背景:对PM2.5成分对心脏代谢危险因素的个体和综合影响的研究很少。此外,PM2.5成分靶向的关键心脏代谢危险因素和生物学机制仍不清楚.
方法:多级,在中国两个典型的空气污染城市进行了分层整群抽样调查。PM2.5及其成分包括硫酸盐,硝酸盐,铵,有机物,和黑碳是使用机器学习模型预测的。同时采用三类中的20种生物标志物作为心脏代谢危险因素。我们使用广义加性模型和基于分位数的g计算,探索了长期PM2.5成分与这些标记的个体和混合关联,分别。为了最小化潜在的混杂效应,我们考虑了协变量,包括人口统计学,生活方式,气象,时间趋势,和疾病相关信息。我们进一步使用ROC曲线和中介分析来确定关键的亚临床指标,并探讨炎症介质是否介导这种关联。分别。
结果:PM2.5成分与HOMA-B呈正相关,TC,TG,LDL-C和LCI,与PP和RC呈负相关。Further,PM2.5组成混合物与DBP呈正相关,MAP,HbA1c,HOMA-B,AC,CRI-1和CRI-2,与PP和HDL-C呈负相关。ROC分析进一步表明,多种心脏代谢危险因素可以共同区分PM2.5成分的暴露(AUC>0.9),其中PP和CRI-2作为单独指标对硝酸盐和铵表现出更好的可识别性能(AUC>0.75)。我们还发现,多种血脂指标可能受PM2.5及其成分的影响,可能通过补体C3或hsCRP介导。
结论:我们的研究表明,个体和联合的PM2.5成分暴露与心脏代谢危险因素有关。PP和CRI-2是长期暴露于硝酸盐和铵的目标标志物。炎症可能是PM2.5成分与血脂异常之间的中介因子,这增强了目前对PM2.5诱导的临床前心血管反应的潜在途径的理解。
BACKGROUND: The individual and combined effects of PM2.5 constituents on cardiometabolic risk factors are sparsely investigated. Besides, the key cardiometabolic risk factor that PM2.5 constituents targeted and the biological mechanisms remain unclear.
METHODS: A multistage, stratified cluster sampling survey was conducted in two typically air-polluted Chinese cities. The PM2.5 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 PM2.5 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.
RESULTS: PM2.5 constituents was positively correlated with HOMA-B, TC, TG, LDL-C and LCI, and negatively correlated with PP and RC. Further, PM2.5 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 PM2.5 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 PM2.5 and its constituents, possibly mediated through complement C3 or hsCRP.
CONCLUSIONS: Our study suggested associations of individual and combined PM2.5 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 PM2.5 constituents and dyslipidemia, which enhance current understanding of potential pathways for PM2.5-induced preclinical cardiovascular responses.