ORIGINAL RESEARCH article
Front. Nutr.
Sec. Nutrition and Metabolism
Volume 12 - 2025 | doi: 10.3389/fnut.2025.1613721
Association Between Urinary Heavy Metals and Cardiovascular-Kidney-Metabolic Syndrome: Mediating Roles of TyG, WWI, and eGFR
Provisionally accepted
- Yancheng Third People's Hospital, Yancheng, China
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Background: Cardiovascular-kidney-metabolic (CKM) syndrome is a multistage disorder with significant global health and socioeconomic impact. Environmental factors are increasingly recognized as key risk factors in disease development. This study aims to assess the relationship between urinary heavy metals and CKM risk, as well as the mediating roles of triglyceride-glucose index (TyG), weight-adjusted waist index (WWI), and estimated glomerular filtration rate (eGFR).Methods: This cross-sectional analysis uses data from the National Health and Nutrition Examination Survey (NHANES). CKM stages 0-2 are classified as non-advanced, and stages 3-4 as advanced. Multivariable logistic regression, restricted cubic splines (RCS), weighted quantile sum (WQS) regression, quantile-based g computation (Qgcomp) regression, and Bayesian kernel machine regression (BKMR) were applied to explore associations between metal exposure and advanced CKM. Mediation analysis examined the roles of TyG, WWI, and eGFR in heavy metal-induced advanced CKM.Results: Among 5,221 participants, 4,340 were non-advanced and 881 had advanced CKM. Both individual and mixed heavy metal exposures were positively linked to advanced CKM risk. Cobalt (Co) was identified as a primary contributor. TyG, WWI, and eGFR partially mediate the relationship between heavy metal exposure and advanced CKM prevalence. Conclusion: Heavy metal exposure is associated with increased CKM risk. TyG, WWI, and eGFR were found to partially mediate the association between heavy metal exposure and advanced CKM prevalence, suggesting potential pathways linking environmental exposures to CKM risk.
Keywords: heavy metals, cardiovascular-kidney-metabolic syndrome, Mediation analysis, NHANES, Bayesian Kernel Machine Regression
Received: 17 Apr 2025; Accepted: 05 Jun 2025.
Copyright: © 2025 Han, Liu, Song, Luo, Wang and Pan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Lijian Han, Yancheng Third People's Hospital, Yancheng, China
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