Molecular and clinical spectrum of epilepsy-dyskinesia syndromes: a cross-sectional study of 609 patients
Notice bibliographique
Résumé
Epilepsy-dyskinesia syndromes (EDS) are a complex group of neurogenetic disorders characterized by the co-occurrence of epilepsy and movement disorders. Despite their increasing clinical recognition, the molecular and clinical spectrum of EDS remains poorly understood. While numerous genetic aetiologies have been implicated, systematic characterization across diverse populations is lacking. This study aimed to delineate the molecular and clinical landscape of EDS in a large, multinational cohort, focusing on movement disorder phenomenologies, genotype-phenotype correlations, and treatment responses. We conducted a multicentre, cross-sectional study involving 609 patients with childhood-onset movement disorders associated with pathogenic variants in 105 predefined genes. Clinical data were collected from over 30 centres across 25 countries using a standardized survey, capturing movement disorder phenomenologies, seizure types, developmental trajectories, motor function and treatment outcomes. We classified EDS-associated genes into biologically meaningful groups by performing unsupervised clustering, which integrated protein-protein interactions and functional data. Genotype-phenotype correlations were assessed using a one-versus-remainder approach to quantify differential enrichment of clinical manifestations and treatment responses. Pathogenic variants were identified in 74 of the 105 predefined genes, with 12 genes accounting for two-thirds of cases. The most frequently reported genes were MECP2, ATP1A3, and GNAO1. Data-driven gene cluster analysis identified 12 functional groups, mapping EDS to relevant biological pathways and informing genotype-phenotype analyses. Dystonia (34.2%), stereotypies (24.6%) and ataxia (16.2%) were the most prevalent movement disorders, with gene- and pathway-specific movement disorder signatures extending beyond previously known associations. Notably, most patients exhibited mixed movement disorders, highlighting the phenotypic complexity of EDS. Epilepsy was diagnosed in only 66.8% of cases, suggesting that some EDS primarily manifest as movement disorders. Developmental trajectories varied by genetic aetiology. Pharmacological responses demonstrated gene- and pathway-specific treatment effects, confirming established therapeutic associations (e.g. PRRT2 variants responding to carbamazepine) and identifying previously unrecognized effects, such as exacerbation of motor symptoms with levodopa/carbidopa in GNAO1 and MECP2 variants. This study provides a detailed characterization of EDS, identifying distinct genetic, phenotypic and therapeutic patterns. The findings underscore the need for early recognition of movement disorders within epilepsy cohorts, offer immediate insights to improve anticipatory guidance and clinical management of EDS, and advocate for personalized treatment strategies. By laying the groundwork for longitudinal studies to refine genotype-phenotype correlations and establish a natural history, this work paves the way for interventional clinical trials and precision medicine approaches.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».