Nonlinear association between visceral fat metabolism score and heart failure: insights from LightGBM modeling and SHAP-Driven feature interpretation in NHANES
Abstract Objective Using 2005–2018 NHANES data, this study examined the association between the visceral fat metabolism score (METS-VF) and heart failure (HF) prevalence in U.S. adults, leveraging machine learning (LightGBM/XGBoost) and SHAP for classfication performance evaluation and feature inter...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-07-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-025-03076-7 |
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| Summary: | Abstract Objective Using 2005–2018 NHANES data, this study examined the association between the visceral fat metabolism score (METS-VF) and heart failure (HF) prevalence in U.S. adults, leveraging machine learning (LightGBM/XGBoost) and SHAP for classfication performance evaluation and feature interpretation. Methods After excluding missing data, 30,704 participants were analyzed via survey-weighted statistics, restricted cubic splines (RCS), stratified analyses, and multivariate logistic regression. Ensemble models were compared for HF classification, with SHAP quantifying feature importance. Results HF patients exhibited higher METS-VF (7.35 ± 0.53 vs. 6.79 ± 0.72, P < 0.001) and worse cardiometabolic profiles. Multivariate adjustment revealed a 2.249-fold increased HF prevalence per 1-unit METS-VF increase (95% CI: 1.503–3.366, P < 0.001), with a nonlinear threshold effect (inflection point = 7.151; OR = 3.321, 95% CI: 3.464–8.494 for METS-VF ≥ 7.151). Obesity (BMI ≥ 30 kg/m²) amplified the association (OR = 5.857). LightGBM outperformed logistic regression in classification (AUC = 0.964 vs. 0.907), with SHAP identifying METS-VF as the top contributor (importance weight = 18.6%), surpassing hypertension (10.8%) and coronary artery disease (11.7%). Correlations validated METS-VF as a composite index of visceral adiposity and metabolic dysfunction (waist circumference r = 0.43, high-density lipoprotein cholesterol r = − 0.38, all P < 0.001). Conclusion METS-VF is independently and nonlinearly associated with HF prevalence, particularly in obese individuals. Machine learning enhances predictive accuracy by capturing complex interactions, while SHAP-based interpretability establishes METS-VF as a key biomarker integrating metabolic-adipose abnormalities, offering a novel target for personalized HF prevention. |
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| ISSN: | 1472-6947 |