Background Exposure to heavy metals has potential adverse impacts on human health, and the concentration of heavy metals in abandoned mining areas may still be higher than that in general areas, so the health risk assessment in such areas cannot be ignored. Objective To explore the health risk of heavy metal exposure and the spatial distribution characteristics of associated main metals in a typical abandoned mining area. Methods Environmental samples of irrigated soil, rice, and drinking water were collected from 13 natural villages under the jurisdiction of a township in Liuzhou, Guangxi from November to December 2019, where a typical abandoned mining was located. Finally, 13 irrigation soil samples, 11 rice samples, and 13 drinking water samples were collected. The concentrations of six metals and metalloid elements in each environmental sample were detected by inductively coupled-plasma mass spectrometry (ICP-MS), including cadmium (Cd), arsenic (As), lead (Pb), chromium (Cr), copper (Cu), and zinc (Zn). At the same time, 251 local residents were recruited for health risk assessment. Model parameters such as body weight, rice intake, and drinking water intake of local residents were obtained through field survey, and the median metal concentration of each environmental sample was taken as the risk assessment parameter of the region. The health risk of heavy metal exposure of local residents was assessed by using oral health risk assessment model of U.S. Environmental Protection Agency. The spatial distribution characteristics of health risks associated with heavy metals were evaluated by empirical Bayes interpolation method using Geographic Information System (GIS) technology. Results The positive rates of Cd, As, Pb, Cr, Cu, and Zn in the irrigated soil samples were 100.00%. The positive rate of Pb was 63.64% in the rice samples, while the rates of other metals were 100.00%. The positive rates of Cd, As, Pb, Cr, Cu, and Zn in the drinking water samples were 53.85%, 76.92%, 92.31%, 15.38%, 84.62%, and 100.00%, respectively. The results of non-carcinogenic risk assessment of oral exposure to heavy metals suggested that the contribution of heavy metals causing non-carcinogenic risk from high to low was As (70.52%) > Cd (18.03%) > Zn (6.63%) > Cu (4.12%) > Pb (0.64%) > Cr (0.06%), and the corresponding estimated non-carcinogenic risk values were 3.54 × 10⁰, 9.05 × 10⁻¹, 3.33 × 10⁻¹, 2.07 × 10⁻¹, 3.23 × 10⁻², and 5.42 × 10⁻⁴, respectively. The results of carcinogenic risk assessment of oral exposure to heavy metals suggested that the contribution of studied metals from high to low was Cd (87.00%) > As (10.24%) > Cr (2.60%) > Pb (0.16%), and the estimated carcinogenic risks were 4.35× 10⁻³, 5.12 × 10⁻⁴, 1.30 × 10⁻⁴, and 3.08 × 10⁻⁷, respectively. Rice was the leading media associated with non-carcinogenic risk and carcinogenic risk (99.4% and 99.8% respectively). The spatial distribution characteristics of GIS showed no obvious regularity in the distribution of As in irrigated soil, rice, and drinking water. In rice and irrigated soil, the content of Cd in the villages adjacent to the mining area was obviously higher than that in the other villages, while in drinking water, the content in the villages far away from the mining area was higher. Conclusion As and Cd are the main heavy metals that increase the health risk of local residents in a typical abandoned mining area, and the distribution characteristics of the two heavy metals in different environmental media are not completely consistent.

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.