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Journal of Autism and Developmental Disorders

, Volume 47, Issue 11, pp 3634–3645 | Cite as

Homogeneous Subgroups of Young Children with Autism Improve Phenotypic Characterization in the Study to Explore Early Development

  • Lisa D. Wiggins
  • Lin H. Tian
  • Susan E. Levy
  • Catherine Rice
  • Li-Ching Lee
  • Laura Schieve
  • Juhi Pandey
  • Julie Daniels
  • Lisa Blaskey
  • Susan Hepburn
  • Rebecca Landa
  • Rebecca Edmondson-Pretzel
  • William Thompson
Original Paper

Abstract

The objective of this study was to identify homogenous classes of young children with autism spectrum disorder (ASD) to improve phenotypic characterization. Children were enrolled in the Study to Explore Early Development between 2 and 5 years of age. 707 children were classified with ASD after a comprehensive evaluation with strict diagnostic algorithms. Four classes of children with ASD were identified from latent class analysis: mild language delay with cognitive rigidity, mild language and motor delay with dysregulation, general developmental delay, and significant developmental delay with repetitive motor behaviors. We conclude that a four-class phenotypic model of children with ASD best describes our data and improves phenotypic characterization of young children with ASD. Implications for screening, diagnosis, and research are discussed.

Keywords

Autism Autism spectrum disorder Characterization Phenotypes Subgroups 

Notes

Acknowledgments

We would like to thank the children and families who participated in this research. This publication was supported by six cooperative agreements from the Centers for Disease Control and Prevention: Cooperative Agreement Number U10DD000180, Colorado Department of Public Health; Cooperative Agreement Number U10DD000181, Kaiser Foundation Research Institute (CA); Cooperative Agreement Number U10DD000182, University of Pennsylvania; Cooperative Agreement Number U10DD000183, Johns Hopkins University; Cooperative Agreement Number U10DD000184, University of North Carolina at Chapel Hill; and Cooperative Agreement Number U10DD000498, Michigan State University. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

Author Contributions

Study concept (LW), study design and methods (all authors), statistical plan (WT, LT, and LW), statistical analysis (LT), statistical review and interpretation (all authors), manuscript preparation and/or review (all authors).

Compliance with Ethical Standards

Conflict of interest

The authors do not have any conflicts of interest to report.

