Association Between Periodic Limb Movement and Sarcopenia: Insights From Polysomnography and Bioelectrical Impedance Analysis
Article information
Abstract
Objectives
Sarcopenia, characterized by loss of skeletal muscle mass and function, is a growing health issue, especially in the elderly. Sleep disorders have recently been implicated as potential contributors to its development. This study investigated the association between Periodic Limb Movements during Sleep (PLMS) and sarcopenia using polysomnography (PSG) and bioelectrical impedance analysis (BIA).
Methods
We analyzed the data of 663 patients (404 males and 259 females) who underwent type I PSG and BIA at Samsung Medical Center from January 2019 to May 2024. PLMS was defined as a periodic limb movement index (PLMI) of ≥15/h. Sarcopenia was defined based on skeletal muscle index (SMI) cutoffs of <7 kg/m2 for males and <5.7 kg/m2 for females. Statistical analyses included chi-squared tests, Fisher’s exact tests, t-tests, and Wilcoxon rank-sum tests using R version 4.3.1.
Results
PLMI of ≥15/h was observed in 204 males and 126 females. Among males, those with PLMS had significantly lower SMI (8.02±0.66 kg/m2 vs. 8.19±0.68 kg/m2) and fat-free mass index (18.90±1.48 kg/m2 vs. 19.31±1.51 kg/m2). In contrast, females with PLMS showed higher muscle mass percentage (37.97%±1.10% vs. 36.89%±3.66%). Sarcopenia prevalence was higher in males with PLMS (6.86%) compared to those without (2.00%), with no significant difference in females. After adjusting for age, exercise, and alcohol consumption, PLMI ≥15/h remained an independent risk factor for sarcopenia in males (adjusted prevalence ratio: 5.808; 95% confidence interval, 1.338–25.215).
Conclusions
PLMS is independently associated with sarcopenia in males, suggesting the need to assess sleep disorders in sarcopenia management.
INTRODUCTION
Sarcopenia, characterized by the loss of muscle mass and function, is a critical health concern, particularly in aging populations [1,2]. Older individuals with sarcopenia often experience a significant decline in physical function, which can lead to a loss of independence. Its prevalence is rising, contributing to increased morbidity, disability, and healthcare costs [3]. Sarcopenia can be diagnosed using body composition analysis tools such as bioelectrical impedance analysis (BIA), with skeletal muscle mass and lean body mass serving as crucial indicators for assessing muscle condition. Although the pathophysiology of sarcopenia is multifactorial and involves metabolic, inflammatory, and neural factors, emerging evidence suggests that sleep disorders may also play a role in its development [4-6].
Periodic limb movements during sleep (PLMS) is a common sleep-related movement disorder characterized by repetitive limb movements that primarily occur during non-rapid eye movement sleep. PLMS is frequently associated with sleep fragmentation and other conditions such as restless legs syndrome (RLS), obstructive sleep apnea (OSA), and cardiovascular diseases [7,8]. This can reduce the sleep quality and negatively affect recovery. Periodic limb movements (PLM) can cause frequent awakening during sleep, increase fatigue, and potentially lead to long-term decreases in muscle strength. This sleep disturbance negatively affects muscle health and may accelerate the progression of sarcopenia. However, the association between PLMS and sarcopenia remains unclear. The mechanism underlying PLM disorders is not yet fully understood. Established treatments beyond pharmacological therapy are lacking, and studies on the interaction between sarcopenia and sleep disturbance remain limited [9].
This study investigated the association between PLMS and sarcopenia in a cohort of patients who underwent overnight type I polysomnography (PSG) and BIA. By analyzing muscle mass and sleep parameters, this study sought to determine whether the presence of PLMS was associated with an increased risk of sarcopenia and to explore the interaction between these conditions. Understanding this relationship may provide critical insights for the management of sarcopenia in patients with sleep disorders.
METHODS
Study population
This study included patients who visited the Department of Neurology at Samsung Medical Center for sleep-related symptoms between January 2019 and May 2024. Initially, 1,119 individuals who completed both type I PSG and BIA were identified. BIA is routinely performed in all patients undergoing PSG as part of a comprehensive sleep evaluation protocol. From this pool, 691 individuals were selected through age-matching to ensure appropriate distribution across age groups. Thus, this study included 691 participants who completed both type I PSG and BIA. We excluded individuals under 18 years of age (n=2) and those with severe medical conditions (such as congestive heart failure and thyroid disease) (n=13) or neurological disorders (such as Parkinson’s disease and stroke) (n=12), because these conditions could potentially influence sleep patterns and muscle mass. Consequently, 663 participants were included in the study. This study was approved by the Institutional Review Board of the Samsung Medical Center, and the requirement for informed consent was waived owing to the retrospective observational nature of the study (IRB number: 2023-05-012).
