A Biological Signature for the Early Differential Diagnosis of Psychosis
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: differential diagnosis.
- Who it may be relevant to
- Registry conditions: Schizophrenia, Bipolar Disorder, Major Depressive Disorder. Basic parameters: 18 years — 65 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
A Biological Signature for the Early Differential Diagnosis of Psychosis: Unveiling the Differences Between Mood Disorders and Schizophrenia With Multimodal Machine Learning Techniques
Overview
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
Interventions
- Other differential diagnosis
this is a retrospective observational study. no intervention has been or will be performed
Primary outcome measures
- Schizophrenia vs Mood disorders [Time frame: baseline]
Secondary outcome measures (1)
- Bipolar vs major depressive disorder [Time frame: baseline]
Eligibility criteria
Inclusion criteria
- 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.
Exclusion criteria
- 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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Study design
- Observational model
- Cohort
Study locations
Center list to be confirmed — check the primary protocol.
Identifiers
NCT: NCT06515522 · PNRR-MCNT2-2023-12378015