Introduction
Multiple sclerosis (MS) is a chronic autoimmune disease that involves the central nervous system, resulting in physical and cognitive disability. 1 MS is considered the most common cause of neurological disability in young adults (aged 18–40 years). 1 MS causes demyelination and axonal degeneration resulting in white and grey matter lesions. Another pathophysiological mechanism that contributes to disability in MS is chronic inflammation or smoldering disease activity. 1
Obstructive sleep apnea (OSA) is a sleep disorder characterized by recurring episodes of apnea or hypopnea resulting in oxygen desaturation and sleep arousals. 2 OSA affects almost 1 billion adults globally 3 with almost half billion having moderate to severe OSA. 4 OSA significantly impacts cardiovascular disease, behavioral conditions, and quality of life. 5 Overall, patients with OSA have lower life expectancy compared to the general population. 6
Patients with MS commonly report fragmented and poor-quality sleep. 7 These sleep problems are associated with worse quality of life, fatigue, mood disorders, and increased disease activity. 8
Identifying methods to improve sleep in patients with MS is challenging because evidence regarding the effects of MS on sleep remains limited, particularly because central sleep apnea secondary to CNS lesions is uncommon. This deficiency in the evidence is mainly due to the reliance on subjective questionnaires to assess sleep problems, limited use of polysomnography, 9 , 10 and the lack of healthy controls for interpreting polysomnography data. Several hypotheses exist regarding the connection between MS and OSA. First, lesions in the spinal cord and brainstem may result in impaired upper airway muscle responsiveness and coordination. 8 Second, inflammation in MS may also play a role in the occurrence of OSA. 11 Similar to other autoimmune diseases, MS is associated with increased levels of pro-inflammatory cytokines. 11 Studies have demonstrated that levels of tumor necrosis factor-α, interleukin-6, and interleukin-1 are elevated in patients with OSA. 12 Additionally, it was found that anti-inflammatory therapy with etanercept can improve OSA severity in the general population. 13
Previous systematic reviews and meta-analyses conducted in 2022 7 and 2023 14 included 13 and 30 articles, respectively, and were limited by sample size, lack of polysomnography data in several of the included studies, and dependence on subjective questionnaires.
Our systematic review and meta-analysis aimed to understand the association between MS and OSA by (1) identifying the prevalence of OSA and its severity grades among patients with MS, (2) assessing the difference between patients with MS and healthy controls in sleep metrics detected by polysomnography, and (3) evaluating the impact of OSA metrics on disability indices.
We hypothesize that the prevalence of OSA among patients with MS is high, and several polysomnography metrics will be different among patients with MS compared to healthy controls and that some of these metrics will be associated with disability scales.
Methods
Registration and Protocol
In this systematic review and meta-analysis, we followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA). The protocol of this study was registered in the international prospective register of systematic reviews (PROSPERO) under registration number CRD420251156995.
Search Strategy
On September 29, 2025, we searched PubMed, Embase, Scopus, cumulative index to nursing and allied health literature (CINAHL), and the Cochrane central register of controlled trials (CENTRAL) using the following keywords and their corresponding medical subject headings (MeSH) terms: (polysomnography OR sleep study OR somnography OR somnographies OR polysomnographies OR sleep monitoring) AND (multiple sclerosis OR MS).
Exposures and Outcomes
The exposure of interest was MS including relapsing-remitting MS (RRMS), secondary progressive MS (SPMS), and primary progressive MS (PPMS). MS had to be diagnosed using the McDonald diagnostic criteria. The outcomes of interest included the prevalence of OSA across its severity grades, polysomnography characteristics, and the correlation between sleep characteristics and MS disability indices. OSA was defined as AHI ≥5. OSA severity grades included mild (AHI=5-14), moderate (AHI=15-29), and severe (AHI≥30). Polysomnography characteristics included total sleep time, REM latency, sleep efficiency, AHI, arousal index (AI), and oxygen saturation nadir (SpO 2 nadir), as defined in the American Academy of Sleep Medicine (AASM) scoring manual. MS disability indices included the expanded disability status scale (EDSS), timed 25-foot walking test (T25FWT), California verbal language testing (CVLT), and symbol digit modality testing (SDMT).
