Data-Driven Phenotyping of Central Disorders of Hypersomnolence With Unsupervised Clustering

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

Data-Driven Phenotyping of Central Disorders of Hypersomnolence With Unsupervised Clustering.

BACKGROUND AND OBJECTIVES Recent 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 whether current diagnostic criteria of hypersomnolence disorders should be reassessed. The main aim of this data-driven observational study was to see if data-driven algorithms would segregate narcolepsy type 1 and identify more reliable subgrouping of individuals without cataplexy with new clinical biomarkers. METHODS We 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. RESULTS We included 1078 unmedicated adolescents and adults. Seven clusters were identified, of which four clusters included predominantly individuals with cataplexy. The two most distinct clusters consisted of 158 and 157 patients respectively, were dominated by those without cataplexy and, amongst other variables, significantly differed in presence of sleep drunkenness, subjective difficulty awakening and weekend-week sleep length difference. Patients formally diagnosed as narcolepsy type 2 and idiopathic hypersomnia were evenly mixed in these two clusters. DISCUSSION Using 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 rapid eye moment periods (SOREMPs) 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

Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning

Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of poorly defined diseases with excessive daytime sleepiness as a core feature. Due to the considerable overlap of symptoms and the rarity of the diseases, it is difficult to identify distinct phenotypes of CH. Machine learning (ML) can help to identify phenotypes as it learns to recognize clinical features invisible for humans. Here we apply ML to data from the huge European Narcolepsy Network (EU-NN) that contains hundreds of mixed features of narcolepsy making it difficult to analyze with classical statistics. Stochastic gradient boosting, a supervised learning model with built-in feature selection, results in high performances in testing set. While cataplexy features are recognized as the most influential predictors, machine find additional features, e.g. mean rapid-eye-movement sleep latency of multiple sleep latency test contributes to classify NT1 and NT2 as confirmed by classical statistical analysis. Our results suggest ML can identify features of CH on machine scale from complex databases, thus providing 'ideas' and promising candidates for future diagnostic classifications.</p

Evaluation of hypersomnolence: From symptoms to diagnosis, a multidimensional approach

Hypersomnolence is a major public health issue given its high frequency, its impact on academic/occupational functioning and on accidentology, as well as its heavy socio-economic burden. The positive and aetiological diagnosis is crucial, as it determines the therapeutic strategy. It must consider the following aspects: i) hypersomnolence is a complex concept referring to symptoms as varied as excessive daytime sleepiness, excessive need for sleep, sleep inertia, or drowsiness, all of which warrant specific dedicated investigations; ii) the boundary between physiological and abnormal hypersomnolence is blurred, since most symptoms can be encountered in the general population to varying degrees without being considered as pathological, meaning that their severity, frequency, context of occurrence and related impairment need to be carefully assessed; iii) investigation of hypersomnolence relies on scales/questionnaires as well as behavioural and neurophysiological tests, which measure one or more dimensions, keeping in mind the possible discrepancy between objective and subjective assessment; iv) aetiological reasoning is driven by knowledge of the main sleep regulation mechanisms, epidemiology, and associated symptoms. The need to assess hypersomnolence is growing, both for its management, and for assessing the efficacy of treatments. The landscape of tools available for investigating hypersomnolence is constantly evolving, in parallel with research into sleep physiology and technical advances. These investigations face the challenges of reconciling subjective perception and objective data, making tools accessible to as many people as possible and predicting the risk of accidents

50th anniversary of the ESRS in 2022—JSR special issue

This article addresses the clinical presentation, diagnosis, pathophysiology and management of narcolepsy type 1 and 2, with a focus on recent findings. A low level of hypocretin-1/orexin-A in the cerebrospinal fluid is sufficient to diagnose narcolepsy type 1, being a highly specific and sensitive biomarker, and the irreversible loss of hypocretin neurons is responsible for the main symptoms of the disease: sleepiness, cataplexy, sleep-related hallucinations and paralysis, and disrupted nocturnal sleep. The process responsible for the destruction of hypocretin neurons is highly suspected to be autoimmune, or dysimmune. Over the last two decades, remarkable progress has been made for the understanding of these mechanisms that were made possible with the development of new techniques. Conversely, narcolepsy type 2 is a less well-defined disorder, with a variable phenotype and evolution, and&nbsp;few reliable biomarkers discovered so far. There is a dearth of knowledge about this disorder, and its aetiology remains unclear and needs to be further explored. Treatment of narcolepsy is still nowadays only symptomatic, targeting sleepiness, cataplexy and disrupted nocturnal sleep. However, new psychostimulants have been recently developed, and the upcoming arrival of non-peptide hypocretin receptor-2 agonists should be a revolution in the management of this rare sleep disease, and maybe also for disorders beyond narcolepsy

