Optimize and Predict Antidepressant Efficacy for Patient With Major Depressive Disorders Using Multi-omics Analysis and AI-predictive Tool
Trial Snapshot
- Phase
- Not Applicable
- Status
- Active, not recruiting
- Sponsor
- Enrollment
- 350
- Locations
- 1
- Primary Endpoint
- Microbiome analysis
Study Overview
Brief Summary
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.
Detailed Description
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.
Study Design
- Study Type
- Observational
- Observational Model
- Case Only
- Time Perspective
- Prospective
Eligibility Criteria
- Ages
- 14 Years to 50 Years (Child, Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •Patients diagnosed with Major Depressive Disorder as certified by a SCID 5 (Structured Clinical Interview for DSM-5) for DSM-S for adults and K-SADS-PL-DSM 5 (Kiddie Schedule for Affective Disorders and Schizophrenia - Present and Lifetime for DSM 5) for adolescents.
- •Currently experiencing a major depressive episode with a HAM-D (Hamilton Depression) score of 18 or greater, or alternatively, a MADRS (Montgomery-Asberg Depression Rating Scale) score of 18 or greater.
- •About to start a new antidepressant.
- •Not concurrently starting a new psychotropic medication.
- •Age 14-50 years.
- •Able to use mobile devices (smart phone, tablet).
- •Willingness to provide written informed consent to participate.
Exclusion Criteria
- •Intellectual disability.
- •Neurological disease (multiple sclerosis, severe neurocognitive disorder, epilepsy).
- •Current psychotic disorder or mood disorder with psychotic features.
- •Primary diagnosis of alcohol or substance use disorder (DSM-5).
- •Patients who started concomitant psychotropic medications less than one week ago.
- •Active, ongoing inflammatory diseases (such as rheumatoid arthritis and rheumatic polymyalgia). or severe and unstable physical illness (such as recent myocardial infarction).
- •A history of hepatitis B or C, human immunodeficiency virus, or evidence of active tuberculosis infection or any active systemic infection within 2 weeks prior to the start of the study.
- •Use of antibiotics or other medications that may have affected the composition of the microbiota during the 30 days prior to baseline.
- •Pregnancy and lactation.
Arms & Interventions
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)
Outcomes
Primary Outcomes
Microbiome analysis
Time Frame: 2 years
Identification of bacterial and fungal components.
Identify neuroinflammatory indices
Time Frame: 2 years
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.
Metabolomic analysis
Time Frame: 2 years
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.
Analysis of lipoprotein profile
Time Frame: 2 years
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.
Identify immune-profile linked and epigenomic signatures
Time Frame: 2 years
Methylome analysis on genomic DNA will be performed.
Mood assessment through brain biomarker
Time Frame: 2 years
Validate a patient tracking tool for mood assessment using brain biomarker.
Patient engagement digital tool
Time Frame: 2 years
Validate a patient engagement digital tool that can be deployed in any patient community to enhance clinical study outcomes.
Discovery of a new set of biomarkers
Time Frame: 2 years
Propose new set of biomarkers that can guide the development of new antidepressants
Investigation of the gut-brain-axis and of the biomarkers of interest in the context of mental diseases starting with MDD
Time Frame: 2 years
Identify indices in MDD to improve diagnostic accuracy for primary prevention and patients' stratification.
Identify neuroinflammatory indices
Time Frame: 2 years
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.
Microbiome analysis
Time Frame: 2 years
Identification of bacterial and fungal components.
Metabolomic analysis
Time Frame: 2 years
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.
Analysis of lipoprotein profile
Time Frame: 2 years
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.
Identify immune-profile linked and epigenomic signatures
Time Frame: 2 years
Methylome analysis on genomic DNA will be performed.
AI-powered diagnostics predictive tool (companion diagnostic-like)
Time Frame: 2 years
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.
Mood assessment through brain biomarker
Time Frame: 2 years
Validate a patient tracking tool for mood assessment using brain biomarker.
Patient engagement digital tool
Time Frame: 2 years
Validate a patient engagement digital tool that can be deployed in any patient community to enhance clinical study outcomes.
Discovery of a new set of biomarkers
Time Frame: 2 years
Propose new set of biomarkers that can guide the development of new antidepressants
Investigation of the gut-brain-axis and of the biomarkers of interest in the context of mental diseases starting with MDD
Time Frame: 2 years
Identify indices in MDD to improve diagnostic accuracy for primary prevention and patients' stratification.
Secondary Outcomes
No secondary outcomes reported
Investigators
Alessio Fasano
Clinical Director
Massachusetts General Hospital