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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.

Background and ObjectivesNarcolepsy type 1 (NT1) is an orphan brain disorder caused by the irreversible destruction of orexin neurons. Metabolic disturbances are common in patients with NT1 who have a body mass index (BMI) 10% to 20% higher than the general population, with one-third being obese (BMI >30 kg/m2). Besides the destruction of orexin neurons in NT1, the metabolic alterations in obese and nonobese patients with NT1 remain unknown. The aim of this study was to identify possible differences in plasma metabolic profiles between patients with NT1 and controls as a function of their BMI status.MethodsWe used a targeted liquid chromatography–mass spectrometry metabolomics approach to measure 141 circulating, low-molecular-weight metabolites in drug-free fasted plasma samples from 117 patients with NT1 (including 41 obese individuals) compared with 116 BMI-matched controls (including 57 obese individuals).ResultsCommon metabolites driving the difference between patients with NT1 and controls, regardless of BMI, were identified, namely sarcosine, glutamate, nonaylcarnitine (C9), 5 long-chain lysophosphatidylcholine acyls, 1 sphingolipid, 12 phosphatidylcholine diacyls, and 11 phosphatidylcholine acyl-akyls, all showing increased concentrations in NT1. Metabolite concentrations significantly affected by NT1 (n = 42) and BMI category (n = 40) showed little overlap (n = 5). Quantitative enrichment analysis revealed common metabolic pathways that were implicated in the NT1/control differences in both normal BMI and obese comparisons, namely glycine and serine, arachidonic acid, and tryptophan metabolism. The metabolites driving these differences were glutamate, sarcosine, and ornithine (glycine and serine metabolism); glutamate and PC aa C34:4 (arachidonic acid metabolism); and glutamate, serotonin, and tryptophan (tryptophan metabolism). Linear metabolite-endophenotype regression analyses highlight that as part of the NT1 metabolic phenotype, most of the relationships between the sleep parameters (i.e., slow-wave sleep duration, sleep latency, and periodic leg movement) and metabolite concentrations seen in the controls were lost.DiscussionThese results represent the most comprehensive metabolic profiling of patients with NT1 as a function of BMI and propose some metabolic diagnostic biomarkers for NT1, namely glutamate, sarcosine, serotonin, tryptophan, nonaylcarnitine, and some phosphatidylcholines. The metabolic pathways identified offer, if confirmed, possible targets for treatment of obesity in NT1.Classification of EvidenceThis study provides Class II evidence that a distinct metabolic profile can differentiate patients with NT1 from patients without the disorder.

ObjectiveWe validated the Narcolepsy Severity Scale (NSS) in adults with narcolepsy type 1 (NT1) to quantify the severity, frequency, and consequences of the 5 key narcolepsy symptoms over the last month, and we now developed the Pediatric NSS (NSS-P); thus, the aims of this study were to assess NSS-P psychometric properties, validity, and reliability, and to evaluate its responsiveness to treatment in a well-characterized sample of children and adolescents with NT1.MethodsThe NSS was reformulated for children, and the item about driving was removed. The total score of the 14-item NSS-P ranges from 0 to 54, and higher scores reflect more severe disease. Children and adolescents (n = 209, 6–17 years of age) with NT1 diagnosed in 2 Reference Centers for Narcolepsy in France were consecutively asked to fill in the NSS-P. The scale was fully and correctly completed by 160 (10–18 years of age, 68 untreated). Moreover, 65 participants completed it twice (33 before/during treatment, and 32 under the same treatment). The NSS-P psychometric properties, score changes before/during treatment, and convergent validity with other clinical parameters were assessed.ResultsThe NSS-P showed adequate psychometric properties with significant item–total score correlations. Factor analysis indicated a 4-factor solution with good reliability. The NSS-P score was lower in treated than untreated patients with a mean difference of 3.71 ± 1.45, with a minimum clinically important difference between untreated and treated patients in the longitudinal sample estimated at 4 points. Four severity levels were defined (mild, moderate, severe, very severe) with between-group differences related to treatment. The NSS-P total score was associated with self-reported sleepiness, insomnia, and depressive symptoms. Its temporal stability was satisfactory.DiscussionWe validated a brief instrument to assess NT1 symptom frequency, severity, and consequences in ≥10-year-old children and adolescents with 4 clinically relevant severity score ranges. This scale constitutes a relevant tool to improve and provide guidance for NT1 management in pediatric populations. The ease of administration, its good psychometric properties, and its sensitivity to detect symptom changes after treatment ensure future use of the NSS-P in clinical and research settings.

ObjectivesTo highlight the slow-wave sleep (SWS) fragmentation and validate the video-polysomnographic (vPSG) criteria and cutoffs for the diagnosis of disorders of arousal (DOA) in children, as already reported in adults.MethodsOne hundred children (66 boys, 11.0 ± 3.3 years) with frequent episodes of DOA and 50 nonparasomniac children (32 boys, 10.9 ± 3.9 years) underwent vPSG recording to quantify SWS characteristics (number of N3 sleep interruptions, fragmentation index, slow/mixed and fast arousal ratios, and indexes per hour) and associated behaviors. We compared SWS characteristics in the 2 groups and defined the optimal cutoff values for the diagnosis of DOA using receiver operating characteristic curves.ResultsPatients with DOA had higher amounts of N3 and REM sleep, number of N3 interruptions, SWS fragmentation, and slow/mixed arousal indexes than controls. The highest area under the curve (AUC) values were obtained for SWS fragmentation and slow/mixed arousal indexes with satisfactory classification performances (AUC 0.80, 95% confidence interval [CI] 0.73–0.87; AUC 0.82, 95% CI 0.75–0.89). SWS fragmentation index cutoff value of 4.1/h reached a sensitivity of 65.0% and a specificity of 84.0%. Slow/mixed arousal index cutoff of 3.8/h reached a sensitivity of 69.0% and a specificity of 82.0%. At least one parasomniac episode was recorded in 63.0% of patients and none of the controls. Combining behavioral component by vPSG increased sensitivity of both biomarkers to 83% and 89%, respectively.ConclusionsWe confirmed that SWS fragmentation and slow/mixed arousal indexes are 2 relevant biomarkers for the diagnosis of DOA in children, with different cutoffs obtained than those validated in adults.Classification of EvidenceThis study provides Class III evidence that SWS fragmentation and slow/mixed arousal indexes on vPSG accurately identify children with DOA.

ObjectivesTo assess the frequency and determinants of depressive symptoms and suicidal thoughts in adults with narcolepsy type 1 (NT1) and controls, as well as the changes after NT1 management and the risk factors of major depressive episode (MDE) and suicide risk (SR) in NT1.MethodsTwo hundred ninety-seven patients with NT1 (age 39 ± 17 years, 172 drug-free) and 346 controls (age 38 ± 16 years) underwent a comprehensive clinical evaluation including the Beck Depression Inventory-II (BDI-II) self-questionnaire, with 1 item on suicidal thoughts. One hundred one drug-free patients with NT1 completed the BDI-II a second time during treatment. In 162 patients with NT1, the face-to-face Mini International Neuropsychiatric Interview was performed to formally diagnose current MDE and SR.ResultsBDI-II total scores were higher in patients with NT1 than controls and in untreated than treated patients. Patients with moderate to severe BDI-II scores (24.9%) were less educated, were more frequently obese, and had more severe narcolepsy symptoms, more autonomic dysfunctions, and poorer quality of life. Results were unchanged in models adjusted for NT1 medication intake. Suicidal thoughts were more frequent in untreated patients than controls (22.7% vs 12.4%). Patients with suicidal thoughts were more likely to be men and to have more severe narcolepsy symptoms. After narcolepsy management, BDI-II total score and suicidal thoughts decreased. MDE was diagnosed in 29 (18.1%) and SR in 27 (16.9%) patients.ConclusionsDepression, depressive symptoms, suicidal thoughts, and SR were frequent in patients with NT1, especially those without treatment, and were associated with NT1 severity. Depressive symptoms and suicidal thoughts improved after NT1 management.

Background and ObjectivesTo explore the first coronavirus disease 2019 (COVID-19) lockdown effect on sleep symptoms in patients with narcolepsy, idiopathic hypersomnia (IH), and restless legs syndrome (RLS).MethodsBetween March and May 2020, a sample of adult patients regularly followed up in a Reference Hospital Sleep Unit (299 with narcolepsy, 260 with IH, and 254 with RLS) was offered an online survey assessing their sleep-wake habits, daily activities, medication intake, and validated scales: International RLS Study Group questionnaire, Narcolepsy Severity Scale (NSS), IH Severity Scale (IHSS), Epworth Sleepiness Scale (ESS), Insomnia Severity Index, Beck Depression Inventory–II, and European Quality of Life (QoL) scale. The survey was proposed once, and the questions were answered for the prelockdown (recall of the month before the confinement) and the lockdown (time of study) periods.ResultsOverall, 331 patients completed the survey (response rate 40.7%): 102 with narcolepsy, 81 with IH, and 148 with RLS. All patients reported later bedtimes, with reduced differences for time in bed (TIB) and total sleep time (TST) over 24 hours between weekdays and weekends. Patients with narcolepsy spent more TIB and increased TST overnight, with more daytime napping. They had more awakenings, higher ESS scores, lower QoL, and no NSS changes. Patients with IH also increased their TIB, TST overnight and 24 hours on weekdays. Nocturnal sleep latency and the number of awakenings increased but with no change in ESS, QoL, and IHSS scores. Patients with RLS reported longer nocturnal sleep latency, more awakenings, more naps, decreased TIB, and TST overnight. RLS severity increased while QoL decreased. A significant portion of patients reported disease worsening during the lockdown (narcolepsy: 39.4%, IH: 43.6%, and RLS: 32.8%), and some patients stopped or lowered their medication (narcolepsy: 22.5%, IH: 28%, and RLS: 9.5%).DiscussionDuring the lockdown, all patients reported later bedtimes; those with narcolepsy and IH extended their sleep duration unlike patients with RLS. These changes were often associated with negative consequences on QoL. In the current context of recurrent COVID-19 waves, the recent development of teleconsultations should enable physicians to monitor patients with chronic sleep disorders more closely and to recommend optimized sleep schedules and duration, in order to prevent psychological problems and improve their QoL.

We thank Dr. Peraita-Adrados for the comment on our article.1 Our finding that narcolepsy type 1 (NT1) was not associated with increased risk of comorbid immune disorders was previously stated,2 which gave us additional confidence in our results and can be explained by a unique NT1 pathophysiology. We agree that the background in Dr. Peraita-Adrados's research was the same3; however, the methodology was different: our cohort was much larger, with patients included prospectively; the study was multicentric; adult and children populations were analyzed separately; and results were controlled for potential confounders.1 Importantly, we classified immune-based disorders in 3 groups (autoimmune, inflammatory, and allergic diseases).4 A recent article from the same group, not published at time of our submission, retrospectively added a poorly defined control group to the already reported NT1 population.3,5 Limited evidence was found for an association between heterogeneous autoimmune diseases and NT1, including psoriasis as an autoimmune, but not inflammatory, disease. We also found higher frequency of autoimmune diseases in narcolepsy type 2 and allergies in idiopathic hypersomnia, being both central disorders of hypersomnolence with unclear pathophysiology, and hypothesized on immune dysregulation mechanisms that require further study.1