Diabetes prevalence by HbA1c and oral glucose tolerance test among HIV-infected and uninfected Tanzanian adults

Abstract

BackgroundThe burden of diabetes is increasing in sub-Saharan Africa, including among people living with HIV. We assessed the prevalence of diabetes and the roles of HIV, antiretroviral therapy (ART) and traditional risk factors among adults in Tanzania. MethodsWe analysed diabetes-relevant baseline data from 1,947 adult participants in the CICADA study in Mwanza, Tanzania: 655 HIV-uninfected, 956 HIV-infected ART-naïve, and 336 HIV-infected persons on ART. WHO guidelines for haemoglobin A1c (HbA1c) and oral glucose tolerance test (OGTT) were used to define diabetes and prediabetes. Risk factors were evaluated using multinomial logistic regression analysis. Relative risk ratios (RRR) were generated comparing participants with diabetes and prediabetes against the reference of those with no diabetes. ResultsMean age was 41 (SD 12) years; 59% were women. The prevalence of diabetes was 13% by HbA1c and 6% by OGTT, with partial overlap among participants identified by the two tests. Relative to HIV-uninfected, HIV-infected ART-naïve persons had increased relative risks of diabetes (HbA1c: RRR = 1.95, 95% CI 1.25-3.03; OGTT: RRR = 1.90, 95% CI 0.96-3.73) and prediabetes (HbA1c: RRR = 2.89, 95% CI 1.93-4.34; OGTT: RRR = 1.61, 95% CI 1.22-2.13). HIV-infected participants on ART showed increased risk of prediabetes PLOS ONE

β‐cell dysfunction and insulin resistance in relation to pre‐diabetes and diabetes among adults in north‐western Tanzania: a cross‐sectional study

“…The study population and main methods have been reported elsewhere [12]. Briefly, participants who were recruited in previous tuberculosis and HIV nutritional supplementation trials in Mwanza from 2006 to 2013 (i.e.…”

“…Data on demographics and NCDs risk factors were collected based on the WHO STEPS manual questionnaire [17]. According to previously reported analysis, of the lifestyle factors, only physical activity was associated with diabetes [12], so was the only such variable included here. Less than 600 MET (metabolic equivalent of tasks) minutes per week was considered as being physically inactive [18].…”

“…We examined the association of the β‐cell dysfunction and insulin resistance overall marker with pre‐diabetes or diabetes and included age, sex, CRP, AGP, HIV/ART, fat mass, fat‐free mass and physical activity in models. HIV/ART, fat mass, fat‐free mass and physical activity were included in models because they were previously found to be associated with diabetes in univariate or multivariable analysis in this study population [12], whereas CRP and AGP were included because inflammation is known to be important in both HIV and insulin resistance and may explain the effect of HIV on insulin resistance. Minimally adjusted multinomial logistic regression models included age and sex and final models included all predictor variables.…”

Influence of hemoglobinopathies and glucose-6-phosphate dehydrogenase deficiency on diagnosis of diabetes by HbA1c among Tanzanian adults with and without HIV: A cross-sectional study

“…Venous blood for HbA1c was drawn from those who had fasted, and participants were then given 82.5 g of dextrose monohydrate (equivalent to 75 g of anhydrous glucose) diluted in 250 ml of drinking water to drink within 5 minutes for OGTT. Blood for OGTT glucose was collected after 30 minutes and 2 hours, and glucose was measured with a point-of-care machine (HemoCue 201RT, Ä ngelholm, Sweden) [19]. HbA1c was measured with a point-of-care device (HemoCue HbA1c 501), which uses a boronate affinity assay to separate the glycated hemoglobin fraction from the non-glycated fraction; the instrument is calibrated to harmonize with HPLC method (HbA1c 501 Analyzer, Operating Manual).…”

“…The study was exploratory and had funds to obtain hemoglobinopathy information only on a sub-set of CICADA participants. In the entire CICADA cohort, diabetes was diagnosed using HbA1c in 13% of participants, and an additional 17% were pre-diabetic [19]. Assuming a higher prevalence of PD/DM among participants without hemoglobinopathies of 32%, with 80% statistical power, the study could detect PD/DM among participants with hemoglobinopathies of 19%, with at least 80% power.…”