Search Results for: Sarcopenia

Circulation, Volume 150, Issue Suppl_1, Page ASu1007-ASu1007, November 12, 2024. Introduction:Previous work has associated frailty with mortality and neurologic outcomes in patients resuscitated from cardiac arrest. The Clinical Frailty Scale (CFS) – a 9-point ordinal scale with higher values indicating a higher degree of frailty – is the most used measure in critically ill patients, however there is no established gold-standard. Temporal muscle thickness (TMT) is a measure of sarcopenia, which represents a specific physical frailty phenotype.Aim:We assessed the correlation between TMT, measured on head computed tomography (CT), and the CFS in patients resuscitated from cardiac arrest.Hypothesis:TMT negatively correlates with pre-arrest CFS.Methods:We enrolled adult patients who were resuscitated from cardiac arrest and had a head CT obtained within 48 hours of collapse at our regional academic facility. Patient characteristics were recorded in our prospective registry. Study investigators prospectively obtained pre-arrest CFS through a patient or surrogate interview. Blinded study investigators reviewed head CTs and measured TMT bilaterally at 5mm above the superior orbital rim, adjacent to the Sylvian fissure. We then averaged the two measurements within each patient. We summarized patient demographics and arrest characteristics with descriptive statistics. We calculated Spearman rank-order correlation to assess the relationship between average TMT and CFS.Results:Of 20 included subjects, mean age was 57 (SD 13), 6 (30%) were female, 17 (85%) were out-of-hospital arrests, 6 (32%) had an initial shockable rhythm, and median Charlson Comorbidity Index was 2 (IQR 0-3). Mean TMT in our cohort was 6.7 millimeters (SD 2.6) and median CFS was 3 (IQR 2-6). There was a negative, moderate correlation between average TMT and CFS; r(18) = -0.50,p= 0.02.Conclusions:We found that TMT exhibits a moderate negative correlation with the CFS, providing face validity that TMT may be a tool to measure frailty. Measuring TMT in patients with early head CTs after resuscitation from cardiac arrest may allow for characterization of pre-arrest frailty.

Objectives
To identify and map the available evidence for whether a test of handgrip strength (HGS) and/or the chair stand test (CST) have been used as screening tools for the detection of sarcopenia in elderly individuals within primary care settings.

Design
This study was designed as a scoping review, in accordance with the methodological framework for scoping reviews, developed by Arksey and O’Malley, and using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews.

Data sources
A literature search was conducted to identify relevant articles listed in PubMed and Scopus databases up to 16 October 2023.

Eligibility criteria
Studies including individuals aged ≥60 years who had undergone assessments of HGS and/or the CST in primary care settings were included.

Data extraction and synthesis
Following the article selection process, based on predetermined criteria for inclusion and exclusion, the selected articles were analysed regarding population demographics, the setting in which the research was conducted, the study design, used diagnostic tools and reported results.

Results
The search yielded 282 unique articles, of which 7 were included in the final analysis. All seven included articles had a cross-sectional study design, whereof one also had a longitudinal 3-year follow-up. The number of participants ranged from 75 to 719. Three of the articles used the diagnostic criteria presented by European Working Group on Sarcopenia in Older People (EWGSOP), two used the criteria by Asian Working Group for Sarcopenia 2019, one used the EWGSOP2 criteria and one applied both the EWGSOP and EWGSOP2 criteria to their data. All the articles used HGS to assess muscle strength. CST was not used for this purpose in any of the articles, although it was used to estimate physical performance or as part of the short physical performance battery. The prevalence of sarcopenia in the included articles was 12.0%–20.7%, while the prevalence of possible sarcopenia was 69.9%–73.3% and that of probable sarcopenia was 25.5%–94%.

Conclusion
None of the included articles aimed to study a test of HGS and/or the CST as screening tools for the detection of sarcopenia. However, four of the articles diagnosed possible or probable sarcopenia by using a test of HGS and/or the CST among elderly patients in a primary care setting. There is a need for more research to elucidate whether a test of HGS and/or the CST might be used for screening of sarcopenia in primary care.

