A Biological Signature for the Early Differential Diagnosis of Psychosis

Not yet recruiting

• Conditions: Schizophrenia; Major Depressive Disorder; Bipolar Disorder
• Interventions: Other: differential diagnosis

• Registration Number: NCT06515522
• Lead Sponsor: IRCCS San Raffaele

Brief Summary

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

Detailed Description

Not available

Study Timeline

• Status Verified: 2024-07
• Study First Submitted: 2024-07-17
• Study First Submitted QC: 2024-07-17
• Last Update Submitted: 2024-07-17
• Study First Posted: 2024-07-23
• Last Update Posted: 2024-07-23
• Study Start: 2024-08-31
• Primary Completion: 2026-08
• Study Completion: 2026-08

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 1850

Inclusion Criteria

1. Aged 18-65
2. diagnosed with Schizophrenia, Bipolar Disorder or Major depressive disorder.
3. For Bipolar and Major depressive disorder, Hamilton Depression Rating Scale scores >8
4. Multimodal 3 T MRI acquisition available (*)
5. Genetic and serum inflammatory data available, or serum and whole blood available for genotyping and inflammatory markers determination.

Exclusion Criteria

1. Presence of major medical or neurological disorders
2. Alcohol or drugs abuse or dependence
3. Conditions known to alter immune-inflammatory status, such as rheumatic diseases, malignancies,
4. ongoing treatment with drugs acting on the immune system, such as corticosteroids, NSAIDs and other immunomodulatory drugs.
5. Pregnancy or lactating

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Mood disorders	differential diagnosis	All patients with bipolar or major depressive disorders recruited from 2007 and 2023
Schizophrenia	differential diagnosis	All patients with schizophrenia recruited from 2007 and 2023
Controls	differential diagnosis	healthy controls

Primary Outcome Measures

Name	Time	Method
Schizophrenia vs Mood disorders	baseline	Predicting the differential diagnosis between Schizophrenia and Mood Disorders combining multimodal neuroimaging, immuno-inflammatory and genetic data

Secondary Outcome Measures

Name	Time	Method
Bipolar vs major depressive disorder	baseline	Predicting the differential diagnosis between major depression and bipolar disorder, and the presence or absence of psychotic symptoms combining multimodal neuroimaging, immuno-inflammatory and genetic data