	66.3 (86.5–74.3)
Combined	14.7 (13.7–15.7)	18.7 (17.5–19.7)	67.8 (53.7–76.3)	9.23 (8.41–10.5)	19.9 (18.8–21.1)	75.7 (64.3–79.3)

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Presented as proportions with 95% confidence intervals; LDL-C calculated using the Friedewald method; LDL-C, low density lipoprotein, HDL-C, high density lipoprotein, TGs, triglycerides; Elevated cholesterol is defined as TC ≥ 5 mmol/L; Elevated LDL-C is LDL-C ≥3 mmol/L; Low HDL-C 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.

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.

Table 3. Sex differences in the association of the various adiposity phenotypes with the lipid fractions in the combined AWI-Gen cohort.

Adiposity phenotype	Sample	Elevated total cholesterol	Elevated LDL-C	Low HDL-C	Elevated TGs	Elevated non-HDL-C
BMI in kg/m²	Combine sample	1.89 (1.57, 2.28)	1.32 (1.06, 1.62)	1.21 (1.16, 1.28)	1.68 (1.32, 2.13)	2.18 (1.84, 2.58)
	P _Sex*BMI	0.027	<0.001	<0.001	0.088	0.014
	Women	1.74 (1.36, 2.25)	1.94 (1.33, 2.82)	1.41 (1.30, 1.56)	1.75 (1.24, 2.46)	2.06 (1.65, 2.57)
	Men	2.15 (1.61, 2.89)	2.36 (1.78, 3.14)	1.45 (1.34, 1.62)	1.84 (1.26, 2.68)	2.51 (1.88, 3.34)
WC in cm	Combine sample	1.64 (1.25, 2.14)	1.26 (1.03, 1.54)	1.12 (1.08, 1.18)	1.83 (1.30, 2.57)	1.67 (1.32. 2.11)
	P _Sex*WC	0.037	<0.001	<0.001	0.001	0.001
	Women	1.96 (1.40, 2.76)	2.89 (1.87, 4.42)	1.59 (1.37, 1.94)	3.04 (1.98, 4.78)	2.44 (1.82, 3.28)
	Men	1.67 (1.03, 2.72)	3.98 (2.91, 5.44)	1.52 (1.24, 1.89)	1.69 (0.99, 2.88)	1.42 (1.09, 2.18)
WTHR	Combine sample	1.47 (1.24, 1.75)	1.77 (1.46, 2.15)	1.16 (1.04, 1.20)	2.21 (1.75, 2.79)	1.63 (1.39, 1.91)
	P _Sex*WTHR	0.007	<0.001	<0.001	0.097	<0.001
	Women	1.29 (1.04, 1.63)	1.95 (1.37, 2.78)	1.18 (1.06, 1.29)	2.13 (1.56, 2.91)	1.51 (1.24, 1.85)
	Men	1.75 (1.32, 2.32)	1.89 (1.46, 2.44)	1.17 (1.03, 1.27)	2.37 (1.65, 3.42)	1.84 (1.42, 2.37)

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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; P _{sex interaction} represent sex differences derived from sex and adiposity phenotype multiplicative interaction term; 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.

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).

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.

(DOC)

pone.0316527.s001.doc^{(87.5KB, 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.

(DOCX)

pone.0316527.s002.docx^{(14.2KB, 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.

(DOCX)

pone.0316527.s003.docx^{(13.7KB, docx)}

S3 Table. STROBE statement—checklist of items that should be included in reports of observational studies.

(DOC)

pone.0316527.s004.doc^{(83.5KB, doc)}

S1 File

(DOCX)

pone.0316527.s005.docx^{(66KB, 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).

Data Availability

All AWI‐Gen data can be accessed from the European Genome‐phenome Archive (https://ega-archive.org/dacs/EGAC00001000648?order=stable_id&sort=asc). The phenotype data set accession IDs is EGAD00010001996.

