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Background: During Intensive Care Unit (ICU) admission, patients demonstrate up to 15% muscle loss per week, contributing to neuromuscular weakness, complicating recovery and delaying return to daily life. Biomarkers for muscle loss could aid in early detection of patients at risk and help guide resources to mitigate muscle loss, e.g. physical therapy and protein supplementation., Aims: To explore serum biomarkers for muscle mass and muscle loss in ICU patients using a metabolomics approach., Methods: Mechanically ventilated patients with an unplanned ICU admission between June and December 2021 were prospectively studied. The cross-sectional area of the rectus femoris muscle was assessed using ultrasound (RF csa ) and 188 serum metabolites were assessed using the Biocrates™ AbsoluteIDQ p180 kit for targeted metabolomics. Patients were eligible for analysis when a serum sample drawn within 5 days of ICU admission and at least 1 RF csa were available. In patients with sequential RF csa measurements, muscle loss was defined as the negative slope of the regression line fitted to the RF csa measurements per patient in the first 10 days of ICU admission. Correlations between baseline metabolite concentrations and baseline muscle mass, as well as between baseline metabolite concentrations and muscle loss were assessed using Pearson's test for correlations. To correct for multiple testing, the Benjamini-Hochberg procedure was used., Results: Seventeen patients were eligible for analysis. Mean age was 62 (SD ± 9) years and the cohort was predominantly male (76%). Four metabolites correlated with baseline muscle mass: creatinine (R = 0.5, p = 0.041), glycerophospholipid PC_ae_C30_0 (R = 0.5, p = 0.034) and two acylcarnitines: C14_2 (R = 0.5, p = 0.042) and C10_2 (R = 0.5, p = 0.049). For muscle loss, significant associations were found for histidine (R = -0.8, p = 0.002) and three glycerophospholipids; PC_aa_C40_2 (R = 0.7, p = 0.015), PC_ae_C40_1 (R = 0.6, p = 0.032) and PC_aa_C42_1 (R = 0.6, p = 0.037). After correction for multiple testing, no significant associations remained., Conclusions: This exploratory analysis found certain metabolites to be associated with muscle mass and muscle loss. Future research, specifically addressing these metabolites is necessary to confirm or refute an association with muscle loss and determine their role as potential muscle loss marker., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background & Aims: Sarcopenia is a muscle disorder associated with loss of muscle mass, strength and function. Early screening, diagnosis and treatment may improve outcome in different disease conditions. A wide variety of tools for estimation of muscle mass is available and each tool has specific technical requirements. However, different investigational settings and lack of homogeneity of populations influence the definition of gold standards, proving it difficult to systematically adopt these tools. Recently, the European Working Group on Sarcopenia in Older People (EWGSOP) published a revised recommendation (EWGSOP-2) and algorithm for using tools for screening and diagnosing sarcopenia. However, agreement of the EWGSOP2 criteria with other classifications is poor and although an overview of available tools is valuable, for the purpose of clinical decision-making the reverse is useful; a given scenario asks for the most suitable tools., Results: Tools were identified for screening, diagnostics and longitudinal monitoring of muscle mass. For each of these clinical scenarios the most appropriate tools were listed and for each technique their usability is specified based on sensitivity and specificity. Based on this information a specific recommendation is made for each clinical scenario., Conclusion: This narrative review provides an overview of currently available tools and future developments for different clinical scenarios such as screening, diagnosis and longitudinal monitoring of alterations in muscle status. It supports clinical decision-making in choosing the right tools for muscle mass quantification depending on the need within a given clinical scenario as well as the local availability and expertise., Competing Interests: Declaration of competing interest There is no conflict of interest., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)