Asset Details
Do you wish to reserve the book?
Nonlinear association between visceral fat metabolism score and heart failure: insights from LightGBM modeling and SHAP-Driven feature interpretation in NHANES
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Nonlinear association between visceral fat metabolism score and heart failure: insights from LightGBM modeling and SHAP-Driven feature interpretation in NHANES
Do you wish to request the book?
Nonlinear association between visceral fat metabolism score and heart failure: insights from LightGBM modeling and SHAP-Driven feature interpretation in NHANES
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Nonlinear association between visceral fat metabolism score and heart failure: insights from LightGBM modeling and SHAP-Driven feature interpretation in NHANES
2025
Overview
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.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
Subject
/ Adult
/ Age
/ Aged
/ Analysis
/ Boosting Machine Learning Algorithms
/ Diabetes
/ Female
/ Heart Failure - epidemiology
/ Humans
/ Information Systems and Communication Service
/ Intra-Abdominal Fat - metabolism
/ Male
/ Management of Computing and Information Systems
/ Medicine
/ METS-VF
/ NHANES
/ Obesity
/ SHAP
