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Abstracts

Deep Phenotyping of Epilepsy Subtypes Reveals Co-Occurrence with Neurodevelopmental Disorders: Insights into Clinical Manifestations

Abstract number : 3.194
Submission category : 4. Clinical Epilepsy / 4B. Clinical Diagnosis
Year : 2023
Submission ID : 1221
Source : www.aesnet.org
Presentation date : 12/4/2023 12:00:00 AM
Published date :

Authors :
Presenting Author: Byron Ramirez, BA – Icahn School of Medicine at Mount Sinai

Lea Erjavc, MSc – Research Associate, Psychiatry, Icahn School of Medicine at Mount Sinai; Clodine Lebrun, BA – Research Intern, Psychiatry, Icahn School of Medicine at Mount Sinai; Xiao Lin, PhD – Psychiatry – Icahn School of Medicine at Mount Sinai; Hillary Raynes, MD – Neurology – Icahn School of Medicine at Mount Sinai; Madeline Fields, MD – Neurology – Icahn School of Medicine at Mount Sinai; Lara Marcuse, MD – Neurology – Icahn School of Medicine at Mount Sinai; Maite LaVega-Talbott, MD – Neurology – Icahn School of Medicine at Mount Sinai; Patricia McGoldrick, NP – Neurology – Boston Children’s Health Physicians, Maria Fareri Children's Hospital at Westchester Medical Center; Orrin Devinsky, MD – Neurology – New York University/Langone Health; RWJ Barnabas Health; Steven Wolf, MD – Neurology – Boston Children’s Health Physicians, Maria Fareri Children's Hospital at Westchester Medical Center; Dalila Pinto, PhD – Psychiatry – Icahn School of Medicine at Mount Sinai

Rationale: Neurodevelopmental disorders (NDDs), including Autism Spectrum Disorder (ASD) and Epilepsy, exhibit complex and heterogeneous clinical manifestations, posing challenges in identifying underlying genetic factors. In this study, we conducted deep phenotyping of 673 individuals with epilepsy to categorize them into two groups: Epilepsy with NDD (Intellectual Disability, Developmental Delays, ASD) and Epilepsy without NDD. Rigorous phenotypic characterization of epilepsy cases preceded genetic studies to ensure the relevance and utility of the findings. By integrating comprehensive phenotype data into genetic research, we aimed to gain deeper insights into specific epilepsy subtypes, associated comorbidities, and clinical manifestations. 

Methods: Detailed information about the diagnosis, including epilepsy type, age of onset, and specific comorbidities, were collected for each group. We divided epilepsy into the following types, namely Idiopathic Generalized Epilepsy (IGE), Non-Acquired Focal Epilepsy (NAFE), Lesional Focal Epilepsy (LFE), Epileptic Encephalopathies (EE), as well as Mixed Focal/Generalized Epilepsy (Mixed) group and an uncategorized group. The analysis revealed NAFE as the most prevalent epilepsy subtype, accounting for 36% (240) of cases, followed by EE at 29% (196), IGE at 24% (163), LFE at 7% (48) and a mixed group comprising 3% (17) of cases. The remaining nine cases (1%) fell into the unknown category. 

Results: Notably, a substantial proportion of our cohort (70%; 471 individuals) were diagnosed with ASD, indicating a noteworthy co-occurrence of epilepsy and ASD. Moreover, a high percentage of individuals presented comorbidities such as Intellectual Disability/Developmental Delays (85%; 572) and other NDDs, underscoring the heterogeneity and challenges associated with epilepsy. 

Conclusions: This research highlights the significance of integrating comprehensive phenotype data into genetic investigations of epilepsy. The findings shed light on the co-occurrence of epilepsy and NDDs while providing insights into specific epilepsy subtypes, associated comorbidities, and diverse clinical manifestations. These findings have implications for improved diagnostic approaches and targeted therapeutic interventions, ultimately enhancing the quality of life for individuals affected by epilepsy. Furthermore, the study contributes to advancing our understanding of the clinical nature of epilepsy and its substantial impact on a significant portion of the population. 

Funding:
NIH R01MH110555


Clinical Epilepsy