Introduction
Environmental heavy-metal mixtures may contribute to diabetes risk, yet their combined effects remain understudied. We investigated the association between the Metal Mixture Inflammatory Index (MMII) and prevalent diabetes in US adults.
Research design and methods
We analyzed data from 23 288 participants in the 1999–2020 National Health and Nutrition Examination Survey. Survey-weighted logistic regression, restricted cubic splines (RCS), and stratified analyses assessed the relationship between MMII and diabetes. Least absolute shrinkage and selection operator (LASSO) regression identified key predictors, which were incorporated into a nomogram. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis.
Results
After multivariable adjustment, each 0.1-unit increase in MMII was associated with 2% higher odds of diabetes (OR=1.02; 95% CI 1.00 to 1.04; p=0.02). Participants in the highest quartile (Q4) had 26% greater odds than those in the lowest quartile (Q1) (OR=1.26; 95% CI 1.04 to 1.52; p=0.016). RCS analysis indicated a linear positive association between MMII and diabetes risk. Subgroup analyses revealed stronger associations among men, non-Hispanic white participants, former smokers, current alcohol users, and individuals without hypertension. The LASSO-based nomogram demonstrated excellent discrimination (AUC=0.869; 95% CI 0.863 to 0.875), good calibration, and net clinical benefit.
Conclusions
MMII is independently and linearly associated with diabetes risk. Metal exposures may enhance future risk stratification for diabetes. Prospective studies are warranted to confirm causal mechanisms.
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