Insulin sensitivity and metabolic flexibility decrease in response to bed rest, but the temporal and causal adaptations in human skeletal muscle metabolism are not fully defined. Here, we use an integrative approach to assess human skeletal muscle metabolism during bed rest and provide a multi-system analysis of how skeletal muscle and the circulatory system adapt to short- and long-term bed rest (German Clinical Trials: DRKS00015677). We uncover that intracellular glycogen accumulation after short-term bed rest accompanies a rapid reduction in systemic insulin sensitivity and less GLUT4 localization at the muscle cell membrane, preventing further intracellular glycogen deposition after long-term bed rest. We provide evidence of a temporal link between the accumulation of intracellular triglycerides, lipotoxic ceramides, and sphingomyelins and an altered skeletal muscle mitochondrial structure and function after long-term bed rest. An intracellular nutrient overload therefore represents a crucial determinant for rapid skeletal muscle insulin insensitivity and mitochondrial alterations after prolonged bed rest.
Background & aims: Optimal nutritional support during the acute phase of critical illness remains controversial. We hypothesized that patients with low skeletal muscle area and -density may specifically benefit from early high protein intake. Aim of the present study was to determine the association between early protein intake (day 2–4) and mortality in critically ill intensive care unit (ICU) patients with normal skeletal muscle area, low skeletal muscle area, or combined low skeletal muscle area and -density. Methods: Retrospective database study in mechanically ventilated, adult critically ill patients with an abdominal CT-scan suitable for skeletal muscle assessment around ICU admission, admitted from January 2004 to January 2016 (n = 739). Patients received protocolized nutrition with protein target 1.2–1.5 g/kg/day. Skeletal muscle area and -density were assessed on abdominal CT-scans at the 3rd lumbar vertebra level using previously defined cut-offs. Results: Of 739 included patients (mean age 58 years, 483 male (65%), APACHE II score 23), 294 (40%) were admitted with normal skeletal muscle area and 445 (60%) with low skeletal muscle area. Two hundred (45% of the low skeletal muscle area group) had combined low skeletal muscle area and -density. In the normal skeletal muscle area group, no significant associations were found. In the low skeletal muscle area group, higher early protein intake was associated with lower 60-day mortality (adjusted hazard ratio (HR) per 0.1 g/kg/day 0.82, 95%CI 0.73–0.94) and lower 6-month mortality (HR 0.88, 95%CI 0.79–0.98). Similar associations were found in the combined low skeletal muscle area and -density subgroup (HR 0.76, 95%CI 0.64–0.90 for 60-day mortality and HR 0.80, 95%CI 0.68–0.93 for 6-month mortality). Conclusions: Early high protein intake is associated with lower mortality in critically ill patients with low skeletal muscle area and -density, but not in patients with normal skeletal muscle area on admission. These findings may be a further step to personalized nutrition, although randomized studies are needed to assess causality.
Background: Manual muscle mass assessment based on Computed Tomography (CT) scans is recognized as a good marker for malnutrition, sarcopenia, and adverse outcomes. However, manual muscle mass analysis is cumbersome and time consuming. An accurate fully automated method is needed. In this study, we evaluate if manual psoas annotation can be substituted by a fully automatic deep learning-based method.Methods: This study included a cohort of 583 patients with severe aortic valve stenosis planned to undergo Transcatheter Aortic Valve Replacement (TAVR). Psoas muscle area was annotated manually on the CT scan at the height of lumbar vertebra 3 (L3). The deep learning-based method mimics this approach by first determining the L3 level and subsequently segmenting the psoas at that level. The fully automatic approach was evaluated as well as segmentation and slice selection, using average bias 95% limits of agreement, Intraclass Correlation Coefficient (ICC) and within-subject Coefficient of Variation (CV). To evaluate performance of the slice selection visual inspection was performed. To evaluate segmentation Dice index was computed between the manual and automatic segmentations (0 = no overlap, 1 = perfect overlap).Results: Included patients had a mean age of 81 ± 6 and 45% was female. The fully automatic method showed a bias and limits of agreement of -0.69 [-6.60 to 5.23] cm2, an ICC of 0.78 [95% CI: 0.74-0.82] and a within-subject CV of 11.2% [95% CI: 10.2-12.2]. For slice selection, 84% of the selections were on the same vertebra between methods, bias and limits of agreement was 3.4 [-24.5 to 31.4] mm. The Dice index for segmentation was 0.93 ± 0.04, bias and limits of agreement was -0.55 [1.71-2.80] cm2.Conclusion: Fully automatic assessment of psoas muscle area demonstrates accurate performance at the L3 level in CT images. It is a reliable tool that offers great opportunities for analysis in large scale studies and in clinical applications.
