A Biological Signature for the Early Differential Diagnosis of Psychosis

Sponsor
IRCCS San Raffaele
Study ID
NCT06515522
Status
Not Yet Recruiting

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Conditions

Eligibility Criteria

Sex
ALL
Age
18 Years - 65 Years
Healthy Volunteers
Not accepted

Interventions

  • differential diagnosis — OTHER
    this is a retrospective observational study. no intervention has been or will be performed

Study Details

Schizophrenia (SZ) and mood disorders (BD, MDD) are among the most disabling disorders worldwide, with a relevant social, functional, and economic burden. Although they are identified as distinct disorders, the potential overlapping symptomatology poses important challenges for the differential diagnosis. A consistent literature affirms that brain structure, and function reflect an intermediate phenotype of an underlying genetic vulnerability for the disorders, shaped by interaction with environmental experiences. Such experiences include early life stress and trauma which seem to characterize psychiatric patients and have been associated with brain abnormalities. Further, early life experiences have been associated with inflammation in a subpopulation of psychiatric patients However imaging, inflammatory, and genetic group-level differences, albeit consistent, do not impact clinical practice since they have not been translated into individual prediction. To address these issues, a rapidly growing body of scientific literature implemented computational techniques, such as machine learning (ML). In this project we will develop cutting-edge ML algorithms to predict the differential diagnosis between mood disorders and SZ from genetic, neuroimaging, inflammatory and environmental data in a unique cohort of 1850 patients and 1000 healthy controls recruited in 4 different centers in Italy. The project will address three different aims: in aim 1 we will develop algorithms for the differential diagnosis between SZ and MD combining multimodal neuroimaging and genetic data; in aim 2 we will predict the differential diagnosis between SZ and MD from immuno-inflammatory and environmental data; finally, with aim three we will exploit an animal model to identify the underlying mechanisms of brain alterations associated with exposure to early life stress. Machine learning analyses will include algorithms for data harmonization and feature reduction, as well as for generating normative models. Finally. different classifying models will be compared considering the specific features to achieve the best performance.The definition of reliable and objective biomarkers, combined with cutting-edge computational methodology, could help clinicians in providing more precise diagnoses and early interventions, also considering dimensional constructs \& factors influencing outcomes such as affective vs non-affective psychosis and breadth of exposure to traumatic events

Key Dates

First listed
Jul 23, 2024
Start date
Aug 31, 2024
Status verified
Jul 2024
Primary completion
Aug 31, 2026
Completion
Aug 31, 2026

Study Design

Enrollment
1,850 participants (estimated)

Arms

  • Arm: Schizophrenia
    All patients with schizophrenia recruited from 2007 and 2023
  • Arm: Mood disorders
    All patients with bipolar or major depressive disorders recruited from 2007 and 2023
  • Arm: Controls
    healthy controls

Primary Outcome Measure

Schizophrenia vs Mood disorders [ Time Frame: baseline ]

Central Contacts

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