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Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning methods
Air pollution exposure has been linked to various diseases, including dementia. However, a novel method for investigating the associations between air pollution exposure and disease is lacking. The objective of this study was to investigate whether long-term exposure to ambient particulate air pollu...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9556552/ https://www.ncbi.nlm.nih.gov/pubmed/36224306 http://dx.doi.org/10.1038/s41598-022-22100-8 |
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author | Yan, Yuan-Horng Chen, Ting-Bin Yang, Chun-Pai Tsai, I-Ju Yu, Hwa-Lung Wu, Yuh-Shen Huang, Winn-Jung Tseng, Shih-Ting Peng, Tzu-Yu Chou, Elizabeth P. |
author_facet | Yan, Yuan-Horng Chen, Ting-Bin Yang, Chun-Pai Tsai, I-Ju Yu, Hwa-Lung Wu, Yuh-Shen Huang, Winn-Jung Tseng, Shih-Ting Peng, Tzu-Yu Chou, Elizabeth P. |
author_sort | Yan, Yuan-Horng |
collection | PubMed |
description | Air pollution exposure has been linked to various diseases, including dementia. However, a novel method for investigating the associations between air pollution exposure and disease is lacking. The objective of this study was to investigate whether long-term exposure to ambient particulate air pollution increases dementia risk using both the traditional Cox model approach and a novel machine learning (ML) with random forest (RF) method. We used health data from a national population-based cohort in Taiwan from 2000 to 2017. We collected the following ambient air pollution data from the Taiwan Environmental Protection Administration (EPA): fine particulate matter (PM(2.5)) and gaseous pollutants, including sulfur dioxide (SO(2)), carbon monoxide (CO), ozone (O(3)), nitrogen oxide (NO(x)), nitric oxide (NO), and nitrogen dioxide (NO(2)). Spatiotemporal-estimated air quality data calculated based on a geostatistical approach, namely, the Bayesian maximum entropy method, were collected. Each subject's residential county and township were reviewed monthly and linked to air quality data based on the corresponding township and month of the year for each subject. The Cox model approach and the ML with RF method were used. Increasing the concentration of PM(2.5) by one interquartile range (IQR) increased the risk of dementia by approximately 5% (HR = 1.05 with 95% CI = 1.04–1.05). The comparison of the performance of the extended Cox model approach with the RF method showed that the prediction accuracy was approximately 0.7 by the RF method, but the AUC was lower than that of the Cox model approach. This national cohort study over an 18-year period provides supporting evidence that long-term particulate air pollution exposure is associated with increased dementia risk in Taiwan. The ML with RF method appears to be an acceptable approach for exploring associations between air pollutant exposure and disease. |
format | Online Article Text |
id | pubmed-9556552 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-95565522022-10-14 Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning methods Yan, Yuan-Horng Chen, Ting-Bin Yang, Chun-Pai Tsai, I-Ju Yu, Hwa-Lung Wu, Yuh-Shen Huang, Winn-Jung Tseng, Shih-Ting Peng, Tzu-Yu Chou, Elizabeth P. Sci Rep Article Air pollution exposure has been linked to various diseases, including dementia. However, a novel method for investigating the associations between air pollution exposure and disease is lacking. The objective of this study was to investigate whether long-term exposure to ambient particulate air pollution increases dementia risk using both the traditional Cox model approach and a novel machine learning (ML) with random forest (RF) method. We used health data from a national population-based cohort in Taiwan from 2000 to 2017. We collected the following ambient air pollution data from the Taiwan Environmental Protection Administration (EPA): fine particulate matter (PM(2.5)) and gaseous pollutants, including sulfur dioxide (SO(2)), carbon monoxide (CO), ozone (O(3)), nitrogen oxide (NO(x)), nitric oxide (NO), and nitrogen dioxide (NO(2)). Spatiotemporal-estimated air quality data calculated based on a geostatistical approach, namely, the Bayesian maximum entropy method, were collected. Each subject's residential county and township were reviewed monthly and linked to air quality data based on the corresponding township and month of the year for each subject. The Cox model approach and the ML with RF method were used. Increasing the concentration of PM(2.5) by one interquartile range (IQR) increased the risk of dementia by approximately 5% (HR = 1.05 with 95% CI = 1.04–1.05). The comparison of the performance of the extended Cox model approach with the RF method showed that the prediction accuracy was approximately 0.7 by the RF method, but the AUC was lower than that of the Cox model approach. This national cohort study over an 18-year period provides supporting evidence that long-term particulate air pollution exposure is associated with increased dementia risk in Taiwan. The ML with RF method appears to be an acceptable approach for exploring associations between air pollutant exposure and disease. Nature Publishing Group UK 2022-10-12 /pmc/articles/PMC9556552/ /pubmed/36224306 http://dx.doi.org/10.1038/s41598-022-22100-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Yan, Yuan-Horng Chen, Ting-Bin Yang, Chun-Pai Tsai, I-Ju Yu, Hwa-Lung Wu, Yuh-Shen Huang, Winn-Jung Tseng, Shih-Ting Peng, Tzu-Yu Chou, Elizabeth P. Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning methods |
title | Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning methods |
title_full | Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning methods |
title_fullStr | Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning methods |
title_full_unstemmed | Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning methods |
title_short | Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning methods |
title_sort | long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning methods |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9556552/ https://www.ncbi.nlm.nih.gov/pubmed/36224306 http://dx.doi.org/10.1038/s41598-022-22100-8 |
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