Exposure to heavy metals and trace elements and risk of dyslipidemia: a nested case-control analysis in rural adults.
J, Q., Q, W., J, Z., C, C., K, C., Y, Z., & Y, Z. (2026). Exposure to heavy metals and trace elements and risk of dyslipidemia: a nested case-control analysis in rural adults.. Frontiers in endocrinology. https://doi.org/10.3389/fendo.2026.1851531
J Q, Q W, J Z, C C, K C, Y Z, et al. Exposure to heavy metals and trace elements and risk of dyslipidemia: a nested case-control analysis in rural adults.. Frontiers in endocrinology. 2026; doi: 10.3389/fendo.2026.1851531
J Q, Q W, J Z, et al. Exposure to heavy metals and trace elements and risk of dyslipidemia: a nested case-control analysis in rural adults.[J]. Frontiers in endocrinology. 2026. DOI: 10.3389/fendo.2026.1851531.
@article{j2026,
author = {Qiu J and Wang Q and Zhang J and Chen C and Chen K and Zhang Y and Zhao Y},
title = {Exposure to heavy metals and trace elements and risk of dyslipidemia: a nested case-control analysis in rural adults.},
journal = {Frontiers in endocrinology},
year = {2026},
doi = {10.3389/fendo.2026.1851531},
note = {PMID: 42255419},
}
TY - JOUR AU - Qiu J AU - Wang Q AU - Zhang J AU - Chen C AU - Chen K AU - Zhang Y AU - Zhao Y TI - Exposure to heavy metals and trace elements and risk of dyslipidemia: a nested case-control analysis in rural adults. T2 - Frontiers in endocrinology PY - 2026 DO - 10.3389/fendo.2026.1851531 AN - PMID:42255419 ER -
BACKGROUND: Exposure to some conventional metal trace elements has been found to be associated with abnormal blood lipids; however, evidence for combined exposure is inconclusive. This study aimed to explore the joint associations between multiple metal trace elements and dyslipidemia. METHODS: In this nested case-control study, 782 subjects (391 cases and 391 controls, 1:1 case-to-control ratio) were selected from a cohort in Ningxia, China. Levels of 15 trace metals in serum were determined by inductively coupled plasma atomic emission spectrometry. Conditional logistic regression was used to analyze association of dyslipidemia. Bayesian kernel machine regression (BKMR) and weighted quantile sum regression (WQS) assessed the relationship between metal mixtures and dyslipidemia. RESULTS: After adjusting for confounders, lithium (Li) and tin (Sn) were negatively associated with dyslipidemia, with OR (CI) of 0.654 (0.436, 0.981) and 0.800 (0.649, 0.985), respectively. In BKMR, Li exhibited a negative correlation with dyslipidemia across the whole exposure range. The interactions between Li and Nickel (Ni), Sn and manganese (Mn), and Ni and Mn were found. In WQS, the overall effect of the metal mixtures was not statistically significant, with Li and Sn accounting for 7.6% and 16.8% of the weights in negative direction. CONCLUSIONS: Serum Li and Sn were associated with lower dyslipidemia risk in an observational study of this rural Chinese cohort. Li had an overall negative association with dyslipidemia in the metal mixtures. The potential interactions between metals warrant further investigation.