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Social psychiatry and psychiatric epidemiology
Mar 18, 2025;

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

Alessandra Martinelli, Silvia Leone, Cesare M Baronio, Damiano Archetti, Alberto Redolfi, Andrea Adorni, Elisa Caselani, Miriam D'Addazio, Marta Di Forti, Laura Laffranchini, Deborah Maffezzoni, Marta Magno, Donato Martella, Robin M Murray, Elena Toffol, Giovanni Battista Tura, Giovanni de Girolamo, DIAPASON consortium
Author Information
  1. Alessandra Martinelli: Unit of Epidemiological and Evaluation Psychiatry, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy. gdegirolamo@fatebenefratelli.eu.
  2. Silvia Leone: Unit of Epidemiological and Evaluation Psychiatry, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy. gdegirolamo@fatebenefratelli.eu.
  3. Cesare M Baronio: Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
  4. Damiano Archetti: Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
  5. Alberto Redolfi: Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
  6. Andrea Adorni: Psychiatry Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
  7. Elisa Caselani: Unit of Epidemiological and Evaluation Psychiatry, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
  8. Miriam D'Addazio: Unit of Epidemiological and Evaluation Psychiatry, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
  9. Marta Di Forti: Department of Social, Genetic and Developmental Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
  10. Laura Laffranchini: Psychiatry Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
  11. Deborah Maffezzoni: Unit of Epidemiological and Evaluation Psychiatry, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
  12. Marta Magno: Unit of Epidemiological and Evaluation Psychiatry, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
  13. Donato Martella: Unit of Epidemiological and Evaluation Psychiatry, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
  14. Robin M Murray: Department of Social, Genetic and Developmental Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
  15. Elena Toffol: Unit of Epidemiological and Evaluation Psychiatry, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
  16. Giovanni Battista Tura: Psychiatry Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
  17. Giovanni de Girolamo: Unit of Epidemiological and Evaluation Psychiatry, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy. gdegirolamo@fatebenefratelli.eu. ORCID
PMID: 40100359 DOI: 10.1007/s00127-025-02855-x

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.

Keywords

Machine Learning Positivity Schizophrenia spectrum disorder Sex differences Time perception

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Created with Highcharts 10.0.0p���=���0SSDdifferencessexfemalesmalesnegativesymptomsbetterperceptionstudyusingdata-drivenapproachSchizophreniaseveretimepositivitymachinelearningaddressItalianpatientsDiAPAsonclinicalfunctioningQoLPCAGMMclustersMalesless004interpersonalSexspectrumPURPOSE:SpectrumDisordersdisplaynotabledifferences:earlieronsetexhibitaffectiveverbalabilitiesfavourableprognosisDespiteextensiveresearchareasremainunderexploredyetadequatelyutilisedaimsgapsexaminingsampleMETHODS:partproject619198421assessedstandardisedtoolsDatasocio-demographicscharacteristicssymptomseverityqualitylifeperspectivecollectedDescriptiveregressionanalysesconductedPrincipalComponentAnalysisGaussianMixtureModeluseddefineleave-one-group-outvalidationperformedRESULTS:likelysinglep���<���0001educated006smokedtobacco011frequentlyprescribedantipsychotics022exhibitedpsychiatric013alsopastevents047viewpsychologicalconditionFemalesshowed008revealedtwomainsignificant027CONCLUSION:identifiessuggestingtailoredtreatmentsenhancingskillsmaintainingpositiveself-assessmentUsinghighlightdistinctphenotypesemphasisingneedsex-specificinterventionsimproveoutcomesfindingsstressimportancemultifacetedinterdisciplinarysex-baseddisparitiesTRIALREGISTRATION:ISRCTNregistryIDISRCTN21141466schizophreniadisorders:insightsMachineLearningPositivitydisorderTime

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