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

项目摘要 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支持这一重要性 两性关系。有全身炎症情况的患者，如类风湿性关节炎(RA)，可以提供 对这种关系的重要见解，因为他们更极端的全身性炎症表型。 研究人员已经认识到类风湿关节炎患者心血管疾病风险的增加：心肌梗死和中风的风险 与普通人群相比，两者在RA中都升高，导致寿命缩短。心血管疾病风险 RA的分层是不精确的，一般的人口工具也不准确。大多数改善心血管疾病的尝试 风险分层将临床类风湿因子添加到现有的人群风险工具中。易于评估的蛋白质 生物标志物可能会增强对心血管疾病风险的预测。文献强烈表明&gt之间的关系； RA和CVD共有20个生物标志物。这些关系从未被系统地研究过。 疾病。该提案的首要目标是确定RA患者心血管疾病的蛋白质生物标记物。 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 用高维细胞免疫图谱补充强大的生物标记物分析，它具有 将特定细胞类型与蛋白质生物标记物机械连接的可能性，以及识别新的细胞生物标记物的可能性。 我们进行了一项随机对照试验，即目标试验，以检查特定的治疗方法 通过FDG PET/CT测量，RA患者的心血管风险降低。本试验由美国国立卫生研究院资助(U01 AR068043) 使我们能够前瞻性地确定RA患者的特征，收集治疗前后的生物标本，以及 进行基线和24周FDG PET/CT扫描，以评估血管炎症。分析工作仍在进行中 以确定不同的RA治疗是否会转化为心血管风险的不同变化。我们建议 利用目标研究队列、数据集和生物信息库实现以下目标。目标1：使用 综合生物标记物小组，以得出和验证RA患者的心血管风险评分。靶向试验 提供生物标记物、患者表型和广泛的生物标记物发现小组，该小组将作为 实物捐赠。我们假设，将生物标志物添加到集合队列方程和变量中 与RA疾病活动相关的疾病活动将显著改善对RA患者心血管预后的预测。目标2：实现 通过scRNA-seq图谱阐明RA和CVD之间的细胞免疫机制。我们将使用Single 细胞转录和表面蛋白质组学(CITE-SEQ)来研究来自目标患者子集的PBMC， 包括基于FDG PET/CT的应答者和无应答者，以识别循环免疫细胞 与心血管风险和心血管生物标志物相关的人群。我们假设特定的免疫细胞群 会在基线时与心血管风险相关，并在治疗后减少丰度或激活状态 与CV风险平行。此外，这些处理对相关细胞群的影响也会有所不同。

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.