Development of machine-learning-driven signatures for diagnosing and monitoring therapeutic response in major depressive disorder using integrated immune cell profiles and plasma cytokines.

ABSTRACT: Background: Diagnosis and treatment efficacy of major depressive disorder (MDD) currently lack stable and reliable biomarkers. Previous research has suggested a potential association between immune cells, cytokines, and the pathophysiology and treatment of MDD. Objective: This study aims to investigate the relationship between immune cells, cytokines, and the diagnosis of MDD and treatment response, further utilizing machine learning algorithms to develop robust diagnostic and treatment response prediction models. Methods: Using mass cytometry by time-of-flight (CyTOF) technology and high-throughput cytokine detection, we analyzed 63 types of immune cells from 134 pre-treatment MDD patients. Among these patients, plasma data for 440 cytokines were obtained from 84 indi

SUBMITTER: He S 

PROVIDER: S-EPMC11610142 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature