ArticleBMC medical informatics and decision making2025
Nonlinear association between visceral fat metabolism score and heart failure: insights from LightGBM modeling and SHAP-Driven feature interpretation in NHANES.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Early prediction of severe Omicron pneumonia using a multimodal a.i. model integrating delta CT radiomics and laboratory indicators.Scientific reports · 2026Article
- Peak strain dispersion as a nonlinear mediator in HFpEF: Unraveling subtype-specific pathways via SHAP-augmented ensemble modeling.PLoS computational biology · 2026Article
- Comparative discriminatory performance of emerging endocrine-metabolic indices versus obesity indices for cardiometabolic multimorbidity in older adults: a cross-sectional study.Frontiers in endocrinology · 2026Article
- The association between frailty and recurrent pregnancy loss in reproductive-aged women: a cross-sectional study.Scientific reports · 2025Article
- Machine learning prediction and SHAP interpretability analysis of heart failure risk in patients with hyperuricemia.Frontiers in cardiovascular medicine · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
objectiveUsing 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.
methodsAfter 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.
resultsHF 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).
conclusionMETS-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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