Sarcopenia and Hypoxia in Patients with Obstructive Sleep Apnea

Kim, Jinseung; Lee, Ho-Joon; Lee, Dong Ah.; Park, Kang Min

doi:https://doi.org/10.1155/2023/4310809

Acta Neurologica Scandinavica

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Abstract Introduction Methods Results Discussion Conclusion Data Availability Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2023 | Article ID 4310809 | https://doi.org/10.1155/2023/4310809

Sarcopenia and Hypoxia in Patients with Obstructive Sleep Apnea

Jinseung Kim,¹Ho-Joon Lee,²Dong Ah. Lee,³and Kang Min Park³

Academic Editor: Paul Cumming

Received17 Oct 2023

Revised27 Nov 2023

Accepted28 Nov 2023

Published06 Dec 2023

Abstract

Objectives. Obstructive sleep apnea (OSA) is associated with a variety of health issues. Both OSA and sarcopenia are associated with metabolic disorders; however, there is limited literature assessing the correlation between them. Therefore, we aimed to investigate sarcopenia using temporal muscle thickness (TMT) in patients with obstructive sleep apnea (OSA) and the association between sarcopenia and hypoxia. Methods. We enrolled patients with OSA and healthy controls. Both groups underwent brain magnetic resonance imaging (MRI) scans, including three-dimensional T1-weighted imaging. TMT, a marker for sarcopenia, was obtained based on the T1-weighted imaging and compared between the groups. Additionally, we analyzed the correlation between TMT and clinical factors in patients with OSA. Results. In total, 40 patients with OSA and 52 healthy controls were evaluated. There was no difference in TMT between patients with OSA and healthy controls ( vs. mm, ). In the correlation analysis, age was negatively correlated with TMT (, ), and lowest oxygen saturation (, ) in patients with OSA. Conclusion. Our results demonstrated negative correlations between TMT and lowest oxygen saturation in the patients with OSA. These findings suggest potential relationships between sarcopenia and hypoxia in patients with OSA. Thus, these results underline the importance of maintaining oxygen saturation in patients with OSA by implementing active treatment. This study also demonstrates the feasibility of sarcopenia assessment by measuring TMT through conventional head MRI in patients with various neurological disorders.

1. Introduction

Obstructive sleep apnea (OSA) is defined by intermittent collapse of the upper airway, a phenomenon contingent upon the individual’s sleep state. This leads to recurring instances of reduced or halted ventilation, causing hypoxia, hypercapnia, and arousals from sleep [1]. The prevalence of OSA ranges from 9 to 38% among the adult population, with a higher occurrence in men compared with that in women [2, 3]. However, the fundamental pathophysiology of OSA is intricate and varies among individuals. Key pathophysiological elements that influence the severity of OSA include anatomical impediments within the upper airway, a diminished threshold for respiratory arousal, elevated loop gain leading to unstable respiratory control, and inadequate responsiveness of the upper airway dilator muscles [4]. Additionally, OSA is associated with a spectrum of persistent health issues, such as cardiovascular risks, metabolic disorders, and psychiatric conditions [5–7].

Sarcopenia is a progressive muscle disorder characterized by rapid loss of muscle mass and function. This condition has been linked to various detrimental health consequences such as fractures, reduced functionality, and increased mortality [8]. In community-dwelling individuals years, the estimated global prevalence of sarcopenia is approximately 10%, slightly higher than previously observed [9]. Sarcopenia, not limited to the elderly, can begin in midlife and is prevalent in various populations, including those with cancer, chronic kidney and liver disease, and metabolic disorders, serving as a significant prognostic indicator for survival and clinical complications in these patients [10–13].

Sarcopenia is initially diagnosed by assessing muscle strength, such as grip strength [14]. Subsequent diagnosis involves measuring muscle mass, primarily estimated through dual-energy X-ray absorptiometry for lean mass, combined with other tools such as bioelectrical impedance analysis and computed tomography scans [15]. However, the assessment of skeletal muscle mass and function is based on additional examinations that result in a higher radiation dose for the patient, in addition to health care costs, or prolonged clinical examinations.

