Cadmium dominates urinary heavy metal mixtures associated with COPD risk: Integrative evidence from NHANES, network pharmacology, and experimental validation.

Urinary heavy metal and trace element mixtures in chronic kidney disease: associations and implications for potential reverse causality

Introduction Chronic kidney disease (CKD) is a growing public health challenge, yet impaired kidney function may substantially alter urinary biomarker profiles, complicating their interpretation. We aimed to investigate the associations between urinary element concentrations and prevalent CKD, and to explore whether impaired kidney function and altered urinary excretion may contribute to reverse causality in the interpretation of urinary biomarkers. Methods We conducted a cross-sectional analysis of urinary element mixtures and prevalent CKD among 6,192 adults (51.7% female; mean age 52.4 ± 10.7 years). Associations between urinary element concentrations and prevalent CKD were evaluated using logistic regression, restricted cubic spline models, and three mixture modeling approaches, including Bayesian kernel machine regression, weighted quantile sum regression, and quantile g-computation. Results Participants with CKD exhibited lower urinary concentrations of selenium, molybdenum, cadmium, mercury, lead, and thallium, whereas urinary zinc concentrations were higher. Non-linear associations were observed for several elements. Across multiple mixture models, higher combined urinary element concentrations were consistently associated with lower odds of prevalent CKD. Cadmium, mercury, selenium, and thallium contributed most strongly to these inverse associations, whereas zinc showed a positive contribution. Discussion These findings are unlikely to reflect protective effects and may instead reflect impaired urinary excretion resulting from kidney dysfunction. Our findings suggest that urinary elements in CKD reflect not only exposure but also disease-related alterations in renal excretory function. These findings highlight the potential for reverse causality when interpreting urinary elements as biomarkers or predictors of kidney outcomes.

Association Between Metal Mixtures and C-Reactive Protein Levels: A Study Based on Occupational Populations in China.

Associations of heavy metals and essential trace elements exposures with rheumatoid arthritis: The potential mediating role of telomere length.

The relationships linking heavy metals and essential trace elements to rheumatoid arthritis (RA) risk have not been fully elucidated. The aim of this study was to explore the relationship between multi-metal exposure and the risk of RA, and to assess the mediating role of telomere length (TL) in these associations.This case-control study recruited 548 RA patients and 548 controls, quantifying peripheral blood concentrations of 15 metals (Cr, Mn, Co, Zn, Se, Sr, Mo, Sn, Sb, Cu, Ni, Cd, Ba, Tl, Pb) via inductively coupled plasma mass spectrometry (ICP-MS). Associations between metals and RA were assessed using logistic regression, quantile-based g-computation (qgcomp), and Bayesian kernel machine regression (BKMR). Overall concentrations of five heavy metals (Cu, Pb, Ni, Cd, and Ba) were positively associated with RA risk, primarily driven by exposures to Cu, Ni, and Cd. Conversely, overall concentrations of seven essential metals (Sn, Se, Mn, Cr, Zn, Sr, and Mo) were inversely associated with RA risk, with Se, Sn, Mn, Sr, and Cr emerging as the main protective factors. A U-shaped relationship emerged between the overall concentrations of these 12 metals and RA risk. TL were inversely associated with RA risk (OR =0.822, 95% CI: 0.689, 0.978). Notably, TL mediated 4.82% of the total effect of Pb exposure on RA risk. Our study reveals that excessive heavy metals and insufficient trace essential elements increase the risk of RA, and emphasizes the potential mediating role of TL in this process.

Potential joint association between urinary metal mixtures and adult asthma: A matched case-control study in China.

Exposure to multi-metal mixtures is a public health concern, but their joint association with adult asthma remains insufficiently characterized, particularly in Asian populations. This study aimed to investigate the joint association between urinary multi-metal mixtures and adult asthma and to identify the metals contributing to this association. We conducted a case-control study including 551 adults with asthma and 551 age- and sex-matched controls in China. Urinary concentrations of 23 metals were measured by ICP-MS. Principal component analysis (PCA), weighted quantile sum regression (WQS), quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR) were used to evaluate mixture associations. Across the mixture models, the 23-metal mixture showed a positive joint association with adult asthma. Titanium (Ti), uranium (U), and molybdenum (Mo) showed relatively consistent positive contributions, whereas iron (Fe), manganese (Mn), and nickel (Ni) showed inverse contributions. Although larger positive point estimates were observed among females and participants aged ≤ 40 years, formal interaction tests did not indicate statistically significant heterogeneity. BKMR suggested potential nonlinear and pairwise patterns involving multiple metals, particularly U, Ti and vanadium (V), but these findings were exploratory and do not establish synergistic toxicity. Overall, these findings suggest an association between urinary multi-metal mixtures and adult asthma but do not establish causality. Prospective studies are needed to clarify the temporal sequence and underlying biological pathways.

Association between metal co-exposure and frailty: exploring the potential explanatory pathway of inflammation

Selenium predominance in heavy metal mixtures is associated with hyperuricemia in Chinese adults: the role of insulin resistance

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. 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. In the single pollutant model, Se was positively associated with SUA (β = 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 118.70–233.12 μg/d based on HUA. 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.