Sarcopenia in patients following stroke: an overlooked problem

Personalised screening tool for early detection of sarcopenia in stroke patients: a machine learning-based comparative study

BACKGROUND

Sarcopenia is a common complication in patients with stroke, adversely affecting recovery and increasing mortality risk. However, no standardised tool exists for its screening in this population. This study aims to identify factors influencing sarcopenia in patients with stroke, develop a risk prediction model and evaluate its predictive performance.

METHODS

Data from 794 patients with stroke were analysed to assess demographic and clinical characteristics. Variable selection was performed using least absolute shrinkage and selection operator (LASSO) regression, followed by multivariate regression analysis. Logistic regression (LR), random forest (RF) and XGBoost algorithms were used to construct prediction models, with the optimal model subjected to external validation. Internal validation was conducted via bootstrap resampling, and external validation involved an additional cohort of 159 patients with stroke. Model performance was assessed using the area under the curve (AUC), calibration curves and decision curve analysis (DCA).

RESULTS

Seven variables were identified through LASSO and multivariate regression analysis. The LR model achieved the highest AUC (0.805), outperforming the RF (0.796) and XGBoost (0.780) models. Additionally, the LR model exhibited superior accuracy, precision, recall, specificity and F1-score. External validation confirmed the LR model's robustness, with an AUC of 0.816. Calibration and DCA curves demonstrated their accuracy and clinical applicability.

CONCLUSIONS

A predictive model, presented as a nomogram and an online risk calculator, was developed to assess sarcopenia risk in patients with stroke. Early screening using this model may facilitate timely interventions and improve patient outcomes.

Risk factors of stroke-related sarcopenia: a systematic review and meta-analysis

Background

The presence of sarcopenia at the time of stroke may deteriorate the rehabilitation and functional outcomes. There is no consensus on the factors associated with stroke-related sarcopenia because previous studies produced inconsistent and disputed results. Therefore, we screened the possible risk factors by meta-analysis.

Methods

Studies published before March 2024 on risk factors with stroke-related sarcopenia were searched through PubMed, Embase, Web of Science, CINAHL, Cochrane Library, CNKI, Wan Fang, CBM, and VIP library databases. Two researchers independently screened the articles to extract the information and to evaluate their quality. Meta-analysis was then performed using Revman 5.4 software to determine the significant risk factors for patients with stroke-related sarcopenia.

Results

A total of 14 studies (n = 3,113) were selected to determine the following factors that were statistically significant in patients with stroke-related sarcopenia: Age (OR = 1.04; 95% CI: 1.02, 1.06; P < 0.0001), tube feeding (OR = 3.98; 95% CI: 2.12, 7.47; P < 0.0001), pre-stroke sarcopenia (OR = 1.84; 95% CI: 1.39, 2.43; P < 0.0001), atrial fibrillation (OR = 1.53; 95% CI: 1.15, 2.02; P = 0.003), NIHSS score (OR = 1.48; 95% CI: 1.21, 1.81; P = 0.0001), and osteoporosis (OR = 1.801; 95% CI: 58, 2.04; P < 0.00001). BMI (P = 0.71), FOIS (P = 0.80), time since stroke (P = 0.34), and calf circumference reduction (P = 0.48) were not identified as risk or protective factors after stroke (P < 0.05).

Conclusion

Our results identified various risk factors for stroke-related sarcopenia which should be considered and studied by healthcare organizations and professionals to improve the health of stroke patients.

Systematic Review Registration

PROSPERO, Identifier CRD42024545757.

Should adrenal incidentaloma patients be evaluated for muscle mass, function, and quality? A cross-sectional study

PURPOSE

Our study evaluated skeletal muscle mass, function and quality among mild autonomous cortisol secretion (MACS) patients and non-functioning adrenal incidentaloma (NFAI) patients in comparison with the control group without adrenal mass.