Moreover, recent studies have established a significant correlation between temporal muscle thickness (TMT) and the risk of developing sarcopenia. TMT, obtained on routinely performed brain magnetic resonance imaging (MRI), has been shown to estimate skeletal muscle mass. Thus, it has been explored as a new proxy indicator for identifying sarcopenia risk in individuals with neurological conditions [16, 17]. Previous studies have demonstrated a strong correlation between TMT and both hand grip strength and skeletal muscle mass, as well as good interrater reliability, making it a reliable marker for assessing sarcopenia [16–18]. Notably, TMT assessment can be conveniently performed using routine brain computed tomography or MRI protocols or retrospectively, providing an optimal method for assessing sarcopenia. Furthermore, the measurement of TMT has proven effective in investigating the impact of sarcopenia on prognosis among patients with brain tumors [19, 20].

Previous literature indicates that among community-dwelling older adults, women with poor sleep quality exhibit a heightened risk of sarcopenia [21]. Moreover, OSA and sarcopenia are recognized for their association with metabolic disorders, and OSA is often accompanied by hypertension and diabetes, which are risk factors for sarcopenia [22]. Therefore, we might expect sarcopenia to be more common in patients with OSA compared to healthy individuals. However, there have been limited studies exploring the association between sarcopenia and OSA, yielding contradictory results [23–25]. Moreover, there is a notable dearth of specific research examining the precise correlation between them using TMT. Therefore, this study is aimed at evaluating the presence of sarcopenia in patients with OSA by employing TMT measurements. Our hypothesis posited a clear and distinct relationship between OSA and sarcopenia. Furthermore, we investigated the association between sarcopenia and hypoxia in patients with OSA.

2. Methods

2.1. Participants

This study was conducted at a tertiary care hospital with the approval of the institutional review board. Patients with OSA who agreed to participate in this study were enrolled between September 2018 and August 2023. Participants who met the following specific criteria for OSA were included: (1) confirmed diagnosis of OSA based on polysomnography (PSG) results, demonstrating an apnea-hypopnea index (AHI) exceeding five, along with accompanying symptoms such as excessive sleepiness or chronic snoring and (2) availability of three-dimensional (3D) T1-weighted (T1-WI) MRI data. We excluded patients with OSA with (1) presence of any other medical or neurological disorders, aside from OSA, or (2) presence of any structural brain lesions, as confirmed by visual inspection of brain MRI scans. Clinical and PSG data were collected from patients with OSA. Healthy individuals were included as a control, matched in terms of age and sex to the patients with OSA. These individuals had no history of medical or neurological disorders and exhibited normal brain MRI findings upon visual examination. Although PSG was not conducted for these healthy participants, none of them reported snoring or displayed any symptoms of OSA.

2.2. MRI Acquisition

Both patients with OSA and the healthy control group underwent brain MRI scans using identical sequences performed on a three-tesla MRI scanner equipped with a 32-channel head coil (AchievaTx, Phillips Healthcare, Best, The Netherlands). The 3D T1-WI images were acquired using the same sequences in both groups. Additionally, patients underwent axial T2-weighted images to detect structural brain lesions. The 3D T1-WI image was acquired in the sagittal plane, using a turbo-field echo sequence with the following parameters: ms, repetition time/echo ms, flip °, and an isotropic voxel size of 1 mm³.

2.3. Temporal Muscle Thickness Measurement

TMT was measured on 3D T1-WI images on the right and left sides by a board-certified radiologist (H.J.L) with 9 years of subspecialty experience in neuroradiology. The images were reformatted to an axial plane parallel to the anterior commissure-posterior commissure line. Thereafter, TMT was measured perpendicular to the long axis of the temporalis muscle, using the orbital roof and the Sylvian fissure as landmarks. Image reformatting and measurements were performed using 3D Slicer (version 5.4.0, https://www.slicer.org) [26, 27]. The measurements for each side were averaged for use in further analysis (Figure 1).

(a)

(b)

(c)

2.4. Statistical Analysis

We employed the independent samples -test to compare age and TMT between the groups, and Fisher’s exact test to analyze sex differences. The Pearson correlation test was used for correlation analysis between TMT and clinical factors. All statistical analyses were performed using MedCalc® software (version 20.014, MedCalc Software, Ostend, Belgium; accessible at https://www.medcalc.org; 2021). Statistical significance was set at .

