Prevalence and predictive factors for obesity, sarcopenia and sarcopenic obesity in patients with chronic stroke
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- Published online on: January 10, 2025 https://doi.org/10.3892/wasj.2025.312
- Article Number: 24
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Copyright : © Nimphan et al. This is an open access article distributed under the terms of Creative Commons Attribution License [CC BY 4.0].
Abstract
Introduction
A progressive decline in muscle mass is associated with ageing. This decrease in muscle mass begins at the age of ~40 years (1). The rate of muscle mass loss accelerates with age, ranging from 4-5% per decade to as high as 10% per decade in individuals >70 years of age (2,3). Sarcopenia, which is defined as a syndrome characterized by a decline of skeletal muscle plus low muscle strength and/or physical performance, occurs at a higher prevalence with ageing, leading to multiple adverse outcomes including falls, fractures, disability, hospitalization or increased mortality rates (4-8).
Stroke significantly affects skeletal muscle. Studies have demonstrated that muscle mass loss occurs early following a stroke, and that the rate of decline is more rapid than that observed with the normal ageing process (9,10). In addition to muscle atrophy resulting from neuronal deafferentation, other mechanisms accounting for the changes in muscle mass, such as catabolic-anabolic imbalance, systemic inflammatory activation and local muscle metabolic alterations, have also been reported (11-13). Furthermore, inactivity, fatigue, deconditioning and poor nutrition, which are common in patients following a stroke, also contribute to sarcopenia (11,13). Since muscle is a critical organ for daily function, the loss of muscle mass has a particularly detrimental impact on patients following a stroke. For example, Jang et al (14) and Matsushita et al (15) found that sarcopenia occurring early following stroke was associated with a poorer functional outcome.
Patients following a stroke exhibit an increased prevalence of sarcopenia when compared to age- and sex-matched individuals who have not suffered a stroke (16,17). According to a recent meta-analysis, the pooled prevalence of sarcopenia in patients with chronic stroke, obtained from three studies in the USA, Taiwan and Korea, was found to be 33.6 [95% confidence interval (CI), 16.5-56.4] (18). The prevalence from each country was 16.8, 48.6 and 41.8%, respectively (14,18-20). The authors of the meta-analysis also reported that there was a limited number of studies focusing on sarcopenia in the stroke population (18). Differences in ethnicity, genetics, diet, lifestyle and living environment play a role in the development of sarcopenia. Furthermore, since sarcopenia and obesity can co-exist, the term sarcopenic obesity (SO) has emerged. SO is characterized as a clinical condition of a high body fat percentage and low lean mass; this combination is considered to have a negative synergistic effect on patient outcomes. SO and sarcopenia should be considered differently, since individuals with obesity may have a comparable or even higher absolute skeletal muscle mass due to a higher overall body mass. The criteria for sarcopenia, which are based on appendicular skeletal mass divided by height squared, were not considered suitable for the diagnosis of SO. Instead, the relative reduction in muscle mass per body mass, as demonstrated by appendicular skeletal mass per body mass, was considered more appropriate (21). SO has not yet been studied in detail in patients with chronic stroke, and the consensus criteria were only recently launched by the European Society for Clinical Nutrition and Metabolism (ESPEN) and the European Association for the Study of Obesity (EASO), in 2022(21). Thus, the aim of the present study was to examine the prevalence of and predictive factors for obesity, sarcopenia and SO in patients with chronic stroke in Thailand.
Patients and methods
Patients and inclusion and exclusion criteria
The present cross-sectional descriptive study was conducted in patients with chronic stroke who attended the Outpatient Rehabilitation Clinic at Srinagarind Hospital (the university hospital of the Faculty of Medicine, Khon Kaen University Khon Kaen, Thailand) between October, 2020 and January, 2022. Srinagarind Hospital is a super-tertiary care facility and the largest university hospital in Northeastern Thailand. The inclusion criteria were as follows: i) An age ≥18 years; ii) ischaemic or haemorrhagic stroke with an onset >6 months; iii) an ability to understand and follow commands; and iv) an ability to provide consent to participate in the study. The exclusion criteria were as follows: i) Unstable vital signs; ii) the use of medication that may affect body mass or composition, e.g. steroids or diuretics; and iii) having underlying diseases that may affect walking performance, e.g., chronic joint pain conditions. The present study was approved by the Khon Kaen University Ethics Committee for Human Research (Ref. HE631455). The study was conducted in accordance with local legislation and institutional requirements. Each patient signed a written consent form prior to participation.
