Skip to main content
Download PDF
  • Systematic Review
  • Open access
  • Published:

Sleep and cognitive outcomes in multiple sclerosis; a systematic review

BMC Psychiatry volume 24, Article number: 638 (2024) Cite this article

Abstract

Background

Multiple sclerosis (MS) is a disabling disease of the central nervous system. People living with MS often have co-existing sleep disorders and cognitive dysfunction. The objective of this study was to scrutinize the relationship between cognitive outcomes and sleep conditions in MS.

Methods

This study followed the Joanna Briggs Institute’s (JBI) and PRISMA guidelines. PubMed, Scopus, Embase, and Web of Science databases were searched and original studies delineating the relationship between sleep status and cognitive findings in MS patients‌ were included. The risk of bias was assessed using the JBI critical appraisal tools.

Results

In the final review, out of 1635 screened records, 35 studies with 5321 participants were included. Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), and polysomnography were the most common assessment tools for evaluation of sleep condition, and cognitive evaluations were conducted using the tests including Paced Auditory Serial Addition Test (PASAT), California Verbal Learning Test (CVLT), Symbol Digit Modalities Test (SDMT) and Brief Visuospatial Memory Test (BVMT). Assessing the quality of studies showed no significant bias in most of the included articles. A link between sleep condition and cognitive abilities was suggested in the literature, especially with objective measurement of sleep condition; however, current evidence did not support a substantial association between self-reported sleep quality and processing speed and working memory in patients with MS.

Discussion

Evidence proposes sleep is an independent factor associated with cognitive outcomes in MS. Given the limitations of the evidence such as the lack of well-designed prospective studies, these findings need to be interpreted with caution.

Peer Review reports

Introduction

Multiple sclerosis (MS) is a disabling condition of the central nervous system (CNS). Signs and symptoms and severity of nerve fiber damage in the CNS vary widely between patients. Fatigue, sensory abnormalities, visual abnormalities, spasticity, pain, depression and anxiety, bladder problems, cerebellar dysfunction, and cognitive dysfunction are the most common manifestations of MS [1]. The incidence of MS is increasing worldwide, in line with the socioeconomic impact of the disease [2]. Relapsing-remitting (RRMS) which is characterized by relapses with stable neurological disability between episodes, primary progressive (PPMS) with a progressive course at onset, and secondary progressive (SPMS) which is a progressive course following an initial relapsing-remitting course, are the phenotypes of MS [3, 4]. As a result of advancements in comprehending the pathogenesis and course of this condition, pharmacological and non-pharmacological strategies for the management of MS symptoms [5,6,7,8], as well as effectual disease-modifying therapies [9,10,11], and supplementations [12, 13] are widely researched and found to be influential in enhancing patients’ quality of life and survival [14, 15].

Sleep disturbance is a general term for a wide spectrum of sleep-related symptoms and disorders, including insomnia, restless legs syndrome, sleep-disordered breathing, narcolepsy, and rapid eye movement (REM) sleep behavior disorder [16]. Low sleep quality and insufficient sleep during adolescence are suggested to increase the risk of developing MS [17]. Sleep disturbance is a prevalent manifestation in MS patients, even in patients with a low level of disability [18]. Poor sleep quality [19, 20], restless legs syndrome, and insomnia [21] are found to be more prevalent in MS [22]. A recent systematic review of polysomnographic findings revealed a considerable reduction in stage N2 sleep and sleep efficiency as well as increases in wake time after sleep onset, the periodic limb movement index, and the periodic limb movement arousal index in patients with MS [23]. Sleep abnormalities are reported to be significantly associated with other debilitating symptoms of the disease, such as fatigue, and can harm substantially patients’ quality of life [24].

On the other side, the literature on cognitive outcomes in MS patients has grown exponentially over the last few years, so that cognitive dysfunction is now recognized as a core symptom of MS [25]. Slowed information process speed, and episodic memory decline as well as impaired verbal fluency, learning abilities, executive function, and visuospatial memory are common among MS patients [26,27,28]. A recent systematic review reported 32.5% prevalence of cognitive dysfunction among patients with RRMS [29]. As one of several behavioral states, sleep can affect cognition. Sleep can provide conditions and make unique contributions to memory formation [30], which is not possible in arousal states. Through this, a possible connection between sleep status and cognitive function is suggested in the literature [31, 32]. In addition, daytime sleepiness is also found to correlate with cognitive status [33, 34].

A systematic review was conducted in 2018 to assess the relationship between sleep disturbance and cognitive dysfunction in MS patients [35], and found a significant correlation between sleep disorders and cognitive impairment. Researchers included 12 studies that were published until the final search in June 2017 all revealed significant associations between sleep disturbance and cognitive dysfunction. Due to growing interest in this contest in recent years, several studies were conducted to assess this correlation on different occasions, therefore, the present systematic review is designed to dissect the possible association between any sleep-related measurement and cognitive status, in patients with MS.

Methods

This systematic review is conducted following the methods mentioned in the Joanna Briggs Institute’s (JBI) Manual for Evidence Synthesis [36], and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [37].

Eligibility criteria

All original clinical studies delineating the relationship between sleep status and cognitive findings in MS patients were included in this systematic review. Review articles, editorials, commentaries, conference abstracts, animal studies, in vitro studies, and non-English publications were excluded.

Information sources and search strategy

Studies were identified through a systematic search of Scopus, Embase, PubMed, and Web of Science databases and carried out by two independent reviewers (A.N., S.M.). The last search was run on July 27, 2023 and updated via handsearching in August 2024. The following strategy was utilized for search in PubMed database: (“sleep“[MeSH Terms] OR “sleep“[All Fields] OR “sleeping“[All Fields] OR “sleeps“[All Fields] OR “sleep s“[All Fields]) AND (“Cognitive Dysfunction“[MeSH Terms] OR “cogni*“[All Fields]) AND (“Multiple Sclerosis“[MeSH Terms] OR “Multiple Sclerosis“[All Fields]). In addition, the references and citations of included studies and the related review articles as well as PubMed related articles were manually checked.

Selection process

Retrieved studies were imported into Endnote and de-duplicated. Moreover, screening was fulfilled independently by two reviewers in title/abstract (H.R., S.S-S., S.M.) and full-text stages (A.N., B.G.) using the Rayyan Intelligent Systematic Review tool [38]. Discrepancies were resolved by discussion or decided upon by a third reviewer (S.S. or M.T.).

Data collection process

A data extraction table comprising the first authors’ surname, publication date, study design, setting, sample size, age, female ratio, severity and phenotype of MS, scales about cognitive and sleep-related assessment, result and conclusion was designed. A review author contributed to fulfilling data extraction (H.R. or N.A. or S.M.), and another reviewer ascertained its accuracy (B.G. or A.N. and H.B.).

Study risk of bias assessment

Two reviewers (B.G. and S.S-S.) independently appraised the risk of bias (RoB) and the risk of bias in the included articles using the Joanna Briggs Institute’s (JBI) critical appraisal tool for cross-sectional or case-control studies [39], and disagreements were resolved by discussion between the reviewers or by consulting with a third reviewer (S.S. or M.T.).

Effect measures and synthesis methods

Any reported associations, including the difference between groups and/or correlations between sleep-related measures, and cognitive outcomes were presented in this systematic review. Due to the significant diversity regarding the assessed outcomes and reporting methods, conducting a meta-analysis was not possible; therefore, the outcomes of the studies were synthesized qualitatively.