METHODS

63 NFAI (49 female, 14 male) and 31 MACS (24 female, 7 male) patients were included in the study. As the control group, 44 patients (31 women, 13 men) who were known to have no radiological adrenal pathology on computed tomography or magnetic resonance imaging performed for other reasons were selected. After recording the laboratory parameters of the patients, anthropometric measurements, handgrip strength test with dynamometer, SARC-F survey and bioelectrical impedance analysis (BIA) measurements were performed.

RESULTS

There was no statistical difference among the groups in terms of age, gender, and BMI parameters. Handgrip strength (HGS), skeletal muscle mass (SMM) index (SMM/BMI), and skeletal muscle quality (HGS/SMM), values used to evaluate muscle strength and quality, were found to be significantly lower in both the MACS and NFAI groups compared to the control group (p = 0.004, p = 0.012 and p = 0.034 respectively). This significance was also present in women subgroup analyses (p = 0.002, p = 0.037 and p = 0.039 respectively), but these parameters lost their statistical significance in men. In the correlation analysis of the female subgroup, 24-h free urine cortisol value was inversely proportional to skeletal muscle quality (rs = -0.417, p = 0.008).

CONCLUSION

Our study showed that there is a decrease in muscle mass and function in female AI patients, and this decrease is more severe in MACS patients. These results may suggest that mild cortisol excess also has negative effects on skeletal muscle metabolism.

Prevalence and risk factors of sarcopenia in patients with stroke: a systematic review and meta-analysis

Stroke is the second leading cause of death and the third-leading cause of disability in the world. The skeletal muscles play a key role in disability following stroke. Although many studies have reported the prevalence and risk factors of sarcopenia in patients with stroke, the results have not been synthesized. This systematic review was conducted to explore the prevalence and risk factors of sarcopenia in patients with stroke. PubMed, Embase, The Cochrane Library, Web of Science, Chinese Biomedical Literature (CBM), China National Knowledge Infrastructure (CNKI), Weipu Database (VIP), Wanfang Database were comprehensively searched for studies exploring the prevalence and risk factors of sarcopenia in patients with stroke from inception to October 5th, 2022. This review included 19 studies involving 4148 subjects. The total prevalence of sarcopenia in patients with stroke is 46% (95% confidence interval, CI: 40% to 52%, I2 = 94%, P < 0.001). Age (MD = 0.43, 95%CI: 0.06 to 0.81, I2 = 88.3%, P < 0.001), stroke duration (MD = 0.20, 95%CI: 0.05 to 0.34, I2 = 37.2%, P = 0.189), length of hospital stay (MD = 0.40, 95%CI: 0.20 to 0.59, I2 = 0, P = 0.393), pneumonia history (OR = 3.21, 95%CI: 1.98 to 5.20, I2 = 0, P = 0.343), female (OR = 1.36, 95%CI: 1.09 to 1.70, I2 = 48.7%, P = 0.069), albumin (MD = -0.43, 95%CI: -0.58 to -0.28, I2 = 13.7%, P < 0.001), hemoglobin (MD = -0.47, 95%CI: -0.62 to -0.32, I2 = 0, P = 0.687) were risk factors for sarcopenia in patients with stroke. The current analysis indicated sarcopenia was common in patients with stroke. Age, low albumin, low hemoglobin, stroke duration, length of hospital stay, and pneumonia history were the risk factors for sarcopenia in patients with stroke. This review will provide evidence for the screening and early identification of high-risk groups and the prevention of sarcopenia to improve the quality of life of stroke patients.

Predicting sarcopenia risk in stroke patients: a comprehensive nomogram incorporating demographic, anthropometric, and biochemical indicators

Objective

Although there is a strong correlation between stroke and sarcopenia, there has been a lack of research into the potential risks associated with post-stroke sarcopenia. Predictors of sarcopenia are yet to be identified. We aimed at developing a nomogram able to predict sarcopenia in patients with stroke.