Clinical assessment
Clinical assessment included body mass, height, body mass index (BMI), handgrip strength, National Institutes of Health Stroke Scale (NIHSS), upper and lower extremities Motricity Index (MI) (22), Modified Rankin Scale (MRS), Functional Ambulation Category (FAC), maximum calf circumference, maximal grip strength (the maximum value achieved from three attempts of forcefully grasping the device using the Baseline Hydraulic Hand dynamometer), 10-metre walk test (a series of 10-metre walks, with the average gait speed calculated from the 6 m covered between the 2-metre and 8-metre points divided by the time to cover this distance), Oral Health Assessment Tool (OHAT), and nutritional status (Mini Nutritional Assessment-Short Form: MNA-SF). Cognitive impairment was defined as a score of ≤23 from the Montreal Cognitive Assessment (23).
Muscle mass was measured using dual-energy X-ray absorptiometry (DXA). Appendicular skeletal muscle mass measurement was performed in a standard manner with a Lunar Prodigy DXA scanner (GE Healthcare; Cytiva). The appendicular skeletal muscle index (ASMI) was calculated as appendicular lean body mass (kg) divided by the square of the height (m2).
Definition of obesity, sarcopenia and SO. Obesity
Obesity was determined based on the BMI, which was calculated as the body mass divided by the square of height (kg/m2). Individuals classified as obese were defined according to the Southeast Asia cut-off point of BMI ≥25 kg/m2 (24).
Sarcopenia. The diagnosis of sarcopenia was based on the Asian Working Group for Sarcopenia (AWGS) 2019 consensus update. Sarcopenia was defined as a ‘low muscle mass’ plus ‘low hand grip strength’ and/or ‘low physical performance’. The cut-off values of ASMI were as follows: Low muscle mass <7.0 kg/m2 in males and <5.4 kg/m2 in females measured using DXA, low hand grip strength <28.0 kg for males and <18.0 kg for females measured using the handgrip dynamometer, reduced physical performance using gait speed <1.0 m/sec (25).
SO. SO was diagnosed according to the European Society for Clinical Nutrition and Metabolism (ESPEN) and the European Association for the Study of Obesity (EASO) consensus (21). The diagnosis of SO was determined if the participant fulfilled the following two criteria: i) Altered skeletal muscle functional parameters assessed by hand grip strength; and ii) altered body composition defined as an increased fat mass (FM%) and reduced muscle mass assessed as appendicular lean muscle divided by body mass (ALM/BM) (21). As regards FM%, the definition from the study by Lee et al (26) we used, which identified high FM% in the Asian population with an age ≥40 years as >25.8% for males and >36.5% for females; these values represent the two highest quintiles of the study population. The definition of a reduced muscle mass based on ALM/BM was defined as <29.1% in males and <23.0% for females, which are the values representing two standard deviations (SDs) from the mean of the young reference group aged 20-39 years (27).
Sample size calculation
The sample size was estimated using a single population proportion formula, considering the proportion of patients who had sarcopenia to be 35%. This proportion was obtained from a previous study conducted in the able-bodied population in Thailand, with a confidence level of 95% and a margin of error of 10% (28). The required sample size was then 88 participants.
