Does Taking a Break Matter-Adaptations in Muscle Strength and Size Between Continuous and Periodic Resistance Training.

We aimed to compare the effects of periodic resistance training (RT) and continuous RT on muscle strength and size. Fifty-five healthy, untrained participants (age 32 ± 5 years) were randomized to periodic (PRT, n = 20 completed the study, 45% females) or continuous (CRT, n = 22 completed the study, 45% females) groups. PRT completed a 10-week RT, a 10-week detraining, and a second identical 10-week RT. CRT began with a 10-week non-RT, followed by a 20-week RT. RT included twice-weekly supervised whole-body RT sessions. Leg press (LP) and biceps curl (BC) one repetition maximum (1RM), countermovement jump (CMJ) height, muscle cross-sectional area (CSA) of vastus lateralis (VL), and biceps brachii (BB) using ultrasound imaging were measured twice at the beginning and every fifth week during the intervention. Both groups increased (p < 0.001) 1RM in LP and BC, CSA in VL and BB, and CMJ height with no differences between the groups. In PRT, 1RM in LP and BC, CSA in VL and BB, and CMJ height decreased during detraining (p < 0.05). During the first 5 weeks of retraining in PRT, increases in LP 1RM, and VL and BB CSA were greater than in CRT during Weeks 10-15 of their CRT (p < 0.01). PRT and CTR ended up in similar postintervention adaptations, as decreased muscle strength and size during detraining in PRT regained rapidly during retraining. Our results therefore suggest that trainees should not be too concerned about occasional short-term training breaks in their daily lives when it comes to lifelong strength training. Trial Registration: ClinicalTrials.gov identifier: NCT05553769.

A novel mean shape based post-processing method for enhancing deep learning lower-limb muscle segmentation accuracy.

This study aims at improving the lower-limb muscle segmentation accuracy of deep learning approaches based on Magnetic Resonance Imaging (MRI) scans, crucial for the diagnostic and therapeutic processes in musculoskeletal diseases. In general, segmentation methods such as U-Net deep learning neural networks can achieve good Dice Similarity Coefficient (DSC) values, e.g. around 0.83 to 0.91 on various cohorts. Some generic post-processing strategies have been studied to incorporate connectivity constraints into the resulting masks for the purpose of further improving the segmentation accuracy. In this paper, a novel mean shape (MS) based post-processing method is proposed, utilizing Statistical Shape Modelling (SSM) to fine-tune the segmentation output, taking into consideration the muscle anatomical shape. The methodology was compared to existing post-processing techniques and a commercial semi-automatic tool on MRI scans from two cohorts of post-menopausal women (10 Training, 8 Testing, voxel size 1.0x1.0x1.0 mm3). The MS based method obtained a mean DSC of 0.83 across the different analysed muscles and the best performance for the Hausdorff Distance (HD, 20.6 mm) and the Average Symmetric Surface Distance (ASSD, 2.1 mm). These findings highlight the feasibility and potential of using anatomical mean shape in post-processing of human lower-limb muscle segmentation task and indicate that the proposed method can be popularized to other biological organ segmentation mission.

Prognostic value of body composition measures in breast cancer patients treated with chemotherapy.

Breast cancer remains a significant public health issue, often resulting in severe side effects such as neutropenia, highlighting the need for reliable predictors of clinical outcomes. This study aimed to evaluate the predictive value of body composition measures for mortality, recurrence, and chemotherapy-induced neutropenia in patients with breast cancer following surgery and chemotherapy. We retrospectively analyzed 85 breast cancer patients who underwent surgery and chemotherapy between 2006 and 2016. Body composition was assessed using computed tomography (CT) or positron emission tomography (PET) at diagnosis and three years and five years post-diagnosis. Metrics included skeletal muscle area (SMA), skeletal muscle index (SMI), subcutaneous adipose tissue area (SAT), and visceral adipose tissue area (VAT). Longitudinal analysis revealed a decrease in muscle mass (P < 0.001 for both SMA and SMI) and nonsignificant changes in fat mass (P = 0.449 for SAT and P = 0.798 for VAT). A lower SMI at diagnosis was significantly associated with increased mortality (P = 0.019) and a higher incidence of grade 4 neutropenia (P = 0.008). There was no significant association between SMI at diagnosis and recurrence (P = 0.691). No associations were found between body composition measurements during the follow-up period and the clinical outcomes. Lower skeletal muscle mass at diagnosis is strongly associated with higher mortality and chemotherapy-induced complications in patients with breast cancer, highlighting the potential of readily available imaging techniques as valuable predictors of clinical outcomes.

