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Associations of long-term exposure to PM(1), PM(2.5), NO(2) with type 2 diabetes mellitus prevalence and fasting blood glucose levels in Chinese rural populations

OBJECTIVES: To evaluate the associations between long-term exposure to particulate matter with an aerodynamic diameter ≤1.0 μm and ≤2.5 μm (PM(1) and PM(2.5)), nitrogen dioxide (NO(2)) and type 2 diabetes prevalence and fasting blood glucose levels in Chinese rural populations. MATERIAL AND METHODS:...

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Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6853163/
https://www.ncbi.nlm.nih.gov/pubmed/31654916
http://dx.doi.org/10.1016/j.envint.2019.105213
Descripción
Sumario:OBJECTIVES: To evaluate the associations between long-term exposure to particulate matter with an aerodynamic diameter ≤1.0 μm and ≤2.5 μm (PM(1) and PM(2.5)), nitrogen dioxide (NO(2)) 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 PM(1), PM(2.5), NO(2) for each subject were estimated by a spatiotemporal model. Logistic regression and linear regression models were applied to estimate the associations between PM(1), PM(2.5), NO(2) exposure and type 2 diabetes prevalence and fasting blood glucose levels. RESULTS: The mean 3-year residential exposure concentrations of PM(1), PM(2.5) and NO(2) was 57.4 μg/m(3), 73.4 μg/m(3) and 39.9 μg/m(3), respectively. Higher exposure concentrations of PM(1), PM(2.5), NO(2) 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 PM(1), PM(2.5) and NO(2) 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.