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

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 of whether current diagnostic criteria of hypersomnolence di...

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Vydáno v:	Neurology Ročník 98; číslo 23; s. e2387
Hlavní autoři:	Gool, Jari K, Zhang, Zhongxing, Oei, Martijn S S L, Mathias, Stephanie, Dauvilliers, Yves, Mayer, Geert, Plazzi, Giuseppe, Del Rio-Villegas, Rafael, Cano, Joan Santamaria, Šonka, Karel, Partinen, Markku, Overeem, Sebastiaan, Peraita-Adrados, Rosa, Heinzer, Raphael, Martins da Silva, Antonio, Högl, Birgit, Wierzbicka, Aleksandra, Heidbreder, Anna, Feketeova, Eva, Manconi, Mauro, Bušková, Jitka, Canellas, Francesca, Bassetti, Claudio L, Barateau, Lucie, Pizza, Fabio, Schmidt, Markus H, Fronczek, Rolf, Khatami, Ramin, Lammers, Gert Jan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States 07.06.2022
Témata:	Adolescent Cataplexy - diagnosis Cluster Analysis Disorders of Excessive Somnolence - diagnosis Disorders of Excessive Somnolence - epidemiology Humans Idiopathic Hypersomnia - diagnosis Narcolepsy - diagnosis Narcolepsy - drug therapy
ISSN:	1526-632X, 1526-632X
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Shrnutí:	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 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. 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. We 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. 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 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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Undefined-3
ISSN:	1526-632X 1526-632X
DOI:	10.1212/WNL.0000000000200519