{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T06:18:22Z","timestamp":1787811502718,"version":"build-2784847793"},"reference-count":68,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Emerging studies indicate exposure to heavy metals is linked with uric acid levels and hyperuricemia risk. However, the combined effects of heavy metals and the underlying mechanisms remain poorly characterized.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this community-based cross-sectional study, plasma concentrations of 20 heavy metals and serum uric acid were measured in 1,312 adults in China. Triglyceride-glucose (TyG) index, a biomarker of insulin resistance, was also calculated. Generalized linear regression model, bayesian kernel machine regression, weighted quantile sum, and quantile G-computation were used to explore the associations between single or co-exposure to heavy metals and serum uric acid levels (SUA), as well as hyperuricemia (HUA). We also explored the mediating role of TyG in these associations. And then, benchmark dose lower bound (BMDL) of selenium (Se) was calculated using benchmark dose model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the single pollutant model, Se was positively associated with SUA (\u03b2 = 0.18, 95% CI: 0.04, 0.32) and HUA (OR = 1.51, 95% CI: 1.08, 2.11). Three multi-pollutant models all revealed positive associations between metal mixtures and SUA as well as HUA, with Se being the primary contributor. Furthermore, TyG significantly mediated the associations of Se with SUA and HUA, with the mediated proportions of 47.85% and 34.72%, respectively. BMDL of Se was 118.70\u2013233.12 \u03bcg\/d based on HUA.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>In conclusion, metal mixtures had positive associations with SUA and HUA. Insulin resistance might be a crucial mediated pathway between Se and outcomes. Our estimated point of departure (POD) for Se was lower than those recommended by other studies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1913054","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T05:34:56Z","timestamp":1787808896000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Selenium predominance in heavy metal mixtures is associated with hyperuricemia in Chinese adults: the role of insulin resistance"],"prefix":"10.3389","volume":"14","author":[{"given":"Jing-Fu","family":"Lai","sequence":"first","affiliation":[{"name":"Joint International Research Laboratory of Environment and Health, Ministry of Education, Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University","place":["Guangzhou, 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