ArticleFrontiers in public health2022
Identification for heavy metals exposure on osteoarthritis among aging people and Machine learning for prediction: A study based on NHANES 2011-2020.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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43 citing papers in PubMed, 61 citations in OpenAlex.
- Applications of Machine Learning in the Research of Heavy Metal(loid)s-Related Risk: A Scoping Review of Methodology.Toxics · 2026Review
- An Exploration of Machine Learning Methods in Human Biomonitoring.International journal of environmental research and public health · 2026Review
- Associations of the Endothelial Activation and Stress Index with breast cancer prevalence and mortality based on NHANES 2001 to 2018.Medicine · 2026Article
- Global Co-Burden and Risk Factors of Low Back Pain and Osteoarthritis: Analysis of Global Burden of Disease 2021 Data Based on Machine Learning and SHAP.Journal of pain research · 2026Article
- Trace Elements-Role in Joint Function and Impact on Joint Diseases.International journal of molecular sciences · 2025Review
- Association between ethylene oxide exposure and osteoarthritis risk mediated by oxidative stress: evidence from NHANES 2013-2020.Medical gas research · 2025Article
- Regional concentrations of heavy metals in surface soils and risk of body pain in elderly residential population: a national cohort study in China.BMC public health · 2025Article
- The interpretable machine learning model for depression associated with heavy metals via EMR mining method.Scientific reports · 2025Article
- Preliminary study on the potential damage of cigarette smoke extract in 3D human chondrocyte culture.In vitro cellular & developmental biology. Animal · 2025Article
- Association between composite dietary antioxidant index and rheumatoid arthritis: results from NHANES 2003-2018.International journal of medical sciences · 2025Article
- Heavy Metal Contamination: Sources, Health Impacts, and Sustainable Mitigation Strategies with Insights from Nigerian Case Studies.F1000Research · 2025Review
- Developing machine learning models for predicting cardiovascular disease survival based on heavy metal serum and urine levels.Frontiers in public health · 2025Article
- Prediction of Hypertension in the Pediatric Population Using Machine Learning and Transfer Learning: A Multicentric Analysis of the SAYCARE Study.International journal of public health · 2025Observational
- Article
- Development and Assessment of a Prediction Model for Alzheimer's Disease Diagnosis Based on Thermoregulation-Related Genes.Combinatorial chemistry & high throughput screening · 2025Article
- Effects of Various Heavy Metal Exposures on Insulin Resistance in Non-diabetic Populations: Interpretability Analysis from Machine Learning Modeling Perspective.Biological trace element research · 2024Article
- Predictive Modeling of Osteoarthritis Using Biochemical Markers: A Cross-Sectional Analysis.Journal of orthopaedic case reports · 2024Article
- Association between sleep duration, depression and breast cancer in the United States: a national health and nutrition examination survey analysis 2009-2018.Annals of medicine · 2024Article
- Machine learning model for age-related macular degeneration based on heavy metals: The National Health and Nutrition Examination Survey 2005 to 2008.Scientific reports · 2024Article
- Association between body roundness index and risk of osteoarthritis: a cross-sectional study.Lipids in health and disease · 2024Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Heavy metals are present in many environmental pollutants, and have cumulative effects on the human body through water or food, which can lead to several diseases, including osteoarthritis (OA). In this research, we aimed to explore the association between heavy metals and OA. Methods: We extracted 18 variables including age, gender, race, education level, marital status, smoking status, body mass index (BMI), physical activity, diabetes mellitus, hypertension, poverty level index (PLI), Lead (Pb), cadmium (Cd), mercury (Hg), selenium (Se), manganese (Mn), and OA status from National Health and Nutrition Examination Survey (NHANES) 2011-2020 datasets. Results: In the baseline data, the Conclusion: The overall and gender subgroup logistic regressions all showed that Pb and Cd promoted the prevalence of OA while Mn could be a protective factor of OA prevalence among the elderly population of the United States. Furthermore, XGBoost model was trained for OA prediction.
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