Evidence map›Paper›PMID 36805686›Full record

ArticleChild and adolescent psychiatry and mental health2023

Identification of subgroups of children in the Australian Autism Biobank using latent class analysis.

Alicia Montgomery, Anne Masi, Andrew Whitehouse, Jeremy Veenstra-VanderWeele, Lauren Shuffrey, Mark D Shen, Lisa Karlov, Mirko Uljarevic, Gail Alvares, Sue Woolfenden and 2 more

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Article in Child and adolescent psychiatry and mental health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Alicia MontgomeryUniversity of New South Wales, Sydney, Australia. a.k.montgomery@unsw.edu.au.
Anne MasiUniversity of New South Wales, Sydney, Australia.
Andrew WhitehouseUniversity of Western Australia, Perth, Australia.
Jeremy Veenstra-VanderWeeleColumbia University, New York, USA.
Lauren ShuffreyColumbia University, New York, USA.
Mark D ShenUniversity of North Carolina, Chapel Hill, USA.
Lisa KarlovUniversity of New South Wales, Sydney, Australia.
Mirko UljarevicUniversity of Melbourne, Melbourne, Australia.
Gail AlvaresUniversity of Western Australia, Perth, Australia.
Sue WoolfendenUniversity of New South Wales, Sydney, Australia.
Natalie SiloveSydney Children's Hospital Network, Randwick, Sydney, Australia.
Valsamma EapenUniversity of New South Wales, Sydney, Australia.

Funding

Cooperative Research Centre for Living with Autism 1.073RURoyal Australasian College of Physicians 2020 NHMRC RACP Fellows Research Entry Scholarship
6 · The paper itself

Abstract

backgroundThe 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).

methodsLatent 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.

resultsOur 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.

conclusionsOur 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.

Indexed as

Autism spectrumLatent class analysisSubgroups

Identifiers

PMID36805686
PMCPMC9940381

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