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https://ir.cnu.edu.tw/handle/310902800/34562
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| 標題: | Long-term exposure to air pollutants and increased risk of chronic kidney disease in a community-based population using a fuzzy logic inference model |
| 作者: | Lin, Hsueh-Chun
Hung, Peir-Haur
Hsieh, Yun-Yu
Lai, Ting-Ju
Hsu, Hui-Tsung
Chung, Mu-Chi
Chung, Chi-Jung |
| 貢獻者: | China Medical University Taiwan
Chia-Yi Christian Hospital
Chia Nan University of Pharmacy & Science
China Medical University Taiwan
China Medical University Taiwan
Taichung Veterans General Hospital
China Medical University Taiwan
China Medical University Hospital - Taiwan |
| 關鍵字: | particulate matter exposure
renal-function
ambient pm2.5
pollution |
| 日期: | 2022 |
| 上傳時間: | 2023-12-11 13:58:01 (UTC+8) |
| 出版者: | OXFORD UNIV PRESS |
| 摘要: | Background Fuzzy inference systems (FISs) based on fuzzy theory in mathematics were previously applied to infer supplementary points for the limited number of monitoring sites and improve the uncertainty of spatial data. Therefore we adopted the FIS method to simulate spatiotemporal levels of air pollutants [particulate matter <2.5 mu m (PM2.5), sulfur dioxide (SO2) and (NO2)] and investigated the association of levels of air pollutants with the community-based prevalence of chronic kidney disease (CKD). Methods A Complex Health Screening program was launched during 2012-2013 and a total of 8284 community residents in Chiayi County, which is located in southwestern Taiwan, received a series of standard physical examinations, including measurement of estimated glomerular filtration rate (eGFR). CKD cases were defined as eGFR <60 mL/min/1.73 m(2) and were matched for age and gender in a 1:4 ratio of cases:controls. Data on air pollutants were collected from air quality monitoring stations during 2006-2016. The longitude, latitude and recruitment month of the individual case were entered into the trained FIS. The defuzzification process was performed based on the proper membership functions and fuzzy logic rules to infer the concentrations of air pollutants. In addition, we used conditional logistic regression and the distributed lag nonlinear model to calculate the prevalence ratios of CKD and the 95% confidence interval. Confounders including Framingham Risk Score (FRS), diabetes, gout, arthritis, heart disease, metabolic syndrome and vegetables consumption were adjusted in the models. Results Participants with a high FRS (>10%), diabetes, heart disease, gout, arthritis or metabolic syndrome had significantly increased CKD prevalence. After adjustment for confounders, PM2.5 levels were significantly increased in CKD cases in both single- and two-pollutant models (prevalence ratio 1.31-1.34). There was a positive association with CKD in the two-pollutant models for NO2. However, similar results were not observed for SO2. Conclusions FIS may be helpful to reduce uncertainty with better interpolation for limited monitoring stations. Meanwhile, long-term exposure to ambient PM2.5 appears to be associated with an increased prevalence of CKD, based on a FIS model. |
| 關聯: | CLINICAL KIDNEY JOURNAL, v.15, n.CB2, pp.CC2, pp.-137, |
| 顯示於類別: | [行政單位] 123
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