Review articlePeripheral biomarkers of major depression and antidepressant treatment response: Current knowledge and future outlooks
Introduction
Major depressive disorder (MDD) is a prevalent psychiatric disorder associated with varied prognosis, chronic course, and duration of illness with reduced quality of life (Beck et al., 1961, Burton et al., 2015, Daly EJ et al., 2010). Most MDD patients stay on ineffective medications for too long, switch treatments too early, or simply drop out of care (Burton et al., 2015, Rush et al., 2008, Warden et al., 2007b). Compared to treatment of several other somatic diseases, antidepressant response rates are low, duration to attain therapeutic benefit is long, and treatment-emergent side effect burden is significant (Rush et al., 2011, Trivedi et al., 2006b, Warden et al., 2007a). Furthermore, treatments are selected not based on efficacy, but instead on patient or provider preferences. The factors that ultimately drive these decisions include cost, side effects, tolerability, and/or response during previous episode(s) (Meron et al., 2015). Unlike other specialty fields of medicine, such as breast cancer (Dowsett and Dunbier, 2008), asthma (Lima et al., 2009), macular degeneration (Lee et al., 2009), and multiple sclerosis (Vosslamber et al., 2009), there are no validated biomarkers for depression, thereby stalling the goal of offering precise, targeted treatment for this devastating disorder. Indeed, personalized treatment has the capacity to maximize the likelihood of treatment response or remission, while simultaneously minimizing detrimental side effects (Kessler et al., 2003, Murray et al., 2013).
The search for biomarkers is hindered by the heterogeneity of MDD (Hasler et al., 2004) and the limitation of its current diagnostic categories such as self-reports, measurement based scales, with a lack of understanding of the molecular blood testing compared to other diseases (Insel et al., 2010a). In clinical practice, efforts are made to understand the demographic features, (e.g., gender (Young et al., 2009), race (Friedman et al., 2009), employment status (Warden et al., 2007a)), illness characteristics (e.g., baseline severity of depression (Friedman et al., 2012), duration of illness (Rush et al., 2012), number of previous episodes (Trivedi et al., 2005), age of onset (Zisook et al., 2007), family history of mood disorders (Trivedi et al., 2005), presence of anxious features (Fava et al., 2008), depression symptoms and its subtypes (Friedman et al., 2009), co-morbid psychiatric disorders (Friedman et al., 2009), psychosocial functioning (Vittengl et al., 2009), and social factors (e.g., marital status (Trivedi et al., 2005), level of social support (Lesser et al., 2008), social status (Lesser et al., 2008)). Unfortunately, these have proven to be of limited utility due to the knowledge gap regarding cellular and molecular pathophysiology, blood tests, and events that occur during brain development and maturation in MDD. (Arnow et al., 2015, Bobo et al., 2011, Chan et al., 2012, Sung et al., 2012, Sung et al., 2013, Sung et al., 2015). The underlying biological factors that drive MDD may be better suited to serve as biomarkers for guiding personalized medicine, as they are objective and can be measured externally (Biomarkers Definitions Working Group, 2001, Strimbu and Tavel, 2010). The heterogeneity of MDD necessitates and/or allows for numerous biomarker classifications, as shown in Fig. 1. Diagnostic biomarkers indicate presence and/or future development of disease. Most of the currently-identified biomarkers, described below, are predictive, such that baseline levels will provide insight as to whether or not a patient will respond to treatment. Moderators are also characterized at baseline, though provide more detailed information, such that clinicians can predict how a patient will respond to a particular treatment. Mediators define markers that change following treatment initiation and may predict future performance with the same or alternative treatment methodology. To maximize the chances of success, we may also need to go beyond individual biomarkers and venture towards generating multidimensional biomarkers (i.e., biosignatures) by systematically evaluating combinations of both clinical and biological markers.
In this report, we briefly review currently available treatment options for depression, though emphasize the necessity for biomarker identification to discriminate depression subtypes and work toward personalized medicine. We present the tools available for biomarker discovery and discuss what these technologies have identified as hits to date. In addition, we discuss our own clinical trial study, EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care), which is exclusively designed to screen numerous putative biomarkers with the aim to identify biosignatures for depression response.
Section snippets
Antidepressant treatment strategies
Numerous modalities are available to treat individuals with depression. Unfortunately, no treatment is universally effective, although different molecules and neural circuits are targeted, promoting distinct physiological changes. Pharmacological medications continue to be the most commonly-recommended first-line treatment for MDD (Olfson and Marcus, 2009). While there are several ADM classes like selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors
Biomarker discovery—Tools and application
Technological advances over the last few decades has fueled the search for biomarkers which may predict individual response to particular antidepressant treatment strategies. In this section we detail the advanced methodologies with a particular focus on the strategies which enable screening of “Omics” biomarkers. Fig. 2 denotes the cascade of events necessary for identifying a biomarker, including discovery and validation processing using high- and low-throughput methodology, respectively.
The future of biomarker identification via clinical trial analyses
The limitations of current diagnostic criteria for psychiatric illnesses have led to the Research Domain Criteria (RDoC) project by the NIMH (Insel et al., 2010b). Especially in the depression field, where clinical syndrome-based subtyping has failed to personalize treatment, the framework postulated in RDoC provides an exciting and promising future. RDoC aims to classify high level domains (or subtypes) from a heterogeneous population by integrating assessments from numerous systems, including
Conclusions and future perspectives
The ineffective treatment of depression necessitates biomarker discovery. We are equipped with complex and high throughput technologies, which combined with large-scale clinical trials, should enable detection of depression biosignatures and ultimately a higher change of remission. As demonstrated in Fig. 3, to date, biomarker research has begun spanning the genome, proteome, and metabolome, and the utility of these technologies will only continue to grow. Presently there exists a potpourri of
Funding
This research was supported in part by the Center for Depression Research and Clinical Care (to MHT) and by the NIMH under Award Number R25MH101078 (to AC).
Financial disclosures
BSG, MKJ, AC, TLM, and MPE report no financial disclosures. JLF has received research funding from the NIA and licensing fees from Regeneron Pharmaceuticals, Janssen Pharmaceuticals, C2N Diagnostics, Treventis Corp., Denali Therapeutics, and ADRx Inc. MHT has received funding support from the Agency for Healthcare Research and Quality (AHRQ), Cyberonics Inc., National Alliance for Research in Schizophrenia and Depression, National Institute of Mental Health (NIMH), National Institute on Drug
Acknowledgements
This research was supported in part by the Center for Depression Research and Clinical Care (to MHT) and by the NIMH under Award Number R25MH101078 (to AC). NIMH had no role in the drafting or review of the manuscript or in the collection or analysis of the data.
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