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Alpha-synucleinopathies can be identified in their prodromal phase, raising several ethical issues. In this review, we first provide definitions of prodromal α-synucleinopathies and discuss the importance of distinguishing between prodromes and risk factors. Next, we discuss the implications of a diagnosis of prodromal α-synucleinopathy and considerations regarding prognostic counseling in both clinical and research settings. We review available data on patient preferences regarding disclosure as well as providers' perspectives. We examine the pros and cons of disclosing a diagnosis of prodromal α-synucleinopathy, taking into consideration the differences between clinical and research settings. Asking about willingness to know in clinical and research settings and the shared decision-making process applied to prognostic counseling is discussed. Concerning research settings, ethical aspects regarding clinical trials are addressed. Availability of direct-to-consumer technologies will likely lead to novel contexts requiring prognostic counseling, and future neuroprotective or neuromodulating treatments may require further considerations on the timing, role, and importance of prognostic counseling. Recommendations on how to address ethical gaps should be a priority for patients, medical professional societies, and research workgroups. Ethical issues must be considered as an integral part of the overall clinical and research approach to prodromal synucleinopathies.

Sleep disorders, daytime sleepiness, and autonomic dysfunction are commonly reported among patients with multiple system atrophy and Parkinson disease (PD). We aimed to assess sleep and autonomic function in these patients to evaluate the relationships between sleep disorders, excessive daytime sleepiness, and autonomic function.Twenty patients with multiple system atrophy (n = 7) and PD (n = 13) underwent clinical assessment including questionnaires for autonomic function and sleep. Cardiovascular autonomic function tests and 2-night video-polysomnography were followed by administration of the Multiple Sleep Latency Test. Rapid eye movement sleep without atonia was quantified in the chin, flexor digitorum superficialis, tibial anterior, and sternocleidomastoid muscles.Rapid eye movement sleep behavior disorder was associated with orthostatic hypotension (The relationship of autonomic dysfunction with rapid eye movement sleep without atonia in PD and multiple system atrophy is accounted for by shared brainstem neuropathology and likely identifies patients in a more advanced stage of disease. Excessive daytime sleepiness is found exclusively in PD and may be secondary to levodopa treatment and not related to α-synuclein disease.Eckhardt C, Fanciulli A, Högl B, et al. Analysis of sleep, daytime sleepiness, and autonomic function and multiple system atrophy and Parkinson disease: a prospective study.

Study Objectives To investigate the frequency and characteristics of large muscle group movements (LMMs) during sleep in healthy adults. Methods LMMs were scored following the International Restless Legs Syndrome Study Group criteria in 100 healthy participants aged 19–77 years. A LMM was defined as a temporally overlapping increase in EMG activity and/or the occurrence of movement artifacts in at least two channels. LMM indices and durations in total sleep time (TST), NREM and REM sleep, and association with arousals, awakenings, and/or respiratory events were calculated. Correlations of LMMs indices and durations with sleep architecture, respiratory and motor events, and subjective sleep quality were investigated. Results Median LMMs index in TST was 6.8/h (interquartile range (IQR), 4.5–10.8/h), median mean duration 12.4 s (IQR 10.7–14.4 s). Mean LMMs duration was longer in NREM (median 12.7 s, IQR 11.1–15.2 s) versus REM sleep (median 10.3 s, IQR 8.0–13.5s), p Conclusions This is the first study providing normative data on LMMs frequency in healthy adults. LMMs are a ubiquitous phenomenon often associated with other events. Correlation with arousals and respiratory events suggests a potential clinical significance of LMMs in adults that awaits further investigation.

