Discovering biomarkers for antidepressant response: protocol from the Canadian biomarker integration network in depression (CAN-BIND) and clinical characteristics of the first patient cohort

发现抗抑郁反应的生物标志物:加拿大抑郁症生物标志物整合网络 (CAN-BIND) 的协议和第一批患者临床特征

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作者:Raymond W Lam, Roumen Milev, Susan Rotzinger, Ana C Andreazza, Pierre Blier, Colleen Brenner, Zafiris J Daskalakis, Moyez Dharsee, Jonathan Downar, Kenneth R Evans, Faranak Farzan, Jane A Foster, Benicio N Frey, Joseph Geraci, Peter Giacobbe, Harriet E Feilotter, Geoffrey B Hall, Kate L Harkness, St

Background

Major Depressive Disorder (MDD) is among the most prevalent and disabling medical conditions worldwide. Identification of clinical and biological markers ("biomarkers") of treatment response could personalize clinical decisions and lead to better outcomes. This paper describes the aims, design, and

Discussion

From June 2013 to February 2015, a cohort of 134 participants (85 outpatients with MDD and 49 healthy participants) has been evaluated at baseline. The clinical characteristics of this cohort are similar to other studies of MDD. Recruitment at all sites is ongoing to a target sample of 290 participants. CAN-BIND will identify biomarkers of treatment response in MDD through extensive clinical, molecular, and imaging assessments, in order to improve treatment practice and clinical outcomes. It will also create an innovative, robust platform and database for future research.

Methods

CAN-BIND-1 is a multisite initiative involving 6 academic health centres working collaboratively with other universities and research centres. In the 16-week protocol, patients with MDD are treated with a first-line antidepressant (escitalopram 10-20 mg/d) that, if clinically warranted after eight weeks, is augmented with an evidence-based, add-on medication (aripiprazole 2-10 mg/d). Comprehensive datasets are obtained using clinical rating scales; behavioural, dimensional, and functioning/quality of life measures; neurocognitive testing; genomic, genetic, and proteomic profiling from blood samples; combined structural and functional magnetic resonance imaging; and electroencephalography. De-identified data from all sites are aggregated within a secure neuroinformatics platform for data integration, management, storage, and analyses. Statistical analyses will include multivariate and machine-learning techniques to identify predictors, moderators, and mediators of treatment response.

Trial registration

ClinicalTrials.gov identifier NCT01655706 . Registered July 27, 2012.

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