TY - JOUR
T1 - Impact of lifetime air pollution exposure patterns on the risk of chronic disease
AU - Tsai, Cheng Yu
AU - Su, Chien Ling
AU - Wang, Yuan Hung
AU - Wu, Sheng Ming
AU - Liu, Wen Te
AU - Hsu, Wen Hua
AU - Majumdar, Arnab
AU - Stettler, Marc
AU - Chen, Kuan Yuan
AU - Lee, Ya Ting
AU - Hu, Chaur Jong
AU - Lee, Kang Yun
AU - Tsuang, Ben Jei
AU - Tseng, Chien Hua
N1 - Funding Information:
This work was supported by the Ministry of Science and Technology of Taiwan (MOST; grant numbers 110-2314-B-038-146-MY3 and 110–2634–F- 002–049 ), National Science and Technology Council of Taiwan (NSTC ; grant number 111-2634-F-002-021 ),and the Featured Research Program “Establishment of Tucheng Health Care Cohort” of Shuang Ho Hospital, Taipei Medical University (grant numbers 107FRP-02 , 108FRP-01 , 109FRP-01 , 110FRP-01 ). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2023 The Authors
PY - 2023/7
Y1 - 2023/7
N2 - Long-term exposure to air pollution can lead to cardiovascular disease, metabolic syndrome, and chronic respiratory disease. However, from a lifetime perspective, the critical period of air pollution exposure in terms of health risk is unknown. This study aimed to evaluate the impact of air pollution exposure at different life stages. The study participants were recruited from community centers in Northern Taiwan between October 2018 and April 2021. Their annual averages for fine particulate matter (PM2.5) exposure were derived from a national visibility database. Lifetime PM2.5 exposures were determined using residential address information and were separated into three stages (<20, 20–40, and >40 years). We employed exponentially weighted moving averages, applying different weights to the aforementioned life stages to simulate various weighting distribution patterns. Regression models were implemented to examine associations between weighting distributions and disease risk. We applied a random forest model to compare the relative importance of the three exposure life stages. We also compared model performance by evaluating the accuracy and F1 scores (the harmonic mean of precision and recall) of late-stage (>40 years) and lifetime exposure models. Models with 89% weighting on late-stage exposure showed significant associations between PM2.5 exposure and metabolic syndrome, hypertension, diabetes, and cardiovascular disease, but not gout or osteoarthritis. Lifetime exposure models showed higher precision, accuracy, and F1 scores for metabolic syndrome, hypertension, diabetes, and cardiovascular disease, whereas late-stage models showed lower performance metrics for these outcomes. We conclude that exposure to high-level PM2.5 after 40 years of age may increase the risk of metabolic syndrome, hypertension, diabetes, and cardiovascular disease. However, models considering lifetime exposure showed higher precision, accuracy, and F1 scores and lower equal error rates than models incorporating only late-stage exposures. Future studies regarding long-term air pollution modelling are required considering lifelong exposure pattern. .1
AB - Long-term exposure to air pollution can lead to cardiovascular disease, metabolic syndrome, and chronic respiratory disease. However, from a lifetime perspective, the critical period of air pollution exposure in terms of health risk is unknown. This study aimed to evaluate the impact of air pollution exposure at different life stages. The study participants were recruited from community centers in Northern Taiwan between October 2018 and April 2021. Their annual averages for fine particulate matter (PM2.5) exposure were derived from a national visibility database. Lifetime PM2.5 exposures were determined using residential address information and were separated into three stages (<20, 20–40, and >40 years). We employed exponentially weighted moving averages, applying different weights to the aforementioned life stages to simulate various weighting distribution patterns. Regression models were implemented to examine associations between weighting distributions and disease risk. We applied a random forest model to compare the relative importance of the three exposure life stages. We also compared model performance by evaluating the accuracy and F1 scores (the harmonic mean of precision and recall) of late-stage (>40 years) and lifetime exposure models. Models with 89% weighting on late-stage exposure showed significant associations between PM2.5 exposure and metabolic syndrome, hypertension, diabetes, and cardiovascular disease, but not gout or osteoarthritis. Lifetime exposure models showed higher precision, accuracy, and F1 scores for metabolic syndrome, hypertension, diabetes, and cardiovascular disease, whereas late-stage models showed lower performance metrics for these outcomes. We conclude that exposure to high-level PM2.5 after 40 years of age may increase the risk of metabolic syndrome, hypertension, diabetes, and cardiovascular disease. However, models considering lifetime exposure showed higher precision, accuracy, and F1 scores and lower equal error rates than models incorporating only late-stage exposures. Future studies regarding long-term air pollution modelling are required considering lifelong exposure pattern. .1
KW - Air pollution
KW - Cardiovascular disease
KW - Fine particulate matter
KW - Metabolic syndrome
KW - Respiratory disease
UR - http://www.scopus.com/inward/record.url?scp=85153037115&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85153037115&partnerID=8YFLogxK
U2 - 10.1016/j.envres.2023.115957
DO - 10.1016/j.envres.2023.115957
M3 - Article
AN - SCOPUS:85153037115
SN - 0013-9351
VL - 229
JO - Environmental Research
JF - Environmental Research
M1 - 115957
ER -