A precision medicine approach to assess progression from undifferentiated arthritis to rheumatoid arthritis

项目总结 类风湿性关节炎(RA)是一种慢性全身性炎症性疾病，影响1%的成年人 人口，包括美国的150万成年人。目前还没有治愈类风湿关节炎的方法。除关节外 炎症时，该病可导致对软骨和骨骼的不可逆转的侵蚀破坏，造成显著的 残疾和生活质量差。因为早期治疗类风湿性关节炎是有益的，导致的疾病不那么严重 当然，有人假设RA的早期阶段可能代表着一个“治疗机会之窗” 在此期间，适当的治疗可能能够改变疾病的进程，防止进一步 进展，并可能关闭疾病的过程。它还表明，它将是更多 有助于识别更接近症状出现的RA患者，以便进行早期治疗，即当症状 入驻时间不到3个月。那时，患者甚至可能不符合早期RA的标准 诊断，但得到的诊断是“未分化关节炎”(UA)，这是最常见的诊断 关节炎症状首先出现，患者不能被诊断出患有任何特定的疾病。因此，预测 需要模型来准确预测所有UA患者中的谁将(A)在30%的 进展到类风湿性关节炎，因此，应开始早期积极治疗，和(B)将进入缓解或 会发展成其他情况，因此不应该不必要地给予激进的RA治疗，因为这些 都与严重的副作用有关。目前可用的预测模型不能准确地预测 不稳定型心绞痛患者将进展为类风湿关节炎。由于与早期类风湿性关节炎相关的基因组标记还不彻底 被调查的，并且不包括在现有预测模型中，我们建议首先确定 在基因表达和遗传多态(表达)水平上从UA到RA的进展 早期UA患者队列中的数量性状基因座(EQTL)。已确定的生物标记物将包括在 具有高特异性和高灵敏度的预测模型，可用于准确预测疾病进展 UA呼叫RA。在早期类风湿关节炎治疗中使用这种精确的医学方法将提供显著的健康 和成本效益。第二，我们还建议研究与疾病进展相关的生物学变化 随着时间的推移，UA到RA，以更好地了解可能参与RA的潜在途径 发病机制。

PROJECT SUMMARY Rheumatoid arthritis (RA) is a chronic systemic inflammatory disease that affects 1% of the adult world population, including 1.5 million adults in the U.S. There is currently no cure for RA. In addition to joint inflammation, the disease can lead to irreversible erosive damage to cartilage and bone, resulting in significant disability and poor quality of life. Since early treatment of RA is beneficial and results in a less severe disease course, it was hypothesized that very early stages of RA may represent a “therapeutic window of opportunity” during which appropriate treatment may be able to alter the course of the disease, preventing further progression, and possibly could switch off the disease process. It has also been shown that it would be more beneficial to identify RA patients closer to the onset of symptoms for early therapy, i.e. when symptoms have been present for less than 3 months. At that time, patients may not even satisfy the criteria for early RA diagnosis but get a diagnosis of “undifferentiated arthritis” (UA), which is the most common diagnosis when arthritis symptoms first appear and patients cannot be diagnosed with any specific disease. Hence, prediction models are needed that can accurately predict who among all UA patients will (a) be among the 30% who progress to RA, and hence, should be started on early aggressive treatment, and (b) will go into remission or will develop other conditions, and hence, should not unnecessarily be given aggressive RA treatment as these are associated with serious side effects. Currently available prediction models do not accurately predict which UA patients will progress to RA. Since genomic markers associated with early RA have not been thoroughly investigated and are not included among existing prediction models, we propose to first identify biomarkers of progression from UA to RA at the levels of gene expression and genetic polymorphisms (expression quantitative trait loci, eQTLs) in a cohort of early UA patients. The biomarkers identified will be included in a prediction model with high specificity and sensitivity that can be applied to accurately predict progression from UA to RA. The use of such a precision medicine approach for early RA treatment will provide significant health and cost benefits. Second, we also propose to investigate biological changes associated with progression from UA to RA over time to gain a better understanding of potential pathways that may be involved in RA pathogenesis.