Background and ObjectivesRecent studies fueled doubts as to whether all currently defined central disorders of hypersomnolence are stable entities, especially narcolepsy type 2 and idiopathic hypersomnia. New reliable biomarkers are needed, and the question arises of whether current diagnostic criteria of hypersomnolence disorders should be reassessed. The main aim of this data-driven observational study was to see whether data-driven algorithms would segregate narcolepsy type 1 and identify more reliable subgrouping of individuals without cataplexy with new clinical biomarkers.MethodsWe used agglomerative hierarchical clustering, an unsupervised machine learning algorithm, to identify distinct hypersomnolence clusters in the large-scale European Narcolepsy Network database. We included 97 variables, covering all aspects of central hypersomnolence disorders such as symptoms, demographics, objective and subjective sleep measures, and laboratory biomarkers. We specifically focused on subgrouping of patients without cataplexy. The number of clusters was chosen to be the minimal number for which patients without cataplexy were put in distinct groups.ResultsWe included 1,078 unmedicated adolescents and adults. Seven clusters were identified, of which 4 clusters included predominantly individuals with cataplexy. The 2 most distinct clusters consisted of 158 and 157 patients, were dominated by those without cataplexy, and among other variables, significantly differed in presence of sleep drunkenness, subjective difficulty awakening, and weekend-week sleep length difference. Patients formally diagnosed as having narcolepsy type 2 and idiopathic hypersomnia were evenly mixed in these 2 clusters.DiscussionUsing a data-driven approach in the largest study on central disorders of hypersomnolence to date, our study identified distinct patient subgroups within the central disorders of hypersomnolence population. Our results contest inclusion of sleep-onset REM periods in diagnostic criteria for people without cataplexy and provide promising new variables for reliable diagnostic categories that better resemble different patient phenotypes. Cluster-guided classification will result in a more solid hypersomnolence classification system that is less vulnerable to instability of single features.

While a growing body of studies suggests a link between sleep disturbances and neurodegenerative diseases’ (NDDs) development, prior studies have been hindered by small sample sizes, short follow-up times and a lack of objective sleep measures.In this cohort study, patients who underwent polysomnography (PSG) at the Innsbruck Sleep Disorders Unit from January 2004 to December 2007, aged ≥18 years, without NDDs at baseline or within five years post PSG, and with at least five years clinical follow-up were included. The main outcome measure was NDDs diagnosis at least five years after polysomnography, assessed until December 2021.Of 1454 patients assessed for eligibility, 999 (68.7%) met inclusion criteria (683 (68.3%) men; median age 54.9 (interquartile range, IQR 33.9-62.7) years. Seventy-five patients (7.5%) developed NDDs, 924 (92.5%) remained disease-free after 12.8 (IQR 9.9-14.6) years median follow-up. After adjusting for demographic, sleep, and clinical covariates, each percent decrease in sleep efficiency, N3 sleep, or REM sleep was associated with 1.9%, 6.5%, and 5.2% increased risk of incident NDDs (hazard ratio, HR, 1.019, CI:1.002-1.035; HR 1.065, CI:1.007-1.118; HR 1.052, CI:1.012-1.085,), respectively whereas one percent decrease in night-time wakefulness represented a 2.2% reduced risk (HR 0.978, CI:0.958-0.997). Random forest analysis identified wake, followed by N3 and REM sleep percentages, as the most important feature associated with NDDs development. Additionally, multiple sleep features combination offered more robust discrimination of incident NDDs compared to single sleep stages.These findings support contribution of sleep architecture changes to NDDs pathogenesis and provide insights into the temporal window during which these changes are detectable, pointing to sleep as early NDDs marker and potential target of neuroprotective strategies.

