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

作　　者：黄伊琪 周家圳 邓耀棠 李国樑[2] 赵志强 欧嘉怡 何水蓉 李和成 李新华 陈平 刘莉莉 HUANG Yiqi;ZHOU Jiazhen;DENG Yaotang;LI Guoliang;ZHAO Zhiqiang;OU Jiayi;HE Shuirong;LI Hecheng;LI Xinhua;CHEN Ping;LIU Lili(School of Public Health,Guangzhou Medical University,Guangzhou,Guangdong 511436,China;Institute of Toxicology,Guangdong Province Hospital for Occupational Disease Prevention and Treatment,Guangzhou,Guangdong 510300,China;Affiliated Cancer Hospital and Institute of Guangzhou Medical University,Guangzhou,Guangdong 510095,China;Shaoguan Hospital for Occupational Disease Prevention and Treatment,Shaoguan,Guangdong 512026,China)

机构地区：[1]广州医科大学公共卫生学院,广东广州511436 [2]广东省职业病防治院毒理实验所,广东广州510300 [3]广州医科大学附属肿瘤医院,广东广州510095 [4]韶关市职业病防治院,广东韶关512026

出　　处：《环境与职业医学》2024年第7期735-743,共9页Journal of Environmental and Occupational Medicine

基　　金：国家自然科学基金项目:(81972990);广东省医学科研基金项目:(A2022102);广州市科技计划项目(202201011000)。

摘　　要：[背景]砷、钴、钡等单个金属暴露被证实与肾结石发病存在关联,然而目前关于金属混合暴露与肾结石发病的研究较少,尤其是在职业人群中金属混合暴露与肾结石发病的关系尚不明确。[目的]探讨冶炼厂职业人群中金属混合暴露与肾结石发病风险的关联性。[方法]通过问卷调查的形式收集2021年7月—2022年1月广东某冶炼厂1158名金属混合暴露工人的社会人口学特征、既往史、生活方式等信息。收集工人中段晨尿,以电感耦合等离子体质谱法测定工人尿锂、钒、铬、锰、钴、镍、铜、锌、砷、硒、锶、钼、镉、铯、钡、钨、铊、铅18种金属水平,以冷原子吸收光谱测定法测量尿汞水平。根据纳入标准最终纳入919名金属混合暴露的工人为研究对象,其中肾结石组117人,非肾结石组802人。以尿金属检出率大于80%为标准,最终纳入16种金属进行后续分析。使用参数或非参数方法比较非肾结石组和肾结石组的连续型变量或离散型变量之间的差异。通过构建logistic回归模型探讨单一金属暴露与肾结石发病的关联。采用加权分位数和(WQS)回归模型以评价金属混合暴露与肾结石发病的关联及各金属对肾结石发病影响的权重。随后采用贝叶斯核机器回归(BKMR)模型探讨金属混合暴露对肾结石发病的整体影响及各金属之间的交互作用。[结果]本研究发现非肾结石组工人和肾结石组在性别、年龄、工龄、体质量指数(BMI)差异有统计学意义(均P<0.05),肾结石组工人尿钼、尿钡水平高于非肾结石组,且差异有统计学意义(均P<0.05)。logistic回归分析结果显示,尿钴、尿砷、尿钼、尿钡与肾结石发病呈正相关(均P趋势<0.05)。WQS回归分析结果显示,钒、钴、砷、钼、钡混合暴露与肾结石发病存在正向关联(P<0.05),其中钼、砷和钡权重分别为0.391、0.337、0.154。BKMR结果显示金属混合暴露与肾结�[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 occupational 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,manganese,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 inte

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 

分 类 号：R13[医药卫生—劳动卫生]