Pretreatment metabotype as a predictor of response to sertraline or placebo in depressed outpatients: a proof of concept

Abstract

The purpose of this study was to determine whether the baseline metabolic profile (that is, metabotype) of a patient with major depressive disorder (MDD) would define how an individual will respond to treatment. Outpatients with MDD were randomly assigned to sertraline (up to 150 mg per day) (N = 43) or placebo (N = 46) in a double-blind 4-week trial. Baseline serum samples were profiled using the liquid chromatography electrochemical array; the output was digitized to create a ‘digital map’ of the entire measurable response for a particular sample. Response was defined as ≥50% reduction baseline to week 4 in the 17-item Hamilton Rating Scale for Depression total score. Models were built using the one-out method for cross-validation. Multivariate analyses showed that metabolic profiles partially separated responders and non-responders to sertraline or to placebo. For the sertraline models, the overall correct classification rate was 81% whereas it was 72% for the placebo models. Several pathways were implicated in separation of responders and non-responders on sertraline and on placebo including phenylalanine, tryptophan, purine and tocopherol. Dihydroxyphenylacetic acid, tocopherols and serotonin were common metabolites in separating responders and non-responders to both drug and placebo. Pretreatment metabotypes may predict which depressed patients will respond to acute treatment (4 weeks) with sertraline or placebo. Some pathways were informative for both treatments whereas other pathways were unique in predicting response to either sertraline or placebo. Metabolomics may inform the biochemical basis for the early efficacy of sertraline.

Metabolic Profiling and Phenotyping of Central Nervous System Diseases: Metabolites Bring Insights into Brain Dysfunctions

“…Importantly, systemic biomarkers detectable in urine, plasma or CSF would be the most relevant for translation to human and several studies demonstrate the great potential of metabolic not only for diagnosis but also for treatment assessment. As such, pilot studies on CSF of schizophrenic and depressed patients reveal that metabotyping predicts the disease status for a given sample, as well as treatment outcome Huang et al 2007;Kaddurah-Daouk et al 2011a;KaddurahDaouk et al 2007;Lan et al 2009;Prabakaran et al 2004). This is particularly relevant for psychiatric conditions of unknown etiology since, up to date, diagnosis rely on psychocognitive tests and no biochemical or genetic test is available for these diseases.…”

“…This personalized medicine approach was coined "pharmacometabonomics" and opens new avenues for metabolic research in CNS diseases. Following the first studies, which focussed on predicting the response of rats (Clayton et al 2006) and humans (Clayton et al 2009) to a sub-toxic dose of acetaminophen using naïve (pre-dose) urine samples, several papers were subsequently published in various contexts, and in particular, in the management of Major Depressive Depression (MDD) (Kaddurah-Daouk et al 2013a;Kaddurah-Daouk et al 2011a). In the latest double-blind pharmacometabolomic study (Kaddurah-Daouk et al 2013a), the authors tested whether the baseline metabolic profile of a patient with MDD can predict patient response to treatment with the antidepressant sertraline over 1-to 4-weeks.…”

Pharmacometabonomics and personalized medicine

“…Hence, a biochemical rationale was presented for these two metabolites. 78 It was not commented upon that 5-HIAA is a direct metabolite of serotonin and is used as a surrogate to measure serotonin in man. 79 Schizophrenia.…”

“…A report on pharmacometabonomics in MDD patients randomly assigned to treatment with sertraline or to placebo has appeared. 78 The study was complicated as, in addition to variable drug responses from patients, the placebo effect is strong in MDD patients. However, a liquid chromatography electrochemical analysis of predose serum was undertaken.…”

Prediction of treatment outcomes in major depressive disorder

“…Structural and functional neuroimaging [10], genetics [11], inflammation [12], electroencephalography [13], metabolomics [14] and hypothalamic-adrenal axis measures [11] have all shown potential as biological variables predictive of treatment outcome. However, only a few have identified moderators of differential outcomes between treatments.…”