Methods

The National Health and Nutrition Examination Survey (NHANES) cycle year of 2011 to 2018 was divided into two groups of 209 participants-one receiving training and the other validation-in a random manner. The Lasso regression analysis was used to identify the risk factors of sarcopenia, and a nomogram model was created to forecast sarcopenia in the stroke population. The model was assessed based on its discrimination area under the receiver operating characteristic curve, calibration curves, and clinical utility decision curve analysis curves.

Results

In this study, we identified several predictive factors for sarcopenia: Gender, Body Mass Index (kg/m2), Standing Height (cm), Alkaline Phosphatase (ALP) (IU/L), Total Calcium (mg/dL), Creatine Phosphokinase (CPK) (IU/L), Hemoglobin (g/dL), and Waist Circumference (cm). Notably, female patients with stroke exhibited a higher risk of sarcopenia. The variables positively associated with increasing risk included Alkaline Phosphatase, Body Mass Index, Waist Circumference, and Hemoglobin, while those negatively associated with risk included Height, Total Calcium, and Creatine Phosphokinase. The nomogram model demonstrated remarkable accuracy in distinguishing between training and validation sets, with areas under the curve of 0.97 and 0.90, respectively. The calibration curve showcased outstanding calibration, and the analysis of the decision curve revealed a broad spectrum of beneficial clinical outcomes.

Conclusion

This study creates a new nomogram which can be used to predict pre-sarcopenia in stroke. The new screening device is accurate, precise, and cost-effective, enabling medical personnel to identify patients at an early stage and take action to prevent and treat illnesses.

Ischemic stroke and sarcopenia have an asymmetric bidirectional relationship based on a two-sample Mendelian randomization study

Background

We investigated the potential relationship between age-related conditions, particularly sarcopenia and ischemic stroke (IS), through a two-sample Mendelian randomization (MR) study.

Methods

We conducted a two-sample bidirectional MR study to investigate the relationship between sarcopenia and stroke. Genetic instruments for sarcopenia were derived from the UK Biobank, while data on IS and its subtypes were obtained from the MEGASTROKE consortium. Inverse variance weighting (IVW) served as the primary analytical method. Additionally, heterogeneity and pleiotropy were assessed to ensure the robustness of the findings.

Results

The analysis indicates a negative correlation between appendicular lean mass (ALM) and small vessel stroke (SVS; OR = 0.790, 95% CI: 0.703-0.888, p < 0.001), a positive correlation with cardioembolic stroke (CES; OR = 1.165, 95% CI: 1.058-1.284, p = 0.002), and no causal relationship with any ischemic stroke (AIS) or large artery stroke (LAS). Additionally, SVS is negatively associated with right-hand grip strength (OR = 0.639, 95% CI: 0.437-0.934, p = 0.021), while AIS, LAS, and CES do not exhibit a causal relationship with grip strength. Furthermore, no causal relationship was identified between left-hand grip strength, usual walking pace, and IS or its subtypes. MR analysis reveals only a negative association between CES and usual walking pace (OR = 0.989, 95% CI: 0.980-0.998, p = 0.013), with no associations found between other IS subtypes and sarcopenia-related traits.

Conclusion

This study demonstrates that a reduction in ALM and right-hand grip strength is associated with SVS, whereas decreased ALM may serve as a protective factor against CES. Conversely, our analysis suggests that CES can impact walking speed. Overall, these findings provide valuable insights into the prevention and treatment of these conditions.

Sarcopenia, frailty prevalence, and related factors in patients with stroke: A cross-sectional analytical study from Turkey

BACKGROUND

Sarcopenia and frailty may develop at varying rates depending on the metabolic, structural, and functional changes in the muscle structure after stroke.

OBJECTIVE

To evaluate the prevalence of sarcopenia and frailty in patients with stroke and affecting clinical parameters.

