BackgroundRising rates of obesity and type 2 diabetes (T2D) are impending major threats to the health of African populations, but the extent to which they differ between rural and urban settings in Africa and upon migration to Europe is unknown. We assessed the burden of obesity and T2D among Ghanaians living in rural and urban Ghana and Ghanaian migrants living in different European countries.MethodsA multi-centre cross-sectional study was conducted among Ghanaian adults (n = 5659) aged 25–70 years residing in rural and urban Ghana and three European cities (Amsterdam, London and Berlin). Comparisons between groups were made using prevalence ratios (PRs) with adjustments for age and education.ResultsIn rural Ghana, the prevalence of obesity was 1.3 % in men and 8.3 % in women. The prevalence was considerably higher in urban Ghana (men, 6.9 %; PR: 5.26, 95 % CI, 2.04–13.57; women, 33.9 %; PR: 4.11, 3.13–5.40) and even more so in Europe, especially in London (men, 21.4 %; PR: 15.04, 5.98–37.84; women, 54.2 %; PR: 6.63, 5.04–8.72). The prevalence of T2D was low at 3.6 % and 5.5 % in rural Ghanaian men and women, and increased in urban Ghanaians (men, 10.3 %; PR: 3.06; 1.73–5.40; women, 9.2 %; PR: 1.81, 1.25–2.64) and highest in Berlin (men, 15.3 %; PR: 4.47; 2.50–7.98; women, 10.2 %; PR: 2.21, 1.30–3.75). Impaired fasting glycaemia prevalence was comparatively higher only in Amsterdam, and in London, men compared with rural Ghana.ConclusionOur study shows high risks of obesity and T2D among sub-Saharan African populations living in Europe. In Ghana, similarly high prevalence rates were seen in an urban environment, whereas in rural areas, the prevalence of obesity among women is already remarkable. Similar processes underlying the high burden of obesity and T2D following migration may also be at play in sub-Saharan Africa as a consequence of urbanisation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12916-016-0709-0) contains supplementary material, which is available to authorized users.

Many ethnic minorities in Europe have a higher type 2 diabetes (T2D) prevalence than their host European populations. The risk size differs between ethnic groups, but the extent of the differences in the various ethnic minority groups has not yet been systematically quantified. We conducted a meta-analysis of published data on T2D in various ethnic minority populations resident in Europe compared to their host European populations. We systematically searched MEDLINE (using PUBMED) and EMBASE for papers on T2D prevalence in ethnic minorities in Europe published between 1994 and 2014. The ethnic minority groups were classified into five population groups by geographical origin: South Asian (SA), Sub-Saharan African (SSA), Middle Eastern and North African (MENA), South and Central American (SCA), and Western Pacific (WP). Pooled odds ratios with corresponding 95 % confidence interval (CI) were calculated using Review Manager 5.3. Twenty articles were included in the analysis. Compared with the host populations, SA origin populations had the highest odds for T2D (3.7, 95 % CI 2.7-5.1), followed by MENA (2.7, 95 % CI 1.8-3.9), SSA (2.6, 95 % CI 2.0-3.5), WP (2.3, 95 % CI 1.2-4.1), and lastly SCA (1.3, 95 % CI 1.1-1.6). Odds ratios were in all ethnic minority populations higher for women than for men except for SCA. Among SA subgroups, compared with Europeans, Bangladeshi had the highest odds ratio of 6.2 (95 % CI 3.9-9.8), followed by Pakistani (5.4, 95 % CI 3.2-9.3) and Indians (4.1, 95 % CI 3.0-5.7). The risk of T2D among ethnic minority groups living in Europe compared to Europeans varies by geographical origin of the group: three to five times higher among SA, two to four times higher among MENA, and two to three times higher among SSA origin. Future research and policy initiatives on T2D among ethnic minority groups should take the interethnic differences into account.