Study Selection
The inclusion criteria were: (1) observational studies including cross-sectional, cohort, and case-control studies, (2) included patients with MS who underwent polysomnography, and (3) reported the prevalence of OSA among the included cohort, compared polysomnography data between patients with MS and healthy controls, or assessed correlations between polysomnography data and disability scales.
The exclusion criteria were (1) reviews, editorials, and case reports, and (2) studies that assessed sleep data subjectively and not using polysomnography.
The study selection was conducted using Rayyan, 14 a screening tool designed to screen papers for systematic reviews and meta-analyses. The selection process was performed by AAT and TNA (two reviewers) independently and any disagreements were resolved through discussion until consensus was reached.
Data Extraction
The data were extracted from the included studies using a pre-defined data sheet that included authors names, year of publication, study design, country of the study, sample size, number of patients diagnosed with OSA, polysomnography data from patients and healthy controls, and the correlation between polysomnography data and disability scales.
The data extraction process was performed by AAT and TNA independently and any disagreements were resolved through discussion until consensus was reached.
Quality Assessment
The quality of the included studies was assessed using the Newcastle-Ottawa Scale (NOS), which evaluates three domains: (1) selection of the included population, (2) comparability of study groups, and (3) ascertainment of exposure (for case-control studies) or outcome (for cohort studies). The maximum score is 9; studies scoring ≤5 were classified as low-quality, and >5 as high-quality. Quality appraisal was performed by AAT.
Data Analysis
Data analysis was performed using MetaXL version 5.3 (EpiGear International, Australia). The effect sizes used in the study included the rate for the prevalence of OSA and its grades, weighted mean difference (WMD) for the comparison in the polysomnography data between patients with MS and healthy controls, and rho (r) correlation coefficient for the correlation between polysomnography data and disability scales along with their associated 95% confidence intervals (95%CI). Conversions between different effect sizes were performed using previously published methods. 15 Specifically, median, range and interquartile range were converted to mean and standard deviation based on the method described by Hozo et al., 2020. 16 The random effect model was used when the heterogeneity was ≥50% while the fixed effect model was used when the heterogeneity was <50%. The heterogeneity was assessed using the heterogeneity statistic (I 2 ), and Cochrane Q test. Among significant results, subgroup analyses of high-quality studies (NOS>5) were conducted when feasible (models with more than 3 studies).
Results
Search Results
A total of 747 articles were retrieved from the search. Of them, 377 were duplicates. The remaining 370 articles were screened using their titles and abstracts and 262 articles were excluded because they were reviews, case reports, editorials, or irrelevant. The remaining 108 articles were screened using their full-text and 68 articles were excluded because they did not include data about the outcomes of interest. Finally, 40 articles were included in the final analysis. References are available in the supplementary material . Figure 1 presents the PRISMA flow chart associated with the selection process. Figure 1. PRISMA flow chart of the study selection process
Characteristics of the Included Studies
A total of 3,264 patients with MS and 679 healthy controls from 40 studies were included in the analysis. Table 1 summarizes the characteristics of the included studies. Table 1. Characteristics of the Included Studies Author Country Design Sample size Case Sample size Control Total Queisi et al., 2024 US Retrospective cohort 299 299 6 Dubessy et al., 2021 France Case-control 44 24 7 Riccitelli et al., 2022 Switzerland Cross-sectional 80 N/A 6 Cascone et al., 2019 US Cross-sectional 292 N/A 5 Giannuzzi et al., 2018 US Cross-sectional 292 N/A 5 McNicholas et al., 2018 Ireland Cross-sectional 23 N/A 4 McNicholas et al., 2018 Ireland Cross-sectional 15 N/A 6 Gudesblatt US Cross-sectional 206 N/A 5 Braky et al., 2015 US Cross-sectional 37 N/A 6 Sater et al., 2015 US Cross-sectional 32 N/A 4 Carnicka et al., 2015 Slovakia Cross-sectional 50 N/A 4 Brakey et al., 2014 US Cross-sectional 13 N/A 6 Suarez-toste et al., 2012 Canada Cross-sectional 37 N/A 4 Kaminska et al., 2011 Canada Retrospective cohort 62 32 9 Sivaci et al., 2018 Turkey Retrospective cohort 30 19 4 Aljundi et al., 2022 US Cross-sectional 24 N/A 5 Braley et al., 2012 US Case-control 48 84 6 Bavli et al., 2021 Turkey Cross-sectional 41 N/A 4 Elkattan et al., 2009 Egypt Retrospective cohort 20 20 4 Beran et al., 2008 Australia Retrospective cohort 12 14 4 Kaynak et al., 2006 Turkey Retrospective cohort 37 13 6 Tachibana et al., 1994 UK Retrospective cohort 28 N/A 4 Ferinin-strambi et al., 1994 Italy Case-control 25 25 4 Hansen et al., 2019 Australia Retrospective cohort 111 N/A 4 Borragan et al., 2018 Belgium Retrospective cohort 10 11 4 Ferri et al., 2022 Italy Case-control 40 28 5 Rzepka et al., 2024 Poland Cross-sectional 51 N/A 4 Levit et al., 2025 US Cross-sectional 122 N/A 4 Chinnadurai et al., 2018 India Cross-sectional 113 N/A 4 McNicholas et al., 2020 Ireland Cross-sectional 23 N/A 5 Veauthier et al., 2018 Germany Cross-sectional 24 N/A 4 Reynolds et al., 2025 Australia Case-control 54 N/A 6 Jian-Hua et al., 2014 China Case-control 21 11 6 Sacmaci et al., 2020 Turkey Prospective cross-sectional 32 32 7 Cousin et al., 2024 France Retrospective cohort 130 N/A 4 Queisi et al., 2024 US Cross-sectional 280 N/A 4 Valentine et al., 2023 US Cross-sectional 134 N/A 4 Sparasci et al., 2022 Switzerland Cross-sectional 67 67 4 Levit et al., 2020 US Cross-sectional 65 N/A 4 Gonzale-platas et al., 2016 Spain Cross-sectional 240 N/A 4 N/A=not available; NOS=new-castle Ottawa scale; US=United states.
Table 1.
Characteristics of the Included Studies
N/A=not available; NOS=new-castle Ottawa scale; US=United states.
| Author | Country | Design | Sample size Case | Sample size Control | Total |
| Queisi et al., 2024 | US | Retrospective cohort | 299 | 299 | 6 |
| Dubessy et al., 2021 | France | Case-control | 44 | 24 | 7 |
| Riccitelli et al., 2022 | Switzerland | Cross-sectional | 80 | N/A | 6 |
| Cascone et al., 2019 | US | Cross-sectional | 292 | N/A | 5 |
| Giannuzzi et al., 2018 | US | Cross-sectional | 292 | N/A | 5 |
| McNicholas et al., 2018 | Ireland | Cross-sectional | 23 | N/A | 4 |
| McNicholas et al., 2018 | Ireland | Cross-sectional | 15 | N/A | 6 |
| Gudesblatt | US | Cross-sectional | 206 | N/A | 5 |
| Braky et al., 2015 | US | Cross-sectional | 37 | N/A | 6 |
| Sater et al., 2015 | US | Cross-sectional | 32 | N/A | 4 |
| Carnicka et al., 2015 | Slovakia | Cross-sectional | 50 | N/A | 4 |
| Brakey et al., 2014 | US | Cross-sectional | 13 | N/A | 6 |
| Suarez-toste et al., 2012 | Canada | Cross-sectional | 37 | N/A | 4 |
| Kaminska et al., 2011 | Canada | Retrospective cohort | 62 | 32 | 9 |
| Sivaci et al., 2018 | Turkey | Retrospective cohort | 30 | 19 | 4 |
| Aljundi et al., 2022 | US | Cross-sectional | 24 | N/A | 5 |
| Braley et al., 2012 | US | Case-control | 48 | 84 | 6 |
| Bavli et al., 2021 | Turkey | Cross-sectional | 41 | N/A | 4 |
