Metabolomic biomarker differences of sarcopenia in older patients with sepsis

BackgroundSarcopenia has a high incidence in older patients with sepsis and is closely associated with poor prognosis, but its metabolic characteristics under this specific stress state remain unclear. This study aims to reveal the specific plasma metabolomic changes related to sarcopenia in older patients with sepsis.MethodsA prospective observational cohort design was adopted, involving 84 older patients with sepsis, who were divided into a sarcopenia group (35 cases) and a non-sarcopenia group (49 cases). Fasting plasma samples were collected within 24 h, and untargeted metabolomic analysis was performed using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) technology. Multivariate statistical methods such as principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were used to screen differential metabolites, and KEGG pathway enrichment analysis was conducted.ResultsA total of 4,752 metabolites were identified, and 203 significantly differential metabolites were screened out (71 upregulated and 132 downregulated), indicating that the plasma metabolic profile of sarcopenic patients presents an “inhibitory” feature. The differential metabolites were mainly enriched in core pathways such as valine/leucine/isoleucine degradation, arachidonic acid metabolism, and steroid hormone synthesis. Specifically, metabolic pathways promoting muscle synthesis (such as steroid hormone and branched-chain amino acid metabolism) were generally downregulated, while pathways representing catabolism and injury (such as inflammatory mediator production) were significantly upregulated. Correlation analysis of differential metabolites revealed an interconnected metabolic disorder network.ConclusionThis study preliminarily reveals the unique plasma metabolic profile of elderly patients with sepsis complicated by sarcopenia, which is characterized by the impact on core pathways such as branched-chain amino acid degradation, steroid hormone synthesis inhibition, and arachidonic acid metabolism activation. These findings systematically depict the potential metabolic imbalance networks associated with this disease state, provide new clues for understanding its pathophysiological mechanisms, and offer an important theoretical basis and potential targets for the future development of early diagnostic biomarkers and targeted intervention strategies for this disease.