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Risk of obstructive sleep apnea in multiple sclerosis: Frequency, clinical and radiological correlates
Multiple Sclerosis and Related Disorders 28 (2019) 184–188
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Multiple Sclerosis and Related Disorders journal homepage: www.elsevier.com/locate/msard
Risk of obstructive sleep apnea in multiple sclerosis: Frequency, clinical and radiological correlates
T
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Osama A. Abdel Salama, , Nesma A.M. Ghonimib, Mohamed H. Ismailb a b
Neurology Department, Faculty of Medicine, Mansoura University, Egypt Neurology Department, Faculty of Medicine, Zagazig University, Egypt
A R T I C LE I N FO
A B S T R A C T
Key words: Multiple sclerosis OSA Fatigue Disability
Background: Primary sleep disorder, especially, obstructive sleep apnea (OSA), are noted to occur in MS patients at higher frequency than the general population. Objectives: Our objectives are: (1) To assess the frequency of OSA risk among MS patients. (2) To evaluate the relationship between OSA risk and self-reported fatigue and sleepiness. (3) To evaluate the relationship between OSA risk and clinical disability, radiological findings and treatment status. Methods: We enrolled 124 patients with multiple sclerosis, there were 53 men and 71 women with mean age (31.12 ± 7.48) All participants completed questionnaires: STOP-Bang, Berlin, Fatigue Severity Scale (FSS) and Epworth sleepiness scale (ESS) and their disability was assessed using Expanded Disability Status Scale (EDSS). Results: Among the completed surveys, 46.8% screened as high risk for OSA based on STOP-BANG questionnaire, and 45.2% based on Berlin questionnaire. About 64.5% of subjects screened positive for fatigue using FSS and 38.7% reported excessive day time sleepiness. Comparing MS patients diagnosed with high risk of OSA and those without, there was significant difference between the two groups regarding age (P < 0.001), gender (P < 0.001), disease duration, (P = 0.04), presence of brainstem lesions (0.04) and disease modifying therapy (DMT) use (P = 0.002). ESS and FSS positive scores were each significantly correlated with positive STOP BANG and Berlin outcomes (p < 0.001). EDSS showed significant correlation with positive STOP-Bang and Berlin scores. Conclusions: OSA risk appears to be high in MS patients with increased risk of fatigue and increasing disability.
1. Introduction Multiple sclerosis (MS) is an autoimmune disease targeting the central nervous system and causes myelin destruction and axonal damage in the brain and spinal cord. Multiple sclerosis is the leading cause of non-traumatic neurological disability among young adults, affecting approximately 350,000 individuals in the USA and 2.5 million individuals worldwide (Hauser and Goodin, 2011). Primary sleep disorders especially obstructive sleep apnea (OSA) are noted to occur in MS patients at higher frequency than the general population (Brass et al., 2005), (Kaminska et al., 2012). While the classic symptoms of sleep apnea are loud snoring, witnessed apneas, and excessive daytime sleepiness (Attarian et al., 2004), (Kaminska et al., 2011), these symptoms may be less prominent in women (Gabbay and Lavie, 2012) and may be less frequently reported in neurologically impaired populations. For example, patients with MS and OSA may be more frequently report symptoms of fatigue rather
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than overt sleepiness (Braley et al., 2014). OSA has been strongly associated with fatigue (Krupp et al., 1989), which is the most common and disabling symptom of the disease, estimated to occur in 90% of patients (Goodin, 1999), (Ghaem and Haghighi, 2008). In addition, OSA is associated with in- creased cardiovascular and cerebrovascular morbidity and mortality, as well as decreased quality of life (Young et al., 2008). Little is known regarding the frequency and predictive factors of OSA in MS patients. Studies have been limited by varying methodologies, small sample sizes and inconsistent results (Kaminska et al., 2011). Given the profound effect that both MS and OSA can have on daytime functioning, it is important to identify those MS patients who have high OSA risk in order to apply early treatment thus improving their overall health status and quality of life. The major objectives of this study are to: (1) Assess frequency of OSA risk among MS patients by utilizing the
Corresponding author. E-mail address: [email protected] (O.A. Abdel Salam).
https://doi.org/10.1016/j.msard.2018.12.015 Received 3 August 2018; Received in revised form 19 November 2018; Accepted 13 December 2018 2211-0348/ © 2018 Published by Elsevier B.V.
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Scores < 6.0 indicate independent ambulation without the need for an assistive device such as a cane, walker, or wheelchair (Kurtzke, 1983). Brain and spinal MRI images were done to all the patients in the MR unit of radiology department. All the MRI examinations were performed using Atchiva MRI scanner Philips (1.5 T). The routine examination consisted of axial, sagittal and coronal slices. We used the following techniques: T1-Weighted images, T2-Weighted images, FLAIR Images (sagittal flair) and T1-Gadolinium (Gd) enhancement images. Number of T2 lesions were calculated and sites either periventricular, corpus callosum, brain stem, cerebellar or spinal were also assessed. Patients were considered to have MS activity if they had one or more T1-Gd enhancing lesion.
STOP-BANG and Berlin questionnaires. (2) Evaluate the relationship be- tween OSA risk and self-reported fatigue and sleepiness using the Fatigue Severity Scale (FSS) and Epworth Sleepiness Scale (ESS), respectively. (3) Evaluate the relationship between OSA risk and clinical disability (assessed by EDSS), radiological findings and treatment status. 2. Subject and methods The current study was conducted at Zagazig Neurology MS unit in collaboration with Saudi German Hospital; from February 2017 to April 2018. It included a total of 136 patients diagnosed with MS according to McDonals criteria 2010, The exclusion criteria were: age < 18 years, patients with other disorders causing OSA (Stroke, Parkinson, ALS) cases with moderate to severe depression and incomplete survey. Twelve patients were excluded, leaving a sample of 124 subjects. Patients were invited to participate in an anonymous survey. The study protocol was approved by The Local Ethical Committee. Patients signed written fully informed consent for study participation and undergoing the assigned investigations. The survey included 4 validated instruments: the STOP Bang Questionnaire; Berlin scale; Epworth Sleepiness Scale and Fatigue Severity Scale. The STOP-Bang questionnaire is a screening tool consisting of 8 questions and measures the items that form the acronym STOP-Bang. Questions include snoring, daytime tiredness, observed apneas, high blood pressure, body mass index (> 35 kg/m2), age (≥ 50 years), neck circumference (> 40 cm), and gender (male). Scores depend on yes/no answers for each item (score: 1/0). STOP-Bang scores ≥ 3 indicate elevated risk for OSA (Chung et al., 2008), (Abrishami et al., 2010). Berlin questionnaire is a 3-category questionnaire that determines the risk of obstructive sleep apnea based on a profile of questions similar to the STOP BANG, but measured quantitatively instead of dichotomously. Patients with responses indicating high risk in ≥ 2 categories were predicted to be at high risk of obstructive sleep apnea (Netzer et al., 1999). Both STOP-BANG and Berlin were used to determine the concordance rate between the surveys, as they are both validated questionnaires to screen for obstructive sleep apnea and have been first used conjointly in the MS population by Brass et al. (2014). The Epworth sleepiness scale (ESS) is the most commonly used measure of daytime sleepiness and involves asking the patient to rate the probability of dozing off or falling asleep (0 = never, 1 = slight chance, 2 = moderate chance, 3 = high chance) in eight different situations, with a total score greater than 10 out of 24 indicating possible excessive daytime sleepiness, and a score of 16 or higher to indicate severe daytime sleepiness (Johns, 1999). The Fatigue Severity Scale (FSS) rates the severity of fatigue symptoms not only in patients with MS but also in those with other chronic diseases through responding to 9 questions related to the patient's experiences in the past week, using a Likert scale (1-strongly disagree to 7-strongly agree). Patients with a score of 36/64 or higher are found to be suffering from fatigue (Krupp et al., 1989). The Clinical variables recorded included age, gender, MS subtype (re- lapsing or progressive), MS disease duration at time of the survey (years), current use of disease modifying or immunosuppressive therapy (yes/no), active symptoms or clinical diagnosis of depression were documented during clinical assessment of the patients on the day of the survey, patients with moderate to severe depression were excluded based on Becker depression inventory scale, and disability was estimated using EDSS [defined as Expanded Disability Status Scale score < 6 (lower disability), or ≥ 6 (higher disability)]. Based on a standard neurological examination, the Expanded Disability Status Scale (EDSS) is a scale that is commonly used to quantify disability level in patients with MS through ratings of 7 functional systems commonly affected in MS: visual, brainstem, pyramidal, cerebellar, sensory, bowel/ bladder, and cerebral function.