PSG evaluation
All participants underwent overnight type I PSG, which included 6-channel electroencephalography, 2-channel electrooculography, electrocardiography, respiratory sounds, oral and nasal airflow, thoracic respiratory movements, abdominal respiratory movements, and a minimum of 4-channel electromyography (chin, intercostal muscles, and left and right anterior tibialis muscles). This comprehensive assessment allowed the analysis of sleep architecture and patterns, arousal and arousal types, apneas/hypopneas, and PLM.
Trained PSG technicians conducted the study, and the collected data were evaluated by certified sleep specialists according to the criteria established by the American Academy of Sleep Medicine. Following examination, PLM disorder was diagnosed based on the International Classification of Sleep Disorders adult criteria. A PLM index (PLMI) of ≥15/h was considered pathological, and participants were divided into two groups accordingly.
BIA assessment
BIA was performed by using a bioelectrical impedance device (InBody 770; Biospace). This device employs direct segmental multifrequency BIA technology to evaluate body composition by conducting 30 impedance measurements at six frequencies (1, 5, 50, 250, 500, and 1,000 kHz) across the arms, legs, and trunk. BIA measurements were conducted with participants standing barefoot on the footpads of the device and holding the hand electrodes with their arms extended away from the body. The participants were instructed to fast for at least 3 hours and void their bladders before the assessment to ensure accuracy. Data were automatically collected while the participants stood still while holding the sensing handles.
The measured parameters included body mass index (BMI), fat mass, fat percentage, fat mass index (FMI), visceral fat area (VFA), fat-free mass index (FFMI), skeletal muscle index (SMI), and muscle percentage reflecting both muscle and fat mass. Sarcopenia was defined using SMI cut-off values of <7 kg/m2 for males and <5.7 kg/m2 for females, according to the Asian Working Group for Sarcopenia 2019 criteria.
Statistical analysis
Data were analyzed using R version 4.3.1 (R Foundation). Comparisons of clinical variables among participants were performed using chi-squared or Fisher’s exact tests for categorical variables and t-tests or Wilcoxon rank-sum tests for continuous variables, as appropriate.
BIA parameters according to the PLMS status were analyzed using t-tests and Wilcoxon rank-sum tests. Logistic regression and standardization were performed to estimate the adjusted prevalence ratios and differences. To account for potential confounding factors, adjustments were made for age (years), physical activity level (frequency per week), and alcohol consumption.
All tests were two-sided, and a p-value<0.05 was considered statistically significant.
RESULTS
Comparison of characteristics by PLMS group
Of the 663 participants, 404 were male and 259 were female. PLMI of ≥15/h was observed in 204 males and 126 females (Table 1). When classified into non-PLMS and PLMS groups based on PLMI ≥15, the mean ages were 60.84±10.51 and 62.90±10.57 years for males, and 62.38±10.06 and 61.58±10.77 years for females, respectively, with no significant difference between groups.
Demographics of the study population
There were no significant differences in the prevalence of hypertension, diabetes, or cardiovascular diseases, which could potentially affect sleep and muscle mass. Alcohol consumption was significantly higher in the non-PLMS group (58%) than in the PLMS group (50%). In the PLMS group, the prevalence of RLS was 1.5% among 204 males (3 cases) and 3.2% among 126 females (4 cases).
Self-reported sleep measures showed no significant differences between the groups in either sex. However, as the participants were patients visiting a sleep center, the average Epworth Sleepiness Scale, Insomnia Severity Index, and Pittsburgh Sleep Quality Index scores were generally high: 9.40±5.40, 12.39±6.48, and 7.61±3.93 for males, and 8.33±5.17, 13.91±6.55, and 10.43±10.92 for females, respectively.
PSG findings revealed no significant differences in total sleep time, sleep latency, or waking after sleep onset between the groups. However, in males, both the apnea-hypopnea index (22.94±18.46 vs. 31.68±23.01/h) and the arousal index (27.70±12.59 vs. 32.18±16.12) were lower in the PLMS group compared to the non-PLMS group.
Comparison of BIA parameters between PLMS groups
We compared indicators reflecting muscle and fat mass between the PLMS groups (Table 2). BMI showed no significant differences between non-PLMS and PLMS groups in both males (25.66±3.06 kg/m2 vs. 25.28±3.26 kg/m2) and females (23.73±3.37 kg/m2 vs. 24.42±3.93 kg/m2). Indicators representing fat mass, including body fat percentage, VFA, and FMI, showed no significant differences, although the PLMS group tended to have a higher fat mass in both sexes than the non-PLMS group.
Comparison of bioelectrical impedance analysis parameters according to periodic limb movements during sleep
However, muscle mass showed sex-specific differences. In males, the PLMS group had lower SMI (8.02±0.66 kg/m2 vs. 8.19±0.68 kg/m2) and FFMI (18.90±1.48 kg/m2 vs. 19.31±1.51 kg/m2) compared to the non-PLMS group. Conversely, females in the PLMS group had higher SMI, FFMI, and muscle mass percentage (37.97%±1.10% vs. 36.89%±3.66%) compared to the non-PLMS group.
Prevalence ratio of sarcopenia in PLMS patients
We examined the incidence of sarcopenia based on the SMI criteria, considering the sex differences in muscle mass between the PLMS groups. The incidence of sarcopenia differed between sex in participants with PLMI ≥15. Among males, the PLMS group had a higher incidence (6.86%) than the non-PLMS group (2%). In females, the non-PLMS group had a higher incidence (10.32%) than the PLMS group (7.7%) (Fig. 1).
Prevalence of sarcopenia among periodic limb movements during sleep patients. While there was no significant difference in sarcopenia between high and low periodic limb movement groups in females, males with a periodic limb movement index (PLMI) of 15 or above showed a markedly higher proportion of sarcopenia.
The association between PLMS and the incidence of sarcopenia was assessed using multivariate regression analysis with predictive margins adjusted for age, exercise frequency, and alcohol consumption. In males, the multivariable-adjusted prevalence ratio for PLMI ≥15 compared to PLMI <15 was 3.0 (95% confidence interval, 1.0–8.8) when adjusted for age only, and 5.8 (95% confidence interval, 1.3–25.2) when additionally adjusted for exercise frequency and alcohol consumption (Table 3).
Association between PLMI and prevalence of sarcopenia
DISCUSSION
In this study, we investigated the relationship between PLMS and sarcopenia by analyzing data from patients who underwent PSG and BIA. Our findings revealed sex-specific differences in the association between PLMS and sarcopenia. In male participants, those with a PLMI ≥15 exhibited a significantly higher prevalence of sarcopenia compared to their counterparts without PLMS. Conversely, no significant association was observed between PLMS and sarcopenia in females. These findings suggest that PLMS may be an underappreciated contributor to sarcopenia development, particularly in males. Importantly, both the PLMS and non-PLMS groups had high AHI values (Table 1). PLMS and OSA commonly coexist, especially in older adults, with some studies reporting PLMS in up to 48% of the patients with OSA. These two conditions may share common mechanisms, such as autonomic instability and sleep fragmentation [10]. This study is one of the first to establish such a link, adding to the growing body of evidence that sleep disorders can be associated with musculoskeletal health.
The observed sex-based differences in the association between PLMS and sarcopenia may be attributable to several factors. PLMS is associated with sleep fragmentation and reduced sleep efficiency, both of which can contribute to muscle wasting through the disruption of the secretion of anabolic hormones, such as growth hormone and testosterone, which are essential for muscle maintenance. Hormonal variations, particularly the protective effects of estrogen on muscle mass in women, may play a significant role. Additionally, the generally higher baseline muscle mass in men may make them more susceptible to the catabolic effects of sleep disturbance [8,11,12]. Additionally, inflammatory responses triggered by repeated arousal during PLMS may exacerbate muscle catabolism [13]. Previous studies have demonstrated that sleep disorders are associated with elevated levels of inflammatory cytokines, such as interleukin-6 and tumor necrosis factor-α, which can accelerate muscle degradation and impede muscle repair mechanisms [14]. Interestingly, the absence of a significant association in females suggests a potential protective mechanism, possibly related to hormonal differences or varying susceptibility to the impact of sleep fragmentation on muscle mass [15]. Estrogen, which has anabolic effects on muscle tissue, may play a role in mitigating the effects of sleep disruption on muscle loss [16-18]. Additionally, females may exhibit greater resilience to the effects of PLMS on sleep quality owing to hormonal differences in sleep–wake cycle regulation, such as higher levels of progesterone, which is known to have sedative effects [19]. This could mitigate the impact of sleep disruption on muscle health in females. Further research is required to clarify these sex-related differences and their underlying mechanisms.