BackgroundParkinson’s Disease (PD) has been associated with a distinct odour, strongest in sebum-rich areas. Thermal Desorption – Gas Chromatography – Mass Spectrometry (TD-GC-MS) has revealed volatile signatures that distinguish individuals with Parkinson’s Disease (PD) from healthy controls. Here, we applied the same method, including subjects with isolated REM sleep behaviour disorder (iRBD) to examine the volatiles in sebum and compare this with that found in PD subjects and control participants. Participants with iRBD have a high likelihood for conversion to overt clinical synucleinopathies like PD, Dementia with Lewy Bodies (DLB) or (less commonly) Multiple System Atrophy (MSA).MethodsSubjects with clinically established PD (n=16) or iRBD (n=9) as well as healthy controls (n=9) were included. Following methods established in our laboratory, sebum was sampled from each participant using cotton gauze and the headspace from these swabs, analysed directly with TD-GC-MS1,2. Univariate and multivariate analysis was employed to probe the differences between volatile metabolites found for each phenotype. Putative identifications were assigned using spectral matching against the Golm metabolome and NIST spectral databases.FindingsWe can completely classify each phenotype using the sampled volatilome from which we built models with logistic regression analysis. The classification between PD and control improved on previously published work, from 85% to 100%. Putatively annotated molecules include alkanes, aldehydes, fatty acid methyl esters (FAMEs), and three metabolites namely purine, tropinone and oleamide. Investigation of highly ranked features revealed 18 features that showed intermediate expression in samples from iRBD participants.InterpretationTD-GC-MS can differentiate volatile metabolite signatures from sebum between PD, RDB and control samples. More than 70% of the identifiable metabolites that permit this discrimination were putatively annotated as hydrocarbons and fatty acid methyl esters (FAMEs). Our prior work indicates that these components arise from larger lipid molecules that decompose during the experiment2. Features putatively annotated as tropinone, oleamide and purine, have previously been linked with neuroprotection, sleep induction and antioxidation, respectively, are significantly different between the three groups of participants, along with FAMEs and hydrocarbons.FundingWe thank Michael J Fox Foundation (grant ref:12921) and Parkinson’s UK (grant ref: K-1504) for funding this study and Community of Analytical and Measurement Sciences (CAMS) for supporting DT’s research position. This work was supported by the BBSRC (award BB/L015048/1) for instrumentation used in this work and by a DTA to CW-D (project ref. 2113640). We also thank our recruitment centres for their enthusiasm and rigor during the recruitment process. We are very grateful to all the participants who took part in this study as well as PIs and nurses at the recruiting centres.

Differentiation of central disorders of hypersomnolence (DOH) is challenging but important for patient care. This study aimed to investigate whether biomarkers derived from sleep structure evaluated both by manual scoring as well as with artificial intelligence (AI) algorithms allow distinction of patients with different DOH. We included video-polysomnography data of 40 narcolepsy type 1 (NT1), 26 narcolepsy type 2 (NT2), 23 patients with idiopathic hypersomnia (IH) and 54 participants with subjective excessive daytime sleepiness (sEDS). Sleep experts manually scored sleep stages. A previously validated AI algorithm was employed to obtain automatic hypnograms and hypnodensity graphs (where each epoch is represented as a mixture of sleep stage probabilities). One-thousand-three features describing sleep architecture and instability were extracted from manual/automatic hypnogram and hypnodensity graphs. After feature selection, random forest classifiers were trained and tested in a 5-fold-cross-validation scheme to distinguish groups pairwise (NT1-vs-NT2, NT1-vs-IH, …) and single groups from the pooled remaining ones (NT1-vs-rest, NT2-vs-rest,…). The accuracy/F1-score values obtained in the test sets were: 0.74 ± 0.04/0.79 ± 0.05 (NT1-vs-NT2), 0.89 ± 0.09/0.91 ± 0.08 (NT1-vs-IH), 0.93 ± 0.06/0.91 ± 0.07 (NT1-vs-sEDS), 0.88 ± 0.04/0.80 ± 0.07 (NT1-vs-rest), 0.65 ± 0.10/0.70 ± 0.09 (NT2-vs-IH), 0.72 ± 0.12/0.60 ± 0.10 (NT2-vs-sEDS), 0.54 ± 0.19/0.38 ± 0.13 (NT2-vs-rest), 0.57 ± 0.11/0.35 ± 0.18 (IH-vs-sEDS), 0.71 ± 0.08/0.35 ± 0.10 (IH-vs-rest) and 0.76 ± 0.08/0.71 ± 0.13 (sEDS-vs-rest). The results confirm previous findings on sleep instability in patients with NT1 and show that combining manual and automatic AI-based sleep analysis could be useful for better distinction of NT2 from IH, but no precise sleep biomarker of NT2 or IH could be identified. Validation in a larger and multi-centric cohort is needed to confirm these findings.

