Prevalence and phenotype of diabetes and prediabetes using fasting glucose vs HbA1c in a Caribbean population.

BACKGROUND: Both fasting plasma glucose (FPG) and HbA1c are recommended for the diagnosis of diabetes and prediabetes by the American Diabetes Association (ADA), and for diabetes by the World Health Organization. The ADA guidance is influential on clinical practice in many developing countries, including in the Caribbean and Latin America. We aimed to compare the prevalence and characteristics of individuals identified as having diabetes and prediabetes by FPG and HbA1c in a predominantly African ancestry Caribbean population. METHODS: A representative population-based sample of 1234 adults (≥25 years of age) resident in Barbados was recruited. Standard methods with appropriate quality control were used to collect data on height, weight, blood pressure, fasting lipids and history of diagnosed diabetes, and to measure fasting glucose and HbA1c. Those with previously diagnosed diabetes (n = 192) were excluded from the analyses. Diabetes was defined as: FPG ≥7.0 mmol/L or HbA1c ≥6.5%; prediabetes as: FPG ≥5.6 to <7mmol/L or HbA1c ≥5.7 to <6.5%. RESULTS: Complete data were available on 939 participants without previously diagnosed diabetes. The prevalence of undiagnosed diabetes was higher, but not significantly so, by HbA1c (4.9%, 95% CI 3.5, 6.8) vs FPG (3.5%, 2.4, 5.1). Overall 79 individuals had diabetes by either measure, but only 21 on both. The prevalence of prediabetes was higher by HbA1c compared to FPG: 41.7% (37.9, 45.6) vs 15.0% (12.8, 17.5). Overall 558 individuals had prediabetes by either measure, but only 107 on both. HbA1c, but not FPG, was significantly higher in women than men; and FPG, but not HbA1c, was significantly associated with raised triglycerides and low HDL cholesterol. CONCLUSION: The agreement between FPG and HbA1c defined hyperglycaemia is poor. In addition, there are some differences in the phenotype of those identified, and HbA1c gives a much higher prevalence of prediabetes. The routine use of HbA1c for screening and diagnosis in this population would have major implications for clinical and public health policies and resources. Given the lack of robust evidence, particularly for prediabetes, on whether intervention in the individuals identified would improve outcomes, this approach to screening and diagnosis cannot be currently recommended for this population

Anthropometric cut-offs to identify hyperglycemia in an Afro-Caribbean population: a cross-sectional population-based study from Barbados.

INTRODUCTION: Body mass index (BMI) and waist circumference (WC) cut-offs associated with hyperglycemia may differ by ethnicity. We investigated the optimal BMI and WC cut-offs for identifying hyperglycemia in the predominantly Afro-Caribbean population of Barbados. RESEARCH DESIGN AND METHODS: A cross-sectional study of 865 individuals aged ≥25 years without known diabetes or cardiovascular disease was conducted. Hyperglycemia was defined as fasting plasma glucose ≥5.6 mmol/L or hemoglobin A1c ≥5.7% (39 mmol/mol). The Youden index was used to identify the optimal cut-offs from the receiver operating characteristic (ROC) curves. Further ROC analysis and multivariable log binomial regression were used to compare standard and data-derived cut-offs. RESULTS: The prevalence of hyperglycemia was 58.9% (95% CI 54.7% to 63.0%). In women, optimal BMI and WC cut-offs (27 kg/m2 and 87 cm, respectively) performed similarly to standard cut-offs. In men, sensitivities of the optimal cut-offs of BMI ≥24 kg/m2 (72.0%) and WC ≥86 cm (74.0%) were higher than those for standard BMI and WC obesity cut-offs (30.0% and 25%-46%, respectively), although with lower specificity. Hyperglycemia was 70% higher in men above the data-derived WC cut-off (prevalence ratio 95% CI 1.2 to 2.3). CONCLUSIONS: While BMI and WC cut-offs in Afro-Caribbean women approximate international standards, our findings, consistent with other studies, suggest lowering cut-offs in men may be warranted to improve detection of hyperglycemia. Our findings do, however, require replication in a new data set.The project was supported by the Ministry of Health of the Government of Barbados. ANW is supported by the Fogarty International Center of the National Institutes of Health under Award Number K43TW010698. This paper describes the views of the authors and does not necessarily represent the official views of the National Institutes of Health (USA)

Leveraging population admixture to characterize the heritability of complex traits.

