Exploring Social Subtypes in Autism: A Preliminary Study

Poster Presentation
Thursday, May 2, 2019: 5:30 PM-7:00 PM
Room: 710 (Palais des congres de Montreal)
R. K. Schuck1, M. Uljarevic2, J. M. Phillips1, R. Libove1, T. W. Frazier3 and A. Y. Hardan1, (1)Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, (2)Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, (3)Autism Speaks, New York, NY
Background: Impairments in the social domain are considered a hallmark diagnostic feature of autism spectrum disorder (ASD). Yet, individuals diagnosed with ASD vary widely with respect to specific presentation, severity, and course across different dimensions of this complex symptom domain. Given that wide phenotypic variability likely reflects diverse etiological mechanisms, identifying more homogenous ASD subgroups based on social domains is necessary for understanding the underlying etiology and pathophysiology.

Objectives: The aim of the current investigation was to utilize the Stanford Social Dimensions Scale (SSDS), a newly developed quantitative measure of social processes, in order to explore the existence of homogeneous subgroups of individuals with ASD who share distinct patterns of strengths and weaknesses across distinct dimensions of social domains. Identified subgroups will be further characterized by examining their association with cognitive ability, severity of core ASD symptoms and impairments in self-regulation.

Methods: Parents of 172 individuals with ASD (31 females, 141 males; Mage = 7.97 years, SD = 5.02) completed the SSDS, the Social Responsiveness Scale (SRS-2) and the Child Behavior Checklist (CBCL). Data on children’s verbal and non-verbal intellectual functioning (FSIQ) was also collected.

Results: The k-means cluster analysis was used to classify participants according to the pattern of SSDS subscale scores (Social Motivation [SM], Social Affiliation [SA], Expressive Social Communication [ESC], Social Reception [SR] and Unusual Approach [UA]). The optimal number of clusters to be specified was derived by plotting the within-group sum of squares for each cluster by applying the k-means. Analysis suggested 6 clusters as optimal solution. There were significant differences between the clusters across all SSDS subscales: SM (F= 51.86, p< .001, Partial η2= .61), SA (F= 55.031, p< .001, Partial η2= .62), ESC (F= 41.09, p< .001, Partial η2= .55), SR (F= 49.48, p< .001, Partial η2= .60) and UA (F= 40.53, p< .001, Partial η2= .55). Both severity and shape differences among six clusters were identified. Derived clusters did not differ in terms of gender distribution (χ2= 3.15, p= .68, Phi= .13) nor chronological age (F= .93, p= .46, Partial η2= .027). Clusters showed distinct profiles of strengths and difficulties across FSIQ (F= 4.62, p= .001, Partial η2= .248), self-regulation (F= 4.08, p= .002, Partial η2= .121), and across SRS-2 factors (social avoidance [F= 9.77, p< .001, Partial η2= .241], emotion recognition [F= 24.17, p< .001, Partial η2= .440], interpersonal relatedness [F= 7.90, p< .001, Partial η2= .204], insistence on sameness [F= 6.75, p< .001, Partial η2= .180] and repetitive motor mannerisms [F= 9.47, p< .001, Partial η2= .235]).

Conclusions: Our study provides a significant contribution by identifying six subgroups of individuals with ASD who shared distinct social domain profiles. Importantly, these clusters reflect differential individual variability in terms of cognitive ability, severity of ASD symptoms, as well as self-regulation skills, and represent an initial step toward reducing phenotypical heterogeneity in the autism spectrum which promises to lead to more personalized interventions.