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

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Title: Sex differences in schizophrenia spectrum disorders: insights from the DiAPAson study using a data-driven approach.
Authors: Martinelli, Alessandra (AUTHOR), Leone, Silvia (AUTHOR), Baronio, Cesare M. (AUTHOR), Archetti, Damiano (AUTHOR), Redolfi, Alberto (AUTHOR), Adorni, Andrea (AUTHOR), Caselani, Elisa (AUTHOR), D'Addazio, Miriam (AUTHOR), Di Forti, Marta (AUTHOR), Laffranchini, Laura (AUTHOR), Maffezzoni, Deborah (AUTHOR), Magno, Marta (AUTHOR), Martella, Donato (AUTHOR), Murray, Robin M. (AUTHOR), Toffol, Elena (AUTHOR), Tura, Giovanni Battista (AUTHOR), de Girolamo, Giovanni (AUTHOR), D'Elia, P. (AUTHOR), Impicci, S. (AUTHOR), Mari, M. (AUTHOR)
Source: Social Psychiatry & Psychiatric Epidemiology. Aug2025, Vol. 60 Issue 8, p1983-1997. 15p.
Subjects: Schizophrenia, Gender differences (Psychology), Patient care, Self-perception, Machine learning, Symptoms, Empirical research
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Social Psychiatry & Psychiatric Epidemiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: 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 &lt; 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. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of Social Psychiatry &amp; Psychiatric Epidemiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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