3. Statistical analysis All statistical analyses were performed using SPSS type 20.0 (SPSS, Inc., Chicago, IL, USA). Software for Windows. Data were expressed as mean ± standard deviation, and categorical variables were compared by using a χ2 test. Student's t-test for unpaired data was used to compare demo- graphic and clinical data between cases with and without OSA. P values < 0.05 were considered statistically significant. Spearsmann and Pearson correlation coefficient used to calculate correlation between quantitative variables mainly the variables correlating with STOP-BANG. Multivariate analysis was performed for confounding variables of OSA, and in assessing the predictors of OSA in MS patients. 4. Results Demographic data are displayed in Table 1, twelve patients were excluded, leaving a sample of 124 subjects that were 42.7% men and 57.3% women. The mean age for the entire cohort was 31.12 ± 7.48, with a mean duration of illness of 7.94 ± 4.98 years. About 69.4% of patients were receiving beta interferon, and no one received other disease modifying therapy (DMT). The majority of them were RRMS (89.09%). They showed the following distribution of MRI lesions (93.5% periventricular, 56.1% cerebellar, 55.65% spinal region while 70.2% had brainstem involvement) with a mean EDSS of 4.06 ± 1.49. Participants’ sleep history was obtained based on the screening questionnaires. Among the completed surveys, 58 (46.8%) screened as high risk for OSA based on STOP-BANG questionnaire, and 45.2% based on Berlin questionnaire. Among responders,38.7% reported excessive Day time sleepiness while 33.9% had sever day time sleepiness based on the ESS. About 64.5% of subjects (no = 80) screened positive for fatigue using FSS (Table 1). Comparing MS patients with high OSA and those with low OSA (based on STOP-BANG and Berlin questionnaire), there was significant difference between the two groups regarding age (P < 0.001), gender (P < 0.001), disease duration, (P = 0.04), presence of brainstem lesions (0.04) and DMT use (P = 0.002), these variables can be considered as predictors of OSA. Multiple sclerosis patients with high OSA risk were more commonly suffering from fatigue and excessive day time sleepiness than those without (P < 0.001) . In addition, higher levels of disability measured by EDSS were observed in patients with high OSA risk than those with- out and the difference was statistically significant (p = 0.04) (Table 2). ESS and FSS positive scores were each significantly correlated with positive STOP BANG and Berlin outcomes p (p < 0.001). In addition, the association between respondents with excessive daytime sleepiness and those with excessive fatigue was statistically significant (p < 0.001), suggesting that individuals experiencing excessive daytime sleepiness also experience excessive fatigue. EDSS showed significant correlation with positive STOP-Bang and Berlin scores (Table 3). Potential variables were analyzed using multivariate Analysis (Table 4), Only the age (OR: 6.098, 95% CI: 1.920–19.367; = 0.002), male gender (OR:10.586; CI:4.005–27.984; < 0.001), not using DMT (OR: 3.37; 95% CI:1.264–8.989; = 0.015) were independently 185
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associated with the risk of OSA.
Table 1 Demographic and clinical data of the studied group.
Age Sex Disease duration DMT MS subtype RRMS SPMS PPMS MRI findings Peri-ventricular Corpus callosum Brain stem Cerebellar Spinal cord FSS
EDSS
ESS
STOP-Bang
Berlin
Variable
(n = 124)
Mean ± SD Range Female N (%) Male N (%) Mean ± SD Median (Range) no N (%) yes N (%)
31.12 ± 7.48 18 – 46 71 (57.3%) 53 (42.7%) 7.94 ± 4.98 7 (1 – 24) 38 (30.6%) 86 (69.4%)
N (%) N (%) N (%)
108 (87.09) 14 (11.2) 2 (1.6)
No N (%) Yes N (%) No N (%) Yes N (%) No N (%) Yes N (%) No N (%) Yes N (%) No N (%) Yes N (%) Mean ± SD Median (Range) No fatigue N (%) Fatigue N (%) Mean ± SD Median (Range) Lower disability N (%) Higher disability N (%) Mean ± SD Median (Range) no daytime sleepiness N (%) excessive Day time sleepiness N (%) sever day time sleepiness N (%) Mean ± SD Median (Range) high OSA risk N (%) low OSA risk N (%) High OSA risk N (%) Low OSA risk N (%)
8 (6.5%) 116 (93.5%) 46 (37.1%) 78 (62.9%) 39 (31.5%) 85 (68.5%) 52 (41.9%) 72 (58.1%) 55 (44.4%) 69 (55.6%) 39.75 ± 14.06 14.06 (10 – 64) 44 (35.5%) 80 (64.5%) 4.06 ± 1.49 4 (1 – 6.6) 103 (83.1%) 21 (16.9%) 12.7 ± 4.91 14 (1 – 21) 34 (27.4%) 48 (38.7%) 42 (33.9%) 4.02 ± 1.78 3(0 – 7) 66 (53.2%) 58 (46.8%) 68 (54.8%) 56 (45.2%)
5. Discussions Obstructive sleep apnea (OSA) is well recognized as a major public health challenge (Young et al., 1993). Despite its importance, its frequency in patients with MS remains unknown, with estimates ranging from 0% to 58% in published studies (Veauthier et al., 2011; Kaynak et al., 2006). According to our results nearly half of MS patients (46%) have high OSA risk based on STOP BANG, a percentage that is very near to that reported by Dias et al. (40%) who also depended on STOP- Bang to detect high risk patients (Dias et al., 2012). There are several potential reasons for the high variability in OSA frequency among different studies. Some of these studies utilized PSG (Veauthier et al., 2011; Kaynak et al., 2006; Neau et al., 2012) with the tendency to select the most severely affected individuals, thus leading to an overestimation of OSA prevalence. Additional factors include differences in sample size and subject selection methods with some restriction related to age and disability status (Kaynak et al., 2006; Ferini-Srambi et al., 1994) which may have led to an underestimation of OSA prevalence among the wider population of all MS patients. Lack of screening for OSA risk remains a common problem, and validated questionnaires provide an effective method of improving detection, as Senthivel et al. have demonstrated in the primary care setting (Senthilvel et al., 2011). The performance levels of the Berlin and STOP-BANG questionnaires in detecting OSA of various severity levels are outlined as follows: for mild OSA, sensitivity levels were 76%, 88%, specificity levels were 59%, 42% respectively. For moderate OSA, sensitivity levels were 77% and 90%; specificity levels were 44% and 36%, respectively. For severe OSA, sensitivity levels were 84%, 93%, specificity levels were 38%, 35%, respectively. Therefore, for mild, moderate, and severe OSA, the pooled sensitivity of the SBQ were significantly higher than those of Berlin (P < 0.05) (Chiu et al., 2017). So, it is important to note that a positive STOP-BANG score indicates being at risk for OSA and does not necessarily equate to a diagnosis of OSA and further validation data with polysomnography is still needed. At this study, we tried to throw light on MS clinical features that may increase OSA risk. The number of females predominates in our study subjects (57.3%), as typically occurring in MS (Hauser and
Table 2 Demographic and clinical data of patients with high and low OSA risk.