Despite recent progress on estimating the heritability explained by genotyped SNPs (h(2)g), a large gap between h(2)g and estimates of total narrow-sense heritability (h(2)) remains. Explanations for this gap include rare variants or upward bias in family-based estimates of h(2) due to shared environment or epistasis. We estimate h(2) from unrelated individuals in admixed populations by first estimating the heritability explained by local ancestry (h(2)γ). We show that h(2)γ = 2FSTCθ(1 - θ)h(2), where FSTC measures frequency differences between populations at causal loci and θ is the genome-wide ancestry proportion. Our approach is not susceptible to biases caused by epistasis or shared environment. We applied this approach to the analysis of 13 phenotypes in 21,497 African-American individuals from 3 cohorts. For height and body mass index (BMI), we obtained h(2) estimates of 0.55 ± 0.09 and 0.23 ± 0.06, respectively, which are larger than estimates of h(2)g in these and other data but smaller than family-based estimates of h(2)

Leveraging population admixture to explain missing heritability of complex traits

Despite recent progress on estimating the heritability explained by genotyped SNPs (hg2), a large gap between hg2 and estimates of total narrow-sense heritability (h2) remains. Explanations for this gap include rare variants, or upward bias in family-based estimates of h2 due to shared environment or epistasis. We estimate h2 from unrelated individuals in admixed populations by first estimating the heritability explained by local ancestry (hγ2). We show that hγ2 = 2FSTCθ(1−θ)h2, where FSTC measures frequency differences between populations at causal loci and θ is the genome-wide ancestry proportion. Our approach is not susceptible to biases caused by epistasis or shared environment. We examined 21,497 African Americans from three cohorts, analyzing 13 phenotypes. For height and BMI, we obtained h2 estimates of 0.55 ± 0.09 and 0.23 ± 0.06, respectively, which are larger than estimates of hg2 in these and other data, but smaller than family-based estimates of h2

Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction.

Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction

Generalizability of established prostate cancer risk variants in men of African ancestry

Genome-wide association studies have identified more than 80 risk variants for prostate cancer, mainly in European or Asian populations. The generalizability of these variants in other racial/ethnic populations needs to be understood before the loci can be used widely in risk modeling. In our study, we examined 82 previously reported risk variants in 4,853 prostate cancer cases and 4,678 controls of African ancestry. We performed association testing for each variant using logistic regression adjusted for age, study and global ancestry. Of the 82 known risk variants, 68 (83%) had effects that were directionally consistent in their association with prostate cancer risk and 30 (37%) were significantly associated with risk at p < 0.05, with the most statistically significant variants being rs116041037 (p = 3.7 × 10(-26) ) and rs6983561 (p = 1.1 × 10(-16) ) at 8q24, as well as rs7210100 (p = 5.4 × 10(-8) ) at 17q21. By exploring each locus in search of better markers, the number of variants that captured risk in men of African ancestry (p < 0.05) increased from 30 (37%) to 44 (54%). An aggregate score comprised of these 44 markers was strongly associated with prostate cancer risk [per-allele odds ratio (OR) = 1.12, p = 7.3 × 10(-98) ]. In summary, the consistent directions of effects for the vast majority of variants in men of African ancestry indicate common functional alleles that are shared across populations. Further exploration of these susceptibility loci is needed to identify the underlying biologically relevant variants to improve prostate cancer risk modeling in populations of African ancestry