Objectives
To examine the association between the sarcopenia index (SI) and the risk of intraprocedural events and post-discharge death during percutaneous coronary intervention (PCI).

Design
A retrospective cohort study.

Setting
The study was conducted at a teaching hospital in Western China.

Participants
The participants were patients aged 45 years and older who underwent PCI at the hospital and had an estimated glomerular filtration rate (eGFR) of ≥15 mL/min/1.73 m2. Patients who died during hospitalisation, as well as those with unknown death dates, those lost to follow-up and those with missing information for the SI calculation, were excluded.

Primary and secondary outcome measures
The SI was calculated as serum creatinine/cystatin C (Cr/CysC) x 100. The high-SI group was defined as the highest quartile, while the remaining participants were included in the low-SI group. Intraprocedural events included intraprocedural coronary slow flow (CSF)/coronary artery no-reflow (CNR) and malignant ventricular arrhythmia (MVA). In the event of death, the date of death was recorded.

Results
The study included 497 patients who underwent PCI in our hospital, of whom 369 (74.25%) were males. A total of 57 (11.47%) patients developed CSF, 100 (20.12%) developed CNR and 4 (0.8%) developed MVA. Forty-four (8.85%) patients died post-discharge. The proportion of patients in the low-SI group who developed CSF was higher than those who did not (16.94% vs 9.65%, p=0.027). In addition, the average SI was lower in patients who developed CSF than in those who did not (81.99 vs 87.11, p=0.043). After adjusting for possible confounding factors, logistic regression analysis showed that the risk of CSF in the low-SI group was higher than that in the high-SI group (OR = 2.01, 95% CI: 1.04 to 3.89). In addition, it was found that the lower the SI, the higher the risk of CSF (OR = 0.983, 95% CI: 0.967 to 0.999).

Conclusions
Patients with lower SI had a greater risk of developing CSF, and the lower the SI, the higher the risk of CSF. However, these data suggest that SI is not associated with CNR and the risk of post-discharge death in patients after PCI.

To the Editor Dr Conte and colleagues assert that concern about marked weight loss induced by glucagon-like peptide–1 (GLP-1)–based antiobesity medications causing physical frailty or sarcopenia is not supported by data. We believe that reassurance is premature. The loss of skeletal muscle mass (SMM) during weight loss is poorly described and has not been directly measured for weight loss resulting from GLP-1 agonists. The Figure in the Viewpoint by Conte and colleagues assumes that SMM is a constant 50% of fat-free mass (FFM). This assumption is not based on actual accurate measurement of SMM. The proportion of FFM that is SMM is highly variable and decreases with advancing age; as a result, FFM is not a surrogate measurement of SMM. The loss of SMM in older patients with obesity is a particular concern. Previous studies have demonstrated an exaggerated loss of FFM in older men and women during weight loss and regain of fat and very little FFM or muscle. Loss of excessive muscle during weight loss in older people is associated with increased mortality risk. This is particularly important for older people, as decreased muscle mass (but not FFM) is associated with increased risk of disability, hip fracture, and mortality. There is an urgent need for studies that accurately measure muscle mass, strength, and mobility during GLP-1 agonist–induced weight loss and regain of weight after they are stopped, especially in older people. Until then, clinicians and patients should take measures to maintain muscle mass and strength during treatment and cessation of these potent agents.

Introduction
Sarcopenia is characterised by the progressive weakening of muscle function that occurs with age. This condition frequently leads to frailty, disability and even death. Research on sarcopenia prevention is growing. Digital health exercise interventions are increasingly gaining attention in this field, with the rapid advancement of the internet and the influence of the COVID-19. However, there is a lack of empirical support for their effectiveness. Our study aims to assess the effect of digital health exercise intervention on sarcopenia in older persons, specifically focusing on its ability to improve muscle strength, muscle mass and physical performance.