Age Sex Disease duration DMT Peri-ventricular Corpus callosum Brain stem Cerebellar Spinal cord FSS EDSS ESS
Variable
Low OSA risk (n = 66)
High OSA risk (n = 58)
Test
P
Mean ± SD Range Female N (%) Male N (%) Mean ± SD Median (Range) No N (%) Yes N (%) Yes N (%) No N (%) No N (%) Yes N (%) No N (%) Yes N (%) No N (%) Yes N (%) No N (%) Yes N (%) No fatigue N (%) Fatigue N (%) Lower disability N (%) Higher disability N (%) no daytime sleepiness N (%) excessive Day time sleepiness N(%) severe day time sleepiness N (%)
28.67 ± 6.8 18 – 45 51 (77.3%) 15 (22.7%) 6.92 ± 4.04 7 (1 – 22) 28 (42.4%) 38 (57.6%) 61 (92.4%) 5 (7.6%) 22 (33.3%) 44 (66.7%) 26 (39.4%) 40 (60.6%) 31 (47%) 35 (53%) 34 (51.5%) 32 (48.5%) 36 (54.5%) 30 (45.5%) 59 (89.4%) 7 (10.6%) 27 (40.9%) 28 (42.4%) 11 (16.7%)
33.91 ± 7.29 20 - 46 20 (34.5%) 38 (65.5%) 9.10 ± 5.68 9 (3 – 24) 10 (17.2%) 48 (82.8%) 55 (94.8%) 3 (5.2%) 24 (41.4%) 34 (58.6%) 13 (22.4%) 45 (77.6%) 21 (36.2%) 37 (63.8%) 21 (36.2%) 37 (63.8%) 8 (13.8%) 50 (86.2%) 44 (75.9%) 14 (24.1%) 7 (12.1%) 20 (34.5%) 31 (53.4%)
t 4.15 χ2 23.10 MW 2.06 χ2 9.21 χ2 0.30 χ2 0.86 χ2 4.13 χ2 1.47 χ2 2.93 χ2 22.4 χ2 4.02 χ2 22.2
<0.001**
186
<0.001** 0.04* 0.002** 0.59 NS 0.65 NS 0.04* 0.23 NS 0.09 NS <0.001** 0.04* <0.001**
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Table 3 Correlation between different variables and scales related to OSA risk. Age Age Disease duration Berlin FSS EDSS Stop-Bang Epworth sleepiness scale
—. 0.761⁎⁎ <0.001 0.23 0.12 0.18 0.45 0.21 0.18 0.16 0.34 0.09 0.79
r P r P r P r P r P r P r P
Disease duration
Berlin
⁎⁎
FSS
⁎⁎
0.334 <0.001 0.164 .069 —. 0.375⁎⁎ <0.001 0.399⁎⁎ <0.001 0.968⁎⁎0 <0.001 0.383⁎⁎ <0.001
0.761 <0.001 —. 0.164 .069 0.490⁎⁎ <0.001 0.394⁎⁎ <0.001 0.170 .059 0.386⁎⁎ <0.001
EDSS ⁎⁎
0.508 <0.001 0.490⁎⁎ <0.001 0.375⁎⁎ <0.001 —. 0.522⁎⁎ <0.001 0.389⁎⁎ <0.001 0.656⁎⁎ <0.001
Stop-Bang ⁎⁎
0.536 <0.001 0.394⁎⁎ <0.001 0.399⁎⁎ <0.001 0.522⁎⁎ <0.001 —. 0.413⁎⁎ <0.001 0.319⁎⁎ <0.001
⁎⁎
0.353 <0.001 0.170 .059 0.968⁎⁎ <0.001 0.389⁎⁎ <0.001 0.413⁎⁎ <0.001 —. 0.405⁎⁎ <0.001
ESS 0.339⁎⁎ <0.001 0.386⁎⁎ <0.001 0.383⁎⁎ <0.001 0.656⁎⁎ <0.001 0.319⁎⁎ <0.001 0.405⁎⁎ <0.001 —.
other sleep disturbances on the pathophysiology of MS, recent associations between OSA and local/systemic inflammation allow speculation that un- treated OSA could contribute to MS-related disability or progression as showed by EDSS which was significantly higher in patients with OSA (based on STOP-BANG and Berlin positive responses) and significantly correlated with their scores. This association has been supported by the recent study of Sloane and Siddiqui (Sloane and Siddiqui, 2017) who reported that features of MS progression such as high ARR and elevated EDSS are more common in OSA patients, a research area that merits further study. On the other hand, Previous studies suggest that progressive MS subtypes and in- creased level of disability are risk factors for OSA in MS (Braley et al., 2012). All these findings support a reciprocal relationship between OSA in MS patients and level of disability. Similarly, another risk factor is age, which correlates positively with positive STOP-BANG and Berlin scores as well as disability progression. It is of interest to note that in our survey, there was an association be- tween MS patients who are at risk for OSA and reported high levels of fatigue (FSS scored positive in 64.5%) with a significant correlation be- tween the STOP-BANG scores and the FSS scores. These results are parallel to those of Veauthier and Colleagues whose study included 66 MS patients, they reported that sleep-related breathing disorders (using portable PSG) were more frequent in MS patients with fatigue (27%) than those without fatigue (2.5%) (Veauthier et al., 2011). They used both FSS and Modified Fatigue Impact Scale (MFIS) in their study (Kurtzke, 1983). Obstructive sleep apnea (OSA) may contribute to fatigue symptoms and augment disability through cerebral perfusion changes, inflammatory cytokine expression and brain atrophy. This may suggest that OSA should be considered in MS patients presenting with fatigue. In the view of these findings, re- searchers are also currently considering screening for OSA in MS patients complaining of fatigue and using CPAP as a therapeutic modality to treat MS fatigue (Gómez-González et al., 2012). MS-related fatigue can be substantially relieved, by early diagnosis and successful treatment of OSA in those patients (Braley et al., 2014). Failure to detect sleep problems in MS patients may lead to missed opportunities to alleviate fatigue which is one of the most common chronic symptoms in those patients
Goodin, 2011). However, substantially more males than females were classified as at high OSA risk (65.5% versus 34.5%) on the STOP-BANG questionnaire. Similarly, Population-based studies have observed a two to three folds greater risk for OSA in males compared to females (Young et al., 2008; 2002; 1993). The gender difference may be explained by several factors including location of body fat distribution and anatomical airway differences, generally, males had a statistically larger neck circumference compared to females (Young et al., 2008; 2002; Chiu et al., 2017), additional factors include breathing control, hormones, and aging, all are thought to play a role (Christine et al., 2008). Since the STOP-BANG questionnaire itself incorporates male gen- der among its items, it is expected that a larger number of males will have higher scores in STOP-BANG questionnaire (Dias et al., 2012). As both multiple sclerosis and OSA influence the immune system and change the cytokine profile of patients, this may provide explanation for their comorbidity. OSA patients (without MS) show elevation in pro-inflammatory cytokines e.g. interleukin- 6, IFN-γ, and tumor necrosis factor-α (TNF-α) compared to BMI-matched controls without OSA (Gómez-González et al., 2012). IFN-γ and TNF- α are also found to have a somnogenic effect, thus causing sleepiness and fatigue if administered to normal subjects. Additionally, interleukin- 6 and TNF-α levels decrease when subjects receive CPAP. Of importance, MS is known to be also associated with a similar pro-inflammatory cytokine profile, with patients showing elevations of IL-6, IFN-γ, and TNF-α, well as other pro-inflammatory cytokines, all these findings support the important role of inflammation in both diseases (Gómez-González et al., 2012). MS patients may be at increased risk for OSA as a result of their neuro- logical condition. In a recent study, MS patients, and particularly patients with evidence of brainstem lesions on MRI, were found to have more severe OSA (and CSA) than matched control subject due to damage of key brainstem respiratory centers located in the pons and medulla (Braley et al., 2012). These findings focus on the important role of brainstem autonomic control on nocturnal airway patency and throw light on vulnerable populations whose OSA risk may be increased due to central nervous system pathology (Dyken and Im, 2009; Dyken et al., 2012). While no data is available regarding the long-term impact of OSA or
Table 4 Multivariate analysis of significantly correlated variables with OSA risk. Variable
B
S.E.
