Serum Lipid Profile and Its Association with Diabetes and Prediabetes in a Rural Bangladeshi Population.

Dyslipidemia is commonly associated with diabetes (T2DM). This has been demonstrated for the Caucasian population, but few data are available for Asian Indians. The paper aims to investigate serum lipids (separately or in combination) and their association with glucose intolerance status (T2DM and prediabetes) in a rural Bangladeshi population. A sample of 2293 adults (≥20 years) were included in a community based cross-sectional survey in 2009. Anthropometric measures, blood pressure, blood glucose (fasting and 2-h oral glucose tolerance test) and fasting serum lipids (total cholesterol, T-Chol; triglycerides, Tg; low density lipoprotein cholesterol, LDL-C and high density lipoprotein cholesterol, HDL-C) were registered. Analysis of covariance (ANCOVA) and regression analysis were performed. High Tg levels were seen in 26% to 64% of the participants, depending on glucose tolerance status. Low HDL-C levels were seen in all groups (>90%). Significant linear trends were observed for high T-Chol, high Tg and low HDL-C with increasing glucose intolerance (p for trend <0.001). T2DM was significantly associated with high T-Chol (Odds ratio (OR): 2.43, p < 0.001), high Tg (OR: 3.91, p < 0.001) and low HDL-C (OR: 2.17, p = 0.044). Prediabetes showed a significant association with high Tg (OR: 1.96, p < 0.001) and low HDL-C (OR: 2.93, p = 0.011). Participants with combined high Tg and low HDL-C levels had a 12.75-fold higher OR for T2DM and 4.89 OR for prediabetes. In Asian Indian populations an assessment of serum lipids is warranted not only for T2DM patients, but also for those with prediabetes.

Simple risk score to detect rural Asian Indian (Bangladeshi) adults at high risk for type 2 diabetes.

AIMS/INTRODUCTION: To develop and evaluate a simple, non-invasive, diabetes risk score for detecting individuals at high risk for type 2 diabetes in rural Bangladesh. MATERIALS AND METHODS: Data from 2,293 randomly selected individuals aged ≥20 years from a cross-sectional study in a rural community of Bangladesh (2009 Chandra Rural Study) was used for model development. The validity of the model was assessed in another rural cross-sectional study (2009 Thakurgaon Rural Study). The logistic regression model used included age, sex, body mass index, waist-to-hip ratio and hypertension status to predict individuals who were at high risk for type 2 diabetes. RESULTS: On applying the developed model to both cohorts, the area under the receiver operating characteristic curve was 0.70 (95% confidence interval 0.68-0.72) for the Chandra cohort and 0.71 (95% confidence interval 0.68-0.74) for the Thakurgaon cohort. The risk score of >9 was shown to have the optimal cut-point to detect diabetes. This score had a sensitivity of 62.4 and 75.7%, and specificity of 67.4 and 61.6% in the two cohorts, respectively. This risk score was shown to have improved sensitivity and specificity to detect type 2 diabetes cases compared with the Thai, Indian, Omani, UK, Dutch, Portuguese and Pakistani diabetes risk scores. CONCLUSIONS: This simple, non-invasive risk score can be used to detect individuals at high risk for type 2 diabetes in rural Bangladesh. Subjects with a score of 9 or above (out of 15) should undergo an oral glucose tolerance test for definitive diagnosis of diabetes.

Incidence of diabetes in Asian-Indian subjects: a five year follow-up study from Bangladesh.

AIMS: To determine the incidence of Type 2 diabetes and its risk factors. Further, to examine the effect of relative changes in obesity (BMI and waist circumference). METHODS: A sample of 2011 non-diabetic adults aged 20 and above were randomly selected and followed from 1999 to 2004. Fasting blood glucose including biophysical and anthropometric measures was measured. RESULTS: The overall 5 year cumulative incidence of diabetes was 16.4 per 1000 person-years and 65.1 per 1000 person-years in those with impaired fasting glucose adjusted for age and sex. Among obesity measures, only waist >85in. increased the risk for diabetes in males (RR=3.0). Relative changes stratified by loss in BMI and WC ≥5% or gain of BMI >15% from the baseline values for men were significantly associated with the incidental cases of diabetes. Increased hip circumference for men was significantly associated with a protective effect while an opposite association was observed for women. CONCLUSION: Relative change in both loss and excessive gain of BMI were risks for increased diabetes. Targeted intervention in those with impaired fasting blood glucose will expectedly reduce the incidental cases. Further investigations are needed for non obese related diabetes in Asian Indian subjects.