STUDY OBJECTIVES: The objective of this study was to evaluate interrater reliability between manual sleep stage scoring performed in 2 European sleep centers and automatic sleep stage scoring performed by the previously validated artificial intelligence–based Stanford-STAGES algorithm. METHODS: Full night polysomnographies of 1,066 participants were included. Sleep stages were manually scored in Berlin and Innsbruck sleep centers and automatically scored with the Stanford-STAGES algorithm. For each participant, we compared (1) Innsbruck to Berlin scorings (INN vs BER); (2) Innsbruck to automatic scorings (INN vs AUTO); (3) Berlin to automatic scorings (BER vs AUTO); (4) epochs where scorers from Innsbruck and Berlin had consensus to automatic scoring (CONS vs AUTO); and (5) both Innsbruck and Berlin manual scorings (MAN) to the automatic ones (MAN vs AUTO). Interrater reliability was evaluated with several measures, including overall and sleep stage-specific Cohen’s κ. RESULTS: Overall agreement across participants was substantial for INN vs BER (κ = 0.66 ± 0.13), INN vs AUTO (κ = 0.68 ± 0.14), CONS vs AUTO (κ = 0.73 ± 0.14), and MAN vs AUTO (κ = 0.61 ± 0.14), and moderate for BER vs AUTO (κ = 0.55 ± 0.15). Human scorers had the highest disagreement for N1 sleep (κ(N1) = 0.40 ± 0.16 for INN vs BER). Automatic scoring had lowest agreement with manual scorings for N1 and N3 sleep (κ(N1) = 0.25 ± 0.14 and κ(N3) = 0.42 ± 0.32 for MAN vs AUTO). CONCLUSIONS: Interrater reliability for sleep stage scoring between human scorers was in line with previous findings, and the algorithm achieved an overall substantial agreement with manual scoring. In this cohort, the Stanford-STAGES algorithm showed similar performances to the ones achieved in the original study, suggesting that it is generalizable to new cohorts. Before its integration in clinical practice, future independent studies should further evaluate it in other cohorts. CITATION: Cesari M, Stefani A, Penzel T, et al. Interrater sleep stage scoring reliability between manual scoring from two European sleep centers and automatic scoring performed by the artificial intelligence–based Stanford-STAGES algorithm. J Clin Sleep Med. 2021;17(6):1237–1247.

International audience; Objective: This multilanguage study used simple speech recording and high-end pattern analysis to provide sensitive and reliable noninvasive biomarkers of prodromal versus manifest α-synucleinopathy in patients with idiopathic rapid eye movement sleep behavior disorder (iRBD) and early-stage Parkinson disease (PD).Methods: We performed a multicenter study across the Czech, English, German, French, and Italian languages at 7 centers in Europe and North America. A total of 448 participants (337 males), including 150 with iRBD (mean duration of iRBD across language groups 0.5-3.4 years), 149 with PD (mean duration of disease across language groups 1.7-2.5 years), and 149 healthy controls were recorded; 350 of the participants completed the 12-month follow-up. We developed a fully automated acoustic quantitative assessment approach for the 7 distinctive patterns of hypokinetic dysarthria.Results: No differences in language that impacted clinical parkinsonian phenotypes were found. Compared with the controls, we found significant abnormalities of an overall acoustic speech severity measure via composite dysarthria index for both iRBD (p = 0.002) and PD (p < 0.001). However, only PD (p < 0.001) was perceptually distinct in a blinded subjective analysis. We found significant group differences between PD and controls for monopitch (p < 0.001), prolonged pauses (p < 0.001), and imprecise consonants (p = 0.03); only monopitch was able to differentiate iRBD patients from controls (p = 0.004). At the 12-month follow-up, a slight progression of overall acoustic speech impairment was noted for the iRBD (p = 0.04) and PD (p = 0.03) groups.Interpretation: Automated speech analysis might provide a useful additional biomarker of parkinsonism for the assessment of disease progression and therapeutic interventions. ANN NEUROL 2021;90:62-75.

