Effects of periodic breathing on sleep at high altitude: a randomized, placebo-controlled, crossover study using inspiratory CO2.

Hypoxia at high altitude facilitates changes in ventilatory control that can lead to nocturnal periodic breathing (nPB). Here, we introduce a placebo-controlled approach to prevent nPB by increasing inspiratory CO2 and used it to assess whether nPB contributes to the adverse effects of hypoxia on sleep architecture. In a randomized, single-blinded, crossover design, 12 men underwent two sojourns (three days/nights each, separated by 4 weeks) in hypobaric hypoxia corresponding to 4000 m altitude, with polysomnography during the first and third night of each sojourn. During all nights, subjects' heads were encompassed by a canopy retaining exhaled CO2 , and CO2 concentration in the canopy (i.e. inspiratory CO2 concentration) was controlled by adjustment of fresh air inflow. Throughout the placebo sojourn inspiratory CO2 was ≤0.2%, whereas throughout the other sojourn it was increased to 1.76% (IQR, 1.07%-2.44%). During the placebo sojourn, total sleep time (TST) with nPB was 54.3% (37.4%-80.8%) and 45.0% (24.5%-56.5%) during the first and the third night, respectively (P = 0.042). Increased inspiratory CO2 reduced TST with nPB by an absolute 38.1% (28.1%-48.1%), the apnoea-hypopnoea index by 58.1/h (40.1-76.1/h), and oxygen desaturation index ≥3% by 56.0/h (38.9.1-73.2/h) (all P < 0.001), whereas it increased the mean arterial oxygen saturation in TST by 2.0% (0.4%-3.5%, P = 0.035). Increased inspiratory CO2 slightly increased the percentage of N3 sleep during the third night (P = 0.045), without other effects on sleep architecture. Increasing inspiratory CO2 effectively prevented hypoxia-induced nPB without affecting sleep macro-architecture, indicating that nPB does not explain the sleep deterioration commonly observed at high altitudes. KEY POINTS: Periodic breathing is common during sleep at high altitude, and it is unclear how this affects sleep architecture. We developed a placebo-controlled approach to prevent nocturnal periodic breathing (nPB) with inspiratory CO2 administration and used it to assess the effects of nPB on sleep in hypobaric hypoxia. Nocturnal periodic breathing was effectively mitigated by an increased inspiratory CO2 fraction in a blinded manner. Prevention of nPB did not lead to relevant changes in sleep architecture in hypobaric hypoxia. We conclude that nPB does not explain the deterioration in sleep architecture commonly observed at high altitude.

Sleep features and long-term incident neurodegeneration: a polysomnographic study.

STUDY OBJECTIVES: Sleep is altered early in neurodegenerative diseases (NDDs) and may contribute to neurodegeneration. Long-term, large sample-size studies assessing NDDs association with objective sleep measures are scant. We aimed to investigate whether video-polysomnography (v-PSG)-based sleep features are associated with long-term NDDs incidence. METHODS: Retrospective cohort study of patients referred 2004-2007 to the Sleep Disorders Unit, Neurology, Medical University Innsbruck, Austria. All patients ≥ 18 years undergoing v-PSG and without NDDs at baseline or within 5 years were included. Main outcome was NDDs diagnosis ≥5 years after v-PSG. RESULTS: Of 1454 patients assessed for eligibility, 999 (68.7%) met inclusion criteria (68.3% men; median age 54.9 (IQR 33.9-62.7) years). Seventy-five patients (7.5%) developed NDDs and 924 (92.5%) remained disease-free after a median of 12.8 (IQR 9.9-14.6) years. After adjusting for demographic, sleep, and clinical covariates, a one-percentage decrease in sleep efficiency, N3-, or rapid-eye-movement (REM)-sleep was associated with 1.9%, 6.5%, or 5.2% increased risk of incident NDDs (HR 1.019, 1.065, and 1.052). One-percentage decrease in wake within sleep period time represented a 2.2% reduced risk of incident NDDs (HR 0.978). Random-forest analysis identified wake, followed by N3 and REM-sleep percentages, as the most important feature associated with NDDs diagnosis. Additionally, multiple sleep features combination improved discrimination of incident NDDs compared to individual sleep stages (concordance-index 0.72). CONCLUSIONS: These findings support contribution of sleep changes to NDDs pathogenesis and provide insights into the temporal window during which these differences are detectable, pointing to sleep as early NDDs marker and potential target of neuroprotective strategies.

