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Lenker KP, Li Y, Fernandez-Mendoza J, Mayes SD, Calhoun SL. Autism Spectrum Disorder Phenotypes Based on Sleep Dimensions and Core Autism Symptoms. J Autism Dev Disord 2025:10.1007/s10803-025-06822-y. [PMID: 40244506 DOI: 10.1007/s10803-025-06822-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/31/2025] [Indexed: 04/18/2025]
Abstract
Previous studies have used cluster analysis to address the diagnostic heterogeneity of autism spectrum disorder, but have been limited by identifying subgroups solely on the basis of core autism symptoms. The present study aimed to identify sleep phenotypes and their clustering with core autism symptoms in youth diagnosed with autism. 1397 patients (1-17y, M = 6.1 ± 3.3y; M IQ = 88.5 ± 27.2; 81.2% male, 89.0% white) with autism. Principal component analysis (PCA) was performed on 10 sleep items from the Pediatric Behavior Scale. Latent class analyses (LCA) determined phenotypes characterized by core autism symptoms and sleep clusters, accounting for age, sex, Intelligence Quotient (IQ), and medication use.PCA identified three distinct sleep clusters (disturbed sleep, insufficient sleep and hypersomnolence) explaining 48.4% of the variance. LCA revealed four phenotypes based on core ASD symptoms and sleep clusters. Compared to Class 1 (54.8%) as the reference group, Class 2 (26.3%) had a similar degree of sleep problems, higher IQ and milder autism symptoms, less problems with selective attention/fearlessness; Class 3 (14.5%) was characterized by insufficient and disturbed sleep, perseveration and somatosensory disturbance, and higher medication use, while Class 4 (4.4%) was by hypersomnolence, problems with social interactions, and higher medication use.We found four distinct clustering of core autism symptoms and sleep problems differing in their sleep profiles as well as in relation to clinical characteristics, demographics, internalizing/externalizing symptoms, and functional outcomes. Our findings underscore the heterogeneity of autism based on sleep-wake problems, advocating for personalized therapeutic interventions targeting nighttime sleep and daytime alertness.
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Affiliation(s)
- Kristina P Lenker
- Sleep Research & Treatment Center, Penn State Milton S. Hershey Medical Center, College of Medicine, Department of Psychiatry and Behavioral Health, Penn State University, Hershey, PA, USA.
| | - Yanling Li
- Social Science Research Institute, Pennsylvania State University, Hershey, PA, USA
| | - Julio Fernandez-Mendoza
- Sleep Research & Treatment Center, Penn State Milton S. Hershey Medical Center, College of Medicine, Department of Psychiatry and Behavioral Health, Penn State University, Hershey, PA, USA
| | - Susan D Mayes
- Department of Psychiatry and Behavioral Health, College of Medicine, Pennsylvania State University, Hershey, PA , USA
| | - Susan L Calhoun
- Sleep Research & Treatment Center, Penn State Milton S. Hershey Medical Center, College of Medicine, Department of Psychiatry and Behavioral Health, Penn State University, Hershey, PA, USA.
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Masi A, Moni MA, Azim SI, Choi B, Heussler H, Lin PI, Diaz AM, Eapen V. Clinical and behavioral attributes leading to sleep disorders in children on the autism spectrum. Autism Res 2022; 15:1274-1287. [PMID: 35596587 DOI: 10.1002/aur.2745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 04/16/2022] [Indexed: 11/07/2022]
Abstract
Sleep disorders are a common comorbid condition in children diagnosed with autism spectrum disorder ("autism"). However, the relationship between the clinical features of autism and sleep disorders remains unclear. A better understanding of the inherent autism-related characteristics linked to comorbid sleep disorders would improve comprehensive assessment and management. This study examined the relationship between sociodemographics, autism symptoms, sleep problems, cognitive status, behavioral attributes, and sensory profiles. Using data from 1268 participants who took part in the Australian Autism Biobank, sleep-related measurements using the Child Sleep Habits Questionnaire (CSHQ) were compared between autistic children aged 2 to 17 (N = 969), their siblings (N = 188), and unrelated children without an autism diagnosis (N = 111). The known relationship between sleep problems and autism was further explored by including scores from the Autism Diagnostic Observation Schedule-2, Mullen Scales of Early Learning, Vineland Adaptive Behavioral Scale-II and the Short Sensory Profile-2; which were included in analyses for autistic participants who had a completed CSHQ. Multiple regression models were used to identify clinical/behavioral variables associated with CSHQ subscales. The autism group had a significantly higher total CSHQ score than the sibling and comparison groups (p < 0.001), indicating worse sleep quality. Within the autism group, lower adaptive behaviors (i.e., VABS-II) and sensory issues (i.e., SSP-2 subclass scores) were positively associated with the severity of sleep problems (i.e., the CSHQ subclass scores) (p < 0.001). The significant functional impact of poor sleep on autistic children warrants an assessment of sleep as a critical part of a holistic approach to supporting autistic children and their families. LAY SUMMARY: Autistic children generally have co-occurring conditions. Sleep disorders impact approximately 50%-80% of autistic children. The impact on the quality of life for both the children and their families can be significant. This study compares sleep problems in autistic children and adolescents with their siblings and children without a diagnosis of autism, and investigates the relationship between specific autistic traits, daily life behaviors and sleep problems. The findings highlight the importance of a holistic assessment for autistic children and matching appropriate sleep intervention and supports where indicated.
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Affiliation(s)
- Anne Masi
- School of Psychiatry, Faculty of Medicine, UNSW Sydney, Kensington, New South Wales, Australia
| | - Mohammod Ali Moni
- School of Psychiatry, Faculty of Medicine, UNSW Sydney, Kensington, New South Wales, Australia
| | - Syeda Ishra Azim
- School of Psychiatry, Faculty of Medicine, UNSW Sydney, Kensington, New South Wales, Australia
| | - Byungkuk Choi
- School of Psychiatry, Faculty of Medicine, UNSW Sydney, Kensington, New South Wales, Australia
| | - Helen Heussler
- Centre for Children's Health Research, University of Queensland, South Brisbane, Queensland, Australia.,Child Development, Child and Youth Community Health Services, Queensland Children's Hospital, South Brisbane, Queensland, Australia
| | - Ping-I Lin
- School of Psychiatry, Faculty of Medicine, UNSW Sydney, Kensington, New South Wales, Australia.,Mental Health Research Unit, South Western Sydney Local Health District, Liverpool, New South Wales, Australia.,Ingham Institute, Liverpool Hospital, Liverpool, New South Wales, Australia
| | - Antonio Mendoza Diaz
- School of Psychiatry, Faculty of Medicine, UNSW Sydney, Kensington, New South Wales, Australia.,Academic Unit of Child Psychiatry, South Western Sydney Local Health District, Liverpool, New South Wales, Australia
| | - Valsamma Eapen
- School of Psychiatry, Faculty of Medicine, UNSW Sydney, Kensington, New South Wales, Australia.,Ingham Institute, Liverpool Hospital, Liverpool, New South Wales, Australia.,Academic Unit of Child Psychiatry, South Western Sydney Local Health District, Liverpool, New South Wales, Australia
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