Background: Gender is a crucial factor in the development of mental illnesses, with an essential influence on clinical characteristics and not only on the prevalence of each disorder. Gender differences in cannabinoid-related disorders are highlighted by different research fields (preclinical, clinical, socio-demographic studies), but few studies focused on differential symptom expression in cannabinoid-induced psychosis. This study aims at investigating qualitative and quantitative gender differences in specific psychopathological domains in a clinical sample of subjects affected by cannabinoid-induced psychotic disorder, without psychiatric comorbidity.
Subjects and methods: The study was carried out at the Psychiatric Inpatient Service of General Hospital of Perugia (Italy). In this cross-sectional gender study, 28 inpatients were enrolled, 14 males (M) and 14 females (F). Participants were administered a psychometric battery consisting of 7 tests (PANSS, NDS-I, YMRS, HAM-D, HAM-A, AQ, SSI) in order to investigate 7 psychopathological domains (Psychosis, Dysphoria, Mania, Depression, Anxiety, Aggressive Behaviour and Suicide Ideation). Scores obtained at each test were compared between male and females by using Mann-Whitney U test (p<0.05).
Results: In this study, we observed that males present higher severity of psychotic symptoms, with prominent scores in PANSS positive and general psychopathology scale (p<0.001), and an important expression of aggressive behavior (p<0.001) compared with females. Female sample, instead, shows a greater expression of dysphoria and depressive domains (p<0.001) and a lower, but statistically significant, prevalence in the anxiety domains expression (p=0.01). By these observations, we could assert that in male group thought disorders are prominent. On the other hand, in female group affective disorder are prominent.
Conclusions: This study confirmed how gender influences the phenomenic expression of psychiatric disorders. In line with the precision medicine paradigm, a further clarification of different clinical profiles based on gender would allow the choice of a personalized treatment plan with better efficacy and accuracy indices.