Unusual presentation of PYGM gene mutation as late-onset McArdle disease with camptocormia: a case report.

BACKGROUND: Glycogen storage disease type 5 (McArdle disease) leads to a deficiency in the activity of myophosphorylase resulting in an impaired glucose utilization. The disease can be caused by a variety of mutations in the PYGM gene, and its typical clinical manifestation is muscles weakness within the first three decades of life. CASE PRESENTATION: In this case report we present the diagnostic work-up of a physically active 78-year-old Caucasian patient suffering from a 2-year history of progressive camptocormia including clinical, radiologic, histological, and genetic tests. There was no history of neuro-muscular diseases in the family. Serum CK levels were moderately increased while other blood/urine parameters were normal. Magnetic resonance imaging showed fatty remodeling of the muscles of the back. Histochemical examination of a muscle biopsy revealed the absence of myophosphorylase activity, while gene analysis identified a known early-onset McArdle mutation in the PYGM gene. CONCLUSION: This case highlights that the clinical spectrum of PYGM gene mutation typically manifest during adolescence, but it is also a differential diagnosis in late onset muscle disorders and emphases the investigation of the role of ACE inhibitors in this disease.

Examining the Safety of Dorsogluteal and Ventrogluteal Sites for Intramuscular Injection in Older Adults.

BACKGROUND: Muscle, subcutaneous tissue and total tissue thicknesses are important factors in successful intramuscular injection. Muscle mass decreases and subcutaneous tissue increases with age. This may negatively affect the safety and effectiveness of intramuscular injection in older adults by increasing the risk of bone contact and subcutaneous drug administration. Intramuscular injection sites should be evaluated in this respect, but no previous study has evaluated the most appropriate sites for safe and effective intramuscular injection in older adults. OBJECTIVES: This study aimed to examine the safety of dorsogluteal and ventrogluteal injection sites in older adults. METHODS: This cross-sectional study included 171 older adults who presented to the radiology clinic of a hospital between November 2022 and February 2023. We collected the study data using a descriptive characteristics form and an ultrasonographic measurement form. To complete the descriptive characteristics form, we interviewed the participants and measured their waist circumference, hip circumference, weight and height. Muscle, subcutaneous tissue and total tissue thicknesses at the ventrogluteal and dorsogluteal sites were determined by ultrasonography. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline. RESULTS: At the ventrogluteal and dorsogluteal sites, respectively, total tissue thicknesses were 59.43 ± 11.21 and 48.78 ± 9.68 mm, subcutaneous tissue thicknesses were 20.07 ± 6.64 and 22.97 ± 7.40 mm and muscle thicknesses were 40.13 ± 5.59 and 25.61 ± 4.30 mm. Tissue thicknesses at both sites differed according to sex, weight, hip circumference and waist circumference (p < 0.05). Although both sites were acceptable according to the tissue thickness thresholds for intramuscular injection given in the literature (subcutaneous tissue < 25 mm, total tissue > 35 mm), the ventrogluteal site was more advantageous in terms of greater muscle thickness and lower subcutaneous tissue thickness. CONCLUSIONS: The results of this study indicated that both the ventrogluteal and dorsogluteal sites are safe for intramuscular injections in older adults in terms of tissue thickness. However, the ventrogluteal site may be safer for older adults because of the lower risk of bone contact and subcutaneous injection. Further studies are needed on this subject. IMPLICATIONS FOR PRACTICE: This study is important in terms of determining the safe and effective gluteal site for IM injection in older people aged 65 and over, preventing complications that may arise from site selection, and developing nursing policies that consider older people as a special group in the selection of IM injection sites.

Reduced muscle mass is an important part of Global Leadership Initiative on Malnutrition criteria in nutritional diagnosis of hepatocellular carcinoma.

