Related Experiment Video
Updated: Jul 16, 2025
Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
Published on: November 29, 2024
Serum metabolomics analysis for quantification of muscle loss in critically ill patients: An explorative study
Leanne L G C Ackermans1, Julia L M Bels2, Benjamin Seethaler3
1Department of Traumatology, Maastricht University Medical Centre, the Netherlands; NUTRIM School for Nutrition and Translational Research in Metabolism, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, the Netherlands.
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 (RFcsa) 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 RFcsa were available. In patients with sequential RFcsa measurements, muscle loss was defined as the negative slope of the regression line fitted to the RFcsa 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.