Wald
P
OR
95% C.I.
Age > 30 y Male Sex Not using DMT Duration > 7 y Brainstem lesions in MRI Higher disability in EDSS
1.808 2.360 1.215 −0.237 0.946 0.660
0.590 0.496 0.500 0.568 0.489 0.596
9.402 22.633 5.898 0.174 3.734 1.226
0.002** <0.001** 0.015* 0.676 0.053 0.268
6.098 10.586 3.371 .789 2.575 1.934
1.920 4.005 1.264 0.259 0.987 0.602
187
19.367 27.984 8.989 2.403 6.719 6.219
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(Veauthier et al., 2013). Prior studies have had conflicting results regarding the relation between fatigue and OSA risk. Wunderlin et al. and Kaynak et al. did not find significant relation between high scores on fatigue measures and OSA risk (Wunderlin et al., 1997), (Kaynak et al., 2006), similarly, Dias et al. reported lack of a significant correlation between the FSS and the overall STOP-BANG score (Dias et al., 2012). They re- ported other factors aside from sleep-related breathing disorders to con- tribute to MS fatigue, including central nervous system abnormalities, altered cytokine profiles, depression, heat sensitivity, physical impairment, pain, nocturia, and degree of psychosocial support (Wunderlin et al., 1997; Mills and Young, 2010) so, further studies are needed to look at this important association given the impact of cytokines on fatigue and sleep. Measures of fatigue and sleepness were significantly correlated in our study (FSS vs ESS, r = 0.65, p < 0.001). Stanton et al. had similar findings on the FSS and ESS in 60 outpatients with MS (Stanton et al., 2006) as did Merkelbach et al. in 80 MS patients (Merkelbach et al., 2011). As previously mentioned, inflammatory cytokines are expressed at higher levels in individuals with MS as well as OSA and are involved in their pathogenesis, so, treatment with agents that influence these cytokine levels may improve apnea severity (Vgntzas et al., 2004). Based on these facts, DMT can be a strong predictor of reduced apnea severity (Braley, 2015).
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MultScler. 12, 481–486. Veauthier, C., Gaede, G., Radbruch, H., Gottschalk, S., Wernecke, K.D., Paul, F., 2013. Treatment of sleep disorders may improve fatigue in multiple sclerosis. ClinNeurol. Neurosurg. 115, 1826–1830. Veauthier, C., Radbruch, H., Gaede, G., Pfueller, C.F., Dörr, J., Bellmann- Strobl, J., Wernecke, K.D., Zipp, F., Paul, F., Sieb, J.P., 2011. Fatigue in multiple sclerosis is closely related to sleep disorders: a polysomnographic cross-sectional study. MultScler 17, 613–622. Vgontzas, A.N., Zoumakis, E., Lin, H.M., Bixler, E.O., Trakada, G., Chrousos, G.P., 2004. Marked decrease in sleepiness in patients with sleep apnea by etanercept, a tumor necrosis factor antagonist. J. Clin. Endocrinol. Metab. 89, 4409–4413. Wunderlin, B.W., Kesselring, J., Ginzler, H., Walser, B., Kuhn, M., Reinhart, W.H., 1997. Fatigue in multiple sclerosis is not due to sleep apnea. Eur. J. Neurol. 4, 72–78. Young, T., Finn, L., Peppard, P.E., Szklo-Coxe, M., Austin, D., Nieto, F.J., Stubbs, R., Hla, K.M., 2008. Sleep disordered breathing and mortality: eighteen-year follow-up of the Wisconsin sleep cohort. Sleep 31, 1071–1078. Young, T., Palta, M., Dempsey, J., Skatrud, J., Weber, S., Badr, S., 1993. The occurrence of sleep-disordered breathing among middle-aged adults. N. Engl. J. Med. 328, 1230–1235. Young T., Peppard P.E., Gottlieb D.J.: Epidemiology of obstructive sleep apnea: a population health perspective. Am. J. Respir. Crit. Care Med. 2002; 165:1217–1239.
6. Conclusion OSA appears to be surprisingly common in MS patients. By virtue to the overlap between OSA and fatigue and the potential risk of further increasing disability from complications related to untreated sleep apnea. Identifying and treating OSA in those patients may be an important way not only to treat fatigue but also to reduce disability. Based on our findings, we should maintain a low threshold for referral to a sleep center for further evaluation by polysomnography if sleep breathing disorders are suspected in patients with MS. 7. Limitations The major limitation of the present study is that scores on the STOPBANG questionnaires were not supported with PSG to determine the sensitivity and specificity of these screening instruments in MS patients however, we are supported by the fact that STOP-Bang scores and Berlin questionnaire have a sensitivity of 75% to 85% to detect OSA as previously mentioned. References Abrishami, A., Khajehdehi, A., Chung, F., 2010. A systematic review of screening questionnaires for obstructive sleep apnea. Can. J. Anaesth. 57, 423–438. Attarian, H.P., Brown, K.M., Duntley, S.P., Carter, J.D., Cross, A.H., 2004. The relationship of sleep disturbances and fatigue in multiple sclerosis. Arch. Neurol. 61, 525–528. Braley, T.J., Segal, B.M., Chervin, R.D., 2014. Obstructive sleep apnea and fatigue in patients with multiple sclerosis. J. Clin. Sleep Med. 10, 155–162. Braley, T.J., 2015. Sleep in patients with multiple sclerosis. Curr. Sleep Medicine Rep. 1, 108–113. Braley, T.J., Segal, B.M., Chervin, R.D., 2012. Sleep-disordered breathing in multiple sclerosis. Neurology 79, 929–936. Brass, S.D., Duquette, P., Proulx-Therrien, J., Auerbach, S., 2005. Sleep disorders in multiple sclerosis. SeminNeurol. 25, 121–129. Brass, S.D., Li, C.S., Auerbach, S., 2014. The under diagnosis of sleep disorders in patients with multiple sclerosis. J. ClinSleep Med. 10 (9), 1025–1031. Chiu, H.Y., Chen, P.Y., Chuang, L.P., Chen, N.H., Tu, Y.K.5, Hsieh, Y.J., Wang, Y.C., Guilleminault, C., 2017. Diagnostic accuracy of the Berlin questionnaire, STOPBANG, STOP, and Epworth sleepiness scale in detecting obstructive sleep apnea: a bivariate meta-analysis. Sleep Med. Rev. 36, 57–70.
