What question did this study set out to answer?

The study aims to assess how mixtures of heavy metals affect liver function biomarkers in a middle-aged population.

May 2, 2026Open Access

Association of heavy metal mixtures with liver function biomarkers: multi-model analysis identifies cadmium as the primary driver

Key Points

The study aims to assess how mixtures of heavy metals affect liver function biomarkers in a middle-aged population.
Conducted a cross-sectional study with 451 participants from the Dongdagou Xinglong cohort.
Employed multiple regression techniques including linear regression, BKMR, WQS, and Qgcomp.
Established a cadmium exposure rat model for further validation of findings.
Blood cadmium was positively correlated with GGT, TBA, ALT, and AST; negatively with DBil, TBil, and IBil (all P < 0.05).
BKMR analysis showed positive associations of metal mixtures with ALT, AST, GGT, and TBA, while negatively with TBil, DBil, and IBil.
Cadmium was consistently identified as the primary factor affecting liver function biomarkers across all models, supported by animal studies.

Abstract

Background Evidence regarding the hepatotoxic effects of co-exposure to multiple heavy metals in the general middle-aged and older adults population remains limited. This study aimed to investigate the association between heavy metal mixtures and liver function in the population of Northwest China, with key findings supported using an animal model. Methods We conducted a cross-sectional study involving 451 participants from the Dongdagou Xinglong cohort. Concentrations of heavy metals and liver function indices were measured. Multiple linear regression, Bayesian kernel machine regression (BKMR), weighted quantile sum (WQS), and quantile-based g-computation (Qgcomp) regression were employed to evaluate the combined effects of co-exposure to multiple heavy metals on liver function. A sub-chronic cadmium (Cd) exposure rat model was further established to validate population-based findings. Results Multiple linear regression analysis revealed that blood Cd was positively correlated with GGT (β = 0.236), TBA (β = 0.162), ALT (β = 0.142) and AST (β = 0.114), while negatively correlated with DBil (β = −0.207), TBil (β = −0.166) and IBil (β = −0.157) (all P 0.05). Similarly, other heavy metals also exhibited significant associations with liver function indicators. BKMR analysis showed that heavy metal mixture exposure was positively associated with ALT, AST, ALP, GGT, CHE, and TBA, but negatively associated with TBil, DBil, and IBil; WQS regression indicated that positive associations between the metal mixture and GGT as well as CHE; and the Qgcomp model demonstrated that the metal mixture was positively associated with ALT, GGT, and TBA, and negatively associated with TBil, DBil, and IBil. Notably, all three statistical models consistently identified Cd as the factor associated with liver function biomarkers. Furthermore, animal experiments provided experimental evidence consistent with the human findings: Cd exposure led to elevated serum GGT and ALP levels and induced histopathological alterations in the liver. Transcriptomic sequencing suggested that hepatic lipid metabolism pathways may be involved in Cd-induced liver injury. Conclusions Overall, our study shows that co-exposure to heavy metals is associated with liver function biomarkers in middle-aged and older adults, with Cd identified as the predominant factor associated with liver function biomarkers.

Perguntar à IA

Bookmark

View Full Paper