The MAKE Biomarker Discovery for Enhancing anTidepressant Treatment Effect and Response (MAKE BETTER) Study: Design and Methodology.
- Author:
Hee Ju KANG
1
;
Ju Wan KIM
;
Seon Young KIM
;
Sung Wan KIM
;
Hee Young SHIN
;
Myung Geun SHIN
;
Jae Min KIM
Author Information
- Publication Type:Original Article
- Keywords: Biological marker; Depression; Observational study; Predictors; Treatment response
- MeSH: Antidepressive Agents; Biomarkers; Depression; Depressive Disorder; Genetic Markers; Gwangju; Humans; Jeollanam-do; Judgment; Korea; Observational Study; Precision Medicine; Prospective Studies; Psychiatry
- From:Psychiatry Investigation 2018;15(5):538-545
- CountryRepublic of Korea
- Language:English
- Abstract: 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.
