From metabolic dysregulation to clinical alert: a novel panel of biomarkers for staging alcohol-associated liver disease.

BACKGROUND AND AIM: ALD is among the most prevalent chronic liver disorders with rising global incidence, the aim of our study is to evaluate the effect of metabolites in the progress of ALD, and build a prediction model for ALC.

METHODS

A total of 161 subjects were prospectively enrolled and classified into HC, AUD, AH, ALC. Plasma samples underwent untargeted LC-MS/MS metabolomics analysis. Data were split into training, validation and test sets at a 6:2:2 ratio. Partial least squares discriminant analysis and random forest were employed for feature selection, followed by logistic regression to build metabolite-based and metabolite-plus-clinical composite models. Discrimination was evaluated by AUC, sensitivity, specificity and accuracy.

RESULTS

Metabolomic profiles showed progressive separation among groups (Q² > 0.65 for all PLS-DA models). Five metabolites (Vecuronium, N-Docosahexaenoyl Cysteine, 7-Acetylintermedine, Hymenoxon, E-3174) were identified as the most influential features for distinguishing ALC from HC, yielding an AUC of 0.986 (95% CI: 0.969-0.999) in the validation set and 100% accuracy in the test set. Integration of these metabolites with AST further elevated the AUC to 0.998 with 96.70% sensitivity and 100% specificity. Similarly, a five-metabolite panel plus PT achieved perfect accuracy (AUC = 0.964) in discriminating ALC from AH.

CONCLUSIONS

We delineate stage-specific metabolic fingerprints of ALD and present robust, non-invasive models for early identification of cirrhosis. Incorporating metabolomic biomarkers with routine clinical variables markedly improves diagnostic precision and offers a practical tool for risk stratification and personalized management of alcohol-related liver disease.