Abstract
Little is known concerning the associations of fine particulate matter (PM(2.5)) and its constituents with atherosclerotic cardiovascular disease (ASCVD). A total of 31,162 participants enrolled from the Henan Rural Cohort were used to specify associations of PM(2.5) and its constituents with ASCVD. Hybrid machine learning was utilized to estimate the 3-year average concentration of PM(2.5) and its constituents (black carbon [BC], nitrate [NO(3)(-)], ammonium [NH(4)(+)], inorganic sulfate [SO(4)(2-)], organic matter [OM], and soil particles [SOIL]). Constituent concentration, proportion, and residual models were utilized to examine the associations of PM(2.5) constituents with 10-year ASCVD risk and to identify the most hazardous constituent. The isochronous substitution model (ISM) was employed to analyze the substitution effect between PM(2.5) constituents. We found that each 1 μg/m(3) increase in PM(2.5), BC, NH(4)(+), NO(3)(-), OM, SO(4)(2-), and SOIL was associated with a 3.5%, 49.3%, 19.4%, 10.5%, 21.4%, 14%, and 28.5% higher 10-year ASCVD risk, respectively (all p < 0.05). Comparable results were observed in proportion and residual models. The ISM found that replacing BC with other constituents will generate the greatest health benefits. The results indicated that long-term exposure to PM(2.5) and its constituents were associated with increased risks of ASCVD, with BC being the most attributable constituent.