Serum metabolomics analysis for quantification of muscle loss in critically ill patients: An explorative study.

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.

Clinical evaluation of automated segmentation for body composition analysis on abdominal L3 CT slices in polytrauma patients.

INTRODUCTION: Sarcopenia is a muscle disease that involves loss of muscle strength and physical function and is associated with adverse health effects. Even though sarcopenia has attracted increasing attention in the literature, many research findings have not yet been translated into clinical practice. In this article, we aim to validate a deep learning neural network for automated segmentation of L3 CT slices and aim to explore the potential for clinical utilization of such a tool for clinical practice. MATERIALS AND METHODS: A deep learning neural network was trained on a multi-centre collection of 3413 abdominal cancer surgery subjects to automatically segment muscle, subcutaneous and visceral adipose tissue at the L3 lumbar vertebral level. 536 Polytrauma subjects were used as an independent test set to show generalizability. The Dice Similarity Coefficient was calculated to validate the geometric similarity. Quantitative agreement was quantified using Bland-Altman's Limits of Agreement interval and Lin's Concordance Correlation Coefficient. To determine the potential clinical usability, randomly selected segmentation images were presented to a panel of experienced clinicians to rate on a Likert scale. RESULTS: Deep learning results gave excellent agreement versus a human expert operator for all of the body composition indices, with Concordance Correlation Coefficient for skeletal muscle index of 0.92, Skeletal muscle radiation attenuation 0.94, Visceral Adipose Tissue index 0.99 and Subcutaneous Adipose Tissue Index 0.99. Triple-blinded visual assessment of segmentation by clinicians correlated only to the Dice coefficient, but had no association to quantitative body composition metrics which were accurate irrespective of clinicians' visual rating. CONCLUSION: A deep learning method for automatic segmentation of truncal muscle, visceral and subcutaneous adipose tissue on individual L3 CT slices has been independently validated against expert human-generated results for an enlarged polytrauma registry dataset. Time efficiency, consistency and high accuracy relative to human experts suggest that quantitative body composition analysis with deep learning should is a promising tool for clinical application in a hospital setting.

Screening, diagnosis and monitoring of sarcopenia: When to use which tool?

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.

MAS: Standalone Microwave Resonator to Assess Muscle Quality.

Microwave-based sensing for tissue analysis is recently gaining interest due to advantages such as non-ionizing radiation and non-invasiveness. We have developed a set of transmission sensors for microwave-based real-time sensing to quantify muscle mass and quality. In connection, we verified the sensors by 3D simulations, tested them in a laboratory on a homogeneous three-layer tissue model, and collected pilot clinical data in 20 patients and 25 healthy volunteers. This report focuses on initial sensor designs for the Muscle Analyzer System (MAS), their simulation, laboratory trials and clinical trials followed by developing three new sensors and their performance comparison. In the clinical studies, correlation studies were done to compare MAS performance with other clinical standards, specifically the skeletal muscle index, for muscle mass quantification. The results showed limited signal penetration depth for the Split Ring Resonator (SRR) sensor. New sensors were designed incorporating Substrate Integrated Waveguides (SIW) and a bandstop filter to overcome this problem. The sensors were validated through 3D simulations in which they showed increased penetration depth through tissue when compared to the SRR. The second-generation sensors offer higher penetration depth which will improve clinical data collection and validation. The bandstop filter is fabricated and studied in a group of volunteers, showing more reliable data that warrants further continuation of this development.

Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients.

Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is a potential bottleneck in early rapid detection and quantification of sarcopenia. A prototype deep learning neural network was trained on a multi-center collection of 3413 abdominal cancer surgery subjects to automatically segment truncal muscle, subcutaneous adipose tissue and visceral adipose tissue at the L3 lumbar vertebral level. Segmentations were externally tested on 233 polytrauma subjects. Although after severe trauma abdominal CT scans are quickly and robustly delivered, with often motion or scatter artefacts, incomplete vertebral bodies or arms that influence image quality, the concordance was generally very good for the body composition indices of Skeletal Muscle Radiation Attenuation (SMRA) (Concordance Correlation Coefficient (CCC) = 0.92), Visceral Adipose Tissue index (VATI) (CCC = 0.99) and Subcutaneous Adipose Tissue Index (SATI) (CCC = 0.99). In conclusion, this article showed an automated and accurate segmentation system to segment the cross-sectional muscle and adipose area L3 lumbar spine level on abdominal CT. Future perspectives will include fine-tuning the algorithm and minimizing the outliers.