Drugs

, Volume 77, Issue 18, pp 1967–1986 | Cite as

Progress in Elucidating Biomarkers of Antidepressant Pharmacological Treatment Response: A Systematic Review and Meta-analysis of the Last 15 Years

  • G. Voegeli
  • M. L. Cléry-Melin
  • N. Ramoz
  • P. Gorwood
Systematic Review

Abstract

Background

Antidepressant drugs are widely prescribed, but response rates after 3 months are only around one-third, explaining the importance of the search of objectively measurable markers predicting positive treatment response. These markers are being developed in different fields, with different techniques, sample sizes, costs, and efficiency. It is therefore difficult to know which ones are the most promising.

Objective

Our purpose was to compute comparable (i.e., standardized) effect sizes, at study level but also at marker level, in order to conclude on the efficacy of each technique used and all analyzed markers.

Methods

We conducted a systematic search on the PubMed database to gather all articles published since 2000 using objectively measurable markers to predict antidepressant response from five domains, namely cognition, electrophysiology, imaging, genetics, and transcriptomics/proteomics/epigenetics. A manual screening of the abstracts and the reference lists of these articles completed the search process.

Results

Executive functioning, theta activity in the rostral Anterior Cingular Cortex (rACC), and polysomnographic sleep measures could be considered as belonging to the best objectively measured markers, with a combined d around 1 and at least four positive studies. For inter-category comparisons, the approaches that showed the highest effect sizes are, in descending order, imaging (combined d between 0.703 and 1.353), electrophysiology (0.294–1.138), cognition (0.929–1.022), proteins/nucleotides (0.520–1.18), and genetics (0.021–0.515).

Conclusion

Markers of antidepressant treatment outcome are numerous, but with a discrepant level of accuracy. Many biomarkers and cognitions have sufficient predictive value (d ≥ 1) to be potentially useful for clinicians to predict outcome and personalize antidepressant treatment.

Notes

Compliance with Ethical Standards

Funding

No funding was used to assist with the preparation of this review.

Conflicts of interest

Geraldine VOEGELI has no conflicts of interest to declare. Marie-Laure CLERY-MELIN has no conflicts of interest to declare. Nicolas RAMOZ has no conflicts of interest to declare. Philip GORWOOD received research grants from Eli Lilly and Servier; honoraria for presentations in congresses from AstraZeneca, Bristol-Myers Squibb, Janssen, Lundbeck, Otsuka and Servier; participated on advisory board of AstraZeneca, Janssen, Roche, and Servier.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.CMME, Hôpital Sainte-AnneUniversité Paris DescartesParisFrance
  2. 2.Centre de Psychiatrie et Neuroscience (INSERM UMR 894)ParisFrance

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