Biomarkers for Outcomes In Late-life Depression (BOLD)

描述（由申请人提供）： 本申请涉及广泛的挑战领域（03）生物标志物发现和验证，以及特定的挑战主题03-MH-101：精神障碍中的生物标志物，在项目BOLD（晚年抑郁症结局的生物标志物）中。重性抑郁障碍（Major Depression Disorder，MDD）是一种常见的精神疾病，对社会和患者个体都有很高的成本。高成本的一个原因是，大多数患者在通过试错法确定成功的药物之前，经历了漫长且最终失败的经验性抗抑郁药试验。如果生物标志物可以在治疗过程的早期确定特定的抗抑郁药是否可能导致反应，缓解或治疗失败，那么护理将得到改善。医生可以迅速改变治疗方法，使用一种生物标志物表明可能对患者有帮助的抗抑郁药。我们已经确定了定量脑电图（QEEG）的变化，出现在早期的治疗过程中与选择性5-羟色胺再摄取抑制剂（SSRIs），似乎预测以后的反应和缓解，在一般的成年患者群体。美国的人口统计学趋势表明，改善对MDD的护理对于越来越多的老年抑郁症患者至关重要;西班牙裔社区老年人队伍的扩大预计将是爆炸性的。虽然长期的试错期以找到成功的治疗方法的后果对于患有晚年抑郁症的老年人来说特别不吉利，但这一患者群体尚未被纳入过去的研究中，这些研究证明了这种生物标志物方法在一般成年人群中的使用。同样，在西班牙裔社区中，关于MDD治疗的信息也很缺乏，特别是关于潜在的生物标志物。我们提出了一个为期12周的治疗试验，以评估一个实用的生物标志物预测结果的基础上，从第一周的抗抑郁药治疗的数据，重点只对抑郁症在晚年（年龄e65）和过度采样的西班牙裔社区在洛杉矶。我们的具体目标是：（1）评估ATR生物标志物在老年抑郁症中的表现，特别是在西班牙裔社区;（2）通过纳入临床、社会人口统计学和遗传因素，评估模型对预测准确性的贡献。我们将测试四个特定的假设：H1：ATR对老年人治疗结果的预测将显示>70%的准确性。H2：通过纳入临床、社会人口统计学和遗传预测因子，将提高模型的预测准确性。H3：老年西班牙裔受试者中ATR预测的准确性将显示>70%的准确性。H4：ATR预测的准确性不会显示出对受试者性别的显著依赖性。共有80名患有MDD的老年人将接受为期一周的SSRI艾司西酞普兰（ESC）药理学挑战，并接受为期11周的ESC单药治疗。主要结局为30项抑郁症状量表的12周应答（e50%改善）。我们将（1）通过比较临床结果与生物标志物预测，以及（2）通过检查纳入非生理因素的预测模型的改善来检验我们的假设。一个数据安全监测委员会将监督该项目。将我们的模型应用于老年人和西班牙裔美国人的治疗选择的初步评估将支持抑郁症个性化医疗方法的进一步发展。该项目非常适合RC 1机制和ARRA的支持。该项目将为那些可能负担不起医疗费用的老年人提供抑郁症治疗，将保留受经济衰退威胁的学术职位，并将改进一项有望改善MDD护理的新技术。最后，该项目得到了与一家小型医疗器械公司的独特公私合作伙伴关系的支持。这一伙伴关系将有助于确保在两年项目期结束后，在该项目下开发的技术将得到进一步发展。此外，公私伙伴关系将扩大这项技术的经济效益，可能创造数百个新的私营部门就业机会。晚年抑郁症是一种常见且昂贵的精神疾病，与医疗费用增加，丧失独立性以及心脏病，中风，癌症和其他疾病的不良后果有关。生物标记物指导的治疗可以通过快速识别每个患者的有效药物来改善结果。该项目研究了一种神经生理学生物标志物，该生物标志物已在成年人中进行了测试，但未在抑郁的老年人中进行测试，特别是西班牙裔老年人，这两个群体将在未来几十年内急剧增长。

DESCRIPTION (provided by applicant): This application addresses broad Challenge Area (03) Biomarker Discovery and Validation, and specific Challenge Topic 03-MH-101: Biomarkers in mental disorders, in Project BOLD (Biomarkers for Outcomes In Late-life Depression). Major depressive disorder (MDD) is a common psychiatric illness with high cost to society and individual patients. One reason for the high cost is that most patients endure lengthy and ultimately unsuccessful empiric antidepressant trials before a successful medication is identified by trial-and-error. Care would be improved if a biomarker could determine, early in the course of treatment, whether a particular antidepressant would likely lead to response, remission, or treatment failure. Physicians could rapidly change treatments to an antidepressant which the biomarker indicated would be likely to help the patient. We have identified quantitative electroencephalographic (QEEG) changes that emerge early in the course of treatment with selective serotonin reuptake inhibitors (SSRIs) that appear to predict later response and remission in a general adult patient population. Demographic trends in the United States suggest that improved care for MDD will be essential for a growing number of elderly with late-life depression; expansion of the ranks of the elderly in the Hispanic community is projected to be explosive. While the consequences of prolonged trial-and- error periods to find a successful treatment are particularly inauspicious for elders with late-life depression, this patient group has not been included in the past studies which demonstrated the use of this