Chia Nan University of Pharmacy & Science Institutional Repository:Item 310902800/34562
English  |  正體中文  |  简体中文  |  Items with full text/Total items : 18055/20253 (89%)
Visitors : 25174240      Online Users : 412
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item:

    Title: Long-term exposure to air pollutants and increased risk of chronic kidney disease in a community-based population using a fuzzy logic inference model
    Authors: Lin, Hsueh-Chun
    Hung, Peir-Haur
    Hsieh, Yun-Yu
    Lai, Ting-Ju
    Hsu, Hui-Tsung
    Chung, Mu-Chi
    Chung, Chi-Jung
    Contributors: China Medical University Taiwan
    Chia-Yi Christian Hospital
    Department of Applied Life Science and Health, Chia Nan University of Pharmacy & Science
    China Medical University Taiwan
    Taichung Veterans General Hospital
    China Medical University Taiwan
    China Medical University Hospital - Taiwan
    Keywords: particulate matter exposure
    ambient pm2.5
    Date: 2022
    Issue Date: 2023-12-11 13:58:01 (UTC+8)
    Publisher: OXFORD UNIV PRESS
    Abstract: 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.
    Relation: CLINICAL KIDNEY JOURNAL, v.15, Issue 10, pp.1872–1880
    Appears in Collections:[Dept. of Life and Health Science] Periodical Articles

    Files in This Item:

    File Description SizeFormat
    ckj-sfac114.pdf930KbAdobe PDF35View/Open

    All items in CNU IR are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback