REM Sleep Behavior Disorders and the Risk of Neurodegenerative Diseases

Abstract

Objectives: This review aims to examine the link between REM sleep behavior disorder (RBD) and neurodegenerative diseases, identifying clinical, environmental, and biological predictors of phenoconversion to support early detection and preventive care.

Methods: Following PRISMA guidelines, searches were performed in PubMed and Cochrane using the terms “REM Sleep Disorders,” “Neurodegenerative Diseases,” and “Risk Factors.” Of 69 screened articles, 15 met the inclusion criteria, and six studies from the World Sleep Society were included for comparative analysis. These studies provided insights into the impact of RBD on disease progression through long-term follow-up. Methodological approaches were analyzed to understand predictive potential and guide preventive strategies.

Results: Sample sizes ranged from 44 to 7,225 participants, with follow-ups up to 12 years. Phenoconversion rates from idiopathic RBD (iRBD) to neurodegenerative conditions ranged from 13% to 73.5%. Common outcomes included Parkinson’s disease, dementia with Lewy bodies, and multiple system atrophy. Risk factors were constipation, hyposmia, orthostatic hypotension, diabetes, family history of parkinsonism or dementia, and exposure to toxins. Protective factors included tea intake, rural living, and certain medications. Biomarkers like reduced dopamine transporter uptake and REM sleep without atonia (RSWA) improved prediction. Stratifying iRBD patients by clinical profiles and comorbidities enhanced risk assessment. The FARPRESTO study demonstrated that combining olfactory testing, DAT-SPECT imaging, and autonomic symptom scales improved predictive accuracy. RSWA has been proposed as an early marker, even without dream enactment. Cognitive patterns differed: PD converters showed executive dysfunction; DLB converters showed visuospatial and memory decline. These findings underscore the need for individualized monitoring strategies.

Conclusion: iRBD is a key early marker for neurodegenerative diseases. Identifying risk factors and biomarkers enhances early detection and intervention. Despite heterogeneity among studies, current evidence supports the integration of iRBD monitoring into personalized care and preventive planning.