Statistical analysis
Outcomes were assessed for normality using the Shapiro-Wilk test. Normally distributed, non-normally distributed, and categorical data are presented as the mean ± SD, median (25 and 75th percentile; (p25, p75), and number (%), respectively. Differences in the baseline characteristics were analysed using one-way ANOVA for continuous variables and Fisher's exact test or a Chi-squared test for categorical variables. When one-way ANOVA indicated significant differences, Bonferroni post hoc tests were performed to assess pairwise comparisons between groups. Univariate logistic regression analysis was performed to evaluate individual effect sizes and the statistical significance of each parameter, reported in crude odds ratio and 95% confidence interval (CI). Factors included in the model were established factors (age and sex) (29,30) and factors that were found to have P-values <0.25(31) (NIHSS, OHAT, FAC, calf circumference and MNA-SF). BMI, maximal hand grip and ASMI were not included as variables in the analysis as these factors were used in the diagnosis of obesity, sarcopenia and SO. Due to the exploratory nature of the study, a full-model approach was used; thus, all factors were entered in a multivariable logistic regression model (32). The results are reported as adjusted odds ratios and 95% CI values. A value of P<0.05 was considered to indicate a statistically significant difference. Statistical analyses were performed using Stata (Stata Statistical Software: Release 18. College Station, TX: Stata Corp LLC).
Results
A total of 88 participants were initially included in the study; however, following the exclusion of incomplete data, the final sample consisted of 84 participants (58 men and 26 women). Their average age was 58.3 years. Almost two thirds (64.6%) of the patients had ischaemic stroke, and more than half of the patients had left hemiparesis (59.5%). The median duration following the stroke was 2.2 years and ~14% had suffered from recurrent strokes.
The prevalence of obesity, sarcopenia and SO for the patients with chronic stroke was 26.2, 21.4 and 23.8%, respectively (Fig. 1). A total of 7 participants met the criteria for both sarcopenia and SO, 10 participants met the criteria for both obesity and SO and 3 participants met the criteria for obesity, sarcopenia and SO. All of them were included in the SO group, resulting in a final total of 20 participants in the SO group (Fig. 1). Males were found to have sarcopenia or SO (50%) more often than females (34.6%). The prevalence of sarcopenia increased with age, with 39.5% of individuals <60 years old affected, increasing to 51.2% in those aged ≥60 years (Table I and Fig. 2).
 | Table IBaseline demographic and clinical characteristics of the patients with chronic stroke (n=84). |
There were significant differences in BMI, NIHSS, FAC, calf circumference, MNA-SF, maximal hand grip and ASMI among the groups. Univariate logistic regression analysis revealed that MNA-SF and calf circumference were associated with obesity. However, only calf circumference [adjusted odds ratio (aOR), 1.38, 95% CI, 1.08-1.77] was a significant predictive factor for obesity in multivariable logistic regression analysis.
As regards sarcopenia, two factors were significant in the both univariate and multivariable logistic regression analysis: The MNA-SF score (aOR, 0.70; 95% CI, 0.53-0.94), and calf circumference (aOR, 0.66; 95% CI, 0.49-0.89).
OHAT, NIHSS and FAC were significant factors associated with SO in the univariate logistic regression analysis. However, the significant predictive factors for SO in the multivariable logistic regression analysis were being male (aOR, 7.96; 95% CI, 1.05-60.49) and FAC (aOR, 0.15; 95% CI, 0.04-0.55) (Table II).
| Table IIFactors associated with obesity, sarcopenia and sarcopenic obesity in patients with chronic stroke. |
Discussion
The aim of the present study was to examine the prevalence of and predictive factors for obesity, sarcopenia and SO in patients with chronic stroke in Thailand. It was found that the prevalence of obesity, sarcopenia and SO for patients with chronic stroke was 26.2, 21.4 and 23.8%, respectively. Overall, the prevalence of obesity, when including obesity cases that were overlapping with SO and sarcopenia, was 41.6%, which was higher than that in a previous study in Brazil (26.0%), but comparable to that in a study in Korea (43.5%) (33,34). It was found that the only predictive factor for obesity was calf circumference.
Similar to multiple previous studies, males and older-aged individuals exhibited a higher prevalence of sarcopenia and SO (35,36). In able-bodied populations, the prevalence of SO was reported to be less than half of the prevalence of sarcopenia, although the age of inclusion into the studies was higher than that in the present study (29,35,36). Notably, patients with chronic stroke exhibited a similarly high prevalence of sarcopenia and SO. This may suggest that following a stroke, patients have a higher gain in body fat compared to the able-bodied population. This notion is supported by a previous systematic review demonstrated that fat mass significantly increased between 6 and 12 months post-stroke with a pooled mean increase of nearly 2 kg (37).