| Elkattan et al., 2009 | Egypt | Retrospective cohort | 20 | 20 | 4 |
| Beran et al., 2008 | Australia | Retrospective cohort | 12 | 14 | 4 |
| Kaynak et al., 2006 | Turkey | Retrospective cohort | 37 | 13 | 6 |
| Tachibana et al., 1994 | UK | Retrospective cohort | 28 | N/A | 4 |
| Ferinin-strambi et al., 1994 | Italy | Case-control | 25 | 25 | 4 |
| Hansen et al., 2019 | Australia | Retrospective cohort | 111 | N/A | 4 |
| Borragan et al., 2018 | Belgium | Retrospective cohort | 10 | 11 | 4 |
| Ferri et al., 2022 | Italy | Case-control | 40 | 28 | 5 |
| Rzepka et al., 2024 | Poland | Cross-sectional | 51 | N/A | 4 |
| Levit et al., 2025 | US | Cross-sectional | 122 | N/A | 4 |
| Chinnadurai et al., 2018 | India | Cross-sectional | 113 | N/A | 4 |
| McNicholas et al., 2020 | Ireland | Cross-sectional | 23 | N/A | 5 |
| Veauthier et al., 2018 | Germany | Cross-sectional | 24 | N/A | 4 |
| Reynolds et al., 2025 | Australia | Case-control | 54 | N/A | 6 |
| Jian-Hua et al., 2014 | China | Case-control | 21 | 11 | 6 |
| Sacmaci et al., 2020 | Turkey | Prospective cross-sectional | 32 | 32 | 7 |
| Cousin et al., 2024 | France | Retrospective cohort | 130 | N/A | 4 |
| Queisi et al., 2024 | US | Cross-sectional | 280 | N/A | 4 |
| Valentine et al., 2023 | US | Cross-sectional | 134 | N/A | 4 |
| Sparasci et al., 2022 | Switzerland | Cross-sectional | 67 | 67 | 4 |
| Levit et al., 2020 | US | Cross-sectional | 65 | N/A | 4 |
| Gonzale-platas et al., 2016 | Spain | Cross-sectional | 240 | N/A | 4 |
Prevalence of Obstructive Sleep Apnea Among Patients With MS
Eight studies including 678 patients with MS reported the prevalence of mild OSA. Pooled analysis demonstrated a prevalence of 24% (95% CI: 14%–36%), with significant heterogeneity (I 2 = 84%, p < 0.001). Moderate OSA was evaluated in eight studies including 690 patients with MS and showed a pooled prevalence of 14% (95% CI: 11%–17%; I 2 = 86%, p < 0.001). Similarly, pooled analysis of eight studies involving 690 patients demonstrated a prevalence of severe OSA of 8% (95% CI: 3%–15%; I 2 = 86%, p < 0.001).
Overall, pooled analysis of 25 studies including 2,371 patients with MS demonstrated an OSA prevalence of 44% ( Figure 2 ; 95% CI: 33%–56%), with substantial heterogeneity across studies (I 2 = 97%, p < 0.001). Figure 2. The prevalence of OSA among patients with MS
Characteristics of Obstructive Sleep Apnea Among Patients With MS
Meta-analysis of 13 studies including 728 patients with MS demonstrated that total sleep time was significantly lower in patients with MS than in healthy controls ( Figure 3 ; WMD = -37.70; 95% CI: -62.27 to -13.12), with significant heterogeneity (I 2 = 93%, p < 0.001). Subgroup analysis restricted to seven high-quality studies showed results consistent with the primary analysis (WMD = -12.66; 95% CI: -37.76 to -3.43; I 2 = 90%, p < 0.001). Figure 3. The association between total sleep time and multiple sclerosis
REM latency was evaluated in nine studies including 548 patients with MS and did not differ significantly between patients with MS and healthy controls (WMD = -4.25; 95% CI: -33.21 to 24.71; I 2 = 93%, p < 0.001).
Sleep efficiency was assessed in 13 studies including 741 patients with MS and was significantly lower among patients with MS compared with healthy controls ( Figure 4 ; WMD = -6.38; 95% CI: -9.71 to -3.06; I 2 = 86%, p < 0.001). Subgroup analysis of seven high-quality studies yielded consistent findings (WMD = -4.09; 95% CI: -8.05 to -0.13; I 2 = 85%, p < 0.001). Figure 4. The association between sleep efficiency and multiple sclerosis
Six studies including 473 patients with MS evaluated the arousal index (AI) and found no significant difference between patients with MS and healthy controls (WMD = 2.71; 95% CI: -3.77 to 9.20; I 2 = 96%, p < 0.001).
