Evaluation of molecular mechanisms of treatment response in late-life depression

描述：在过去的几十年里，抗抑郁药物和心理治疗一直是第一线 LLD的治疗。尽管是安全和耐受性良好的，但大量患者没有达到完全和 经过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 抗抑郁药物试验，符合治疗耐药的共识定义(TR-LLD)。坚持不懈的 老年人的慢性和重度抑郁症状具有重要的临床和公共卫生意义 这意味着什么。这与一般健康状况不佳、生活质量下降以及罹患癌症的风险较高有关。 与治疗后持续缓解的患者相比，死亡率更高。尽管这与公共卫生有关 关于其生物学机制，目前尚无可靠的临床预测模型。 在开始抗抑郁治疗时，评估谁对治疗有反应或不有反应。 利用NIMH资助的临床试验，晚年抑郁症的不完全反应：获得 (IRL-GREAD)，在这项研究中，我们建议评估其生物学机制 与老年抑郁症的治疗反应相关，并开发一种基于机器学习的算法 预测这些受试者的治疗反应。我们将进行全面的、多元化的蛋白质组学研究 对完成临床试验1期和2期患者的542份样本进行分析。我们 假设与衰老相关的生物通路(即炎症反应控制、蛋白抑制控制、 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记物小组将具有敏感性和特异性。 大于80%可预测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有关。 这项工作将为生物驱动的治疗反应模型奠定基础，这将有助于指导 开始抗抑郁治疗时，那些将从特定治疗中受益更多的人。如果成功，我们的工作 可以加快针对抑郁症的治疗努力和创新，减少大量 老年人及其家人。

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.