3. Results

3.1. Clinical Characteristics and Polysomnographic Findings in Participants

We included 40 patients with OSA and 52 healthy controls. No significant differences in age and sex were found between the two groups. Table 1 presents the clinical characteristics and PSG findings in patients with OSA. Eighty percent of patients with OSA were male.

3.2. Difference of the Temporal Muscle Thickness between Patients with OSA and Healthy Controls

No difference was found in TMT between patients with OSA and healthy controls ( vs. mm; ) (Figure 2).

3.3. Correlation Analysis between Temporal Muscle Thickness and Clinical Factors

Age and lowest oxygen saturation were negatively correlated with TMT (, ; , , respectively) in patients with OSA (Figure 3). However, other clinical factors, such as body mass index (, ), Epworth sleep scale (, ), sleep efficiency (, ), AHI (, ), average oxygen saturation (, ), and sleep time (, ), were not correlated with TMT.

(a)

(b)

4. Discussion

In the present study, we investigated the difference in the presence of sarcopenia using TMT measurement between 40 patients with OSA and 52 healthy controls. Contrary to our initial hypothesis, there was no significant difference in TMT between the two groups. However, our results demonstrated a negative correlation between age and TMT, as well as between the lowest oxygen saturation and TMT in patients with OSA.

Our results are consistent with those of previous studies using dual-energy X-ray absorptiometry, wherein OSA was strongly associated with total and upper body obesity, but not with sarcopenia in patients with nondialyzed chronic kidney disease [23, 25]. Traditionally, OSA has been characterized and measured mainly based on the frequency of apneas and hypopneas during sleep, known as the AHI [28–30]. However, in our study, AHI did not correlate with the TMT measurement, but hypoxia alone showed a negative correlation with TMT. This suggests that hypoxia is more strongly associated with sarcopenia than the severity of OSA.

Clinical and epidemiological investigations have shown a standalone correlation between OSA and occurrences of cardiovascular events, suggesting that OSA might instigate cardiometabolic dysregulation [31–33]. Two primary patterns of hypoxemia have been established. First, patients with OSA often exhibit short, intermittent, high-frequency hypoxemia, characterized by a cyclical pattern of oxygen desaturation lasting 15–60 seconds followed by reoxygenation, persisting for 8–9 hours during sleep and extending over weeks to months or longer. Second, sustained, or low-frequency hypoxemia, with oxygen saturation ranging between 80 and 85% and lasting from a few minutes to hours, is observed during rapid ascent and descent from altitude as well as in chronic lung disease during sleep [34]. Persistent and continuous hypoxemia, prevalent at high altitudes or in cases of chronic lung disease, can trigger both adaptive and maladaptive reactions. These responses may involve heightened erythropoiesis and the development of pulmonary hypertension. Conversely, intermittent hypoxia typically induces maladaptive reactions, distinctly modulating hypoxia-inducible factors 1 and 2 [35]. Chronic obstructive pulmonary disease is a lung condition resulting in diminished tissue oxygenation, consequently inducing a state of tissue hypoxia. In patients with chronic obstructive pulmonary disease, skeletal muscle fiber size is reduced compared to that of healthy individuals [36]. Muscle size reduction is likely a result of changes in molecular pathways that control both muscle growth and breakdown. The mTORC1 signaling pathway, a widely studied anabolic pathway, is activated in skeletal muscle in response to nutrients, growth factors, and muscle loading. This activation leads to an enhanced mRNA translational capacity, ultimately promoting muscle growth [37]. Research conducted in mice revealed that a brief intermittent hypoxia regimen, a characteristic feature of OSA, induced significant oxygen fluctuations in the liver, closely resembling the variations in SpO₂ levels. The oscillations in oxygen levels were mitigated in muscle tissue and significantly reduced in adipose tissue in both lean and obese mice. These findings indicate that OSA intensifies tissue-level hypoxia [38]. The heightened hypoxia within adipose tissue could represent a significant mechanism contributing to cardiometabolic dysfunction in OSA. This heightened hypoxia is a primary initiator of processes such as lipolysis, chronic inflammation, infiltration of macrophages, reduction in adiponectin levels, increased leptin levels, adipocyte cell death, endoplasmic reticulum stress, and impaired mitochondrial function [39].

