A Biological Signature for the Early Differential Diagnosis of Psychosis

NCT06515522 | Not Yet Recruiting

• 1 other identifier: PNRR-MCNT2-2023-12378015
• Study Type: observational
• Target: 1,850
• Locations: 0 countries
• Sites: N/A

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

Outcome Measures

Primary Outcomes (1)

• Schizophrenia vs Mood disorders

  Predicting the differential diagnosis between Schizophrenia and Mood Disorders combining multimodal neuroimaging, immuno-inflammatory and genetic data

  baseline

Secondary Outcomes (1)

• Bipolar vs major depressive disorder

  baseline

Study Arms (3)

Schizophrenia

All patients with schizophrenia recruited from 2007 and 2023

Other: differential diagnosis

Mood disorders

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

Other: differential diagnosis

Controls

healthy controls

Other: differential diagnosis

Interventions

differential diagnosis OTHER

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

Controls; Mood disorders; Schizophrenia

Eligibility Criteria

• Age: 18 Years - 65 Years
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• 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

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• IRCCS San Raffaele: lead
• Ministry of Health, Italy: collaborator

Biospecimen

Retention: SAMPLES WITH DNA

Plasma and live cells

MeSH Terms

Conditions

Schizophrenia; Bipolar Disorder; Depressive Disorder, Major

Condition Hierarchy (Ancestors)

Schizophrenia Spectrum and Other Psychotic Disorders; Mental Disorders; Bipolar and Related Disorders; Mood Disorders; Depressive Disorder

Study Officials

• Francesco Benedetti, Prof

  IRCCS Ospedale San Raffaele

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Francesco Benedetti, Prof

CONTACT

00390226433156; benedetti.francesco@hsr.it

Sara Poletti, PhD

CONTACT

00390226436833; poletti.sara@hsr.it

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: July 17, 2024
• First Posted: July 23, 2024
• Study Start: August 31, 2024
• Primary Completion (Estimated): August 1, 2026
• Study Completion (Estimated): August 1, 2026
• Last Updated: July 23, 2024

Record last verified: 2024-07