Metabolomic Signatures Predictive of Outcomes to Treatments for Major Depression

 描述（由申请人提供）：重度抑郁症（MDD）是一个严重的全球公共卫生问题。然而，尽管药物和心理治疗对MDD的可用性，只有不到40%的患者在初始治疗后达到缓解。如果我们能够对MDD进行个体化治疗，这将是精准医学的一个重大进步。分析化学的最新进展导致了代谢组学的出现，代谢组学是一门允许同时测量100到1000个代谢物以映射代谢途径和网络中的扰动的学科，从而可能使MDD及其治疗的研究成为一种系统方法。在过去的十年中，我们的工作开创了代谢组学应用于研究选择性5-羟色胺再摄取抑制剂（SSRIs）。我们已经绘制了SSRI反应中涉及的代谢途径，并发现了与该反应相关的新机制。在本提案中，我们开始应用代谢组学，通过使用先前收集的样本和来自两项大型研究（Emory PReDICT和马约药物基因组学研究网络（PGRN）试验）的综合临床数据，极大地扩展我们对MDD治疗反应的了解。这两项独立研究均使用SSRI艾司西酞普兰和阿托宁-去甲肾上腺素再摄取抑制剂度洛西汀作为治疗。PReDICT研究还包括一个非药物治疗组，即认知行为疗法（CBT）。我们的目标是通过应用综合代谢组学-基因组学-神经影像学方法来表征和功能验证预测MDD治疗结果的生物系统，从而利用NIH的这些大型投资。在具体目标1中，我们将定义来自PReDICT研究的初治MDD患者暴露于3种治疗的代谢组学特征。在目标2中，我们将评估和建模与艾司西酞普兰、度洛沙坦和CBT治疗期间改善相关的代谢组学特征，然后在马约研究中复制我们的发现。在目标3中，我们将使用“药物代谢组学-知情药物基因组学”来比较马约研究中发现的生物标志物与PReDICT中鉴定的生物标志物，并使用基于细胞系的系统鉴定与治疗反应变化相关的机制中涉及的代谢物相关基因和单核苷酸多态性。最后，我们的探索性目标将检查中枢神经系统功能和外周代谢组学特征之间的联系。这一提议是创新的，因为它将代谢组学的新工具应用于MDD的治疗选择问题，并且因为它将代谢组学与基因组学和神经影像学数据相结合，从而能够更深入地理解治疗作用机制。这也是非常重要的，因为它将增加以证据为基础的方法，为个别MDD患者选择最佳治疗，并将扩大我们对这种主要疾病治疗反应的生物学机制的理解。

 DESCRIPTION (provided by applicant): 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-naïve 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.