Clustering of Schizotypal Features in Unaffected First-Degree Relatives of Schizophrenia Patients.

ABSTRACT: Meehl conceptualized schizotypy as the phenotypic manifestations of a neural integrative defect resulting from a schizophrenia diathesis. The majority of schizotypy studies recruited subjects from the general population and revealed a multidimensional construct. This 2-phase investigation first examined the clustering of schizotypy in 194 unaffected relatives of schizophrenia patients using the Chapman Psychosis Proneness scales and then directly compared the cognitive profiles of negative schizotypal individuals and positive schizotypal individuals with schizophrenia patients and controls. In the first phase, cluster analysis categorized 194 unaffected relatives of schizophrenia patients into positive schizotypy (n = 33), negative schizotypy (n = 66), mixed schizotypy (n = 27), and low schizotypy (n = 64). Positive schizotypal participants showed more self-report pleasure experiences than negative schizotypal participants, replicating earlier cluster analytic findings. In the second phase, 27 negative schizotypal individuals, 18 positive schizotypal individuals, 19 schizophrenia patients, and 29 controls were recruited. Although the groups were matched in terms of age, gender, and IQ, they differed significantly in cognitive profiles. While schizophrenia patients exhibited the broadest cognitive impairments, negative schizotypal participants exhibited visual memory, working memory, and verbal fluency impairments, and positive schizotypal participants exhibited logical memory, visual memory, working memory, and theory-of-mind impairments. Among people with familial risk of schizophrenia, individuals exhibiting positive rather than negative schizotypal features resembled schizophrenia patients in cognitive profiles. Using the psychometric-familial method to identify schizotypy, our findings support the heterogeneity of schizotypy as well as the potential utility of the positive schizotypy dimension in genetically high-risk individuals to predict the risk of developing schizophrenia.

SUBMITTER: Lui SSY 

PROVIDER: S-EPMC6188519 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature