Optimize and Predict Antidepressant Efficacy for Patient With MDD Using Multi-omics Analysis and AI-predictive Tool

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.