Comparative Study of Paraneoplastic and Nonparaneoplastic Autoimmune Encephalitis With GABABR Antibodies.

BACKGROUND AND OBJECTIVES: While patients with paraneoplastic autoimmune encephalitis (AE) with gamma-aminobutyric-acid B receptor antibodies (GABABR-AE) have poor functional outcomes and high mortality, the prognosis of nonparaneoplastic cases has not been well studied. METHODS: Patients with GABABR-AE from the French and the Dutch Paraneoplastic Neurologic Syndromes Reference Centers databases were retrospectively included and their data collected; the neurologic outcomes of paraneoplastic and nonparaneoplastic cases were compared. Immunoglobulin G (IgG) isotyping and human leukocyte antigen (HLA) genotyping were performed in patients with available samples. RESULTS: A total of 111 patients (44/111 [40%] women) were enrolled, including 84 of 111 (76%) paraneoplastic and 18 of 111 (16%) nonparaneoplastic cases (cancer status was undetermined for 9 patients). Patients presented with seizures (88/111 [79%]), cognitive impairment (54/111 [49%]), and/or behavioral disorders (34/111 [31%]), and 54 of 111 (50%) were admitted in intensive care unit (ICU). Nonparaneoplastic patients were significantly younger (median age 54 years [range 19-88] vs 67 years [range 50-85] for paraneoplastic cases, p < 0.001) and showed a different demographic distribution. Nonparaneoplastic patients more often had CSF pleocytosis (17/17 [100%] vs 58/78 [74%], p = 0.02), were almost never associated with KTCD16-abs (1/16 [6%] vs 61/70 [87%], p < 0.001), and were more frequently treated with second-line immunotherapy (11/18 [61%] vs 18/82 [22%], p = 0.003). However, no difference of IgG subclass or HLA association was observed, although sample size was small (10 and 26 patients, respectively). After treatment, neurologic outcome was favorable (mRS ≤2) for 13 of 16 (81%) nonparaneoplastic and 37 of 84 (48%) paraneoplastic cases (p = 0.03), while 3 of 18 (17%) and 42 of 83 (51%) patients had died at last follow-up (p = 0.008), respectively. Neurologic outcome no longer differed after adjustment for confounding factors but seemed to be negatively associated with increased age and ICU admission. A better survival was associated with nonparaneoplastic cases, a younger age, and the use of immunosuppressive drugs. DISCUSSION: Nonparaneoplastic GABABR-AE involved younger patients without associated KCTD16-abs and carried better neurologic and vital prognoses than paraneoplastic GABABR-AE, which might be due to a more intensive treatment strategy. A better understanding of immunologic mechanisms underlying both forms is needed.

Strengths, weaknesses, opportunities and threats of using AI-enabled technology in sleep medicine: a commentary.

Over the past few years, artificial intelligence (AI) has emerged as a powerful tool used to efficiently automate several tasks across multiple domains. Sleep medicine is perfectly positioned to leverage this tool due to the wealth of physiological signals obtained through sleep studies or sleep tracking devices and abundance of accessible clinical data through electronic medical records. However, caution must be applied when utilizing AI, due to intrinsic challenges associated with novel technology. The Artificial Intelligence in Sleep Medicine committee of the American Academy of Sleep Medicine (AASM) reviews advancements in AI within the sleep medicine field. In this article, the Artificial Intelligence in Sleep Medicine committee members provide a commentary on the scope of AI technology in sleep medicine. The commentary identifies three pivotal areas in sleep medicine which can benefit from AI technologies: clinical care, lifestyle management and population health management. This article provides a detailed analysis of the strengths, weaknesses, opportunities, and threats associated with using AI enabled technologies in each pivotal area. Finally, the article broadly reviews barriers and challenges associated with using AI enabled technologies and offers possible solutions.

A deep transfer learning approach for sleep stage classification and sleep apnea detection using wrist-worn consumer sleep technologies.

Obstructive sleep apnea (OSA) is a common, underdiagnosed sleep-related breathing disorder with serious health implications Objective - We propose a deep transfer learning approach for sleep stage classification and sleep apnea (SA) detection using wrist-worn consumer sleep technologies (CST). Methods - Our model is based on a deep convolutional neural network (DNN) utilizing accelerometers and photo-plethysmography signals from nocturnal recordings. The DNN was trained and tested on internal datasets that include raw data from clinical and wrist-worn devices; external validation was performed on a hold-out test dataset containing raw data from a wrist-worn CST. Results - Training on clinical data improves performance significantly, and feature enrichment through a sleep stage stream gives only minor improvements. Raw data input outperforms feature-based input in CST datasets. The system generalizes well but performs slightly worse on wearable device data compared to clinical data. However, it excels in detecting events during REM sleep and is associated with arousal and oxygen desaturation. We found; cases that were significantly underestimated were characterized by fewer of such event associations. Conclusion - This study showcases the potential of using CSTs as alternate screening solution for undiagnosed cases of OSA. Significance - This work is significant for its development of a deep transfer learning approach using wrist-worn consumer sleep technologies, offering comprehensive validation for data utilization, and learning techniques, ultimately improving sleep apnea detection across diverse devices.

HLA-DQB1*05 subtypes and not DRB1*10:01 mediates risk in anti-IgLON5 disease.

Anti-IgLON5 disease is a rare and likely underdiagnosed subtype of autoimmune encephalitis. The disease displays a heterogeneous phenotype that includes sleep, movement, and bulbar-associated dysfunction. Presence of IgLON5-antibodies in CSF/serum, together with a strong association with HLA-DRB1*10:01∼DQB1*05:01, support an autoimmune basis. In this study, a multicentric HLA study of 87 anti-IgLON5 patients revealed a stronger association with HLA-DQ than HLA-DR. Specifically, we identified a predisposing rank-wise association with HLA-DQA1*01:05∼DQB1*05:01, HLA-DQA1*01:01∼DQB1*05:01 and HLA-DQA1*01:04∼DQB1*05:03 in 85% of patients. HLA sequences and binding cores for these three DQ heterodimers were similar, unlike those of linked DRB1 alleles, supporting a causal link to HLA-DQ. This association was further reflected in an increasingly later age of onset across each genotype group, with a delay of up to 11 years, while HLA-DQ-dosage dependent effects were also suggested by reduced risk in the presence of non-predisposing DQ1 alleles. The functional relevance of the observed HLA-DQ molecules was studied with competition binding assays. These proof-of-concept experiments revealed preferential binding of IgLON5 in a post-translationally modified, but not native, state to all three risk-associated HLA-DQ receptors. Further, a deamidated peptide from the Ig2-domain of IgLON5 activated T cells in two patients, compared to one control carrying HLA-DQA1*01:05∼DQB1*05:01. Taken together, these data support a HLA-DQ-mediated T cell response to IgLON5 as a potentially key step in the initiation of autoimmunity in this disease.

Fatigued but not sleepy? An empirical investigation of the differentiation between fatigue and sleepiness in sleep disorder patients in a cross-sectional study.

OBJECTIVE: Sleepiness and fatigue are common complaints among individuals with sleep disorders. The two concepts are often used interchangeably, causing difficulty with differential diagnosis and treatment decisions. The current study investigated sleep disorder patients to determine which factors best differentiated sleepiness from fatigue. METHODS: The study used a subset of participants from a multi-site study (n = 606), using a cross-sectional study design. We selected 60 variables associated with either sleepiness or fatigue, including demographic, mental health, and lifestyle factors, medical history, sleep questionnaires, rest-activity rhythms (actigraphy), polysomnographic (PSG) variables, and sleep diaries. Fatigue was measured with the Fatigue Severity Scale and sleepiness was measured with the Epworth Sleepiness Scale. A Random Forest machine learning approach was utilized for analysis. RESULTS: Participants' average age was 47.5 years (SD 14.0), 54.6% female, and the most common sleep disorder diagnosis was obstructive sleep apnea (67.4%). Sleepiness and fatigue were moderately correlated (r = 0.334). The model for fatigue (explained variance 49.5%) indicated depression was the strongest predictor (relative explained variance 42.7%), followed by insomnia severity (12.3%). The model for sleepiness (explained variance 17.9%), indicated insomnia symptoms was the strongest predictor (relative explained variance 17.6%). A post hoc receiver operating characteristic analysis indicated depression could be used to discriminate fatigue (AUC = 0.856) but not sleepiness (AUC = 0.643). CONCLUSIONS: The moderate correlation between fatigue and sleepiness supports previous literature that the two concepts are overlapping yet distinct. Importantly, depression played a more prominent role in characterizing fatigue than sleepiness, suggesting depression could be used to differentiate the two concepts.

Stability and Volatility of Human Rest-Activity Rhythms: Insights from Very Long Actograms (VLAs).

Importance: Wrist-worn activity monitors provide biomarkers of health by non-obtrusively measuring the timing and amount of rest and physical activity (rest-activity rhythms, RARs). The morphology and robustness of RARs vary by age, gender, and sociodemographic factors, and are perturbed in various chronic illnesses. However, these are cross-sectionally derived associations from recordings lasting 4-10 days, providing little insights into how RARs vary with time. Objective: To describe how RAR parameters can vary or evolve with time (~months). Design Setting and Participants: 48 very long actograms ("VLAs", ≥90 days in duration) were identified from subjects enrolled in the STAGES (Stanford Technology, Analytics and Genomics in Sleep) study, a prospective cross-sectional, multi-site assessment of individuals > 13 years of age that required diagnostic polysomnography to address a sleep complaint. A single 3-year long VLA (author GD) is also described. Exposures/Intervention: None planned. Main Outcomes and Measures: For each VLA, we assessed the following parameters in 14-day windows: circadian/ultradian spectrum, pseudo-F statistic ("F"), cosinor amplitude, intradaily variability, interdaily stability, acrophase and estimates of "sleep" and non-wearing. Results: Included STAGES subjects (n = 48, 30 female) had a median age of 51, BMI of 29.4kg/m2, Epworth Sleepiness Scale score (ESS) of 10/24 and a median recording duration of 120 days. We observed marked within-subject undulations in all six RAR parameters, with many subjects displaying ultradian rhythms of activity that waxed and waned in intensity. When appraised at the group level (nomothetic), averaged RAR parameters remained remarkably stable over a ~4 month recording period. Cohort-level deficits in average RAR robustness associated with unemployment or high BMI (>29.4) also remained stable over time. Conclusions and Relevance: Through an exemplary set of months-long wrist actigraphy recordings, this study quantitatively depicts the longitudinal stability and dynamic range of human rest-activity rhythms. We propose that continuous and long-term actigraphy may have broad potential as a holistic, transdiagnostic and ecologically valid monitoring biomarker of changes in chronobiological health. Prospective recordings from willing subjects will be necessary to precisely define contexts of use.

Validation of the RBD Symptom Severity Scale in the North American Prodromal Synucleinopathy Consortium.

BACKGROUND AND OBJECTIVES: REM sleep behavior disorder (RBD) is a parasomnia characterized by dream enactment. The International RBD Study Group developed the RBD Symptom Severity Scale (RBDSSS) to assess symptom severity for clinical or research use. We assessed the psychometric and clinimetric properties of the RBDSSS in participants enrolled in the North American Prodromal Synucleinopathy (NAPS) Consortium for RBD. METHODS: NAPS participants, who have polysomnogram-confirmed RBD, and their bedpartners completed the RBDSSS (participant and bedpartner versions). The RBDSSS contains 8 questions to assess the frequency and severity/impact of (1) dream content, (2) vocalizations, (3) movements, and (4) injuries associated with RBD. Total scores for participant (maximum score = 54) and bedpartner (maximum score = 38) questionnaires were derived by multiplying frequency and severity scores for each question. The Clinical Global Impression Scale of Severity (CGI-S) and RBD symptom frequency were assessed by a physician during a semistructured clinical interview with participants and, if available, bedpartners. Descriptive analyses, correlations between overall scores, and subitems were assessed, and item response analysis was performed to determine the scale's validity. RESULTS: Among 261 study participants, the median (interquartile range) score for the RBDSSS-PT (participant) was 10 (4-18) and that for the RBDSSS-BP (bedpartner) was 8 (4-15). The median CGI-S was 3 (3-4), indicating moderate severity. RBDSSS-BP scores were significantly lower in women with RBD (6 vs 9, p = 0.02), while there were no sex differences in RBDSSS-PT scores (8 vs 10.5, p = 0.615). Positive correlations were found between RBDSSS-PT vs RBDSSS-BP (Spearman rs = 0.561), RBDSSS-PT vs CGI-S (rs = 0.556), and RBDSSS-BP vs CGI-S (rs = 0.491, all p < 0.0001). Item response analysis showed a high discriminatory value (range 1.40-2.12) for the RBDSSS-PT and RBDSSS-BP (1.29-3.47). DISCUSSION: We describe the RBDSSS with adequate psychometric and clinimetric properties to quantify RBD symptom severity and good concordance between participant and bedpartner questionnaires and between RBDSSS scores and clinician-assessed global severity.

Prevalence and Course of Idiopathic Hypersomnia in the Wisconsin Sleep Cohort Study.

BACKGROUND AND OBJECTIVES: Idiopathic hypersomnia (IH) is a CNS disorder of hypersomnolence of unknown etiology. Due to the requirement for objective sleep testing to diagnose the disorder, there are currently no population-based estimates of the prevalence of IH nor data regarding the longitudinal course of IH in naturalistic settings. METHODS: Subjective and objective data from the Wisconsin Sleep Cohort study were used to identify cases with probable IH from participants with polysomnography and multiple sleep latency test data. Demographic, polysomnographic, and symptom-level data were compared between those with and without IH. Longitudinal trajectories of daytime sleepiness among those with IH were assessed to evaluate symptom persistence or remission over time. RESULTS: From 792 cohort study participants with available polysomnography and multiple sleep latency test data, 12 cases with probable IH were identified resulting in an estimated prevalence of IH of 1.5% (95% CI 0.7-2.5, p < 0.0001). Consistent with inclusion/exclusion criteria, cases with IH had more severe sleepiness and sleep propensity, despite similar or longer sleep times. Longitudinal data (spanning 12.1 ± 4.3 years) demonstrated a chronic course of sleepiness for most of the cases with IH, though pathologic somnolence remitted in roughly 40% of cases. DISCUSSION: These results demonstrate IH is more common in the working population than generally assumed with a prevalence on par with other common neurologic and psychiatric conditions. Further efforts to identify and diagnose those impaired by unexplained daytime somnolence may help clarify the causes of IH and the mechanisms underlying symptomatic remission.

Role of Daytime Continuous Polysomnography in the Diagnosis of Pediatric Narcolepsy Type 1.