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Multiple Sclerosis and Related Disorders journal homepage: www.elsevier.com/locate/msard
Risk of obstructive sleep apnea in multiple sclerosis: Frequency, clinical and radiological correlates
T
⁎
Osama A. Abdel Salama, , Nesma A.M. Ghonimib, Mohamed H. Ismailb a b
Neurology Department, Faculty of Medicine, Mansoura University, Egypt Neurology Department, Faculty of Medicine, Zagazig University, Egypt
A R T I C LE I N FO
A B S T R A C T
Key words: Multiple sclerosis OSA Fatigue Disability
Background: Primary sleep disorder, especially, obstructive sleep apnea (OSA), are noted to occur in MS patients at higher frequency than the general population. Objectives: Our objectives are: (1) To assess the frequency of OSA risk among MS patients. (2) To evaluate the relationship between OSA risk and self-reported fatigue and sleepiness. (3) To evaluate the relationship between OSA risk and clinical disability, radiological findings and treatment status. Methods: We enrolled 124 patients with multiple sclerosis, there were 53 men and 71 women with mean age (31.12 ± 7.48) All participants completed questionnaires: STOP-Bang, Berlin, Fatigue Severity Scale (FSS) and Epworth sleepiness scale (ESS) and their disability was assessed using Expanded Disability Status Scale (EDSS). Results: Among the completed surveys, 46.8% screened as high risk for OSA based on STOP-BANG questionnaire, and 45.2% based on Berlin questionnaire. About 64.5% of subjects screened positive for fatigue using FSS and 38.7% reported excessive day time sleepiness. Comparing MS patients diagnosed with high risk of OSA and those without, there was significant difference between the two groups regarding age (P < 0.001), gender (P < 0.001), disease duration, (P = 0.04), presence of brainstem lesions (0.04) and disease modifying therapy (DMT) use (P = 0.002). ESS and FSS positive scores were each significantly correlated with positive STOP BANG and Berlin outcomes (p < 0.001). EDSS showed significant correlation with positive STOP-Bang and Berlin scores. Conclusions: OSA risk appears to be high in MS patients with increased risk of fatigue and increasing disability.
1. Introduction Multiple sclerosis (MS) is an autoimmune disease targeting the central nervous system and causes myelin destruction and axonal damage in the brain and spinal cord. Multiple sclerosis is the leading cause of non-traumatic neurological disability among young adults, affecting approximately 350,000 individuals in the USA and 2.5 million individuals worldwide (Hauser and Goodin, 2011). Primary sleep disorders especially obstructive sleep apnea (OSA) are noted to occur in MS patients at higher frequency than the general population (Brass et al., 2005), (Kaminska et al., 2012). While the classic symptoms of sleep apnea are loud snoring, witnessed apneas, and excessive daytime sleepiness (Attarian et al., 2004), (Kaminska et al., 2011), these symptoms may be less prominent in women (Gabbay and Lavie, 2012) and may be less frequently reported in neurologically impaired populations. For example, patients with MS and OSA may be more frequently report symptoms of fatigue rather
⁎
than overt sleepiness (Braley et al., 2014). OSA has been strongly associated with fatigue (Krupp et al., 1989), which is the most common and disabling symptom of the disease, estimated to occur in 90% of patients (Goodin, 1999), (Ghaem and Haghighi, 2008). In addition, OSA is associated with in- creased cardiovascular and cerebrovascular morbidity and mortality, as well as decreased quality of life (Young et al., 2008). Little is known regarding the frequency and predictive factors of OSA in MS patients. Studies have been limited by varying methodologies, small sample sizes and inconsistent results (Kaminska et al., 2011). Given the profound effect that both MS and OSA can have on daytime functioning, it is important to identify those MS patients who have high OSA risk in order to apply early treatment thus improving their overall health status and quality of life. The major objectives of this study are to: (1) Assess frequency of OSA risk among MS patients by utilizing the
Corresponding author. E-mail address: [email protected] (O.A. Abdel Salam).
https://doi.org/10.1016/j.msard.2018.12.015 Received 3 August 2018; Received in revised form 19 November 2018; Accepted 13 December 2018 2211-0348/ © 2018 Published by Elsevier B.V.
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Scores < 6.0 indicate independent ambulation without the need for an assistive device such as a cane, walker, or wheelchair (Kurtzke, 1983). Brain and spinal MRI images were done to all the patients in the MR unit of radiology department. All the MRI examinations were performed using Atchiva MRI scanner Philips (1.5 T). The routine examination consisted of axial, sagittal and coronal slices. We used the following techniques: T1-Weighted images, T2-Weighted images, FLAIR Images (sagittal flair) and T1-Gadolinium (Gd) enhancement images. Number of T2 lesions were calculated and sites either periventricular, corpus callosum, brain stem, cerebellar or spinal were also assessed. Patients were considered to have MS activity if they had one or more T1-Gd enhancing lesion.