Five studies involving 463 patients with MS assessed SpO 2 nadir and demonstrated no significant difference between patients with MS and healthy controls (WMD = -0.04; 95% CI: -0.61 to 0.53; I 2 = 10%, p = 0.350).
Analysis of ten studies including 674 patients with MS demonstrated significantly higher AHI values among patients with MS compared with healthy controls ( Figure 5 ; WMD = 1.76; 95% CI: 0.20–3.32; I 2 = 93%, p < 0.001). Subgroup analysis restricted to eight high-quality studies showed similar findings (WMD = 2.36; 95% CI: 0.55–4.18; I 2 = 94%, p < 0.001). Figure 5. The association between apnea hypo-pnea index and multiple sclerosis
Correlation Between Obstructive Sleep Apnea Characteristics and Disability Among Patients With MS
Only one study by Rzepka et al. (2024), which included 51 patients with MS, evaluated the correlation between total sleep time and EDSS and reported a weak negative correlation (r = -0.21).
Two studies including 164 patients with MS assessed the correlation between REM latency and EDSS and found no significant correlation (r = 0.32; 95% CI: -0.20 to 0.70; I 2 = 90%, p < 0.001).
Three studies involving 444 patients with MS demonstrated a significant negative correlation between sleep efficiency and EDSS ( Figure 6 ; r = -0.16; 95% CI: -0.29 to -0.02), with low heterogeneity (I 2 = 43%, p = 0.170). Figure 6. The correlation between sleep efficiency and expanded disability status scale
Among the included studies, only Rzepka et al. (2024) evaluated the correlation between AI and EDSS and reported a weak positive correlation (r = 0.04).
Similarly, only one study by Sacmaci et al. (2020), which included 32 patients with MS, evaluated the correlation between SpO 2 nadir and EDSS and found a moderate negative correlation (r = -0.44).
Three studies including 147 patients with MS assessed the correlation between AHI and EDSS and demonstrated a significant positive correlation ( Figure 7 ; r = 0.41; 95% CI: 0.26–0.54; I 2 = 0%, p = 0.680). Figure 7. The correlation between apnea hypo-pnea index and expanded disability status scale
Meta-analysis of two studies including 149 patients found no significant correlation between total sleep time and CVLT scores (r = 0.31; 95% CI: -0.28 to 0.73; I 2 = 77%, p = 0.040). No studies evaluated the correlation between REM latency and CVLT scores.
Two studies including 149 patients with MS assessed the correlation between sleep efficiency and CVLT scores and found no significant association (r = 0.48; 95% CI: -0.43 to 0.91; I 2 = 91%, p < 0.001). No studies evaluated the correlation between AI and CVLT scores.
Three studies including 184 patients with MS evaluated the correlation between SpO 2 nadir and CVLT scores and found no significant association (r = -0.25; 95% CI: -0.55 to 0.11; I 2 = 71%, p = 0.030).
Similarly, three studies involving 170 patients with MS found no significant correlation between AHI and CVLT scores (r = 0.42; 95% CI: -0.10 to 0.76; I 2 = 83%, p < 0.001).
Two studies including 214 patients assessed the association between total sleep time and SDMT scores and found no significant correlation (r = 0.07; 95% CI: -0.06 to 0.21; I 2 = 0%, p = 0.400).
REM latency and SDMT scores were evaluated in two studies including 193 patients with MS, with no significant correlation observed (r = 0.02; 95% CI: -0.33 to 0.37; I 2 = 84%, p = 0.010).
Three studies including 327 patients with MS demonstrated no correlation between sleep efficiency and SDMT scores (r = -0.22; 95% CI: -0.41 to 0.02; I 2 = 71%, p = 0.030).
Two studies involving 93 patients found no significant correlation between AI and SDMT scores (r = -0.39; 95% CI: -0.83 to 0.34; I 2 = 82%, p = 0.020).