BACKGROUND: The Global Leadership Initiative on Malnutrition criteria (GLIM) was established to build a global consensus on the diagnostic criteria for malnutrition. The study aimed to assess the prevalence of the malnutrition diagnosed by GLIM criteria for patients with hepatocellular carcinoma (HCC), and to determine the role of the reduced muscle mass defined by CT scans in the GLIM criteria. METHODS: This cohort research was conducted on adult cirrhotic patients with HCC. The risk of malnutrition was screened by Nutritional Risk Screening 2002 (NRS-2002), and malnutrition was diagnosed by GLIM criteria. The third lumbar vertebrae (L3-SMI) were used to represent the muscle mass in GLIM criteria. The variables associated with overall mortality were assessed by multivariate Cox regression analyses. RESULTS: The incidence of malnutrition diagnosed by GLIM criteria was 49.7% (179/360) in patients with HCC. If reduced muscle mass was not included in GLIM criteria, the prevalence of malnutrition was 31.7% (114/360). GLIM-defined malnutrition (HR = 1.979, 95%CI 1.019-3.841, P = 0.044) was independently associated with overall mortality in patients with HCC. However, the GLIM-defined malnutrition (without muscle mass) was not associated with overall mortality (HR = 0.863, 95%CI 0.399-1.867, P = 0.709). CONCLUSIONS: Skeletal muscle mass is an integral component of the GLIM criteria for patients with HCC. The malnutrition is common in patients with HCC, and malnourishment is associated with higher overall mortality. GLIM criteria are recommended to assess the nutritional status of hospitalized patients with HCC, which is recommended and can be used as the basis for nutritional interventions.

Accelerated muscle mass estimation from CT images through transfer learning.

BACKGROUND: The cost of labeling to collect training data sets using deep learning is especially high in medical applications compared to other fields. Furthermore, due to variances in images depending on the computed tomography (CT) devices, a deep learning based segmentation model trained with a certain device often does not work with images from a different device. METHODS: In this study, we propose an efficient learning strategy for deep learning models in medical image segmentation. We aim to overcome the difficulties of segmentation in CT images by training a VNet segmentation model which enables rapid labeling of organs in CT images with the model obtained by transfer learning using a small number of manually labeled images, called SEED images. We established a process for generating SEED images and conducting transfer learning a model. We evaluate the performance of various segmentation models such as vanilla UNet, UNETR, Swin-UNETR and VNet. Furthermore, assuming a scenario that a model is repeatedly trained with CT images collected from multiple devices, in which is catastrophic forgetting often occurs, we examine if the performance of our model degrades. RESULTS: We show that transfer learning can train a model that does a good job of segmenting muscles with a small number of images. In addition, it was confirmed that VNet shows better performance when comparing the performance of existing semi-automated segmentation tools and other deep learning networks to muscle and liver segmentation tasks. Additionally, we confirmed that VNet is the most robust model to deal with catastrophic forgetting problems. CONCLUSION: In the 2D CT image segmentation task, we confirmed that the CNN-based network shows better performance than the existing semi-automatic segmentation tool or latest transformer-based networks.

Body composition and muscle composition phenotypes in patients on waitlist and shortly after liver transplant - results from a pilot study.

BACKGROUND: Sarcopenia is common in end-stage liver disease and negatively impacts patients awaiting or undergoing liver transplantation (LT). Magnetic resonance imaging (MRI) may be used to measure body composition and sarcopenia. We aimed to evaluate the feasibility of MRI-based LT body composition profiling, describe waitlist body composition, and assess the natural rate of change in body composition while on the waitlist and post-LT. METHODS: This prospective pilot study recruited adults listed for LT at an urban, tertiary care facility. Eighteen participants were scanned at time of waitlisting and 15 had follow-up MRIs (waitlist and/or post-LT). An 8-min MRI was used to measure body composition (AMRA® Researcher) including thigh fat-free muscle volume (FFMV) and fat infiltration (MFI), visceral (VAT) and abdominal subcutaneous (ASAT) adipose tissue volumes, and liver fat. A sex- and BMI invariant FFMV z-score (z-FFMV) was calculated, and muscle composition (MC) phenotypes were defined using the muscle assessment score (consisting of the FFMV z-score and sex-adjusted MFI). Rate of body composition change was calculated using mixed-effect modelling and is presented as rate per 30 days. RESULTS: At time of waitlisting, 73% of the 18 participants had high MFI and 39% had the adverse MC (low FFMV z-score and high MFI) phenotype. Seven participants received an LT. Post-LT serial MRIs, at a median of 147 days apart within the first 200 days post-LT, demonstrated increased z-FFMV 0.22 SDs/(30 days) (p = 0.002), VAT 0.23 (p < 0.001), and ASAT 0.52 (p = 0.001) L/(30 days), but no change in MFI (p = 0.200) nor liver fat (p = 0.232). CONCLUSION: MRI-based body composition profiling is feasible in LT patients and shortly after LT. This can be amended to routine clinical scans and may help in early identification of patients who may benefit from interventions to improve body composition. In addition, body composition changes significantly over time after LT.

