Influence of inflammation-related genetic variants on PT treatment response in a population affected by CLBP

CLBP 人群中炎症相关基因变异对 PT 治疗反应的影响

• 批准号: 10208162
• 负责人: Gwendolyn A Sowa
• 金额: $ 10万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-26 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10208162
• 关键词: Academia; Affect; Age; Area; Attention; Autoimmune Diseases; Back Pain; Caring; Chronic low back pain; Clinical; Clinical Data; Clinical Research; Clinical Treatment; Communities; Complex; DNA; Data; Data Set; Development; Diagnosis; Doctor of Philosophy; Enrollment; Epigenetic Process; Failure; Financial Hardship; Future; Genes; Genetic; Genetic Variation; Goals; Grant; Individual; Inflammation; Inflammation Mediators; Inflammatory; Intervention; Journals; Knowledge; Learning; Literature; Logistic Regressions; Machine Learning; Measures; Mediating; Medical center; Mentors; Methodology; Molecular; Musculoskeletal Diseases; Outcome; Outcome Measure; Pain; Pain Measurement; Pain Research; Pain interference; Pain management; Patients; Peer Review; Phenotype; Physical therapy; Physicians; Population; Publications; Regression Analysis; Research; Research Personnel; Research Project Grants; Resource Allocation; Resources; Screening procedure; Single Nucleotide Polymorphism; Societies; Solid; Statistical Data Interpretation; Testing; Translating; Treatment outcome; United States National Institutes of Health; Universities; Variant; Vertebral column; base; career; clinically relevant; cohort; comorbidity; diagnostic screening; disability; genetic variant; human DNA; impression; improved; indexing; individual variation; individualized medicine; inflammatory marker; innovation; insight; interest; learning network; meetings; microbiome; novel; pain outcome; pain patient; pain score; patient response; physical therapist; predict clinical outcome; primary outcome; programs; rehabilitation science; response; sex; standard of care; success; symposium; tool; treatment response

Project Summary Chronic Low Back Pain (CLBP) is a complex multi-factorial condition, as well as the most prevalent painful musculoskeletal disorder worldwide. Its causes and mechanisms are numerous and still poorly understood, which leads to common failure in its clinical treatment (e.g. physical therapy - PT). Systemic inflammation has been gaining attention in the literature as a likely contributing aspect to CLBP, but its causes are diverse and still lack thorough insight. One of these causes seems to be the presence of specific SNPs (Single Nucleotide Polymorphism, the simplest and most common variation in human DNA) in certain genes that are known to be related to both inflammation and pain. It is unclear how these variations may affect the outcomes of different types of PT treatment for CLBP, which is an innovative aspect of the proposed research project. Overall, the main objective is to be able to better tailor specific PT treatment to each patient. More specifically, this research aims to understand if there is an association between gene variations and outcomes of PT treatment; it also intends to better understand the mechanisms behind how gene variations affect inflammation, to support the formation of novel research questions in the area of pain. Furthermore, it seeks to determine clinical sub- phenotypes of CLBP using machine learning (Network Phenotyping Strategy - NPS) based on patients’ responsiveness to PT treatment. From a career standpoint, it aims at supporting my professional development for a future in pain research and academia. The project will use genetic and clinical data from 200-250 patients, gathered from the 1000 patients’ cohort that the Pitt LB3P Mechanistic Research Center will collect; it will include all the patients with CLBP and no diagnosis of inflammatory or auto-immune disease who will undergo PT treatment as part of their standard of care within the University of Pittsburgh Medical Center (UPMC). The data that will be used for this study includes the variations for selected genes, PT treatment (to be collected as a novel aspect facilitated by this proposed supplement), the outcomes variables (disability and pain scores, pain interference, the impression of change), as well as other clinically relevant information (e.g. age, sex, comorbidities). The analysis will be performed with two different approaches: first, it will be analyzed with the traditional statistical testing for genetic variations, to look for associations between SNPs and PT treatment outcomes. The analysis will include Logistic regression analysis for the association between genetic variations and outcome measures, Fisher's exact test for proper group determination, Cochran’s Q test for multiple groups if a non-paired assumption will be found to be more clinically relevant. Subsequently, the dataset will be analyzed using the NPS approach, which will help delineate clinical outcome-based phenotypes based on clinical response to PT treatment. By adding this new layer of information to the clinical approach, this research seeks to improve the assessment and treatment of CLBP in a PT setting and to make it more resource-efficient and tailored to each patient’s needs.

项目摘要 慢性腰痛（CLBP）是一种复杂的多因素疾病，也是最常见的疼痛 肌肉骨骼疾病其原因和机制很多，但仍然知之甚少， 这导致其临床治疗（例如物理治疗- PT）的常见失败。全身性炎症具有 作为CLBP的一个可能的贡献方面，在文献中得到了关注，但其原因是多种多样的， 缺乏透彻的洞察力。这些原因之一似乎是存在特定的SNP（单核苷酸多态性）。 多态性，人类DNA中最简单和最常见的变异）在某些基因中，已知是 与炎症和疼痛有关。目前还不清楚这些变化如何影响不同的结果。 CLBP的PT治疗类型，这是拟议研究项目的一个创新方面。总体看 主要目标是能够更好地为每位患者定制特定的PT治疗。更具体地说，这项研究 旨在了解基因变异与PT治疗结果之间是否存在关联;它还 打算更好地了解基因变异如何影响炎症的机制，以支持 在疼痛领域形成新的研究问题。此外，它旨在确定临床亚- 使用机器学习（网络表型分型策略）基于患者的 对PT治疗的反应。从职业的角度来看，它旨在支持我的专业发展 在疼痛研究和学术界的未来。 该项目将使用从1000名患者队列中收集的200-250名患者的遗传和临床数据， 皮特LB 3 P机制研究中心将收集;它将包括所有CLBP患者， 将接受PT治疗作为其标准治疗的一部分的炎性或自身免疫性疾病患者， 匹兹堡大学医学中心（UPMC）本研究将使用的数据包括 所选基因的变异，PT治疗（作为由本发明所提出的促进的新方面收集）， 补充），结果变量（残疾和疼痛评分，疼痛干扰，变化的印象）， 以及其他临床相关信息（例如年龄、性别、合并症）。分析将使用 两种不同的方法：第一，将用传统的遗传变异统计检验进行分析， 寻找SNP与PT治疗结果之间的关联。分析将包括Logistic回归 分析遗传变异和结果测量之间的关联，Fisher精确检验， 组确定，如果非配对假设将发现多个组的Cochran Q检验 临床相关。随后，将使用NPS方法分析数据集，这将有助于描述 基于PT治疗临床应答的临床结局表型。通过添加这一新层 信息的临床方法，这项研究旨在改善评估和治疗CLBP在一个 PT设置，使其更有效地利用资源，并根据每位患者的需求量身定制。