Identification of subgroups of children in the Australian Autism Biobank using latent class analysis
Alicia Montgomery1, Anne Masi2, Andrew Whitehouse3
1University of New South Wales, Sydney, Australia. a.k.montgomery@unsw.edu.au.
Insights
This study identified four distinct subgroups of autistic children, revealing varying needs based on autism traits, cognitive abilities, and co-occurring medical and psychiatric conditions. Understanding these profiles is crucial for personalized support and treatment strategies.
Area of Science:
- Neurodevelopmental disorders
- Autism spectrum disorder research
- Child psychiatry and psychology
Background:
- Identifying reproducible autism spectrum disorder (ASD) subtypes is critical for biomarker discovery and tailored interventions.
- Previous research has often overlooked the interplay of medical comorbidities with behavioral, cognitive, and psychiatric data in ASD subgrouping.
- This study aimed to delineate ASD subgroups within the Australian Autism Biobank (AAB) using a comprehensive data profile.
Purpose of the Study:
- To identify distinct subgroups of children with autism spectrum disorder (ASD).
- To analyze differences in behavioral, cognitive, medical, and psychiatric profiles across these subgroups.
- To inform targeted support and treatment strategies for diverse ASD populations.
Main Methods:
- Latent profile analysis (LPA) was employed on data from 1151 children in the AAB.
- Utilized data included social communication, restricted and repetitive behaviors (RRBs), cognitive, medical, and psychiatric profiles.
- The analysis aimed to identify distinct clusters based on these multifaceted profiles.
Main Results:
- Four distinct subgroups of children with ASD were identified, each with unique profiles of autism traits and comorbidities.
- Two subgroups exhibited more severe clinical and cognitive phenotypes, indicating higher support needs.
- One subgroup showed prominent language and cognitive challenges with sensory seeking behaviors; another had high rates of medical/psychiatric comorbidity and RRBs.
- A third subgroup presented moderate autism traits with elevated risks for depression and suicidality, while a fourth group had fewer challenges.
Conclusions:
- The findings underscore the importance of a holistic assessment approach for children with ASD, considering core traits, adaptive functioning, cognition, and co-occurring conditions.
- The identified subgroups highlight the heterogeneity within ASD and the need for individualized support plans.
- Further research involving replication in diverse cohorts is recommended to validate the identified subgroup structure and its broader applicability.
Background:
The identification of reproducible subtypes within autistic populations is a priority research area in the context of neurodevelopment, to pave the way for identification of biomarkers and targeted treatment recommendations. Few previous studies have considered medical comorbidity alongside behavioural, cognitive, and psychiatric data in subgrouping analyses. This study sought to determine whether differing behavioural, cognitive, medical, and psychiatric profiles could be used to distinguish subgroups of children on the autism spectrum in the Australian Autism Biobank (AAB).
Methods:
Latent profile analysis was used to identify subgroups of children on the autism spectrum within the AAB (n = 1151), utilising data on social communication profiles and restricted, repetitive, and stereotyped behaviours (RRBs), in addition to their cognitive, medical, and psychiatric profiles.
Results:
Our study identified four subgroups of children on the autism spectrum with differing profiles of autism traits and associated comorbidities. Two subgroups had more severe clinical and cognitive phenotype, suggesting higher support needs. For the 'Higher Support Needs with Prominent Language and Cognitive Challenges' subgroup, social communication, language and cognitive challenges were prominent, with prominent sensory seeking behaviours. The 'Higher Support Needs with Prominent Medical and Psychiatric and Comorbidity' subgroup had the highest mean scores of challenges relating to social communication and RRBs, with the highest probability of medical and psychiatric comorbidity, and cognitive scores similar to the overall group mean. Individuals within the 'Moderate Support Needs with Emotional Challenges' subgroup, had moderate mean scores of core traits of autism, and the highest probability of depression and/or suicidality. A fourth subgroup contained individuals with fewer challenges across domains (the 'Fewer Support Needs Group').
Limitations:
Data utilised to identify subgroups within this study was cross-sectional as longitudinal data was not available.
Conclusions:
Our findings support the holistic appraisal of support needs for children on the autism spectrum, with assessment of the impact of co-occurring medical and psychiatric conditions in addition to core autism traits, adaptive functioning, and cognitive functioning. Replication of our analysis in other cohorts of children on the autism spectrum is warranted, to assess whether the subgroup structure we identified is applicable in a broader context beyond our specific dataset.
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