Cognitive Performance, Sleep Disturbances and Fatigue in Multiple Sclerosis

Fatigue is a prevalent symptom in patients with multiple sclerosis (MS) and is associated with considerable impairment in quality of life as well as loss of occupational capacity. Sleep disturbances are regarded as a critical factor in the development of fatigue and are frequently observed in individuals with MS. However, they often remain underrecognized, undiagnosed, and consequently untreated. Polysomnography, the gold standard for assessing sleep architecture and quality, has rarely been applied in the investigation of sleep disorders in MS. Accordingly, uncertainties remain regarding the prevalence and extent to which sleep disturbances contribute to fatigue in this population. Moreover, emerging evidence suggests an association between sleep disorders and cognitive dysfunction in MS. Yet, it is unclear whether cognitive impairment arises from the sleep disorder itself, from the resulting fatigue, or from other independent factors. Pharmacological treatments for MS-related fatigue remain limited, given heterogeneous and frequently non-replicable effects. Non-pharmacological interventions such as physical activity, cognitive behavioral therapy, and psychoeducation have shown promise but yield variable outcomes. The development of novel and effective therapeutic strategies requires a more comprehensive understanding of the etiology of fatigue. To date, the role of sleep disturbances and their relationship to cognitive performance in MS have not been adequately investigated. The objective of this project is to determine the prevalence and characteristics of sleep disorders in MS patients with fatigue using polysomnography and to examine their relationship with cognitive impairment. In addition, the study will compare sleep quality parameters and the prevalence of sleep disorders across different MS subtypes (relapsing-remitting, primary progressive, and secondary progressive). Furthermore, within a sub-study, it will be investigated whether the type of immunotherapy has an influence on the aforementioned aspects. Finally, the project seeks to integrate artificial intelligence (AI) into polysomnography analysis to streamline data evaluation and facilitate the future assessment of therapeutic interventions. The study will be conducted as a non-invasive, non-interventional, longitudinal observational trial including MS patients with fatigue and a control group of patients with subjective sleep complaints but without MS. Recruitment will take place over 36 months at two centers: the Department of Neurology at the University Hospital Düsseldorf and the Maria Hilf Clinics in Mönchengladbach. Additional recruitment will be supported by community-based neurologists in the Mönchengladbach region to broaden the study cohort and ensure representativeness of the study population. Approximately 382 MS patients are expected to be enrolled. The number of control participants will be determined by the proportion of MS patients presenting with sleep disorders and will be recruited consecutively from the neurological sleep laboratory of the Maria Hilf Clinics. For AI training, retrospective polysomnography data from the past five years (N ≥ 10,000 patients) at the Maria Hilf Clinics will be utilized. The study protocol includes overnight polysomnography to assess sleep quality, along with comprehensive clinical evaluation, neuropsychological testing, and validated questionnaires addressing fatigue, subjective sleep quality, daytime sleepiness, depression, and anxiety. Based on manually scored polysomnography, AI models will be trained to identify key parameters of sleep quality. The findings of this study will advance the understanding of the role of sleep disturbances in MS-related fatigue and will facilitate the integration of AI into sleep research, thereby streamlining the evaluation of future therapeutic approaches.