Martinelli Alessandra, Leone Silvia, Baronio Cesare M, Archetti Damiano, Redolfi Alberto, Adorni Andrea, Caselani Elisa, D'Addazio Miriam, Di Forti Marta, Laffranchini Laura, Maffezzoni Deborah, Magno Marta, Martella Donato, Murray Robin M, Toffol Elena, Tura Giovanni Battista, de Girolamo Giovanni
Unit of Epidemiological and Evaluation Psychiatry, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
Soc Psychiatry Psychiatr Epidemiol. 2025 Mar 18. doi: 10.1007/s00127-025-02855-x.
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.
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.
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).
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.
ISRCTN registry ID ISRCTN21141466.
精神分裂症谱系障碍（SSD）存在显著的性别差异：男性发病更早，阴性症状更严重，而女性表现出情感症状、语言能力更强且预后更佳。尽管进行了广泛研究，但时间感知和积极性等领域仍未得到充分探索，机器学习也尚未得到充分利用。本研究旨在通过采用数据驱动方法，对一组意大利SSD患者样本中的性别差异进行研究，以填补这些空白。
作为DiAPAson项目的一部分，使用标准化临床工具对619名意大利SSD患者（198名女性；421名男性）进行了评估。收集了社会人口统计学、临床特征、症状严重程度、功能、积极性、生活质量（QoL）和时间观念等方面的数据。进行了描述性和回归分析。主成分分析（PCA）和高斯混合模型（GMM）用于定义数据驱动的聚类。进行了留一组交叉验证。
男性更可能单身（p < 0.001）且受教育程度较低（p = 0.006），而女性吸烟更多（p = 0.011）。男性更频繁地被开抗精神病药物（p = 0.022），且表现出更严重的精神症状（p = 0.004）和阴性症状（p = 0.013）。他们对过去事件的负面看法也较少（p = 0.047），对自身心理状况的看法更好（p = 0.004）。女性表现出更好的人际功能（p = 0.008）。PCA和GMM揭示了两个存在显著性别差异的主要聚类（p = 0.027）。
本研究确定了SSD中的性别差异，提示了针对性的治疗方法，如提高女性的人际技能和维持男性的积极自我评估。通过机器学习，我们突出了不同的SSD表型，强调了针对性别进行干预以改善治疗效果和生活质量的必要性。我们的研究结果强调了采用多方面、跨学科方法来解决SSD中基于性别的差异的重要性。
ISRCTN注册编号ISRCTN21141466。