A 24% prevalence of excessive fragmentary myoclonus in 500 consecutive sleep laboratory patients.

Excessive fragmentary myoclonus (EFM) is a frequent finding during routine video-polysomnography (VPSG). We aimed to automatically measure the prevalence of EFM according to current American Academy of Sleep Medicine (AASM) criteria, and the fragmentary myoclonus index (FMI) in sleep stage N1, N2, N3, rapid eye movement (REM) sleep and wake in a large patient population. A total of 500 VPSG recordings of patients admitted to the Sleep Laboratory, Department of Neurology, Medical University of Innsbruck, Austria, between May 1, 2022 and February 28, 2023, were included. EFM according to AASM criteria and FMI were computed by applying a previously validated algorithm. EFM was automatically detected in 121 of the 500 Sleep Laboratory patients (24.2%, 95% confidence interval 20.1%-28.9%). FMI increased with age, male gender, apnea-hypopnea-index (AHI), oxygen desaturation index (ODI), and periodic leg movements of sleep (PLMS) index. FMI was highest in REM sleep behaviour disorder (RBD), followed by neurodegenerative and internal medicine diseases, but the increase in the FMI was not explained by the disease itself but rather by the age and sex of the patients. Almost a quarter of our patient population had EFM. However, the prevalence of EFM does not allow the drawing of any conclusions about the pathophysiology of EFM or even the determination of a pathological FMI cut-off value. Associations of the FMI with age, sex, AHI, ODI and PLMS are in line with previous studies, but the FMI needs to be evaluated in different disease entities to learn more about its pathophysiology.

Sleep disorders as both risk factors for, and a consequence of, stroke: A narrative review.

BACKGROUND AND PURPOSE: Sleep disorders are increasingly implicated as risk factors for stroke, as well as a determinant of stroke outcome. They can also occur secondary to the stroke itself. In this review, we describe the variety of different sleep disorders associated with stroke and analyze their effect on stroke risk and outcome. METHODS: A search term-based literature review ("sleep," "insomnia," "narcolepsy," "restless legs syndrome," "periodic limb movements during sleep," "excessive daytime sleepiness" AND "stroke" OR "cerebrovascular" in PubMed; "stroke" and "sleep" in ClinicalTrials.gov) was performed. English articles from 1990 to March 2023 were considered. RESULTS: Increasing evidence suggests that sleep disorders are risk factors for stroke. In addition, sleep disturbance has been reported in half of all stroke sufferers; specifically, an increase is not only sleep-related breathing disorders but also periodic limb movements during sleep, narcolepsy, rapid eye movement (REM) sleep behavior disorder, insomnia, sleep duration, and circadian rhythm sleep-wake disorders. Poststroke sleep disturbance has been associated with worse outcome. CONCLUSION: Sleep disorders are risk factors for stroke and associated with worse stroke outcome. They are also a common consequence of stroke. Recent guidelines suggest screening for sleep disorders after stroke. It is possible that treatment of sleep disorders could both reduce stroke risk and improve stroke outcome, although further data from clinical trials are required.

Is REM Sleep Behavior Disorder Changing? Secular Changes Versus Referral Patterns.

Background: Since 2014, there has been increasing public outreach effort regarding isolated/idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) in Montreal. Objective: To assess if, over time, milder iRBD cases are presenting earlier. Methods: Disease-free survival was compared in two iRBD recruitment epochs: 2004 to 2013 ("earlier") versus 2014to 2022 ("later") and by referral type ("self-referral" vs. "conventional-referral") in three large centers. Results: In Montreal, among 209 subjects followed prospectively, shorter time to phenoconversion was observed in the earlier epoch (5-year phenoconversion = 42% earlier vs. 23% later); diagnosis before 2014 had a 1.8-fold phenoconversion hazard. However, no difference was observed in 248 subjects from Barcelona and 166 from Innsbruck. Analysis of Montreal data found that increased survival in the later epoch was driven by an increasing number of self-referrals, who phenoconverted at 1/3 the rate of physician-referred subjects. Conclusions: Increased patient awareness of iRBD results in earlier presentation to clinical attention, with a longer time to phenoconversion.

HLA in isolated REM sleep behavior disorder and Lewy body dementia.

Synucleinopathies-related disorders such as Lewy body dementia (LBD) and isolated/idiopathic REM sleep behavior disorder (iRBD) have been associated with neuroinflammation. In this study, we examined whether the human leukocyte antigen (HLA) locus plays a role in iRBD and LBD. In iRBD, HLA-DRB1*11:01 was the only allele passing FDR correction (OR = 1.57, 95% CI = 1.27-1.93, p = 2.70e-05). We also discovered associations between iRBD and HLA-DRB1 70D (OR = 1.26, 95%CI = 1.12-1.41, p = 8.76e-05), 70Q (OR = 0.81, 95%CI = 0.72-0.91, p = 3.65e-04) and 71R (OR = 1.21, 95%CI = 1.08-1.35, p = 1.35e-03). Position 71 (pomnibus = 0.00102) and 70 (pomnibus = 0.00125) were associated with iRBD. Our results suggest that the HLA locus may have different roles across synucleinopathies.

Improved automatic identification of isolated rapid eye movement sleep behavior disorder with a 3D time-of-flight camera.

BACKGROUND AND PURPOSE: Automatic 3D video analysis of the lower body during rapid eye movement (REM) sleep has been recently proposed as a novel tool for identifying people with isolated REM sleep behavior disorder (iRBD), but, so far, it has not been validated on unseen subjects. This study aims at validating this technology in a large cohort and at improving its performances by also including an analysis of movements in the head, hands and upper body. METHODS: Fifty-three people with iRBD and 128 people without RBD (of whom 89 had sleep disorders considered RBD differential diagnoses) were included in the study. An automatic algorithm identified movements from 3D videos during REM sleep in four regions of interest (ROIs): head, hands, upper body and lower body. The movements were divided into categories according to duration: short (0.1-2 s), medium (2-15 s) and long (15-300 s). For each ROI and duration range, features were obtained from the identified movements. Logistic regression models using as predictors the features from one single ROI or a combination of ROIs were trained and tested in a 10-runs 10-fold cross-validation scheme on the task of differentiating people with iRBD from people without RBD. RESULTS: The best differentiation was achieved using short movements in all four ROIs (test accuracy 0.866 ± 0.007, test F1 score = 0.783 ± 0.010). Single group analyses showed that people with iRBD were distinguished successfully from subjects with RBD differential diagnoses. CONCLUSIONS: Automatic 3D video analysis might be implemented in clinical routine as a supportive screening tool for identifying people with RBD.

Low Specificity of Rapid Eye Movement Sleep Behavior Disorder Questionnaires: Need for Better Screening Methods.

BACKGROUND: Correct diagnosis of rapid eye movement sleep behavior disorder (RBD) is critical due to its link to α-synucleinopathies and risk of injuries and requires video-polysomnography (V-PSG). Usefulness of screening questionnaires outside the context of validation studies is limited. OBJECTIVE: The aim was to assess the performance of three validated RBD screening questionnaires compared with gold-standard V-PSG. METHODS: In this bicentric prospective study, 400 consecutive subjects referred to a sleep center for the first time filled three RBD questionnaires (RBD Screening Questionnaire, RBD Single Question, and Innsbruck RBD Inventory) in random order before sleep experts' interview. Subjects positive for at least one questionnaire were invited to undergo V-PSG. Data from patients negative for all questionnaires undergoing V-PSG for other reasons were also evaluated. Questionnaire performances were compared to gold-standard V-PSG RBD diagnosis. RESULTS: Three hundred ninety-nine patients (median age: 51 [interquartile range: 37-64] years, 54.9% men) participated. Two hundred thirty-eight (59.6%) were positive for at least one questionnaire, and RBD was diagnosed using V-PSG in 30 patients (7.5%). Questionnaire specificity was 48.1% to 67.4%, sensitivity 80% to 92%, accuracy 51% to 68.3%, negative predictive value 94.2% to 98%, and positive predictive value 14.1% to 20.7%, with no relevant differences in performances among the evaluated questionnaires. CONCLUSIONS: RBD questionnaires have low specificity and low positive predictive value and should not be used as a standalone tool for the diagnosis of RBD. Further development of RBD screening methods is needed, particularly for upcoming neuroprotective trials. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

A reliable automatic algorithm to score fragmentary myoclonus.

Excessive fragmentary myoclonus (EFM) is an incidental polysomnographic finding requiring documentation of ≥20 minutes of NREM sleep with ≥5 fragmentary myoclonus (FM) potentials per minute. Manual FM scoring is time-consuming and prone to inter-rater variability. This work aimed to validate an automatic algorithm to score FM in whole-night recordings. One expert scorer manually scored FM in the anterior tibialis muscles in 10 polysomnographies of as many subjects. The algorithm consisted of two steps. First, parameters of the automatic leg movement identification algorithm of the BrainRT software (OSG, Belgium) were modified to identify FM-like activity. Second, a post-processing algorithm was implemented to remove FM activity not meeting sufficient amplitude criteria. The parameter choice and the post-processing were optimised with leave-one-out cross-validation. Agreement with the human scorer was measured with Cohen's kappa (k) and correlation between manual and automatic FM indices in different sleep stages was evaluated. Agreement in identifying patients with EFM was computed. The algorithm showed substantial agreement (average k > 0.62) for all sleep stages, except for W, where a moderate agreement was observed (average k = 0.58). Nonetheless, the agreement between human scorer and the algorithm was similar to previously reported values of inter-rater variability for FM scoring. Correlation coefficients were over 0.96 for all sleep stages. Furthermore, the presence/absence of EFM was correctly identified in 80% of the subjects. In conclusion, this work presents a reliable algorithm for automatic scoring of FM and EFM. Future studies will apply it to objectively and consistently evaluate FM indices and the presence of EFM in large populations.

Large muscle group movements during sleep in healthy people: normative values and correlation to sleep features.

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 < 0.001. LMMs associated with awakening increased with age (p = 0.029). LMMs indices in TST were higher in men than women (p = 0.018). LMMs indices correlated positively with N1 sleep percentage (ρ = 0.49, p < 0.001), arousal index (ρ = 0.40, p = 0.002), sleep stages shift index (ρ = 0.43, p < 0.001, apnea index (ρ = 0.36, p = 0.017), and video-visible movements indices (ρ = 0.45, p < 0.001), and negatively with N3 sleep (ρ = -0.38, p= 0.004) percentage. 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.

NPC1 variants are not associated with Parkinson's disease, REM-sleep behavior disorder or dementia with Lewy bodies in European cohorts.

NPC1 encodes a lysosomal protein involved in cholesterol transport. Biallelic mutations in this gene may lead to Niemann-Pick disease type C (NPC), a lysosomal storage disorder. The role of NPC1 in alpha synucleinopathies is still unclear, as different genetic, clinical, and pathological studies have reported contradictory results. This study aimed to evaluate the association of NPC1 variants with the synucleinopathies Parkinson's disease (PD), dementia with Lewy bodies (DLB), and rapid eye movement-sleep behavior disorder (RBD). We analyzed common and rare variants from 3 cohorts of European descent: 1084 RBD cases and 2945 controls, 2852 PD cases and 1686 controls, and 2610 DLB cases and 1920 controls. Logistic regression models were used to assess common variants while optimal sequence Kernel association tests were used to assess rare variants, both adjusted for sex, age, and principal components. No variants were associated with any of the synucleinopathies, supporting that common and rare NPC1 variants do not play an important role in alpha synucleinopathies.

HLA in isolated REM sleep behavior disorder and Lewy body dementia.

Background and Objectives: Isolated/idiopathic REM sleep behavior disorder (iRBD) and Lewy body dementia (LBD) are synucleinopathies that have partial genetic overlap with Parkinson's disease (PD). Previous studies have shown that neuroinflammation plays a substantial role in these disorders. In PD, specific residues of the human leukocyte antigen ( HLA ) were suggested to be associated with a protective effect. This study examined whether the HLA locus plays a similar role in iRBD, LBD and PD. Methods: We performed HLA imputation on iRBD genotyping data (1,072 patients and 9,505 controls) and LBD whole-genome sequencing (2,604 patients and 4,032 controls) using the multi-ethnic HLA reference panel v2 from the Michigan Imputation Server. Using logistic regression, we tested the association of HLA alleles, amino acids and haplotypes with disease susceptibility. We included age, sex and the top 10 principal components as covariates. We also performed an omnibus test to examine which HLA residue positions explain the most variance. Results: In iRBD, HLA-DRB1 *11:01 was the only allele passing FDR correction (OR=1.57, 95% CI=1.27-1.93, p =2.70e-05). We also discovered associations between iRBD and HLA-DRB1 70D (OR=1.26, 95%CI=1.12-1.41, p =8.76e-05), 70Q (OR=0.81, 95% CI=0.72-0.91, p =3.65e-04) and 71R (OR=1.21, 95% CI=1.08-1.35, p =1.35e-03). In HLA-DRB1 , position 71 ( p omnibus =0.00102) and 70 ( p omnibus =0.00125) were associated with iRBD. We found no association in LBD. Discussion: This study identified an association between HLA-DRB1 11:01 and iRBD, distinct from the previously reported association in PD. Therefore, the HLA locus may play different roles across synucleinopathies. Additional studies are required better to understand HLA's role in iRBD and LBD.