Effectiveness of High-Protein Energy-Dense Oral Supplements on Patients with Malnutrition Using Morphofunctional Assessment with AI-Assisted Muscle Ultrasonography: A Real-World One-Arm Study.

Background: User-friendly tools for assessing nutrition status and interventions in malnourished patients are crucial. This study evaluated the effectiveness of a personalised nutrition intervention using a novel oral nutritional supplement and AI-supported morphofunctional assessment to monitor clinical outcomes in patients with disease-related malnutrition (DRM). Methods: This prospective observational study involved patients receiving concentrated high-protein, high-calorie ONS (cHPHC-ONS), per usual clinical practice. Comprehensive assessments were performed at baseline (B0) and three months (M3) post-intervention. Results: 65 patients participated in the study. Significant decreases were observed in the percentage weight loss from B0 (-6.75 ± 7.5%) to M3 (0.5 ± 3.48%) (p < 0.01), in the prevalence of malnutrition (B0: 93.4%; M3: 78.9%; p < 0.01), severe malnutrition (B0: 60.7%; M3: 40.3%; p < 0.01), and sarcopenia (B0: 19.4%; M3: 15.5%; p < 0.04). Muscle area increased (p = 0.03), and there were changes in the echogenicity of the rectus femoris muscle (p = 0.03) from B0 to M3. In patients aged ≥60, an increase in muscle thickness (p = 0.04), pennation angle (p = 0.02), and handgrip strength (p = 0.04) was observed. There was a significant reduction in the prevalence of malnutrition (B0: 93.4%; M3: 78.9%; p < 0.01) and severe malnutrition (B0: 60.7%; M3: 40.3%; p < 0.01). Conclusions: In patients with DRM, a personalised intervention with cHPHC-ONS significantly reduces the prevalence of malnutrition, severe malnutrition, and sarcopenia and improves muscle mass and function.

Influence of Body Composition Assessed by Computed Tomography on Mortality Risk in Young Women with Breast Cancer.

Background/Objectives: Increasing evidence indicates that body composition can significantly influence prognosis in women with breast cancer. However, alterations in body composition, particularly among young women (<40 years), remain largely unknown and underexplored. This study aimed to investigate the relationship of computed tomography (CT)-derived body composition with mortality rates among young women recently diagnosed with breast cancer, identifying the best-correlated cutoff value. Methods: This is a bi-set cohort study with retrospective data collection. Women newly diagnosed with ductal invasive breast cancer, aged 20 to 40 years, treated in reference oncology units were included. Body composition was assessed using CT scans at the third lumbar vertebra (L3) level, including muscle and adipose compartments. The outcome of interest was the incidence of overall mortality. A maximally selected log-rank Cox-derived analysis was employed to assess the cutoffs associated with mortality. Results: A total of 192 women were included before any form of treatment (median age of 35 years, IQ range: 31-37). Overall mortality occurred in 12% of the females. Stages III-IV were the most frequent (69.5%). Patients who died had a significantly lower muscle area index. CT-derived muscle area was inversely associated with mortality. Each 1 cm2/m2 decrease in skeletal muscle index increased the mortality hazard by 9%. Higher values of adiposity compartments were independently associated with higher mortality. Conclusions: Our study highlights the predictive significance of skeletal muscle area and adipose tissue in predicting survival among young women recently diagnosed with breast cancer.

Magnetic Resonance Imaging Biomarkers of Muscle.

This review is focused on the current status of quantitative MRI (qMRI) of skeletal muscle. The first section covers the techniques of qMRI in muscle with the focus on each quantitative parameter, the corresponding imaging sequence, discussion of the relation of the measured parameter to underlying physiology/pathophysiology, the image processing and analysis approaches, and studies on normal subjects. We cover the more established parametric mapping from T1-weighted imaging for morphometrics including image segmentation, proton density fat fraction, T2 mapping, and diffusion tensor imaging to emerging qMRI features such as magnetization transfer including ultralow TE imaging for macromolecular fraction, and strain mapping. The second section is a summary of current clinical applications of qMRI of muscle; the intent is to demonstrate the utility of qMRI in different disease states of the muscle rather than a complete comprehensive survey.

