Personalized Models of Depression and Chronic Pain

项目摘要 抑郁症是一种常见的致残性心理健康状况，也可能对 身体健康多达41%的抑郁症患者报告患有慢性疼痛1。人士 抑郁症患者可能会被处方更高剂量的阿片类药物，导致滥用，过量， 自杀4 -6心理干预可以减轻身体和情感上的痛苦;然而， 目前难以确定最有可能从这些方法中获益的患者2，3。个性化 (i.e.,具体的或N = 1）抑郁和疼痛模型可能会导致更精确的治疗目标 对于有共同症状的患者。例如，如果抑郁症状的增加预示着 对于患者A日常生活中的疼痛，抑郁症的治疗可以有效地减少心理和 身体症状。相反，如果抑郁症状是由日常生活中疼痛的增加来预测的， 患者B可能需要疼痛干预。在本研究中，我将收集生态 抑郁症状和慢性疼痛并存患者的瞬时评估数据 （N = 75）。每个人将提供大约105个数据点，以便制定 使用动态结构方程建模的个性化症状模型14.将使用个性化模型 评估抑郁症预测疼痛和疼痛预测抑郁症的程度， (Aim 1）。我假设抑郁情绪和疼痛之间的关系在一个特定的时间段内会有所不同。 个体之间的连续统一体，与这些关系存在的替代假设相反 对所有人或没有人。如果抑郁症的增加预示着一些人日常生活中疼痛的严重程度增加， 对于个体而言，这表明个性化模型可能有助于定制治疗。我也会收集 动态血压和心率数据，以评估抑郁情绪增加的程度 生理唤醒，这反过来又预测感知疼痛（目的2）。我假设这些关系 也在个体之间变化，其中一些个体表现出指示生理唤醒的关系 抑郁症使疼痛持续的机制。最后，为了促进理解和 为了开发个性化模型，我将评估个人层面关系的主持人（目标3）。这 该提案将提供个性化医疗方法应用方面的培训， 评估抑郁症和疼痛的共同发生。我的长期目标是领导临床试验， 基于个性化模型定制抑郁症和并发疼痛治疗的可行性和实用性。 在本提案中，我将为开发这些模型迈出第一步。我还将接受跨学科的 我在华盛顿的导师团队对我进行了情绪障碍、慢性疼痛和生理功能方面的培训 圣路易斯大学。这个奖学金将作为完美的顶点，我的研究生学习，因为我准备 开始我的职业生涯作为一个独立的临床研究员。

Project Summary Depression is a common and disabling mental health condition that can also have serious implications for physical health. As many as 41% of individuals with depression report disabling chronic pain1. Individuals with depression are likely to be prescribed higher doses of opioids, contributing to greater risk of abuse, overdose, and suicide4–6. Psychological interventions may alleviate both physical and emotional suffering; however, it is currently difficult to identify patients who are most likely to benefit from these approaches2,3. Personalized (i.e., idiographic or N = 1) models of depression and pain could lead to more precise treatment targets for patients with co-occurring symptoms. For example, if increased depressive symptoms predict increased pain in the daily life of Patient A, treatment for depression may be effective in reducing both psychological and physical symptoms. In contrast, if depressive symptoms are predicted by increased pain in the daily life of Patient B, pain-focused interventions may be indicated. In the current study, I will collect ecological momentary assessment data from patients with co-occurring depressive symptoms and chronic pain (N = 75). Each individual will provide approximately 105 data points, allowing for the development of personalized symptom models using dynamic structural equation modeling14. Personalized models will be used to assess the degree to which depression predicts pain and pain predicts depression for different individuals (Aim 1). I hypothesize that prospective relationships between depressed mood and pain will vary on a continuum between individuals, as opposed to the alternative hypothesis that these relationships are present for all or no individuals. If increased depression predicts increased pain severity in daily life for some individuals, this would suggest that personalized models might be useful in tailoring treatment. I will also collect ambulatory blood pressure and heart rate data to assess the degree to which depressed mood increases physiological arousal, which in turn predicts perceived pain (Aim 2). I hypothesize that these relationships will also vary between individuals, with some individuals exhibiting a relationship indicative of physiological arousal as a mechanism whereby depression perpetuates pain. Finally, in order to foster understanding and development of personalized models, I will assess moderators of individual-level relationships (Aim 3). This proposal will provide training in the application of personalized medicine approaches to the assessment of co-occurring depression and pain. My long-term goal is to lead clinical trials assessing the feasibility and utility of tailoring treatment for depression and co-occurring pain based on personalized models. In this proposal, I will take a first step towards developing these models. I will also receive interdisciplinary training in mood disorders, chronic pain, and physiological functioning from my mentorship team at Washington University in St. Louis. This fellowship will serve as the perfect capstone to my graduate studies as I prepare to begin my career as an independent clinical researcher.