Identification of subgroups of children in the Australian Autism Biobank using latent class analysis
Tóm tắt
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). 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. 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’). Data utilised to identify subgroups within this study was cross-sectional as longitudinal data was not available. 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.
Tài liệu tham khảo
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Arlington: American Psychiatric Association; 2013.
Lord C, Elsabbagh M, Baird G, Veenstra-Vanderweele J. Autism spectrum disorder. Lancet. 2018;392(10146):508–20.
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.
Beversdorf DQ. Phenotyping, etiological factors, and biomarkers: toward precision medicine in autism spectrum disorders. J Dev Behav Pediatr. 2016;37(8):659–73.
Hu VW. Subphenotype-dependent disease markers for diagnosis and personalized treatment of autism spectrum disorders. Dis Markers. 2012;33(5):277–88.
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.
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.
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.
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.
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.
Beglinger LJ, Smith TH. A review of subtyping in autism and proposed dimensional classification model. J Autism Dev Disord. 2001;31(4):411–22.
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.
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.
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.
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.
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.
Grzadzinski R, Huerta M, Lord C. DSM-5 and autism spectrum disorders (ASDs): an opportunity for identifying ASD subtypes. Mol Autism. 2013;4(1):12.
Hyman SL, Levy SE, Myers SM. Identification, evaluation, and management of children with autism spectrum disorder. Pediatrics. 2020;145(1): e20193447.
Montgomery A, Yoong T, Eapen V. Autism spectrum disorder in general practice. Med Today. 2021;22(1–2):14–23.
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.
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.
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.
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.
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.
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.
Sparrow SS, Balla DA, Cicchetti DV. Vineland-II adaptive behavior scales. Circle Pines, MN: AGS Publishing; 2005.
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.
Mullen EM. Mullen Scales of Early Learning (AGS Edition). Circle Pines: American Guidance Service Inc; 1995.
Wechsler D. Wechsler intelligence scale for children (WISC-IV), vol. 4. San Antonia: PsychCorp; 2003.
Lazarsfeld P, Henry N. Latent Structure Analysis. Boston: Houghton Mifflin; 1968.
McCutcheon A, Hagenaars J. Applied Latent Class Analysis. Cambridge: Cambridge University Press; 2002.
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.
Lo Y, Mendell NR, Rubin DB. Testing the number of components in a normal mixture. Biometrika. 2001;88(3):767–78.
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.
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.
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.
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.