Cross-sectional study identifies cognitive and emotional-behavioral difficulties in children with DMD, suggesting need for screening.
Background: Duchenne muscular dystrophy (DMD) is an X-linked disorder caused by out-of-frame variants in the DMD gene, resulting in dystrophin deficiency and progressive muscle degeneration. Beyond motor involvement, evidence links DMD to cognitive impairment and an emotional–behavioral (EB) burden, potentially related to the altered expression of brain dystrophin isoforms (Dp71, Dp140, Dp427). Objectives: To screen for major neurodevelopmental, cognitive and/or EB difficulties in a monocentric cohort of children with DMD. Methods: This cross-sectional study included 21 children with DMD. Neuropsychiatric difficulties were assessed using a multimodal psychometric battery. Cognitive, neurodevelopmental and genetic data were retrospectively collected and analyzed. Results: In the cohort, attention-deficit/hyperactivity disorder (ADHD)-related findings were predominantly inattentive, with 3/20 children (15.0%) scoring within the clinical range on at least one inattention subscale. For measures assessing autism spectrum disorder (ASD)-related features, scores above the normative cutoff emerged in 7/20 children (35.0%), while only 2/20 (10.0%) scored within the clinical range. Internalizing problems represented the predominant EB difficulties, and emotional dysregulation (ED) emerged as a plausible area of vulnerability in the cohort. Among the 11 of 21 children with available Full-Scale Intelligence Quotient (FSIQ) data, five (45.5%) had an FSIQ below 85. Of these, four of five (80.0%) presented the predicted Dp140−/Dp71+ brain dystrophin isoform expression pattern, whereas one of five (20.0%) presented the Dp140+/Dp71+ pattern. Conclusions: Children with DMD showed heterogeneous neuropsychiatric and cognitive features in the employed screening battery. These preliminary findings, if confirmed in larger cohorts, support the potential of a broader neuropsychiatric screening assessment to optimize care pathways.
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D’Alessandro et al. (2026) studied this question.
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