Results

Study selection

The systematic search identified 1635 records. After duplicate removal, 973 publications remained and screened for title/abstract; after that, 44 articles were selected for full-text review. Following the evaluation of selected studies for eligibility, nine articles were excluded due to the following reasons: seven studies didn’t report the association between cognition and sleep [40,41,42,43,44,45,46], one study didn’t report cognitive outcomes [47], and one study didn’t evaluate the sleep condition [48]. Finally, 35 articles met the eligibility criteria and were included. Figure 1 presents the details of the screening process.

Fig. 1
figure 1

PRISMA flow diagram. From: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: https://doi.org/10.1136/bmj.n71. For more information, visit: http://www.prisma-statement.org/

Full size image

Risk of bias assessments

The results of RoB assessments are presented in Tables 1 and 2.; Figs. 2 and 3. Ten studies did not clarify the setting and six studies did not clearly define the inclusion/exclusion criteria. Generally, we did not find a considerable bias in a great number of studies, and none were excluded due to the high risk of bias. 61% of cross-sectional and all of the case-control studies were completely fitted to the JBI critical appraisal tools.

Table 1 Summary of the results of risk of bias assessments for cross-sectional studies
Full size table
Table 2 summary of the results of risk of bias assessments for case-control studies
Full size table
Fig. 2
figure 2

Summary of the quality assessment for cross-sectional studies. Question 1: Were the criteria for inclusion in the sample clearly defined? Question 2: Were the study subjects and the setting described in detail? Question 3: Was the exposure measured in a valid and reliable way? Question 4: Was objective, standard criteria used for measurement of the condition? Question 5: Were confounding factors identified? Question 6: Were strategies to deal with confounding factors stated? Question 7: Were the outcomes measured in a valid and reliable way? Question 8: Was appropriate statistical analysis used?

Full size image
Fig. 3
figure 3

Articles quality regarding the answer to the questions

Full size image

Study characteristics and results of individual studies

The design of 29 studies was cross-sectional, three studies were longitudinal, one article was a pilot study, and two studies were case-control. Most studies were conducted in the United States (14 studies). Sample sizes varied between nine and 1717 participants, with a total number of 5321. The age of participants ranged from 32.4 to 59.7 years. The female ratio was between 54.5 and 100%.

The severity of MS was mostly reported by the Expanded Disability Status Scale (EDSS) score, although four studies didn’t report the severity of MS and some of the studies reported the severity of MS by other scales like the Multiple Sclerosis Functional Composite (MSFC), and Patient Determined Disease Steps (PDDS). MS phenotype in five studies was RRMS; in three studies it was not reported, and in the remaining 27 studies, a mixture of different phenotypes was investigated.

The studies used different kinds of assessment scales, both for cognition and sleep. Sleep-related assessment scales include but are not limited to actinography, polysomnography (PSG), Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), and Insomnia Severity Index (ISI). Cognition assessment scales also included but not limited to the Paced Auditory Serial Addition Test (PASAT), California Verbal Learning Test (CVLT), Brief Visuospatial Memory Test (BVMT), Symbol Digit Modalities Test (SDMT), Judgement of Line Orientation Test (JLO), Controlled Oral Word Association Test (COWAT), Hopkins Verbal Learning Test (HVLT), Cambridge Neuropsychological Test Automated Battery (CATNAB), Letter Digit Substitution Task (LDST), and Delis-Kaplan Executive Function System–Sorting Test (D-KEFS-S).

Detailed characteristics and findings of the included studies are shown in Table 3. Considering the substantial diversity in both sleep and cognitive evaluation scales, quantitative synthesis was not feasible. Meta-synthesis of the most common combinations of sleep and cognitive evaluations are presented in Table 4.

Table 3 Summary of the characteristics and findings of the included studies
Full size table
Table 4 Associations of common cognition scales with common sleep-related scales
Full size table

PSQI and cognitive outcomes

PSQI is a self-report questionnaire that considers sleep quality and disturbances over one month [84]. PSQI was the most common sleep assessment instrument in the included studies, used in 17 studies, among which PASAT and SDMT were the most frequent cognition tests.

PSQI and PASAT

PASAT is a valid cognitive assessment in MS which assessed auditory processing speed and working memory [85]. Seven studies have investigated the associations between the PASAT and PSQI. Aristotelous et al.’s linear regression showed that the PASAT score had an association with the performance score in PSQI [50]. Berard et al.’s results showed that sleep quality could be a predictor factor of decreased cognitive performance with sustained cognitive effort, which is defined as cognitive fatigue based on PASAT [52]. Ozkul et al. enrolled 112 MS in their study, and they couldn’t find any relationships between PASAT and PSQI [70]. According to Hughes et al.’s study in which 121 patients participated, the total PSQI was not associated with the PASAT score [61]. Fifty-three MS patients were enrolled in Mackay et al.’s study, and the findings showed that there was no statistically significant relation between PASAT and PSQI scores [66]. Siengsukon et al.’s results demonstrated that there was no association between cognitive fatigue through the PASAT test and sleep quality [76]. Evaluating the sleep quality in Whibley et al.’s study indicated that no associations were identified between PASAT scores and sleep domains [81]. Current evidence did not support a considerable link between sleep quality assessed by PSQI and auditory processing speed and working memory, based on PASAT in MS.

PSQI and SDMT

SDMT assessed visual processing speed and working memory [85]. Evaluating the relation between SDMT and PSQI was accomplished in four studies. In Mackay et al.’s study, the results demonstrated no statistically significant relation between SDMT and sleep quality [66]. Ozkul et al.’s findings showed that there is no statistically significant relation between PSQI and SDMT scores in MS patients [70]. Siengsukon et al. also identified that patients with good sleep quality performed slightly better, but not statistically, on SDMT. Similarly, in Whibley et al.’s study, there weren’t any statistically significant associations between sleep variables and SDMT [76, 81]. It can be concluded that, based on the existing evidence, sleep quality cannot be considered a predictor of impaired visual processing speed and working memory, as evaluated by SDMT.

PSQI and other cognitive assessments

Aldughmi et al. enrolled 51 MS patients and used the cognitive fatigability (based on continuous performance test, response speed variability [CPT-RSV]) test to demonstrate that cognitive performance was associated with only the daytime dysfunction component of the PSQI [49, 55]. Lamis et al. evaluated 77 MS patients’ cognitive function through the PDQ test, and the results showed that cognitive deficits, assessed by PDQ are associated with poorer sleep quality [63]. Laslett et al. enrolled 1717 patients and used MSSymS to evaluate their cognitive function, and they pointed out that the “fatigue and cognition” cluster is associated with poorer sleep quality [64]. According to Lehmann et al.’s study, in which 42 MS patients were included, n-back-hits and n-back-RT tests were used to evaluate the cognitive function, and the association between sleep disturbances and cognitive decline during tasks of sustained attention was observed [65]. Odintsova and Kopchak didn’t find a correlation in their study based on MoCA [68]. Riccitelli et al. enrolled 80 MS patients and used the BRB-N test to evaluate their cognitive function. They concluded that worse performances in the global, memory, and attention cognitive domains are correlated with poor sleep quality [72]. Sharhbanian et al. exhibited that cognitive deficits are significantly correlated with sleep disorders [75]. According to Siengsukon, Aldughmi, et al.’s results, visuospatial memory was correlated with poor sleep quality [76]. Siengsukon, Alshehri, et al.’s study revealed that bad sleepers have a significantly higher level of cognitive fatigue [77].

ESS and cognitive outcomes

ESS is a simple, self-administered questionnaire that considers the patients’ general level of daytime sleepiness [86].