Funding Statement

The AWI-Gen Collaborative Centre is funded by the National Human Genome Research Institute (NHGRI), Office of the Director (OD), Eunice Kennedy Shriver National Institute Of Child Health & Human Development (NICHD), the National Institute of Environmental Health Sciences (NIEHS), the Office of AIDS research (OAR) and the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), of the National Institutes of Health (NIH) under award number U54HG006938 and its supplements, as part of the H3Africa Consortium. The study was also partly funded the Department of Science and Technology, South Africa, award number DST/CON 0056/2014, and by the African Partnership for Chronic Disease Research (APCDR). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

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. doi: 10.1016/j.jacl.2013.04.001 [DOI] [PubMed] [Google Scholar]
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. doi: 10.1016/j.jacc.2020.11.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.1016/S0140-6736(16)31679-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.1136/heartjnl-2012-302300 [DOI] [PubMed] [Google Scholar]
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. doi: 10.1371/journal.pone.0155753 [DOI] [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.3402/gha.v7.25365 [DOI] [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.1371/journal.pone.0157281 [DOI] [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.1093/ije/dyr050 [DOI] [PubMed] [Google Scholar]
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. doi: 10.1093/ije/dyx078 [DOI] [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.1038/s41586-019-1171-x [DOI] [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.3389/fcell.2022.840866 [DOI] [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.1161/CIRCULATIONAHA.114.015000 [DOI] [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.1016/j.jacl.2015.02.003 [DOI] [PubMed] [Google Scholar]
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. doi: 10.1161/CIRCULATIONAHA.105.563452 [DOI] [PubMed] [Google Scholar]
15.Grundy SM. Small LDL, atherogenic dyslipidemia, and the metabolic syndrome. Circulation. 1997;95(1):1–4. doi: 10.1161/01.cir.95.1.1 [DOI] [PubMed] [Google Scholar]
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. doi: 10.1186/s12889-017-4318-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
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. [DOI] [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.1016/j.metabol.2009.12.018 [DOI] [PubMed] [Google Scholar]
19.Hunter DJ, Reddy KS. Noncommunicable diseases. New England Journal of Medicine. 2013;369(14):1336–43. [DOI] [PubMed] [Google Scholar]
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. doi: 10.1371/journal.pone.0126166 [DOI] [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.7196/samj.3180 [DOI] [PubMed] [Google Scholar]
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. doi: 10.1371/journal.pone.0046855 [DOI] [PMC free article] [PubMed] [Google Scholar]
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. [DOI] [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.1080/16549716.2018.1507133 [DOI] [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.1093/ije/dyv187 [DOI] [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.1093/ije/dym016 [DOI] [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.3389/fcvm.2021.778891 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Cochran W. G. (1977). Sampling techniques (3rd ed.). New York: John Wiley & Sons [Google Scholar]
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. [Google Scholar]
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. doi: 10.1161/CIRCULATIONAHA.109.192644 [DOI] [PubMed] [Google Scholar]
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. [PubMed] [Google Scholar]
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. [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.7196/samj.5502 [DOI] [PubMed] [Google Scholar]
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. doi: 10.1016/j.atherosclerosis.2019.02.030 [DOI] [PubMed] [Google Scholar]
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. doi: 10.1093/eurpub/ckz051 [DOI] [PMC free article] [PubMed] [Google Scholar]
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. [Google Scholar]
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. doi: 10.1136/bmjgh-2018-001017 [DOI] [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.1371/journal.pone.0223189 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Omran AR. The Epidemiologic Transition: A Theory of the Epidemiology of Population Change. The Milbank Quarterly. 2005;18(4):731–57. [DOI] [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.1016/S2214-109X(19)30443-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.1016/S2214-109X(18)30275-4 [DOI] [PubMed] [Google Scholar]
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). doi: 10.1186/s12916-020-01648-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.3389/fmed.2022.914016 [DOI] [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.1016/j.gheart.2017.01.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.1038/s41467-022-30098-w [DOI] [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.1016/j.mam.2012.10.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
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). doi: 10.5334/gh.863 [DOI] [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.1371/journal.pmed.1003841 [DOI] [PMC free article] [PubMed] [Google Scholar]
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. doi: 10.1371/journal.pone.0187347 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0316527.r001

Decision Letter 0

Neftali Eduardo Antonio-Villa

4 Nov 2024

PONE-D-24-06681Obesity phenotypes and dyslipidemia in adults from four African countries: An H3Africa AWI-Gen StudyPLOS ONE

Dear Dr. Nonterah,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

This is a well-written manuscript that sought to report the burden of abnormal lipid fraction, dyslipidemia and their associations with obesity phenotypes stratified by sex among four SSA countries. I agree with two external referees that the manuscript has some minor issues that could be adressed within a revised version of the manuscript.

