Association between PM constituents and cardiometabolic risk factors: Exploring individual and combined effects, and mediating inflammation.

Chemosphere

Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100005, China. Electronic address:

Published: July 2024

AI Article Synopsis

  • The study explores how particulate matter (PM) and its components like sulfate and black carbon affect cardiometabolic risk factors in people living in air-polluted cities in China.
  • Using advanced statistical methods and machine learning models, researchers analyzed the relationship between long-term exposure to PM components and various health biomarkers, considering other influencing factors.
  • Findings indicate that certain PM constituents are linked to negative and positive changes in cardiometabolic health markers, with some indicators showing strong potential for identifying exposure risks.

Article Abstract

Background: The individual and combined effects of PM constituents on cardiometabolic risk factors are sparsely investigated. Besides, the key cardiometabolic risk factor that PM constituents targeted and the biological mechanisms remain unclear.

Method: A multistage, stratified cluster sampling survey was conducted in two typically air-polluted Chinese cities. The PM and its constituents including sulfate, nitrate, ammonium, organic matter, and black carbon were predicted using a machine learning model. Twenty biomarkers in three category were simultaneously adopted as cardiometabolic risk factors. We explored the individual and mixture association of long-term PM constituents with these markers using generalized additive model and quantile-based g-computation, respectively. To minimize potential confounding effects, we accounted for covariates including demographic, lifestyle, meteorological, temporal trends, and disease-related information. We further used ROC curve and mediation analysis to identify the key subclinical indicators and explore whether inflammatory mediators mediate such association, respectively.

Result: PM constituents was positively correlated with HOMA-B, TC, TG, LDL-C and LCI, and negatively correlated with PP and RC. Further, PM constituent mixture was positive associated with DBP, MAP, HbA1c, HOMA-B, AC, CRI-1 and CRI-2, and negative associated with PP and HDL-C. The ROC analysis further reveals that multiple cardiometabolic risk factors can collectively discriminate exposure to PM constituents (AUC>0.9), among which PP and CRI-2 as individual indicators exhibit better identifiable performance for nitrate and ammonium (AUC>0.75). We also found that multiple blood lipid indicators may be affected by PM and its constituents, possibly mediated through complement C3 or hsCRP.

Conclusion: Our study suggested associations of individual and combined PM constituents exposure with cardiometabolic risk factors. PP and CRI-2 were the targeted markers of long-term exposure to nitrate and ammonium. Inflammation may serve as a mediating factor between PM constituents and dyslipidemia, which enhance current understanding of potential pathways for PM-induced preclinical cardiovascular responses.

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Source
http://dx.doi.org/10.1016/j.chemosphere.2024.142251DOI Listing

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