References

  1. Achenbach, T. (1992). Child behavior checklist. Burlington, VT: Achenbach System of Empirically Based Assessment.Google Scholar
  2. Allen, C., Silove, N., Williams, K., & Hutchins, P. (2007). Validity of the Social Communication Questionnaire in assessing risk of autism in preschool children with developmental problems. Journal of Autism and Developmental Disorders, 37(7), 1272–1278.CrossRefPubMedGoogle Scholar
  3. American Psychiatric Association (1980). Diagnostic and statistical manual of mental disorders (3rd edn.). Washington, DC: American Psychiatric Association.Google Scholar
  4. American Psychiatric Association (1987). Diagnostic and statistical manual of mental disorders (3rd ed., rev.). Washington, DC: American Psychiatric Association.Google Scholar
  5. American Psychiatric Association (1994). Diagnostic and statistical manual of mental disorders (4th edn.). Washington, DC: American Psychiatric Association.Google Scholar
  6. American Psychiatric Association (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: American Psychiatric Association.Google Scholar
  7. American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders (5th edn.). Arlington, VA: American Psychiatric Publishing.CrossRefGoogle Scholar
  8. Asparouhov, T., & Muthén, B. (2013). Auxiliary variables in mixture modeling: 3-Step approaches using Mplus. Accessed on January 22, 2016 from http://www.statmodel.com/examples/webnotes/webnote15.pdf.
  9. Autism and Developmental Disabilities Monitoring Network Surveillance Year 2010 Principal Investigators. (2014). Prevalence of autism spectrum disorder among children aged 8 years—Autism and developmental disabilities monitoring network, 11 sites, United States, 2010. Morbidity and Mortality Weekly Report Surveillance Summaries, 63(SS02), 1–21.Google Scholar
  10. Close, H. A., Lee, L. C., Kaufmann, C. N., & Zimmerman, A. W. (2012). Co-occurring conditions and change in diagnosis in autism spectrum disorder. Pediatrics, 129, 305–316.CrossRefGoogle Scholar
  11. Constantino, J. (2011). The quantitative nature of autistic social impairment. Pediatric Research, 69, 55R-62R.CrossRefPubMedPubMedCentralGoogle Scholar
  12. Frazier, T. W., Youngstrom, E. A., Kuba, C. S., Sinclair, L., & Rezai, A. (2008). Exploratory and confirmatory factor analysis of the autism diagnostic interview—Revised. Journal of Autism and Developmental Disorders, 38, 474–480.CrossRefPubMedGoogle Scholar
  13. Frazier, T. W., Youngstron, E. A., Speer, L., Embacher, R., Law, P., Constantino, J., et al. (2012). Validation of proposed DSM-5 criteria for autism spectrum disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 51(1), 28–40.CrossRefPubMedGoogle Scholar
  14. Georgiades, S., Szatmari, P., Boyle, M., Hanna, S., Duku, E., Zwaigenbaum, L., et al. (2013). Investigating phenotypic heterogeneity in children with autism spectrum disorder: A factor mixture modeling approach. Journal of Child Psychology and Psychiatry, 54, 206–215.CrossRefPubMedGoogle Scholar
  15. Gotham, K., Risi, S., Pickles, A., & Lord, C. (2007). The autism diagnostic observation schedule: Revised algorithms for improved diagnostic validity. Journal of Autism and Developmental Disorders, 37, 613–627.CrossRefPubMedGoogle Scholar
  16. Grzadzinski, R., Huerta, M., & Lord, C. (2013). DSM-5 and autism spectrum disorders (ASDs): An opportunity for identifying ASD subtypes. Molecular Autism, 4, 1–6.CrossRefGoogle Scholar
  17. Hu, V. W., Sarachana, T., Kim, K. S., Nguyen, A., Kulkarni, S., Steinberg, M. E., et al. (2009). Gene expression profiling differentiates autism case-controls and phenotypic variants of autism spectrum disorders: Evidence for circadian rhythm dysfunction in severe autism. Autism Research, 2, 78–97.CrossRefPubMedPubMedCentralGoogle Scholar
  18. Hu, V. W., & Steinberg, M. E. (2009). Novel clustering of items from the autism diagnostic interview – revised to define phenotypes within autism spectrum disorders. Autism Research, 2, 67–77.CrossRefPubMedPubMedCentralGoogle Scholar
  19. Kozlowski, A. M., Matson, J. L., Horovitz, M., Worley, J. A., & Neal, D. (2011). Parents’ first concerns of their child’s development in toddlers with autism spectrum disorder. Developmental Neurorehabilitation, 14(2), 72–78.CrossRefPubMedGoogle Scholar
  20. Lai, M.-C., Lombardo, M. V., Chakrabarti, B., & Baron-Cohen, S. (2013) Subgrouping the autism “spectrum”: Reflections on DSM-5. PLoS Biology 11(4), e1001544. doi: 10.1371/journal.pbio.1001544.CrossRefPubMedPubMedCentralGoogle Scholar
  21. Lee, L.-C., David, A. B., Rusyniak, J., Landa, R., & Newschaffer, C. J. (2007). Performance of the social communication questionnaire in children receiving preschool special education services. Research in Autism Spectrum Disorders, 1, 126–138.CrossRefGoogle Scholar
  22. Levy, S. E., Giarelli, E., Lee, L. C., Schieve, L., Kirby, R., Cunniff, C., et al. (2010). Autism spectrum disorder and co-occurring developmental, psychiatric, and medical conditions among children in multiple populations of the United States. Journal of Developmental and Behavioral Pediatrics, 31(4), 267–275.CrossRefPubMedGoogle Scholar
  23. Lord, C., Risi, S., Lambrecht, L., Cook, E. H., Leventhal, B. L., DiLavore, P. C., et al. (2000). The autism diagnostic observation schedule-generic: A standard measure of social and communication deficits associated with the spectrum of autism. Journal of Autism and Developmental Disorders, 30, 205–223.CrossRefPubMedGoogle Scholar
  24. Lord, C., Rutter, M., DiLavore, P. C., & Risi, S. (1999). Autism diagnostic observation schedule. Los Angeles, CA: Western Psychological Services.Google Scholar
  25. Lord, C., Rutter, M., & Le Couteur, A. L. (1994). Autism diagnostic interview-revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of Autism and Developmental Disorders, 24, 659–685.CrossRefPubMedGoogle Scholar