Please submit your revised manuscript by Dec 19 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Neftali Eduardo Antonio-Villa, MD PhD

Academic Editor

PLOS ONE

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When submitting your revision, we need you to address these additional requirements.

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2. Please include a complete copy of PLOS’ questionnaire on inclusivity in global research in your revised manuscript. Our policy for research in this area aims to improve transparency in the reporting of research performed outside of researchers’ own country or community. The policy applies to researchers who have travelled to a different country to conduct research, research with Indigenous populations or their lands, and research on cultural artefacts. The questionnaire can also be requested at the journal’s discretion for any other submissions, even if these conditions are not met. Please find more information on the policy and a link to download a blank copy of the questionnaire here: https://journals.plos.org/plosone/s/best-practices-in-research-reporting. Please upload a completed version of your questionnaire as Supporting Information when you resubmit your manuscript.

3. We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match.

When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section.

4. Thank you for stating the following financial disclosure:

"The AWI-Gen Collaborative Centre is funded by the National Human Genome Research Institute (NHGRI), Office of the Director (OD), Eunice Kennedy Shriver National Institute Of Child Health & Human Development (NICHD), the National Institute of Environmental Health Sciences (NIEHS), the Office of AIDS research (OAR) and the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), of the National Institutes of Health (NIH) under award number U54HG006938 and its supplements, as part of the H3Africa Consortium. The study was also partly funded the Department of Science and Technology, South Africa, award number DST/CON 0056/2014, and by the African Partnership for Chronic Disease Research (APCDR)."

Please state what role the funders took in the study. If the funders had no role, please state: ""The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.""

If this statement is not correct you must amend it as needed.

Please include this amended Role of Funder statement in your cover letter; we will change the online submission form on your behalf.

5. Please note that your Data Availability Statement is currently missing the repository name and/or the DOI/accession number of each dataset OR a direct link to access each database. If your manuscript is accepted for publication, you will be asked to provide these details on a very short timeline. We therefore suggest that you provide this information now, though we will not hold up the peer review process if you are unable.

6. One of the noted authors is a group or consortium [Freedom Mukomana, Annoy Choudary, Juliana Kagura, Zane Lombard, Scot Hezelhurst]. In addition to naming the author group, please list the individual authors and affiliations within this group in the acknowledgments section of your manuscript. Please also indicate clearly a lead author for this group along with a contact email address.

7. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Please, find attach some additional comments:

Abstract

* Adjust the abstract headings to align with the Plos One format.

* Remove the sentence: “(odds ratio [95% confidence interval])” from the Findings section.

* Clarify that obesity phenotypes are defined as BMI >30, increased waist-to-hip circumference, and increased waist-to-hip ratio.

Methods and Results

* Include the STROBE checklist as a supplementary material file.

* Although the H3Africa AWI-Gen study is a previously published paper, specify the sampling method used and clarify if population weights were applied.

* Indicate whether “behavioral risk factors” were self-reported by the study participants.

* In the “mixed-effect linear regression” section, specify the random effect used in the model.

* Correct the typo in Figure 1, y-label of panel A, which should read “Mean lipid Levels.” Also, consider adding the 95% CI for the adjusted mean levels.

* Estimating the age-standardized prevalence rates of any dyslipidemia in the sample would be insightful.

* Review for typos, especially where “non-HLD-C” is written as “non-HLDC.”

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: No

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This is a well written manuscript; I have not seen anything untoward. It is for this reason that I am accepting it without any revisions. The structure of the manuscript is well written and adhering to PLOS one requirement.

Reviewer #2: Summary

This study provides current estimates of the prevalence of dyslipidemia in several urban and rural African settings, as well as the associations of dyslipidemia and obesity phenotypes.

General comments

• Check spelling and grammar throughout the manuscript; there were many typos and sentence structure issues scattered throughout the sections.

Introduction

• Page 4, paragraph 2, sentence 1: Suggest adding some more context to why obesity is rising in rural areas, especially as you have several rural study sites.