Hypoxemia resulting from OSA triggers the activation of arterial sympathetic nerves and the renin-angiotensin-aldosterone system, primarily driving the pathophysiology underlying complications such as cardiovascular morbidity and mortality. Notably, angiotensin II/angiotensin II type 1 receptor activity also contributes to fibrotic, atrophic, and inflammatory effects on muscles [40]. Age-related sleep changes, encompassing sleep apnea, insomnia, reduced sleep efficiency, and disrupted circadian rhythms, elevate cortisol levels, impair endothelial function, and induce arousal and insulin resistance, while diminishing growth hormone, insulin-like growth factor 1 secretion, and protein synthesis, culminating in a hormonal imbalance that skews muscle metabolism towards catabolism and contributes to sarcopenia [41]. Moreover, prolonged hypoxia has an adverse impact on skeletal muscle protein metabolism [42–44]. In cases where protein breakdown exceeds synthesis, there is a decline in skeletal muscle mass and in the cross-sectional area of myofibers, ultimately resulting in diminished muscle strength and function [45, 46]. Therefore, hypoxia should be recognized as a potent initiator of inflammation. Additionally, cellular responses to hypoxia hinge on the transcription factor hypoxia-inducible factor 1α. While inactive in normoxemic conditions, this dimeric protein becomes active in the presence of hypoxia [47]. Together, these aspects may explain our results which showed a negative correlation between the degree of hypoxia and TMT in OSA patients.

Nonetheless, this study encountered several limitations. First, as this was a single-center study with a relatively small sample size, generalizing the findings to the general population is challenging. Second, due to the study’s cross-sectional design, the establishment of a definitive temporal relationship between hypoxia and sarcopenia was not possible. Third, despite incorporating a control group without OSA symptoms, PSG examinations were not conducted, potentially allowing for the inclusion of individuals with undiagnosed OSA. Fourth, TMT is a well-known marker for sarcopenia and is associated with hand grip strength, indicating its potential to reflect skeletal muscle mass. However, we were unable to directly measure muscle strength, muscle mass, and physical performance, which is crucial for diagnosis of sarcopenia. Finally, factors such as body mass index (BMI) or lifestyle factors may have caused bias, which may affect the TMT in patients with OSA and healthy controls. However, a previous study demonstrated sarcopenia as an independent factor for TMT, even after adjusting for age, sex, and BMI [18].

Despite these limitations, this was the first study to investigate sarcopenia in patients with OSA compared to healthy controls. We identified negative correlations between TMT and lowest oxygen saturation in the patients with OSA. Thus, these results underline the importance of maintaining oxygen saturation in patients with OSA by implementing active treatment, such as positive airway pressure. This study also demonstrates the feasibility of sarcopenia assessment by measuring TMT through conventional head MRI in patients with various neurological disorders. This study marks the pioneering exploration of the association between OSA and sarcopenia using the assessment of TMT through brain MRI in patients with OSA. It could be better studied to measure and compare TMT again after treating OSA with large a sample size in the future.

5. Conclusion

We observed a negative correlation between TMT and lowest oxygen saturation in the patients with OSA. This suggests a potential association between sarcopenia and hypoxia in patients with OSA. Thus, these results underline the importance of maintaining oxygen saturation in patients with OSA by implementing active treatment. This study also demonstrates the feasibility of sarcopenia assessment by measuring TMT through conventional head MRI in patients with various neurological disorders.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

Neither of the authors has any conflict of interest to disclose.

Authors’ Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Jinseung Kim, Ho-Joon Lee, Dong Ah Lee, and Kang Min Park. The first draft of the manuscript was written by Kang Min Park, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Jinseung Kim and Ho-Joon Lee contributed equally to this study.

Acknowledgments

This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HR21C1003).

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Copyright © 2023 Jinseung Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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