Although the exact mechanisms linking PLMS to sarcopenia remain unclear, several hypotheses have been proposed. First, sleep disruption caused by frequent limb movements may lead to chronic activation of the hypothalamic–pituitary–adrenal axis, resulting in elevated cortisol levels [20,21]. Chronic exposure to cortisol, a catabolic hormone, promotes muscle breakdown and inhibits protein synthesis, thereby contributing to muscle atrophy [22]. Additionally, sleep disorders such as PLMS can lead to insulin resistance and impaired glucose metabolism, both of which are associated with increased fat mass and decreased muscle mass [5]. Insulin is a key anabolic hormone in the muscles, and its dysfunction may reduce muscle protein synthesis, further accelerating the development of sarcopenia [23,24]. Furthermore, physical inactivity, which is frequently observed in individuals with poor sleep quality, may mediate the relationship between PLMS and sarcopenia [25]. Sleep disturbances often lead to daytime fatigue and reduced physical activity, which are essential for maintaining muscle mass and strength. Over time, reduced physical activity may exacerbate muscle loss and functional decline, particularly among older adults [26,27].
The strengths of this study include the use of objective measurements for both PLMS and sarcopenia, which provided robust data on their association. However, this study also has certain limitations. First, its retrospective design limited the ability to establish causality. The cross-sectional nature of this study limited the ability to establish a causal relationship between PLMS and sarcopenia. Future longitudinal studies are required to elucidate the temporal relationships between these conditions. Second, we did not assess the role of comorbid conditions such as type 2 diabetes mellitus and cardiovascular diseases, which could confound the relationship between PLMS and sarcopenia. Finally, although our sample size was substantial, the generalizability of our findings may be limited to patients with sleep-related symptoms. Further studies with larger and more diverse populations are required to confirm these findings.
The findings of this study suggest that clinicians should consider screening for sleep disorders such as PLMS in older adults, especially those at risk for sarcopenia. Addressing sleep disturbances through interventions, such as cognitive-behavioral therapy, medication, or lifestyle changes, could potentially mitigate the progression of sarcopenia. Future research should also explore the effectiveness of such interventions in reducing the risk of sarcopenia and improving muscle health in patients with PLMS. Moreover, longitudinal studies are required to establish a causal relationship between PLMS and sarcopenia. Understanding whether PLMS precedes muscle loss or sarcopenia contributes to the development of PLMS could have significant implications for both diagnosis and treatment. Investigating the roles of inflammatory markers, anabolic hormone levels, and metabolic pathways in this relationship may provide insights into the underlying biological mechanisms.
In conclusion, our study suggests that PLMS may be a risk factor for sarcopenia in males, but not in females. These findings highlight the importance of sleep disorders when evaluating and managing sarcopenia, particularly in older male patients. These findings underscore the importance of considering sleep disorders in the broader context of aging and musculoskeletal health. Future prospective studies should investigate the mechanisms linking PLMS and sarcopenia, explore potential interventions to mitigate sarcopenia in patients with sleep disturbance, and explore the potential benefits of sleep-focused interventions for the prevention or management of sarcopenia.
Notes
Hea Ree Park and Eun Yeon Joo, contributing editors of the Journal of Sleep Medicine, were not involved in the editorial evaluation or decision to publish this article. All remaining authors have declared no conflicts of interest.
Author Contributions
Conceptualization: Eun Yeon Joo, Heewon Bae. Data curation: all authors. Formal analysis: Yeo Ju Sohn, Eun Yeon Joo. Funding acquisition: Eun Yeon Joo. Investigation: all authors. Methodology: Hea Ree Park, Eun Yeon Joo, Heewon Bae. Project administration: Eun Yeon Joo. Supervision: Eun Yeon Joo. Validation: Yeo Ju Sohn, Hea Ree Park. Visualization: : all authors. Writing—original draft: Yeo Ju Sohn, Heewon Bae. Writing—review & editing: Eun Yeon Joo, Hea Ree Park.
Funding Statement
This research was supported by a Samsung Medical Center Grant (OTC 1190671).
Acknowledgments
None