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Alicia Montgomery, Anne Masi, Andrew Whitehouse, Jeremy Veenstra-VanderWeele, Lauren Shuffrey, Mark D. Shen, Lisa Karlov, Mirko Uljarevic, et al.
(2023).
Identification of subgroups of children in the Australian Autism Biobank using latent class analysis.
Child and Adolescent Psychiatry and Mental Health, 17(1), 27.
Springer Science and Business Media LLC.
- Date de publication
-
20/02/2023
- Identifiant
-
10.1186/s13034-023-00565-3
- Auteurs
-
Alicia Montgomery,
Anne Masi,
Andrew Whitehouse,
Jeremy Veenstra-VanderWeele,
Lauren Shuffrey,
Mark D. Shen,
Lisa Karlov,
Mirko Uljarevic,
Gail Alvares,
Sue Woolfenden,
Natalie Silove,
Valsamma Eapen
- Source
- Child and Adolescent Psychiatry and Mental Health
- Détails
- 17(1), 27
- Type de référence
- article
- Éditeur
- Springer Science and Business Media LLC
- Source de métadonnées
- crossref
Résumé
Abstract 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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