Urinary metal mixtures and cardio-kidney-metabolic risk in adults from a legacy-contaminated area: Repeated-measures cohort evidence and computational validation.

Cardiovascular-kidney-metabolic (CKM) syndrome links metabolic dysfunction, kidney injury, and cardiovascular disease; however, how real-world toxic exposures and social disadvantage accelerate CKM progression is not well defined. We followed a pollution-exposed rural cohort in Northeast China from 2016 to 2021 (n = 472; 2360 person-visits). Urinary chromium (Cr), cadmium (Cd), manganese (Mn), and lead (Pb) were measured by inductively coupled plasma mass spectrometry, and CKM stage was assigned using the current American Heart Association framework. We modeled advanced CKM (stages 3-4) versus non-advanced CKM (0-2) using generalized linear mixed-effects models (LME) with participant-level random intercepts, and evaluated nonlinearity, mixture effects, and metal-metal interactions using random-intercept Bayesian kernel machine regression (BKMR). Higher urinary Cd [odds ratio (OR) 1.43, confidence interval (CI) 1.01-2.03], Pb (1.38, 1.05-1.80), and Mn (1.35, 1.02-1.80) were associated with advanced CKM, whereas Cr showed an inverse association (0.79, 0.60-0.97). The metal mixture as a whole increased advanced CKM risk and displayed nonlinear, interacting behavior (notably Cd- and Mn-driven effects and Cr×Cd, Cr×Mn, Pb×Cd, Pb×Mn interactions). Although single social determinants of health (education, income, employment, insurance) did not independently predict advanced CKM, cumulative disadvantage (≥2 adverse factors) amplified the Cd-CKM association (interaction OR 2.12, 1.00-4.54), indicating that inequity modifies biological susceptibility. Network and pathway analysis highlighted STAT3 as a central inflammatory-metabolic hub linking metal-responsive signaling to cardio-renal-metabolic injury, and molecular docking suggested direct coordination of Cd²⁺, Mn²⁺, and Pb²⁺ to STAT3. Notably, this study leverages a longitudinal repeated-measures design and mixture modeling framework to assess combined metal exposures in relation to CKM progression, and integrates epidemiological inference with systems-level analyses to generate mechanistic hypotheses. These findings outline an exposure-inequity-inflammation axis and nominate mixture reduction and social protection as dual prevention targets.