BACKGROUND AND OBJECTIVES: Narcolepsy type 1 (NT1) is still largely underdiagnosed or diagnosed too late in children. Difficulties in obtaining rapid and reliable diagnostic evaluations of the condition in clinical practice partially explain this problem. Predictors of NT1 include cataplexy and sleep-onset REM periods (SOREMPs), documented during nocturnal polysomnography (N-PSG) or through the multiple sleep latency test (MSLT), although low CSF hypocretin-1 (CSF hcrt-1) is the definitive biological disease marker. Obtaining reliable MSLT results is not always feasible in children; therefore, this study aimed to validate daytime continuous polysomnography (D-PSG) as an alternative diagnostic tool. METHODS: Two hundred consecutive patients aged younger than 18 years (112 with NT1; 25 with other hypersomnias, including narcolepsy type 2 and idiopathic hypersomnia; and 63 with subjective excessive daytime sleepiness) were randomly split into 2 groups: group 1 (n = 133) for the identification of diagnostic markers and group 2 (n = 67) for the validation of the detected markers. The D-PSG data collected included the number of spontaneous naps, total sleep time, and the number of daytime SOREMPs (d-SOREMP). D-PSG data were tested against CSF hcrt-1 deficiency (NT1 diagnosis) as the gold standard using receiver operating characteristic (ROC) curve analysis in group 1. ROC diagnostic performances of single and combined D-PSG parameters were tested in group 1 and validated in group 2. RESULTS: In group 1, the areas under the ROC curve (AUCs) were 0.91 (95% CI 0.86-0.96) for d-SOREMPs, 0.81 (95% CI 0.74-0.89) for the number of spontaneous naps, and 0.70 (95% CI 0.60-0.79) for total sleep time. A d-SOREMP count ≥1 (sensitivity of 95% and specificity of 72%), coupled with a diurnal total sleep time above 60 minutes (sensitivity of 89% and specificity of 91%), identified NT1 in group 1 with high reliability (area under the ROC curve of 0.93, 95% CI 0.88-0.97). These results were confirmed in the validation group with an AUC of 0.88 (95% CI 0.79-0.97). DISCUSSION: D-PSG recording is an easily performed, cost-effective, and reliable tool for identifying NT1 in children. Further studies should confirm its validity with home D-PSG monitoring. These alternative procedures could be used to confirm NT1 diagnosis and curtail diagnostic delay.

The immunopathogenesis of narcolepsy type 1.

Narcolepsy type 1 (NT1) is a chronic sleep disorder resulting from the loss of a small population of hypothalamic neurons that produce wake-promoting hypocretin (HCRT; also known as orexin) peptides. An immune-mediated pathology for NT1 has long been suspected given its exceptionally tight association with the MHC class II allele HLA-DQB1*06:02, as well as recent genetic evidence showing associations with polymorphisms of T cell receptor genes and other immune-relevant loci and the increased incidence of NT1 that has been observed after vaccination with the influenza vaccine Pandemrix. The search for both self-antigens and foreign antigens recognized by the pathogenic T cell response in NT1 is ongoing. Increased T cell reactivity against HCRT has been consistently reported in patients with NT1, but data demonstrating a primary role for T cells in neuronal destruction are currently lacking. Animal models are providing clues regarding the roles of autoreactive CD4+ and CD8+ T cells in the disease. Elucidation of the pathogenesis of NT1 will allow for the development of targeted immunotherapies at disease onset and could serve as a model for other immune-mediated neurological diseases.

Perceptions of exercise and restless legs syndrome: Results from a nationwide survey.

Restless legs syndrome is a prevalent, sensorimotor sleep disorder temporarily relieved by movement, with evidence of symptomatic improvement with regular exercise. The present study describes perceptions of the effects of exercise on symptoms of restless legs syndrome. Participants (N = 528) completed a mixed-methods (i.e. numerical and narrative), nationwide survey including items assessing personal experiences with exercise and restless legs syndrome (both positive and negative), as well as restless legs syndrome diagnosis, restless legs syndrome severity, and demographic and clinical characteristics. Responses varied widely on specific experiences with exercise, but a higher percentage of participants indicated positive experiences with exercise than those who reported negative experiences (72%-40%, respectively) with exercise. Further, 54% of respondents reported that exercise only improves restless legs syndrome, while 24% reported exercise only worsens symptoms. Participants described that any abrupt change in exercise routine would almost always elicit restless legs syndrome symptoms (e.g. hiking for a long time, stopping an exercise routine), and that a consistent pattern of exercise improved restless legs syndrome symptoms with an overall beneficial effect on the frequency of symptomatic bouts. Participants further described time of day as impactful for their exercise experience, with > 50% indicating morning exercise improves symptoms and evening exercise worsens symptoms. Participants described several questions that they wanted answered regarding the evidence for exercise in restless legs syndrome and specific exercise prescription recommendations. The present study describes information crucial to the creation of stakeholder-informed health promotion programs for individuals with restless legs syndrome to optimize personalized treatment plans that could prevent and manage symptoms.

Apparent resolution of hypersomnia episodes in two patients with Kleine-Levin syndrome following treatment with the melatonin receptor agonist ramelteon.

Kleine-Levin syndrome (KLS) is a rare disorder characterized by episodic bouts of severe hypersomnia associated with cognitive and behavioral abnormalities and normal alertness and functioning in between episodes. The pathophysiology is unclear but may involve neurotransmitter abnormalities, hypothalamic/thalamic dysfunction, viral/autoimmune etiology, or circadian abnormalities. No single treatment has been shown to be reliably efficacious; lithium has demonstrated the most consistent efficacy, although many do not respond and its use is limited by side effects. Due to the evidence of circadian involvement, we hypothesized that strengthening circadian signals may ameliorate symptoms. Ramelteon is a potent melatonin receptor agonist. In this report, two patients with KLS are described with apparent resolution of hypersomnia episodes following ramelteon initiation. CITATION: Dominguez D, Rudock R, Tomko S, Pathak S, Mignot E, Licis A. Apparent resolution of hypersomnia episodes in two patients with Kleine-Levin syndrome following treatment with the melatonin receptor agonist ramelteon. J Clin Sleep Med. 2024;20(4):657-662.

Stakeholder involvement in the optimization of a patient-centered exercise intervention for people with restless legs syndrome.

STUDY OBJECTIVES: Previous research supports exercise as a behavioral approach to manage symptoms of restless legs syndrome (RLS); however, completion rates in exercise studies are low. This study obtained key stakeholder feedback from people with RLS to modify and optimize a 12-week, evidence-based exercise program for RLS. METHODS: Participants with RLS(N=513) completed a nationwide survey to provide feedback on the necessity, interest, feasibility, and efficacy of the program as well as perceived barriers and proposed modifications to improve the exercise program. RESULTS: Most respondents(67%) expressed the need for an exercise program designed specifically for people with RLS and 64% were interested in the program. Only 6% of participants thought the program wouldn't be well tolerated and 6% responded that it would likely exacerbate symptoms. However, only 58% said they would be likely to participate in the program if it was available to them locally. Key barriers to participation were (1)accessibility; (2)personal factors; (3)trustworthiness; and (4)fear of injury, illness, or symptom exacerbations. Respondents highlighted modification considerations for the individualization of exercise features, adaptations for specific impairments/personal factors, inclusion of flexibility and balance exercises, and flexibility for more home-based activities. CONCLUSIONS: Interest in the program was driven by the desire to reduce medications and improve overall quality of life. Appropriately educated and trained exercise providers knowledgeable about RLS are integral to buy-in from stakeholders. This study provides an imperative step in clinical research that can increase the success of subsequent implementation efforts and may accelerate the adoption of exercise programs into practice.

Circadian protein expression patterns in healthy young adults.

OBJECTIVES: To explore how the blood plasma proteome fluctuates across the 24-hour day and identify a subset of proteins that show endogenous circadian rhythmicity. METHODS: Plasma samples from 17 healthy adults were collected hourly under controlled conditions designed to unmask endogenous circadian rhythmicity; in a subset of 8 participants, we also collected samples across a day on a typical sleep-wake schedule. A total of 6916 proteins were analyzed from each sample using the SomaScan aptamer-based multiplexed platform. We used differential rhythmicity analysis based on a cosinor model with mixed effects to identify a subset of proteins that showed circadian rhythmicity in their abundance. RESULTS: One thousand and sixty-three (15%) proteins exhibited significant daily rhythmicity. Of those, 431 (6.2%) proteins displayed consistent endogenous circadian rhythms on both a sleep-wake schedule and under controlled conditions: it included both known and novel proteins. When models were fitted with two harmonics, an additional 259 (3.7%) proteins exhibited significant endogenous circadian rhythmicity, indicating that some rhythmic proteins cannot be solely captured by a simple sinusoidal model. Overall, we found that the largest number of proteins had their peak levels in the late afternoon/evening, with another smaller group peaking in the early morning. CONCLUSIONS: This study reveals that hundreds of plasma proteins exhibit endogenous circadian rhythmicity in humans. Future analyses will likely reveal novel physiological pathways regulated by circadian clocks and pave the way for improved diagnosis and treatment for patients with circadian disorders and other pathologies. It will also advance efforts to include knowledge about time-of-day, thereby incorporating circadian medicine into personalized medicine.

Fully Automated Detection of Isolated Rapid-Eye-Movement Sleep Behavior Disorder Using Actigraphy.

Isolated rapid-eye-movement (REM) sleep behavior disorder (iRBD) is caused by motor disinhibition during REM sleep and is a strong early predictor of Parkinson's disease. However, screening questionnaires for iRBD lack specificity due to other sleep disorders that mimic the symptoms. Nocturnal wrist actigraphy has shown promise in detecting iRBD by measuring sleep-related motor activity, but it relies on sleep diary-defined sleep periods, which are not always available. Our aim was to precisely detect iRBD using actigraphy alone by combining two actigraphy-based markers of iRBD - abnormal nighttime activity and 24-hour rhythm disruption. In a sample of 42 iRBD patients and 42 controls (21 clinical controls with other sleep disorders and 21 community controls) from the Stanford Sleep Clinic, the nighttime actigraphy model was optimized using automated detection of sleep periods. Using a subset of 38 iRBD patients with daytime data and 110 age-, sex-, and body-mass-index-matched controls from the UK Biobank, the 24-hour rhythm actigraphy model was optimized. Both nighttime and 24-hour rhythm features were found to distinguish iRBD from controls. To improve the accuracy of iRBD detection, we fused the nighttime and 24-hour rhythm disruption classifiers using logistic regression, which achieved a sensitivity of 78.9%, a specificity of 96.4%, and an AUC of 0.954. This study preliminarily validates a fully automated method for detecting iRBD using actigraphy in a general population.Clinical relevance- Actigraphy-based iRBD detection has potential for large-scale screening of iRBD in the general population.

Automatic Detection of Chronic Insomnia from Polysomnographic and Clinical Variables Using Machine Learning.

Insomnia is defined subjectively by the presence and frequency of specific clinical symptoms and an association with distress. Although sleep study data has shown some weak associations, no objective test can currently be used to predict insomnia. The purpose of this study was to use previously reported and relatively crafted insomnia-related polysomnographic variables in machine learning models to classify groups with and without insomnia. Demographics, diagnosed depression, Epworth Sleepiness Scale (ESS), and features derived from electroencephalography (EEG), arousals, and sleep stages from 3,407 sleep clinic patients (2,617 without insomnia and 790 insomnia patients based on responses to a set of questions) were included in this analysis. The number of features were reduced using pair-wise correlation and recursive feature elimination. Predictive value of three machine learning models (logistic regression, neural network, and support vector machine) was investigated, and the best performance was achieved with logistic regression, yielding a balanced accuracy of 71%. The most important features in predicting insomnia were depression, age, sex, duration of longest arousal, ESS score, and EEG power in theta and sigma bands across all sleep stages. Results indicate potential of machine learning-based screening for insomnia using clinical variables and EEG.

Mutations in human DNA methyltransferase DNMT1 induce specific genome-wide epigenomic and transcriptomic changes in neurodevelopment.

DNA methyltransferase type 1 (DNMT1) is a major enzyme involved in maintaining the methylation pattern after DNA replication. Mutations in DNMT1 have been associated with autosomal dominant cerebellar ataxia, deafness and narcolepsy (ADCA-DN). We used fibroblasts, induced pluripotent stem cells (iPSCs) and induced neurons (iNs) generated from patients with ADCA-DN and controls, to explore the epigenomic and transcriptomic effects of mutations in DNMT1. We show cell type-specific changes in gene expression and DNA methylation patterns. DNA methylation and gene expression changes were negatively correlated in iPSCs and iNs. In addition, we identified a group of genes associated with clinical phenotypes of ADCA-DN, including PDGFB and PRDM8 for cerebellar ataxia, psychosis and dementia and NR2F1 for deafness and optic atrophy. Furthermore, ZFP57, which is required to maintain gene imprinting through DNA methylation during early development, was hypomethylated in promoters and exhibited upregulated expression in patients with ADCA-DN in both iPSC and iNs. Our results provide insight into the functions of DNMT1 and the molecular changes associated with ADCA-DN, with potential implications for genes associated with related phenotypes.