Four studies including 264 patients demonstrated no significant association between SpO 2 nadir and SDMT scores (r = 0.07; 95% CI: -0.08 to 0.21; I 2 = 19%, p = 0.290).
Finally, three studies including 227 patients with MS found no significant correlation between AHI and SDMT scores (r = -0.18; 95% CI: -0.46 to 0.14; I 2 = 74%, p = 0.020).
Quality of the Included Studies
Based on NOS scores, 30.0% of the included studies were of good quality (NOS>5). The studies lost points in the assessment mainly due to lack or incomplete adjustment of confounding variables (90.0%), representativeness of the included cohort (97.5%) and improper assessment of the exposure and/or the outcome (72.5%).
Discussion
In this systematic review and meta-analysis, we found that the prevalence of OSA was 44% with a prevalence of 24% for mild OSA, 14% for moderate OSA and 8% for severe OSA. In terms of polysomnographic data, patients with MS had lower total sleep time and sleep efficiency, and higher AHI. In contrast, no significant differences were observed in REM latency, AI, and SpO 2 nadir. AHI and sleep efficiency were correlated with EDSS. Subgroup analysis among high-quality studies (NOS>5) confirmed these findings.
In terms of prevalence, the previous meta-analysis conducted in 2023 estimated the prevalence of OSA among patients with MS to be around 36% which was lower compared to our estimates. 17 The reason for this discrepancy can be explained by the fact that the study included a smaller number of studies and patients, and included pooled prevalence estimates derived from subjective questionnaires.
Compared to other neurological diseases, previous studies among patients with Alzheimer’s disease demonstrated similar prevalence ranging between 40%-50%. 18 Among patients with stroke, the prevalence of OSA was estimated to range between 30% and 72%. 19 - 21 A lower prevalence of OSA was reported among patients with Parkinson’s disease and multi-system atrophy ranging between 15%-35%. 22 , 23 Similarly, the prevalence of OSA among patients with epilepsy was around 30%. 24 Compared to the general population, a previous systematic review demonstrated that the prevalence of OSA among adults ranges between 6% to 17% with higher prevalence rates among individuals with obesity, 25 which is lower compared to the prevalence of OSA among patients with MS and other neurological diseases.
In a previous meta-analysis conducted in 2022 that included 323 patients with MS from 13 articles and compared patients with MS and healthy controls in polysomnography data demonstrated that there was no significant difference between the two groups in total sleep time or AHI, however they found that patients with MS had lower sleep efficiency. 7 The previous meta-analysis done in 2023 did not conduct a comparison in the polysomnography data between patients with MS and healthy controls. 17 In contrast to the previous meta-analyses, we found that patients with MS had lower total sleep time and higher AHI. The differences between our results and the previous meta-analyses can be explained by the fact that our search was more updated, and included larger number of studies and patients, thus our study had greater statistical power to detect significant differences.
We also found that higher AHI, and lower sleep efficiency were correlated with worsening EDSS. Overall, our results indicate that sleep fragmentation is associated with worse physical ability in MS. The previous meta-analysis conducted in 2022 correlated polysomnography data with EDSS and did not find a correlation between any of the variables. 7 In our study, we expanded on the aforementioned analysis by including higher number of studies and patients and demonstrating that sleep characteristics such as AHI and sleep efficiency might be correlated with EDSS. However, it is important to highlight that these analyses were of small sample size and included a small number of studies, thus they should be interpreted cautiously. The low number of studies in these models also limited our ability to perform subgroup analysis among the high-quality studies.