IntroductionObesity and type 2 diabetes (T2D) are highly prevalent among African migrants compared with European descent populations. The underlying reasons still remain a puzzle. Gene–environmental interaction is now seen as a potential plausible factor contributing to the high prevalence of obesity and T2D, but has not yet been investigated. The overall aim of the Research on Obesity and Diabetes among African Migrants (RODAM) project is to understand the reasons for the high prevalence of obesity and T2D among sub-Saharan Africans in diaspora by (1) studying the complex interplay between environment (eg, lifestyle), healthcare, biochemical and (epi)genetic factors, and their relative contributions to the high prevalence of obesity and T2D; (2) to identify specific risk factors within these broad categories to guide intervention programmes and (3) to provide a basic knowledge for improving diagnosis and treatment.Methods and analysisRODAM is a multicentre cross-sectional study among homogenous sub-Saharan African participants (ie, Ghanaians) aged >25 years living in rural and urban Ghana, the Netherlands, Germany and the UK (http://rod-am.eu/). Standardised data on the main outcomes, genetic and non-genetic factors are collected in all locations. The aim is to recruit 6250 individuals comprising five subgroups of 1250 individuals from each site. In Ghana, Kumasi and Obuasi (urban stratum) and villages in the Ashanti region (rural stratum) are served as recruitment sites. In Europe, Ghanaian migrants are selected through the municipality or Ghanaian organisations registers.Ethics and disseminationEthical approval has been obtained in all sites. This paper gives an overview of the rationale, conceptual framework and methods of the study. The differences across locations will allow us to gain insight into genetic and non-genetic factors contributing to the occurrence of obesity and T2D and will inform targeted intervention and prevention programmes, and provide the basis for improving diagnosis and treatment in these populations and beyond.

Background: West African immigrants in Europe are disproportionally affected by metabolic conditions compared to European host populations. Nutrition transition through urbanisation and migration may contribute to this observations, but remains to be characterised.Objective: We aimed to describe the dietary behaviour and its socio-demographic factors among Ghanaian migrants in Europe and their compatriots living different Ghanaian settings.Methods: The multi-centre, cross-sectional RODAM (Research on Obesity and Diabetes among African Migrants) study was conducted among Ghanaian adults in rural and urban Ghana, and Europe. Dietary patterns were identified by principal component analysis.Results: Contributions of macronutrient to the daily energy intake was different across the three study sites. Three dietary patterns were identified. Adherence to the ‘mixed’ pattern was associated with female sex, higher education, and European residency. The ‘rice, pasta, meat, and fish’ pattern was associated with male sex, younger age, higher education, and urban Ghanaian environment. Adherence to the ‘roots, tubers, and plantain’ pattern was mainly related to rural Ghanaian residency.Conclusion: We observed differences in food preferences across study sites: in rural Ghana, diet concentrated on starchy foods; in urban Ghana, nutrition was dominated by animal-based products; and in Europe, diet appeared to be highly diverse.

BackgroundEpigenome-wide association studies (EWAS) have identified DNA methylation loci involved in adiposity. However, EWAS on adiposity in sub-Saharan Africans are lacking despite the high burden of adiposity among African populations. We undertook an EWAS for anthropometric indices of adiposity among Ghanaians aiming to identify DNA methylation loci that are significantly associated.MethodsThe Illumina 450k DNA methylation array was used to profile DNA methylation in whole blood samples of 547 Ghanaians from the Research on Obesity and Diabetes among African Migrants (RODAM) study. Differentially methylated positions (DMPs) and differentially methylation regions (DMRs) were identified for BMI and obesity (BMI ≥ 30 kg/m2), as well as for waist circumference (WC) and abdominal obesity (WC ≥ 102 cm in men, ≥88 cm in women). All analyses were adjusted for age, sex, blood cell distribution estimates, technical covariates, recruitment site and population stratification. We also did a replication study of previously reported EWAS loci for anthropometric indices in other populations.ResultsWe identified 18 DMPs for BMI and 23 for WC. For obesity and abdominal obesity, we identified three and one DMP, respectively. Fourteen DMPs overlapped between BMI and WC. DMP cg00574958 annotated to gene CPT1A was the only DMP associated with all outcomes analysed, attributing to 6.1 and 5.6% of variance in obesity and abdominal obesity, respectively. DMP cg07839457 (NLRC5) and cg20399616 (BCAT1) were significantly associated with BMI, obesity and with WC and had not been reported by previous EWAS on adiposity.ConclusionsThis first EWAS for adiposity in Africans identified three epigenome-wide significant loci (CPT1A, NLRC5 and BCAT1) for both general adiposity and abdominal adiposity. The findings are a first step in understanding the role of DNA methylation in adiposity among sub-Saharan Africans. Studies on other sub-Saharan African populations as well as translational studies are needed to determine the role of these DNA methylation variants in the high burden of adiposity among sub-Saharan Africans.Electronic supplementary materialThe online version of this article (10.1186/s13148-017-0403-x) contains supplementary material, which is available to authorized users.

In this first EWAS for T2D in sub-Saharan Africans, we have identified four DMPs at epigenome-wide level, one of which is novel. These findings provide insight into the epigenetic loci that underlie the burden of T2D in sub-Saharan Africans.

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Hypertension prevalence, awareness, and treatment levels were generally higher in African migrants, but blood pressure control level was lower in Ghanaian migrant men compared with their nonmigrant peers. Further work is needed to identify key underlying factors to support prevention and management efforts.Supplement Figure 1, http://links.lww.com/HJH/A831.