ArticleFrontiers in public health2022
Machine learning model for depression based on heavy metals among aging people: A study with National Health and Nutrition Examination Survey 2017-2018.
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 38 papers, 1 of them a synthesis that pooled it.
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38 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- Developing a translational research framework for MDD: combining biomolecular mechanisms with a spiraling risk factor model.Frontiers in psychiatry · 2024Pooled it
- Applications of Machine Learning in the Research of Heavy Metal(loid)s-Related Risk: A Scoping Review of Methodology.Toxics · 2026Review
- Integrating epidemiologic modeling and explainable machine learning to predict and identify factors associated with self-reported depression among adults in Tennessee, United States.Discover mental health · 2026Article
- An Exploration of Machine Learning Methods in Human Biomonitoring.International journal of environmental research and public health · 2026Review
- Explainable machine learning for depression risk prediction in adults with obesity: development of an online tool.BMC medical informatics and decision making · 2026Article
- Association between heavy metals exposure and depression: findings of the NHANES from 2003 to 2020.European journal of medical research · 2026Article
- Development, validation, and visualization of a machine learning-based predictive model for depression risk in sleep disorder patients.BMC psychiatry · 2025Article
- Burden and future trends of facial fractures in China: a global perspective with machine learning projections to 2036.BMC oral health · 2025Article
- Development and Validation of a Machine Learning‑Based Predictive Model for Assessing the Risk of Comorbid Depression in Patients With Asthma.Alpha psychiatry · 2025Article
- Temporal trends and machine learning prediction of depressive symptoms among Chinese middle-aged and elderly individuals: a national cohort study.BMC public health · 2025Article
- Development and interpretation of a machine learning risk prediction model for post-stroke depression in a Chinese population.Scientific reports · 2025Article
- The associations between mixed blood heavy metal exposure and depressive symptom: a cross-sectional study in Shandong, China.BMC public health · 2025Article
- A prospective study of a whole blood metal mixture and depressive symptoms among Black women from Detroit, Michigan.Neurotoxicology · 2025Article
- The interpretable machine learning model for depression associated with heavy metals via EMR mining method.Scientific reports · 2025Article
- Developing machine learning models for predicting cardiovascular disease survival based on heavy metal serum and urine levels.Frontiers in public health · 2025Article
- Global burden of schizophrenia in 204 countries and regions from 1990 to 2021 and machine learning-based projections to 2036: an analysis of the 2021 global burden of disease study.Frontiers in psychiatry · 2025Article
- Relationships between vitamin C intake and COPD assessed by machine learning approaches from the NHANES (2017-2023).Frontiers in nutrition · 2025Article
- Prediction of non-suicidal self-injury (NSSI) among rural Chinese junior high school students: a machine learning approach.Annals of general psychiatry · 2024Article
- Association Between Vitamin Intake and Colorectal Cancer: Evidence from NHANES Data.Journal of gastrointestinal cancer · 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
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4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To explore the association between depression and blood metal elements, we conducted this machine learning model fitting research. Methods: Datasets from the National Health and Nutrition Examination Survey (NHANES) in 2017-2018 were downloaded (https://www.cdc.gov/nchs/nhanes). After screening, 3,247 aging samples with 10 different metals [lead (Pb), mercury (Hg), cadmium (Cd), manganese (Mn), selenium (Se), chromium (Cr), cobalt (Co), inorganic mercury (InHg), methylmercury (MeHg) and ethyl mercury (EtHg)] were included. Eight machine learning algorithms were compared for analyzing metal and depression. After comparison, XGBoost showed optimal effects. Poisson regression and XGBoost model (a kind of decision tree algorithm) were conducted to find the risk factors and prediction for depression. Results: A total of 344 individuals out of 3247 participants were diagnosed with depression. In the Poisson model, we found Cd (β = 0.22, Conclusion: Blood heavy metals, especially Cd, EtHg, and Hg were significantly associated with depression and the prediction of depression was imperative.
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