STOP-BANG and Berlin questionnaires. (2) Evaluate the relationship be- tween OSA risk and self-reported fatigue and sleepiness using the Fatigue Severity Scale (FSS) and Epworth Sleepiness Scale (ESS), respectively. (3) Evaluate the relationship between OSA risk and clinical disability (assessed by EDSS), radiological findings and treatment status. 2. Subject and methods The current study was conducted at Zagazig Neurology MS unit in collaboration with Saudi German Hospital; from February 2017 to April 2018. It included a total of 136 patients diagnosed with MS according to McDonals criteria 2010, The exclusion criteria were: age < 18 years, patients with other disorders causing OSA (Stroke, Parkinson, ALS) cases with moderate to severe depression and incomplete survey. Twelve patients were excluded, leaving a sample of 124 subjects. Patients were invited to participate in an anonymous survey. The study protocol was approved by The Local Ethical Committee. Patients signed written fully informed consent for study participation and undergoing the assigned investigations. The survey included 4 validated instruments: the STOP Bang Questionnaire; Berlin scale; Epworth Sleepiness Scale and Fatigue Severity Scale. The STOP-Bang questionnaire is a screening tool consisting of 8 questions and measures the items that form the acronym STOP-Bang. Questions include snoring, daytime tiredness, observed apneas, high blood pressure, body mass index (> 35 kg/m2), age (≥ 50 years), neck circumference (> 40 cm), and gender (male). Scores depend on yes/no answers for each item (score: 1/0). STOP-Bang scores ≥ 3 indicate elevated risk for OSA (Chung et al., 2008), (Abrishami et al., 2010). Berlin questionnaire is a 3-category questionnaire that determines the risk of obstructive sleep apnea based on a profile of questions similar to the STOP BANG, but measured quantitatively instead of dichotomously. Patients with responses indicating high risk in ≥ 2 categories were predicted to be at high risk of obstructive sleep apnea (Netzer et al., 1999). Both STOP-BANG and Berlin were used to determine the concordance rate between the surveys, as they are both validated questionnaires to screen for obstructive sleep apnea and have been first used conjointly in the MS population by Brass et al. (2014). The Epworth sleepiness scale (ESS) is the most commonly used measure of daytime sleepiness and involves asking the patient to rate the probability of dozing off or falling asleep (0 = never, 1 = slight chance, 2 = moderate chance, 3 = high chance) in eight different situations, with a total score greater than 10 out of 24 indicating possible excessive daytime sleepiness, and a score of 16 or higher to indicate severe daytime sleepiness (Johns, 1999). The Fatigue Severity Scale (FSS) rates the severity of fatigue symptoms not only in patients with MS but also in those with other chronic diseases through responding to 9 questions related to the patient's experiences in the past week, using a Likert scale (1-strongly disagree to 7-strongly agree). Patients with a score of 36/64 or higher are found to be suffering from fatigue (Krupp et al., 1989). The Clinical variables recorded included age, gender, MS subtype (re- lapsing or progressive), MS disease duration at time of the survey (years), current use of disease modifying or immunosuppressive therapy (yes/no), active symptoms or clinical diagnosis of depression were documented during clinical assessment of the patients on the day of the survey, patients with moderate to severe depression were excluded based on Becker depression inventory scale, and disability was estimated using EDSS [defined as Expanded Disability Status Scale score < 6 (lower disability), or ≥ 6 (higher disability)]. Based on a standard neurological examination, the Expanded Disability Status Scale (EDSS) is a scale that is commonly used to quantify disability level in patients with MS through ratings of 7 functional systems commonly affected in MS: visual, brainstem, pyramidal, cerebellar, sensory, bowel/ bladder, and cerebral function.
3. Statistical analysis All statistical analyses were performed using SPSS type 20.0 (SPSS, Inc., Chicago, IL, USA). Software for Windows. Data were expressed as mean ± standard deviation, and categorical variables were compared by using a χ2 test. Student's t-test for unpaired data was used to compare demo- graphic and clinical data between cases with and without OSA. P values < 0.05 were considered statistically significant. Spearsmann and Pearson correlation coefficient used to calculate correlation between quantitative variables mainly the variables correlating with STOP-BANG. Multivariate analysis was performed for confounding variables of OSA, and in assessing the predictors of OSA in MS patients. 4. Results Demographic data are displayed in Table 1, twelve patients were excluded, leaving a sample of 124 subjects that were 42.7% men and 57.3% women. The mean age for the entire cohort was 31.12 ± 7.48, with a mean duration of illness of 7.94 ± 4.98 years. About 69.4% of patients were receiving beta interferon, and no one received other disease modifying therapy (DMT). The majority of them were RRMS (89.09%). They showed the following distribution of MRI lesions (93.5% periventricular, 56.1% cerebellar, 55.65% spinal region while 70.2% had brainstem involvement) with a mean EDSS of 4.06 ± 1.49. Participants’ sleep history was obtained based on the screening questionnaires. Among the completed surveys, 58 (46.8%) screened as high risk for OSA based on STOP-BANG questionnaire, and 45.2% based on Berlin questionnaire. Among responders,38.7% reported excessive Day time sleepiness while 33.9% had sever day time sleepiness based on the ESS. About 64.5% of subjects (no = 80) screened positive for fatigue using FSS (Table 1). Comparing MS patients with high OSA and those with low OSA (based on STOP-BANG and Berlin questionnaire), there was significant difference between the two groups regarding age (P < 0.001), gender (P < 0.001), disease duration, (P = 0.04), presence of brainstem lesions (0.04) and DMT use (P = 0.002), these variables can be considered as predictors of OSA. Multiple sclerosis patients with high OSA risk were more commonly suffering from fatigue and excessive day time sleepiness than those without (P < 0.001) . In addition, higher levels of disability measured by EDSS were observed in patients with high OSA risk than those with- out and the difference was statistically significant (p = 0.04) (Table 2). ESS and FSS positive scores were each significantly correlated with positive STOP BANG and Berlin outcomes p (p < 0.001). In addition, the association between respondents with excessive daytime sleepiness and those with excessive fatigue was statistically significant (p < 0.001), suggesting that individuals experiencing excessive daytime sleepiness also experience excessive fatigue. EDSS showed significant correlation with positive STOP-Bang and Berlin scores (Table 3). Potential variables were analyzed using multivariate Analysis (Table 4), Only the age (OR: 6.098, 95% CI: 1.920–19.367; = 0.002), male gender (OR:10.586; CI:4.005–27.984; < 0.001), not using DMT (OR: 3.37; 95% CI:1.264–8.989; = 0.015) were independently 185
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associated with the risk of OSA.
Table 1 Demographic and clinical data of the studied group.
Age Sex Disease duration DMT MS subtype RRMS SPMS PPMS MRI findings Peri-ventricular Corpus callosum Brain stem Cerebellar Spinal cord FSS
EDSS
ESS
STOP-Bang
Berlin
Variable
(n = 124)
Mean ± SD Range Female N (%) Male N (%) Mean ± SD Median (Range) no N (%) yes N (%)
31.12 ± 7.48 18 – 46 71 (57.3%) 53 (42.7%) 7.94 ± 4.98 7 (1 – 24) 38 (30.6%) 86 (69.4%)
N (%) N (%) N (%)
108 (87.09) 14 (11.2) 2 (1.6)
No N (%) Yes N (%) No N (%) Yes N (%) No N (%) Yes N (%) No N (%) Yes N (%) No N (%) Yes N (%) Mean ± SD Median (Range) No fatigue N (%) Fatigue N (%) Mean ± SD Median (Range) Lower disability N (%) Higher disability N (%) Mean ± SD Median (Range) no daytime sleepiness N (%) excessive Day time sleepiness N (%) sever day time sleepiness N (%) Mean ± SD Median (Range) high OSA risk N (%) low OSA risk N (%) High OSA risk N (%) Low OSA risk N (%)
8 (6.5%) 116 (93.5%) 46 (37.1%) 78 (62.9%) 39 (31.5%) 85 (68.5%) 52 (41.9%) 72 (58.1%) 55 (44.4%) 69 (55.6%) 39.75 ± 14.06 14.06 (10 – 64) 44 (35.5%) 80 (64.5%) 4.06 ± 1.49 4 (1 – 6.6) 103 (83.1%) 21 (16.9%) 12.7 ± 4.91 14 (1 – 21) 34 (27.4%) 48 (38.7%) 42 (33.9%) 4.02 ± 1.78 3(0 – 7) 66 (53.2%) 58 (46.8%) 68 (54.8%) 56 (45.2%)
5. Discussions Obstructive sleep apnea (OSA) is well recognized as a major public health challenge (Young et al., 1993). Despite its importance, its frequency in patients with MS remains unknown, with estimates ranging from 0% to 58% in published studies (Veauthier et al., 2011; Kaynak et al., 2006). According to our results nearly half of MS patients (46%) have high OSA risk based on STOP BANG, a percentage that is very near to that reported by Dias et al. (40%) who also depended on STOP- Bang to detect high risk patients (Dias et al., 2012). There are several potential reasons for the high variability in OSA frequency among different studies. Some of these studies utilized PSG (Veauthier et al., 2011; Kaynak et al., 2006; Neau et al., 2012) with the tendency to select the most severely affected individuals, thus leading to an overestimation of OSA prevalence. Additional factors include differences in sample size and subject selection methods with some restriction related to age and disability status (Kaynak et al., 2006; Ferini-Srambi et al., 1994) which may have led to an underestimation of OSA prevalence among the wider population of all MS patients. Lack of screening for OSA risk remains a common problem, and validated questionnaires provide an effective method of improving detection, as Senthivel et al. have demonstrated in the primary care setting (Senthilvel et al., 2011). The performance levels of the Berlin and STOP-BANG questionnaires in detecting OSA of various severity levels are outlined as follows: for mild OSA, sensitivity levels were 76%, 88%, specificity levels were 59%, 42% respectively. For moderate OSA, sensitivity levels were 77% and 90%; specificity levels were 44% and 36%, respectively. For severe OSA, sensitivity levels were 84%, 93%, specificity levels were 38%, 35%, respectively. Therefore, for mild, moderate, and severe OSA, the pooled sensitivity of the SBQ were significantly higher than those of Berlin (P < 0.05) (Chiu et al., 2017). So, it is important to note that a positive STOP-BANG score indicates being at risk for OSA and does not necessarily equate to a diagnosis of OSA and further validation data with polysomnography is still needed. At this study, we tried to throw light on MS clinical features that may increase OSA risk. The number of females predominates in our study subjects (57.3%), as typically occurring in MS (Hauser and
Table 2 Demographic and clinical data of patients with high and low OSA risk.
