Toxics 2025, 13(7), 562; https://doi.org/10.3390/toxics13070562 (registering DOI) - 30 Jun 2025
Abstract
Transcriptomic profiling has shown that exposure to PM2.5, 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 PM2.5 exposure and
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Transcriptomic profiling has shown that exposure to PM2.5, 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 PM2.5 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 PM2.5 and to identify PM2.5-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 PM2.5. Logistic regression and decision tree algorithms were then utilized to construct predictive models for PM2.5 exposure based on these gene expression profiles. Our results indicated that the expression of five genes (FAM102B, PPP2R1B, OXR1, ITGAM, and PRP38B) increased with PM2.5 exposure in both cell-based assay and population-based cohort studies. Furthermore, the predictive models demonstrated high accuracy in classifying high-and-low PM2.5 exposure, potentially supporting the integration of gene biomarkers into public health practices.
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(This article belongs to the Section Air Pollution and Health)
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