Differentiation of central disorders of hypersomnolence with manual and artificial-intelligence-derived polysomnographic measures.

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.

Genome-wide association study of REM sleep behavior disorder identifies polygenic risk and brain expression effects.

Rapid-eye movement (REM) sleep behavior disorder (RBD), enactment of dreams during REM sleep, is an early clinical symptom of alpha-synucleinopathies and defines a more severe subtype. The genetic background of RBD and its underlying mechanisms are not well understood. Here, we perform a genome-wide association study of RBD, identifying five RBD risk loci near SNCA, GBA, TMEM175, INPP5F, and SCARB2. Expression analyses highlight SNCA-AS1 and potentially SCARB2 differential expression in different brain regions in RBD, with SNCA-AS1 further supported by colocalization analyses. Polygenic risk score, pathway analysis, and genetic correlations provide further insights into RBD genetics, highlighting RBD as a unique alpha-synucleinopathy subpopulation that will allow future early intervention.

Myoclonus and other jerky movement disorders.

Myoclonus and other jerky movements form a large heterogeneous group of disorders. Clinical neurophysiology studies can have an important contribution to support diagnosis but also to gain insight in the pathophysiology of different kind of jerks. This review focuses on myoclonus, tics, startle disorders, restless legs syndrome, and periodic leg movements during sleep. Myoclonus is defined as brief, shock-like movements, and subtypes can be classified based the anatomical origin. Both the clinical phenotype and the neurophysiological tests support this classification: cortical, cortical-subcortical, subcortical/non-segmental, segmental, peripheral, and functional jerks. The most important techniques used are polymyography and the combination of electromyography-electroencephalography focused on jerk-locked back-averaging, cortico-muscular coherence, and the Bereitschaftspotential. Clinically, the differential diagnosis of myoclonus includes tics, and this diagnosis is mainly based on the history with premonitory urges and the ability to suppress the tic. Electrophysiological tests are mainly applied in a research setting and include the Bereitschaftspotential, local field potentials, transcranial magnetic stimulation, and pre-pulse inhibition. Jerks due to a startling stimulus form the group of startle syndromes. This group includes disorders with an exaggerated startle reflex, such as hyperekplexia and stiff person syndrome, but also neuropsychiatric and stimulus-induced disorders. For these disorders polymyography combined with a startling stimulus can be useful to determine the pattern of muscle activation and thus the diagnosis. Assessment of symptoms in restless legs syndrome and periodic leg movements during sleep can be performed with different validated scoring criteria with the help of electromyography.

TeaSpam: A Novel Method of TEmporal And SPAtial Movement Encoding during Sleep.

Movements during sleep characterize sleep disorders, which can disturb sleep or its onset, impacting sleep quantity and quality. Video-polysomnography is the current gold standard to assess movements during sleep, but its availability is limited. Using data recorded with a 3D time of flight sensor, we developed a novel method of encoding temporal and spatial information of automatically identified movements during sleep. In a cohort of 20 insomnia patients and 18 controls, we showed that this novel method holds important information able to discriminate the groups. Future studies will explore the methodology in the context of other sleep disorders.

Risk Factors for Phenoconversion in Rapid Eye Movement Sleep Behavior Disorder.