Skeletal Muscle Segmentation at the Level of the Third Lumbar Vertebra (L3) in Low-Dose Computed Tomography: A Lightweight Algorithm.

BACKGROUND: The cross-sectional area of skeletal muscles at the level of the third lumbar vertebra (L3) measured from computed tomography (CT) images is an established imaging biomarker used to assess patients' nutritional status. With the increasing prevalence of low-dose CT scans in clinical practice, accurate and automated skeletal muscle segmentation at the L3 level in low-dose CT images has become an issue to address. This study proposed a lightweight algorithm for automated segmentation of skeletal muscles at the L3 level in low-dose CT images. METHODS: This study included 57 patients with rectal cancer, with both low-dose plain and contrast-enhanced pelvic CT image series acquired using a radiotherapy CT scanner. A training set of 30 randomly selected patients was used to develop a lightweight segmentation algorithm, and the other 27 patients were used as the test set. A radiologist selected the most representative axial CT image at the L3 level for both the image series for all the patients, and three groups of observers manually annotated the skeletal muscles in the 54 CT images of the test set as the gold standard. The performance of the proposed algorithm was evaluated in terms of the Dice similarity coefficient (DSC), precision, recall, 95th percentile of the Hausdorff distance (HD95), and average surface distance (ASD). The running time of the proposed algorithm was recorded. An open source deep learning-based AutoMATICA algorithm was compared with the proposed algorithm. The inter-observer variations were also used as the reference. RESULTS: The DSC, precision, recall, HD95, ASD, and running time were 93.2 ± 1.9% (mean ± standard deviation), 96.7 ± 2.9%, 90.0 ± 2.9%, 4.8 ± 1.3 mm, 0.8 ± 0.2 mm, and 303 ± 43 ms (on CPU) for the proposed algorithm, and 94.1 ± 4.1%, 92.7 ± 5.5%, 95.7 ± 4.0%, 7.4 ± 5.7 mm, 0.9 ± 0.6 mm, and 448 ± 40 ms (on GPU) for AutoMATICA, respectively. The differences between the proposed algorithm and the inter-observer reference were 4.7%, 1.2%, 7.9%, 3.2 mm, and 0.6 mm, respectively, for the averaged DSC, precision, recall, HD95, and ASD. CONCLUSION: The proposed algorithm can be used to segment skeletal muscles at the L3 level in either the plain or enhanced low-dose CT images.

Poor muscle health and cardiometabolic risks associated with antidepressant treatment.

OBJECTIVE: This study aims to investigate whether antidepressant users display differences in fat distribution and muscle composition relative to non-users and to explore risk factors for developing cardiovascular disease (CVD) and type 2 diabetes. METHODS: The study used quantitative adipose and muscle tissue measures derived from magnetic resonance imaging data from UK Biobank (N = 40,174). Fat distribution and muscle composition of selective serotonin reuptake inhibitor (SSRI) and tricyclic antidepressant (TCA) users were compared with sex-, age-, and BMI-matched control individuals. Cox regression models were used to test for increased risk of developing CVD and type 2 diabetes. RESULTS: SSRI users had more visceral fat, smaller muscle volume, and higher muscle fat infiltration compared with matched control individuals. Female users showed a larger increase in BMI over time compared with male users. However, male users displayed an unhealthier body composition profile. Male SSRI users also had an increased risk of developing CVD. Both male and female TCA users showed lower muscle volume and an increased risk of developing type 2 diabetes. CONCLUSIONS: Adverse changes in body composition of antidepressant users are not captured by tracking the body weight or the BMI of the patients. These changes may lead to a worsened cardiometabolic risk profile.

Loss of Skeletal Muscle Mass During Neoadjuvant Chemotherapy for Pancreatic Cancer Is Related to the Continuation of S-1 Adjuvant Chemotherapy After Pancreatectomy.

