ArticleFrontiers in public health2024
Association between machine learning-assisted heavy metal exposures and diabetic kidney disease: a cross-sectional survey and Mendelian randomization analysis.
Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Applications of Machine Learning in the Research of Heavy Metal(loid)s-Related Risk: A Scoping Review of Methodology.Toxics · 2026Review
- Interpretable Machine Learning in Heavy Metal-Associated Cardiovascular and Metabolic Disease: A Review of Current Methodologies, Toxicological Insights, and Clinical Implications.Cardiovascular toxicology · 2026Review
- An Exploration of Machine Learning Methods in Human Biomonitoring.International journal of environmental research and public health · 2026Review
- Environmental Exposure to Cadmium and Lead Exacerbates Kidney Function in People with Diabetes.Journal of xenobiotics · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and objective: Heavy metals, ubiquitous in the environment, pose a global public health concern. The correlation between these and diabetic kidney disease (DKD) remains unclear. Our objective was to explore the correlation between heavy metal exposures and the incidence of DKD. Methods: We analyzed data from the NHANES (2005-2020), using machine learning, and cross-sectional survey. Our study also involved a bidirectional two-sample Mendelian randomization (MR) analysis. Results: Machine learning reveals correlation coefficients of -0.5059 and - 0.6510 for urinary Ba and urinary Tl with DKD, respectively. Multifactorial logistic regression implicates urinary Ba, urinary Pb, blood Cd, and blood Pb as potential associates of DKD. When adjusted for all covariates, the odds ratios and 95% confidence intervals are 0.87 (0.78, 0.98) ( Conclusion: In the general population, urinary Ba demonstrates a nonlinear inverse association with DKD, whereas in the diabetic population, urinary Tl displays a linear inverse relationship with DKD.
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Registered trials
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