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Latent Class Analysis Identifies Distinctive Behavioral Subtypes in Children with Fragile X Syndrome.

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Title: Latent Class Analysis Identifies Distinctive Behavioral Subtypes in Children with Fragile X Syndrome.
Authors: Kaufmann, Walter E., Raspa, Melissa, Bann, Carla M., Gable, Julia M., Harris, Holly K., Budimirovic, Dejan B., Lozano, Reymundo, Berry-Kravis, Elizabeth, Velinov, Milen, Talboy, Amy L, Sherman, Stephanie L, Kaufmann, Walter E, Schuster, Marcy, Tartaglia, Nicole, Filipink, Robyn A, Budimirovic, Dejan B, Barbouth, Deborah, Lightbody, Amy, Reiss, Allan, Delahunty, Carol M
Source: Journal of Autism & Developmental Disorders. Feb2024, Vol. 54 Issue 2, p725-737. 13p.
Subjects: Cross-sectional method, Neurologic manifestations of general diseases, Research funding, Autism, Questionnaires, Fragile X syndrome, Structural equation modeling, Sensory disorders, Intellectual disabilities, Asperger's syndrome, Child behavior, Sleep disorders
Abstract: Fragile X syndrome (FXS) is characterized by variable neurobehavioral abnormalities, which leads to difficulties in developing and evaluating treatments and in determining accurate prognosis. We employed a pediatric cross-sectional sample (1,072 males, 338 females) from FORWARD, a clinic-based natural history study, to identify behavioral subtypes by latent class analysis. Input included co-occurring behavioral conditions, sleep and sensory problems, autistic behavior scales (SCQ, SRS-2), and the Aberrant Behavior Checklist revised for FXS (ABCFX). A 5-class solution yielded the most clinically meaningful, pharmacotherapy independent behavioral groups with distinctive SCQ, SRS-2, and ABCFX profiles, and adequate non-overlap (≥ 71%): "Mild" (31%), "Moderate without Social Impairment" (32%), "Moderate with Social Impairment" (7%), "Moderate with Disruptive Behavior" (20%), and "Severe" (9%). Our findings support FXS subtyping, for improving clinical management and therapeutic development. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Autism & Developmental Disorders is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Latent Class Analysis Identifies Distinctive Behavioral Subtypes in Children with Fragile X Syndrome.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Autism+%26+Developmental+Disorders%22">Journal of Autism & Developmental Disorders</searchLink>. Feb2024, Vol. 54 Issue 2, p725-737. 13p.
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  Data: Fragile X syndrome (FXS) is characterized by variable neurobehavioral abnormalities, which leads to difficulties in developing and evaluating treatments and in determining accurate prognosis. We employed a pediatric cross-sectional sample (1,072 males, 338 females) from FORWARD, a clinic-based natural history study, to identify behavioral subtypes by latent class analysis. Input included co-occurring behavioral conditions, sleep and sensory problems, autistic behavior scales (SCQ, SRS-2), and the Aberrant Behavior Checklist revised for FXS (ABCFX). A 5-class solution yielded the most clinically meaningful, pharmacotherapy independent behavioral groups with distinctive SCQ, SRS-2, and ABCFX profiles, and adequate non-overlap (≥ 71%): "Mild" (31%), "Moderate without Social Impairment" (32%), "Moderate with Social Impairment" (7%), "Moderate with Disruptive Behavior" (20%), and "Severe" (9%). Our findings support FXS subtyping, for improving clinical management and therapeutic development. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Autism & Developmental Disorders is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10803-022-05821-7
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              Text: Feb2024
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