Personalized Models of Depression and Chronic Pain

项目总结

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.