ESS and SDMT

Braley et al. evaluated the cognitive function of 38 MS patients and the results indicated no association between ESS and SDMT [53]. Patel et al. evaluated the sleep quality and cognitive function in 102 MS patients and found that there are no differences in cognitive performance between MS participants with normal versus excessive daytime sleepiness [71]. The noted studies were the only evidence that assessed the possible association between daytime sleepiness assessed by ESS and visual processing speed and working memory, as evaluated by SDMT, which did not support a substantial affinity.

ESS and other cognitive assessments

Laslett et al. enrolled 1717 MS patients, used the MSSymS test to assess cognitive function, and concluded that fatigue and cognition are associated with sleep quality [64]. In Sater et al.’s study, in which 32 MS patients’ cognitive function was evaluated with MFIS-Cognitive and NeuroTrax tests, ESS was mildly correlated with the cognitive component of the MFIS [73]. Siengsukon, Alshehri, et al. demonstrated that bad sleepers have a significantly higher level of cognitive fatigue [77].

Objective sleep measures and cognitive outcomes

PSG is a multi-parameter kind of sleep investigation, which is considered the gold standard for diagnosing sleep-related breathing disorders [87]. Braley et al. in 2016 concluded that OSA and sleep disturbance are associated with memory, executive function, and processing speed [53]. In an assessment of 113 patients, Chinnadurai et al.‘s results showed a significant relationship between sleep impairments and cognitive fatigue [58]. Riccitelli et al. concluded that reduced REM sleep may affect attention abilities [72]. The findings of Sater et al.’s study also revealed that poor sleep efficacy may contribute to reduced cognitive function [73].

Actigraphy is also a valid and reliable instrument to assess sleep objectively, with approximately 90% agreement with polysomnography [88]. In three studies, actigraphy was used for sleep assessment. Aldughmi et al. showed that cognitive fatigability assessed by RSV is significantly associated with sleep efficiency and wake after sleep onset measures of actigraphy which is suggested to be mediated by depression [49]. The findings of Opelt et al.’s study revealed that discrepancies between self-report and actigraphy-based measures of sleep outcomes are linked with cognitive impairment in MS [69]. Whibley et al.’s outcomes pointed out associations between visual-spatial function and sleep duration and continuity [81].

Contrary to the self-reported techniques, it seems objective measurement of sleep condition, can significantly affect MS patients’ cognitive performance. By contrast, a polysomnographic study conducted by Anne-Laure Dubessy et al., found no significant association between central hyposmia and cognitive outcomes in MS. Cognitive evaluations using a poor-sensitive and non-specific method for MS, Mattis Dementia Rating Scale, and lack of appropriate addressing the possible confounders, may be the reasons for lack of significant findings in this study [59].

Discussion

The objective of this systematic review was to provide an overview of the studies evaluating the relationship between sleep outcomes and cognition function in adults with MS, as assessed by either questionnaires or neuropsychological tests. Collectively, 35 studies were included in the final evaluation, and the majority of the studies discovered a link between poorer sleep quality and impairment in at least one domain of cognition in MS, which was more evident in the studies with objective measurement of sleep condition.

Despite the majority of evidence revealed a significant association between sleep conditions and cognitive abilities, a lack of a significant relationship was evident in some studies. The study conducted by Odintsova et al. used MoCA which is considered a sensitive and specific method for cognitive evolution in MS [68, 89], but there was a lack of appropriate addressing of the confounders in this study. Ozkul et al.’s research only reported the correlations between cognitive scales and PSQI as one of the secondary outcomes of the study with no control for cofounders [70]. Chen et al.‘s study also investigated the short-term association between prior night sleep measures and cognitive outcomes and could not detect a considerable link in this regard [90]. Therefore, this contrast can be explained by confounding factors influencing the relationship between sleep and cognition as well as using the different cognitive and sleep assessment tools with a wide range of specificity or sensitivity, and the difference between subjective and objective evaluations. Despite the observed associations between sleep quality, assessed by PSQI and cognitive outcomes, current evidence did not support a substantial association between sleep quality assessed by PSQI and processing speed and working memory in patients with MS.

RLS is found to be associated with an increased risk of incidence of all-cause dementia in older adults [91]. RLS is frequent among patients with MS [24, 92]. Cederberg et al. in 2020 enrolled 275 MS patients and used the MSNQ test to evaluate the cognitive function, and demonstrated that sleep disturbance may be an intermediary factor in the connection between RLS and cognitive impairment, and in 2022 concluded that those with more severe RLS may experience worse cognitive function, particularly slower processing speed and more memory difficulties [55, 56, 93].

Evidence regarding the association between insomnia and the risk of cognitive dysfunction in the general population is not deterministic yet [90, 94]. Regarding MS, Hare et al. revealed a significant relationship between insomnia and cognitive function, which is only mediated by fatigue catastrophizing [91]. Schellaert et al.‘s findings showed that cognitive factors were associated with insomnia disorder [92]. Sumowski et al.‘s study also connected sleep disturbance to poor memory [93]. Future studies are needed to discover the possible link between insomnia and different aspects of cognitive function in MS.

Recent studies have reported that sleep disruption deteriorates cognitive performance by facilitating the pathogenesis of AD throughout the entire course of AD, from the preclinical to the advanced phase [95]. It is widely known that MCI and developing AD are both linked with sleep and circadian rhythm disturbances, making the management of sleep disturbance crucial [96]. Memory redistribution interruption while sleeping, is suggested as the consequence of prefrontal dysfunction observed in dementia and cognitive impairment conditions [97]. Also, recent evidence suggests that sleep disruption is correlated with disturbed verbal fluency, executive function, attention, spatial memory, and processing speed, particularly in AD patients [98,99,100,101]. A recent systematic review concluded that REM-sleep decline status is associated with Alzheimer’s Disease (AD) cognitive impairments [102]. Such an association was also evident in MS patients, too [58, 72].

Cognitive impairment is considered to be poorly managed in patients with MS [103]. A recent systematic literature review found minimal efficacy of pharmacological interventions in the management of MS-related cognitive dysfunction [104]. Despite the spectacular progress in MS management, current strategies confer only partial protection against the neurodegenerative component of MS [14], which is significantly correlated to sleep condition [105]. The observed association, suggests sleep is a modifiable risk factor for cognitive dysfunction which can be targeted to improve neuropsychiatric outcomes in MS [106]. A longitudinal study conducted by McNicholas et al. demonstrated the potential for OSA treatment to improve verbal learning in people with MS [67]. In addition, cognitive efficacy of the interventions for improving the sleep condition such as melatonin supplementation was suggested [107,108,109,110,111]; however, there is a lack of evidence regarding the effect of treatment methods for sleep management in MS which shed light on the importance of future studies on this topic [112]. In addition, improving sleep status is challenges in MS. For example, physical activity has been discussed as an approach to expanding sleep quality which is not sufficient for the MS population [93]. Through understanding the cause of sleep disorders and their consequences, specific meditations and treatment can be obtained. Anxiety, depression, and neuropathic pain are among the initial symptoms concerning sleep quality [113], which can lead to cognitive dysfunction.

Given the limitations of the included studies, findings of this study need to be interpreted with caution. The majority of them were cross-sectional studies, which provided no insight into when exactly the association between sleep and cognition in MS patients first appeared, whether it was immediately following diagnosis or during treatment courses. Additionally, most studies did not provide clear information on confounding variables, medical history, comorbidities, and medication use, all of which could have influenced the relationship between sleep and cognition. To address this issue, empirical validations are necessary to establish whether successful interventions for improving sleep quality are aimed at enhancing cognitive functioning and confirm the link between sleep disturbance and cognitive dysfunction.

