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.

项目概要 慢性腰痛 (CLBP) 是一种复杂的多因素病症，也是最常见的疼痛 世界各地的肌肉骨骼疾病的原因和机制有很多，但人们仍然知之甚少。 这导致其临床治疗（例如物理治疗 - PT）常见失败。 作为 CLBP 的一个可能的促成因素，它在文献中引起了人们的关注，但其原因多种多样，而且仍然 缺乏彻底的洞察力似乎是特定 SNP（单核苷酸）的存在。 已知某些基因中的多态性（人类 DNA 中最简单和最常见的变异） 目前尚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。 更具体地说，本研究的主要目标是能够更好地为每位患者量身定制特定的 PT 治疗。 旨在了解基因变异与 PT 治疗结果之间是否存在关联； 旨在更好地了解基因变异如何影响炎症背后的机制，以支持 此外，它还寻求确定疼痛领域的新研究问题。 使用机器学习（网络表型策略 - NPS）根据患者的 CLBP 表型 从职业角度来看，它旨在支持我的职业发展。 为了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 LB3P 机制研究中心将收集所有 CLBP 且未诊断的患者； 患有炎症或自身免疫性疾病的患者将接受 PT 治疗作为其标准护理的一部分 本研究将使用的数据包括匹兹堡大学医学中心 (UPMC)。 选定基因的变异、PT 治疗（作为本提议促进的新方面收集 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）。 两种不同的方法：首先，将使用传统的遗传变异统计测试进行分析，以 寻找 SNP 和 PT 治疗结果之间的关联 该分析将包括逻辑回归。 分析遗传变异和结果测量之间的关联，Fisher 精确检验 组确定，如果发现非配对假设更有效，则对多个组进行 Cochran Q 检验 随后，将使用 NPS 方法对数据集进行分析，这将有助于描述。 通过添加这一新层，基于对 PT 治疗的临床反应的基于临床结果的表型。 为临床方法提供信息，本研究旨在改善 CLBP 的评估和治疗 PT 设置，使其更加资源有效，并根据每个患者的需求进行定制。