Age Sex Disease duration DMT Peri-ventricular Corpus callosum Brain stem Cerebellar Spinal cord FSS EDSS ESS
Variable
Low OSA risk (n = 66)
High OSA risk (n = 58)
Test
P
Mean ± SD Range Female N (%) Male N (%) Mean ± SD Median (Range) No N (%) Yes N (%) Yes N (%) No N (%) No N (%) Yes N (%) No N (%) Yes N (%) No N (%) Yes N (%) No N (%) Yes N (%) No fatigue N (%) Fatigue N (%) Lower disability N (%) Higher disability N (%) no daytime sleepiness N (%) excessive Day time sleepiness N(%) severe day time sleepiness N (%)
28.67 ± 6.8 18 – 45 51 (77.3%) 15 (22.7%) 6.92 ± 4.04 7 (1 – 22) 28 (42.4%) 38 (57.6%) 61 (92.4%) 5 (7.6%) 22 (33.3%) 44 (66.7%) 26 (39.4%) 40 (60.6%) 31 (47%) 35 (53%) 34 (51.5%) 32 (48.5%) 36 (54.5%) 30 (45.5%) 59 (89.4%) 7 (10.6%) 27 (40.9%) 28 (42.4%) 11 (16.7%)
33.91 ± 7.29 20 - 46 20 (34.5%) 38 (65.5%) 9.10 ± 5.68 9 (3 – 24) 10 (17.2%) 48 (82.8%) 55 (94.8%) 3 (5.2%) 24 (41.4%) 34 (58.6%) 13 (22.4%) 45 (77.6%) 21 (36.2%) 37 (63.8%) 21 (36.2%) 37 (63.8%) 8 (13.8%) 50 (86.2%) 44 (75.9%) 14 (24.1%) 7 (12.1%) 20 (34.5%) 31 (53.4%)
t 4.15 χ2 23.10 MW 2.06 χ2 9.21 χ2 0.30 χ2 0.86 χ2 4.13 χ2 1.47 χ2 2.93 χ2 22.4 χ2 4.02 χ2 22.2
<0.001**
186
<0.001** 0.04* 0.002** 0.59 NS 0.65 NS 0.04* 0.23 NS 0.09 NS <0.001** 0.04* <0.001**
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Table 3 Correlation between different variables and scales related to OSA risk. Age Age Disease duration Berlin FSS EDSS Stop-Bang Epworth sleepiness scale
—. 0.761⁎⁎ <0.001 0.23 0.12 0.18 0.45 0.21 0.18 0.16 0.34 0.09 0.79
r P r P r P r P r P r P r P
Disease duration
Berlin
⁎⁎
FSS
⁎⁎
0.334 <0.001 0.164 .069 —. 0.375⁎⁎ <0.001 0.399⁎⁎ <0.001 0.968⁎⁎0 <0.001 0.383⁎⁎ <0.001
0.761 <0.001 —. 0.164 .069 0.490⁎⁎ <0.001 0.394⁎⁎ <0.001 0.170 .059 0.386⁎⁎ <0.001
EDSS ⁎⁎
0.508 <0.001 0.490⁎⁎ <0.001 0.375⁎⁎ <0.001 —. 0.522⁎⁎ <0.001 0.389⁎⁎ <0.001 0.656⁎⁎ <0.001
Stop-Bang ⁎⁎
0.536 <0.001 0.394⁎⁎ <0.001 0.399⁎⁎ <0.001 0.522⁎⁎ <0.001 —. 0.413⁎⁎ <0.001 0.319⁎⁎ <0.001
⁎⁎
0.353 <0.001 0.170 .059 0.968⁎⁎ <0.001 0.389⁎⁎ <0.001 0.413⁎⁎ <0.001 —. 0.405⁎⁎ <0.001
ESS 0.339⁎⁎ <0.001 0.386⁎⁎ <0.001 0.383⁎⁎ <0.001 0.656⁎⁎ <0.001 0.319⁎⁎ <0.001 0.405⁎⁎ <0.001 —.
other sleep disturbances on the pathophysiology of MS, recent associations between OSA and local/systemic inflammation allow speculation that un- treated OSA could contribute to MS-related disability or progression as showed by EDSS which was significantly higher in patients with OSA (based on STOP-BANG and Berlin positive responses) and significantly correlated with their scores. This association has been supported by the recent study of Sloane and Siddiqui (Sloane and Siddiqui, 2017) who reported that features of MS progression such as high ARR and elevated EDSS are more common in OSA patients, a research area that merits further study. On the other hand, Previous studies suggest that progressive MS subtypes and in- creased level of disability are risk factors for OSA in MS (Braley et al., 2012). All these findings support a reciprocal relationship between OSA in MS patients and level of disability. Similarly, another risk factor is age, which correlates positively with positive STOP-BANG and Berlin scores as well as disability progression. It is of interest to note that in our survey, there was an association be- tween MS patients who are at risk for OSA and reported high levels of fatigue (FSS scored positive in 64.5%) with a significant correlation be- tween the STOP-BANG scores and the FSS scores. These results are parallel to those of Veauthier and Colleagues whose study included 66 MS patients, they reported that sleep-related breathing disorders (using portable PSG) were more frequent in MS patients with fatigue (27%) than those without fatigue (2.5%) (Veauthier et al., 2011). They used both FSS and Modified Fatigue Impact Scale (MFIS) in their study (Kurtzke, 1983). Obstructive sleep apnea (OSA) may contribute to fatigue symptoms and augment disability through cerebral perfusion changes, inflammatory cytokine expression and brain atrophy. This may suggest that OSA should be considered in MS patients presenting with fatigue. In the view of these findings, re- searchers are also currently considering screening for OSA in MS patients complaining of fatigue and using CPAP as a therapeutic modality to treat MS fatigue (Gómez-González et al., 2012). MS-related fatigue can be substantially relieved, by early diagnosis and successful treatment of OSA in those patients (Braley et al., 2014). Failure to detect sleep problems in MS patients may lead to missed opportunities to alleviate fatigue which is one of the most common chronic symptoms in those patients
Goodin, 2011). However, substantially more males than females were classified as at high OSA risk (65.5% versus 34.5%) on the STOP-BANG questionnaire. Similarly, Population-based studies have observed a two to three folds greater risk for OSA in males compared to females (Young et al., 2008; 2002; 1993). The gender difference may be explained by several factors including location of body fat distribution and anatomical airway differences, generally, males had a statistically larger neck circumference compared to females (Young et al., 2008; 2002; Chiu et al., 2017), additional factors include breathing control, hormones, and aging, all are thought to play a role (Christine et al., 2008). Since the STOP-BANG questionnaire itself incorporates male gen- der among its items, it is expected that a larger number of males will have higher scores in STOP-BANG questionnaire (Dias et al., 2012). As both multiple sclerosis and OSA influence the immune system and change the cytokine profile of patients, this may provide explanation for their comorbidity. OSA patients (without MS) show elevation in pro-inflammatory cytokines e.g. interleukin- 6, IFN-γ, and tumor necrosis factor-α (TNF-α) compared to BMI-matched controls without OSA (Gómez-González et al., 2012). IFN-γ and TNF- α are also found to have a somnogenic effect, thus causing sleepiness and fatigue if administered to normal subjects. Additionally, interleukin- 6 and TNF-α levels decrease when subjects receive CPAP. Of importance, MS is known to be also associated with a similar pro-inflammatory cytokine profile, with patients showing elevations of IL-6, IFN-γ, and TNF-α, well as other pro-inflammatory cytokines, all these findings support the important role of inflammation in both diseases (Gómez-González et al., 2012). MS patients may be at increased risk for OSA as a result of their neuro- logical condition. In a recent study, MS patients, and particularly patients with evidence of brainstem lesions on MRI, were found to have more severe OSA (and CSA) than matched control subject due to damage of key brainstem respiratory centers located in the pons and medulla (Braley et al., 2012). These findings focus on the important role of brainstem autonomic control on nocturnal airway patency and throw light on vulnerable populations whose OSA risk may be increased due to central nervous system pathology (Dyken and Im, 2009; Dyken et al., 2012). While no data is available regarding the long-term impact of OSA or
Table 4 Multivariate analysis of significantly correlated variables with OSA risk. Variable
B
S.E.
Wald
P
OR
95% C.I.
Age > 30 y Male Sex Not using DMT Duration > 7 y Brainstem lesions in MRI Higher disability in EDSS
1.808 2.360 1.215 −0.237 0.946 0.660
0.590 0.496 0.500 0.568 0.489 0.596
9.402 22.633 5.898 0.174 3.734 1.226
0.002** <0.001** 0.015* 0.676 0.053 0.268
6.098 10.586 3.371 .789 2.575 1.934
1.920 4.005 1.264 0.259 0.987 0.602
187
19.367 27.984 8.989 2.403 6.719 6.219
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(Veauthier et al., 2013). Prior studies have had conflicting results regarding the relation between fatigue and OSA risk. Wunderlin et al. and Kaynak et al. did not find significant relation between high scores on fatigue measures and OSA risk (Wunderlin et al., 1997), (Kaynak et al., 2006), similarly, Dias et al. reported lack of a significant correlation between the FSS and the overall STOP-BANG score (Dias et al., 2012). They re- ported other factors aside from sleep-related breathing disorders to con- tribute to MS fatigue, including central nervous system abnormalities, altered cytokine profiles, depression, heat sensitivity, physical impairment, pain, nocturia, and degree of psychosocial support (Wunderlin et al., 1997; Mills and Young, 2010) so, further studies are needed to look at this important association given the impact of cytokines on fatigue and sleep. Measures of fatigue and sleepness were significantly correlated in our study (FSS vs ESS, r = 0.65, p < 0.001). Stanton et al. had similar findings on the FSS and ESS in 60 outpatients with MS (Stanton et al., 2006) as did Merkelbach et al. in 80 MS patients (Merkelbach et al., 2011). As previously mentioned, inflammatory cytokines are expressed at higher levels in individuals with MS as well as OSA and are involved in their pathogenesis, so, treatment with agents that influence these cytokine levels may improve apnea severity (Vgntzas et al., 2004). Based on these facts, DMT can be a strong predictor of reduced apnea severity (Braley, 2015).
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6. Conclusion OSA appears to be surprisingly common in MS patients. By virtue to the overlap between OSA and fatigue and the potential risk of further increasing disability from complications related to untreated sleep apnea. Identifying and treating OSA in those patients may be an important way not only to treat fatigue but also to reduce disability. Based on our findings, we should maintain a low threshold for referral to a sleep center for further evaluation by polysomnography if sleep breathing disorders are suspected in patients with MS. 7. Limitations The major limitation of the present study is that scores on the STOPBANG questionnaires were not supported with PSG to determine the sensitivity and specificity of these screening instruments in MS patients however, we are supported by the fact that STOP-Bang scores and Berlin questionnaire have a sensitivity of 75% to 85% to detect OSA as previously mentioned. References Abrishami, A., Khajehdehi, A., Chung, F., 2010. A systematic review of screening questionnaires for obstructive sleep apnea. Can. J. Anaesth. 57, 423–438. Attarian, H.P., Brown, K.M., Duntley, S.P., Carter, J.D., Cross, A.H., 2004. The relationship of sleep disturbances and fatigue in multiple sclerosis. Arch. Neurol. 61, 525–528. Braley, T.J., Segal, B.M., Chervin, R.D., 2014. Obstructive sleep apnea and fatigue in patients with multiple sclerosis. J. Clin. Sleep Med. 10, 155–162. Braley, T.J., 2015. Sleep in patients with multiple sclerosis. Curr. Sleep Medicine Rep. 1, 108–113. Braley, T.J., Segal, B.M., Chervin, R.D., 2012. Sleep-disordered breathing in multiple sclerosis. Neurology 79, 929–936. Brass, S.D., Duquette, P., Proulx-Therrien, J., Auerbach, S., 2005. Sleep disorders in multiple sclerosis. SeminNeurol. 25, 121–129. Brass, S.D., Li, C.S., Auerbach, S., 2014. The under diagnosis of sleep disorders in patients with multiple sclerosis. J. ClinSleep Med. 10 (9), 1025–1031. Chiu, H.Y., Chen, P.Y., Chuang, L.P., Chen, N.H., Tu, Y.K.5, Hsieh, Y.J., Wang, Y.C., Guilleminault, C., 2017. Diagnostic accuracy of the Berlin questionnaire, STOPBANG, STOP, and Epworth sleepiness scale in detecting obstructive sleep apnea: a bivariate meta-analysis. Sleep Med. Rev. 36, 57–70.
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