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

References

  1. McGinley MP, Goldschmidt CH, Rae-Grant AD. Diagnosis and treatment of multiple sclerosis: a review. JAMA. 2021;325(8):765–79. https://doi.org/10.1001/jama.2020.26858.

    Article  CAS  PubMed  Google Scholar 

  2. Walton C, King R, Rechtman L, Kaye W, Leray E, Marrie RA, et al. Rising prevalence of multiple sclerosis worldwide: insights from the Atlas of MS, third edition. Mult Scler. 2020;26(14):1816–21. https://doi.org/10.1177/1352458520970841.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018;17(2):162–73. https://doi.org/10.1016/S1474-4422(17)30470-2.

    Article  PubMed  Google Scholar 

  4. Carroll WM. 2017 McDonald MS diagnostic criteria: evidence-based revisions. Mult Scler J. 2018;24(2):92–5. https://doi.org/10.1177/1352458517751861.

    Article  Google Scholar 

  5. Tahmasbi F, Salehi-Pourmehr H, Hajebrahimi S, Soleimanzadeh F. Effects of tibial nerve electrical stimulation on bowel dysfunction in multiple sclerosis: a systematic review. Int J Drug Res Clin. 2023;1(1):e24 e. https://doi.org/10.34172/ijdrc.2023.e24.

    Article  Google Scholar 

  6. Tahmasbi F, Hosseini S, Hajebrahimi S, Heris RM, Salehi-Pourmehr H. Efficacy of tibial nerve stimulation in neurogenic lower urinary tract dysfunction among patients with multiple sclerosis: a systematic review and meta-analysis. Urol Res Pract. 2023;49(2):100–11. https://doi.org/10.5152/tud.2023.22241.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Chisari CG, Sgarlata E, Arena S, D’Amico E, Toscano S, Patti F. An update on the pharmacological management of pain in patients with multiple sclerosis. Expert Opin Pharmacother. 2020;21(18):2249–63. https://doi.org/10.1080/14656566.2020.1757649.

    Article  CAS  PubMed  Google Scholar 

  8. Abbasi H, Shakouri F, Mosaddeghi-Heris R, Gholipour-Khalili E, Jahanshahlou F, Sanaie S, et al. Mediterranean-like diets in multiple sclerosis: a systematic review. Rev Neurol. 2023. https://doi.org/10.1016/j.neurol.2023.07.017.

    Article  Google Scholar 

  9. Langer-Gould AM, Smith JB, Gonzales EG, Piehl F, Li BH. Multiple sclerosis, disease-modifying therapies, and infections. Neurol Neuroimmunol Neuroinflammation. 2023;10(6): e200164. https://doi.org/10.1212/NXI.0000000000200164.

    Article  Google Scholar 

  10. Wu X, Wang S, Xue T, Tan X, Li J, Chen Z, et al. Disease-modifying therapy in progressive multiple sclerosis: a systematic review and network meta-analysis of randomized controlled trials. Front Neurol. 2024;15(1295770): 1295770. https://doi.org/10.3389/fneur.2024.1295770.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Drudge C, Samjoo IA, Brennan R, Badgujar L, Khurana V, Tiwari S, et al. An overview of reviews with network meta-analyses comparing disease-modifying therapies for relapsing multiple sclerosis. Future Neurol. 2023;18(3):FNL70. https://doi.org/10.2217/fnl-2023-0009.

    Article  CAS  Google Scholar 

  12. Feige J, Moser T, Bieler L, Schwenker K, Hauer L, Sellner J. Vitamin D supplementation in multiple sclerosis: a critical analysis of potentials and threats. Nutrients. 2020;12(3): 783. https://doi.org/10.3390/nu12030783.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Tryfonos C, Mantzorou M, Fotiou D, Vrizas M, Vadikolias K, Pavlidou E, et al. Dietary supplements on controlling multiple sclerosis symptoms and relapses: current clinical evidence and future perspectives. Med (Basel). 2019;6(3). https://doi.org/10.3390/medicines6030095.

  14. Hauser SL, Cree BAC. Treatment of multiple sclerosis: a review. Am J Med. 2020;133(12):1380-90.e2. https://doi.org/10.1016/j.amjmed.2020.05.049.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lunde HMB, Assmus J, Myhr KM, Bø L, Grytten N. Survival and cause of death in multiple sclerosis: a 60-year longitudinal population study. J Neurol Neurosurg Psychiatry. 2017;88(8):621–5. https://doi.org/10.1136/jnnp-2016-315238.

    Article  PubMed  Google Scholar 

  16. Veauthier C. Sleep disorders in multiple sclerosis. Review. Curr Neurol Neurosci Rep. 2015;15(5):21. https://doi.org/10.1007/s11910-015-0546-0.

    Article  CAS  PubMed  Google Scholar 

  17. Åkerstedt T, Olsson T, Alfredsson L, Hedström AK. Insufficient sleep during adolescence and risk of multiple sclerosis: results from a Swedish case-control study. J Neurol Neurosurg Psychiatry. 2023;94(5):331–6. https://doi.org/10.1136/jnnp-2022-330123.

    Article  PubMed  Google Scholar 

  18. Yazdchi M, Khanalizadeh R, Nasiri E, Naseri A, Talebi M, Talebi M. Sleep status in multiple sclerosis: role of vitamin D and body mass index. Curr J Neurol. 2022;21(2):66–73. https://doi.org/10.18502/cjn.v21i2.10489.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Zhang GX, Zhang WT, Gao SS, Zhao RZ, Yu WJ, Izquierdo G. Sleep disorders in patients with multiple sclerosis in Spain. Neurología. 2024;39(1):29–35. https://doi.org/10.1016/j.nrl.2021.03.012.

    Article  CAS  PubMed  Google Scholar 

  20. Moradi A, Ebrahimian A, Naseri A, Talebi M. Multiple sclerosis patients has lower sleep quality; a systematic review and meta-analysis. J Neurol Sci. 2023;455:101016jjns2023121871.

    Article  Google Scholar 

  21. Zeng X, Dorstyn DS, Edwards G, Kneebone I. The prevalence of insomnia in multiple sclerosis: a meta-analysis. Sleep Med Rev. 2023;72: 101842. https://doi.org/10.1016/j.smrv.2023.101842.

    Article  PubMed  Google Scholar 

  22. Čarnická Z, Kollár B, Šiarnik P, Krížová L, Klobučníková K, Turčáni P. Sleep disorders in patients with multiple sclerosis. J Clin Sleep Med. 2015;11(5):553–7. https://doi.org/10.5664/jcsm.4702.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Zhang Y, Ren R, Yang L, Zhang H, Shi Y, Vitiello MV, et al. Sleep in multiple sclerosis: a systematic review and meta-analysis of polysomnographic findings. J Clin Sleep Med. 2023;19(2):253–65. https://doi.org/10.5664/jcsm.10304.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Mnif S, Saoussen D, Nadia B, Nouha F, Sonda MK, Salma S, et al. Sleep disorders in relapsing-remitting multiple sclerosis patients. Mult Scler Relat Disorders. 2023;71: 104334. https://doi.org/10.1016/j.msard.2022.104334.

    Article  Google Scholar 

  25. Sumowski JF, Benedict R, Enzinger C, Filippi M, Geurts JJ, Hamalainen P, et al. Cognition in multiple sclerosis: state of the field and priorities for the future. Neurology. 2018;90(6):278–88. https://doi.org/10.1212/wnl.0000000000004977.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Portaccio E, Amato MP. Cognitive impairment in multiple sclerosis: an update on assessment and management. NeuroSci. 2022;3(4):667–76. https://doi.org/10.3390/neurosci3040048.

