ArticleSkeletal muscle2024
Metabolic signatures and potential biomarkers of sarcopenia in suburb-dwelling older Chinese: based on untargeted GC-MS and LC-MS.
Article in Skeletal muscle, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Association of handgrip strength with the plasma metabolomic profile: secondary analysis of a protein intervention study.Metabolomics : Official journal of the Metabolomic Society · 2026Trial
- A distinct serum metabolic profile characterizes osteosarcopenia: identifying potential metabolic biomarkers.Frontiers in endocrinology · 2026Observational
- Advancing Precision Diagnosis of Sarcopenic Obesity Through Digital Technologies, Wearables and Omics Data.Life (Basel, Switzerland) · 2025Review
- Machine learning to identify potential biomarkers for sarcopenia in liver cirrhosis.World journal of hepatology · 2025Article
- Pathophysiological mechanisms and emerging therapeutic strategies for muscle wasting: an integrative review.Frontiers in physiology · 2025Review
- Lipid metabolites and sarcopenia-related traits: a Mendelian randomization study.Diabetology & metabolic syndrome · 2024Article
- LC/MS-Based Untargeted Lipidomics Reveals Lipid Signatures of Sarcopenia.International journal of molecular sciences · 2024Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundUntargeted metabolomics can be used to expand our understanding of the pathogenesis of sarcopenia. However, the metabolic signatures of sarcopenia patients have not been thoroughly investigated. Herein, we explored metabolites associated with sarcopenia by untargeted gas chromatography (GC)/liquid chromatography (LC)-mass spectrometry (MS) and identified possible diagnostic markers.
methodsForty-eight elderly subjects with sarcopenia were age and sex matched with 48 elderly subjects without sarcopenia. We first used untargeted GC/LC-MS to analyze the plasma of these participants and then combined it with a large number of multivariate statistical analyses to analyze the data. Finally, based on a multidimensional analysis of the metabolites, the most critical metabolites were considered to be biomarkers of sarcopenia.
resultsAccording to variable importance in the project (VIP > 1) and the p-value of t-test (p < 0.05), a total of 55 metabolites by GC-MS and 85 metabolites by LC-MS were identified between sarcopenia subjects and normal controls, and these were mostly lipids and lipid-like molecules. Among the top 20 metabolites, seven phosphatidylcholines, seven lysophosphatidylcholines (LysoPCs), phosphatidylinositol, sphingomyelin, palmitamide, L-2-amino-3-oxobutanoic acid, and palmitic acid were downregulated in the sarcopenia group; only ethylamine was upregulated. Among that, three metabolites of LysoPC(17:0), L-2-amino-3-oxobutanoic acid, and palmitic acid showed very good prediction capacity with AUCs of 0.887 (95% CI = 0.817-0.957), 0.836 (95% CI = 0.751-0.921), and 0.805 (95% CI = 0.717-0.893), respectively.
conclusionsThese findings show that metabonomic analysis has great potential to be applied to sarcopenia. The identified metabolites could be potential biomarkers and could be used to study sarcopenia pathomechanisms.
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