OBJECTIVE: This study was undertaken to follow up predictive factors for α-synuclein-related neurodegenerative diseases in a multicenter cohort of idiopathic/isolated rapid eye movement sleep behavior disorder (iRBD). METHODS: Patients with iRBD from 12 centers underwent a detailed assessment for potential environmental and lifestyle risk factors via a standardized questionnaire at baseline. Patients were then prospectively followed and received assessments for parkinsonism or dementia during follow-up. The cumulative incidence of parkinsonism or dementia was estimated with competing risk analysis. Cox regression analyses were used to evaluate the predictive value of environmental/lifestyle factors over a follow-up period of 11 years, adjusting for age, sex, and center. RESULTS: Of 319 patients who were free of parkinsonism or dementia, 281 provided follow-up information. After a mean follow-up of 5.8 years, 130 (46.3%) patients developed neurodegenerative disease. The overall phenoconversion rate was 24.2% after 3 years, 44.8% after 6 years, and 67.5% after 10 years. Patients with older age (adjusted hazard ratio [aHR] = 1.05) and nitrate derivative use (aHR = 2.18) were more likely to phenoconvert, whereas prior pesticide exposure (aHR = 0.21-0.64), rural living (aHR = 0.53), lipid-lowering medication use (aHR = 0.59), and respiratory medication use (aHR = 0.36) were associated with lower phenoconversion risk. Risk factors for those converting to primary dementia and parkinsonism were generally similar, with dementia-first converters having lower coffee intake and beta-blocker intake, and higher occurrence of family history of dementia. INTERPRETATION: Our findings elucidate the predictive values of environmental factors and comorbid conditions in identifying RBD patients at higher risk of phenoconversion. ANN NEUROL 2022;91:404-416.

Rare PSAP Variants and Possible Interaction with GBA in REM Sleep Behavior Disorder.

BACKGROUND: PSAP encodes saposin C, the co-activator of glucocerebrosidase, encoded by GBA. GBA mutations are associated with idiopathic/isolated REM sleep behavior disorder (iRBD), a prodromal stage of synucleinopathy. OBJECTIVE: To examine the role of PSAP mutations in iRBD. METHODS: We fully sequenced PSAP and performed Optimized Sequence Kernel Association Test in 1,113 iRBD patients and 2,324 controls. We identified loss-of-function (LoF) mutations, which are very rare in PSAP, in three iRBD patients and none in controls (uncorrected p = 0.018). RESULTS: Two variants were stop mutations, p.Gln260Ter and p.Glu166Ter, and one was an in-frame deletion, p.332_333del. All three mutations have a deleterious effect on saposin C, based on in silico analysis. In addition, the two carriers of p.Glu166Ter and p.332_333del mutations also carried a GBA variant, p.Arg349Ter and p.Glu326Lys, respectively. The co-occurrence of these extremely rare PSAP LoF mutations in two (0.2%) GBA variant carriers in the iRBD cohort, is unlikely to occur by chance (estimated co-occurrence in the general population based on gnomAD data is 0.00035%). Although none of the three iRBD patients with PSAP LoF mutations have phenoconverted to an overt synucleinopathy at their last follow-up, all manifested initial signs suggestive of motor dysfunction, two were diagnosed with mild cognitive impairment and all showed prodromal clinical markers other than RBD. Their probability of prodromal PD, according to the Movement Disorder Society research criteria, was 98% or more. CONCLUSION: These results suggest a possible role of PSAP variants in iRBD and potential genetic interaction with GBA, which requires additional studies.

Automatic 3D Video Analysis of Upper and Lower Body Movements to Identify Isolated REM Sleep Behavior Disorder: A Pilot Study.

Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by dream enactment, abnormal jerks and movements during REM sleep. Isolated RBD (iRBD) is recognized as the early stage of alpha-synucleinopathies, i.e. dementia with Lewy bodies, Parkinson's disease and multiple system atrophy. The certain diagnosis of iRBD requires video-polysomnography, evaluated by experts with time-consuming visual analyses. In this study, we propose automatic analysis of movements detected with 3D contactless video as a promising technology to assist sleep experts in the identification of patients with iRBD. By using automatically detected upper and lower body movements occurring during REM sleep with a duration between 4s and 5s, we could discriminate 20 iRBD patients from 24 patients with sleep-disordered breathing with an accuracy of 0.91 and F1-score of 0.90. This pilot study shows that 3D contactless video can be successfully used as a non-invasive technology to assist clinicians in identifying abnormal movements during REM sleep, and therefore to recognize patients with iRBD. Future investigations in larger cohorts are needed to validate the proposed technology and methodology.

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.

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 (κN1 = 0.40 ± 0.16 for INN vs BER). Automatic scoring had lowest agreement with manual scorings for N1 and N3 sleep (κN1 = 0.25 ± 0.14 and κN3 = 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.