• Page 4, paragraph 2, sentence 6: What does 120 mean?

• Page 4, paragraph 2, sentence 7: If you distinguish between the two study approaches (epidemiologic versus Mendelian randomization), I suggest adding some context for why both types are needed.

• Page 4, paragraph 3, sentences 1-2: What are the costs associated with dyslipidemia, e.g., healthcare utilization, morbidity, mortality, etc. These could be important reasons for screening, too.

Methods

• Page 6, paragraph 4, sentence 2: What is the rationale for those specific cutoffs?

• Page 8: paragraph 1, sentence 3: Do you mean least transitioned instead of “least exposed?”

• Page 8, paragraph 1, sentence 4: Can you provide some rationale for using the West African sites as the reference, if they are classified as least transitioned?

• Page 8, paragraph 2, sentence 1: Please provide more information on the WHO package, such as a general description of how the statistics are calculated and the criteria for needing screening.

Results

• Table 1: You may want to only report the proportions, not the counts, since those are more difficult to compare across study sites; plus, the number of participants per study site is already reported in the first table row.

• Table 1: How is fruit and vegetable intake measured?

• Table 2: Check the number of decimal places of Nanoro and Navrongo compared to the other study sites.

• Table 2: Double check the combined prevalence rate of low HDL-C in men.

• Table 2: Check the formatting for confidence intervals.

Discussion

• Page 17, paragraph 1, sentence 2: Consider adding information about how the urban Ghanaian population that was also studied.

• Page 17, paragraph 1, sentence 5: What are those studies that describe the sociodemographic characteristics of those study sites? Please cite them.

• Page 17, paragraph 2, sentence 2: Check the reference to the RODAM study; I think there is an incorrect citation. Also, please clarify by adding more description to observations you are discussing.

• Page 18, paragraph 2: While all the measures were associated with higher odds of dyslipidaemia, there were some stark differences in the magnitudes of association. Was this consistent with the results described in the systematic review?

• Page 18, paragraph 3: You may want to also discuss the benefits of better screening on cardiovascular disease in Africa; i.e., what could be achieved by scaling up screening to the levels you ascertained in that part of the analysis?

**********

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Reviewer #1: No

Reviewer #2: No

**********

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PLoS One. 2025 Jan 30;20(1):e0316527. doi: 10.1371/journal.pone.0316527.r002

Author response to Decision Letter 0

2 Dec 2024

Comments Response

Editors Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. We have done this

Please include a complete copy of PLOS’ questionnaire on inclusivity in global research in your revised manuscript. We have attached this as “supporting information”

We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match. When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section. We have corrected this

If this statement is not correct you must amend it as needed.

Please include this amended Role of Funder statement in your cover letter; we will change the online submission form on your behalf. We had indicated this in the funding section. We have amended the statement to reflect the suggested wording

Please note that your Data Availability Statement is currently missing the repository name and/or the DOI/accession number of each dataset OR a direct link to access each database. If your manuscript is accepted for publication, you will be asked to provide these details on a very short timeline. We therefore suggest that you provide this information now, though we will not hold up the peer review process if you are unable. We have now provided the data repository name, link and access number: “The AWI-Gen data used in the current study can be accessed from the European Genome-Phenome Archive (https://ega-archive.org/datasets) with phenotype dataset accession ID of EGA00001002482.”

One of the noted authors is a group or consortium [Freedom Mukomana, Annoy Choudary, Juliana Kagura, Zane Lombard, Scot Hezelhurst]. In addition to naming the author group, please list the individual authors and affiliations within this group in the acknowledgments section of your manuscript. Please also indicate clearly a lead author for this group along with a contact email address. We amended this to include the list of the group authors in the acknowledgement section as requested

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. We have revised this accordingly

Abstract

* Adjust the abstract headings to align with the Plos One format.

* Remove the sentence: “(odds ratio [95% confidence interval])” from the Findings section.

* Clarify that obesity phenotypes are defined as BMI >30, increased waist-to-hip circumference, and increased waist-to-hip ratio. *We have adjusted the heading (page 3)

*The suggested sentence has been removed (page 3) and *we have clarified as suggested (refer to page 3)

Methods and Results

* Include the STROBE checklist as a supplementary material file.

