Data-Driven Insights into Hyperkinetic Disorders in Neurodevelopmental Syndromes and Epileptic Encephalopathies
Hugo Morales-Briceño1,2, Shekeeb S Mohammad3,4, Rajeshwar Reddy Angiti5
1Movement Disorders Unit, Department of Neurology, Westmead Hospital, Westmead, New South Wales, Australia.
This study identifies genetic conditions causing childhood hyperkinetic movement disorders, linking them to neurodevelopmental delay (NDD) and epileptic encephalopathies (DEE). It proposes a diagnostic framework to aid clinicians in identifying these rare genetic disorders.
Area of Science:
- Genetics
- Neurology
- Pediatrics
Background:
- Childhood-onset hyperkinetic movement disorders are linked to various genetic conditions.
- Monogenic disorders associated with neurodevelopmental delay (NDD) and developmental and epileptic encephalopathies (DEE) increasingly present with hyperkinetic movement disorders.
- The full spectrum of genotypes and phenotypes for these conditions remains underexplored.
Purpose of the Study:
- To comprehensively review literature identifying monogenic NDD and DEE disorders associated with hyperkinetic movement disorders (dystonia, chorea, dyskinesia).
- To analyze the frequency of neurological, extra-neurological, and phenomenological features in these disorders.
- To develop data-driven phenotypic groupings for diagnostic strategies.
Main Methods:
- Comprehensive literature review.
- Cluster analysis to identify phenotypic groupings.
- Analysis of neurological, extra-neurological, and phenomenological features.
Main Results:
- 210 monogenic conditions were identified, with 131 segregating into NDD or DEE clusters.
- Dystonia was the most frequent movement disorder, followed by chorea, ataxia, and dyskinesia.
- Specific features like paroxysmal movement disorders and alternating hemiplegia of childhood were identified as diagnostic clues.
Conclusions:
- A clinical and syndromic diagnostic framework is proposed for NDD, DEE, and hyperkinetic movement disorders.
- This framework can assist clinicians, especially in resource-limited settings.
- The approach supports reverse phenotyping to enhance diagnostic precision and genomic data interpretation.
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