Department of Environmental Science, Baylor University, Waco, TX, USA.
Department of Preventive Medicine, Dankook University College of Medicine, Cheonan, Republic of Korea.
Environ Sci Pollut Res Int. 2024 May;31(24):35938-35951. doi: 10.1007/s11356-024-33567-5. Epub 2024 May 14.
This study aimed to develop an environmental risk score (ERS) of multiple pollutants (MP) causing kidney damage (KD) in Korean residents near abandoned metal mines or smelters and evaluate the association between ERS and KD by a history of occupational chemical exposure (OCE). Exposure to MP, consisting of nine metals, four polycyclic aromatic hydrocarbons, and four volatile organic compounds, was measured as urinary metabolites. The study participants were recruited from the Forensic Research via Omics Markers (FROM) study (n = 256). Beta-2-microglobulin (β2-MG), N-acetyl-β-D-glucosaminidase (NAG), and estimated glomerular filtration rate (eGFR) were used as biomarkers of KD. Bayesian kernel machine regression (BKMR) was selected as the optimal ERS model with the best performance and stability of the predicted effect size among the elastic net, adaptive elastic net, weighted quantile sum regression, BKMR, Bayesian additive regression tree, and super learner model. Variable importance was estimated to evaluate the effects of metabolites on KD. When stratified with the history of OCE after adjusting for several confounding factors, the risks for KD were higher in the OCE group than those in the non-OCE group; the odds ratio (OR; 95% CI) for ERS in non-OCE and OCE groups were 2.97 (2.19, 4.02) and 6.43 (2.85, 14.5) for β2-MG, 1.37 (1.01, 1.86) and 4.16 (1.85, 9.39) for NAG, and 4.57 (3.37, 6.19) and 6.44 (2.85, 14.5) for eGFR, respectively. We found that the ERS stratified history of OCE was the most suitable for evaluating the association between MP and KD, and the risks were higher in the OCE group than those in the non-OCE group.
本研究旨在开发一种由多种污染物（MP）引起的肾脏损伤（KD）的环境风险评分（ERS），该污染物存在于韩国废弃金属矿山或冶炼厂附近的居民环境中，并通过职业性化学暴露（OCE）的历史来评估 ERS 与 KD 之间的关联。MP 的暴露通过测量尿液代谢物来评估，MP 由九种金属、四种多环芳烃和四种挥发性有机化合物组成。研究参与者是从法医研究通过组学标志物（FROM）研究（n=256）中招募的。β2-微球蛋白（β2-MG）、N-乙酰-β-D-氨基葡萄糖苷酶（NAG）和估算肾小球滤过率（eGFR）被用作 KD 的生物标志物。贝叶斯核机器回归（BKMR）被选为最佳 ERS 模型，在弹性网络、自适应弹性网络、加权分位数总和回归、BKMR、贝叶斯加性回归树和超级学习者模型中，该模型具有最佳的预测效果大小的性能和稳定性。通过估计变量的重要性来评估代谢物对 KD 的影响。当按 OCE 历史分层并调整几个混杂因素后，与非 OCE 组相比，OCE 组的 KD 风险更高；非 OCE 和 OCE 组 ERS 的比值比（OR；95%CI）分别为 2.97（2.19，4.02）和 6.43（2.85，14.5），β2-MG 为 1.37（1.01，1.86）和 4.16（1.85，9.39），NAG 为 4.57（3.37，6.19）和 6.44（2.85，14.5），eGFR 为 4.57（3.37，6.19）和 6.44（2.85，14.5）。我们发现，分层 OCE 历史的 ERS 最适合评估 MP 与 KD 之间的关联，并且 OCE 组的风险高于非 OCE 组。
