Major Depressive Disorder (MDD) is a significant public health problem worldwide. However, despite the availability of medication and psychotherapeutic treatments for MDD, fewer than 40% of patients achieve remission after initial treatment. It would represent a major advance for Precision Medicine if we were able to individualize the therapy of MDD. Recent advances in analytical chemistry have led to the emergence of Metabolomics, a discipline that allows the simultaneous measurement of 100's to 1000's of metabolites to map perturbations in metabolic pathways and networks, thus potentially enabling a systems approach to the study of MDD and its treatment. Our work over the past decade has pioneered the application of metabolomics to study selective serotonin reuptake inhibitors (SSRIs). We have mapped metabolic pathways implicated in SSRI response and discovered novel mechanisms associated with that response. In this proposal, we set out to apply metabolomics to greatly expand our knowledge of MDD treatment response by the use of previously collected samples and comprehensive clinical data from two large studies, the Emory PReDICT and the Mayo Pharmacogenomics Research Network (PGRN) trials. Both of these independent studies used the SSRI escitalopram and the serotonin-norepinephrine reuptake inhibitor duloxetine as treatments. The PReDICT study also included a non-pharmacologic treatment arm, cognitive behavior therapy (CBT). Our goal is to leverage these large investments made by the NIH by applying an integrated metabolomics- genomics- neuroimaging approach to characterize and functionally validate the biological systems predictive of MDD treatment outcomes.
In Specific Aim 1, we will define metabolomic signatures of exposure to the 3 therapies in the treatment-nave MDD patients from the PReDICT study.
In Aim 2, we will evaluate and model the metabolomic signatures associated with improvement during treatment with escitalopram, duloxetine, and CBT, and then replicate our findings in the Mayo study.
In Aim 3 we will use pharmacometabolomics-informed pharmacogenomics both to compare the biomarkers discovered in the Mayo study with those identified in PReDICT, and to identify the metabolite-associated genes and single nucleotide polymorphisms involved in mechanisms associated with variation in treatment response using cell-line based systems. Finally, our Exploratory Aim will examine linkages between central nervous system function and peripheral metabolomic signatures. This proposal is innovative because it applies the novel tool of metabolomics to the question of treatment selection in MDD, and because it integrates metabolomics with genomics and neuroimaging data to enable a deeper understanding of therapeutic mechanisms of action. It is also highly significant because it will add to evidence-based methods for the selection of optimal treatments for individual MDD patients and will expand our understanding of biological mechanisms underlying response to the therapy for this major disease.
Major Depressive Disorder (MDD) is a leading cause of disability and suicide in the United States and around the world, but currently only 40% of patients achieve remission with their initial treatment. Thus, one of the greatest needs in mental health care delivery is to more accurately match each individual MDD patient with the specific treatment most likely to benefit them. We propose to bring the power of metabolomics and to use metabolomics to 'inform' genomics to define biological and genomic profiles that characterize both the metabolic state of individual MDD patients and the effects of three different MDD treatments, with the aim of improving outcomes for the therapy of MDD.
|Liu, Duan; Ray, Balmiki; Neavin, Drew R et al. (2018) Beta-defensin 1, aryl hydrocarbon receptor and plasma kynurenine in major depressive disorder: metabolomics-informed genomics. Transl Psychiatry 8:10|
|Ho, Ming-Fen; Correia, Cristina; Ingle, James N et al. (2018) Ketamine and ketamine metabolites as novel estrogen receptor ligands: Induction of cytochrome P450 and AMPA glutamate receptor gene expression. Biochem Pharmacol 152:279-292|
|Adler, Angela; Kirchmeier, Pia; Reinhard, Julian et al. (2018) PhenoDis: a comprehensive database for phenotypic characterization of rare cardiac diseases. Orphanet J Rare Dis 13:22|