When focusing only on the diagnosis of sarcopenia, it was noted that 28 patients fulfilled the criteria for sarcopenia (10 patients were classified as SO in the present study), leading to a prevalence of sarcopenia of 33.3%. The prevalence of sarcopenia in patients following stroke in the present study was comparable to a previous study on patients with acute stroke in Japan (32.5%) (38), although the age of the participants in the present study was lower (median age, of 58.5 vs. 72 years). The prevalence of sarcopenia was lower in the present study than that in previous studies in Korea (41.8%) (14) and Taiwan (48.6%) (19), but higher than that in a study conducted in the USA (16.8%) (20). The higher prevalence observed in Korea and Taiwan may be due to the older age of the patients recruited (age, 64.3±13.0 years) (14) and (64.6±15.1 years) (19), respectively (mean ± SD), compared to the present study (age, 58.3±9.5 years). The lower prevalence in the USA may be due to the inclusion criteria of that study, which were limited to participants with stroke who had mild to moderate hemiplegic gait deficits and had completed conventional rehabilitation therapy. Different diagnostic criteria and participant characteristics may also contribute to the differences in prevalence. Previous studies in Japan and Korea used similar criteria to define sarcopenia and found that the prevalence of sarcopenia in patients with stroke was 39.7 and 53.5% (39,40). The results of the present study are comparable to the pooled prevalence estimate of sarcopenia in patients with chronic stroke (≥6 months) in a recent systematic review and meta-analysis (34%) (18). The lower prevalence of sarcopenia in the present study may be attributed to the lower average age of the participants.
It was found that calf circumference and nutritional status determined using the MNA-SF score were predictive factors for sarcopenia. Calf circumference was positively and moderately to highly associated with appendicular skeletal muscle and skeletal muscle index; thus, it was recommended as a surrogate marker of muscle mass for the diagnosis of sarcopenia (41). This finding confirms the importance of calf circumference and supports earlier studies that used calf circumference as a screening test itself or as an additional part of a screening test to increase the diagnostic accuracy for sarcopenia (41,42). The importance of nutritional status for sarcopenia, as highlighted in the present study, aligns with findings from previous studies across various populations (43-47). Several mechanisms may explain this. A reduced protein intake and low vitamin D levels are associated with muscle mass loss (48,49). Malnutrition leads to decreased levels of insulin-like growth factor-1 and growth factors which are essential for muscle growth (50). Malnutrition may increase the production of reactive oxygen and nitrogen species, causing oxidative stress, which reduces the regenerative potential of skeletal muscle and leads to muscle dysfunction (51). Moreover, in patients with disability, such as following a stroke, there may be a possible inter-association between disability and malnutrition, which leads to further detrimental effects (52). A recent longitudinal study demonstrated that malnutrition preceded the onset of sarcopenia (53). These findings, together with those of multiple previous studies, highlight the importance of screening the nutritional status in patients with stroke or older adults who are at risk of developing sarcopenia, in order to provide early intervention to prevent the progression of malnutrition and sarcopenia (52-54). It was found that MNA-SF was significantly associated with confirmed and severe sarcopenia and it was noted that the assessment of sarcopenia in individuals with a MNA-SF score <13 can be beneficial (46).
