Abstracts Division 2
30. Serum metabolomics analysis for muscle loss in critically ill patients: an explorative study
Julia L.M. Bels1,2*, Leanne L.G.C. Ackermans2,3*, Benjamin Seethaler4, Maarten van Dinter1, Maryam Basrai4, Anna Schweinlin4, Marcel C.G. van de Poll1,2,5, Stephan C. Bischoff4, Jan Ten Bosch3, Taco Jan Blokhuis3
* JB and LA are joint first authors.
1Department of Intensive Care Medicine, Maastricht University Medical Centre, P. Debyelaan 25, 6202 AZ Maastricht, the Netherlands
2 NUTRIM School for Nutrition and Translational Research in Metabolism, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, the Netherlands
3Department of Traumatology, Maastricht University Medical Centre, The Netherlands
4 Institute of Nutritional Medicine, University of Hohenheim, Fruwirthstr. 12, 70599 Stuttgart, Germany
5 Department of Surgery, Maastricht University Medical Centre, The Netherlands
Abstract
Importance. During Intensive Care Unit (ICU) admission, patients demonstrate up to 15% muscle loss per week, contributing to neuromuscular weakness and complicating recovery and return to daily life. Metabolomics markers for muscle loss could aid in early detection of patients at risk and help guide resources (e.g. physical therapy, protein supplementation) to mitigate muscle loss.
Objective
To determine whether serum metabolites are associated with muscle mass and muscle loss.
Methods
Explorative, prospective cohort study including 38 ICU patients admitted between June and December 2021 with assessment of the cross-sectional area of the Rectus Femoris muscle using ultrasound (RFcsa) and serum metabolites using the Biocrates AbsoluteIDQ p180 kit. Seventeen patients were analyzed, for whom a baseline serum sample and RFcsa were available. 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. A Pearson correlation was calculated between baseline metabolites and baseline muscle mass and between baseline metabolites and muscle loss. To correct for multiple testing, the Benjamini-Hochberg procedure was used.
Results
Mean age was 62 (SD ±9) years and the cohort was predominantly male (76%). Four metabolites were significantly correlated to baseline muscle mass: PC_ae_C30_0 (R=0.5, p=0.034), Creatinine (R=0.5, p=0.041), 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), 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, no significant associations remained.
Conclusions and Relevance
This exploratory analysis found some metabolites to be associated with muscle mass and muscle loss, yet these associations did not remain after controlling for false discovery rate. In more targeted future research, these metabolites should further be explored to confirm or refute an association with muscle loss and determine their role as potential muscle loss marker.
NUTRIM | School of Nutrition and Translational Research in Metabolism
NUTRIM aims to contribute to health maintenance and personalised medicine by unraveling lifestyle and disease-induced derangements in metabolism and by developing targeted nutritional, exercise and drug interventions. This is facilitated by a state of the art research infrastructure and close interaction between scientists, clinicians, master and PhD students.
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