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

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

Novel Approach Identifies SNPs in SLC2A10 and KCNK9 with Evidence for Parent-of-Origin Effect on Body Mass Index

Marja-Liisa Lokki työryhmien Generation Scotland Consortium, LifeLines Cohort Study ja GIANT Consortium jäsenPeer reviewe

A patient with narcolepsy with cataplexy and multiple sclerosis: two different diseases that may share pathophysiologic mechanisms?

Paroxysmal Cerebral Disorder

Prevalence of EEG Paroxysmal Activity in a Population of Children with Obstructive Sleep Apnea Syndrome

Study Objectives: Sleep breathing disorders may trigger paroxysmal events during sleep such as parasomnias and may exacerbate preexisting seizures. We verified the hypothesis that the amount of EEG paroxysmal activity (PA) may be high in children with obstructive sleep apnea syndrome (OSAS). Design: Prospective study Settings: Sleep unit of an academic center. Participants: Polysomnographic studies were performed in a population of children recruited prospectively, for suspected OSAS, from January to December 2007, with no previous history of epileptic seizures or any other medical conditions. All sleep studies included 8 EEG channels, including centrotemporal leads. We collected data about clinical and respiratory parameters of children with OSAS and with primary snoring, then we performed sleep microstructure analysis in 2 OSAS subgroups, matched for age and sex, with and without paroxysmal activity. Measurements and Results: We found 40 children who met the criteria for primary snoring, none of them showed PA, while 127 children met the criteria for OSAS and 18 of them (14.2%) showed PA. Children with PA were older, had a predominance of boys, a longer duration of OSAS, and a lower percentage of adenotonsillar hypertrophy than children without PA. Moreover, PA occurred over the centrotemporal regions in 9 cases, over temporal-occipital regions in 5, and over frontocentral regions in 4. Children with PA showed a lower percentage of REM sleep, a lower CAP rate and lower A1 index during slow wave sleep, and lower total A2 and arousal index than children without EEG abnormalities. Conclusions: We found a higher percentage of paroxysmal activity in children with OSAS, compared to children with primary snoring, who did not exhibit EEG abnormalities. The children with paroxysmal activity have peculiar clinical and sleep microstructure characteristics that may have implications in the neurocognitive outcome of OSAS