ArticleWorld journal of hepatology2025
Machine learning to identify potential biomarkers for sarcopenia in liver cirrhosis.
Article in World journal of hepatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- CIRRHOSIS-ASSOCIATED SARCOPENIA: INTEGRATING IMAGING, FUNCTION, AND METABOLISM FOR CLINICAL DECISION-MAKING.Arquivos de gastroenterologia · 2026Review
- Sarcopenia and inflammatory markers in early-stage breast cancer: a retrospective case-control study of NLR, MLR, RDW-CV, and HRR in middle-aged and elderly women.Frontiers in surgery · 2026Article
- Neural Cues and Genomic Clues: NGS Insights into Neurogenic Sarcopenia and Muscle Atrophy.International journal of molecular sciences · 2025Review
- Metabolic and immune links between sarcopenia and liver disease.World journal of hepatology · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe prevalence of sarcopenia progressively increases with as liver function deteriorates. Muscle wasting has been shown to independently predict adverse outcomes in liver cirrhosis patients.
aimTo screen effective biomarkers for sarcopenia in liver cirrhosis.
methodsUntargeted 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.
resultsA 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.
conclusionThis 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.
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