The orexin story, sleep and sleep disturbances

The orexins, also known as hypocretins, are two neuropeptides (orexin A and B or hypocretin 1 and 2) produced by a few thousand neurons located in the lateral hypothalamus that were independently discovered by two research groups in 1998. Those two peptides bind two receptors (orexin/hypocretin receptor 1 and receptor 2) that are widely distributed in the brain and involved in the central physiological regulation of sleep and wakefulness, orexin receptor 2 having the major role in the maintenance of arousal. They are also implicated in a multiplicity of other functions, such as reward seeking, energy balance, autonomic regulation and emotional behaviours. The destruction of orexin neurons is responsible for the sleep disorder narcolepsy with cataplexy (type 1) in humans, and a defect of orexin signalling also causes a narcoleptic phenotype in several animal species. Orexin discovery is unprecedented in the history of sleep research, and pharmacological manipulations of orexin may have multiple therapeutic applications. Several orexin receptor antagonists were recently developed as new drugs for insomnia, and orexin agonists may be the next-generation drugs for narcolepsy. Given the broad range of functions of the orexin system, these drugs might also be beneficial for treating various conditions other than sleep disorders in the near future

Absence of NMDA receptor antibodies in the rare association between Type 1 Narcolepsy and Psychosis

International audienceFrequency and mechanisms underlying the association between narcolepsy type 1 (NT1) and psychosis remain unclear with potential role for a common immune pathway. We estimated the frequency of psychosis and its characteristics in NT1 at two European sleep centers (France, n = 381; Spain, n = 161) and measured IgG autoantibodies that recognize the GluN1 subunit of the NMDAR in 9 patients with NT1 with psychosis, and 25 NT1 patients without psychosis. Ten NT1 patients (6 in France, 4 in Spain) were diagnosed with comorbid psychosis, a frequency of 1.8%. One patient reported psychotic symptoms few months before narcolepsy onset, two patients few months after onset, and one patient one year after onset but after modafinil introduction. The six remaining patients reported long delays between NT1 and psychosis onset. Half the patients, mostly male adults, reported onset or worsening of psychotic symptoms after medication. We found no IgG antibodies to NR1/NR2B heteromers of the NMDARs in patients with NT1 with or without psychosis. To conclude, psychosis is rare in NT1, with limited evidence for a key impact of stimulants, and no association with anti-NMDAR antibodies. However, dramatic NT1 and schizophrenia exists especially in early onset NT1, which may lead to inappropriate diagnosis and management

Smoking, Alcohol, Drug Use, Abuse and Dependence in Narcolepsy and Idiopathic Hypersomnia: A Case-Control Study.

STUDY OBJECTIVES: Basic experiments support the impact of hypocretin on hyperarousal and motivated state required for increasing drug craving. Our aim was to assess the frequencies of smoking, alcohol and drug use, abuse and dependence in narcolepsy type 1 (NT1, hypocretin-deficient), narcolepsy type 2 (NT2), idiopathic hypersomnia (IH) (non-hypocretin-deficient conditions), in comparison to controls. We hypothesized that NT1 patients would be less vulnerable to drug abuse and addiction compared to other hypersomniac patients and controls from general population. METHODS: We performed a cross-sectional study in French reference centres for rare hypersomnia diseases and included 450 adult patients (median age 35 years; 41.3% men) with NT1 (n = 243), NT2 (n = 116), IH (n = 91), and 710 adult controls. All participants were evaluated for alcohol consumption, smoking habits, and substance (alcohol and illicit drug) abuse and dependence diagnosis during the past year using the Mini International Neuropsychiatric Interview. RESULTS: An increased proportion of both tobacco and heavy tobacco smokers was found in NT1 compared to controls and other hypersomniacs, despite adjustments for potential confounders. We reported an increased regular and frequent alcohol drinking habit in NT1 versus controls but not compared to other hypersomniacs in adjusted models. In contrast, heavy drinkers were significantly reduced in NT1 versus controls but not compared to other hypersomniacs. The proportion of patients with excessive drug use (codeine, cocaine, and cannabis), substance dependence, or abuse was low in all subgroups, without significant differences between either hypersomnia disorder categories or compared with controls. CONCLUSIONS: We first described a low frequency of illicit drug use, dependence, or abuse in patients with central hypersomnia, whether Hcrt-deficient or not, and whether drug-free or medicated, in the same range as in controls. Conversely, heavy drinkers were rare in NT1 compared to controls but not to other hypersomniacs, without any change in alcohol dependence or abuse frequency. Although disruption of hypocretin signaling in rodents reduces drug-seeking behaviors, our results do not support that hypocretin deficiency constitutes a protective factor against the development of drug addiction in humans