Population Cohort-Validated PM<sub>2.5</sub>-Induced Gene Signatures: A Machine Learning Approach to Individual Exposure Prediction

Population Cohort-Validated PM<sub>2.5</sub>-Induced Gene Signatures: A Machine Learning Approach to Individual Exposure Prediction

Transcriptomic profiling has shown that exposure to PM<sub>2.5</sub>, a common air pollutant, can modulate gene expression, which has been linked to negative health effects and diseases. However, there are few population-based cohort studies on the association between PM<sub>2.5<...

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Bibliographic Details
Main Authors: Yu-Chung Wei, Wen-Chi Cheng, Pinpin Lin, Zhi-Yao Zhang, Chi-Hsien Chen, Chih-Da Wu, Yue Leon Guo, Hung-Jung Wang
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Toxics
Subjects:
PM<sub>2.5</sub>
transcriptomic profiling
biomarker
machine learning
predictive model
Online Access:https://www.mdpi.com/2305-6304/13/7/562
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Summary:Transcriptomic profiling has shown that exposure to PM<sub>2.5</sub>, a common air pollutant, can modulate gene expression, which has been linked to negative health effects and diseases. However, there are few population-based cohort studies on the association between PM<sub>2.5</sub> exposure and specific gene set expression. In this study, we used an unbiased transcriptomic profiling approach to examine gene expression in a mouse model exposed to PM<sub>2.5</sub> and to identify PM<sub>2.5</sub>-responsive genes. The gene expressions were further validated in both the human cell lines and a population-based cohort study. Two cohorts of healthy older adults (aged ≥ 65 years) were recruited from regions characterized by differing levels of PM<sub>2.5</sub>. Logistic regression and decision tree algorithms were then utilized to construct predictive models for PM<sub>2.5</sub> exposure based on these gene expression profiles. Our results indicated that the expression of five genes (<i>FAM102B</i>, <i>PPP2R1B</i>, <i>OXR1</i>, <i>ITGAM</i>, and <i>PRP38B)</i> increased with PM<sub>2.5</sub> exposure in both cell-based assay and population-based cohort studies. Furthermore, the predictive models demonstrated high accuracy in classifying high-and-low PM<sub>2.5</sub> exposure, potentially supporting the integration of gene biomarkers into public health practices.
ISSN:2305-6304

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