  26. McGuire, K., Fung, L. K., Hagopian, L., Vasa, R. A., Mahajan, R., Bernal, P., et al. (2016). Irritability and problem behavior in autism spectrum disorder: A practice pathway for pediatric primary care. Pediatrics, 137, S136-S148.CrossRefGoogle Scholar
  27. Mullen, E. (1995). Mullen scales of early learning. San Antonio, TX: Pearson.Google Scholar
  28. Munson, J., Dawson, G., Sterling, L., Beauchaine, T., Zhou, A., Koehler, E., et al. (2008). Evidence for latent classes of IQ in young children with autism spectrum disorder. American Journal of Mental Retardation, 113, 439–452.CrossRefPubMedPubMedCentralGoogle Scholar
  29. Muthén, L. K., & Muthén, B. O. (2015). Mplus user’s guide (7th ed.). Los Angeles, CA: Publishing.Google Scholar
  30. Nylund, K. L., Asparouhov, T., & Muthén, B. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo Simulation Study. SEM: A Multidisciplinary Journal, 14, 535–569.Google Scholar
  31. Owens, J. A., Spirito, A., & McGuinn, M. (2000). The children’s sleep habits questionnaire (CSHQ): Psychometric properties of a survey instrument for school-aged children. Sleep, 23(8), 1043–1051.CrossRefPubMedGoogle Scholar
  32. Ozonoff, S., Williams, B. J., & Landa, R. (2005). Parental report of the early development of children with regressive autism: The delays-plus regression phenotype. Autism: The International Journal of Research and Practice, 9, 461–486.CrossRefGoogle Scholar
  33. Rapin, I. (Ed.). (1996). Preschool children with inadequate communication. New Jersey: Mac Keith Press.Google Scholar
  34. Ring, H., Woodbury-Smith, M., Watson, P., Wheelwright, S., & Baron-Cohen, S. (2008). Clinical heterogeneity among people with high functioning autism spectrum conditions: Evidence f avouring a continuous severity gradient. Behavior and Brain Functioning, 4(11), 1–6.Google Scholar
  35. Rutter, M. A., Bailey, A., & Lord, C. (2003). The social communication questionnaire. Los Angeles, CA: Western Psychological Services.Google Scholar
  36. Sacco, R., Curatolo, P., Manzi, B., Militerni, R., Bravaccio, C., Frolli, A., et al. (2010). Principal pathogenetic components and biological endophenotypes in autism spectrum disorders. Autism Research, 3, 237–252.CrossRefPubMedGoogle Scholar
  37. Sacco, R., Lenti, C., Saccani, M., Curatolo, P., Manzi, B., Bravaccio, C., et al. (2012). Cluster analysis of autistic patients based on principal pathogenetic components. Autism Research, 5, 137–147.CrossRefPubMedGoogle Scholar
  38. Schendel, D., DiGuiseppi, C., Croen, L., Fallin, D., Reed, P., Schieve, L., et al. (2012). The Study to Explore Early Development (SEED): A multi-site epidemiologic study of autism by the centers for autism and developmental disabilities research and epidemiology (CADDRE) network. Journal of Autism and Developmental Disorders, 42, 2121–2140.CrossRefPubMedPubMedCentralGoogle Scholar
  39. Snow, A. V., Lecavalier, L., & Houts, C. (2009). The structure of the Autism diagnostic interview—Revised: Diagnostic and phenotypic implications. Journal of Child Psychology and Psychiatry, 50(6), 734–742.CrossRefPubMedGoogle Scholar
  40. Sparrow, S., Balla, D., & Cicchetti, D. (2005). Vineland adaptive behavior scales, 2nd edition. San Antonio, TX: Pearson.Google Scholar
  41. Spiker, D., Lotspeich, I. J., Dimiceli, S., Myers, R. M., & Risch, N. (2002). Behavioral phenotypic variation in autism multiplex families: Evidence for a continuous severity gradient. Journal of Medical Genetics, 11, 129–136.CrossRefGoogle Scholar
  42. Stevens, M. C., Fein, D. A., Dunn, M., Allen, D., Waterhouse, L. H., Feinstein, C., et al. (2000). Subgroups of children with autism by cluster analysis: A longitudinal examination. Journal of the American Academy of Child and Adolescent Psychiatry, 39(3), 346–352.CrossRefPubMedGoogle Scholar
  43. Vermunt, J. K. (2010). Latent class modeling with covariates: Two improved three-step approaches. Political Analysis, 18, 450–469.CrossRefGoogle Scholar
  44. Viding, E., & Blakemore, S. J. (2007). Endophenotype approach to developmental psychopathology: implications for autism research. Behavioral Genetics, 37, 51–60.CrossRefGoogle Scholar
  45. Waterhouse, L., & Gillberg, C. (2014). Why autism must be taken apart. Journal of Autism and Developmental Disorders, 44, 1788–1792.CrossRefPubMedGoogle Scholar
  46. Wiggins, L. D., Bakeman, R., Adamson, L. B., & Robins, D. L. (2007). The utility of the social communication questionnaire in screening for autism in children referred for early intervention. Focus on Autism and Developmental Disabilities, 22, 33–38.CrossRefGoogle Scholar
  47. Wiggins, L. D., Levy, S. E., Daniels, J., Schieve, L., Croen, L. A., DiGuiseppi, C., et al. (2015a). Symptoms of autism spectrum disorder among children enrolled in the Study to Explore Early Development. Journal of Autism and Developmental Disorders, 45, 3183–3194.CrossRefPubMedPubMedCentralGoogle Scholar
  48. Wiggins, L. D., Reynolds, A., Rice, C., Moody, E. J., Bernal, P., Blaskey, L., et al. (2015b). Using standardized diagnostic instruments to classify children with autism in the Study to Explore Early Development. Journal of Autism and Developmental Disorders, 45, 1271–1280.CrossRefPubMedPubMedCentralGoogle Scholar
  49. Wiggins, L. D., Robins, D. L., Adamson, L. B., Bakeman, R., & Henrich, C. C. (2012). Support for a dimensional view of autism spectrum disorders in toddlers. Journal of Autism and Developmental Disorders, 42, 191–200.CrossRefPubMedPubMedCentralGoogle Scholar
  50. Wurpts, I. C., & Geiser, C. (2014). Is adding more indicators to a latent class analysis beneficial or detrimental? Results of a Monte–Carlo study. Frontiers in Psychology, 5, 1–15.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC (outside the USA) 2017

