Meta-analytic evaluation for the spatio-temporal patterns of the associations between common risk factors and type 2 diabetes in mainland China

Spatiotemporal Analysis and Risk Assessment Model Research of Diabetes among People over 45 Years Old in China

Diabetes, which is a chronic disease with a high prevalence in people over 45 years old in China, is a public health issue of global concern. In order to explore the spatiotemporal patterns of diabetes among people over 45 years old in China, to find out diabetes risk factors, and to assess its risk, we used spatial autocorrelation, spatiotemporal cluster analysis, binary logistic regression, and a random forest model in this study. The results of the spatial autocorrelation analysis and the spatiotemporal clustering analysis showed that diabetes patients are mainly clustered near the Beijing−Tianjin−Hebei region, and that the prevalence of diabetes clusters is waning. Age, hypertension, dyslipidemia, and smoking history were all diabetes risk factors (p < 0.05), but the spatial heterogeneity of these factors was weak. Compared with the binary logistic regression model, the random forest model showed better accuracy in assessing diabetes risk. According to the assessment risk map generated by the random forest model, the northeast region and the Beijing−Tianjin−Hebei region are high-risk areas for diabetes.

Oxidative Stress Biomarkers in the Relationship between Type 2 Diabetes and Air Pollution

The incidence and prevalence of type 2 diabetes have increased in the last decades and are expected to further grow in the coming years. Chronic hyperglycemia triggers free radical generation and causes increased oxidative stress, affecting a number of molecular mechanisms and cellular pathways, including the generation of advanced glycation end products, proinflammatory and procoagulant effects, induction of apoptosis, vascular smooth-muscle cell proliferation, endothelial and mitochondrial dysfunction, reduction of nitric oxide release, and activation of protein kinase C. Among type 2 diabetes determinants, many data have documented the adverse effects of environmental factors (e.g., air pollutants) through multiple exposure-induced mechanisms (e.g., systemic inflammation and oxidative stress, hypercoagulability, and endothelial and immune responses). Therefore, here we discuss the role of air pollution in oxidative stress-related damage to glycemic metabolism homeostasis, with a particular focus on its impact on health. In this context, the improvement of new advanced tools (e.g., omic techniques and the study of epigenetic changes) may provide a substantial contribution, helping in the evaluation of the individual in his biological totality, and offer a comprehensive assessment of the molecular, clinical, environmental, and epidemiological aspects.

Hypertriglyceridemic Waist Phenotype and Lipid Accumulation Product: Two Comprehensive Obese Indicators of Waist Circumference and Triglyceride to Predict Type 2 Diabetes Mellitus in Chinese Population

PURPOSE

To determine whether hypertriglyceridemic waist (HTGW) and high lipid accumulation product (LAP) preceded the incidence of type 2 diabetes mellitus (T2DM), and to investigate the interactions of HTGW and LAP with other components of metabolic syndrome on the risk of T2DM.

METHODS

A total of 15,717 eligible participants without baseline T2DM and aged 35 and over were included from a Chinese rural cohort. Cox proportional hazards regression models were used to estimate the association of HTGW and LAP with the incidence of T2DM, and the restricted cubic spline model was used to evaluate the dose-response association.

RESULTS

Overall, 867 new T2DM cases were diagnosed after 7.77 years of follow-up. Participants with HTGW had a higher hazard ratio for T2DM (hazard ratio (HR): 6.249, 95% confidence interval (CI): 5.199-7.511) after adjustment for potential confounders. The risk of incident T2DM was increased with quartiles 3 and 4 versus quartile 1 of LAP, and the adjusted HRs (95% CIs) were 2.903 (2.226-3.784) and 6.298 (4.911-8.077), respectively. There were additive interactions of HTGW (synergy index (SI): 1.678, 95% CI: 1.358-2.072) and high LAP (SI: 1.701, 95% CI: 1.406-2.059) with increased fasting plasma glucose (FPG) on the risk of T2DM. Additionally, a nonlinear (P nonlinear < 0.001) dose-response association was found between LAP and T2DM.

CONCLUSION

The subjects with HTGW and high LAP were at high risk of developing T2DM, and the association between LAP and the risk of T2DM may be nonlinear. Our study further demonstrates additive interactions of HTGW and high LAP with increased FPG on the risk of T2DM.