Cluster analysis reveals subclinical subgroups with shared autistic and schizotypal traits. (July 2018)

• Record Type: Journal Article
• Authors: Ford, Talitha C.; Apputhurai, Pragalathan; Meyer, Denny; Crewther, David P.
• Is Part Of: Psychiatry research. Volume 265(2018)
• Page Start: 111
• Page End: 117
• Publication Date: 2018-07
• Subjects: Autism -- Schizotypy -- Cluster analysis -- Autism spectrum quotient -- Schizotypal personality questionnaire; Psychiatry -- Periodicals; Psychiatry -- periodicals; Psychiatrie -- Périodiques; 616.89
• DOI: 10.1016/j.psychres.2018.04.037
• Languages: English
• ISSNs: 0165-1781

Abstract

Highlights: Psychosocial domains across the autism and schizophrenia spectra overlap to a large degree. 8 subclinical population clusters of traits emerge from latent profile analysis. Three clusters represent the spectrum of psychosocial functioning. Three clusters represent the spectrum of shared autistic-schizotypal traits. Two clusters represent schizotypy and high psychosis-proneness. Abstract: Autism and schizophrenia spectrum research is typically based on coarse diagnostic classification, which overlooks individual variation within clinical groups. This method limits the identification of underlying cognitive, genetic and neural correlates of specific symptom dimensions. This study, therefore, aimed to identify homogenous subclinical subgroups of specific autistic and schizotypal traits dimensions, that may be utilised to establish more effective diagnostic and treatment practices. Latent profile analysis of subscale scores derived from an autism-schizotypy questionnaire, completed by 1678 subclinical adults aged 18–40 years (1250 females), identified a local optimum of eight population clusters: High, Moderate and Low Psychosocial Difficulties; High, Moderate and Low Autism-Schizotypy; High Psychosis-Proneness ; and Moderate Schizotypy . These subgroups represent the convergent and discriminant dimensions of autism and schizotypy in the subclinical population, and highlight the importance of examining subgroups of specific symptom characteristics across these spectra in order to identify the underlying genetic and neural correlates that can be utilised to advance diagnostic and treatment practices.