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）生物标志物的发现和验证，以及项目BOLD（晚期抑郁症预后的生物标志物）中特定的挑战主题03- mh -101：精神障碍中的生物标志物。重度抑郁障碍（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，大多数患者在通过反复试验确定一种成功的药物之前，都要经历漫长而最终不成功的抗抑郁药物经验试验。如果一种生物标志物能够在治疗过程的早期确定一种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，使用生物标志物显示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，出现在选择性血清素再摄取抑制剂（SSRIs）治疗过程的早期，似乎可以预测一般成年患者群体的后期反应和缓解。美国的人口趋势表明，改善对重度抑郁症的护理对越来越多的老年抑郁症患者至关重要；西班牙裔社区的老年人口预计将呈爆炸性增长。虽然为了找到一种成功的治疗方法而进行的长时间的试验和错误期的结果对患有晚年抑郁症的老年人来说尤其不吉利，但这一患者群体并没有被包括在过去的研究中，这些研究证明了在一般成年人中使用这种生物标志物方法。同样，西班牙裔社区总体上缺乏关于重度抑郁症治疗的信息，特别是关于潜在生物标志物的信息。我们建议进行一项为期12周的治疗试验，根据抗抑郁药物治疗第一周的数据，评估一种实用的生物标志物，用于预测结果，该试验只关注晚年（65岁）的抑郁症，并对洛杉矶的西班牙裔社区进行过采样。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总体表现，特别是在西班牙裔社区；(2)通过包括临床、社会人口统计学和遗传因素来评估模型预测准确性的附加贡献。我们将检验四个特定的假设：H1: ATR对老年人治疗结果的预测将显示bb0 70%的准确性。H2：模型的预测准确性将通过纳入临床、社会人口统计学和遗传预测因素而得到提高。H3：老年西班牙裔受试者的ATR预测准确率为70%左右。H4: ATR预测的准确性不受受试者性别的显著影响。共有80名老年MDD患者将接受为期一周的SSRI艾司西酞普兰（ESC）药理学挑战，并接受11周的ESC单药治疗。主要结局将是在抑郁症状量表30项中12周的缓解（改善50%）。我们将检验我们的假设：(1)通过比较临床结果与生物标志物预测，(2)通过结合非生理因素检查预测模型的改进。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患者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RC1机制并得到ARRA的支持。该项目将为可能无法负担护理费用的老年人提供抑郁症治疗，将保留因经济衰退而受到威胁的学术地位，并将改进一项有望改善重度抑郁症护理的新技术。最后，该项目得到了一家小型医疗设备公司独特的公私合作伙伴关系的支持。这种伙伴关系将有助于确保在两年的项目期后，在项目下开发的技术将得到进一步开发。此外，公私合作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。老年抑郁症是一种常见且昂贵的精神疾病，与医疗费用增加、独立性丧失以及心脏病、中风、癌症和其他疾病的预后较差有关。生物标志物引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生物标志物，这种标志物已经在成年人身上进行了测试，但在抑郁的老年人身上却没有，尤其是西班牙裔老年人，这两个群体在未来几十年将急剧增长。

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.