Study objectives Rapid eye movement sleep behavior disorder (RBD) is frequent in dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD), and poses a risk of injury to patients and their bed partners. We assessed the efficacy of nelotanserin, a selective 5-HT (2A) inverse agonist, for symptomatic treatment of RBD using systematic video analysis. Methods This was a phase 2 multicenter study in DLB or PDD with video polysomnography (vPSG)-confirmed RBD. After a single-blind placebo run-in period, patients meeting eligibility criteria entered a 4-week double-blind treatment period (1:1 ratio with nelotanserin 80 mg/placebo). Whole-night vPSG was conducted during the run-in and at the end of the treatment period. Videos of all rapid eye movement (REM) sleep periods were analysed for RBD behaviors (movements and vocalizations) using the Innsbruck classification system by two of the central reviewers, and a third reviewer adjudicated ambiguous cases. Results 34 patients (N = 26 DLB, N = 8 PDD; 85.3% men; mean age 71.3 ± 6.36 years) were included in the analyses. Two (5.9%) patients were excluded due to protocol deviation in treatment compliance. Systematic video analysis demonstrated no difference between nelotanserin and placebo in RBD behaviors. Bland–Altman plot showed high interrater reliability. Conclusions Despite negative results, this is the first randomized, placebo-controlled study on symptomatic RBD treatment using objective outcome measures based on systematic video analysis. This study provides a new method for outcome research in RBD and proves that movement analysis is a feasible and meaningful outcome for studies evaluating changes in RBD severity. Clinical trial information ClinicalTrials.gov. NCT Number NCT02708186. https://clinicaltrials.gov/ct2/show/NCT02708186 .

Objective Sleep complaints are frequent after acute COVID-19. Aim of this study was to videopolysomnographically evaluate sleep and sleep disorders after SARS-Cov2 infection. Methods Patients with suspected sleep disorders after acute COVID-19 underwent video-polysomnography (v-PSG) at the Sleep Disorders Clinic, Department of Neurology, Medical University Innsbruck. V-PSG was conducted 4.2 (SD = 1.3) months after diagnosis of SARS-CoV-2 infection. Results Eleven patients [nine men, age 52.5 (SD = 11.7) years; BMI 29 (SD = 5.2) kg/m2] were included. At 60 days follow-up after diagnosis, persisting breathing complaints were present in 7/11 (64%) patients. After v-PSG four patients (36%) were diagnosed with obstructive sleep apnea (OSA). Respiratory frequency during sleep was normal and no tachypnea, thoracoabdominal asynchrony, or periodic deep sighing were detected. Four patients (36%) showed REM sleep without atonia (RWA), and two additional patients showed an RWA index within the highest range of normality. Conclusion We report videopolysomnographic findings in a series of eleven patients after acute COVID-19. A major finding of this study was the presence of isolated RWA, a recognized prodromal stage of RBD, in more than one third of the patients. Future videopolysomnographic investigations including quantification of RWA in patients after COVID-19 will give more insights into a possible acute or post-infectious CNS pathology related to the SARS-CoV-2 infection.

The role of central sleep apnea (CSA) in pacing-induced cardiomyopathy (PICM) remains speculative. In a prospective trial entitled UPGRADE, the presence of CSA was assessed by single-night polysomnography (PSG) in 54 PICM patients within 1 month after left ventricular lead implantation (with biventricular stimulation still not activated). CSA was diagnosed in half of patients (n = 27). Patients with moderate or severe CSA were randomized to cardiac resynchronization therapy (CRT) versus right ventricular pacing (RVP) in a double-blinded cross-over design and re-scheduled for a follow-up PSG within 3 to 5 months. After crossing-over of stimulation mode another PSG was conducted 3 to 5 months later. CRT led to a significant increase in left ventricular ejection fraction and significant reduction in left ventricular end systolic volumes and N-terminal pro brain natriuretic peptide plasma levels, whereas no significant effects were observed with ongoing RVP. CSA was significantly improved after 3.9 (3.2 to 4.4) months of CRT: apnea-hypopnea index decreased from 39.1 (32.1 to 54.0) events per hour at baseline to 22.2/h (10.9 to 36.7) by CRT (p

