Sleepwalking is a common non-rapid eye movement (NREM) parasomnia and a significant cause of sleep-related injuries. While evidence suggest that the occurrence of this condition is partly determined by genetic factors, its pattern of inheritance remains unclear, and few molecular studies have been conducted. One promising candidate is the adenosine deaminase (ADA) gene. Adenosine and the ADA enzyme play an important role in the homeostatic regulation of NREM sleep. In a single sleepwalking family, genome-wide analysis identified a locus on chromosome 20, where ADA lies. In this study, we examined if variants in the ADA gene were associated with sleepwalking. In total, 251 sleepwalking patients were clinically assessed, and DNA samples were compared to those from 94 unaffected controls. Next-generation sequencing of the whole ADA gene was performed. Bio-informatic analysis enabled the identification of variants and assessed variants enrichment in our cohort compared to controls. We detected 25 different coding and non-coding variants, of which 22 were found among sleepwalkers. None were enriched in the sleepwalking population. However, many missense variants were predicted as likely pathogenic by at least two in silico prediction algorithms. This study involves the largest sleepwalking cohort in which the role of a susceptibility gene was investigated. Our results did not reveal an association between ADA gene and sleepwalking, thus ruling out the possibility of ADA as a major genetic factor for this condition. Future work is needed to identify susceptibility genes.

STUDY OBJECTIVES:Sleep spindles, a defining feature of stage N2 sleep, are maximal at central electrodes and are found in the frequency range of the electroencephalogram (EEG) (sigma 11-16 Hz) that is known to be heritable. However, relatively little is known about the heritability of spindles. Two recent studies investigating the heritability of spindles reported moderate heritability, but with conflicting results depending on scalp location and spindle type. The present study aimed to definitively assess the heritability of sleep spindle characteristics.METHODS:We utilized the polysomnography data of 58 monozygotic and 40 dizygotic same-sex twin pairs to identify heritable characteristics of spindles at C3/C4 in stage N2 sleep including density, duration, peak-to-peak amplitude, and oscillation frequency. We implemented and tested a variety of spindle detection algorithms and used two complementary methods of estimating trait heritability.RESULTS:We found robust evidence to support strong heritability of spindles regardless of detector method (h2 > 0.8). However not all spindle characteristics were equally heritable, and each spindle detection method produced a different pattern of results.CONCLUSIONS:The sleep spindle in stage N2 sleep is highly heritable, but the heritability differs for individual spindle characteristics and depends on the spindle detector used for analysis.

Spindle event detection is a key component in analyzing human sleep. However, detection of these oscillatory patterns by experts is time consuming and costly. Automated detection algorithms are cost efficient and reproducible but require robust datasets to be trained and validated. Using the MODA (Massive Online Data Annotation) platform, we used crowdsourcing to produce a large open-source dataset of high quality, human-scored sleep spindles (5342 spindles, from 180 subjects). We evaluated the performance of three subtype scorers: "experts, researchers and non-experts", as well as 7 previously published spindle detection algorithms. Our findings show that only two algorithms had performance scores similar to human experts. Furthermore, the human scorers agreed on the average spindle characteristics (density, duration and amplitude), but there were significant age and sex differences (also observed in the set of detected spindles). This study demonstrates how the MODA platform can be used to generate a highly valid open source standardized dataset for researchers to train, validate and compare automated detectors of biological signals such as the EEG.

Study objectivesUntreated obstructive sleep apnea (OSA) patients have an increased risk of cardiovascular disease (CVD). Adhesion molecules, including soluble E-selectin (sE-selectin), intercellular adhesion molecule-1 (ICAM-1), and vascular adhesion molecule-1 (VCAM-1), are associated with incident CVD. We hypothesized that specific genetic variants will be associated with plasma levels of adhesion molecules in suspected OSA patients. We also hypothesized that there may be an interaction between these variants and OSA.MethodsWe measured levels of sE-selectin, sICAM-1 and sVCAM-1 in 491 patients with suspected OSA and genotyped them for 20 polymorphisms.ResultsThe most significant association was between the ABO rs579459 polymorphism and sE-selectin levels (P = 7×10-21), with the major allele T associated with higher levels. The direction of effect and proportion of the variance in sE-selectin levels accounted for by rs579459 (16%) was consistent with estimates from non-OSA cohorts. In a multivariate regression analysis, addition of rs579459 improved the model performance in predicting sE-selectin levels. Three polymorphisms were nominally associated with sICAM-1 levels but none with sVCAM-1 levels. The combination of severe OSA and two rs579459 T alleles identified a group of patients with high sE-selectin levels; however, the increase in sE-selectin levels associated with severe OSA was greater in patients without two T alleles (P = 0.05 test for interaction).ConclusionsThese genetic polymorphisms may help to identify patients at greatest risk of incident CVD and may help in developing a more precision-based approach to OSA care.

