{"title":"Complex metal interaction networks and the mediating role of biological aging in dyslipidemia","authors":"Guohuan Yin , Xingyu Chen , Meiduo Zhao , Jing Xu , Qun Xu","doi":"10.1016/j.envpol.2025.126047","DOIUrl":null,"url":null,"abstract":"<div><div>Metal mixture exposure is a major risk factor for dyslipidemia. Numerous studies have shown an association between metal mixture exposure, biological aging, and dyslipidemia. However, the interactions between metals, their directions, and the potential mechanisms through which they influence dyslipidemia remain unclear. This study utilized data from a repeated-measures cohort collected between 2016 and 2021, including 403 participants (1612 observations). Levels of metals, including chromium (Cr), cadmium (Cd), lead (Pb), and manganese (Mn), were measured in urine, along with four dyslipidemia biomarkers and their extended indicators. Generalized Estimating Equations (GEE) and Bayesian Kernel Machine Regression (BKMR) were used to analyze the effects of single and combined metal exposures on dyslipidemia. BKMR and the Synergy Index were employed to explore binary metal interactions and their directions. Marginal effects analysis assessed the impact of multiple metal interactions on dyslipidemia, and mediation analysis was conducted to explore the role of KDM.Accel in mediating the relationship between metal exposure and dyslipidemia. The findings indicated that both individual and combined exposures to Cr, Cd, Pb, and Mn significantly affected dyslipidemia. Multiple binary metal interactions exhibited synergistic effects on lipid outcomes. Pb∗Cd∗Cr and Pb∗Cd∗Mn showed an antagonistic effect on non-high-density lipoprotein cholesterol (NHC), while Cd∗Cr∗Mn∗Pb demonstrated synergistic effects on NHC. Additionally, KDM.Accel was identified as a key mediator in the relationship between Pb exposure and dyslipidemia, influencing the associations between Pb and HDL-C, LDL-C, and AC abnormalities. Mixed heavy metal exposure and their interactions are associated with dyslipidemia outcomes, with KDM.Accel playing a mediating role in the relationship between metals and dyslipidemia. This study highlights the potential interactions between metals and the mechanisms by which KDM.Accel may influence dyslipidemia, offering new insights into the connection between metal mixtures and dyslipidemia.</div></div>","PeriodicalId":311,"journal":{"name":"Environmental Pollution","volume":"372 ","pages":"Article 126047"},"PeriodicalIF":7.6000,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Pollution","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0269749125004208","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Metal mixture exposure is a major risk factor for dyslipidemia. Numerous studies have shown an association between metal mixture exposure, biological aging, and dyslipidemia. However, the interactions between metals, their directions, and the potential mechanisms through which they influence dyslipidemia remain unclear. This study utilized data from a repeated-measures cohort collected between 2016 and 2021, including 403 participants (1612 observations). Levels of metals, including chromium (Cr), cadmium (Cd), lead (Pb), and manganese (Mn), were measured in urine, along with four dyslipidemia biomarkers and their extended indicators. Generalized Estimating Equations (GEE) and Bayesian Kernel Machine Regression (BKMR) were used to analyze the effects of single and combined metal exposures on dyslipidemia. BKMR and the Synergy Index were employed to explore binary metal interactions and their directions. Marginal effects analysis assessed the impact of multiple metal interactions on dyslipidemia, and mediation analysis was conducted to explore the role of KDM.Accel in mediating the relationship between metal exposure and dyslipidemia. The findings indicated that both individual and combined exposures to Cr, Cd, Pb, and Mn significantly affected dyslipidemia. Multiple binary metal interactions exhibited synergistic effects on lipid outcomes. Pb∗Cd∗Cr and Pb∗Cd∗Mn showed an antagonistic effect on non-high-density lipoprotein cholesterol (NHC), while Cd∗Cr∗Mn∗Pb demonstrated synergistic effects on NHC. Additionally, KDM.Accel was identified as a key mediator in the relationship between Pb exposure and dyslipidemia, influencing the associations between Pb and HDL-C, LDL-C, and AC abnormalities. Mixed heavy metal exposure and their interactions are associated with dyslipidemia outcomes, with KDM.Accel playing a mediating role in the relationship between metals and dyslipidemia. This study highlights the potential interactions between metals and the mechanisms by which KDM.Accel may influence dyslipidemia, offering new insights into the connection between metal mixtures and dyslipidemia.
期刊介绍:
Environmental Pollution is an international peer-reviewed journal that publishes high-quality research papers and review articles covering all aspects of environmental pollution and its impacts on ecosystems and human health.
Subject areas include, but are not limited to:
• Sources and occurrences of pollutants that are clearly defined and measured in environmental compartments, food and food-related items, and human bodies;
• Interlinks between contaminant exposure and biological, ecological, and human health effects, including those of climate change;
• Contaminants of emerging concerns (including but not limited to antibiotic resistant microorganisms or genes, microplastics/nanoplastics, electronic wastes, light, and noise) and/or their biological, ecological, or human health effects;
• Laboratory and field studies on the remediation/mitigation of environmental pollution via new techniques and with clear links to biological, ecological, or human health effects;
• Modeling of pollution processes, patterns, or trends that is of clear environmental and/or human health interest;
• New techniques that measure and examine environmental occurrences, transport, behavior, and effects of pollutants within the environment or the laboratory, provided that they can be clearly used to address problems within regional or global environmental compartments.