* Although the H3Africa AWI-Gen study is a previously published paper, specify the sampling method used and clarify if population weights were applied.

* Indicate whether “behavioral risk factors” were self-reported by the study participants.

* In the “mixed-effect linear regression” section, specify the random effect used in the model.

* Correct the typo in Figure 1, y-label of panel A, which should read “Mean lipid Levels.” Also, consider adding the 95% CI for the adjusted mean levels.

* Estimating the age-standardized prevalence rates of any dyslipidemia in the sample would be insightful.

* Review for typos, especially where “non-HLD-C” is written as “non-HLDC.” We have included the *STROBE checklist as supplementary material now

*We have included a summary statement on sampling methods used in methods section (page 6)

*Behavioural risk was self-reported and this has been indicated now (page 8).

*We have corrected the label for the y-axis now (see page 12)

*We have now specified the random effects used in the models (page 9).

*We have computed the standardized prevalence of dyslipidaemia now (see 13) and results in page 14 table 2

* We have reviewed these typos

Reviewer #1 This is a well written manuscript; I have not seen anything untoward. It is for this reason that I am accepting it without any revisions. The structure of the manuscript is well written and adhering to PLOS one requirement. Thank you for the kind words. We appreciate your appraisal of the manuscript

Reviewer #2 General comments

• Check spelling and grammar throughout the manuscript; there were many typos and sentence structure issues scattered throughout the sections. We have tried to correct any grammatical errors and typos throughout the manuscript

Introduction

• Page 4, paragraph 2, sentence 1: Suggest adding some more context to why obesity is rising in rural areas, especially as you have several rural study sites.

• Page 4, paragraph 2, sentence 6: What does 120 mean?

• Page 4, paragraph 2, sentence 7: If you distinguish between the two study approaches (epidemiologic versus Mendelian randomization), I suggest adding some context for why both types are needed.

• Page 4, paragraph 3, sentences 1-2: What are the costs associated with dyslipidemia, e.g., healthcare utilization, morbidity, mortality, etc. These could be important reasons for screening, too. *We have added more to the reasons driving this phenomenon (page 4)

*We have deleted it as we realized it is an error (page 4)

*A phrase has been added to clarify (page 4)

*We have revised this for clarity and included suggestions by the reviewer (page 5)

Methods • Page 6, paragraph 4, sentence 2: What is the rationale for those specific cutoffs?

• Page 8: paragraph 1, sentence 3: Do you mean least transitioned instead of “least exposed?”

• Page 8, paragraph 1, sentence 4: Can you provide some rationale for using the West African sites as the reference, if they are classified as least transitioned?

• Page 8, paragraph 2, sentence 1: Please provide more information on the WHO package, such as a general description of how the statistics are calculated and the criteria for needing screening. *There are no specific cutoffs for Africa hence we used WHO recommendations

*We have corrected to “least transitioned” (page 8)

*In comparative analyses the interest is risk hence it is assumed least transitioned of the sites will have lower risk.

*We added a phrase to describe this (page 9)

Results • Table 1: You may want to only report the proportions, not the counts, since those are more difficult to compare across study sites; plus, the number of participants per study site is already reported in the first table row.

• Table 1: How is fruit and vegetable intake measured?

• Table 2: Check the number of decimal places of Nanoro and Navrongo compared to the other study sites.

• Table 2: Double check the combined prevalence rate of low HDL-C in men.

• Table 2: Check the formatting for confidence intervals. *We appreciate the comment and have thus edited the table as requested

*We have included this in the methods now (page 7&8)

*Number of decimal places have been corrected now to be consistent

*The combined prevalence rate has been amended after rerunning the analyses

*Confidence intervals have been reformatted and are now consistent

Discussion • Page 17, paragraph 1, sentence 2: Consider adding information about how the urban Ghanaian population that was also studied.

• Page 17, paragraph 1, sentence 5: What are those studies that describe the sociodemographic characteristics of those study sites? Please cite them.

*The first paragraph was a summary of our results hence we didn’t cite.

*We have corrected the citation for the RODAM study and we have added more to the observations reported (page 18)

*Yes, and we have revised for clarity (page 20 lines 549-552)

The magnitudes of the association in the systematic review are consistent with our findings where the odds of dyslipidaemia are higher in East and Southern Africa compared to those in West Africa.