Background: Sleep spindles are a marker of stage 2 NREM sleep that are linked to learning & memory and are altered by many neurological diseases. Although visual inspection of the EEG is considered the gold standard for spindle detection, it is time-consuming, costly and can introduce inter/ra-scorer bias. New method: Our goal was to develop a simple and efficient sleep-spindle detector (algorithm #7, or 'A7') that emulates human scoring. 'A7' runs on a single EEG channel and relies on four parameters: the absolute sigma power, relative sigma power, and correlation/covariance of the sigma band-passed signal to the original EEG signal. To test the performance of the detector, we compared it against a gold standard spindle dataset derived from the consensus of a group of human experts. Results: The by-event performance of the 'A7' spindle detector was 74% precision, 68% recall (sensitivity), and an F1-score of 0.70. This performance was equivalent to an individual human expert (average F1-score = 0.67). Comparison with existing method(s): The F1-score of 'A7' was 0.17 points higher than other spindle detectors tested. Existing detectors have a tendency to find large numbers of false positives compared to human scorers. On a by-subject basis, the spindle density estimates produced by A7 were well correlated with human experts (r(2) = 0.82) compared to the existing detectors (average r(2) = 0.27). Conclusions: The 'A7' detector is a sensitive and precise tool designed to emulate human spindle scoring by minimizing the number of 'hidden spindles' detected. We provide an open-source implementation of this detector for further use and testing.

Epidemiologic and mechanistic evidence is increasingly supporting the notion that obstructive sleep apnea is a risk factor for dementia. Hence, the identification of patients at risk of cognitive decline due to obstructive sleep apnea may significantly improve preventive strategies and treatment decision-making. Cerebrospinal fluid and blood biomarkers obtained through genomic, proteomic and metabolomic approaches are improving the ability to predict incident dementia. Therefore, fluid biomarkers have the potential to predict vulnerability to neurodegeneration in individuals with obstructive sleep apnea, as well as deepen our understanding of pathophysiological processes linking obstructive sleep apnea and dementia. Many fluid biomarkers linked to Alzheimer's disease and vascular dementia show abnormal levels in individuals with obstructive sleep apnea, suggesting that these conditions share common underlying mechanisms, including amyloid and tau protein neuropathology, inflammation, oxidative stress, and metabolic disturbances. Markers of these processes include amyloid-β, tau proteins, inflammatory cytokines, acute-phase proteins, antioxydants and oxidized products, homocysteine and clusterin (apolipoprotein J). Thus, these biomarkers may have the ability to identify adults with obstructive sleep apnea at high risk of dementia and provide an opportunity for therapeutic intervention. Large cohort studies are necessary to establish a specific fluid biomarker panel linking obstructive sleep apnea to dementia risk.