Methods and analysis
Searching will be performed in the following 11 databases (Medline, Embase, Cochrane Central Register of Controlled Trials, CINAHL, PsycINFO, WOS, Scopus, CBM, CNKI, WANFANG and VIP) for published trials and 2 trial registries (ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform) for unpublished trials. Google Scholar will be used to find grey literature. The criterion of inclusion will be clinical trials involving digital health exercise interventions in older adults (≥60 years) diagnosed with sarcopenia (possible, confirmed or severe sarcopenia). For data synthesis, we will use a summary table to show the major characteristics of selected trials and a summary graph to demonstrate the risk of bias for each outcome using RoB 2, which will be further discussed in a narrative synthesis. The possibility of meta-analysis for quantitative data will be assessed according to the homogeneity analysis of the trials, using the methods of fixed or random effects model. If meta-analysis is possible, subgroup analysis and sensitivity analysis will be performed as well. Publication bias will be assessed through the use of the funnel plot and Egger’s linear regression test when an adequate number of trials are available. Finally, the Grading of Recommendations, Assessment, Development and Evaluation approach will be used to classify the certainty of evidence body into four categories (high, moderate, low and very low).

Ethics and dissemination
The findings of the systematic review will be shared through publishing in a peer-reviewed journal and presentation at appropriate conferences. Since we will not be using specific patient data, ethical approval is unnecessary.

PROSPERO registration number
CRD42024516930.

Circulation, Volume 150, Issue 16, Page 1288-1298, October 15, 2024. Recent studies have shown that pharmacologic weight loss with glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and combination therapies is approaching magnitudes achieved with surgery. However, as more weight loss is achieved, there is concern for potential adverse effects on muscle quantity, composition, and function. This primer aims to address whether muscle-related changes associated with weight loss treatments such as GLP-1 RAs may be maladaptive (ie, adversely affecting muscle health or function), adaptive (ie, a physiologic response to weight loss maintaining or minimally affecting muscle health or function), or perhaps an enhanced response to weight loss (ie, improved muscle health or function after treatment). Based on contemporary evidence with the addition of studies using magnetic resonance imaging, skeletal muscle changes with GLP-1 RA treatments appear to be adaptive: changes in muscle volume z-score indicate a change in muscle volume that is commensurate with what is expected given aging, disease status, and weight loss achieved, and the improvement in insulin sensitivity and muscle fat infiltration likely contributes to an adaptive process with improved muscle quality, lowering the probability for loss in strength and function. Nevertheless, factors such as older age and prefrailty may influence the selection of appropriate candidates for these therapies because of risk for sarcopenia. Several pharmacologic treatments to maintain or improve muscle mass designed in combination with GLP-1–based therapies are under development. For future development of GLP-1–based therapies (and other therapies) designed for weight loss, as well as for patient-centered treatment optimization, the introduction of more objective and comprehensive ways of assessing muscle health (including accurate and meaningful assessments of muscle quantity, composition, function, mobility, and strength) is important for the substantial numbers of patients who will likely be taking these medications well into the future.

Objectives
This study aims to determine whether machine learning can identify specific combinations of long-term conditions (LTC) associated with increased sarcopenia risk and hence address an important evidence gap—people with multiple LTC (MLTC) have increased risk of sarcopenia but it has not yet been established whether this is driven by specific combinations of LTC.

Design
Decision trees were used to identify combinations of LTC associated with increased sarcopenia risk. Participants were classified as being at risk of sarcopenia based on maximum grip strength of

Objectives
We aimed to develop an automated method for measuring the volume of the psoas muscle using CT to aid sarcopenia research efficiently.

Methods
We used a data set comprising the CT scans of 520 participants who underwent health check-ups at a health promotion centre. We developed a psoas muscle segmentation model using deep learning in a three-step process based on the nnU-Net method. The automated segmentation method was evaluated for accuracy, reliability, and time required for the measurement.

Results
The Dice similarity coefficient was used to compare the manual segmentation with automated segmentation; an average Dice score of 0.927 ± 0.019 was obtained, with no critical outliers. Our automated segmentation system had an average measurement time of 2 min 20 s ± 20 s, which was 48 times shorter than that of the manual measurement method (111 min 6 s ± 25 min 25 s).

Conclusion
We have successfully developed an automated segmentation method to measure the psoas muscle volume that ensures consistent and unbiased estimates across a wide range of CT images.