背景:关于细颗粒物(PM2.5)结合的重金属与肺功能之间的关联的报道很少。
目的:评估单一和混合PM2.5重金属与肺功能的关系。
方法:本研究包括武汉-珠海队列中224名中国成年人在两个研究期间的316个观察结果,并测量参与者的个人PM2.5结合重金属和肺功能。三种线性混合模型,包括单成分模型,PM2.5调整成分模型,和成分残差模型用于评估单一金属与肺功能之间的关联。混合暴露模型包括贝叶斯核机回归(BKMR)模型,加权分位数和(WQS)模型,和可解释的机器学习模型用于评估PM2.5结合的重金属混合物与肺功能之间的关系。
结果:在单次暴露分析中,PM2.5结合铅的显著负相关,锑,观察到峰值呼气流量(PEF)的镉。在混合暴露分析中,1s用力呼气量(FEV1)/用力肺活量(FVC)显著下降,最大呼气中流量(MMF),75%的肺容积(FEF75)的用力呼气流量与PM2.5结合的重金属混合物增加有关。BKMR模型表明PM2.5结合的铅和锑与肺功能呈负相关。此外,PM2.5结合铜与FEV1/FVC呈正相关,MMF,FEF75可解释的机器学习模型表明,FEV1/FVC,MMF,FEF75随着PM2.5结合铅的升高而降低,锰,和钒,并随着PM2.5结合铜的升高而增加。
结论:在PM2.5结合的重金属混合物与FEV1/FVC之间检测到负相关,MMF,以及FEF75。在PM2.5结合的重金属混合物中,PM2.5结合铅,锑,锰,钒与FEV1/FVC呈负相关,MMF,和FEF75,而PM2.5结合铜与FEV1/FVC呈正相关,MMF,FEF75
BACKGROUND: There are a few reports on the associations between fine particulate matter (PM2.5)-bound heavy metals and lung function.
OBJECTIVE: To evaluate the associations of single and mixed PM2.5-bound heavy metals with lung function.
METHODS: This study included 316 observations of 224 Chinese adults from the Wuhan-Zhuhai cohort over two study periods, and measured participants\' personal PM2.5-bound heavy metals and lung function. Three linear mixed models, including the single constituent model, the PM2.5-adjusted constituent model, and the constituent residual model were used to evaluate the association between single metal and lung function. Mixed exposure models including Bayesian kernel machine regression (BKMR) model, weighted quantile sum (WQS) model, and Explainable Machine Learning model were used to assess the relationship between PM2.5-bound heavy metal mixtures and lung function.
RESULTS: In the single exposure analyses, significant negative associations of PM2.5-bound lead, antimony, and cadmium with peak expiratory flow (PEF) were observed. In the mixed exposure analyses, significant decreases in forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC), maximal mid-expiratory flow (MMF), and forced expiratory flow at 75% of the pulmonary volume (FEF75) were associated with the increased PM2.5-bound heavy metal mixture. The BKMR models suggested negative associations of PM2.5-bound lead and antimony with lung function. In addition, PM2.5-bound copper was positively associated with FEV1/FVC, MMF, and FEF75. The Explainable Machine Learning models suggested that FEV1/FVC, MMF, and FEF75 decreased with the elevated PM2.5-bound lead, manganese, and vanadium, and increased with the elevated PM2.5-bound copper.
CONCLUSIONS: The negative relationships were detected between PM2.5-bound heavy metal mixture and FEV1/FVC, MMF, as well as FEF75. Among the PM2.5-bound heavy metal mixture, PM2.5-bound lead, antimony, manganese, and vanadium were negatively associated with FEV1/FVC, MMF, and FEF75, while PM2.5-bound copper was positively associated with FEV1/FVC, MMF, and FEF75.