Background Chronic obstructive pulmonary disease (COPD) is a leading global cause of mortality, with ambient fine particulate matter (PM 2.5 ) recognized as a key environmental risk factor. However, the circulating proteomic alterations that may underlie the association between long-term PM 2.5 exposure and incident COPD remain incompletely characterized. Methods We included 48,219 participants from the UK Biobank. Baseline plasma proteins were measured using the Olink Explore 3072 platform, and PM 2.5 concentrations were assigned using the ESCAPE land-use regression model. The PM 2.5 -protein associations were estimated using multivariable linear regression. An elastic net model was used to construct a PM 2.5 -related proteomic score. Cox proportional hazards models were then applied to evaluate the associations of PM 2.5 and PM 2.5 -related proteomic score with incident COPD. Statistical mediation, pathway enrichment, protein–protein interaction, and druggability analyses were further performed. Results Over a median follow-up of 12.6 years, 1955 participants developed COPD. We identified 1629 proteins associated with PM 2.5 exposure and 1185 proteins associated with incident COPD. Among proteins associated with both PM 2.5 exposure and incident COPD, 500 showed evidence consistent with statistical mediation in exploratory analyses. The largest estimated statistical mediation proportions were observed for PLAUR (24.7%), GDF15 (17.4%), and MMP12 (16.3%). Enrichment analyses highlighted inflammatory and stress-response pathways, and protein-protein interaction analysis identified IL1B, TGFB1, and CXCL8 as network hubs. Database screening and molecular docking further prioritized PLAUR, MMP12, and CXCL8 as potentially druggable candidate proteins for future experimental evaluation. Conclusion Long-term PM 2.5 exposure was associated with widespread plasma proteomic alterations, and a subset of proteins showed evidence consistent with statistical mediation of the PM 2.5 –COPD association. These systems-level findings provide pathway-level biological plausibility and prioritize candidate biomarkers and potentially druggable proteins for external replication, mechanistic validation, and future translational research.
Wang et al. (Mon,) studied this question.
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