Yu Yunjiang, Meng Wenjie, Zhu Xiaohui, Li Zongrui, Zheng Tong, He Ping, Yu Ying, Dong Chenyin, Li Zhenchi, Kuang Hongxuan, Xiang Mingdeng, Qin Xiaodi, Zhou Yang
State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, PR China.
State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, PR China; Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, PR China.
Ecotoxicol Environ Saf. 2025 Jul 15;302:118695. doi: 10.1016/j.ecoenv.2025.118695.
Emerging evidence suggests that exposure to metals is associated with dyslipidemia; however, very little data is available on metal exposure in hair, and data regarding their joint and interactive effects for different metals on dyslipidemia are still sparse.
Between 2020 and 2021, a cross-sectional study was conducted among 407 adults across eight provinces in China. Hair samples were analyzed for concentrations of 13 metals: arsenic (As), calcium (Ca), cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), magnesium (Mg), manganese (Mn), molybdenum (Mo), nickel (Ni), lead (Pb), selenium (Se), and zinc (Zn)-using inductively coupled plasma mass spectrometry (ICP-MS). Blood lipid biomarkers, including total cholesterol (CHOL), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C), were measured using an automated clinical chemistry analyzer. Multivariable linear and logistic regression models were used to examine associations between individual hair metal levels and lipid profiles or dyslipidemia status. Bayesian Kernel Machine Regression (BKMR) models were applied to assess the combined and interactive effects of metal mixtures on dyslipidemia.
We observed hair concentrations of Mo and Pb were positively associated with CHOL levels, while Ca and Cd were inversely associated with LDL-C. Hair Cd was also negatively associated with HDL-C. Logistic regression models showed that elevated Mo and Pb levels were significantly associated with higher odds of dyslipidemia [Mo: OR = 1.58, 95 % CI: 1.20-2.09; Pb: OR = 1.25, 95 % CI: 1.04-1.51] in single-metal models. In BKMR models, Mo and Pb showed the strongest and most consistent associations across exposure percentiles. The risk of dyslipidemia increased steadily with higher metal mixture exposure. Interactive analyses suggested a potential antagonistic effect between Cd and Pb, and a synergistic effect between Mo and Pb, where their joint exposures amplified the risk of dyslipidemia beyond their individual effects.
This study revealed a positive association between hair metal mixtures, predominantly driven by Pb, and the risk of dyslipidemia, while also revealing an inverse Pb-Cd interaction effect on dyslipidemia.
新出现的证据表明，接触金属与血脂异常有关；然而，关于头发中金属暴露的数据非常少，而且关于不同金属对血脂异常的联合和交互作用的数据仍然很少。
2020年至2021年期间，在中国八个省份的407名成年人中进行了一项横断面研究。使用电感耦合等离子体质谱法（ICP-MS）分析头发样本中13种金属的浓度：砷（As）、钙（Ca）、镉（Cd）、铬（Cr）、铜（Cu）、铁（Fe）、镁（Mg）、锰（Mn）、钼（Mo）、镍（Ni）、铅（Pb）、硒（Se）和锌（Zn）。使用自动临床化学分析仪测量血脂生物标志物，包括总胆固醇（CHOL）、甘油三酯（TG）、高密度脂蛋白胆固醇（HDL-C）和低密度脂蛋白胆固醇（LDL-C）。多变量线性和逻辑回归模型用于检验个体头发金属水平与血脂谱或血脂异常状态之间的关联。应用贝叶斯核机器回归（BKMR）模型评估金属混合物对血脂异常的联合和交互作用。
我们观察到头发中钼和铅的浓度与总胆固醇水平呈正相关，而钙和镉与低密度脂蛋白胆固醇呈负相关。头发中的镉也与高密度脂蛋白胆固醇呈负相关。逻辑回归模型显示，在单金属模型中，钼和铅水平升高与血脂异常几率显著增加相关[钼：比值比（OR）=1.58，95%置信区间（CI）：1.20-2.09；铅：OR=1.25，95%CI：1.04-1.51]。在BKMR模型中，钼和铅在各暴露百分位数中显示出最强且最一致的关联。随着金属混合物暴露量增加，血脂异常风险稳步上升。交互分析表明镉和铅之间存在潜在的拮抗作用，钼和铅之间存在协同作用，它们的联合暴露使血脂异常风险超出了各自单独作用的范围。
本研究揭示了以铅为主导的头发金属混合物与血脂异常风险之间存在正相关，同时也揭示了铅-镉对血脂异常的反向交互作用。
