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Published on: July 31, 2017
Latent Profile Analysis of Sleep Patterns in Children With Autism Spectrum Disorder
Ke Wang1,2, Qiuhong Wei1, Ting Yang1
1Children Nutrition Research Center Children's Hospital of Chongqing Medical University National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders Chongqing Key Laboratory of Child Nuerodevelopment and Cognitive Disorders Chongqing China.
Insights
Children with autism spectrum disorder (ASD) show distinct sleep patterns. Identifying these sleep phenotypes early can help predict treatment response and guide personalized interventions for better outcomes.
Area of Science:
- Neurodevelopmental Disorders
- Pediatric Sleep Medicine
- Autism Spectrum Disorder Research
Background:
- Sleep disturbances are common and impactful in children with autism spectrum disorder (ASD).
- The diverse nature of sleep problems in ASD necessitates a deeper understanding of specific sleep phenotypes.
- Previous research has not fully characterized the heterogeneity of sleep disturbances in young children with ASD.
Purpose of the Study:
- To identify distinct sleep phenotypes in young children with ASD using latent profile analysis.
- To compare sleep profiles between children with ASD and typically developing (TD) children.
- To investigate the relationship between identified sleep phenotypes and treatment response over one year.
Main Methods:
- A multicenter prospective cohort study involving 631 children with ASD and 768 TD children (aged 3-6 years).
- Latent profile analysis applied to data from the Children's Sleep Habits Questionnaire.
- One-year follow-up data collected to assess changes in core ASD symptoms.
Main Results:
- Three distinct sleep phenotypes were identified in children with ASD: severe multi-domain disturbances (Cluster 1), mixed profile with elevated sleep-disordered breathing (Cluster 2), and elevated night waking/bedtime resistance with reduced sleep-disordered breathing (Cluster 3).
- Children with ASD generally exhibited poorer sleep patterns compared to TD controls.
- Children in Cluster 3 showed significant improvements in core ASD symptoms, particularly social cognition and communication, after one year, while Clusters 1 and 2 showed modest changes.
Conclusions:
- Distinct sleep phenotypes exist in young children with ASD, varying in severity and type of sleep disturbance.
- Early identification of these sleep phenotypes may serve as a predictor for treatment response in ASD.
- Personalized sleep management strategies tailored to specific phenotypes are crucial for improving core symptoms in children with ASD.
Abstract:
Sleep disturbances significantly impact children with autism spectrum disorder (ASD), yet their heterogeneous manifestations remain poorly understood. This multicenter prospective cohort study employed latent profile analysis to identify distinct sleep phenotypes among 631 children with ASD (aged 3-6 years) and 768 typically developing (TD) controls across three Chinese cities representing Northern, Central, and Western regions. Analysis of Children's Sleep Habits Questionnaire data revealed three distinct sleep phenotypes based on optimal model fit determined by Bayesian information criterion. Compared to TD children who showed generally better sleep patterns with lower sleep onset delay and fewer disturbances overall, the ASD groups exhibited distinctive profiles: Cluster 1 (9.2%) exhibited severe disturbances across multiple domains (sleep anxiety, parasomnias, night wakings and sleep-disordered breathing) and demonstrated the most severe autism symptoms; Cluster 2 (36.0%) presented a mixed profile with comparable bedtime resistance, sleep duration, and daytime sleepiness to TD children but elevated sleep-disordered breathing; and Cluster 3 (54.8%) showed reduced sleep-disordered breathing but elevated night waking and bedtime resistance. One-year follow-up data indicated that Cluster 3, characterized by mild sleep-disordered breathing, showed significant improvements in core symptoms particularly in social cognition, communication, and motivation domains, whereas Clusters 1 and 2 demonstrated modest changes. These findings suggest that early identification of sleep phenotypes may predict treatment response and inform personalized intervention strategies. Our results underscore the importance of incorporating comprehensive sleep assessment and management into ASD care protocols.
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