Genetic and Metabolic Insights in Myoclonic Epilepsies: A Cohort Study from the NIH Undiagnosed Diseases Program
Abstract number :
1.538
Submission category :
4. Clinical Epilepsy / 4B. Clinical Diagnosis
Year :
2024
Submission ID :
1403
Source :
www.aesnet.org
Presentation date :
12/7/2024 12:00:00 AM
Published date :
Authors :
Presenting Author: Madison LaRoche, – National Institutes of Health
Shino Shimada, MD, PhD – Juntendo University Faculty of Medicine
Ellen Macnamara, ScM, CGC – National Institutes of Health
William Gahl, MD, PhD – National Institutes of Health
Cynthia Tifft, MD, PhD – National Institutes of Health
Maria Acosta, MD – National Institutes of Health
May Malicdan, MD, PhD – National Institutes of Health
Rationale: Myoclonic epilepsies (ME) encompass a spectrum of disorders marked by sudden, involuntary jerks of a single muscle or a group of muscles often co-occurring with seizures. These conditions pose significant diagnostic challenges, particularly in the context of rare and genetically complex disorders. This study leverages the NIH Undiagnosed Disease Program (NIHUDP) to explore the genetic and metabolic landscape of ME.
Methods: We retrospectively analyzed data from patients enrolled in the NIHUDP utilizing human phenotype ontology (HPO) terms that indicate a clinical presentation of seizure and epilepsy. Clinical data, including genetic and MRI findings, were collected and analyzed. Advanced genomic techniques, such as exome and genome sequencing, were employed to identify pathogenic variants.
Results: Our search included NIHUDP patients from 2008 to 2021. Using seizure-associated HPO terms, a total of 209 patients presented with epilepsy, 73 of whom exhibited myoclonus or myoclonic epilepsy. We classified the genetically confirmed ME cohort into three groups: developmental and epileptic encephalopathy (DEE), progressive myoclonic epilepsy (PME), and neurodevelopmental disorder (NDD) with epilepsy. Out of 38 individuals, 19 were categorized as DEE, 4 as adult-onset PME, 3 as NDD with epilepsy, while the remaining 12 individuals were unclassified. Analysis of genomic data uncovered 61 pathogenic or likely pathogenic variants in 25 different genes, with 80% of the variants inherited in an autosomal recessive pattern. Many of these genes are implicated in key metabolic pathways, underscoring the importance of considering metabolic dysfunction in ME. Many of these were loss-of-function or damaging missense mutations, with unaffected carrier parents. Additionally, five probands harbored de novo damaging ultrarare variants in genes such as ADGRV1, GNB1, ASXL3, GRIN2A, SYNGAP1, and DNM1L. Small copy number variants were identified in 3.3% of patients. MRI findings revealed that cortical and midbrain atrophy were common, alongside white matter abnormalities and ventricular enlargement. These neuroimaging abnormalities were often associated with genetic variants impacting metabolic pathways, reinforcing the need for integrated diagnostic approaches.
Conclusions: Our findings underscore the diagnostic complexity of ME, particularly in cases with refractory seizures. The high diagnostic yield, combined with distinct neuroimaging patterns, suggests that early and comprehensive genetic testing should be integrated with neuroimaging to enhance diagnostic accuracy and inform precision therapies. This work presents a novel, in-depth analysis of ME, demonstrating the power of next-generation sequencing in uncovering the molecular bases of these challenging disorders. Our work contributes to the evolving landscape of epilepsy research, with significant implications for improving patient outcomes through targeted therapies.
Funding: This work was funded by the Undiagnosed Diseases Program at the National Institutes of Health.
Clinical Epilepsy