SO is an emerging clinical condition characterized by excessive fat mass in the presence of reduced muscle mass (55). SO produces a double metabolic burden from low muscle mass and excess adiposity. This negatively affects the muscle and gives rise to muscle anabolic resistance, and to deteriorating cardio-metabolic and physical function (55,56). Formerly, there was no universal definition of SO. Researchers classify individuals as having SO if they have appendicular mass fulfilling the criteria of sarcopenia in the presence of a body mass index in the obese category (55). SO has a negative effect on patients following a stroke, as it has been shown to be a predictive factor for physical limitations, falls and all-cause mortality (57-59). Additionally, it has been found to be associated with a lower level of activities of daily living capability in patients with stroke (60). Although the prevalence of SO in participants with stroke was previously reported by Matsushita et al (60), that study was published before the standard criteria were established and it was conducted in a cohort with sub-acute stroke. Compared to the previous study by Matsushita et al (60), which demonstrated the prevalence of simple obesity, sarcopenia and SO as 17, 32 and 28%, respectively in 376 patients with sub-acute stroke, the present study revealed a lower prevalence of sarcopenia and SO. However, the cut-off point used herein for high body fat percentage (>25.8% for males and >36.5% for females) (27) was lower than the cut-off point used in the previous study (≥27% in males and ≥38% in females) (60). This may be due to the older age of their participants, with an average age of 77.5 years. It should be noted that the criteria for SO in the study by Matsushita et al (60) required the patient to fulfil the criteria of sarcopenia and to have a higher cut-off body fat percentage to define obesity. By contrast, SO, as defined in the present study, was based on the ESPEN and the European EASO consensus (21).
Being male and the ambulation status assessed by FAC were found to be predictive factors for SO in the present study. Being male increases the probability of developing SO, while a higher FAC (indicating a better walking ability) decreases the probability of having SO. The strong association with functional ability was previously observed in the study by Matsushita et al (60), which found that SO, but not sarcopenia or obesity, was significantly associated with FIM scores in participants with sub-acute stroke. Similarly, Auyeung et al (57) demonstrated that SO could predict the incidence of physical limitations in older women. Furthermore, Broadwin et al (61) demonstrated that an increased percentage of fat mass and a decreased percentage of fat-free mass were significantly associated with decreased functional ability in both females and males.
A notable strength of the present study is that its diagnosis of sarcopenia and SO followed the latest AWGS 2019 consensus, the ESPEN and the EASO consensus. However, the present study had several limitations which should be mentioned. Although DXA is considered highly accurate, fast and non-invasive, it cannot delineate intramuscular fat and lean body mass. Thus, the lean body mass measured from DXA may overestimate the real muscle mass in patients with stroke (62,63). In addition, the present study was a single-centre study; thus, the generalizability of the data may be limited. The cross-sectional design of the study limits the ability to claim causative associations between each factor and the outcomes of obesity, sarcopenia and SO. The effect size of association may have to be interpreted with caution. The sample size calculation was based on a prevalence study design, which may result in insufficient power for logistic regression analysis. It is recommended that the sample size for logistic regression analysis should be at least 500; small to moderate sample sizes may overestimate the effect size (64,65). Consequently, these findings should be considered to be preliminary. In the future, incorporating additional predictors, such as genetic markers, lifestyle factors, vitamin D levels, micronutrient status and inflammatory biomarkers could lead to a more comprehensive model (48,66-68). A prospective study with a larger population could provide greater statistical power to examine the associations between obesity, sarcopenia and SO.
In conclusion, in the present study, sarcopenia and SO were observed in almost half of the participants with chronic stroke. Calf circumference was a predictor for both obesity and sarcopenia. The nutritional status assessed using MNA-SF was a predictor for sarcopenia. In addition, male sex and FAC were predictive factors for SO.
Acknowledgements
Not applicable.
Funding
Funding: The present study received funding from the Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand (grant no. IN64215).
Availability of data and materials
The data generated in the present study may be requested from the corresponding author.
Authors' contributions
CN, PA, CS and JS were responsible for the research conceptualization, study design and manuscript drafting. CN and JS were involved in data collection, analysis and interpretation. CN and JS confirm the authenticity of all the raw data All authors (CN, PA, CS, and JS) contributed to the manuscript drafting and read and approved the submitted version.
Ethics approval and consent to participate
Ethical approval for the present study was obtained from the Khon Kaen University Ethics Committee for Human Research (Ref. HE631455). The study was conducted in strict accordance with the principles outlined in the Declaration of Helsinki and in compliance with all relevant guidelines and regulations governing human research ethics. Each patient provided written informed consent prior to participation.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Use of artificial intelligence tools
During the preparation of this work, AI tools were used to improve the readability and language of the manuscript, and subsequently, the authors revised and edited the content produced by the AI tools as necessary, taking full responsibility for the ultimate content of the present manuscript.
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