Background In current clinical practice, sleep is manually scored in discrete stages of 30-s duration. We hypothesize that modelling sleep automatically as continuous and dynamic process predicts healthy ageing better than traditional scoring. Methods Sleep electroencephalography of 15 young healthy subjects (aged ≤40 years) was used to train the modelling method. Each 3-s sleep mini-epoch was modelled as a probabilistic combination of wakefulness, light and deep sleep. For 79 healthy sleepers (aged 20–77 years), 15 sleep features were derived from manual traditional scoring (manual features), 7 from the automatic modelling (automatic features) and 24 from a combination of automatic modelling with traditional scoring (combined features). Age was predicted with seven multiple linear regression models with i) manual, ii) automatic, iii) combined, iv) manual + automatic, v) manual + combined, vi) automatic + combined, and vii) manual + automatic + combined sleep features. Using the same seven sleep feature groups, two support vector machine and one random forest classifiers were used to discriminate younger (aged Results The linear model and the classifiers using only manual features achieved the lowest values of adjusted coefficient of determination and classification validation accuracy (adj-R2 = 0.295, ACC = 63.00% ± 16.22%) compared to the ones using automatic (adj-R2 = 0.354, ACC = 65.83% ± 9.39%), combined (adj-R2 = 0.321, ACC = 63.42% ± 8.78%), manual + automatic (adj-R2 = 0.416, ACC = 67.00% ± 8.60%), manual + combined (adj-R2 = 0.355, ACC = 72.17% ± 12.90%), automatic + combined (adj-R2 = 0.448, ACC = 65.92% ± 7.97%), and manual + automatic + combined sleep features (adj-R2 = 0.464, ACC = 70.92% ± 10.33%). Conclusions Continuous and dynamic sleep modelling captures healthy ageing better than traditional sleep scoring.

Introduction Individuals with idiopathic rapid eye movement sleep behavior disorder (iRBD) are at high risk for a clinical diagnosis of an α‐synucleinopathy (aSN). They could serve as a key population for disease‐modifying trials. Abnormal dopamine transporter (DAT) imaging is a strong candidate biomarker for risk of aSN diagnosis in iRBD. Our primary objective was to identify a quantitative measure of DAT imaging that predicts diagnosis of clinically‐defined aSN in iRBD. Methods The sample included individuals with iRBD, early Parkinson’s Disease (PD), and healthy controls (HC) enrolled in the Parkinson Progression Marker Initiative, a longitudinal, observational, international, multicenter study. The iRBD cohort was enriched with individuals with abnormal DAT binding at baseline. Motor and nonmotor measures were compared across groups. DAT specific binding ratios (SBR) were used to calculate the percent of expected DAT binding for age and sex using normative data from HCs. Receiver operative characteristic analyses identified a baseline DAT binding cutoff that distinguishes iRBD participants diagnosed with an aSN in follow‐up versus those not diagnosed. Results The sample included 38 with iRBD, 205 with PD, and 92 HC who underwent DAT‐SPECT at baseline. Over 4.7 years of mean follow‐up, 14 (36.84%) with iRBD were clinically diagnosed with aSN. Risk of aSN diagnosis was significantly elevated among those with baseline putamen SBR ≤ 48% of that expected for age and sex, relative to those above this cutoff (hazard ratio = 17.8 [95%CI: 3.79–83.3], P = 0.0003). Conclusion We demonstrate the utility of DAT SBR to identify individuals with iRBD with increased short‐term risk of an aSN diagnosis.

