A Biological Signature for the Early Differential Diagnosis of Psychosis

NCT ID: NCT06515522

Last Updated: 2024-07-23

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Total Enrollment: 1850 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-08-31
• Study Completion Date: 2026-08-31

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

Conditions

Schizophrenia; Bipolar Disorder; Major Depressive Disorder

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

Schizophrenia

All patients with schizophrenia recruited from 2007 and 2023

differential diagnosis

Intervention Type: OTHER

this is a retrospective observational study. no intervention has been or will be performed

Mood disorders

All patients with bipolar or major depressive disorders recruited from 2007 and 2023

differential diagnosis

Intervention Type: OTHER

this is a retrospective observational study. no intervention has been or will be performed

Controls

healthy controls

differential diagnosis

Intervention Type: OTHER

this is a retrospective observational study. no intervention has been or will be performed

Interventions

differential diagnosis

this is a retrospective observational study. no intervention has been or will be performed

Intervention Type: OTHER

Eligibility Criteria

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

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Ministry of Health, Italy

OTHER_GOV

Sponsor Role: collaborator

IRCCS San Raffaele

OTHER

Sponsor Role: lead

Responsible Party

Francesco Benedetti

Principal Investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Francesco Benedetti, Prof

Role: PRINCIPAL_INVESTIGATOR

IRCCS Ospedale San Raffaele

Central Contacts

Francesco Benedetti, Prof

Role: CONTACT

benedetti.francesco@hsr.it

00390226433156

Sara Poletti, PhD

Role: CONTACT

poletti.sara@hsr.it

00390226436833

Other Identifiers

PNRR-MCNT2-2023-12378015

Identifier Type: -

Identifier Source: org_study_id