BACKGROUND/AIM: Although perioperative chemotherapy has improved patient survival, sarcopenia may occur during chemotherapy owing to decreased food intake and physical strength. However, reports on the occurrence of sarcopenia and changes in body composition in patients with pancreatic cancer during neoadjuvant chemotherapy are scarce. This study aimed to determine the effect of changes in skeletal muscle mass during neoadjuvant chemotherapy on the S-1 adjuvant chemotherapy clinical course in patients who underwent perioperative chemotherapy and surgical resection. PATIENTS AND METHODS: We retrospectively enrolled 159 patients with pancreatic cancer who underwent neoadjuvant chemotherapy and surgical resection, followed by S-1 adjuvant chemotherapy. We evaluated changes in skeletal muscle mass during neoadjuvant chemotherapy using abdominal computed tomography and the SliceOmatic software. The association between the rate of change in skeletal muscle mass index (Δ%SMI) during neoadjuvant chemotherapy and the continuation of S-1 adjuvant chemotherapy was investigated. RESULTS: Eighty-eight (55.3%) patients lost skeletal muscle mass (Δ%SMI <0) during neoadjuvant chemotherapy with a significantly low S-1 adjuvant completion rate (p=0.02). Δ%SMI <0 was an independent risk factor for the continuation of S-1 adjuvant chemotherapy (hazard ratio=1.924, 95% confidence interval=1.002-3.695, p=0.049). Moreover, the lower the Δ%SMI, the lower the S-1 continuation rate (p=0.022). CONCLUSION: Loss of skeletal muscle mass during neoadjuvant chemotherapy for pancreatic cancer affected the continuation of S-1 adjuvant chemotherapy after pancreatic resection. Therefore, ameliorating loss of skeletal muscle mass during neoadjuvant chemotherapy should be carefully considered to improve the continuation rate of adjuvant chemotherapy and the survival of patients with pancreatic cancer.

Relative muscle indices and healthy reference values for sarcopenia assessment using T10 through L5 computed tomography skeletal muscle area.

Sarcopenia is the age-related loss of skeletal muscle mass and function. Computed tomography (CT) assessments of sarcopenia utilize measurements of skeletal muscle cross-sectional area (SMA), radiation attenuation (SMRA), and intramuscular adipose tissue (IMAT). Unadjusted SMA is strongly correlated with both height and body mass index (BMI); therefore, SMA must be adjusted for body size to assess sarcopenic low muscle mass fairly in individuals of different heights and BMI. SMA/height (rather than S M A / h e i g h t 2 ) provides optimal height adjustment, and vertebra-specific relative muscle index (RMI) equations optimally adjust for both height and BMI. Since L3 measurement is not available in all CT scans, sarcopenic low muscle mass may be assessed using other levels. Both a mid-vertebral slice and an inferior slice have been used to define 'L3 SMA', but the effect of vertebral slice location on SMA measurements is unexplored. Healthy reference values for skeletal muscle measures at mid- and inferior vertebra slices between T10 and L5, have not yet been reported. We extracted T10 through L5 SMA, SMRA, and IMAT at a mid-vertebral and inferior slice using non-contrast-enhanced CT scans from healthy, adult kidney donor candidates between age 18 and 73. We compared paired differences in SMA between the mid-vertebral slice versus the inferior slice. We calculated the skeletal muscle gauge as S M G HT = S M R A ∗ S M I HT . We used allometric analysis to find the optimal height scaling power for SMA. To enable comparisons with other published reference cohorts, we computed two height-adjusted measures; S M I HT = S M A / h e i g h t (optimal) and S M I H T 2 = S M A / h e i g h t 2 (traditional). Using the young, healthy reference cohort, we utilized multiple linear regression to calculate relative muscle index z-scores ( R M I HT , R M I H T 2 ), which adjust for both height and BMI, at each vertebra level. We assessed Pearson correlations of each muscle area measure versus age, height, weight, and BMI separately by sex and vertebra number. We assessed the differences in means between age 18-40 versus 20-40 as the healthy, young adult reference group. We reported means, standard deviations, and sarcopenia cutpoints (mean-2SD and 5th percentile) by sex and age group for all measures. Sex-specific allometric analysis showed that height to the power of one was the optimal adjustment for SMA in both men and women at all vertebra levels. Differences between mid-vertebra and inferior slice SMA were statistically significant at each vertebra level, except for T10 in men. S M I HT was uncorrelated with height, whereas S M I H T 2 was negatively correlated with height at all vertebra levels. Both S M I HT and S M I H T 2 were positively correlated with BMI at all vertebra levels. R M I HT was uncorrelated with BMI, weight, and height (minimal positive correlation in women at L3 inf , L4 mid , and L5 inf ) whereas R M I H T 2 was uncorrelated with BMI, but negatively correlated with height and weight at all levels. There were no significant differences in SMA between 18-40 versus 20-40 age groups. Healthy reference values and sarcopenic cutpoints are reported stratified by sex, vertebra level, and age group for each measure. Height to the power of one (SMA/height) is the optimal height adjustment factor for SMA at all levels between T10 mid through L5 inf . The use of S M A / h e i g h t 2 should be discontinued as it retains a significant negative correlation with height and is therefore biased towards identifying sarcopenia in taller individuals. Measurement of SMA at a mid-vertebral slice is significantly different from measurement of SMA at an inferior aspect slice. Reference values should be used for the appropriate slice. We report sarcopenic healthy reference values for skeletal muscle measures at the mid-vertebral and inferior aspect slice for T10 through L5 vertebra levels. Relative muscle index (RMI) equations developed here minimize correlation with both height and BMI, producing unbiased assessments of relative muscle mass across the full range of body sizes. We recommend the use of these RMI equations in other cohorts.

