Optimizing and predicting antidepressant efficacy in patients with major depressive disorder using multi-omics analysis and the opade ai prediction tools

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dc.authorid0000-0003-0918-3395
dc.contributor.authorCorrivetti, Giulio
dc.contributor.authorMonaco, Francesco
dc.contributor.authorVignapiano, Annarita
dc.contributor.authorMarenna, Alessandra
dc.contributor.authorPalm, Kaia
dc.contributor.authorFernandez Arroyo, Salvador
dc.contributor.authorFrigola Capell, Eva
dc.contributor.authorLeen, Volker
dc.contributor.authorIbarrola, Oihane
dc.contributor.authorAmil, Burak
dc.contributor.authorCaruson, Mattia Marco
dc.contributor.authorChiariotti, Lorenzo
dc.contributor.authorPalacios Ariza, Maria Alejandra
dc.contributor.authorHoekstra, Pieter J.
dc.contributor.authorChiang, Hsin Yin
dc.contributor.authorFloares, Alexandru
dc.contributor.authorFagiolini, Andrea
dc.contributor.authorFasano, Alessio
dc.date.accessioned2024-08-29T05:33:43Z
dc.date.available2024-08-29T05:33:43Z
dc.date.issued2024
dc.departmentİstanbul Medipol Üniversitesi, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümü, Ruh Sağlığı ve Hastalıkları Ana Bilim Dalı
dc.description.abstractAccording to the World Health Organization (WHO), major depressive disorder (MDD) is the fourth leading cause of disability worldwide and the second most common disease after cardiovascular events. Approximately 280 million people live with MDD, with incidence varying by age and gender (female to male ratio of approximately 2:1). Although a variety of antidepressants are available for the different forms of MDD, there is still a high degree of individual variability in response and tolerability. Given the complexity and clinical heterogeneity of these disorders, a shift from "canonical treatment" to personalized medicine with improved patient stratification is needed. OPADE is a non-profit study that researches biomarkers in MDD to tailor personalized drug treatments, integrating genetics, epigenetics, microbiome, immune response, and clinical data for analysis. A total of 350 patients between 14 and 50 years will be recruited in 6 Countries (Italy, Colombia, Spain, The Netherlands, Turkey) for 24 months. Real-time electroencephalogram (EEG) and patient cognitive assessment will be correlated with biological sample analysis. A patient empowerment tool will be deployed to ensure patient commitment and to translate patient stories into data. The resulting data will be used to train the artificial intelligence/machine learning (AI/ML) predictive tool.
dc.identifier.citationCorrivetti, G., Monaco, F., Vignapiano, A., Marenna, A., Palm, K., Fernandez Arroyo, S. ... Fasano, A. (2024). Optimizing and predicting antidepressant efficacy in patients with major depressive disorder using multi-omics analysis and the opade ai prediction tools. Brain Sciences, 14(7). http://dx.doi.org/10.3390/brainsci14070658
dc.identifier.doi10.3390/brainsci14070658
dc.identifier.issn2076-3425
dc.identifier.issue7
dc.identifier.pmid39061399
dc.identifier.scopus2-s2.0-85199754927
dc.identifier.scopusqualityQ2
dc.identifier.urihttp://dx.doi.org/10.3390/brainsci14070658
dc.identifier.urihttps://hdl.handle.net/20.500.12511/12792
dc.identifier.volume14
dc.identifier.wos001278149700001en_US
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorAmil, Burak
dc.language.isoen
dc.relation.ispartofBrain Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsAttribution 4.0 International*
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectMajor Depressive Disorders
dc.subjectMicrobiome
dc.subjectMetabolomic
dc.subjectTranscriptomics
dc.subjectInflammation
dc.subjectGenetics
dc.subjectArtificial Intelligence
dc.subjectEEG
dc.subjectChatbot
dc.subjectPersonalized Medicine
dc.titleOptimizing and predicting antidepressant efficacy in patients with major depressive disorder using multi-omics analysis and the opade ai prediction tools
dc.typeArticle

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