环境与职业医学2024，Vol.41Issue(7)：735-743,9.DOI:10.11836/JEOM23442
金属冶炼厂工人尿金属水平与肾结石发病的关联
Association between urinary metal levels and kidney stones in metal smelter workers
摘要
Abstract
[Background]Arsenic,cobalt,barium,and other individual metal exposure have been confirmed to be associated with the incidence of kidney stones.However,there are few studies on the association between mixed metal exposure and kidney stones,especially in occu-pational groups. [Objective]To investigate the association between mixed metal exposure and kidney stones in an occupational population from a metal smelting plant. [Methods]A questionnaire survey was conducted to collect sociodemographic characteristics,medical history,and lifestyle information of 1158 mixed metal-exposed workers in a metal smelting plant in Guangdong Province from July 2021 to January 2022.Midstream morning urine samples were collected from the workers,the concentrations of 18 metals including lithium,vanadium,chromium,man-ganese,cobalt,nickel,copper,zinc,arsenic,selenium,strontium,molybdenum,cadmium,cesium,barium,tungsten,titanium,and lead were measured by inductively coupled plasma mass spectrometry,and the urinary mercury levels were measured by cold atomic absorption spectroscopy.Based on predetermined inclusion criteria,a total of 919 mixed metal-exposed workers were included in the study,including 117 workers in the kidney stone group and 802 workers in the non-kidney stone group.With a detection rate of urinary metals greater than 80%as entry criterion,16 eligible metals were finally included for further analysis.Parametric or non-parametric methods were used to compare the differences between continuous or categorical variables of the non-kidney stone group and the kidney stone group.Logistic regression models were constructed to explore the association between individual metal exposures and kidney stones.Weighted quantile sum(WQS)regression models were used to evaluate the association between mixed metal exposure and kidney stones,as well as the weights of each metal on kidney stones.Then Bayesian kernel machine regression(BKMR)models were used to explore the overall effect of mixed metal exposure on renal calculi and the potential interactions between metals. [Results]We found that there were significant differences in sex,age,length of service,and body mass Index(BMI)between the non-kidney stone group and the kidney stone group(P＜0.05).The urinary concentrations of molybdenum and barium in the kidney stone group were higher than those in the non-kidney stone group,and the differences were statistically significant(P＜0.05).The logistic regression models demonstrated that urinary cobalt,arsenic,molybdenum,and barium were positively correlated with the risk of kidney stones(Ptrend＜0.05).The WQS regression models showed that the mixed exposure to vanadium,cobalt,arsenic,molybdenum,and barium was positively associated with the risk of kidney stones(P＜0.05).Among them,molybdenum,arsenic,and barium accounted for 0.391,0.337,and 0.154,respectively.The BKMR results revealed a positive association between metal mixture exposure and the risk of kidney stones(P＜0.05).When other metals were fixed at the 25th,50th,or 75th percentile,arsenic,molybdenum,cobalt,and barium exhibited significant positive effects on the risk of kidney stones(P＜0.05),while vanadium showed a significant negative effect(P＜0.05).The interaction analysis demonstrated interactions between barium and cobalt,as well as between vanadium and cobalt(P＜0.05). [Conclusion]In the occupational population of this smelter,occupational mixed metal exposure could increase the risk of kidney stones,and the main metals are molybdenum,arsenic,barium,and cobalt.关键词
金属混合暴露/职业人群/肾结石/logistic回归模型/加权分位数和回归模型/贝叶斯核机器回归模型Key words
mixed metal exposure/occupational population/kidney stone/logistic regression model/weighted quantile sum regression model/Bayesian kernel machine regression model分类
医药卫生引用本文复制引用
黄伊琪,陈平,刘莉莉,周家圳,邓耀棠,李国樑,赵志强,欧嘉怡,何水蓉,李和成,李新华..金属冶炼厂工人尿金属水平与肾结石发病的关联[J].环境与职业医学,2024,41(7):735-743,9.基金项目
国家自然科学基金项目(81972990) （81972990）
广东省医学科研基金项目(A2022102) （A2022102）
广州市科技计划项目(202201011000) （202201011000）
