AUTHOR=Zhou Jingjing , Zhou Jia , Sun Zuoli , Feng Lei , Zhu Xuequan , Yang Jian , Wang Gang 

TITLE=Development and Internal Validation of a Novel Model to Identify Inflammatory Biomarkers of a Response to Escitalopram in Patients With Major Depressive Disorder

JOURNAL=Frontiers in Psychiatry

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2021.593710

DOI=10.3389/fpsyt.2021.593710

ISSN=1664-0640

ABSTRACT=Objective: The aim of our study was to identify immune- and inflammation-related factors with clinical utility for prediction of clinical efficacy in treatment of depression. Study Design: This is a followed up study. Participants meeting entry criteria were administered escitalopram (10 mg /day) as the initial treatment. Self-evaluation and observer valuations were arranged at the end of weeks 0, 4, 8, and 12, with blood samples collected at baseline and weeks 2 and 12. A multivariable logistic regression analysis was then refitted by incorporating three cytokines selected by the LASSO regression model. Internal validation was estimated using the bootstrap method (1000 repetitions). Results: A total of 85 patients with MDD were analyzed in the study. MCP-1, VCAM-1 and lipocalin-2 were selected in the LASSO regression model. The AUC from the logistic model was 0.811 and was confirmed as 0.7887 through bootstrapping validation, which suggested the model’s good prediction and discrimination capability. Conclusions: We have proposed and validated a relatively accurate prediction model to facilitate individualized prediction of escitalopram treatment in MDD and lay a path towards machine-learning-driven personalized approaches to treatment in depression.