Article Publish Status: FREE
Abstract Title:

Risk Analysis of Air Pollution and Meteorological Factors Affecting the Incidence of Diabetes in the Elderly Population in Northern China.

Abstract Source:

J Diabetes Res. 2020 ;2020:3673980. Epub 2020 Oct 20. PMID: 33134393

Abstract Author(s):

Yao Lin, Saijun Zhou, Hongyan Liu, Zhuang Cui, Fang Hou, Siyuan Feng, Yourui Zhang, Hao Liu, Chunlan Lu, Pei Yu

Article Affiliation:

Yao Lin


Background: Research investigating the effect of air pollution on diabetes incidence is mostly conducted in Europe and the United States and often produces conflicting results. The link between meteorological factors and diabetes incidence remains to be explored. We aimed to explore associations between air pollution and diabetes incidence and to estimate the nonlinear and lag effects of meteorological factors on diabetes incidence.

Methods: Our study included 19,000 people aged≥60 years from the Binhai New District without diabetes at baseline. The generalized additive model (GAM) and the distributed lag nonlinear model (DLNM) were used to explore the effect of air pollutants and meteorological factors on the incidence of diabetes. In the model combining the GAM and DLNM, the impact of each factor (delayed by 30 days) was first observed separately to select statistically significant factors, which were then incorporated into the final multivariate model. The association between air pollution and the incidence of diabetes was assessed in subgroups based on age, sex, and body mass index (BMI).

Results: We found that cumulative RRs for diabetes incidence were 1.026 (1.011-1.040), 1.019 (1.012-1.026), and 1.051 (1.019-1.083) per 10 g/mincrease in PM, PM, and NO, respectively, as well as 1.156 (1.058-1.264) per 1 mg/mincrease in CO in a single-pollutant model. Increased temperature, excessive humidity or dryness, and shortened sunshine duration were positively correlated with the incidence of diabetes in single-factor models. After adjusting for temperature, humidity, and sunshine, the risk of diabetes increased by 9.2% (95% confidence interval (CI):2.1%-16.8%) per 10 g/mincrease in PM. We also found that women, the elderly (≥75 years), and obese subjects were more susceptible to the effect of PM.

Conclusion: Our data suggest that PMis positively correlated with the incidence of diabetes in the elderly, and the relationship between various meteorological factors and diabetes in the elderly is nonlinear.

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Sayer Ji
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