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

与慢性口面部疼痛和安慰剂效应相关的现有数据的二次分析和整合 - 行政补充

• 批准号: 10741330
• 负责人: Luana Colloca
• 金额: $ 8.78万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2024-09-21
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10741330
• 关键词: Absence of pain sensation; Administrative Supplement; Affect; Age; Algorithms; Area; Behavioral; Biological; Biological Markers; Blood; Blood specimen; Candidate Disease Gene; Caring; Chronic; Classification; Clinical; Complex; Consensus; Data; Data Analyses; Dopamine; Endocannabinoids; Engineering; Ensure; Expectancy; Fostering; Funding; Future; Genes; Genetic; Genomics; Goals; Health; Individual; Interdisciplinary Study; Knowledge; Laboratories; Machine Learning; Medicine; Modeling; Molecular; Neural Pathways; Neurons; Opioid; Orofacial Pain; Pain; Pain intensity; Pain management; Participant; Pathway interactions; Patients; Phenotype; Placebo Effect; Placebos; Population; Predictive Factor; Preparation; Process; Prospective Studies; Proteomics; Psychological Factors; Publishing; Quality of life; Recommendation; Reporting; Research; Research Priority; Sampling; Serotonin; Severities; Severity of illness; Single Nucleotide Polymorphism; System; Temporomandibular Joint Disorders; Testing; Therapeutic; Twin Multiple Birth; United States National Academy of Sciences; Validation; Variant; Visual; Work; analog; chronic pain; chronic pain patient; chronic painful condition; clinical phenotype; clinically relevant; cohort; endophenotype; genetic variant; genome wide association study; machine learning model; molecular marker; non-opioid analgesic; orofacial; pain inhibition; pain reduction; pain score; pain signal; pill; precision medicine; psychologic; psychosocial; randomized, clinical trials; response; secondary analysis; sex; single-cell RNA sequencing; sociodemographics; trait; transcriptome; transcriptome sequencing; transcriptomic profiling; transcriptomics

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.

该项目响应RFA-DE-22-011，旨在分析代表参与者子集的现有数据 与经历了深入临床，行为和心理的颞下颌疾病（TMD） 在R01 DE025946（PI：Coloca，截至2022年9月30日）下的表型。拟议的目的是 与原始的R01工作大不相同，探索与期望诱导的遗传变异 慢性口面疼痛中的镇痛，预测安慰剂反应者的心理因素和基因有关 与预期诱导的镇痛有关的前额叶和边缘区域的神经元变化。使用 Dean的倡议资金分配给了Coloca博士，我们从子集中收集了血液并提取了RNA-Seq数据 在74位TMD参与者中。使用该子集的行为，心理，临床和现在的转录组数据 在TMD参与者中，中心假设明显不同地表达的基因（DEG）和途径 与内源性疼痛调节（EPM）相关的那些表现最高的TMD参与者 安慰剂效果。我们将通过安慰剂比较与高EPM相关的转录组轮廓 在TMD参与者中测试的效果（AIM1），我们将预测EPM的高度集成转录组， 社会人口统计学，临床和心理数据（探索性特定目的2）使用机器学习 型号。为了识别高安慰剂响应能力的转录组概况，TMD参与者将是 根据平均报告 疼痛评分在视觉模拟量表（VAS）上的疼痛评分截止为零=无疼痛至100 =最大 可以想象的疼痛。根据我们先前发布的结果，信息丰富的初步结果和DEG Power 计算，我们期望与那些做到的TMD相比，可以识别HPR中的关键DEG关联关联 在控制性别，年龄和疼痛严重程度时，没有反应和/或安慰剂反应较低。重要的是， 将进行公正的富集分析，以识别与EPM相关的转录组过程。 机器学习方法（例如，广义增强模型）将使我们能够整合社会人口统计学， 带有转录组标记的临床和心理学，以进一步表征TMD参与者中的HPR。我们的团队 具有完善专业知识非常强大，确保这项研究将为步骤提供综合模型 表征那些表现出EPM激活最大的分子机制的逐步发现 安慰剂效果。这是第一个使用转录组分析和机器学习模型来预测的项目 EPM在一个理解的TMD人群中。调查结果将具有很高的临床相关性，并将告知更多 广泛的研究产生知识至关重要，这对于指导未来的融合步骤和 翻译精度医学。

项目成果

How negative and positive constructs and comorbid conditions contribute to disability in chronic orofacial pain.

消极和积极的结构以及合并症如何导致慢性口面部疼痛的残疾。

• DOI: 10.1002/ejp.2042
• 发表时间: 2023
• 期刊: European journal of pain (London, England)
• 作者: Thomas,Sharon;Wang,Yang;Cundiff-O'Sullivan,Rachel;Massalee,Rachel;Colloca,Luana
• 通讯作者: Colloca,Luana

Clinical Phenotypes Supporting the Relationship Between Sleep Disturbance and Impairment of Placebo Effects.

支持睡眠障碍与安慰剂效应受损之间关系的临床表型。

• DOI: 10.1016/j.jpain.2023.10.013
• 发表时间: 2024
• 期刊: The journal of pain
• 作者: Wang,Yang;Varghese,Jeril;Muhammed,Salim;Lavigne,Gilles;Finan,Patrick;Colloca,Luana
• 通讯作者: Colloca,Luana