Associations between urinary and blood heavy metal exposure and heart failure in elderly adults: Insights from an interpretable machine learning model based on NHANES (2003–2020)
Background: The relationship between heavy metal exposure and heart failure is complex and poorly understood. This study employs machine learning techniques to model these associations in a population aged 50 years and older from the National Health and Nutrition Examination Survey (NHANES). Our fin...
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Elsevier
2025-06-01
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| Series: | International Journal of Cardiology. Cardiovascular Risk and Prevention |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S277248752500056X |
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| author | Yang Yuting Deng Shan |
| author_facet | Yang Yuting Deng Shan |
| author_sort | Yang Yuting |
| collection | DOAJ |
| description | Background: The relationship between heavy metal exposure and heart failure is complex and poorly understood. This study employs machine learning techniques to model these associations in a population aged 50 years and older from the National Health and Nutrition Examination Survey (NHANES). Our findings emphasize the need for continued investigation into the mechanisms of these associations and highlight the importance of monitoring and regulatory measures to mitigate heavy metal exposure in populations at risk. Methods: Five machine learning models were evaluated, with Gradient Boosting Decision Trees (GBDT) selected as the optimal model based on accuracy, interpretability, and ability to capture nonlinear relationships. Model performance was assessed through various metrics, and interpretability was enhanced using SHAP (SHapley Additive exPlanations), permuted Feature Importance, Individual Conditional Expectation (ICE), and Partial Dependence Plots (PDP). Results: The GBDT model achieved an accuracy of 0.78, with a sensitivity of 0.93 and an AUC of 0.92. Our analysis revealed that higher levels of urinary iodine, blood cadmium, urinary cobalt, urinary tungsten, and urinary arsenic acid were significantly associated with heart failure. Synergistic effects involving age and body mass index (BMI) were also observed, further strengthening these associations. |
| format | Article |
| id | doaj-art-c5cb8ef45c5e4911b43017b10da3ec0a |
| institution | OA Journals |
| issn | 2772-4875 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Cardiology. Cardiovascular Risk and Prevention |
| spelling | doaj-art-c5cb8ef45c5e4911b43017b10da3ec0a2025-08-20T01:51:53ZengElsevierInternational Journal of Cardiology. Cardiovascular Risk and Prevention2772-48752025-06-012520041810.1016/j.ijcrp.2025.200418Associations between urinary and blood heavy metal exposure and heart failure in elderly adults: Insights from an interpretable machine learning model based on NHANES (2003–2020)Yang Yuting0Deng Shan1Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Ave, Wuhan, 430022, ChinaDepartment of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Ave, Wuhan, 430022, China; Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, China; Hubei Clinical Research Center for Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, China; Corresponding author. Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.Background: The relationship between heavy metal exposure and heart failure is complex and poorly understood. This study employs machine learning techniques to model these associations in a population aged 50 years and older from the National Health and Nutrition Examination Survey (NHANES). Our findings emphasize the need for continued investigation into the mechanisms of these associations and highlight the importance of monitoring and regulatory measures to mitigate heavy metal exposure in populations at risk. Methods: Five machine learning models were evaluated, with Gradient Boosting Decision Trees (GBDT) selected as the optimal model based on accuracy, interpretability, and ability to capture nonlinear relationships. Model performance was assessed through various metrics, and interpretability was enhanced using SHAP (SHapley Additive exPlanations), permuted Feature Importance, Individual Conditional Expectation (ICE), and Partial Dependence Plots (PDP). Results: The GBDT model achieved an accuracy of 0.78, with a sensitivity of 0.93 and an AUC of 0.92. Our analysis revealed that higher levels of urinary iodine, blood cadmium, urinary cobalt, urinary tungsten, and urinary arsenic acid were significantly associated with heart failure. Synergistic effects involving age and body mass index (BMI) were also observed, further strengthening these associations.http://www.sciencedirect.com/science/article/pii/S277248752500056X |
| spellingShingle | Yang Yuting Deng Shan Associations between urinary and blood heavy metal exposure and heart failure in elderly adults: Insights from an interpretable machine learning model based on NHANES (2003–2020) International Journal of Cardiology. Cardiovascular Risk and Prevention |
| title | Associations between urinary and blood heavy metal exposure and heart failure in elderly adults: Insights from an interpretable machine learning model based on NHANES (2003–2020) |
| title_full | Associations between urinary and blood heavy metal exposure and heart failure in elderly adults: Insights from an interpretable machine learning model based on NHANES (2003–2020) |
| title_fullStr | Associations between urinary and blood heavy metal exposure and heart failure in elderly adults: Insights from an interpretable machine learning model based on NHANES (2003–2020) |
| title_full_unstemmed | Associations between urinary and blood heavy metal exposure and heart failure in elderly adults: Insights from an interpretable machine learning model based on NHANES (2003–2020) |
| title_short | Associations between urinary and blood heavy metal exposure and heart failure in elderly adults: Insights from an interpretable machine learning model based on NHANES (2003–2020) |
| title_sort | associations between urinary and blood heavy metal exposure and heart failure in elderly adults insights from an interpretable machine learning model based on nhanes 2003 2020 |
| url | http://www.sciencedirect.com/science/article/pii/S277248752500056X |
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