AUTHOR=Cao Xiao , Xiao Xiaohua , Jiang Peipei , Fu Nian 

TITLE=Construction and evaluation of a diagnostic model for metabolic dysfunction-associated steatotic liver disease based on advanced glycation end products and their receptors

JOURNAL=Frontiers in Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1539708

DOI=10.3389/fmed.2025.1539708

ISSN=2296-858X

ABSTRACT=BackgroundEffective biomarkers for the diagnosis of metabolic dysfunction-associated steatotic liver disease (MASLD) remain limited. This study aims to evaluate the potential of advanced glycation end products (AGEs) and their endogenous secretory receptor (esRAGE) as non-invasive biomarkers for diagnosing MASLD, to explore differences between obese and non-obese MASLD patients, and to develop a novel diagnostic model based on these biomarkers.MethodsThis study enrolled 341 participants, including 246 MASLD patients (118 non-obese, 128 obese) and 95 healthy controls. Serum AGEs and esRAGE levels were measured by ELISA. Key predictors were identified using the Lasso algorithm, and a diagnostic model was developed with logistic regression and visualized as nomograms. Diagnostic accuracy and utility were evaluated through the area under the curve (AUC), bootstrap validation, calibration curves, and decision curve analysis (DCA).ResultsSerum AGEs and esRAGE levels were significantly higher in MASLD patients compared to controls. Moreover, obese MASLD patients had higher esRAGE levels than non-obese ones, but no significant difference in AGEs levels was found. A diagnostic model incorporating age, WC, BMI, ALT, TG, HDL, AGEs, and esRAGE achieved an AUC of 0.963, with 94.3% sensitivity and 85.3% specificity. The AUC for bootstrap internal validation was 0.963 (95% CI: 0.944–0.982). Calibration curves showed strong predictive accuracy, and DCA demonstrated high net clinical benefit.ConclusionSerum AGEs and esRAGE serve as non-invasive biomarkers for distinguishing MASLD patients. We developed and validated diagnostic models for MASLD, offering valuable tools to identify at-risk populations and improve prevention and treatment strategies.