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Updated: Jun 11, 2026
Optimized Analysis of In Vivo and In Vitro Hepatic Steatosis
Published on: March 11, 2017
Incorporating Insulin Resistance Biomarkers into Machine Learning Models Enhances Diagnostic Accuracy for Metabolic
Jingyuan Nie1, Jing Zhou1, Yuanjia Hu1
1Department of Epidemiology and Health Statistics, School of Public Health, Chongqing Medical University, Chongqing, 400016, People's Republic of China.
Introduction:
Metabolic dysfunction-associated steatotic liver disease (MASLD) has emerged as the most prevalent chronic liver disorder worldwide. Despite its increasing prevalence, MASLD is often asymptomatic in its early course, leading to considerable underdiagnosis. Insulin resistance (IR) plays a key role in MASLD development; however, the incremental value of IR-related biomarkers in machine learning (ML) diagnostic models for MASLD has not been fully investigated.
Methods:
This retrospective single-center study included 18,535 adults (5992 with MASLD and 12,543 without MASLD) undergoing routine health examinations at our research center. Two feature sets were created: Set 1 included demographic, anthropometric, clinical, and biochemical indicators, while Set 2 further incorporated IR-related biomarkers, including triglyceride-glucose (TyG)-based indices and the metabolic score for insulin resistance (METS-IR). Six ML algorithms were used to develop diagnostic models. Model performance was assessed using sensitivity, specificity, F1-score, and area under the curve (AUC) on the training and validation sets, with clinical utility evaluated using decision curve analysis (DCA). Model interpretability was further explored using Shapley Additive exPlanations (SHAP).
Results:
All six ML models demonstrated a statistically significant improvement in AUC values after incorporating IR-related biomarkers (all P < 0.05). In the validation set, the extremely randomized trees (ERT) model with Set 2 achieved the highest AUC (0.901). DCA further indicated that models incorporating IR-related biomarkers generally provided a higher net clinical benefit. Overall, the ERT model provided the most favorable combination of discrimination and clinical utility. SHAP analysis further emphasized the important role of IR-related biomarkers in MASLD diagnosis.
Conclusion:
Incorporating IR-related biomarkers into ML diagnostic models improved the accuracy and clinical utility of MASLD detection without increasing testing costs or patient burden. These findings highlight the incremental diagnostic value of IR-related biomarkers in ML models and support their use as a decision-support tool for MASLD screening in general populations.
Insights
Adding insulin resistance (IR) biomarkers to machine learning (ML) models significantly improves the diagnosis of metabolic dysfunction-associated steatotic liver disease (MASLD). This approach enhances accuracy and clinical utility for early MASLD detection in the general population.
Area of Science:
- Hepatology
- Medical Informatics
- Biomarkers
Background:
- Metabolic dysfunction-associated steatotic liver disease (MASLD) is the leading cause of chronic liver disease globally.
- Early MASLD is often asymptomatic, leading to underdiagnosis.
- Insulin resistance (IR) is a key factor in MASLD development, but its role in diagnostic models needs further investigation.
Purpose of the Study:
- To investigate the incremental value of IR-related biomarkers in machine learning (ML) diagnostic models for MASLD.
- To assess the impact of incorporating IR biomarkers on the accuracy and clinical utility of MASLD detection.
Main Methods:
- A retrospective study of 18,535 adults, with and without MASLD.
- Development of ML models using demographic, clinical, biochemical, and IR-related biomarkers (e.g., TyG indices, METS-IR).
- Evaluation of model performance using AUC, sensitivity, specificity, F1-score, and decision curve analysis (DCA).
Main Results:
- All ML models showed significant improvement in AUC after including IR biomarkers (P < 0.05).
- The extremely randomized trees (ERT) model with IR biomarkers achieved the highest AUC (0.901) in the validation set.
- DCA indicated improved net clinical benefit with IR biomarker incorporation; ERT model showed optimal discrimination and utility.
Conclusions:
- Incorporating IR-related biomarkers into ML models enhances MASLD diagnostic accuracy and clinical utility.
- These biomarkers offer incremental diagnostic value for MASLD screening in the general population.
- ML models with IR biomarkers can serve as effective decision-support tools for MASLD detection without increasing costs or patient burden.
