Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, And State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.
The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310003, China.
Chemosphere. 2024 Sep;364:143084. doi: 10.1016/j.chemosphere.2024.143084. Epub 2024 Aug 13.
There are a few reports on the associations between fine particulate matter (PM)-bound heavy metals and lung function.
To evaluate the associations of single and mixed PM-bound heavy metals with lung function.
This study included 316 observations of 224 Chinese adults from the Wuhan-Zhuhai cohort over two study periods, and measured participants' personal PM-bound heavy metals and lung function. Three linear mixed models, including the single constituent model, the PM-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 PM-bound heavy metal mixtures and lung function.
In the single exposure analyses, significant negative associations of PM-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 PM-bound heavy metal mixture. The BKMR models suggested negative associations of PM-bound lead and antimony with lung function. In addition, PM-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 PM-bound lead, manganese, and vanadium, and increased with the elevated PM-bound copper.
The negative relationships were detected between PM-bound heavy metal mixture and FEV1/FVC, MMF, as well as FEF75. Among the PM-bound heavy metal mixture, PM-bound lead, antimony, manganese, and vanadium were negatively associated with FEV1/FVC, MMF, and FEF75, while PM-bound copper was positively associated with FEV1/FVC, MMF, and FEF75.
已有少量研究报告了细颗粒物（PM）中结合的重金属与肺功能之间的关联。
评估单一和混合 PM 结合重金属与肺功能的关联。
本研究纳入了武汉-珠海队列中 224 名中国成年人在两个研究期间的 316 个观测值，并测量了参与者的个人 PM 结合重金属和肺功能。使用三个线性混合模型，包括单一成分模型、PM 调整成分模型和成分残差模型，评估单一金属与肺功能之间的关系。使用混合暴露模型，包括贝叶斯核机器回归（BKMR）模型、加权分位数总和（WQS）模型和可解释机器学习模型，评估 PM 结合重金属混合物与肺功能之间的关系。
在单一暴露分析中，PM 结合的铅、锑和镉与呼气峰流速（PEF）呈显著负相关。在混合暴露分析中，用力肺活量（FEV1）/用力肺活量（FVC）、最大中期呼气流量（MMF）和 75%肺活量时的用力呼气流量（FEF75）的显著降低与 PM 结合的重金属混合物的增加有关。BKMR 模型表明 PM 结合的铅和锑与肺功能呈负相关。此外，PM 结合的铜与 FEV1/FVC、MMF 和 FEF75 呈正相关。可解释机器学习模型表明，随着 PM 结合的铅、锰和钒的升高，FEV1/FVC、MMF 和 FEF75 降低，而随着 PM 结合的铜的升高，FEV1/FVC、MMF 和 FEF75 升高。
PM 结合的重金属混合物与 FEV1/FVC、MMF 以及 FEF75 之间存在负相关关系。在 PM 结合的重金属混合物中，PM 结合的铅、锑、锰和钒与 FEV1/FVC、MMF 和 FEF75 呈负相关，而 PM 结合的铜与 FEV1/FVC、MMF 和 FEF75 呈正相关。
