Sex differences in schizophrenia spectrum disorders: insights from the DiAPAson study using a data-driven approach

Purpose Schizophrenia Spectrum Disorders (SSD) display notable sex differences: males have an earlier onset and more severe negative symptoms, while females exhibit affective symptoms, better verbal abilities, and a more favourable prognosis. Despite extensive research, areas such as time perception and positivity remain underexplored, and machine learning has not yet been adequately utilised. This study aims to address these gaps by examining sex differences in a sample of Italian patients with SSD using a data-driven approach. Methods As part of the DiAPAson project, 619 Italian patients with SSD (198 females; 421 males) were assessed using standardised clinical tools. Data on socio-demographics, clinical characteristics, symptom severity, functioning, positivity, quality of life (QoL), and time perspective were collected. Descriptive and regression analyses were conducted. Principal Component Analysis (PCA) and the Gaussian Mixture Model (GMM) was used to define data-driven clusters. A leave-one-group-out validation was performed. Results Males were more likely to be single (p < 0.001) and less educated (p = 0.006), while females smoked more tobacco (p = 0.011). Males were more frequently prescribed antipsychotics (p = 0.022) and exhibited more severe psychiatric (p = 0.004) and negative symptoms (p = 0.013). They also had a less negative perception of past events (p = 0.047) and a better view of their psychological condition (p = 0.004). Females showed better interpersonal functioning (p = 0.008). PCA and GMM revealed two main clusters with significant sex differences (p = 0.027). Conclusion This study identifies sex differences in SSD, suggesting tailored treatments such as enhancing interpersonal skills for females and maintaining positive self-assessment for males. Using machine learning, we highlight distinct SSD phenotypes, emphasising the need for sex-specific interventions to improve outcomes and QoL. Our findings stress the importance of a multifaceted, interdisciplinary approach to address sex-based disparities in SSD. Trial registration ISRCTN registry ID ISRCTN21141466

Working alliance in treating staff and patients with Schizophrenia Spectrum Disorder living in Residential Facilities.

Objectives: Working Alliance (WA) is important in the care of patients with Schizophrenia Spectrum Disorders (SSD). This study aims to determine which sociodemographic and clinical factors are associated with WA, as assessed by pa tients and staff members in Residential Facilities (RFs), and may predict WA dyads' discrepancies. Methods: Three hundred and three SSD patients and 165 healthcare workers were recruited from 98 RFs and characterized for sociodemographic features. WA was rated by the Working Alliance Inventory (WAI) for patients (WAI-P) and staff members (WAI-T). SSD patients were assessed for the severity of psychopathology and psychosocial functioning. Results: Pearson's correlation revealed a positive correlation (ρ=.314; p<.001) between WAI-P and WAI-T rat ings. Linear regression showed that patients with higher education reported lower WAI-P ratings (β = −.50, p=.044), while not being engaged in work or study was associated with lower WAI-T scores (β = −4.17, p=.015). A shorter lifetime hospitalization was associated with higher WAI-P ratings (β=5.90, p=.008), while higher psychopathology severity negatively predicted WAI-T (β = −.10, p=.002) and WAI-P ratings (β = −.19, p<.001). Better functioning level positively foresaw WAI-T (β=.14, p<.001) and WAI-P rat ings (β=.12, p<.001). Regarding discrepancies, staff mem bers' age was associated with higher dyads discrepancy in Total scale and Agreement subscale scores, which were also associated with more severe negative symptoms, while pa tients' age was negatively correlated to relationship subscale discrepancy. Conclusions: This study provides insight into the factors that influence WA in SSD patients and health workers in RFs. The findings address interventions to improve WA and ultimately patient outcomes