Secondary Analysis and Integration of Existing Data Related to Chronic Orofacial Pain and Placebo Effects - Administrative Supplement

响应RFA-DE-22-011的本项目旨在分析代表参与者子集的现有数据 颞下颌关节紊乱病（TMD）患者接受了深入的临床、行为和心理治疗， 根据R 01 DE 025946进行表型分型（PI：Colloca，2022年9月30日结束）。建议的目标是 实质上不同于原始R 01-探索与预期诱导的遗传变异相关的工作， 慢性口面疼痛的镇痛，心理因素预测安慰剂反应，基因相关 前额叶和边缘区的神经元变化与预期诱导的镇痛有关。使用 在分配给Colloca博士的Dean's Initiative Funds中，我们收集了血液并从一个子集中提取了RNA-seq数据 74名TMD参与者。从这个子集的行为，心理，临床和现在，转录组数据 在TMD参与者中，中心假设是不同的重复表达基因（DEG）和途径 与内源性疼痛调节（Endogenous Pain Modulation，ESTA）相关的特征是那些表现出最高的 安慰剂效应我们将通过安慰剂比较与高与低剂量相关的转录组学特征 在TMD参与者（AIM 1）中测试的效果，我们将预测高整合转录组， 使用机器学习的社会人口统计学、临床和心理数据（探索性特定目标2） 模型为了确定高安慰剂反应性的转录组学特征，TMD参与者将被 根据报告的平均值分为安慰剂高应答者（RPR）和安慰剂低应答者（LPR） 视觉模拟量表（VAS）上的疼痛评分临界值为30，从0 =无疼痛锚定到100=最大疼痛 想象的痛苦。根据我们先前发表的结果、信息丰富的初步结果和DEG功效， 计算，我们希望有足够的权力，以确定关键的DEG协会在HPR相比，那些TMD谁做 在控制性别、年龄和疼痛严重程度的同时，没有反应和/或具有较低的安慰剂反应。重要的是， 将进行无偏见的富集分析，以识别与EPM相关的转录组过程。 机器学习方法（例如，广义提升模型）将允许我们整合社会人口统计学， 临床和心理与转录组学标记，以进一步表征HPR在TMD参与者。我们的团队 具有强大的互补专业知识，确保这项研究将提供迈向下一步的综合模型 逐步发现的分子机制的特点，那些谁表现出最大的激活， 安慰剂效应这是第一个使用转录组学分析和机器学习模型来预测 在一个未被充分研究的TMD人群中。研究结果将具有很高的临床相关性，并将提供更多信息。 广泛的研究产生的知识，将是至关重要的，以指导未来的步骤， 转化精准医学

This project in response to RFA-DE-22-011 aims to analyze existing data representing a subset of participants with Temporomandibular disorders (TMD) who underwent in-depth clinical, behavioral, and psychological phenotyping under R01 DE025946 (PI: Colloca, ending September 30, 2022). The proposed aims are substantially different from the original R01-work exploring genetic variants associated with expectancy-induced analgesia in chronic orofacial pain, psychological factors predicting placebo responders, and genes-related neuronal changes in the prefrontal and limbic areas associated with expectancy-induced analgesia. Using Dean’s Initiative Funds allocated to Dr. Colloca, we collected blood and extracted RNA-seq data from a subset of 74 TMD participants. With the behavioral, psychological, clinical and now, transcriptomic data from this subset of TMD participants, the central hypothesis is distinct Differently Expressed Genes (DEG) and pathways associated with Endogenous Pain Modulation (EPM) characterize those TMD participants who show the highest placebo effects. We will compare transcriptomic profiles associated with high versus low EPM via placebo effects tested in TMD participants (AIM1) and we will predict high EPM integrating transcriptomic, sociodemographic, clinical and psychological data (Exploratory Specific Aim 2) using machine learning models. In order to identify transcriptomic profiles of high placebo responsiveness, TMD participants will be divided into High Placebo Responders (HLR) and Low Placebo Responders (LPR) based on an average reported pain score cut-off of 30 on a visual analogue scale (VAS) anchored from zero=no pain to 100=maximum imaginable pain. Based on our prior published results, informative preliminary results, and DEG power calculation, we expect enough power to identify key DEG associations in HPR compared to those TMD who do not respond and/or have lower placebo responses while controlling for sex, age and pain severity. Importantly, unbiased enrichment analyses will be conducted to identify transcriptomic processes associated with EPM. Machine learning approaches (e.g., generalized boosted models) will allow us to integrate sociodemographic, clinical and psychological with transcriptomic markers to further characterize HPR in TMD participants. Our team is strong with complementary expertise, ensuring that this research will provide integrative models towards step- by-step discoveries of molecular mechanisms characterizing those who show the largest activation of EPM via placebo effects. This is the first project to use transcriptomic profiling and machine learning models to predict EPM in an understudied TMD population. Findings will have high clinical relevance and will inform more extensive studies generating knowledge that will be critical to guide future steps towards integrative and translational precision medicine.