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Gender differences of patients at-risk for psychosis regarding symptomatology, drug use, comorbidity and functioning - Results from the EU-GEI study
Journal
European Psychiatry
Volume
59
Pages / Article-Number
52-59
Keywords
Comorbidity; Functioning; Gender differences; Risk for psychosis; Sex differences
Mesh terms
Adolescent; Adult; Anxiety Disorders, epidemiology; Comorbidity; Early Diagnosis; Europe, epidemiology; Female; Humans; Male; Psychotic Disorders, psychology; Schizophrenia, epidemiology; Sex Distribution; Sex Factors; Substance-Related Disorders, epidemiology; Young Adult
Abstract
Gender differences in symptomatology in chronic schizophrenia and first episode psychosis patients have often been reported. However, little is known about gender differences in those at risk of psychotic disorders. This study investigated gender differences in symptomatology, drug use, comorbidity (i.e. substance use, affective and anxiety disorders) and global functioning in patients with an at-risk mental state (ARMS) for psychosis.; The sample consisted of 336 ARMS patients (159 women) from the prodromal work package of the EUropean network of national schizophrenia networks studying Gene-Environment Interactions (EU-GEI; 11 centers). Clinical symptoms, drug use, comorbidity and functioning were assessed at first presentation to an early detection center using structured interviews.; In unadjusted analyses, men were found to have significantly higher rates of negative symptoms and current cannabis use while women showed higher rates of general psychopathology and more often displayed comorbid affective and anxiety disorders. No gender differences were found for global functioning. The results generally did not change when corrected for possible cofounders (e.g. cannabis use). However, most differences did not withstand correction for multiple testing.; Findings indicate that gender differences in symptomatology and comorbidity in ARMS are similar to those seen in overt psychosis and in healthy controls. However, observed differences are small and would only be reliably detected in studies with high statistical power. Moreover, such small effects would likely not be clinically meaningful.
