Epidemiology and determinants of type 2 diabetes in south Asia.

Type 2 diabetes has rapidly developed into a major public health problem in south Asia (defined here as Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka) in recent decades. During this period, major lifestyle changes associated with economic transition, industrialisation, urbanisation, and globalisation have been key determinants in the increasing burden of non-communicable diseases. A decline in nutrition quality, reduced physical activity, and increased sedentary behaviours are reflected in the increasing prevalence of type 2 diabetes and related risk factors in the region. The International Diabetes Federation 2017 estimates of the prevalence of diabetes in adults in the region range from 4·0% in Nepal to 8·8% in India. The prevalence of overweight ranges from 16·7% in Nepal to 26·1% in Sri Lanka, and the prevalence of obesity ranges from 2·9% in Nepal to 6·8% in Sri Lanka. An increasing proportion of children, adolescents, and women are overweight or obese, leading to a heightened risk of type 2 diabetes. Ethnic south Asians present with greater metabolic risk at lower levels of BMI compared with other ethnic groups (referred to as the south Asian phenotype), with type 2 diabetes often developing at a younger age, and with rapid progression of diabetic complications. Because of the presence of multiple risk factors and a body composition conducive to the development of type 2 diabetes, south Asians should be aggressively targeted for prevention. In this Series paper, we detail trends in the prevalence of diabetes in the region and address major determinants of the disease in the context of nutrition and physical activity transitions and the south Asian phenotype.

Particulate matter and markers of glycemic control and insulin resistance in type 2 diabetic patients: result from Wellcome Trust Genetic study.

There is growing evidence that air pollution is associated with increased risk of type 2 diabetes (T2DM). However, information related to whether particulate matter (PM) contributing to worsened metabolic control in T2DM patients is inconsistent. We examined the association of PM10 exposure with glucose-function parameters in young-onset T2DM patients. We investigated the association between a year ambient concentration of PM10 at residential places, using AERMOD dispersion model, with fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), 2 h post meal plasma glucose (2hPG), homeostasis model assessment of insulin resistance (HOMA-IR), ß-cell function (HOMA-ß) and disposition index (DI) in 1213 diabetic patients from the Wellcome Trust Genetic study at the Diabetes Unit, KEM Hospital Research Center, Pune, India. We used linear regression models and adjusted for a variety of individual and environmental confounding variables. Possible effect modification by age, gender, waist-to-hip ratio (WHR) and smoking status were investigated. Sensitivity analysis assessed the impact of relative humidity (RH) and temperature a day before examination and anti-diabetic and HHR medication (Hydralazine, Hydrochlorothiazide and Reserpine). We found that 1 SD increment in background concentration of PM10 at residential places (43.83 µg/m3) was significantly associated with 2.25 mmol/mol and 0.38 mmol/l increase in arithmetic means of HbA1c and 2hPG, respectively. A similar increase in PM10 was also associated with 4.89% increase in geometric mean of HOMA-IR. The associations remained significant after adjustment to RH and temperature, and WHR and smoking enhanced the size of the effect. Our study suggests that long-term exposure to PM10 is associated with higher glycaemia and insulin resistance. In context of our previous demonstration of association of SO2 and NO x and plasma C-reactive protein, we suggest that air pollution could influence progression of diabetes complications. Prospective studies and interventions are required to define mechanism and confirm causality.

Air pollution and respiratory health among diabetic and non-diabetic subjects in Pune, India-results from the Wellcome Trust Genetic Study.

Diabetics may be more vulnerable to the harmful effects of ambient air pollutants than healthy individuals. But, the risk factors that lead to susceptibility to air pollution in diabetics have not yet been identified. We examined the effect of exposure to ambient PM10 on chronic symptoms and the pulmonary function tests (PFT) in diabetic and non-diabetic subjects. Also, to investigate possible determinants of susceptibility, we recruited 400 type 2 diabetic and 465 healthy subjects who were investigated for chronic respiratory symptoms (CRSs) and then underwent measurement of forced vital capacity (FVC) and forced expiratory volume 1 (FEV1) according to standard protocol. Percent predicted FEV1 and FVC (FEV1% and FVC%, respectively) for each subject were calculated. Particulate matter (PM10) concentrations at residence place of subjects were estimated using AERMOD dispersion model. The association between PM10 and CRSs was explored using logistic regression. We also used linear regression models controlling for potential confounders to study the association between chronic exposure to PM10 and FEV1% and FVC%. Prevalence of current wheezing, allergy symptom, chest tightness, FEV1/FVC <70%, and physician-diagnosed asthma and COPD was significantly higher among diabetic subjects than non-diabetics. There was no significant difference between percent predicted value of PFT among diabetic and non-diabetic subjects (P < 0.05). We estimated that 1 SD increase in PM10 concentration was associated with a greater risk of having dyspnea by 1.50-fold (95% CI, 1.12-2.01). Higher exposure to PM10 concentration was also significantly associated with lower FVC%. The size of effect for 1 SD µg/m3 (=98.38) increase in PM10 concentration was 3.71% (95% CI, 0.48-4.99) decrease in FVC%. In addition, we indicated that strength of these associations was higher in overweight, smoker, and aged persons. We demonstrated a possible contribution of air pollution to reduced lung function independent of diabetes status. This study suggests that decline in exposure may significantly reduce disease manifestation as dyspnea and impaired lung function. We conduct that higher BMI, smoking, and older age were associated with higher levels of air pollution effects.

Differential white blood cell count and type 2 diabetes: systematic review and meta-analysis of cross-sectional and prospective studies.

OBJECTIVE: Biological evidence suggests that inflammation might induce type 2 diabetes (T2D), and epidemiological studies have shown an association between higher white blood cell count (WBC) and T2D. However, the association has not been systematically investigated. RESEARCH DESIGN AND METHODS: Studies were identified through computer-based and manual searches. Previously unreported studies were sought through correspondence. 20 studies were identified (8,647 T2D cases and 85,040 non-cases). Estimates of the association of WBC with T2D were combined using random effects meta-analysis; sources of heterogeneity as well as presence of publication bias were explored. RESULTS: The combined relative risk (RR) comparing the top to bottom tertile of the WBC count was 1.61 (95% CI: 1.45; 1.79, p = 1.5*10(-18)). Substantial heterogeneity was present (I(2) = 83%). For granulocytes the RR was 1.38 (95% CI: 1.17; 1.64, p = 1.5*10(-4)), for lymphocytes 1.26 (95% CI: 1.02; 1.56, p = 0.029), and for monocytes 0.93 (95% CI: 0.68; 1.28, p = 0.67) comparing top to bottom tertile. In cross-sectional studies, RR was 1.74 (95% CI: 1.49; 2.02, p = 7.7*10(-13)), while in cohort studies it was 1.48 (95% CI: 1.22; 1.79, p = 7.7*10(-5)). We assessed the impact of confounding in EPIC-Norfolk study and found that the age and sex adjusted HR of 2.19 (95% CI: 1.74; 2.75) was attenuated to 1.82 (95% CI: 1.45; 2.29) after further accounting for smoking, T2D family history, physical activity, education, BMI and waist circumference. CONCLUSIONS: A raised WBC is associated with higher risk of T2D. The presence of publication bias and failure to control for all potential confounders in all studies means the observed association is likely an overestimate.