A Biological Signature for the Early Differential Diagnosis of Psychosis

A Biological Signature for the Early Differential Diagnosis of Psychosis: Unveiling the Differences Between Mood Disorders and Schizophrenia With Multimodal Machine Learning Techniques

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

Study Overview

• Status: Not yet recruiting
• Conditions: Schizophrenia; Bipolar Disorder; Major Depressive Disorder
• Intervention / Treatment: Other: differential diagnosis

• Study Type: Observational
• Enrollment (Estimated): 1850

Contacts and Locations

• Study Contact: Name: Francesco Benedetti, Prof; Phone Number: 00390226433156; Email: benedetti.francesco@hsr.it
• Study Contact Backup: Name: Sara Poletti, PhD; Phone Number: 00390226436833; Email: poletti.sara@hsr.it

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: N/A

• Sampling Method: Non-Probability Sample

Study Population

Psychiatric patients with a diagnosis of Schizophrenia or Bipolar disorder or Major depressive disorder, neuroimaging data and peripheral blood sampling.

Healthy controls with neuroimaging data and peripheral blood sampling

Description

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 Plan

How is the study designed?

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Controls; healthy controls	Other: differential diagnosis; this is a retrospective observational study. no intervention has been or will be performed
Schizophrenia; All patients with schizophrenia recruited from 2007 and 2023	Other: differential diagnosis; 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	Other: differential diagnosis; this is a retrospective observational study. no intervention has been or will be performed

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Schizophrenia vs Mood disorders	Predicting the differential diagnosis between Schizophrenia and Mood Disorders combining multimodal neuroimaging, immuno-inflammatory and genetic data	baseline

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Bipolar vs major depressive disorder	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	baseline

Collaborators and Investigators

• Sponsor: IRCCS San Raffaele
• Collaborators: Ministry of Health, Italy
• Investigators: Principal Investigator: Francesco Benedetti, Prof, IRCCS Ospedale San Raffaele

Study record dates

Study Major Dates

• Study Start (Estimated): August 31, 2024
• Primary Completion (Estimated): August 1, 2026
• Study Completion (Estimated): August 1, 2026

Study Registration Dates

• First Submitted: July 17, 2024
• First Submitted That Met QC Criteria: July 17, 2024
• First Posted (Actual): July 23, 2024

Study Record Updates

• Last Update Posted (Actual): July 23, 2024
• Last Update Submitted That Met QC Criteria: July 17, 2024
• Last Verified: July 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Mental Disorders; Mood Disorders; Schizophrenia Spectrum and Other Psychotic Disorders; Bipolar and Related Disorders; Depressive Disorder; Schizophrenia; Bipolar Disorder; Depressive Disorder, Major
• Other Study ID Numbers: PNRR-MCNT2-2023-12378015

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No