Adults born preterm have lower peripheral skeletal muscle area and strength.

Prematurity is associated with lower exercise capacity, which relies on the integrity of the cardiovascular, pulmonary, and skeletal muscle systems. Our animal model mimicking prematurity-associated conditions showed altered muscle composition and atrophy in adulthood. This study aimed to compare muscle composition and strength in adults born preterm versus full-term controls. This observational cohort study recruited 55 adults born preterm, ≤ 29 weeks' of gestation and 53 full-term controls who underwent musculoskeletal ultrasound imaging to assess morphology of the rectus femoris at rest and during a maximal voluntary contraction. Maximal voluntary contraction of the hands and legs were measured by manual dynamometry. In adults born preterm, there was lower muscle strength (handgrip: - 4.8 kg, 95% CI - 9.1, - 0.6; knee extensor: - 44.6 N/m, 95% CI - 63.4, - 25.8) and smaller muscle area (- 130 mm2, 95% CI - 207, - 53), which was more pronounced with a history of bronchopulmonary dysplasia. Muscle stiffness was increased in the preterm versus term group (0.4 m/s, 95% CI 0.04, 0.7). Prematurity is associated with alterations in skeletal muscle composition, area, and function in adulthood. These findings highlight the necessity to implement preventive and/or curative approaches to improve muscle development and function following preterm birth to enhance overall health in this population.

Distribution of MRI-derived T2 values as a biomarker for in vivo rapid screening of phenotype severity in mdx mice.

BACKGROUND: The pathology in Duchenne muscular dystrophy (DMD) is characterized by degenerating muscle fibers, inflammation, fibro-fatty infiltrate, and edema, and these pathological processes replace normal healthy muscle tissue. The mdx mouse model is one of the most commonly used preclinical models to study DMD. Mounting evidence has emerged illustrating that muscle disease progression varies considerably in mdx mice, with inter-animal differences as well as intra-muscular differences in pathology in individual mdx mice. This variation is important to consider when conducting assessments of drug efficacy and in longitudinal studies. We developed a magnetic resonance imaging (MRI) segmentation and analysis pipeline to rapidly and non-invasively measure the severity of muscle disease in mdx mice. METHODS: Wildtype and mdx mice were imaged with MRI and T2 maps were obtained axially across the hindlimbs. A neural network was trained to rapidly and semi-automatically segment the muscle tissue, and the distribution of resulting T2 values was analyzed. Interdecile range and Pearson Skew were identified as biomarkers to quickly and accurately estimate muscle disease severity in mice. RESULTS: The semiautomated segmentation tool reduced image processing time approximately tenfold. Measures of Pearson skew and interdecile range based on that segmentation were repeatable and reflected muscle disease severity in healthy wildtype and diseased mdx mice based on both qualitative observation of images and correlation with Evans blue dye uptake. CONCLUSION: Use of this rapid, non-invasive, semi-automated MR image segmentation and analysis pipeline has the potential to transform preclinical studies, allowing for pre-screening of dystrophic mice prior to study enrollment to ensure more uniform muscle disease pathology across treatment groups, improving study outcomes.

Comparative analysis of body composition using torso CT from PET/CT with bioelectrical impedance and muscle strength in healthy adults.