METHODS

This was a cross-sectional and analytical study. Twenty-six (49.1%) women and 27 men (50.9%) with stroke with an average age of 60.2 ± 10.3 (median = 62) years were included in the study. To evaluate physical performance, balance, and mobility, we used the 4-meter walk test (4-MWT), timed up and go test, chair sit and stand test, and the Berg Balance Scale (BBS). Sarcopenia was diagnosed according to the Asian Working Group for Sarcopenia 2019 criteria. The SARC-F scale was used for sarcopenia.

RESULTS

The average stroke duration was 21.6 ± 18.2 months. Prefrailty was present in 14 (26.4%) patients, frailty was present in 34 (64.2%) patients, and two (3.8%) patients were normal. Sarcopenia was detected in 28 (52.8%) patients. According to handgrip strength, the prevalence of low muscle strength was 17% (n= 9). In the sarcopenic group, female sex (p= 0.005) was more frequent, 4-MWT was longer (p= 0.003), chair stand test results were lower (p< 0.001), the clinical frailty scale (CFS) was higher (p< 0.001), fall risk was higher compared with the BBS (p< 0.001), there was lower HG strength (p= 0.009), lower malnutrition scores (PNI, prognostic nutritional index, p< 0.001), and lower lymphocyte counts (p= 0.037). EAT-10 scores and dysphagia presence (p= 0.026), the presence of malnutrition (p< 0.021) and Nottingham Health Profile score (p< 0.001) were higher in the sarcopenic group.

CONCLUSION

Sarcopenia and frailty frequently develop in patients with chronic stroke, with low lymphocyte counts, physical performance, muscle strength, and mobility and balance scores, and it is associated with poor prognostic factors including high disease severity, dysphagia, malnutrition, risk of falling, and poor quality of life.

Trajectories of the Prevalence of Sarcopenia in the Pre- and Post-Stroke Periods: A Systematic Review

Interventions for stroke-related sarcopenia in patients with stroke are needed, but the details of the target population are unclear. This systematic review aimed to identify trajectories of the prevalence of sarcopenia in the pre- and post-stroke periods and to determine the diagnostic criteria used in patients with stroke. We searched for literature in six databases: MEDLINE, EMBASE, Web of Science, Cochrane Central Register of Controlled Trials, CINAHL, and Ichushi-web (in Japanese). We included 1627 studies in the primary screening, and 35 studies were finally included. Of the 35 studies, 32 (91.4%) included Asian patients, and the criteria of the Asian Working Group for Sarcopenia was mainly used as the diagnostic criteria. Nineteen studies used muscle strength and muscle mass to diagnose sarcopenia, whereas a full assessment, including physical performance, was performed in five studies. The estimated prevalences of sarcopenia in pre-stroke, within 10 days of stroke, and from 10 days to 1 month after stroke were 15.8%, 29.5%, and 51.6%, respectively. Sarcopenia increased by approximately 15% from pre-stroke to 10 days, and increased by approximately 20% from 10 days to 1 month. Healthcare providers should note that the prevalence of sarcopenia increases during the acute phase in patients with stroke.

Stroke-Related Sarcopenia among Two Different Developing Countries with Diverse Ethnic Backgrounds (Cross-National Study in Egypt and China)

The prevalence of stroke-related sarcopenia differs according to the diagnostic criteria, the cut-off point for assessment, and ethnicity. Egypt and China are developing countries with different races where no research concerning stroke-related sarcopenia has been performed yet. We aimed to evaluate the prevalence of possible sarcopenia and confirmed sarcopenia among Egyptian and Chinese stroke survivors using the Asian Working Group of Sarcopenia (AWGS-2019) criteria and to assess the variables associated with the prevalence rate. A prospective cross-sectional study was carried out among 200 Egyptian and 195 Chinese stroke survivors from 2019 to 2021 using a structural health questionnaire. A hand-held dynamometer was used to measure grip strength. Anthropometric measures were used to estimate muscle mass. Data were analyzed using SPSS statistics version 20. p-values < 0.05 were considered statistically significant. The prevalence of possible sarcopenia ranged from 20.0% to 34.4% among Egyptian and Chinese groups, except for the Egyptian females where it was 52.0%. The prevalence of sarcopenia in both populations ranged from 13.6% to 18.6%. Pre-stroke independent variables that accelerated possible sarcopenia were age, history of dyslipidemia, diabetes mellitus, and ischemic heart disease, but stroke severity was a post-stroke risk factor. Age was the only pre-stroke variable for sarcopenia, while quitting smoking and having good nutritional status were variables for the reduction of possible sarcopenia. Quitting smoking, having a good nutritional status, and early rehabilitation reduced sarcopenia development. Controlling vascular risk factors, enhancing rehabilitation, and nutritional therapy are protective measures against sarcopenia. Longitudinal studies are required to identify further risk factors.

Aging after stroke: how to define post-stroke sarcopenia and what are its risk factors?

BACKGROUND

Sarcopenia, generally described as "aging-related loss of skeletal muscle mass and function", can occur secondary to a systemic disease.

AIM

This project aimed to study the prevalence of sarcopenia in chronic ambulatory stroke survivors and its associated risk factors using the two most recent diagnostic criteria.

DESIGN

A cross-sectional observational study.

SETTING

A scientific laboratory.

POPULATION

Chronic stroke.

METHODS

Twenty-eight ambulatory chronic stroke survivors (12 females; mean age=57.8±11.8 years; time after stroke=76±45 months), hand-grip strength, gait speed, and appendicular skeletal muscle mass (ASM) were measured to define sarcopenia. Risk factors, including motor impairment and spasticity, were identified using regression analysis.

RESULTS

The prevalence of sarcopenia varied between 18% and 25% depending on the diagnostic criteria used. A significant difference was seen in the prevalence of low hand grip strength on the affected side (96%) when compared to the contralateral side (25%). The prevalence of slow gait speed was 86% while low ASM was present in 89% of the subjects. Low ASM was marginally negatively correlated with time since stroke and gait speed, but no correlation was observed with age, motor impairment, or spasticity. ASM loss, bone loss and fat deposition were significantly greater in the affected upper limb than in the affected lower limb. Regression analyses showed that time since stroke was a factor associated with bone and muscle loss in the affected upper limb, spasticity had a protective role for muscle loss in the affected lower limb, and walking had a protective role for bone loss in the lower limb.

CONCLUSIONS

The prevalence of sarcopenia in stroke survivors is high and is a multifactorial process that is not age-related. Different risk factors contribute to muscle loss in the upper and lower limbs after stroke.

CLINICAL REHABILITATION IMPACT

Clinicians need to be aware of high prevalence of sarcopenia in chronic stroke survivors. Sarcopenia is more evident in the upper than lower limbs. Clinicians also need to understand potential protective roles of some factors, such as spasticity and walking for the muscles in the lower limb.

Paving the way for applying GLIM criteria in clinical practice and research: how to define mild to moderate and severe reduced muscle mass

PURPOSE

Among approaches to adjust skeletal muscle mass (SMM) for body size, adjustment for body mass index (BMI) correlated better with functional measures. To enhance applicability of GLIM criteria, we report grade 1 and grade 2 reduced muscle mass cut-offs for SMM adjusted by BMI.

METHODS

301 young, healthy adults involving students, patient relatives and staff working in the university hospital were included. SMM was estimated by bio-impedance analysis (BIA). SMM index (BMI) [SMMI(BMI)] was calculated as SMM/BMI. Grade 1 low muscle mass (LMM) was defined as "mean young SMMI(BMI)-one standard deviation" and grade 2 LMM as "mean young SMMI(BMI)-two standard deviations".

RESULTS

Mean age was 26.5 ± 4.6 (62.1% male). Grade 1 vs grade 2 LMM thresholds were 1.189 and 0.954 kg/BMI vs 1.049 and 0.823 kg/BMI in males and females, respectively.

CONCLUSION

This is the first report identifying stage 1 and stage 2 LMM thresholds for SMMI adjusted by BMI. Studies are needed to evaluate their predictive validity.