Background: Multiple metals exposure has been revealed to be related to metabolic syndrome (MetS). However, the associations and interactions between multiple metals exposure and MetS are remains controversial, and the potential mechanism of the above-mentioned is still unclear.
Methods: The associations between urinary metals and the MetS were analyzed by multivariable logistic regression model and restricted cubic spline (RCS). Bayesian kernel machine regression (BKMR) model and quantile-based g-computation (qgcomp) were applied to explore the mixed exposure and interaction effect of metals. Mediation analysis was used to explore the role of liver function.
Results: In the single metal model, multiple metals were significantly associated with MetS. RCS analysis further verified the associations between 8 metals and MetS. BKMR model and qgcomp showed that zinc (Zn), iron (Fe), and tellurium (Te) were the main factors affecting the overall effect. In addition, mediation analysis indicated that serum alanine aminotransferase (ALT) mediated 21.54% and 13.29% in the associations of vanadium (V) and Zn with the risk of MetS, respectively.
Conclusions: Elevated urinary concentration of Zn, V, Te, copper (Cu), molybdenum (Mo), and thallium (Tl) were related to the increased risk of MetS. Conversely, Fe and selenium (Se) may be protective factors for MetS in mixed exposure. Liver function may play a key role in the association of V and Zn exposure with MetS.
Keywords: Community-dwelling elderly; Liver function; Mediation; Metabolic syndrome; Urinary metals.
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