    Article  Google Scholar 

  27. Talebi M, Sadigh-Eteghad S, Talebi M, Naseri A, Zafarani F. Predominant domains and associated demographic and clinical characteristics in multiple sclerosis-related cognitive impairment in mildly disabled patients. Egypt J Neurol Psychiatry Neurosurg. 2022;58(1):48. https://doi.org/10.1186/s41983-022-00485-7.

    Article  Google Scholar 

  28. Podda J, Ponzio M, Pedullà L, Monti Bragadin M, Battaglia MA, Zaratin P, et al. Predominant cognitive phenotypes in multiple sclerosis: insights from patient-centered outcomes. Multiple Scler Relat Disorders. 2021;51:102919. https://doi.org/10.1016/j.msard.2021.102919.

    Article  CAS  Google Scholar 

  29. Wu W, Francis H, Lucien A, Wheeler T-A, Gandy M. The prevalence of cognitive impairment in relapsing-remitting multiple sclerosis: a systematic review and meta-analysis. Neuropsychol Rev. 2024. https://doi.org/10.1007/s11065-024-09640-8.

    Article  PubMed  Google Scholar 

  30. Dastgheib M, Kulanayagam A, Dringenberg HC. Is the role of sleep in memory consolidation overrated? Neurosci Biobehav Rev. 2022;140: 104799. https://doi.org/10.1016/j.neubiorev.2022.104799.

    Article  PubMed  Google Scholar 

  31. Belia M, Keren-Portnoy T, Vihman M. Systematic review of the effects of sleep on memory and word learning in infancy. Lang Learn. 2023;73(2):613–51. https://doi.org/10.1111/lang.12544.

    Article  Google Scholar 

  32. Newbury CR, Crowley R, Rastle K, Tamminen J. Sleep deprivation and memory: meta-analytic reviews of studies on sleep deprivation before and after learning. Psychol Bull. 2021;147(11):1215–40. https://doi.org/10.1037/bul0000348.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Helfer B, Bozhilova N, Cooper RE, Douzenis JI, Maltezos S, Asherson P. The key role of daytime sleepiness in cognitive functioning of adults with attention deficit hyperactivity disorder. Eur Psychiatry. 2020;63(1): e31. https://doi.org/10.1192/j.eurpsy.2020.28.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Rosenberg R, Thorpy MJ, Doghramji K, Morse AM. Brain fog in central disorders of hypersomnolence: a review. J Clin Sleep Med. 2024;20(4):643–51. https://doi.org/10.5664/jcsm.11014.

    Article  PubMed  Google Scholar 

  35. Hughes AJ, Dunn KM, Chaffee T. Sleep disturbance and cognitive dysfunction in multiple sclerosis: a systematic review. Curr Neurol Neurosci Rep. 2018;18(1):2. https://doi.org/10.1007/s11910-018-0809-7.

    Article  PubMed  Google Scholar 

  36. Aromataris E MZE. JBI manual for evidence synthesis. 2020. Available from https://doi.org/10.46658/JBIMES-20-01.

  37. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372(71):10.1136/bmj.n71.

    Google Scholar 

  38. Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan—a web and mobile app for systematic reviews. Systematic Reviews. 2016;5:5;10.1186/s13643-016-0384–4.

    Article  Google Scholar 

  39. Moola S, Munn Z, Tufanaru C, Aromataris E, Sears K, Sfetcu R, Currie M, Lisy K, Qureshi R, Mattis P, Mu P. Systematic reviews of etiology and risk (2020). Aromataris E, Lockwood C, Porritt K, Pilla B, Jordan Z, editors. JBI Manual for Evidence Synthesis. JBI; 2024. Available from: https://synthesismanual.jbi.global. https://doi.org/10.46658/JBIMES-24-06.

  40. Bahmani DS, Kesselring J, Papadimitriou M, Bansi J, Pühse U, Gerber M, et al. In patients with multiple sclerosis, both objective and subjective sleep, depression, fatigue, and paresthesia improved after 3 weeks of regular exercise. Front Psychiatry. 2019;10(MAY):265. https://doi.org/10.3389/fpsyt.2019.00265.

    Article  Google Scholar 

  41. Hu M, Muhlert N, Robertson N, Winter M. Perceived fatigue and cognitive performance change in multiple sclerosis: uncovering predictors beyond baseline fatigue. Mult Scler Relat Disord. 2019;32:46–53. https://doi.org/10.1016/j.msard.2019.04.011.

    Article  CAS  PubMed  Google Scholar 

  42. Bruce JM, Arnett P. Clinical correlates of generalized worry in multiple sclerosis. J Clin Exp Neuropsychol. 2009;31(6):698–705. https://doi.org/10.1080/13803390802484789.

    Article  PubMed  Google Scholar 

  43. Cehelyk EK, Harvey DY, Grubb ML, Jalel R, El-Sibai MS, Markowitz CE, et al. Uncovering the association between fatigue and fatigability in multiple sclerosis using cognitive control. Mult Scler Relat Disord. 2019;27:269–75. https://doi.org/10.1016/j.msard.2018.10.112.

    Article  PubMed  Google Scholar 

  44. Jain V, Arunkumar A, Kingdon C, Lacerda E, Nacul L. Prevalence of and risk factors for severe cognitive and sleep symptoms in ME/CFS and MS. BMC Neurol. 2017;17(1):117. https://doi.org/10.1186/s12883-017-0896-0.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Turkoglu R, Benbir G, Ozyurt S, Arsoy E, Akbayir E, Turan S, et al. Sleep disturbance and cognitive decline in multiple sclerosis patients with isolated optic neuritis as the first demyelinating event. International Ophthalmology. 2020;40(1):151–8. https://doi.org/10.1007/s10792-019-01157-x.

    Article  PubMed  Google Scholar 

  46. Hoogerwerf AEW, Bol Y, Lobbestael J, Hupperts R, van Heugten CM. Mindfulness-based cognitive therapy for severely fatigued multiple sclerosis patients: a waiting list controlled study. J Rehabil Med. 2017;49(6):497–504. https://doi.org/10.2340/16501977-2237.

    Article  PubMed  Google Scholar 

  47. Bahmani DS, Esmaeili L, Shaygannejad V, Gerber M, Kesselring J, Lang UE, et al. Stability of mental toughness, sleep disturbances, and physical activity in patients with multiple sclerosis (MS)-A longitudinal and pilot study. Front Psychiatry. 2018;9(MAY):182. https://doi.org/10.3389/fpsyt.2018.00182.

    Article  Google Scholar 

  48. Arnett PA. Longitudinal consistency of the relationship between depression symptoms and cognitive functioning in multiple sclerosis. CNS Spectr. 2005;10(5):372 – 82;10.1017/S1092852900022744.

    Article  PubMed  Google Scholar 

  49. Aldughmi M, Huisinga J, Lynch SG, Siengsukon CF. The relationship between fatigability and sleep quality in people with multiple sclerosis. Mult Scler J Exp Transl Clin. 2016;2(2055217316682774):10.1177/2055217316682774.

    Google Scholar 

  50. Aristotelous P, Stefanakis M, Pantzaris M, Pattichis C, Hadjigeorgiou GM, Giannaki CD. Associations between functional capacity, isokinetic leg strength, sleep quality and cognitive function in multiple sclerosis patients: a cross-sectional study. Postgrad Med. 2019;131(7):453–60. https://doi.org/10.1080/00325481.2019.1662271.

    Article  PubMed  Google Scholar 

  51. Beier M, Amtmann D, Ehde DM. Beyond depression: predictors of self-reported cognitive function in adults living with MS. Rehabil Psychol. 2015;60(3):254.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Berard JA, Smith AM, Walker LAS. Predictive models of cognitive fatigue in multiple sclerosis. Arch Clin Neuropsychol. 2019;34(1):31–8. https://doi.org/10.1093/arclin/acy014.

    Article  PubMed  Google Scholar 

  53. Braley TJ, Kratz AL, Kaplish N, Chervin RD. Sleep and cognitive function in multiple sclerosis. Sleep. 2016;39(8):1525–33. https://doi.org/10.5665/sleep.6012.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Braley TJ, Shieu MM, Zaheed AB, Dunietz GL. Pathways between multiple sclerosis, sleep disorders, and cognitive function: Longitudinal findings from The Nurses’ Health Study. Mult Scler. 2023;29(3):436–46. https://doi.org/10.1177/13524585221144215.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Cederberg KLJ, Jeng B, Sasaki JE, Motl RW. Restless legs syndrome, sleep quality, and perceived cognitive impairment in adults with multiple sclerosis. Mult Scler Relat Disord. 2020;43(102176):10.1016/j.msard.2020.102176.

    Google Scholar 

  56. Cederberg KLJ, Mathison B, Schuetz ML, Motl RW. Restless legs syndrome severity and cognitive function in adults with multiple sclerosis: an exploratory pilot study. Int J MS Care. 2022;24(4):154–61. https://doi.org/10.7224/1537-2073.2020-120.

    Article  PubMed  Google Scholar 

  57. Chen MH, Cherian C, Elenjickal K, Rafizadeh CM, Ross MK, Leow A, et al. Real-time associations among MS symptoms and cognitive dysfunction using ecological momentary assessment. Front Med (Lausanne). 2022;9(1049686):10.3389/fmed.2022.1049686.

    Google Scholar 

  58. Chinnadurai SA, Gandhirajan D, Pamidimukala V, Kesavamurthy B, Venkatesan SA. Analysing the relationship between polysomnographic measures of sleep with measures of physical and cognitive fatigue in people with multiple sclerosis. Mult Scler Relat Disord. 2018;24:32–7. https://doi.org/10.1016/j.msard.2018.05.016.

    Article  PubMed  Google Scholar 

  59. Dubessy AL, du Montcel ST, Viala F, Assouad R, Tiberge M, Papeix C, et al. Association of central hypersomnia and fatigue in patients with multiple sclerosis a polysomnographic study. Neurology. 2021;97(1):E23–33. https://doi.org/10.1212/wnl.0000000000012120.

    Article  CAS  PubMed  Google Scholar 

  60. Hare CJ, Crangle CJ, Carney CE, Hart T. Insomnia symptoms. Subjective appraisals, and fatigue: a multiple mediation model. Behav Sleep Med. 2019;17(3):269–80. https://doi.org/10.1080/15402002.2017.1342167.

    Article  PubMed  Google Scholar 

  61. Hughes AJ, Parmenter BA, Haselkorn JK, Lovera JF, Bourdette D, Boudreau E, et al. Sleep and its associations with perceived and objective cognitive impairment in individuals with multiple sclerosis. J Sleep Res. 2017;26(4):428–35. https://doi.org/10.1111/jsr.12490.

    Article  PubMed  Google Scholar 

  62. Hughes AJ, Turner AP, Alschuler KN, Atkins DC, Beier M, Amtmann D, et al. Association between sleep problems and perceived cognitive dysfunction over 12 months in individuals with multiple sclerosis. Behav Sleep Med. 2018;16(1):79–91. https://doi.org/10.1080/15402002.2016.1173553.

    Article  PubMed  Google Scholar 

  63. Lamis DA, Hirsch JK, Pugh KC, Topciu R, Nsamenang SA, Goodman A, et al. Perceived cognitive deficits and depressive symptoms in patients with multiple sclerosis: perceived stress and sleep quality as mediators. Mult Scler Relat Disord. 2018;25:150–5. https://doi.org/10.1016/j.msard.2018.07.019.

    Article  PubMed  Google Scholar 

  64. Laslett LL, Honan C, Turner JA, Dagnew B, Campbell JA, Gill TK et al. Poor sleep and multiple sclerosis: associations with symptoms of multiple sclerosis and quality of life. J Neurol Neurosurg Psychiatry. 2022. https://doi.org/10.1136/jnnp-2022-329227.

  65. Lehmann P, Eling P, Kastrup A, Grothues O, Hildebrandt H. Self-reported sleep problems, but not fatigue, lead to decline in sustained attention in MS patients. Mult Scler. 2013;19(4):490–7. https://doi.org/10.1177/1352458512457719.

    Article  PubMed  Google Scholar 

  66. Mackay L, Johnson AM, Moodie ST, Rosehart H, Morrow SA. Predictors of cognitive fatigue and fatigability in multiple sclerosis. Mult Scler Relat Disord. 2021;56:103316. https://doi.org/10.1016/j.msard.2021.103316.

    Article  PubMed  Google Scholar 

  67. McNicholas N, Russell A, Nolan G, Tubridy N, Hutchinson M, Garvey JF, et al. Impact of obstructive sleep apnoea on cognitive function in multiple sclerosis: a longitudinal study. J Sleep Res. 2021;30(3): e13159. https://doi.org/10.1111/jsr.13159.

    Article  PubMed  Google Scholar 

  68. Odintsova TA, Kopchak OO. Sleep disorders in relapsing-remitting multiple sclerosis patients. Wiad Lek. 2021;74(2):257–6233813482.

    Article  PubMed  Google Scholar 

  69. Opelt BL, Lewis C, Hughes AJ. Discrepancies between self-report and objective sleep outcomes are associated with cognitive impairment and fatigue in people with multiple sclerosis and insomnia. Mult Scler Relat Disord. 2023;71(104588):10.1016/j.msard.2023.104588.

    Google Scholar 

  70. Ozkul C, Guclu-Gunduz A, Eldemir K, Apaydin Y, Yazici G, Irkec C. Clinical features and physical performance in multiple sclerosis patients with and without cognitive impairment: a cross-sectional study. Int J Rehabil Res. 2020;43(4):316–23. https://doi.org/10.1097/mrr.0000000000000428.

    Article  PubMed  Google Scholar 

  71. Patel VP, Walker LA, Feinstein A. Processing speed and distractibility in multiple sclerosis: the role of sleep. Mult Scler Relat Disord. 2017;11:40–2. https://doi.org/10.1016/j.msard.2016.11.012.

    Article  PubMed  Google Scholar 

  72. Riccitelli GC, Pacifico D, Manconi M, Sparasci D, Sacco R, Gobbi C, et al. Relationship between cognitive disturbances and sleep disorders in multiple sclerosis is modulated by psychiatric symptoms. Mult Scler Relat Disord. 2022;64(103936):10.1016/j.msard.2022.103936.

    Google Scholar 

  73. Sater RA, Gudesblatt M, Kresa-Reahl K, Brandes DW, Sater PA. The relationship between objective parameters of sleep and measures of fatigue, depression, and cognition in multiple sclerosis. Mult Scler J Exp Transl Clin. 2015;1(2055217315577828):10.1177/2055217315577828.

    Google Scholar 

  74. Schellaert V, Labauge P, Lebrun C, Maudarbocus KH, Bernard J, Blache JB et al. Psychological processes associated with insomnia in patients with multiple sclerosis. Sleep. 2018;41(3). https://doi.org/10.1093/sleep/zsy002

  75. Shahrbanian S, Duquette P, Kuspinar A, Mayo NE. Contribution of symptom clusters to multiple sclerosis consequences. Qual Life Res. 2015;24(3):617–29. https://doi.org/10.1007/s11136-014-0804-7.

    Article  PubMed  Google Scholar 

  76. Siengsukon CF, Aldughmi M, Kahya M, Lynch S, Bruce J, Glusman M, et al. Individuals with mild MS with poor sleep quality have impaired visuospatial memory and lower perceived functional abilities. Disabil Health J. 2018;11(1):116–21. https://doi.org/10.1016/j.dhjo.2017.04.011.

    Article  PubMed  Google Scholar 

  77. Siengsukon CF, Alshehri M, Aldughmi M. Self-report sleep quality combined with sleep time variability distinguishes differences in fatigue, anxiety, and depression in individuals with multiple sclerosis: a secondary analysis. Mult Scler J Exp Transl Clin. 2018;4(4):2055217318815924. https://doi.org/10.1177/2055217318815924.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Sumowski JF, Horng S, Brandstadter R, Krieger S, Leavitt VM, Sand IK, et al. Sleep disturbance and memory dysfunction in early multiple sclerosis. Annals of Clinical and Translational Neurology. 2021;8(6):1172–82. https://doi.org/10.1002/acn3.51262.

    Article  PubMed  PubMed Central  Google Scholar 

  79. van Geest Q, Westerik B, van der Werf YD, Geurts JJ, Hulst HE. The role of sleep on cognition and functional connectivity in patients with multiple sclerosis. J Neurol. 2017;264(1):72–80. https://doi.org/10.1007/s00415-016-8318-6.

    Article  PubMed  Google Scholar 

  80. Valentine TR, Kratz AL, Kaplish N, Chervin RD, Braley TJ. Sleep-disordered breathing and neurocognitive function in multiple sclerosis: differential associations across cognitive domains. Mult Scler. 2023;29(7):832 – 45;10.1177/13524585231169465.

    Article  CAS  PubMed  Google Scholar 

  81. Whibley D, Goldstein C, Kratz AL, Braley TJ. A multidimensional approach to sleep health in multiple sclerosis. Mult Scler Relat Disord. 2021;56(103271):10.1016/j.msard.2021.103271.

    Google Scholar 

  82. Borragán G, Gilson M, Atas A, Slama H, Lysandropoulos A, De Schepper M, et al. Cognitive fatigue, sleep and cortical activity in multiple sclerosis disease. a behavioral, polysomnographic and functional near-infrared spectroscopy investigation. Front Hum Neurosci. 2018;12(378):10.3389/fnhum.2018.00378.

    Google Scholar 

  83. Terauchi T, Mizuno M, Suzuki M, Akasaka H, Maeta M, Tamura K, Hosokawa K, Nishijima T, Maeda T. Clinical features of sleep apnea syndrome and cognitive impairment in multiple sclerosis. Mult Scler Relat Disord. 2024;82:105407. https://doi.org/10.1016/j.msard.2023.105407. Epub 2023 Dec 23.

  84. Buysse DJ, Reynolds CF 3, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193–213 10.1016/0165–1781(89)90047-4.

    Article  CAS  PubMed  Google Scholar 

  85. Naseri A, Forghani N, Sadigh-Eteghad S, Shanehbandi D, Asadi M, Nasiri E, et al. Circulatory antioxidant and oxidative stress markers are in correlation with demographics but not cognitive functions in multiple sclerosis patients. Mult Scler Relat Disord. 2022;57(103432):10.1016/j.msard.2021.103432.

    Google Scholar 

  86. Johns MW. A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep. 1991;14(6):540–5. https://doi.org/10.1093/sleep/14.6.540.

    Article  CAS  PubMed  Google Scholar 

  87. Rundo JV, Downey R 3rd. Polysomnography. Handb Clin Neurol. 2019;160:381–92. https://doi.org/10.1016/b978-0-444-64032-1.00025-4.

    Article  PubMed  Google Scholar 

  88. Berger AM, Wielgus KK, Young-McCaughan S, Fischer P, Farr L, Lee KA. Methodological challenges when using actigraphy in research. J Pain Symptom Manag. 2008;36(2):191–9. https://doi.org/10.1016/j.jpainsymman.2007.10.008.

    Article  Google Scholar 

  89. Rosca EC, Simu M. Montreal cognitive assessment for evaluating cognitive impairment in multiple sclerosis: a systematic review. Acta Neurol Belg. 2020;120(6):1307–21. https://doi.org/10.1007/s13760-020-01509-w.

    Article  PubMed  Google Scholar 

  90. Zhao JL, Cross N, Yao CW, Carrier J, Postuma RB, Gosselin N, et al. Insomnia disorder increases the risk of subjective memory decline in middle-aged and older adults: a longitudinal analysis of the Canadian longitudinal study on aging. Sleep. 2022;45(11):zsac176. https://doi.org/10.1093/sleep/zsac176.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Kim KY, Kim EH, Lee M, Ha J, Jung I, Kim E. Restless leg syndrome and risk of all-cause dementia: a nationwide retrospective cohort study. Alzheimer’s Research and Therapy. 2023;15(1):46. https://doi.org/10.1186/s13195-023-01191-z.

    Article  CAS  PubMed Central  Google Scholar 

  92. Aljarallah S, Alkhawajah N, Aldosari O, Alhuqbani M, Alqifari F, Alkhuwaitir B, et al. Restless leg syndrome in multiple sclerosis: a case-control study. Front Neurol. 2023;14(1194212):10.3389/fneur.2023.1194212.

    Google Scholar 

  93. Cederberg KLJ, Jeng B, Sasaki JE, Sikes EM, Cutter G, Motl RW. Physical activity and self-reported sleep quality in adults with multiple sclerosis. Disabil Health J. 2021;14(4): 101133. https://doi.org/10.1016/j.dhjo.2021.101133.

    Article  PubMed  PubMed Central  Google Scholar 

  94. Selbaek-Tungevåg S, Selbaek G, Strand BH, Myrstad C, Livingston G, Lydersen S, et al. Insomnia and risk of dementia in a large population-based study with 11-year follow-up: the HUNT study. J Sleep Res. 2023;32(4): e13820. https://doi.org/10.1111/jsr.13820.

    Article  PubMed  Google Scholar 

  95. Ju YE, Lucey BP, Holtzman DM. Sleep and Alzheimer disease pathology–a bidirectional relationship. Nat Rev Neurol. 2014;10(2):115–9. https://doi.org/10.1038/nrneurol.2013.269.

    Article  CAS  PubMed  Google Scholar 

  96. Kang DW, Lee CU, Lim HK. Role of sleep disturbance in the trajectory of Alzheimer’s disease. Clin Psychopharmacol Neurosci. 2017;15(2):89–99. https://doi.org/10.9758/cpn.2017.15.2.89.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. da Silva RA. Sleep disturbances and mild cognitive impairment: a review. Sleep Sci. 2015;8(1):36–41. https://doi.org/10.1016/j.slsci.2015.02.001.

    Article  PubMed  PubMed Central  Google Scholar 

  98. Zhu B, Dong Y, Xu Z, Gompf HS, Ward SA, Xue Z, et al. Sleep disturbance induces neuroinflammation and impairment of learning and memory. Neurobiol Dis. 2012;48(3):348–55. https://doi.org/10.1016/j.nbd.2012.06.022.

    Article  PubMed  PubMed Central  Google Scholar 

  99. Lim AS, Kowgier M, Yu L, Buchman AS, Bennett DA. Sleep fragmentation and the risk of incident Alzheimer’s Disease and cognitive decline in older persons. Sleep. 2013;36(7):1027–32. https://doi.org/10.5665/sleep.2802.

    Article  PubMed  PubMed Central  Google Scholar 

  100. Emamian F, Khazaie H, Tahmasian M, Leschziner GD, Morrell MJ, Hsiung GY, et al. The association between obstructive sleep apnea and Alzheimer’s disease: a meta-analysis perspective. Front Aging Neurosci. 2016;8(78):10.3389/fnagi.2016.00078.

    Google Scholar 

  101. Yaffe K, Laffan AM, Harrison SL, Redline S, Spira AP, Ensrud KE, et al. Sleep-disordered breathing, hypoxia, and risk of mild cognitive impairment and dementia in older women. Jama. 2011;306(6):613–9. https://doi.org/10.1001/jama.2011.1115.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Zhang Y, Ren R, Yang L, Zhang H, Shi Y, Okhravi HR, et al. Sleep in Alzheimer’s disease: a systematic review and meta-analysis of polysomnographic findings. Translational Psychiatry. 2022;12(1):136. https://doi.org/10.1038/s41398-022-01897-y.

    Article  PubMed  PubMed Central  Google Scholar 

  103. DeLuca J, Chiaravalloti ND, Sandroff BM. Treatment and management of cognitive dysfunction in patients with multiple sclerosis. Nature Reviews Neurology. 2020;16(6):319–32. https://doi.org/10.1038/s41582-020-0355-1.

    Article  PubMed  Google Scholar 

  104. Motavalli A, Majdi A, Hosseini L, Talebi M, Mahmoudi J, Hosseini SH, et al. Pharmacotherapy in multiple sclerosis-induced cognitive impairment: a systematic review and meta-analysis. Multiple Scler Relat Disorders. 2020;46: 102478. https://doi.org/10.1016/j.msard.2020.102478.

    Article  Google Scholar 

  105. Ferini-Strambi L, Liguori C, Lucey BP, Mander BA, Spira AP, Videnovic A, et al. Role of sleep in neurodegeneration: the consensus report of the 5th Think Tank World Sleep Forum. Neurological Sciences. 2024;45(2):749–67. https://doi.org/10.1007/s10072-023-07232-7.

    Article  PubMed  Google Scholar 

  106. Novak AM, Lev-Ari S. Resilience, stress, well-being, and sleep quality in multiple sclerosis. J Clin Med. 2023;12(2):716. https://doi.org/10.3390/jcm12020716.

    Article  PubMed  PubMed Central  Google Scholar 

  107. Naseri A, Morsali S, Sabahi Z, Kakaei J, Hakimzadeh Z, Hamidi S, et al. The effects of Melatonin supplementation on cognitive outcomes in multiple sclerosis: a systematic review. Multiple Scler Relat Disorders. 2023;80: 105167. https://doi.org/10.1016/j.msard.2023.105167.

    Article  Google Scholar 

  108. Morsali S, Sabahi Z, Kakaei J, Hakimzadeh Z, Hamidi S, Gholipour-Khalili E, et al. Clinical efficacy and safety of melatonin supplementation in multiple sclerosis: a systematic review. Inflammopharmacology. 2023;31(5):2213–20. https://doi.org/10.1007/s10787-023-01271-4.

    Article  CAS  PubMed  Google Scholar 

  109. Razmaray H, Nasiri E, Vakilipour P, Morsali S, Moradi A, Ebrahimian A, et al. The effects of melatonin supplementation on neurobehavioral outcomes and clinical severity in rodent models of multiple sclerosis; a systematic review and meta-analysis. Inflammopharmacology. 2024;32(2):927–44. https://doi.org/10.1007/s10787-023-01414-7.

    Article  CAS  PubMed  Google Scholar 

  110. Bandehagh H, Gozalpour F, Mousavi A, Ghavshough MH. Effects of melatonin on the management of multiple sclerosis: a scoping review on animal studies. AIMS Med Sci. 2024;11:137–56. https://doi.org/10.3934/medsci.2024012.

    Article  Google Scholar 

  111. Bocheva G, Bakalov D, Iliev P, Tafradjiiska-Hadjiolova R. The vital role of melatonin and its metabolites in the neuroprotection and retardation of brain aging. Int J Mol Sci. 2024;25(10);https://doi.org/10.3390/ijms25105122

  112. Mogavero MP, Lanza G, Bruni O, DelRosso LM, Ferri R, Ferini-Strambi L. Sleep counts! Role and impact of sleep in the multimodal management of multiple sclerosis. J Neurol. 2023;270(7):3377–90. https://doi.org/10.1007/s00415-023-11655-9.

    Article  PubMed  Google Scholar 

  113. Fleming WE, Pollak CP. Sleep disorders in multiple sclerosis. Semin Neurol. 2005;25(1):64–8. https://doi.org/10.1055/s-2005-867075.

Download references

Acknowledgements

The research protocol was approved and supported by the Student Research Committee, Tabriz University of Medical Sciences (grant number: 71908).

Funding

The research protocol was approved and supported by the Student Research Committee, Tabriz University of Medical Sciences (grant number: 71908).

Author information

Author notes

  1. Behnam Golabi, Hadis Razmaray and Sepideh Seyedi-Sahebari contributed equally to this work.

Authors and Affiliations

  1. Student Research Committee, Tabriz University of Medical Sciences, Golgasht Street, Tabriz, East Azerbaijan, 5166/15731, Iran

    Behnam Golabi, Hadis Razmaray, Sepideh Seyedi-Sahebari, Heliya Bandehagh, Ailin Khosroshahi & Amirreza Naseri

  2. Neurosciences Research Center (NSRC), Tabriz University of Medical Sciences, Tabriz, 5166/15731, Iran

    Zahra Hakimzadeh, Sarvin Sanaie & Mahnaz Talebi

  3. Davis School of Medicine, University of California, Davis, Davis, CA, United States of America

    Seyedehyasmin Moghaddamziabari

  4. Faculty of Medicine, Tabriz Medical Sciences, Islamic Azad University, Tabriz, Iran

    Negar Aghaei

  5. Research Center for Evidence-Based Medicine, Iranian EBM Centre: A Joanna Briggs Institute (JBI) Center of Excellence, Tabriz University of Medical Sciences, Tabriz, Iran

    Amirreza Naseri

  6. Tabriz USERN Office, Universal Scientific Education and Research Network (USERN), Tabriz, Iran

    Amirreza Naseri

Authors
  1. Behnam Golabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hadis Razmaray
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sepideh Seyedi-Sahebari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Heliya Bandehagh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zahra Hakimzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ailin Khosroshahi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Seyedehyasmin Moghaddamziabari
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Negar Aghaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Sarvin Sanaie
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Mahnaz Talebi
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Amirreza Naseri
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

B.G., H.R., S.S-S., Z.H., A.K., S.M., H.B., and A.N: systematic search; study selection, data extraction, risk of bias assessment, preparing the figures, and drafting the manuscript; N.A., S.S, M.T, and A.N.: conceptualization; supervision and critically editing the manuscript. All authors approved the final version for submission.

Corresponding authors

Correspondence to Mahnaz Talebi or Amirreza Naseri.

Ethics declarations

Ethics approval and consent to participate

The ethics committee of the Tabriz University of Medical Science reviewed and approved the study protocol (Ethics code: IR.TBZMED.VCR.REC.1402.067).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Golabi, B., Razmaray, H., Seyedi-Sahebari, S. et al. Sleep and cognitive outcomes in multiple sclerosis; a systematic review. BMC Psychiatry 24, 638 (2024). https://doi.org/10.1186/s12888-024-06103-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12888-024-06103-5

Keywords

BMC Psychiatry

ISSN: 1471-244X

Contact us