Genetic Markers of Chronic Postsurgical Pain

慢性术后疼痛的遗传标记

• 批准号: 10644470
• 负责人: Stephan Frangakis
• 金额: $ 17.5万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-17 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10644470
• 关键词: Academy; Analgesics; Applications Grants; Authorization documentation; Biological; Biometry; Bypass; Candidate Disease Gene; Chronic; Chronic low back pain; Clinical Trials; Clinical Trials Design; Collaborations; Complement; DNA Sequence Alteration; Data; Data Set; Development; Diabetes Mellitus; Discipline; Environmental Risk Factor; Epidemic; Epidemiology; Funding; Future; Genes; Genetic; Genetic Markers; Genetic Predisposition to Disease; Genetic Research; Genetic Variation; Genetic study; Genome; Genomics; Genotype; Goals; Grouping; Heart Diseases; Hospitalization; Impairment; Individual; Institution; K-Series Research Career Programs; Lead; Low Back Pain; Malignant Neoplasms; Measures; Medicine; Mentors; Mentorship; Michigan; Molecular; Morbidity - disease rate; Operative Surgical Procedures; Pain; Pain management; Participant; Pathway Analysis; Pathway interactions; Patient Self-Report; Patients; Perioperative; Phenotype; Physical Function; Physicians; Positioning Attribute; Postoperative Pain; Quality of life; Recovery; Research; Research Methodology; Resources; Role; Sampling; Scientist; Site; Surveys; Testing; Training; Translational Research; United States; United States National Institutes of Health; Universities; Variant; Work; antinociception; authority; biobank; care costs; career; career development; chronic pain; clinical training; cohort; duloxetine; experience; feeding; gene function; genetic analysis; genetic approach; genetic association; genetic information; genetic predictors; genetic risk factor; genetic variant; genome wide association study; genome-wide; insight; meetings; multidisciplinary; neuroimaging; novel; opioid use; pain outcome; pain patient; patient oriented; patient response; patient subsets; pharmacologic; potential biomarker; precision medicine; predict responsiveness; programs; prospective; response; response biomarker; societal costs; success; systematic review; treatment response

PROJECT SUMMARY / ABSTRACT Over 100 million surgical procedures are performed in the United States each year, with up to 80% of patients experiencing postoperative pain. Higher levels of pain after surgery are associated with the development chronic post-surgical pain (CPSP) and chronic opioid use, feeding into a pain epidemic that has a greater annual societal cost than that for cancer, heart disease, and diabetes combined. Though there have been multiple phenotypic and environmental factors that have been identified to be predisposing for the development of CPSP, much less data is available regarding genetic predictors of CPSP. Previous studies have been limited by small samples or candidate-gene approaches that are often not reproducible in different cohorts. It therefore remains difficult to predict patients genetically predisposed to severe postoperative pain or those who will progress to develop CPSP, and to tailor antinociceptive therapy to a patient’s specific genetic predisposition to the development of pain. The goal of the proposed work is to overcome these limitations by using the largest and most well genotyped cohort employed thus far in the study of postsurgical pain genetics. Over 3400 patients with both pre/postsurgical self-reported pain and genomic sequencing data will make up this cohort. We hypothesize that 1) identifiable variations in genes and genetic pathways are associated with postsurgical pain, and 2) these variations can contribute to differing responses to pharmacologic therapy. This study will use candidate gene, genome-wide association study, and pathway-based analyses to identify genetic variations that influence postsurgical pain. I will then leverage one of the most granular clinical trials of low back pain (NIH HEAL BACPAC) to study genetic associations of responsiveness for a common pain medication (duloxetine). The candidate for this mentored Patient-Oriented Career Development Award aims to complement his previous training with additional expertise in large-scale genomics, biostatistics, and clinical trial design. In order to facilitate future collaboration with colleagues across multiple disciplines, in concert with the proposed study the candidate with also pursue didactive and experiential translational science training related to his research aims. The University of Michigan has committed abundant resources to support this proposal, and it is also supported by a multidisciplinary mentorship group comprised of authorities in genetics, genomics, biostatistics, perioperative pain, and clinical trial design and execution. The proposed studies will provide novel insights into the genetics of chronic postoperative pain and form the basis for future studies into personalized pain therapy.

项目摘要 /摘要 每年在美国进行超过1亿个手术程序，最多80％的患者 经历术后疼痛。手术后较高的疼痛水平与发育有关 慢性手术后疼痛（CPSP）和慢性阿片类药物使用，进食疼痛流行，具有更大的疼痛流行 每年的社会成本比癌症，心脏病和糖尿病总和。虽然有 已确定为发展的多种表型和环境因素 在CPSP中，关于CPSP的遗传预测因子的数据少得多。以前的研究是 受小样本或候选基因方法的限制，这些方法通常在不同的队列中不可再现。它 因此，仍然很难预测一般易于术后疼痛的患者或 将进步以开发CPSP，并根据患者的特定通用量调整抗伤害感受疗法 疼痛发展的倾向。 拟议的工作的目的是通过使用最大，最良好的基因分型来克服这些局限性 到目前为止，在术后疼痛遗传学研究中使用的队列。超过3400名患者 前/术后自我报告的疼痛和基因组测序数据将构成该队列。我们假设这一点 1）基因和遗传途径的可识别变化与术后疼痛有关，2） 变化可以导致对药物治疗的不同反应。这项研究将使用候选基因， 全基因组关联研究和基于途径的分析，以鉴定影响影响的遗传变异 术后疼痛。然后，我将利用下背部疼痛最细粒的临床试验之一（NIH治愈） BACPAC）研究常见止痛药（Duloxetine）的反应性遗传关联。 这个重要的面向患者职业发展奖的候选人旨在完成他的 以前的培训具有大规模基因组学，生物统计学和临床​​试验设计方面的其他专业知识。在 为了与拟议的 研究候选人还具有纯粹的教学和经验丰富的转化科学培训 研究目的。密歇根大学已投入大量资源来支持这一建议，IT 还得到了一个跨学科心态的支持，该小组完成了遗传学，基因组学的权威机构 生物统计学，周期性疼痛以及临床试验设计和执行。拟议的研究将提供新颖 洞悉慢性术后疼痛的遗传学，并为将来研究构成个性化研究的基础 疼痛疗法。