Personalized prediction of antidepressant v. placebo response: evidence from the EMBARC study

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Abstract

Background

Major depressive disorder (MDD) is a highly heterogeneous condition in terms of symptom presentation and, likely, underlying pathophysiology. Accordingly, it is possible that only certain individuals with MDD are well-suited to antidepressants. A potentially fruitful approach to parsing this heterogeneity is to focus on promising endophenotypes of depression, such as neuroticism, anhedonia, and cognitive control deficits.

Methods

Within an 8-week multisite trial of sertraline v. placebo for depressed adults ( n = 216), we examined whether the combination of machine learning with a Personalized Advantage Index (PAI) can generate individualized treatment recommendations on the basis of endophenotype profiles coupled with clinical and demographic characteristics.

Results

Five pre-treatment variables moderated treatment response. Higher depression severity and neuroticism, older age, less impairment in cognitive control, and being employed were each associated with better outcomes to sertraline than placebo. Across 1000 iterations of a 10-fold cross-validation, the PAI model predicted that 31% of the sample would exhibit a clinically meaningful advantage [post-treatment Hamilton Rating Scale for Depression (HRSD) difference ⩾3] with sertraline relative to placebo. Although there were no overall outcome differences between treatment groups ( d = 0.15), those identified as optimally suited to sertraline at pre-treatment had better week 8 HRSD scores if randomized to sertraline (10.7) than placebo (14.7) ( d = 0.58).

Conclusions

A subset of MDD patients optimally suited to sertraline can be identified on the basis of pre-treatment characteristics. This model must be tested prospectively before it can be used to inform treatment selection. However, findings demonstrate the potential to improve individual outcomes through algorithm-guided treatment recommendations.

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Depression sum-scores don’t add up: why analyzing specific depression symptoms is essential

Eiko I Fried, Randolph Nesse (2015)

Most measures of depression severity are based on the number of reported symptoms, and threshold scores are often used to classify individuals as healthy or depressed. This method – and research results based on it – are valid if depression is a single condition, and all symptoms are equally good severity indicators. Here, we review a host of studies documenting that specific depressive symptoms like sad mood, insomnia, concentration problems, and suicidal ideation are distinct phenomena that differ from each other in important dimensions such as underlying biology, impact on impairment, and risk factors. Furthermore, specific life events predict increases in particular depression symptoms, and there is evidence for direct causal links among symptoms. We suggest that the pervasive use of sum-scores to estimate depression severity has obfuscated crucial insights and contributed to the lack of progress in key research areas such as identifying biomarkers and more efficacious antidepressants. The analysis of individual symptoms and their causal associations offers a way forward. We offer specific suggestions with practical implications for future research. Electronic supplementary material The online version of this article (doi:10.1186/s12916-015-0325-4) contains supplementary material, which is available to authorized users.

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Toward an objective characterization of an anhedonic phenotype: a signal-detection approach.

L Jahn, D Pizzagalli, Robert P. O'Shea (2005)

Difficulties in defining and characterizing phenotypes has hindered progress in psychiatric genetics and clinical neuroscience. Decreased approach-related behavior and anhedonia (lack of responsiveness to pleasure) are considered cardinal features of depression, but few studies have used laboratory-based measures to objectively characterize these constructs. To assess hedonic capacity in relation to depressive, particularly anhedonic, symptoms, 62 participants completed a signal-detection task based on a differential reinforcement schedule. Anhedonia was operationalized as decreased reward responsiveness. Unequal frequency of reward between two correct responses produced a response bias (i.e., a systematic preference to identify the stimulus paired with the more frequent reward). Subjects with elevated depressive symptoms (Beck Depression Inventory scores >/= 16) failed to show a response bias. Impaired reward responsiveness predicted higher anhedonic symptoms 1 month later, after controlling for general negative affectivity. Impaired tendency to modulate behavior as a function of prior reinforcement might underline diminished hedonic capacity in depression. When applied to a clinical population, objective assessments of participants' propensity to modulate behavior as a function of reward might provide a powerful tool for improving the phenotypic definition of depression and thus offer a reliable behavioral screening approach for neuroscience studies of depression.