Quantitative analysis of phenotypic elements augments traditional electroclinical classification of common familial epilepsies. (17th October 2019)

• Record Type: Journal Article
• Other Names: Abou‐Khalil Bassel investigator.; Afawi Zaid investigator.; Allen Andrew S. investigator.; Bautista Jocelyn F. investigator.; Bellows Susannah T. investigator.; Berkovic Samuel F. investigator.; Bluvstein Judith investigator.; Burgess Rosemary investigator.; Cascino Gregory investigator.; Cossette Patrick investigator.; Cristofaro Sabrina investigator.; Crompton Douglas E. investigator.; Delanty Norman investigator.; Devinsky Orrin investigator.; Dlugos Dennis investigator.; Ellis Colin A. investigator.; Epstein Michael P. investigator.; Fountain Nathan B. investigator.; Freyer Catharine investigator.; Geller Eric B. investigator.; Glauser Tracy investigator.; Glynn Simon investigator.; Goldberg‐Stern Hadassa investigator.; Goldstein David B. investigator.; Gravel Micheline investigator.; Haas Kevin investigator.; Haut Sheryl investigator.; Heinzen Erin L. investigator.; Kirsch Heidi E. investigator.; Kivity Sara investigator.; Knowlton Robert investigator.; Korczyn Amos D. investigator.; Kossoff Eric investigator.; Kuzniecky Ruben investigator.; Loeb Rebecca investigator.; Lowenstein Daniel H. investigator.; Marson Anthony G. investigator.; McCormack Mark investigator.; McKenna Kevin investigator.; Mefford Heather C. investigator.; Motika Paul investigator.; Mullen Saul A. investigator.; J. O'Brien Terence investigator.; Ottman Ruth investigator.; Paolicchi Juliann investigator.; Parent Jack M. investigator.; Paterson Sarah investigator.; Petrou Steven investigator.; Petrovski Slavé investigator.; Owen Pickrell William investigator.; Poduri Annapurna investigator.; Rees Mark I. investigator.; Sadleir Lynette G. investigator.; Scheffer Ingrid E. investigator.; Shih Jerry investigator.; Singh Rani investigator.; Sirven Joseph investigator.; Smith Michael investigator.; Smith Phil E. M. investigator.; Thio Liu Lin investigator.; Thomas Rhys H. investigator.; Venkat Anu investigator.; Vining Eileen investigator.; Von Allmen Gretchen investigator.; Weisenberg Judith investigator.; Widdess‐Walsh Peter investigator.; Winawer Melodie R. investigator.;
• Is Part Of: Epilepsia. Volume 60:issue 11(2019)
• Page Start: 2194
• Page End: 2203
• Publication Date: 2019-10-17
• Subjects: epilepsy -- genetics -- latent class analysis -- phenotype; Epilepsy -- Periodicals; 616.853
• DOI: 10.1111/epi.16354
• Languages: English
• ISSNs: 0013-9580

Abstract

Abstract: Objective: Classification of epilepsy into types and subtypes is important for both clinical care and research into underlying disease mechanisms. A quantitative, data‐driven approach may augment traditional electroclinical classification and shed new light on existing classification frameworks. Methods: We used latent class analysis, a statistical method that assigns subjects into groups called latent classes based on phenotypic elements, to classify individuals with common familial epilepsies from the Epi4K Multiplex Families study. Phenotypic elements included seizure types, seizure symptoms, and other elements of the medical history. We compared class assignments to traditional electroclinical classifications and assessed familial aggregation of latent classes. Results: A total of 1120 subjects with epilepsy were assigned to five latent classes. Classes 1 and 2 contained subjects with generalized epilepsy, largely reflecting the distinction between absence epilepsies and younger onset (class 1) versus myoclonic epilepsies and older onset (class 2). Classes 3 and 4 contained subjects with focal epilepsies, and in contrast to classes 1 and 2, these did not adhere as closely to clinically defined focal epilepsy subtypes. Class 5 contained nearly all subjects with febrile seizures plus or unknown epilepsy type, as well as a few subjects with generalized epilepsy and a few with focal epilepsy. Family concordance of latent classes was similar to or greater than concordance of clinically defined epilepsy types. Significance: Quantitative classification of epilepsy has the potential to augment traditional electroclinical classification by (1) combining some syndromes into a single class, (2) splitting some syndromes into different classes, (3) helping to classify subjects who could not be classified clinically, and (4) defining the boundaries of clinically defined classifications. This approach can guide future research, including molecular genetic studies, by identifying homogeneous sets of individuals that may share underlying disease mechanisms.