The association between sleep characteristics and physical disability might be mediated through hypoxia. Studies showed that hypoxia might be implicated in MS specifically with acute 26 and chronic 27 inflammation. A previous in vivo study demonstrated that watershed areas of the brain harbor a higher number of T 2 -lesions and chronic blackholes and lesions in these areas carry a higher degree of demyelination and axonal degeneration compared to lesions in non-watershed zones. 26 In addition, a previous study also demonstrated that watershed areas of the brain had higher number and larger chronic active lesions compared to non-watershed ones. 27 These findings were explained by the role of hypoxia in inflammation. Hypoxia can cause vascular leaks that start the inflammation cycle. Leakage of fibrinogen and fibronectin activates microglia resulting in secretion of reactive oxidative species and nitric oxide which activate and propagate inflammation resulting in demyelination and axonal loss. 28 Another consideration is that sleep disorders precipitate fatigue through sleep fragmentation resulting in higher physical and cognitive disability scores. 29
From a clinical standpoint, referring all patients with MS to polysomnography would not be feasible. While there are no studies that previously evaluated which patients should be screened for OSA among patients with MS, using validated methods among other diseases might be feasible. For example, using previously validated tools such as Epworth sleepiness scale to screen which patients should be referred to polysomnography can be a feasible approach. 30 Another possible strategy is to screen patients with treatment-resistant symptoms or disease progression despite treatment, similar to the approach used in resistant hypertension. 31 Whether treating OSA or preventing sleep fragmentation and hypoxia improves outcomes in patients with MS remains unknown. However, a previous clinical trial that included 34 patients with MS and OSA who were randomly distributed between CPAP or sham for 6 months demonstrated an improvement in fatigue and sleepiness scales. 32 An open label continuation of the same trial demonstrated that CPAP-treated patients also had improvement in their sleep quality, depression, and quality of life scores compared to their counterparts. 33 Future larger clinical trials examining the impact of CPAP on MS-related disability and MRI outcomes are needed.
Before concluding, few limitations should be acknowledged. First, some of the included models had high heterogeneity which might be due to the difference in the characteristics of the included studies such as MS phenotype, disease duration, age, BMI and disease modifying treatments. Unfortunately, the included studies did not report stratified data based on these variables. We tried to mitigate this high heterogeneity by conducting a subgroup analysis based on the quality of the studies. However, future studies are still recommended to investigate the association between MS and OSA across different MS phenotypes, age categories, BMI, disease duration and disease modifying treatments. Second, the models regarding the correlation between polysomnography characteristics and disability included a low number of studies and patients which limits the generalizability of the findings. Third, no studies included MRI data to correlate polysomnography data with MRI outcomes. Moreover, there were no studies that evaluated OSA subtypes such as REM vs non-REM and positional vs non-positional. Finally, the majority of the included papers did not account for confounding variables. Several factors are known to impact OSA severity and MS risk. For example, younger age is known to have both higher prevalence of OSA 34 and MS. 35 Another example is obesity which is also a known risk factor for both OSA 36 and MS. 37 Smoking is also considered a risk factor for both diseases and their progression. 38 , 39 Future studies that investigate the relationship between MS and OSA are recommended to adjust for confounding factors either through matching or statistical models.
Conclusion
In conclusion, the prevalence of OSA among patients with MS is high and reaches up to 44% with 24% being mild, 14% being moderate, and 8% being severe. Patients with MS had lower total sleep time and sleep efficiency, but higher AHI. AHI and sleep efficiency were correlated with EDSS. Taken together, our paper highlights the high prevalence of OSA among patients with MS and the difference in sleep characteristics among patients with MS compared to healthy controls. OSA significantly impacts physical and cognitive disability, as well as quality of life, in patients with MS. Therefore, it is essential to screen these patients for OSA and offer management options, such as CPAP, when appropriate.
Supplemental Material
Supplemental Material - The Prevalence and Characteristics of Obstructive Sleep Apnea Among Patients With Multiple Sclerosis: A Systematic Review and Meta-Analysis
Supplemental Material for The Prevalence and Characteristics of Obstructive Sleep Apnea Among Patients With Multiple Sclerosis: A Systematic Review and Meta-Analysis by Ahmad A. Toubasi, Thuraya N. Al-Sayegh in Journal of Central Nervous System Disease.
Authors Contributions: AAT was involved in conceptualization; AAT, and TA were involved in project administration; AAT, and TA were involved in data acquisition; AAT was involved in data analysis; AAT, and TA were involved in manuscript writing; AAT was involved in editing and reviewing the manuscript.
Funding: The authors received no financial support for the research, authorship, and/or publication of this article.
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Supplemental Material: Supplemental material for this article is available online.
ORCID iD
Ahmad A. Toubasi https://orcid.org/0000-0002-4688-9728
Data Availability Statement
The data is available from the corresponding author upon reasonable request. *
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