4.2 Article

The MAKE Biomarker Discovery for Enhancing anTidepressant Treatment Effect and Response (MAKE BETTER) Study: Design and Methodology

Journal

PSYCHIATRY INVESTIGATION
Volume 15, Issue 5, Pages 538-545

Publisher

KOREAN NEUROPSYCHIATRIC ASSOC
DOI: 10.30773/pi.2017.10.2

Keywords

Biological marker; Depression; Observational study; Predictors; Treatment response

Categories

Funding

  1. Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea [HI12C0003]
  2. Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Science, ICT and future Planning [NRF-2016R1C1B2006793]

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Objective Depression is associated with a major disease burden, and many individuals suffer from depressive symptoms due to an insufficient response to ostensibly adequate antidepressant treatment. Therefore, it is important to identify reliable treatment response predictors for use in developing personalized treatment strategies. Methods The MAKE Biomarker discovery for Enhancing anTidepressant Treatment Effect and Response (MAKE BETTER) study was performed to identify predictors of antidepressant response using a 2-year naturalistic prospective design. Participants in the MAKE BETTER study were consecutively recruited from patients who visited the Psychiatry Department of Chonnam National University Hospital, Gwangju, South Korea for treatment of a depressive disorder. Data on demographic and clinical characteristics, genetic markers measured by whole-exome sequencing, and blood markers were obtained. The types and doses of antidepressants were determined based on the clinical judgment of the psychiatrist, and the treatment outcomes (e.g., depressive and other psychiatric symptoms and issues related to safety) were assessed. Results We will be able to use the data collected in this study to develop a treatment-response prediction index composed of biomarkers. Conclusion The MAKE BETTER study will provide an empirical basis for a personalized medicine approach to depression by enabling the prediction of antidepressant treatment response according the characteristics of each patient. It will thereby support evidencebased decision-making that decreases the use of a trial-and-error approach to the treatment of depressive disorders.

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