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| Name | Class |
|---|---|
| University of Siena | OTHER |
| Fundació Eurecat | OTHER |
| Perseus Biomics | UNKNOWN |
| ARTIFICIAL INTELLIGENCE EXPERT SRL |
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OPADE is a non-profit, observational, multicenter, open-label study aimed at defining personalized treatment for Major Depressive Disorder (MDD). In particular, we will combine genetics, epigenetics, microbiome, immune response data together with anamnesis, questionnaires, electroencephalography (EEG) collected from subjects suffering MDD. Eventually, an Artificial Intelligence (AI)/Machine Learning (ML) predictive tool will be created to guide clinicians in improving MDD treatment and patient's stratification.
Three hundred and fifty patients diagnosed with MDD will be enrolled for 24 months and divided into 4 groups according to age: 14-17 years (70 pediatric patients), 18-30 years (100 adult patients), 31-39 years (90 adult patients), 40-50 years (90 adult patients).
The study protocol includes 6 follow-up visits: T0 (enrollment), T1, T2, T3, T4, and T5. At each medical visit, psychometric questionnaires will be administered to the patients and contextual biological samples including blood, stool and saliva will be collected. The study will use a multi-omics approach including: metagenomic sequencing to characterize the microbiome composition; metabolomics to detect circulating metabolites; transcriptomics to quantify microRNAs; epigenomics to assess methylation variability between and within groups and immune assays to analyze the antibody immune response and inflammatory profiles (cytokines, interleukins and growth factors). Cortisol and lipoproteins will also be quantified. In parallel, cognitive assessment and emotional status will be recorded remotely by each patient via chatbot and wearable EEG devices, respectively. Specifically, the chatbot will collect patient's conversations and monitoring her/his feelings; the chat conversation will be than transformed in a machine-readable data. The EEG device is a mobile app that will also allows to associate brainwaves with patients' feelings.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Pediatric patients affected by MDD | 14-17 years (70 pediatric patients) | ||
| Group 1 of adult patients affected by MDD | 18-30 years (100 adult patients) | ||
| Group 2 of adult patients affected by MDD | 31-39 years (90 adult patients) | ||
| Group 3 of adult patients affected by MDD | 40-50 years (90 adult patients) |
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| Measure | Description | Time Frame |
|---|---|---|
| Identify neuroinflammatory indices | Several inflammatory markers such as G-CSF, GM-CSF, IFN-γ IL-10, IL-12p40, IL-15, IL-1α, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8/CXCL8, MCP-1/CCL2, TNF-α, TNFβ will be analysed. | 2 years |
| Microbiome analysis | Identification of bacterial and fungal components. | 2 years |
| Metabolomic analysis | The metabolomic analysis will involve three different groups of metabolites: 1) Intermediate of tryptophan metabolism (tryptophan, serotonin, 5-HIAA, quinurenin, quinurenic acid and other hormones and derivatives involved in the pathway) and others related to purines (paraxanthin/xanthin ratio); 2) L-acylcarnitines (including short chain, medium long-lasting acylcarnitine), with particular emphasis on laurylcarnitine and acetylcarnitine; 3) Phenolic (and related), such as phenolic acid, mandelic acid or methoxy-hydroxyphenyl glycol. | 2 years |
| Analysis of lipoprotein profile | Different forms of lipoproteins will be evaluated: Apolipoproteins A1 and A2, HDL-apolipoproteins A1 and A2,free cholesterol HDL3, HDL3-apolipoprotein A1, HDL2-apolipoprotein A2, apolipoprotein A2, IDL, HDL-apolipoprotein A2, VLDL and its subtypes, VLDL2-triglycerides, VLDL3-triglyceridestriglycerides, VLDL2- cholesterol, VLDL3 cholesterol, VLDL4 cholesterol free of VLDL4, phospholipids VLDL2, Phospholipids VLDL3, Cholesterol LDL5, Cholesterol free LDL5, Phospholipids LDL5, LDL5-apolipoprotein B, HDL3 cholesterol, HDL4 cholesterol HDL4, HDL3 cholesterol free, free cholesterol HDL4, HDL3-phospholipids, HDL4-phospholipids, HDL3-apolipoprotein A1, HDL4-apolipoprotein A1, HDL3-apolipoprotein A2 and HDL4-apolipoprotein A2. | 2 years |
| Identify immune-profile linked and epigenomic signatures | Methylome analysis on genomic DNA will be performed. |
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Inclusion Criteria:
Exclusion Criteria:
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Patients with Major Depressive Disorders
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Università Degli Studi Di Siena | Siena | 53100 | Italy |
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| Label | URL |
|---|---|
| OPADE Official Website | View source |
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| ID | Term |
|---|---|
| D003865 | Depressive Disorder, Major |
| D007249 | Inflammation |
| ID | Term |
|---|---|
| D003866 | Depressive Disorder |
| D019964 | Mood Disorders |
| D001523 | Mental Disorders |
| D010335 | Pathologic Processes |
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| INDUSTRY |
| Mama Health Technologies GmbH | INDUSTRY |
| Protobios OU | UNKNOWN |
| Cephalgo | INDUSTRY |
| Biokeralty Research Institute | INDUSTRY |
| Sanitas University | OTHER |
| Accare | OTHER |
| Institut d'Investigació Biomèdica de Girona Dr. Josep Trueta | OTHER |
| Istanbul Medipol University Hospital | OTHER |
| CEINGE | UNKNOWN |
| Fondazione di ricerca biomedica EBRIS | UNKNOWN |
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Periodic collection of blood, stool and saliva samples from enrolled patients.
| 2 years |
| AI-powered diagnostics predictive tool (companion diagnostic-like) | Deploy an AI-powered predictive tool (companion diagnostic-like) in clinical practice for the prescription of anti-depressants. OPADE AI-powered predictive tool will be a class C medical device under the In vitro diagnostic classification. | 2 years |
| Mood assessment through brain biomarker | Validate a patient tracking tool for mood assessment using brain biomarker. | 2 years |
| Patient engagement digital tool | Validate a patient engagement digital tool that can be deployed in any patient community to enhance clinical study outcomes. | 2 years |
| Discovery of a new set of biomarkers | Propose new set of biomarkers that can guide the development of new antidepressants | 2 years |
| Investigation of the gut-brain-axis and of the biomarkers of interest in the context of mental diseases starting with MDD | Identify indices in MDD to improve diagnostic accuracy for primary prevention and patients' stratification. | 2 years |
| D013568 |
| Pathological Conditions, Signs and Symptoms |