STUDY OBJECTIVES: Reduced gray matter volume in the posterior cingulate cortex (PCC) has recently been found in patients with non–rapid eye movement (NREM) parasomnia, providing a neuroanatomical substrate for the arousal state dissociation. It remains unclear whether PCC changes in NREM parasomnias might also play a role in cognitive or affective dysfunction in these patients. The aim of this exploratory study was to investigate neurobehavioral correlates of PCC abnormalities in patients with NREM parasomnia. METHODS: The Reinforcement Sensitivity Theory of Personality Questionnaire and the Stress Coping Questionnaire were used to assess personality and stress coping in 15 patients with NREM parasomnia and 15 age- and sex-matched healthy controls. Patients’ left PCC gray matter volume was quantified with voxel-based morphometry on 3 Tesla T1-weighted magnetic resonance imaging data. RESULTS: In the Reinforcement Sensitivity Theory of Personality Questionnaire, increased trait reactivity of the behavioral inhibition system and goal-drive persistence contributed most to the discrimination of patients and controls. In the Stress Coping Questionnaire, patients showed an increased negative coping trait (ie, anxious rumination) related to an increase in adjusted left PCC volume. CONCLUSIONS: The results suggest subclinical behavioral abnormalities in patients with NREM parasomnias. Such traits might trigger maladaptive emotion regulation processes related to a relative PCC volume increase. The findings encourage further longitudinal studies on this topic, which can provide insights into the causal relations underlying the PCC volume–behavior correlation. Such future studies will have a more direct implication for the clinical management of patients with NREM parasomnias. CITATION: Ramm M, Urbanek A, Failing A, et al. Increased behavioral inhibition trait and negative stress coping in non–rapid eye movement parasomnias. J Clin Sleep Med. 2020;16(10):1737–1744.

Objective: Rapid eye movement sleep behavior disorder (RBD) is a prodromal synucleinopathy, as >80% will eventually convert to overt synucleinopathy. We performed an in-depth analysis of the SNCA locus to identify RBD-specific risk variants. Methods: Full sequencing and genotyping of SNCA was performed in isolated/idiopathic RBD (iRBD, n = 1,076), Parkinson disease (PD, n = 1,013), dementia with Lewy bodies (DLB, n = 415), and control subjects (n = 6,155). The iRBD cases were diagnosed with RBD prior to neurodegeneration, although some have since converted. A replication cohort from 23andMe of PD patients with probable RBD (pRBD) was also analyzed (n = 1,782 cases; n = 131,250 controls). Adjusted logistic regression models and meta-analyses were performed. Effects on conversion rate were analyzed in 432 RBD patients with available data using Kaplan–Meier survival analysis. Results: A 5′-region SNCA variant (rs10005233) was associated with iRBD (odds ratio [OR] = 1.43, p = 1.1E-08), which was replicated in pRBD. This variant is in linkage disequilibrium (LD) with other 5′ risk variants across the different synucleinopathies. An independent iRBD-specific suggestive association (rs11732740) was detected at the 3′ of SNCA (OR = 1.32, p = 4.7E-04, not statistically significant after Bonferroni correction). Homozygous carriers of both iRBD-specific SNPs were at highly increased risk for iRBD (OR = 5.74, p = 2E-06). The known top PD-associated variant (3′ variant rs356182) had an opposite direction of effect in iRBD compared to PD. Interpretation: There is a distinct pattern of association at the SNCA locus in RBD as compared to PD, with an opposite direction of effect at the 3′ of SNCA. Several 5′ SNCA variants are associated with iRBD and with pRBD in overt synucleinopathies. ANN NEUROL 2020;87:584–598.

Restless legs syndrome (RLS) is a common sleep-related movement disorder in populations of European descent and disease risk is strongly influenced by genetic factors. Common variants have been assessed extensively in several genome-wide association studies, but the contribution of rarer genetic variation has not been investigated at this scale. We therefore genotyped a case–control set of 9246 individuals for mainly rare and low frequency exonic variants using the Illumina ExomeChip. However, standard single variant and gene-level association tests were negative. This does not preclude a role of rare variants in RLS, but is likely due to the small sample size and the limited selection of rare genetic variation captured on the array. Therefore, exome or whole genome sequencing should be performed rather than increasing the sample size of ExomeChip studies in order to identify rare risk variants for RLS.