Idiopathic REM sleep behaviour disorder (iRBD) is a powerful early sign of Parkinson’s disease, dementia with Lewy bodies, and multiple system atrophy. This provides an unprecedented opportunity to directly observe prodromal neurodegenerative states, and potentially intervene with neuroprotective therapy. For future neuroprotective trials, it is essential to accurately estimate phenoconversion rate and identify potential predictors of phenoconversion. This study assessed the neurodegenerative disease risk and predictors of neurodegeneration in a large multicentre cohort of iRBD. We combined prospective follow-up data from 24 centres of the International RBD Study Group. At baseline, patients with polysomnographically-confirmed iRBD without parkinsonism or dementia underwent sleep, motor, cognitive, autonomic and special sensory testing. Patients were then prospectively followed, during which risk of dementia and parkinsonsim were assessed. The risk of dementia and parkinsonism was estimated with Kaplan-Meier analysis. Predictors of phenoconversion were assessed with Cox proportional hazards analysis, adjusting for age, sex, and centre. Sample size estimates for disease-modifying trials were calculated using a time-to-event analysis. Overall, 1280 patients were recruited. The average age was 66.3 ± 8.4 and 82.5% were male. Average follow-up was 4.6 years (range = 1–19 years). The overall conversion rate from iRBD to an overt neurodegenerative syndrome was 6.3% per year, with 73.5% converting after 12-year follow-up. The rate of phenoconversion was significantly increased with abnormal quantitative motor testing [hazard ratio (HR) = 3.16], objective motor examination (HR = 3.03), olfactory deficit (HR = 2.62), mild cognitive impairment (HR = 1.91–2.37), erectile dysfunction (HR = 2.13), motor symptoms (HR = 2.11), an abnormal DAT scan (HR = 1.98), colour vision abnormalities (HR = 1.69), constipation (HR = 1.67), REM atonia loss (HR = 1.54), and age (HR = 1.54). There was no significant predictive value of sex, daytime somnolence, insomnia, restless legs syndrome, sleep apnoea, urinary dysfunction, orthostatic symptoms, depression, anxiety, or hyperechogenicity on substantia nigra ultrasound. Among predictive markers, only cognitive variables were different at baseline between those converting to primary dementia versus parkinsonism. Sample size estimates for definitive neuroprotective trials ranged from 142 to 366 patients per arm. This large multicentre study documents the high phenoconversion rate from iRBD to an overt neurodegenerative syndrome. Our findings provide estimates of the relative predictive value of prodromal markers, which can be used to stratify patients for neuroprotective trials.

Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 normal and abnormal sleep recordings to automate sleep stage scoring, producing a hypnodensity graph—a probability distribution conveying more information than classical hypnograms. Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers. The best model performed better than any individual scorer (87% versus consensus). It also reliably scores sleep down to 5 s instead of 30 s scoring epochs. A T1N marker based on unusual sleep stage overlaps achieved a specificity of 96% and a sensitivity of 91%, validated in independent datasets. Addition of HLA-DQB1*06:02 typing increased specificity to 99%. Our method can reduce time spent in sleep clinics and automates T1N diagnosis. It also opens the possibility of diagnosing T1N using home sleep studies.

Restless legs syndrome is a prevalent chronic neurological disorder with potentially severe mental and physical health consequences. Clearer understanding of the underlying pathophysiology is needed to improve treatment options. We did a meta-analysis of genome-wide association studies (GWASs) to identify potential molecular targets. In the discovery stage, we combined three GWAS datasets (EU-RLS GENE, INTERVAL, and 23andMe) with diagnosis data collected from 2003 to 2017, in face-to-face interviews or via questionnaires, and involving 15 126 cases and 95 725 controls of European ancestry. We identified common variants by fixed-effect inverse-variance meta-analysis. Significant genome-wide signals (p≤5 × 10-8) were tested for replication in an independent GWAS of 30 770 cases and 286 913 controls, followed by a joint analysis of the discovery and replication stages. We did gene annotation, pathway, and gene-set-enrichment analyses and studied the genetic correlations between restless legs syndrome and traits of interest. We identified and replicated 13 new risk loci for restless legs syndrome and confirmed the previously identified six risk loci. MEIS1 was confirmed as the strongest genetic risk factor for restless legs syndrome (odds ratio 1·92, 95% CI 1·85-1·99). Gene prioritisation, enrichment, and genetic correlation analyses showed that identified pathways were related to neurodevelopment and highlighted genes linked to axon guidance (associated with SEMA6D), synapse formation (NTNG1), and neuronal specification (HOXB cluster family and MYT1). Identification of new candidate genes and associated pathways will inform future functional research. Advances in understanding of the molecular mechanisms that underlie restless legs syndrome could lead to new treatment options. We focused on common variants; thus, additional studies are needed to dissect the roles of rare and structural variations. Deutsche Forschungsgemeinschaft, Helmholtz Zentrum München-Deutsches Forschungszentrum für Gesundheit und Umwelt, National Research Institutions, NHS Blood and Transplant, National Institute for Health Research, British Heart Foundation, European Commission, European Research Council, National Institutes of Health, National Institute of Neurological Disorders and Stroke, NIH Research Cambridge Biomedical Research Centre, and UK Medical Research Council.