biomarker approach in a general adult population. Similarly, there has been a dearth of information about treatment for MDD in the Hispanic community in general, and about potential biomarkers in particular. We propose a 12- week treatment trial to evaluate a practical biomarker for predicting outcome based on data from the first week of antidepressant treatment, with a focus only on depression in late life (age e65) and with oversampling of the Hispanic community in Los Angeles. Our specific aims are (1) to evaluate the performance of the ATR biomarker in late-life depression generally, and in the Hispanic community in particular and (2) to evaluate the additive contributions to predictive accuracy of the model by including clinical, socio-demographic, and genetic factors. We will test four specific hypotheses: H1: ATR prediction of treatment outcome in older adults will show >70% accuracy. H2: The predictive accuracy of the model will be enhanced by including clinical, socio- demographic, and genetic predictors. H3: The accuracy of ATR prediction in older Hispanic subjects will show >70% accuracy. H4: The accuracy of ATR prediction will not show a significant dependence on subject gender. A total of 80 older adults with MDD will receive a one-week pharmacologic challenge with the SSRI escitalopram (ESC), and receive 11 more weeks of ESC monotherapy. The primary outcome will be 12-week response (e50% improvement) on the 30-item Inventory of Depressive Symptomatology. We will test our hypotheses (1) by comparing clinical outcomes with biomarker predictions, and (2) by examining improvement in the predictive model from incorporating non-physiologic factors. A Data Safety Monitoring Board will oversee the project. This preliminary evaluation of applying our model to treatment selection in the elderly and in Hispanics will support further developments in personalized medicine approaches for depression. This project is highly appropriate for the RC1 mechanism and for support by the ARRA. The project will provide treatment for depression to elders who may not be able to afford care, will preserve academic positions endangered by the economic downturn, and will refine a new technology that promises to improve care for MDD. Finally, the project is supported by a unique public-private partnership with a small medical device company. This partnership will help ensure that the technology developed under the project will be developed further after the two-year project period. Furthermore, the public-private partnership will amplify the economic benefits of this technology by potentially creating hundreds of new private sector jobs. Late-life depression is a common and costly psychiatric illness, and is linked to increased healthcare costs, loss of independence, and poorer outcomes with heart disease, stroke, cancer and other illnesses. Biomarker- guided treatment could improve outcomes by rapidly identifying an effective medication for each patient. This project examines a neurophysiologic biomarker that has been tested in adults but not in depressed elders, particularly Hispanic elders, two groups that will grow dramatically in the next decades.