Machine learning-based plasma metabolomics for improved cirrhosis risk stratification.

ABSTRACT:

Background

Cirrhosis 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.

Methods

This 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.

Results

SUBMITTER: Song J 

PROVIDER: S-EPMC11800577 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature