Abstract
Limited evidence exists on the effect of submicronic particulate matter(PM1)on hypertension hospital-ization.Evidence based on causal inference and large cohorts is even more scarce.In 2015,36,271 par-ticipants were enrolled in South China and followed up through 2020.Each participant was assigned single-year,lag0-1,and lag0-2 moving average concentration of PM1 and fine inhalable particulate mat-ter(PM2.5)simulated based on satellite data at a 1-km resolution.We used an inverse probability weight-ing approach to balance confounders and utilized a marginal structural Cox model to evaluate the underlying causal links between PM1 exposure and hypertension hospitalization,with PM2.5-hypertension association for comparison.Several sensitivity studies and the analyses of effect modifica-tion were also conducted.We found that a higher hospitalization risk from both overall(HR:1.13,95％CI:1.05-1.22)and essential hypertension(HR:1.15,95％CI:1.06-1.25)was linked to each 1 pg/m3 increase in the yearly average PM1 concentration.At lag0-1 and lag0-2,we observed a 17％-21％higher risk of hypertension associated with PM1.The effect of PM1 was 6％-1 1％higher compared with PM2.5.Linear concentration-exposure associations between PM1 exposure and hypertension were identified,without safety thresholds.Women and participants that engaged in physical exercise exhibited higher suscepti-bility,with 4％-22％greater risk than their counterparts.This large cohort study identified a detrimental relationship between chronic PM1 exposure and hypertension hospitalization,which was more pro-nounced compared with PM2.5 and among certain groups.
基金项目
国家重点研发计划(2022YFC3600804)
国家自然科学基金(82204162)
国家自然科学基金(82204154)
Young Elite Scientist Sponsorship Program by China Association for Science and Technology(2023QNRC001)
Guangdong Provincial Pearl River Talents Program(0920220207)
Basic and Applied Basic Research Foundation of Guangdong Province(2022A1515010823)
广州市科技局项目(2023A04J2072)
Fundamental Research Funds for the Central Universities,Sun Yatsen University(23qnpy108)
NETL
NSTL
万方数据