ArticleThe Journal of clinical endocrinology and metabolism2025
Machine Learning-Based Biomarker Identification for Early Diagnosis of Metabolic Dysfunction-Associated Steatotic Liver Disease.
Article in The Journal of clinical endocrinology and metabolism, 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.
- [Research progress of metabolomics in the pathogenesis and treatment of constipation in children].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2026Review
- Review
- Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Transforming diagnosis and therapeutic approaches.World journal of gastroenterology · 2026Review
- Development of an AI algorithm for early identification of MASLD in the electronic health record.Hepatology communications · 2025Article
- Machine learning models for predicting metabolic dysfunction-associated steatotic liver disease prevalence using basic demographic and clinical characteristics.Journal of translational medicine · 2025Article
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12 authors.
Funding
Abstract
contextMetabolic dysfunction-associated steatotic liver disease (MASLD) is an umbrella term for simple hepatic steatosis and the more severe metabolic dysfunction-associated steatohepatitis. The current reliance on liver biopsy for diagnosis and a lack of validated biomarkers are major factors contributing to the overall burden of MASLD.
objectiveThis study investigates the association between biomarkers and hepatic steatosis and stiffness measurements, measured by FibroScan®.
methodsData from the National Health and Nutritional Examination Survey (2017-2020) were collected for 15 560 patients. Propensity score matching balanced the data with a 1:1 case to control for age and sex allowing for preliminary trend assessment. Random Forest machine learning determined variable importance for the incorporation of key biomarkers (age, sex, race, BMI, HbA1c, plasma fasting glucose, insulin, total cholesterol, LDL-cholesterol, HDL-cholesterol, triglycerides, ALT, AST, ALP, albumin, GGT, LDH, iron, total bilirubin, total protein, uric acid, BUN, and hs-CRP) into logistic regression models predicting steatosis (MASLD indicated by a controlled attenuation parameter score of ≥238 dB/m) and stiffness (hepatic fibrosis indicated by a median liver stiffness ≥7 kPa). Sensitivity analysis using XGBoost and Recursive Feature Elimination was performed.
resultsThe Random Forest models (the most accurate) predicted MASLD with 79.59% accuracy (P < .001) and specificity of 84.65% and predicted hepatic fibrosis with 86.07% accuracy (P < .001) and sensitivity of 98.01%. Both the steatosis and stiffness models identified statistically significant biomarkers, with age, BMI, and insulin appearing significant to both.
conclusionThese findings indicate that assessing a variety of biomarkers, across demographic, metabolic, lipid, and standard biochemistry categories, may provide valuable initial insights for diagnosing patients for MASLD and hepatic fibrosis.
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