Multi-omic modeling of antidepressant response implicates dynamic immune and inflammatory changes in individuals who respond to treatment.

ABSTRACT:

Background

Major depressive disorder (MDD) is a leading cause of disability worldwide, and is commonly treated with antidepressant drugs (AD). Although effective, many patients fail to respond to AD treatment, and accordingly identifying factors that can predict AD response would greatly improve treatment outcomes. In this study, we developed a machine learning tool to integrate multi-omic datasets (gene expression, DNA methylation, and genotyping) to identify biomarker profiles associated with AD response in a cohort of individuals with MDD.

Materials and methods

Individuals with MDD (N = 111) were treated for 8 weeks with antidepressants and were separated into responders and non-responders based on the Montgomery-Åsberg Depression Rating Scale (MADRS). Using peripheral bl

SUBMITTER: Fuh SC 

PROVIDER: S-EPMC10184917 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature