Background Heavy metal exposure may be associated with the risk of metabolic syndrome (MetS) and serum uric acid. The role of serum uric acid in the relationship between heavy metal exposure and MetS is currently unclear.

Objective To evaluate the relationships of heavy metal exposure with MetS and serum uric acid, and to quantify the role of serum uric acid in the relationship.

Methods In 2021, convenience sampling was used to select 571 local adults in Liuzhou, Guangxi. Demographic characteristics, lifestyle habits, and physiological and biochemical indicators were collected through questionnaire surveys and physical examinations. Fasting blood and mid-stream morning urine were also collected. The concentrations of 16 heavy metals in urine were measured using inductively coupled plasma mass spectrometry. Least absolute shrinkage and selection operator (LASSO) regression was employed to identify heavy metals associated with MetS. Logistic regression and linear regression models were employed to evaluate the association between the selected heavy metals and MetS as well as serum uric acid. Bayesian kernel machine regression (BKMR) model was utilized to assess the impact of combined exposures to multiple metals on the risk of MetS and identify the main effect metals. Generalized structural equation model was used to evaluate potential mediating effect of serum uric acid on the relationship between heavy metal exposure and MetS.

Results The LASSO regression identified a total of 9 heavy metals that were associated with MetS. The logistic regression revealed a positive correlation between zinc and copper in urine and MetS (P _trend<0.05), while vanadium showed a negative correlation with MetS (P _trend<0.05). Compared to the low concentration groups, the high concentration groups of zinc (OR=2.37, 95%CI: 1.33, 4.20) and copper (OR=2.29, 95%CI: 1.26, 4.18) had an increased risk of MetS, while the high concentration group of vanadium showed a decreased risk of MetS (OR=0.47, 95%CI: 0.27, 0.84). The main effect metals identified by the BKMR model were consistent with the results of logistic regression. The linear regression analysis demonstrated an association between urinary zinc and vanadium concentrations and serum uric acid levels (P _trend<0.05). Compared to the low concentration group, the high concentration group of zinc showed an increase in serum uric acid level (β=0.07, 95%CI: 0.03, 0.11), while the high concentration group of vanadium showed a decrease in serum uric acid level (β=-0.06, 95%CI: -0.09, -0.02). The mediation analysis revealed that serum uric acid played a mediating role in the relationship between urinary zinc and vanadium concentrations and MetS, with mediation proportions of 8.33% and 16.67%, respectively.

Conclusion Exposure to heavy metals zinc, copper, and vanadium are closely associated with MetS. Zinc and vanadium exposures are correlated with serum uric acid levels, and serum uric acid plays a partial mediating role in the relationship between zinc and vanadium exposures and MetS.