EVALUATION OF MOLECULAR MECHANISMS OF TREATMENT RESPONSE IN LATE LIFE DEPRESSION

晚年抑郁症治疗反应的分子机制评估

• 批准号: 10451378
• 负责人: Breno Satler Diniz
• 金额: $ 72.12万
• 依托单位: UNIVERSITY OF CONNECTICUT SCH OF MED/DNT
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10451378
• 关键词: Address; Aftercare; Aging; Antidepressive Agents; Bioinformatics; Biological; Biological Aging; Biological Markers; Biological Process; Blood specimen; Brain; Cell Cycle; Chronic; Clinical; Clinical Trials; Consensus; Data; Disease remission; Double-Blind Method; Elderly; Endothelium; Family; Funding; Geroscience; Goals; Growth Factor; Health; Immune; Impaired cognition; Inflammatory; Inflammatory Response; Knowledge; Machine Learning; Mental Depression; Modeling; Molecular; National Institute of Mental Health; Neurocognitive; Outcome; Pathway interactions; Patients; Pattern; Phase; Phase II Clinical Trials; Placebos; Population; Prediction of Response to Therapy; Prevalence; Proteins; Proteomics; Psychotherapy; Public Health; Quality of life; Sample Size; Sampling; Sensitivity and Specificity; Site; Structure; Testing; Therapeutic; Tissues; Work; angiogenesis; aripiprazole; base; biomarker panel; cell injury; cohort; depressive symptoms; design; endothelial dysfunction; geriatric depression; high risk; innovation; meetings; mortality risk; neuroimaging; new therapeutic target; novel; novel marker; novel strategies; open label; prediction algorithm; predictive modeling; predictive tools; proteostasis; recruit; response; therapy resistant; treatment response; treatment-resistant depression; venlafaxine

DESCRIPTION: Over the past decades, antidepressants and psychotherapy have been the first-line treatments for LLD. Despite being safe and well-tolerated, a large number of patients do not achieve full and persistent remission after initial treatment. About 50% of patients with LLD do not respond after two antidepressant trials, meeting the consensus definition of treatment resistance (TR-LLD). The persistence of chronic and elevated depressive symptoms in older adults has significant clinical and public health implications. This has been correlated to poor general health, reduced quality of life, and a higher risk of mortality when compared to those with sustained remission after treatment. Despite the relevance to public health of TR-LLD, there is little information about the biological mechanisms and no robust clinical prediction model to evaluate at the outset of antidepressant therapy who will or will not respond to treatment. Leveraging an NIMH funded clinical trial, the Incomplete Response in Late-Life Depression: Getting to Remission” (IRL-GREY), across 3 sites, in this study, we propose to evaluate the biological mechanisms related to treatment response in late-life depression and to develop a machine learning based algorithm for prediction of treatment response in these subjects. We will carry out a comprehensive, multiplexed proteomic analysis on 542 samples from patients who completed phase 1 and phase 2 of the clinical trial. We hypothesise that ageing-related biological pathways (i.e. inflammatory response control, proteostasis control, cell damage response, endothelial function) will be associated with poorer treatment response in LLD. Moreover, we hypothesize that a machine learning derived biomarker panel will have sensitivity and specificity greater than 80% to predict treatment response in LLD. Finally, we will evaluate the biological mechanisms related to different depressive symptoms trajectories after treatment. This work will set the stage for a biologically-driven model of treatment response that will be useful to guide, at the outset of antidepressant treatment, those who will benefit more from a specific treatment. If successful, our work can accelerate therapeutic efforts and innovation targeting depression and reduce suffering for large numbers of elderly and their families.

描述：在过去的几十年里，抗抑郁药和心理治疗一直是一线治疗 LLD 的治疗。尽管安全且耐受性良好，但仍有大量患者未达到完全且耐受的状态。 初步治疗后持续缓解。大约 50% 的 LLD 患者在两次治疗后没有反应 抗抑郁试验，符合治疗抵抗的共识定义（TR-LLD）。的坚持 老年人的慢性和加重的抑郁症状具有重要的临床和公共卫生意义 影响。这与总体健康状况不佳、生活质量下降以及较高的患病风险有关 与治疗后持续缓解的患者相比，死亡率。尽管与公共卫生相关 TR-LLD 的生物学机制信息很少，也没有稳健的临床预测模型 在抗抑郁治疗开始时评估谁会对治疗产生反应或不会产生反应。 利用 NIMH 资助的临床试验，晚年抑郁症的不完全反应： 缓解”（IRL-GREY），跨越 3 个地点，在本研究中，我们建议评估生物学机制 与晚年抑郁症的治疗反应相关，并开发基于机器学习的算法 预测这些受试者的治疗反应。我们将开展全面的、多重的蛋白质组学研究 对完成临床试验第一阶段和第二阶段患者的 542 份样本进行分析。我们 假设与衰老相关的生物途径（即炎症反应控制、蛋白质稳态控制、 细胞损伤反应、内皮功能）将与 LLD 较差的治疗反应相关。 此外，我们假设机器学习衍生的生物标志物组将具有敏感性和特异性 大于 80% 来预测 LLD 的治疗反应。最后，我们将评估其生物学机制 与治疗后不同的抑郁症状轨迹相关。 这项工作将为生物驱动的治疗反应模型奠定基础，该模型将有助于指导 在抗抑郁治疗开始时，那些将从特定治疗中受益更多的人。如果成功的话，我们的工作 可以加速针对抑郁症的治疗工作和创新，并减少大量患者的痛苦 老年人及其家人。