Sleep spindles are a marker of stage 2 NREM sleep, have been linked to the process of learning & memory, and are altered by many neurological diseases. For human clinical polysomnography, the visual scoring of sleep spindles by human experts is generally considered the gold standard, but it is time-consuming, costly and can introduce inter/ra-scorer bias. Automated spindle detection methods are efficient and reproducible, but are not well-correlated with human scoring. Typically, automated detectors find large numbers of false positives (‘hidden spindles’) relative to human scorers. While it is plausible that the false positives are biologically meaningful, these ‘hidden spindles’ present several problems, including: i) Lack of gold standard for ‘hidden spindles’; ii) Lack of agreement between automated detectors for ‘hidden spindles’; iii) ‘Hidden spindles’ can be found throughout NREM, REM and wake, and therefore no longer are consistent with the original concept of the sleep spindle. To reduce the problem of ‘hidden spindles’, we have developed an automated spindle detector (‘A7’) that emulates how a human scores spindles. The ‘A7’ detector relies on the correlation/covariation of the sigma band-passed signal to the original broadband filtered (0.3-30Hz) EEG signal. To test the performance of the detector, we compared it against a gold standard spindle dataset derived from the consensus of a crowd-sourced group of human experts. The by-event performance of the ‘A7’ spindle detector was similar to individual experts (f1 score: 0.70 vs 0.67) against the consensus of a group of human experts. This was 0.17 points higher than other spindle detectors we tested. The ‘A7’ detector is designed to emulate human spindle scoring by minimizing the number of ‘hidden spindles’ detected and thereby detecting spindles that have the highest signal/noise ratio. We provide an open-source implementation of this detector for further use and testing. Funding for this work was provided by the Chaire Pfizer, Bristol-Myers Squibb, SmithKline Beecham, Eli Lilly en psychopharmacologie de l’ Université de Montréal, the Centre de Recherche Hôpital du Sacré-Coeur de Montréal, and the Canadian Institutes of Health Research (CIHR).

Two genome-wide association studies (GWAS) suggest that insomnia and restless legs syndrome (RLS) share a common genetic basis. While the identified genetic variation in the MEIS1 gene was previously associated with RLS, the two GWAS suggest a novel and independent association with insomnia symptoms. To test the potential pleiotropic effect of MEIS1, we genotyped three MEIS1 variants in 646 chronic insomnia disorder (CID) patients with and without RLS. To confirm our results, we compared the allelic and genotypic distributions of the CID cohort with ethnically matched controls and RLS cases in the French Canadian cohort. The CID cohort was diagnosed by sleep medicine specialists and 26% of the sample received the combined diagnosis of CID+RLS. We find significant differences in allele and genotype distributions between CID-only and CID+RLS groups, suggesting that MEIS1 is only associated with RLS. Genotype distributions and minor allele frequencies of the three MEIS1 SNPs of the CID-only and control groups were similar (rs113851554: 5.3% vs. 5.6%; rs2300478: 25.3% vs. 26.5%; rs12469063: 23.6% vs. 24.4%; all p > 0.05). Likewise, there were no differences between CID+RLS and RLS-only groups (all p > 0.05). In conclusion, our data confirms that MEIS1 is a genetic risk factor for the development of RLS, but it does not support the pleiotropic effect of MEIS1 in CID. While a lack of power precluded us from refuting small pleiotropic effects, our findings emphasize the critical importance of isolating CID from other disorders that can cause sleep difficulties, particularly RLS, for future genetic studies.

Chronic insomnia disorder (CID) and restless leg syndrome (RLS) are two common sleep disorders in the general population and are frequently comorbid. Two recent genome-wide assocaition studies (GWAS) have suggested that MEIS1 gene is a shared genetic basis between these two disorders, which is an intriguing finding that can change our understanding of the etiology of insomnia. While GWAS play a critical role in advancing our knowledge of sleep disorders, independent replication is a cardinal part of the GWAS methodology. Hence, the objective of our study is to evaluate the assoaiction between CID and MEIS1. We genotyped three MEIS1 variants in 705 CID patients with and without RLS to test for the suggested independent effect of MEIS1. Subjects were assigned to insomnia-only or insomnia+RLS groups by sleep medicine specialists. To confirm our genotyping results, we compared the allelic and genotypic distributions of our CID patients with ethnically matched controls and RLS cases. Polysomnography was used to quantify periodic leg movements in sleep, which supports the diagnosis of RLS, and to screen for apnea-hypopnea index. Overall, 27% of the cohort was diagnosed with insomnia+RLS. Genotyping results have shown that the three MEIS1 gene variants are only associated to the presence of RLS (all p-value<0.05). The comparison of the allele and the genotype distributions of the CID cohort to the ethnically matched controls and RLS cases confirmed our finding by showing the genotyping distribution similarities between the insomnia-only and the control groups and between the insomnia+RLS and RLS-only groups. Hence, our data does not support the pleiotropic effect of MEIS1 gene, suggesting that this gene is only associated to RLS. It is now clear for future genetic studies the need to isolate CID from other disorders that can cause sleep difficulties, particularly RLS. NA.