Repeated polysomnography and multiple sleep latency test in narcolepsy type 1 and other hypersomnolence disorders.

BACKGROUND: The diagnosis of narcolepsy is based on clinical information, combined with polysomnography (PSG) and the Multiple Sleep Latency Test (MSLT). PSG and the MSLT are moderately reliable at diagnosing narcolepsy type 1 (NT1) but unreliable for diagnosing narcolepsy type 2 (NT2). This is a problem, especially given the increased risk of a false-positive MSLT in the context of circadian misalignment or sleep deprivation, both of which commonly occur in the general population. AIM: We aimed to clarify the accuracy of PSG/MSLT testing in diagnosing NT1 versus controls without sleep disorders. Repeatability and reliability of PSG/MSLT testing and temporal changes in clinical findings of patients with NT1 versus patients with hypersomnolence with normal hypocretin-1 were compared. METHOD: 84 patients with NT1 and 100 patients with non-NT1-hypersomnolence disorders, all with congruent cerebrospinal fluid hypocretin-1 (CSF-hcrt-1) levels, were included. Twenty-five of the 84 NT1 patients and all the hypersomnolence disorder patients underwent a follow-up evaluation consisting of clinical assessment, PSG, and a modified MSLT. An additional 68 controls with no sleep disorders were assessed at baseline. CONCLUSION: Confirming results from previous studies, we found that PSG and our modified MSLT accurately and reliably diagnosed hypocretin-deficient NT1 (accuracy = 0.88, reliability = 0.80). Patients with NT1 had stable clinical and electrophysiological presentations over time that suggested a stable phenotype. In contrast, the PSG/MSLT results of patients with hypersomnolence, and normal CSF-hcrt-1 had poor reliability (0.32) and low repeatability.

Proteomic insights into the pathophysiology of periodic limb movements and restless legs syndrome.

OBJECTIVES: We used a high-throughput assay of 5000 plasma proteins to identify biomarkers associated with periodic limb movements (PLM) and restless legs syndrome (RLS) in adults. METHODS: Participants (n = 1410) of the Stanford Technology Analytics and Genomics in Sleep (STAGES) study had blood collected, completed a sleep questionnaire, and underwent overnight polysomnography with the scoring of PLMs. An aptamer-based array (SomaScan) was used to quantify 5000 proteins in plasma. A second cohort (n = 697) that had serum assayed using a previous iteration of SomaScan (1300 proteins) was used for replication and in a combined analysis (n = 2107). A 5% false discovery rate was used to assess significance. RESULTS: Multivariate analyses in STAGES identified 68 proteins associated with the PLM index after correction for multiple testing (ie, base model). Most significantly decreased proteins were iron-related and included Hepcidin (LEAP-1), Ferritin, and Ferritin light chain. Most significantly increased proteins included RANTES, Cathepsin A, and SULT 1A3. Of 68 proteins significant in the base model, 17 were present in the 1300 panel, and 15 of 17 were replicated. The most significant proteins in the combined model were Hepcidin (LEAP-1), Cathepsin A, Ferritin, and RANTES. Exploration of proteins in RLS versus non-RLS identified Cathepsin Z, Heme oxygenase 2 (HO-2), Interleukin-17A (upregulated in the combined cohort), and Megalin (upregulated in STAGES only) although results were less significant than for proteins associated with PLM index. CONCLUSIONS: These results confirm the association of PLM with low iron status and suggest the involvement of catabolic enzymes in PLM/RLS.