Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China; Key Laboratory of Coal Environmental Pathogenicity and Prevention (Shanxi Medical University), Ministry of Education, Taiyuan 030001, China.
Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China.
J Trace Elem Med Biol. 2024 Jul;84:127438. doi: 10.1016/j.jtemb.2024.127438. Epub 2024 Mar 19.
Occupation, environmental heavy metal exposure, and renal function impairment are closely related. The relationship between mixed metal exposure and chronic renal injury is inadequately described, and the interaction between each metal is poorly explored.
This cross-sectional study assessed mixed heavy metal exposure in the general population and their relationship with early renal impairment, as well as possible interactions between metals.
The study was conducted in two communities in Taiyuan City in northern China. Multiple linear regression, weighted quantile sum (WQS) and bayesian kernel machine regression (BKMR) regression were used to explore the relationship of mixed heavy metal exposure with indicators of early kidney injury (N-acetyl-β-D- glucosidase (UNAG), urinary albumin (UALB)). Meanwhile, BKMR was used to explore the possible interactions between mixed heavy metal and indicators of early kidney injury.
Based on the WQS regression results, we observed adjusted WQS coefficient β (β-WQS) of 0.711 (95% CI: 0.543, 0.879). Notably, this change was primarily driven by As (35.6%) and Cd (22.5%). In the UALB model, the adjusted β-WQS was 0.657 (95% CI: 0.567, 0.747), with Ni (30.5%), Mn (22.1%), Cd (21.2%), and As (18.6%) exhibiting higher weights in the overall effect. The BKMR results showed a negative interaction between As and other metals in the UNAG and UALB models, a positive interaction between Mn and Ni and other metals. No significant pairwise interaction was observed in the association of metals with indicators of early kidney injury.
Through multiple linear regression, WQS regression, and BKMR analyses, we found that exposure to mixed heavy metals such as Cd, Cr, Pb, Mn, As, Co and Ni was positively correlated with UNAG and UALB. Moreover, there are complex interactions between two or more heavy metals in more than one direction.
职业、环境重金属暴露和肾功能损害密切相关。混合金属暴露与慢性肾损伤的关系描述不足,各金属之间的相互作用研究甚少。
本横断面研究评估了一般人群中混合重金属暴露及其与早期肾功能损害的关系,并探讨了金属之间可能存在的相互作用。
该研究在中国北方太原市的两个社区进行。采用多元线性回归、加权总量得分(WQS)和贝叶斯核机器回归(BKMR)回归来探讨混合重金属暴露与早期肾损伤指标(N-乙酰-β-D-葡萄糖苷酶(UNAG)、尿白蛋白(UALB))之间的关系。同时,采用 BKMR 来探讨混合重金属与早期肾损伤指标之间可能存在的相互作用。
根据 WQS 回归结果,我们观察到调整后的 WQS 系数β(β-WQS)为 0.711(95%CI:0.543,0.879)。值得注意的是,这种变化主要由 As(35.6%)和 Cd(22.5%)驱动。在 UALB 模型中,调整后的β-WQS 为 0.657(95%CI:0.567,0.747),Ni(30.5%)、Mn(22.1%)、Cd(21.2%)和 As(18.6%)在总效应中权重较高。BKMR 结果显示,在 UNAG 和 UALB 模型中,As 与其他金属之间存在负相互作用,Mn 与 Ni 和其他金属之间存在正相互作用。在金属与早期肾损伤指标的关联中,未观察到金属之间有显著的两两相互作用。
通过多元线性回归、WQS 回归和 BKMR 分析,我们发现 Cd、Cr、Pb、Mn、As、Co 和 Ni 等混合重金属暴露与 UNAG 和 UALB 呈正相关。此外,两种或两种以上重金属之间存在复杂的相互作用,且方向不止一个。