Improving the Prediction of Treatment Response in Depression:Integration of Clinical, Cognitive, Psychophysiological, Neuroimaging, and Genetic Measures

Abstract

Antidepressants are important in the treatment of depression, and selective serotonin reuptake inhibitors are first-line pharmacologic options. However, only 50% to 70% of patients respond to first treatment and <40% remit. Since depression is associated with substantial morbidity, mortality, and family burden, it is unfortunate and demanding on health resources that patients must remain on their prescribed medications for at least 4 weeks without knowing whether the particular antidepressant will be effective. Studies have suggested a number of predictors of treatment response, including clinical, psychophysiological, neuroimaging, and genetics, each with varying degrees of success and nearly all with poor prognostic sensitivity and specificity. Studies are yet to be conducted that use multiple measures from these different domains to determine whether sensitivity and specificity can be improved to predict individual treatment response. It is proposed that a focus on standardized testing methodologies across multiple testing modalities and their integration will be crucial for translation of research findings into clinical practice.

Interactions between BDNF Val66Met polymorphism and early life stress predict brain and arousal pathways to syndromal depression and anxiety

“…These include the international study to predict optimized treatment in depression (iSPOT-D). 2 Further, to explore temporal dynamics and reciprocal relations, longitudinal or experimental data need to be applied; for instance to confirm the direction of effects between body arousal and the neuroticism measure of emotional stability. It would also be interesting to examine if there are additional interactions between BDNF alleles and sex differences, given previous indications of such differences in relation to temperament.…”

“…52 Met allele carriers show comparatively lower hippocampal gray matter 53,54 and poorer cognitive performance. 2,3,52,54 It has also been shown that, in otherwise healthy individuals with the BDNF Met variant, low emotional stability (higher neuroticism) with higher syndromal depression is associated with lower hippocampal volume. 55 Animal evidence indicates that stress exacerbates the effects of reduced BDNF on both hippocampal networks and autonomic arousal.…”

“…There is therefore an urgent need to identify objective indicators of risk for depression, and to understand their link to underlying biological mechanisms and treatment. 1,2 To date, candidate risk markers such as genetic polymorphisms, vulnerability to stress, personality traits, and alterations in brain function, structure and cognition have tended to be assessed in separate studies. 3 The integrative relationships between these factors, and how they may interact to predict depression and anxiety, remain unknown.…”

Resting-State Connectivity Predictors of Response to Psychotherapy in Major Depressive Disorder

“…Undoubtedly, robust algorithms predicting treatment response will need to incorporate genetic testing (Frieling andTadic, 2013), pharmacogenetics (de Leon, 2009), and neuroendocrine function (Holsboer, 2000). It is also well documented that a number of patient factors predict response to antidepressant treatment across treatment modalities, including disease severity, longer duration and frequency of the episodes, comorbid anxiety disorders, and an older age of onset (Kemp et al, 2008). Moreover, longitudinal clinical followup data would be critical to assess relapse after BATD termination as well as rs-fcMRI predictors of relapse, and posttreatment resting-state scans would be needed to address potential mechanisms of antidepressant action on canonical resting-state brain networks.…”

Prediction of treatment outcomes in major depressive disorder

“…Most published prediction studies report predictors associated with better or poorer response to a single treatment [4].…”