摘要
目的探究铅、镉、砷、汞4种重金属联合接触对职业人群早期肾损伤的影响。方法采用随机数表方法,选择广东省某有色金属冶炼厂384名重金属联合接触工人为研究对象。以电感耦合等离子体质谱法测定血铅、尿镉和尿砷水平,以冷原子吸收光谱测定法(酸性氯化亚锡还原法)测定尿汞水平;以酶联免疫吸附试验法检测尿中β_(2)-微球蛋白(β_(2)-MG)、肾损伤分子-1(Kim-1)和中性粒细胞明胶酶相关脂质运载蛋白(NGAL)的水平。采用Spearman相关分析、线性回归、加权分位数和(WQS)回归和贝叶斯核机器回归(BKMR)模型探究上述4种重金属联合接触与早期肾损伤标志物的关联。结果研究对象血铅、尿镉、尿砷、尿汞水平的中位数分别为0.47μmol/L和4.450、27.790和0.520μg/gCr,尿中β_(2)-MG、Kim-1、NGAL水平中位数分别为62.960、1.130和18.150μg/gCr。Spearman相关分析结果显示,尿中β_(2)-MG、Kim-1、NGAL水平与血铅、尿汞水平均呈弱相关(P值均<0.01),但与尿镉、尿砷水平均不相关(P值均>0.05)。多重线性回归分析结果显示,尿汞与尿中β_(2)-MG、Kim-1、NGAL水平均呈正向关联(P值均<0.01),尿砷与尿β_(2)-MG水平呈正向关联(P<0.01),血铅与尿中β_(2)-MG、Kim-1均存在负向关联(P值均<0.05)。WQS回归分析结果显示,4种重金属的联合效应与尿中β_(2)-MG、Kim-1、NGAL水平均存在正向关联(P值均<0.01);其中,影响权重最高的均为汞,权重最低的均为铅。BKMR模型分析结果显示,尿中β_(2)-MG、Kim-1和NGAL水平均有随4种重金属联合接触程度增加而上升的趋势;当尿汞水平从第25百分位数增加至第75百分位数且其他金属相应固定于某水平时,尿中β_(2)-MG、Kim-1和NGAL水平均随之降低;当其他金属内暴露水平保持在相应中位数的水平时,尿中β_(2)-MG、Kim-1、NGAL水平均与尿砷水平呈正向关联,但与其余3种重金属之间均未观察到明显的线性剂量-反应
Objective To investigate the effect of combined exposure to four heavy metals(lead,cadmium,arsenic,mercury) on early kidney injury in occupational population.Methods A total of 384 workers exposed to combined heavy metals in a nonferrous metal smelting plant in Guangdong Province were selected as the research subjects using judgment sampling method.The levels of blood lead,urinary cadmium and urinary arsenic were detacted by inductively coupled plasma mass spectrometry,while urinary mercury levels were measured using cold atomic absorption spectroscopy(acidic tin chloride reduction method).The levels of biomarkers such as urinary β_(2)-microglobulin(β_(2)-MG),kidney injury molecule-1(Kim-1) and neutrophil gelatinase-associated lipocalin(NGAL) were detected by enzyme-linked immunosorbent assay.Spearman correlation analysis,linear regression,weighted quantile sum(WQS) regression and Bayesian kernel machine regression(BKMR) models were used to analyze the association between the exposure to the four heavy metals and early kidney injury biomarkers.Results The median of blood lead,urinary cadmium,urinary arsenic and urinary mercury were 0.47 μmol/L and 4.450,27.790 and 0.520 μg/gCr,respectively.The median of urinary β_(2)-MG,Kim-1 and NGAL were 62.960,1.130 and 18.150 μg/gCr,respectively.Spearman correlation analysis showed that urinary levels of β_(2)-MG,Kim-1,and NGAL were weakly correlated with blood lead and urinary mercury levels(all P>0.01),but not correlated with urinary cadmium and urinary arsenic(all P<0.05).The results of multiple linear regression analysis showed that urinary mercury was positively correlated with urinary β_(2)-MG,Kim-1and NGAL(all P<0.01),urinary arsenic was positively correlated with urinary β_(2)-MG level(P<0.01),and blood lead was negatively correlated with urinary β_(2)-MG and Kim-1(all P<0.05).The WQS regression analysis showed that the combined effect of the four heavy metals was positively correlated with urinary β_(2)-MG,Kim-1 and NGAL(all P<0.01),with mercury having the
作者
欧嘉怡
邓耀棠
周家圳
张伟鹏
陈星宇
李新华
陈平
刘莉莉
OU Jiayi;DENG Yaotang;ZHOU Jiazhen;ZHANG Weipeng;CHEN Xingyu;LI Xinhua;CHEN Ping;LIU Lili(School of Public Health,Guangzhou Medical University,Guangdong 511436,China;不详)
出处
《中国职业医学》
CAS
北大核心
2023年第1期23-30,共8页
China Occupational Medicine
基金
国家自然科学基金(81972990)
国家临床重点专科建设项目(2011-09)
广东省医学科学技术研究基金(A2021421,A2022276)
广州市科技计划基础与应用基础研究项目(202201010887)
广东省职业健康工程技术研究中心(D:2019A069)。
关键词
职业人群
重金属
铅
镉
砷
汞
肾损伤
生物标志物
Occupational population
Heavy metal
Lead
Cadmium
Arsenic
Mercury
Kidney injury
Biomarkers
