Associations of Fine Particulate Matter Components With Acute Aortic Dissection: A Time-Stratified Case-Crossover Study.

Authors

Min Lei, Renyue Ji, Shiqi Wang, Chunren Zhang, Qiang Tu, Yaping Zhang, Nianqiang Hu, Yijin Yu, Haisheng Wu

Published in

Journal of the American Heart Association. Pages e049278. Sep 09, 2026. Epub Sep 09, 2026.

Abstract

Acute aortic dissection (AAD) is among the most life-threatening cardiovascular emergencies. Ambient exposure to fine particulate matter (PM_2.5) has been linked with AAD, particularly in urban areas. Yet the associations of key PM_2.5 components with specific AAD subtypes remain insufficiently understood.
We used a time-stratified case-crossover design (2015-2023) in Hangzhou, China, investigating associations of major PM_2.5 components with AAD and its Stanford subtypes. Conditional logistic regression and weighted quantile sum models were applied to explore associations of single and joint exposure to PM_2.5 components with AAD, respectively. Stratified analyses based on age, sex, lifestyle factors, and existing comorbidities were performed.
At lag 0 to 1 days, black carbon, organic matter, and sulfate were significantly associated with AAD, especially type A, while positive but nonsignificant associations were observed for type B. An interquartile range increase in black carbon, organic matter, and sulfate was associated with a 42% (odds ratio, 1.42 [95% CI, 1.15-1.75]), 35% (odds ratio, 1.35 [95% CI, 1.10-1.64]), and 36% (odds ratio, 1.36 [95% CI, 1.09-1.69]) increased risk of type A AAD, respectively. The weighted quantile sum model revealed significant joint associations of coexposure to major PM_2.5 components with AAD and its type A, with black carbon contributing >50% to the joint associations. Age, smoking, alcohol consumption, and hypertension modified the PM_2.5 components-AAD associations.
Precise regulation of ambient PM_2.5 components, particularly black carbon, is necessary to mitigate AAD risk. Tailored clinical management is critical for susceptible populations during high-pollution periods.

PMID:
42714430
Bibliographic data and abstract were imported from PubMed on 09 Sep 2026.

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