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 一般来说，炎症在动脉粥样硬化性心血管疾病（CVD）中起重要作用 人口流行病学、基础科学和随机临床试验数据都支持这一点的重要性 关系全身炎症性疾病患者，如类风湿性关节炎（RA），可以提供 因为它们更极端的系统性炎症表型，所以对这种关系有重要的见解。 研究者已经认识到RA患者经历的CVD风险升高：MI和卒中风险 与普通人群相比，RA患者的这两种水平均升高，导致寿命缩短。CVD风险 RA分层不精确，一般人群工具不准确。改善CVD的大多数尝试 风险分层在现有人群风险工具中增加了临床RA因素。易于评估的蛋白质 生物标志物可能会增强CVD风险预测。这些文献强烈表明了> RA和CVD共有20种生物标志物。这些关系从未被系统地研究过。 疾病该提案的首要目标是鉴定RA患者中CVD的蛋白质生物标志物， 利用对照试验的结构和用于推导和验证风险评分的严格方法。我们 用高维细胞免疫分析补充强大的生物标志物分析， 将特定细胞类型与蛋白质生物标志物机械地联系起来并鉴定新的细胞生物标志物的潜力。 我们进行了一项随机对照试验，目标试验，以检查是否有特定的治疗， 通过FDG PET/CT测量，RA可降低CV风险。该试验由NIH资助（U 01 AR 068043） 使我们能够前瞻性地描述RA患者的特征，在治疗前后收集生物标本， 进行基线和24周FDG PET/CT扫描，以评估血管炎症。分析仍在进行中 以确定不同的RA治疗是否转化为CV风险的差异变化。我们建议 利用TARGET研究队列、数据集和生物储存库实现以下目标。目标1：使用 一个综合的生物标志物面板，用于推导和验证RA患者的CV风险评分。目标审判 提供生物标本、患者表型和广泛的生物标志物发现面板， 实物捐赠我们假设，将生物标志物添加到合并队列方程和变量中， 与RA疾病活动性相关的心血管事件预测将显著改善RA患者的心血管结局预测。目标2： 通过scRNA-seq分析阐明连接RA和CVD的细胞免疫机制。我们将使用单一 细胞转录组学和表面蛋白质组学（CITE-seq）研究来自TARGET患者亚组的PBMC， 包括基于FDG PET/CT的应答者和非应答者，以识别循环免疫细胞 与CV风险和CV生物标志物相关的人群。我们假设特定的免疫细胞群 将与基线时的CV风险相关，并在治疗后降低丰度或激活状态， 与CV风险平行。此外，这些处理在它们对相关细胞群体的影响方面将不同。