Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
Child and Adolescent Psychiatry and Mental Health
Published in: Child and Adolescent Psychiatry and Mental Health 1/2023

Open Access 01-12-2023 | Research

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

Authors: 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

Published in: Child and Adolescent Psychiatry and Mental Health | Issue 1/2023

Login to get access

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.
Appendix
Available only for authorised users
Literature
1.
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Arlington: American Psychiatric Association; 2013.CrossRef
2.
3.
Masi A, DeMayo MM, Glozier N, Guastella AJ. An overview of autism spectrum disorder, heterogeneity and treatment options. Neurosci Bull. 2017;33(2):183–93.CrossRefPubMedPubMedCentral
4.
Beversdorf DQ. Phenotyping, etiological factors, and biomarkers: toward precision medicine in autism spectrum disorders. J Dev Behav Pediatr. 2016;37(8):659–73.CrossRefPubMedPubMedCentral
5.
6.
Loth E, Charman T, Mason L, Tillmann J, Jones EJH, Wooldridge C, Ahmad J, Auyeung B, Brogna C, Ambrosino S, et al. The EU-AIMS longitudinal european autism project (LEAP): design and methodologies to identify and validate stratification biomarkers for autism spectrum disorders. Mol Autism. 2017;8:24.CrossRefPubMedPubMedCentral
7.
Wiggins L, Tian L, Levy S, Rice C, Lee L-C, Schieve L, Pandey J, Daniels J, Blaskey L, Hepburn S, et al. Homogeneous subgroups of young children with autism improve phenotypic characterization in the study to explore early development. J Autism Dev Disord. 2017;47(11):3634–45.CrossRefPubMedPubMedCentral
8.
Cholemkery H, Medda J, Lempp T, Freitag C. Classifying autism spectrum disorders by ADI-R: subtypes or severity gradient? J Autism Dev Disord. 2016;46(7):2327–39.CrossRefPubMed
9.
Munson J, Dawson G, Sterling L, Beauchaine T, Zhou A, Elizabeth K, Lord C, Rogers S, Sigman M, Estes A, et al. Evidence for latent classes of IQ in young children with autism spectrum disorder. Am J Mental Retardation. 2008;113(6):439–52.CrossRef
10.
Veatch OJ, Veenstra-Vanderweele J, Potter M, Pericak-Vance MA, Haines JL. Genetically meaningful phenotypic subgroups in autism spectrum disorders. Genes Brain Behav. 2014;13(3):276–85.CrossRefPubMed
11.
Beglinger LJ, Smith TH. A review of subtyping in autism and proposed dimensional classification model. J Autism Dev Disord. 2001;31(4):411–22.CrossRefPubMed
12.
Georgiades S, Szatmari P, Boyle M, Hanna S, Duku E, Zwaigenbaum L, Bryson S, Fombonne E, Volden J, Mirenda P, et al. Investigating phenotypic heterogeneity in children with autism spectrum disorder: a factor mixture modeling approach. J Child Psychol Psychiatry. 2013;54(2):206–15.CrossRefPubMed
13.
Montgomery AK, Shuffrey LC, Guter SJ, Anderson GM, Jacob S, Mosconi MW, Sweeney JA, Turner JB, Sutcliffe JS, Cook EH Jr, et al. Maternal serotonin levels are associated with cognitive ability and core symptoms in autism spectrum disorder. J Am Acad Child Adolesc Psychiatry. 2018;57(11):867–75.CrossRefPubMedPubMedCentral
14.
Frazier TW, Youngstrom EA, Kubu CS, Sinclair L, Rezai A. Exploratory and confirmatory factor analysis of the autism diagnostic interview-revised. J Autism Dev Disord. 2008;38(3):474–80.CrossRefPubMed
15.
Frazier TW, Youngstrom EA, Speer L, Embacher R, Law P, Constantino J, Findling RL, Hardan AY, Eng C. Validation of proposed DSM-5 criteria for autism spectrum disorder. J Am Acad Child Adolesc Psychiatry. 2012;51(1):28-40.e23.CrossRefPubMed
16.
Snow AV, Lecavalier L, Houts C. The structure of the autism diagnostic interview-revised: diagnostic and phenotypic implications. J Child Psychol Psychiatry. 2009;50(6):734–42.CrossRefPubMed
17.
18.
Hyman SL, Levy SE, Myers SM. Identification, evaluation, and management of children with autism spectrum disorder. Pediatrics. 2020;145(1): e20193447.CrossRefPubMed
19.
Montgomery A, Yoong T, Eapen V. Autism spectrum disorder in general practice. Med Today. 2021;22(1–2):14–23.
20.
Sacco R, Lenti C, Saccani M, Curatolo P, Manzi B, Bravaccio C, Persico AM. Cluster analysis of autistic patients based on principal pathogenetic components. Autism Res. 2012;5(2):137–47.CrossRefPubMed
21.
