ArticleHormones (Athens, Greece)2026
Combining body roundness index and triglyceride-glucose index to enhance MASLD prediction: insights from NHANES and machine learning.
Article in Hormones (Athens, Greece), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Applying Artificial Intelligence to Childhood Obesity: T2DM and MASLD Risk Predictive Models.Diagnostics (Basel, Switzerland) · 2026Review
- The Value of Five Novel Anthropometric Indicators in Evaluating MASLD and Liver Fibrosis in MASLD: A Cross-Sectional Study.JGH open : an open access journal of gastroenterology and hepatology · 2026Article
- Parathyroid disease, gut microbiome, biologic age, and more.Hormones (Athens, Greece) · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundNon-alcoholic fatty liver disease (MASLD), associated with obesity and metabolic syndrome, necessitates effective biomarkers for diagnosis and management.
aimsThis study examines the combined utility of the body roundness index (BRI) and the triglyceride-glucose (TyG) index in predicting MASLD.
methodsWe analyzed NHANES 2017-2018 cross-sectional data from 1,211 individuals without hepatitis or significant alcohol use. Logistic regression models assessed relationships between BRI, body mass index (BMI), TyG, and MASLD. Predictive performance was enhanced using machine learning techniques and a nomogram. Validation methods included the Net Reclassification Index, the integrated discriminant improvement index, receiver operating characteristic analysis, decision curve analysis, and calibration curve analysis.
resultsA significant nonlinear relationship was observed between BRI, BMI, and TyG with MASLD incidence, along with notable additive interactions (all p < 0.05). TyG emerged as a key mediator of BRI or BMI effects on MASLD. The combined indices (BRI and TyG) achieved a predictive area under the curve (AUC) of 0.797, exceeding individual metrics and BMI + TyG. Among 105 machine learning models, random forest excelled with a mean AUC of 0.918. A nomogram demonstrated strong accuracy (AUC = 0.83).
conclusionThe combined BRI and TyG indices serve as effective surrogate biomarkers for managing MASLD, facilitating personalized interventions, and enhancing clinical outcome predictions.
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40991182What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.