Quantitative analysis of phenotypic elements augments traditional electroclinical classification of common familial epilepsies
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.
National Institute of Health (NIH) National Institute of Neurological Disorders and Stroke grant (U01NS077367)
Australian National Health and Medical Research Council program grant (628952)
National Institutes of Health (NIH) grants R01 NS078419, R01 NS104076, RM1 HG007257, R01 GM117946
National Institute of Social Care and Health Research, Epilepsy Research UK
Health Research Council of New Zealand grant (10/402)
Ruth L. Kirschstein National Research Service Award institutional research training grant (T32 NS091008-01)
National Institutes of Health, Grant/Award Number: P50 HG007257, R01 GM117946, R01 NS078419, R01 NS104076 and RM1 HG007257;
CommentsThis is the peer reviewed version of the following article: Epi4K Consortium. Quantitative analysis of phenotypic elements augments traditional electroclinical classification of common familial epilepsies. Epilepsia. 2019;60(11):2194-2203, which has been published in final form at https://doi.org/10.1111/epi.16354.This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Published CitationEpi4K Consortium. Quantitative analysis of phenotypic elements augments traditional electroclinical classification of common familial epilepsies. Epilepsia. 2019;60(11):2194-2203.
Publication Date17 October 2019
- Beaumont Hospital
- School of Pharmacy and Biomolecular Sciences
- Accepted Version (Postprint)