*We have stated the benefit of better screening on cardiovascular disease in Africa (see page 20, lines 549-552)

PLoS One. doi: 10.1371/journal.pone.0316527.r003

Decision Letter 1

Neftali Eduardo Antonio-Villa

13 Dec 2024

Obesity phenotypes and dyslipidemia in adults from four African countries: An H3Africa AWI-Gen Study

PONE-D-24-06681R1

Dear Dr. Nonterah,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter, and your manuscript will be scheduled for publication.

An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Neftali Eduardo Antonio-Villa, MD PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

I want to congratulate the authors for doing an impressive job in addressing all the suggestions made by the reviewers. The main messages have strength, and I can now recommend this manuscript for publication.

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0316527.r004

Acceptance letter

Neftali Eduardo Antonio-Villa

14 Jan 2025

PONE-D-24-06681R1

PLOS ONE

Dear Dr. Nonterah,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Neftali Eduardo Antonio-Villa

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Checklist. STROBE statement—checklist of items that should be included in reports of observational studies.

(DOC)

pone.0316527.s001.doc^{(87.5KB, 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.

(DOCX)

pone.0316527.s002.docx^{(14.2KB, docx)}

S2 Table. Prevalence ratio of abnormal lipid levels sub-regional blocks with West Africa as the reference.

(DOCX)

pone.0316527.s003.docx^{(13.7KB, docx)}

S3 Table. STROBE statement—checklist of items that should be included in reports of observational studies.

(DOC)

pone.0316527.s004.doc^{(83.5KB, doc)}

S1 File

(DOCX)

pone.0316527.s005.docx^{(66KB, docx)}

Data Availability Statement

[pone.0316527.ref001] 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. doi: 10.1016/j.jacl.2013.04.001 [DOI] [PubMed] [Google Scholar]

[pone.0316527.ref002] 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. doi: 10.1016/j.jacc.2020.11.010 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref003] 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. doi: 10.1016/S0140-6736(16)31679-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref004] 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. doi: 10.1136/heartjnl-2012-302300 [DOI] [PubMed] [Google Scholar]

[pone.0316527.ref005] 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. doi: 10.1371/journal.pone.0155753 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref006] 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. doi: 10.3402/gha.v7.25365 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref007] 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. doi: 10.1371/journal.pone.0157281 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref008] 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. doi: 10.1093/ije/dyr050 [DOI] [PubMed] [Google Scholar]

[pone.0316527.ref009] 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. doi: 10.1093/ije/dyx078 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref010] 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. doi: 10.1038/s41586-019-1171-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref011] 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. doi: 10.3389/fcell.2022.840866 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref012] 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. doi: 10.1161/CIRCULATIONAHA.114.015000 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref013] 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. doi: 10.1016/j.jacl.2015.02.003 [DOI] [PubMed] [Google Scholar]

[pone.0316527.ref014] 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. doi: 10.1161/CIRCULATIONAHA.105.563452 [DOI] [PubMed] [Google Scholar]

[pone.0316527.ref015] 15.Grundy SM. Small LDL, atherogenic dyslipidemia, and the metabolic syndrome. Circulation. 1997;95(1):1–4. doi: 10.1161/01.cir.95.1.1 [DOI] [PubMed] [Google Scholar]

[pone.0316527.ref016] 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. doi: 10.1186/s12889-017-4318-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref017] 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. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref018] 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. doi: 10.1016/j.metabol.2009.12.018 [DOI] [PubMed] [Google Scholar]

[pone.0316527.ref019] 19.Hunter DJ, Reddy KS. Noncommunicable diseases. New England Journal of Medicine. 2013;369(14):1336–43. [DOI] [PubMed] [Google Scholar]

[pone.0316527.ref020] 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. doi: 10.1371/journal.pone.0126166 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref021] 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. doi: 10.7196/samj.3180 [DOI] [PubMed] [Google Scholar]

