Associations of long-term exposure to PM1, PM2.5, NO2 with type 2 diabetes mellitus prevalence and fasting blood glucose levels in Chinese rural populations. (December 2019)

• Record Type: Journal Article
• Authors: Liu, Feifei; Guo, Yuming; Liu, Yisi; Chen, Gongbo; Wang, Yuxin; Xue, Xiaowei; Liu, Suyang; Huo, Wenqian; Mao, Zhenxing; Hou, Yitan; Lu, Yuanan; Wang, Chongjian; Xiang, Hao; Li, Shanshan
• Is Part Of: Environment international. Volume 133(2019)Part B
• Publication Date: 2019-12
• Subjects: Air pollution -- Type 2 -- Prevalence -- Fasting blood glucose -- Rural health; Environmental protection -- Periodicals; Environmental health -- Periodicals; Environmental monitoring -- Periodicals; Environmental Monitoring -- Periodicals; Environnement -- Protection -- Périodiques; Hygiène du milieu -- Périodiques; Environnement -- Surveillance -- Périodiques; Environmental health; Environmental monitoring; Environmental protection; Periodicals; 333.705
• DOI: 10.1016/j.envint.2019.105213
• Languages: English
• ISSNs: 0160-4120

Abstract

Graphical abstract: Highlights: Higher PM1, PM2.5, NO2 exposure concentrations were associated with increased odds of type 2 diabetes. Higher levels of PM1, PM2.5, NO2 exposure were associated with an elevated fasting blood glucose levels. Males and populations aged 65 years or older may susceptible to air pollution. Abstract: Objectives: To evaluate the associations between long-term exposure to particulate matter with an aerodynamic diameter ≤1.0 μm and ≤2.5 μm (PM1 and PM2.5 ), nitrogen dioxide (NO2 ) and type 2 diabetes prevalence and fasting blood glucose levels in Chinese rural populations. Material and methods: A total of 39, 259 participants were enrolled in The Henan Rural Cohort study. Questionnaires and medical examination were conducted from July 2015 to September 2017 in rural areas of Henan province, China. Three-year average residential exposure levels of PM1, PM2.5, NO2 for each subject were estimated by a spatiotemporal model. Logistic regression and linear regression models were applied to estimate the associations between PM1, PM2.5, NO2 exposure and type 2 diabetes prevalence and fasting blood glucose levels. Results: The mean 3-year residential exposure concentrations of PM1, PM2.5 and NO2 was 57.4 μg/m 3, 73.4 μg/m 3 and 39.9 μg/m 3, respectively. Higher exposure concentrations of PM1, PM2.5, NO2 by 1 μg/m 3 was positively related to a 4.0% (95%CIs: 1.026, 1.054), 6.8% (1.052, 1.084) and 5.0% (1.039, 1.061) increase in odds of type 2 diabetes in the final adjusted models. Besides, a 1 μg/m 3 increase of PM1, PM2.5 and NO2 was related to a 0.020 mmol/L (95%CIs: 0.014, 0.026), 0.036 mmol/L (95%CIs: 0.030, 0.042) and 0.030 mmol/L (95%CIs: 0.026, 0.034) mmol/L higher fasting blood glucose levels. Conclusions: Higher exposure concentrations of air pollutants were positively related to the increased odds of type 2 diabetes, as well as higher fasting blood glucose levels in Chinese rural populations.