Aldinger K, Lane C, Veenstra-VanderWeele J, Levitt P. Patterns of risk for multiple co-occurring medical conditions replicate across distinct cohorts of children with autism spectrum disorder. Autism Res. 2015;8:771–81.CrossRefPubMedPubMedCentral
22.
Alvares GA, Dawson PA, Dissanayake C, Eapen V, Gratten J, Grove R, Henders A, Heussler H, Lawson L, Masi A, et al. Study protocol for the Australian autism biobank: an international resource to advance autism discovery research. BMC Pediatr. 2018;18(1):284.CrossRefPubMedPubMedCentral
23.
Lord C, Rutter M, DiLavore PC, Risi S, Gotham K, Bishop SL. Autism diagnostic observation schedule, (ADOS-2), Part 1: Modules 1–4. Los Angeles: Western Psychological Services; 2012.
24.
Lord C, Risi S, Lambrecht L, Cook EH Jr, Leventhal BL, DiLavore PC, Pickles A, Rutter M. The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism. J Autism Dev Disord. 2000;30(3):205–23.CrossRefPubMed
25.
Skuse D, Warrington R, Bishop D, Chowdhury U, Lau J, Mandy W, Place M. The developmental, dimensional and diagnostic interview (3di): a novel computerized assessment for autism spectrum disorders. J Am Acad Child Adolesc Psychiatry. 2004;43(5):548–58.CrossRefPubMed
26.
Sparrow SS, Balla DA, Cicchetti DV. Vineland-II adaptive behavior scales. Circle Pines, MN: AGS Publishing; 2005.
27.
McIntosh DN, Miller LJ, Shyu V, Dunn W. Overview of the short sensory profile. In: Dunn W, editor. Sensory profile user’s manual. San Antonio: San Antonio Pearsons; 1999. p. 59–73.
28.
Mullen EM. Mullen Scales of Early Learning (AGS Edition). Circle Pines: American Guidance Service Inc; 1995.
29.
Wechsler D. Wechsler intelligence scale for children (WISC-IV), vol. 4. San Antonia: PsychCorp; 2003.
30.
Lazarsfeld P, Henry N. Latent Structure Analysis. Boston: Houghton Mifflin; 1968.
31.
McCutcheon A, Hagenaars J. Applied Latent Class Analysis. Cambridge: Cambridge University Press; 2002.
32.
Ramaswamy V, Desarbo WS, Reibstein DJ, Robinson WT. An empirical pooling approach for estimating marketing mix elasticities with PIMS data. Mark Sci. 1993;12(1):103–24.CrossRef
33.
Lo Y, Mendell NR, Rubin DB. Testing the number of components in a normal mixture. Biometrika. 2001;88(3):767–78.CrossRef
34.
Agelink van Rentergem JA, Deserno MK, Geurts HM. Validation strategies for subtypes in psychiatry: a systematic review of research on autism spectrum disorder. Clin Psychol Rev. 2021;87:102033.CrossRefPubMed
35.
Vargason T, Frye RE, McGuinness DL, Hahn J. Clustering of co-occurring conditions in autism spectrum disorder during early childhood: a retrospective analysis of medical claims data. Autism Res. 2019;12(8):1272–85.CrossRefPubMedPubMedCentral
36.
Cohen S, Fulcher BD, Rajaratnam SMW, Conduit R, Sullivan JP, Hilaire MAS, Phillips AJ, Loddenkemper T, Kothare SV, McConnell K, et al. Behaviorally-determined sleep phenotypes are robustly associated with adaptive functioning in individuals with low functioning autism. Sci Rep. 2017;7(1):14228.CrossRefPubMedPubMedCentral
37.
Careaga M, Rogers S, Hansen RL, Amaral DG, Van de Water J, Ashwood P. Immune endophenotypes in children with Autism Spectrum disorder. Biol Psych. 2017;81(5):434–41.CrossRef
Metadata
Title
Identification of subgroups of children in the Australian Autism Biobank using latent class analysis
Authors
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
Publication date
01-12-2023
Publisher
BioMed Central
Published in
Child and Adolescent Psychiatry and Mental Health / Issue 1/2023
Electronic ISSN: 1753-2000
DOI
https://doi.org/10.1186/s13034-023-00565-3

Other articles of this Issue 1/2023

Child and Adolescent Psychiatry and Mental Health 1/2023 Go to the issue

Correction

Correction: Resilient phenotypes among bereaved youth: a comparison of trajectory, relative, and cross-domain approaches

Research

The impact of family alexithymia on the severity of restrictive eating disorders in adolescent patients

Correction

Correction to: Work-family interface and children’s mental health: a systematic review

Review

Gender dysphoria in adolescents and young adults

Research

The effectiveness of psychoeducation and problem-solving on depression and treatment adherence in adolescents living with HIV in Botswana: an exploratory clinical trial

Research

The impacts of anxiety and depressive symptoms on emotional processing in children and their parents: an event-related potential study