| Title: | Long-term PM2.5 component exposure, genetic susceptibility, and myocardial infarction risk |
| Journal: | European Journal of Preventive Cardiology |
| Published: | 30 Jul 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40736025/ |
| DOI: | https://doi.org/10.1093/eurjpc/zwaf476 |
Publication 16075
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Abstract
AIMS: Associations between components of fine particulate matter (PM2.5) exposure and myocardial infarction (MI) risk remain unclear. The aim of this study was to investigate the connection between chronic PM2.5 components exposure and MI risk, as well as the impact of genetic susceptibility on these relationships.</p>
METHODS: This cohort study included 452,693 participants free of cardiovascular disease at baseline. Fine-Gray competing risk regression models were conducted to determine the links between PM2.5 components and MI risk, treating all-cause mortality as a competing event. Quantile-based g-Computation was performed to identify the major contributor of PM2.5 components. A polygenic risk score was developed to reflect genetic susceptibility to MI.</p>
RESULTS: During a median follow-up of 11.75 years, 9,327 participants have developed MI. Chronic PM2.5 components exposures were significantly associated with MI risk. The hazard ratios (95% confidence intervals) for each interquartile range increase were 1.171 (1.141, 1.202) for black carbon, 1.356 (1.332, 1.381) for ammonium, 1.073 (1.048, 1.100) for nitrate, and 1.232 (1.210, 1.254) for sulfate, respectively. Sulfate contributed most to the combined effects of PM2.5 components. Moreover, significant additive interactions and joint effects between genetic susceptibility and different PM2.5 components on MI risk were all observed.</p>
CONCLUSION: Chronic PM2.5 component exposure was linked to an increased MI risk and genetic susceptibility may modify these associations. Our study emphasizes the importance of implementing targeted pollution control strategies for different sources of PM2.5 components, as well as taking additional protective measures for populations at high genetic risk.</p>
11 Authors
- Ning Chen
- Yudiyang Ma
- Feipeng Cui
- Jianing Wang
- Linxi Tang
- Lei Zheng
- Meiqi Xing
- Xinru Zhao
- Jing Zhang
- Jian Yang
- Yaohua Tian
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