ArticleBMC gastroenterology2025
Machine learning-based plasma metabolomics for improved cirrhosis risk stratification.
Article in BMC gastroenterology, 2025. 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.
- Metabolic alterations associated with cirrhosis after HCV cure in people with HIV: metabolomic and lipidomic findings at one and five years.Frontiers in immunology · 2026Article
- Amino Acid Metabolism in Chronic Liver Disease: from Pathogenic Driver to Therapeutic Target.International journal of biological sciences · 2026Review
- A machine learning model for non-invasive prediction of advanced liver fibrosis in patients with chronic hepatitis B.American journal of translational research · 2025Article
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11 authors.
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Abstract
backgroundCirrhosis is a leading cause of mortality in patients with chronic liver disease (CLD). The rapid development of metabolomic technologies has enabled the capture of metabolic changes related to the progression of cirrhosis.
methodsThis study used proton nuclear magnetic resonance (1 H-NMR) serum metabolomics data from the UK Biobank (UKB) and employed elastic net-regularized Cox proportional hazards models to explore the role of metabolomics in cirrhosis risk stratification in patients with CLD. Metabolomic data were integrated with aspartate aminotransferase to platelet ratio index (APRI) and fibrosis-4 score (FIB-4) to construct predictive models for cirrhosis risk. The model performance was assessed in both the derivation and validation cohorts.
resultsA total of 2,738 eligible patients were included in the analysis. Several metabolites showed an independent association with cirrhosis events (68 out of 168 metabolites after adjustment for age and sex, and 21 out of 168 metabolites after full adjustment). The integration of metabolomics with FIB-4 improved the predictive performance compared to FIB-4 alone (Harrell's C: 0.717 vs. 0.696, ΔC = 0.021, 95% confidence interval [CI] 0.014-0.028, Net Reclassification Improvement [NRI]: 0.504 [0.488-0.520]). Similarly, the combination of metabolomics with APRI also improved predictive performance compared to APRI alone (Harrell's C: 0.747 vs. 0.718, ΔC = 0.029, 95% CI 0.022-0.035, NRI: 0.378 [0.366-0.389]). Key metabolites, including branched-chain amino acids (BCAAs), lipids, and markers of oxidative stress, were identified as significant predictors. Pathway enrichment analysis revealed that disruptions in lipid and amino acid metabolism play a central role in the progression of cirrhosis.
conclusion1 H-NMR serum metabolomics significantly improves the prediction of cirrhosis risk in patients with CLD. The APRI + Metabolomics model demonstrated strong discriminatory power, with key metabolites involved in fatty acid and amino acid metabolism, providing a promising tool for the early screening of cirrhosis risk.
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