Predictors of Treatment Response Relapse and Recurrence in Major Depression

描述（由申请人提供）：重度抑郁症（MDD）是一种高度流行的疾病，与显著的发病率和死亡率相关，估计是全球残疾的主要原因之一。DSM-IV定义的重度抑郁症是一种异质性疾病，以一系列独特的遗传、神经和神经内分泌疾病和异常为特征。与此同时，个体生活和成长的环境会影响发育神经生物学、神经化学、思维模式、感觉和行为，从而进一步影响他们对重度抑郁症的易感性。各种抗抑郁药物、心理疗法和非药物躯体疗法在急性治疗试验中被证明是有效的。然而，在这些试验中，大多数患者没有得到缓解，增加了患慢性抑郁症、自杀、药物滥用和几种严重疾病的风险。未满足的一个主要需求是确定治疗缓解的预测因素，这已在其他医学分支（例如肿瘤学和传染病）中用于改善患者的结果。鉴于脑功能成像、分子和细胞神经生物学、遗传学和人格障碍方面的进展以及小型研究中的一些有希望的发现，开展研究以确定这些因素的组合是否预测抗抑郁治疗的缓解以及复发和复发是有利的。在本申请中，我们提出两项研究。第一项研究是我们最近资助的NIMH CIDAR 3组12周治疗试验的扩展；我们将把CIDAR的样本量从400个增加到600个，并对所有出院的患者进行为期两年的随访。在这项扩大的研究中，600名未接受治疗的成年抑郁症患者将被随机分配到以下治疗方案之一：1)艾司西酞普兰，一种SSRI；2)度洛西汀，一种SNRI；3) CBT。CIDAR的主要目标是采用多变量方法预测单次急性治疗后的缓解。本研究的主要目的是确定三种单药治疗缓解后21个月内复发和复发的预测因素。第二项研究旨在研究对那些单药治疗无效的患者进行额外药物治疗和心理治疗联合治疗的短期和长期效果的预测。我们的团队在识别成像程序（例如，静息BOLD功能磁共振成像）、遗传多态性和人格障碍测量方面取得了许多进展，这些方法可以预测重度抑郁症的缓解、复发和复发。此外，我们将采用额外的最先进的MDD及其治疗方法。统计学上，我们将试图确定哪种测量和/或测量的组合可以预测所研究的治疗的缓解、复发和复发。描述治疗缓解、复发和复发的预测因素将显著改善患者的预后，降低治疗不充分的风险。重度抑郁症的终生发病率为16%，是所有医学疾病中最常见和最使人衰弱的疾病之一。尽管在了解抑郁症的原因、性质和治疗方法方面取得了相当大的进步，但我们仍然不知道哪种治疗方法对单个患者有效。根据建议的研究结果，希望未来的社区临床医生能够为患有严重抑郁症的个别患者开出特定的治疗方法，并相信所规定的治疗方法将有效和持久地缓解抑郁症的症状。

DESCRIPTION (provided by applicant): Major depression (MDD) is a highly prevalent disorder associated with significant morbidity and mortality and is estimated to be one of the leading causes of disability worldwide. The DSM-IV defined syndrome of MDD is a heterogeneous disorder characterized by a range of distinctive genetic, neural, and neuroendocrine diatheses and abnormalities. At the same time, individuals live and grow within an environment that influences developmental neurobiology, neurochemistry, and patterns of thought, feeling and behavior that further impact their vulnerability to the development of MDD. A variety of antidepressant drugs, psychotherapies, and non- drug somatic therapies have been demonstrated to be efficacious in acute treatment trials. The majority of patients in these trials, however, do not attain remission, increasing the risk for the development of chronic depression, suicide, substance abuse and several serious medical disorders. A major unmet need is the identification of predictors of remission to treatments, as has been utilized in other branches of medicine (e.g., oncology and infectious disease) to improve patient outcome. In view of advances in functional brain imaging, molecular and cellular neurobiology, genetics, and personality disorders and a number of promising findings in small studies, it is propitious to conduct studies to determine whether a concatenation of these factors predict antidepressant treatment remission as well as relapse and recurrence. In this application, we propose two studies. The first study is an expansion of our recently funded NIMH CIDAR 3-arm, 12-week treatment trial; we will increase the CIDAR sample size from 400 to 600 and follow all remitted patients for a total of two years. In this expanded study, 600 treatment na¿ve adult depressed patients will be randomized to one of the following treatments: 1) escitalopram, an SSRI; 2) duloxetine, an SNRI; and 3) CBT. The primary goal of the CIDAR is to employ a multivariate approach to predict remission following acute treatment with a single therapy. The primary purpose of study of this proposal is to identify predictors of relapse and recurrence during the 21 months following remission to these three monotherapy treatments. The second study is designed to study prediction of short-term and long-term effects of additional pharmacotherapy and psychotherapy combination treatments for those who fail to remit with monotherapy. Our group has made a number of advances in identifying imaging procedures (e.g., resting BOLD fMRI), genetic polymorphisms, and personality disorder measures that predict remission, relapse, and recurrence of MDD. In addition, we will employ additional state- of-the-science measures of MDD and its treatment. Statistically, we will seek to determine which measure and/or combination of measures leads to prediction of remission, relapse, and recurrence to the treatments under study. Delineation of predictors of treatment remission, relapse, and recurrence of MDD will dramatically improve patient outcomes and reduce the risk of inadequate treatment. Major Depressive Disorder has a lifetime occurrence rate of 16% and is among the most frequent and debilitating of all medical disorders. Despite considerable advances in understanding the causes, nature, and treatment of depression, we still do not know which treatment will work for an individual patient. As a result of the proposed studies, it is hoped that the community clinician of tomorrow will be able to prescribe a specific treatment for an individual patient with major depression, with confidence that the prescribed treatment will provide effective and lasting relief from the symptoms of depression.