Comprehensive literature review identifies monogenic disorders linked to hyperkinetic movement disorders, suggesting improved diagnostic strategies.
Background Childhood‐onset hyperkinetic movement disorders occur in a range of genetic conditions. Recently, there has been an increase in recognition of hyperkinetic movement disorders, mainly dystonia, chorea and dyskinesia, with monogenic conditions associated with neurodevelopmental delay (NDD) and also with developmental and epileptic encephalopathies (DEE), however, the full spectrum of genotypes and phenotypes remains underexplored. Objectives We conducted a comprehensive literature review to identify monogenic NDD and DEE disorders that are reported with hyperkinetic movement disorders—specifically dystonia, chorea, and dyskinesia—and analyzed the frequency of neurological, extra‐neurological and phenomenological features. Methods Using cluster analysis, we identified data‐driven phenotypic groupings to inform diagnostic strategies. Results Among 210 monogenic conditions (44.7% autosomal dominant, 45.2% autosomal recessive, 7.6% X‐linked), 131 (62.3%) segregated into two major clusters: one predominantly NDD and the other predominantly DEE. A variable combination of neurological, extra‐neurological and imaging features, as well as phenomenologies, distinguished subclusters within the NDD and DEE groups. Across both groups, dystonia was the most frequently reported movement disorder, followed in order by chorea, ataxia, dyskinesia, and myoclonus. Only a small subset of conditions expressed paroxysmal movement disorders and alternating hemiplegia of childhood, highlighting these clinical features as specific diagnostic clues. Conclusion Based on these findings, we propose a clinical and syndromic diagnostic framework for clinicians evaluating patients with NDD, DEE, and hyperkinetic movement disorders. This approach may aid diagnosis in settings lacking access to next‐generation sequencing and support reverse phenotyping to improve diagnostic precision and interpretation of genomic data.
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Morales‐Briceño et al. (2026) studied this question.
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