[pone.0316527.ref022] 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. doi: 10.1371/journal.pone.0046855 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref023] 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. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref024] 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. doi: 10.1080/16549716.2018.1507133 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref025] 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. doi: 10.1093/ije/dyv187 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref026] 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. doi: 10.1093/ije/dym016 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref027] 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. doi: 10.3389/fcvm.2021.778891 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref028] 28.Cochran W. G. (1977). Sampling techniques (3rd ed.). New York: John Wiley & Sons [Google Scholar]

[pone.0316527.ref029] 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. [Google Scholar]

[pone.0316527.ref030] 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. doi: 10.1161/CIRCULATIONAHA.109.192644 [DOI] [PubMed] [Google Scholar]

[pone.0316527.ref031] 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. [PubMed] [Google Scholar]

[pone.0316527.ref032] 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. [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref033] 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. doi: 10.7196/samj.5502 [DOI] [PubMed] [Google Scholar]

[pone.0316527.ref034] 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. doi: 10.1016/j.atherosclerosis.2019.02.030 [DOI] [PubMed] [Google Scholar]

[pone.0316527.ref035] 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. doi: 10.1093/eurpub/ckz051 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref036] 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. [Google Scholar]

[pone.0316527.ref037] 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. doi: 10.1136/bmjgh-2018-001017 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref038] 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. doi: 10.1371/journal.pone.0223189 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref039] 39.Omran AR. The Epidemiologic Transition: A Theory of the Epidemiology of Population Change. The Milbank Quarterly. 2005;18(4):731–57. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref040] 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. doi: 10.1016/S2214-109X(19)30443-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref041] 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. doi: 10.1016/S2214-109X(18)30275-4 [DOI] [PubMed] [Google Scholar]

[pone.0316527.ref042] 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). doi: 10.1186/s12916-020-01648-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref043] 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. doi: 10.3389/fmed.2022.914016 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref044] 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. doi: 10.1016/j.gheart.2017.01.011 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref045] 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. doi: 10.1038/s41467-022-30098-w [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref046] 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. doi: 10.1016/j.mam.2012.10.001 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref047] 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). doi: 10.5334/gh.863 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref048] 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. doi: 10.1371/journal.pmed.1003841 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0316527.ref049] 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. doi: 10.1371/journal.pone.0187347 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Obesity phenotypes and dyslipidemia in adults from four African countries: An H3Africa AWI-Gen study

Engelbert A Nonterah

Godfred Agongo

Nigel J Crowther

Shukri F Mohamed

Lisa K Micklesfield

Palwendé Romuald Boua

Alisha N Wade

Solomon S R Choma

Hermann Sorgho

Isaac Kissiangani

Gershim Asiki

Patrick Ansah

Abraham R Oduro

Shane A Norris

Stephen M Tollman

Frederick J Raal

Marianne Alberts

Michele Ramsay

Roles

Abstract

Introduction

Methods

Results

Conclusion

Introduction

Methods

Study design, setting, population and sampling

Sample size determination

Data collection

Measures of obesity phenotypes

BMI in Kg/m2 (an indicator of general obesity)

Waist and hip circumference

Waist-to-hip ratio (WTHR)

Serum lipid fractions and dyslipidemia

Other risk factors of CVD

Statistical analysis

Results

Participant characteristics

Table 1. Basic characteristics of AWI-Gen participants in six sites from sub-Saharan Africa stratified by sex.

Mean serum lipid levels

Fig 1.

Age-standardised prevalence of abnormal lipid levels

Table 2. Age-standardised prevalence rates (with 95% confidence intervals) of elevated lipid fractions among women and men in the six sites of the AWI-Gen study.

Obesity measures and dyslipidaemia

Table 3. Sex differences in the association of the various adiposity phenotypes with the lipid fractions in the combined AWI-Gen cohort.

Regional difference

Number recommended for screening

Fig 2. The total number of participants recommended for dyslipidaemia screening computed using the protocol of World Health Organization Package of Essential Non communicable disease intervention for primary healthcare in low resource settings (WHO PEN).

Discussion

Strengths and limitations

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Neftali Eduardo Antonio-Villa

Roles

Author response to Decision Letter 0

Decision Letter 1

Neftali Eduardo Antonio-Villa

Roles

Acceptance letter

Neftali Eduardo Antonio-Villa

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Links to NCBI Databases

BMI in Kg/m² (an indicator of general obesity)