Authors and Affiliations

  • Lisa D. Wiggins
    • 1
  • Lin H. Tian
    • 1
  • Susan E. Levy
    • 2
  • Catherine Rice
    • 3
  • Li-Ching Lee
    • 4
  • Laura Schieve
    • 1
  • Juhi Pandey
    • 2
  • Julie Daniels
    • 5
  • Lisa Blaskey
    • 2
  • Susan Hepburn
    • 6
  • Rebecca Landa
    • 7
  • Rebecca Edmondson-Pretzel
    • 8
  • William Thompson
    • 1
  1. 1.National Center on Birth Defects and Developmental Disabilities (NCBDDD)Centers for Disease Control and PreventionAtlantaUSA
  2. 2.Center for Autism ResearchChildren’s Hospital of PhiladelphiaPhiladelphiaUSA
  3. 3.Emory Autism Resource CenterEmory UniversityAtlantaUSA
  4. 4.Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreUSA
  5. 5.Department of EpidemiologyUniversity of North CarolinaChapel HillUSA
  6. 6.JFK PartnersUniversity of Colorado-Anschutz Medical CampusAuroraUSA
  7. 7.Center for Autism and Related DisordersKennedy Krieger InstituteBaltimoreUSA
  8. 8.Carolina Institute for Developmental DisabilitiesUniversity of North CarolinaChapel HillUSA

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