Rheumatoid Arthritis and the Risk of Cardiovascular Disease: Biomarkers Risk Prediction and Underlying Mechanisms

类风湿关节炎和心血管疾病的风险：生物标志物风险预测和潜在机制

• 批准号: 10416473
• 负责人: Daniel Hal Solomon
• 金额: $ 68.32万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10416473
• 关键词: Aftercare; Atherosclerosis; Basic Science; Biological Markers; Blood Coagulation Factor; Blood Vessels; Calibration; Cardiovascular Diseases; Cells; Cellular Immunology; Cellular Indexing of Transcriptomes and Epitopes by Sequencing; Characteristics; Clinical; Clinical Sciences; Cohort Studies; Complement; Cross-Sectional Studies; Data; Data Set; Discrimination; Disease; Epidemiology; Equation; Event; Funding; Gene Expression; General Population; Goals; Image; Immune; Individual; Inflammation; Inflammatory; Ligands; Link; Lipids; Literature; Longevity; Measurable; Measures; Methods; Modeling; Morbidity - disease rate; Myocardial Infarction; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Outcome; PET/CT scan; Pathway interactions; Patient Care; Patient risk; Patients; Performance; Peripheral Blood Mononuclear Cell; Phenotype; Population; Proteins; Proteomics; Randomized Clinical Trials; Randomized Controlled Trials; Reporting; Research Personnel; Rheumatoid Arthritis; Risk; Running; Sampling; Source; Stroke; Structure; Surface; Testing; Translating; United States National Institutes of Health; Visit; arthritis registry; base; biobank; biomarker discovery; biomarker panel; cardiovascular disorder risk; cell type; cohort; cytokine; experience; fluorodeoxyglucose positron emission tomography; high dimensionality; improved; insight; longitudinal analysis; patient subsets; programs; prospective; protein biomarkers; receptor; responders and non-responders; risk prediction; risk stratification; single-cell RNA sequencing; tool; transcriptomics; treatment arm; uptake; vascular inflammation

PROJECT ABSTRACT Inflammation contributes substantially to atherosclerotic cardiovascular disease (CVD) in the general population. Epidemiology, basic science and randomized clinical trial data support the importance of this relationship. Patients with systemic inflammatory conditions, such as rheumatoid arthritis (RA), can provide important insights into this relationship because of their more extreme systemic inflammatory phenotype. Investigators have appreciated the elevated risk of CVD experienced by RA patients: the risk of MI and stroke are both elevated in RA compared with the general population, contributing to a shortened lifespan. CVD risk stratification in RA is imprecise and general population tools are not accurate. Most attempts at improving CVD risk stratification have added clinical RA factors to existing population risk tools. Easily assessed protein biomarkers would likely enhance CVD risk prediction. The literature strongly suggests relationships between > 20 biomarkers shared by RA and CVD. These relationships have never been studied systematically across diseases. The overarching goal of this proposal is to identify protein biomarkers for CVD in RA patients, leveraging the structure of a controlled trial and rigorous methods for deriving and validating a risk score. We complement the robust biomarker analyses with high-dimensional cellular immuneprofiling, which has the potential to link specific cell types mechanistically to protein biomarkers and to identify new cellular biomarkers. We conducted a randomized controlled trial, the TARGET trial, to examine whether specific treatments for RA produce reductions in CV risk as measured by FDG PET/CT. This trial, funded by NIH (U01 AR068043) allowed us to prospectively characterize RA patients, collect biospecimens before and after treatment, and conduct baseline and 24-week FDG PET/CT scans to assess vascular inflammation. Analyses are still ongoing to determine whether different RA treatments translate into differential changes in CV risk. We propose to leverage the TARGET study cohort, dataset and biorepository for the following aims. Aim 1: To use a comprehensive biomarker panel to derive and validate a CV risk score for patients with RA. The TARGET trial provides biospecimens, patient phenotypes, and a broad biomarker discovery panel that will have been run as an in-kind donation. We hypothesize that adding biomarkers to the Pooled Cohort Equation and variables related to RA disease activity will significantly improve prediction of CV outcomes in RA patients. Aim 2: To elucidate cellular immune mechanisms linking RA and CVD through scRNA-seq profiling. We will use single cell transcriptomic and surface proteomics (CITE-seq) to study PBMCs from a subset of TARGET patients, including both responders and non-responders based on FDG PET/CT, to identify circulating immune cell populations associated with CV risk and CV biomarkers. We hypothesize that specific immune cell populations will associate with CV risk at baseline and will decrease in abundance or activation state after treatment in parallel with CV risk. Further, these treatments will differ in their effects on relevant cell populations.

项目摘要 炎症在一般中有助于动脉粥样硬化心血管疾病（CVD） 人口。流行病学，基础科学和随机临床试验数据支持了这一点的重要性 关系。患有全身性炎症性疾病的患者，例如类风湿关节炎（RA），可以提供 由于它们更极端的系统性炎症表型，对这种关系的重要见解。 调查人员对RA患者经历的CVD风险升高：MI和中风的风险 与普通人群相比，RA的均升高，导致寿命缩短。 CVD风险 RA中的分层不精确，一般人口工具不准确。大多数改善CVD的尝试 风险分层为现有人群风险工具增加了临床RA因素。容易评估蛋白质 生物标志物可能会增强CVD风险预测。文献强烈建议> RA和CVD共享的20个生物标志物。这些关系从未系统地研究 疾病。该提案的总体目标是鉴定RA患者中CVD的蛋白质生物标志物， 利用对照试验的结构和严格的方法来得出和验证风险评分。我们 补充具有高维细胞免疫局体的可靠生物标志物分析，该分析具有 可能将特定细胞类型机械上的特定细胞类型与蛋白质生物标志物联系起来并鉴定新的细胞生物标志物。 我们进行了一项随机对照试验，即目标试验，以检查特定治疗 对于RA，通过FDG PET/CT测量的CV风险降低。该试验由NIH（U01 AR068043）资助 允许我们前瞻性地表征RA患者，在治疗前后收集生物测量以及 进行基线和24周的FDG PET/CT扫描以评估血管炎症。分析仍在进行中 确定不同的RA处理是否转化为简历风险的差异变化。我们建议 利用目标研究队列，数据集和生物座位的目的。目标1：使用 综合生物标志物面板可为RA患者得出和验证CV风险评分。目标试验 提供生物测量，患者表型和广泛的生物标志物发现面板，该面板将被运行为 实物捐赠。我们假设将生物标志物添加到合并的队列方程和变量 与RA疾病活动有关的活动将显着改善RA患者CV结果的预测。目标2：到 阐明通过SCRNA-SEQ分析连接RA和CVD的细胞免疫机制。我们将使用单个 细胞转录组和表面蛋白质组学（CITE-seq），从目标患者子集中研究PBMC， 包括基于FDG PET/CT的响应者和非反应者，以识别循环免疫细胞 与简历风险和简历生物标志物相关的人群。我们假设特定的免疫细胞群体 将在基线时与CV风险相关联，并且在治疗后的丰度或激活状态下降 与简历风险平行。此外，这些治疗对相关细胞群体的影响会有所不同。