Machine learning to identify potential biomarkers for sarcopenia in liver cirrhosis.

Abstract

Background: The prevalence of sarcopenia progressively increases with as liver function deteriorates. Muscle wasting has been shown to independently predict adverse outcomes in liver cirrhosis patients.

Aim: To screen effective biomarkers for sarcopenia in liver cirrhosis.

Methods: Untargeted metabolomics were performed on serum from 62 liver cirrhosis patients, including 41 with sarcopenia and 21 without sarcopenia. Candidate metabolite biomarkers were screened based on three machine-learning algorithms. The diagnostic or predictive value of potential biomarkers was evaluated by drawing receiver operating characteristic curves.

Results: A total of 60 differential metabolites between cirrhotic sarcopenia and the non-sarcopenia group were identified. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed differential metabolites primarily involved in glycerophospholipid metabolism, alpha-linolenic acid metabolism, retrograde endocannabinoid signaling, and choline metabolism in cancer. Finally, four potential biomarkers were screened through machine learning algorithms, namely N-Acetylcarnosine, 2-Stearylcitrate, CerP (d18:1/12:0), and 3-Methyl-alpha-ionylacetate. Among these, N-Acetylcarnosine can provide better diagnostic accuracy.

Conclusion: This study unveiled different plasma metabolic profiles of liver cirrhosis patients with and without sarcopenia. These valuable biomarkers have the potential to improve the prognosis of liver patients with cirrhosis by early detection or prediction of sarcopenia.