The role of torso computed tomography (CT) in evaluating body composition has been unexplored. This study assessed the potential of low-dose torso CT from positron emission tomography (PET)/CT for analyzing body composition and its relation to muscle strength. We retrospectively recruited 384 healthy Korean adults (231 men, 153 women) who underwent torso 18F-FDG PET/CT, bioelectrical impedance analysis (BIA), and muscle strength tests (handgrip strength [HGS] and knee extension strength [KES]). CT images were segmented into three compartments: torso volumetric, abdominal volumetric, and abdominal areal. Muscle amounts from each compartment were indexed to height (m2). BIA and HGS served as reference standards, with correlation coefficients (r) calculated. Torso muscle volumetric index (TorsoMVI) had the strongest correlations with BIA-derived values (r = 0.80 for men; r = 0.73 for women), surpassing those from the abdominal compartments. TorsoMVI was also correlated significantly with HGS (r = 0.39, p < 0.01) and differentiated between normal and possible sarcopenia in men (n = 225, 5960 ± 785 cm3/m2 vs. n = 6, 5210 ± 487 cm3/m2, p = 0.02). In women, KES correlated more strongly with muscle parameters than HGS. Despite gender-specific variations, torso CT-derived parameters show promise for evaluating body composition and sarcopenia.

A detailed analysis of body composition in relation to cardiopulmonary exercise test indices.

A cardiopulmonary exercise test (CPET) is a test assessing an individual's physiological response during exercise. Results may be affected by body composition, which is best evaluated through imaging techniques like magnetic resonance imaging (MRI). The aim of this study was to assess relationships between body composition and indices obtained from CPET. A total of 234 participants (112 female), all aged 50 years, underwent CPETs and whole-body MRI scans (> 1 million voxels). Voxel-wise statistical analysis of tissue volume and fat content was carried out with a method called Imiomics and related to the CPET indices peak oxygen consumption (V̇O2peak), V̇O2peak scaled by body weight (V̇O2kg) and by total lean mass (V̇O2lean), ventilatory efficiency (V̇E/V̇CO2-slope), work efficiency (ΔV̇O2/ΔWR) and peak exercise respiratory exchange ratio (RERpeak). V̇O2peak showed the highest positive correlation with volume of skeletal muscle. V̇O2kg negatively correlated with tissue volume in subcutaneous fat, particularly gluteal fat. RERpeak negatively correlated with tissue volume in skeletal muscle, subcutaneous fat, visceral fat and liver. Some associations differed between sexes: in females ΔV̇O2/ΔWR correlated positively with tissue volume of subcutaneous fat and V̇E/V̇CO2-slope with tissue volume of visceral fat, and, in males, V̇O2peak correlated positively to lung volume. In conclusion, voxel-based Imiomics provided detailed insights into how CPET indices were related to the tissue volume and fat content of different body structures.

A personalized clinical assessment: multi-sensor approach for understanding musculoskeletal health in the frail population.

BACKGROUND: Sarcopenia is a muscle disorder causing a progressive reduction of muscle mass and strength, but the mechanism of its manifestation is still partially unknown. The three main parameters to assess are: muscle strength, muscle volume or quality and low physical performance. There is not a definitive approach to assess the musculoskeletal condition of frail population and often the available tests to be performed in those clinical bedridden patients is reduced because of physical impairments. In this paper, we propose a novel instrumental multi-domain and non-invasive approach during a well-defined protocol of measurements for overcoming these limitations. A group of 28 bedridden elder people, subjected to surgery after hip fracture, was asked to perform voluntary isometric contractions at the 80% of their maximum voluntary contraction with the non-injured leg. The sensor employed before and/or during the exercise were: ultrasound to determine the muscle architecture (vastus lateralis); force acquisition with a load cell placed on the chair, giving an indication of the muscle strength; surface electromyography (EMG) for monitoring muscular electrical activity; time-domain (TD) near-infrared spectroscopy (NIRS) for evaluating muscle oxidative metabolism. RESULTS: A personalized "report card" for each subject was created. It includes: the force diagram (both instantaneous and cumulative, expected and measured); the EMG-force diagram for a comparison between EMG derived median frequency and measured force; two graphs related to the hemodynamic parameters for muscle oxidative metabolism evaluation, i.e., oxy-, deoxy-, total-hemoglobin and tissue oxygen saturation for the whole exercise period. A table with the absolute values of the previous hemodynamic parameters during the rest and the ultrasound related parameters are also included. CONCLUSIONS: In this work, we present the union of protocols, multi-domain sensors and parameters for the evaluation of the musculoskeletal condition. The novelties are the use of sensors of different nature, i.e., force, electrical and optical, together with a new way to visualize and combine the results, by means of a concise, exhaustive and personalized medical report card for each patient. This assessment, totally non-invasive, is focused on a bedridden population, but can be extended to the monitoring of rehabilitation progresses or of the training of athletes.