Obstructive sleep apnea (OSA) is a common heritable disorder displaying marked sexual dimorphism in disease prevalence and progression. Previous genetic association studies have identified a few genetic loci associated with OSA and related quantitative traits, but they have only focused on single ethnic groups, and a large proportion of the heritability remains unexplained. The apnea-hypopnea index (AHI) is a commonly used quantitative measure characterizing OSA severity. Because OSA differs by sex, and the pathophysiology of obstructive events differ in rapid eye movement (REM) and non-REM (NREM) sleep, we hypothesized that additional genetic association signals would be identified by analyzing the NREM/REM-specific AHI and by conducting sex-specific analyses in multiethnic samples. We performed genomewide association tests for up to 19,733 participants of African, Asian, European, and Hispanic/Latino American ancestry in 7 studies. We identified rs12936587 on chromosome 17 as a possible quantitative trait locus for NREM AHI in men (N = 6,737; P = 1.7310(-8)) but not in women (P = 0.77). The association with NREMAHI was replicated in a physiological research study (N = 67; P = 0.047). This locus overlapping the RAI1 gene and encompassing genes PEMT1, SREBF1, and RASD1 was previously reported to be associated with coronary artery disease, lipid metabolism, and implicated in Potocki-Lupski syndrome and Smith-Magenis syndrome, which are characterized by abnormal sleep phenotypes. We also identified gene-by-sex interactions in suggestive association regions, suggesting that genetic variants for AHI appear to vary by sex, consistent with the clinical observations of strong sexual dimorphism.

Book 2nd International Conference on Sleep Spindling and Related Phenomena May 24–26, 2018 • Budapest, Hungary Danubius Hotel Gellért Szent Gellért tér 2, H-1114 Budapest, Hungary Akadémiai Kiadó / AKCongress P.O.Box 245, H-1519 Budapest, Hungary

Wakefulness and sleep are dynamic states during which brain functioning is modified and shaped. Sleep loss is detrimental to many brain functions and results in structural changes localized at synapses in the nervous system. In this review, we present and discuss some of the latest observations of structural changes following sleep loss in some vertebrates and insects. We also emphasize that these changes are region-specific and cell type-specific and that, most importantly, these structural modifications have functional roles in sleep regulation and brain functions. Selected mechanisms driving structural modifications occurring with sleep loss are also discussed. Overall, recent research highlights that extending wakefulness impacts synapse number and shape, which in turn regulate sleep need and sleep-dependent learning/memory.

Background: A significant proportion of individuals with REM sleep behavior disorder (RBD) will progress to dementia with Lewy bodies (DLB). We aimed to examine whether the APOE ε4 allele, associated with DLB, is also associated with idiopathic RBD. Methods: The two SNPs tagging the different APOE alleles (rs429358 and rs7412) were genotyped in individuals who were initially diagnosed with RBD (n=480) and in controls (n=823). Results: APOE ε4 allele frequency was 0.14 among RBD patients and 0.13 among controls (OR=1.11, 95% CI 0.88-1.40, p=0.41), and this lack of association remained after adjustment for age and sex. Furthermore, allele frequencies of APOE ε4 were similar in those who converted to DLB (0.14) and those who converted to Parkinson’s disease (0.12) or multiple system atrophy (0.14, p=1.0). Conclusions: The APOE ε4 allele is neither a risk factor for RBD nor it is associated with conversion from RBD to DLB or other synucleinopathies.

The present study aimed to examine whether the APOE epsilon 4 allele, associated with dementia with Lewy bodies (DLB), and possibly with dementia in Parkinson's disease (PD), is also associated with idiopathic rapid eye movement sleep behavior disorder (RBD). Two single nucleotide polymorphisms, rs429358 and rs7412, were genotyped in RBD patients (n = 480) and in controls (n = 823). APOE epsilon 4 allele frequency was 0.14 among RBD patients and 0.13 among controls (OR = 1.11, 95% CI: 0.88-1.40, p = 0.41). APOE epsilon 4 allele frequencies were similar in those who converted to DLB (0.14) and those who converted to Parkinson's disease (0.12) or multiple system atrophy ( 0.14, p = 1.0). The APOE epsilon 4 allele is neither a risk factor for RBD nor it is associated with conversion from RBD to DLB or other synucleinopathies. (C) 2016 Elsevier Inc. All rights reserved.