Inova Diagnostics, Inc., San Diego, CA, USA.
Inova Diagnostics, Inc., San Diego, CA, USA.
Autoimmun Rev. 2020 May;19(5):102506. doi: 10.1016/j.autrev.2020.102506. Epub 2020 Mar 12.
There is an emerging understanding that an individual's risk for future rheumatoid arthritis (RA) can be determined using a combination of factors while they are still in a state where clinically-apparent inflammatory arthritis (IA) is not yet present. Indeed, this concept has underpinned several completed and ongoing prevention trials in RA. Importantly, risk factors can be divided into modifiable (e.g. smoking, exercise, dental care and diet) and non-modifiable factors (e.g. genetics, sex, age). In addition, there are now several biomarkers including autoantibodies, inflammatory markers and imaging techniques that are highly predictive of future clinically-apparent IA/RA. Although none of the prevention studies have yet provided major breakthroughs, several of them have provided valuable insights that can help to improve the design of future clinical trials and enable RA prevention. In aggregate, these findings suggest that the most accurate disease prediction models will require the combination of demographic and clinical information, biomarkers and potentially medical imaging data to identify individuals for intervention. This review summarizes some of the key aspects around precision medicine in RA with special focus on disease prediction and prevention.
人们逐渐认识到，即使在尚未出现临床明显的炎症性关节炎（IA）的情况下，也可以通过结合多种因素来确定个体未来患类风湿关节炎（RA）的风险。事实上，这一概念为已经完成和正在进行的几项 RA 预防试验提供了依据。重要的是，风险因素可以分为可改变的（例如吸烟、运动、口腔护理和饮食）和不可改变的因素（例如遗传、性别、年龄）。此外，现在有许多生物标志物，包括自身抗体、炎症标志物和影像学技术，它们对未来的临床明显的 IA/RA 具有高度预测性。尽管目前还没有一项预防研究取得重大突破，但其中一些研究提供了有价值的见解，可以帮助改进未来临床试验的设计，并促进 RA 的预防。总的来说，这些发现表明，最准确的疾病预测模型将需要结合人口统计学和临床信息、生物标志物和潜在的医学影像学数据来识别需要干预的个体。本综述总结了 RA 精准医学的一些关键方面，特别关注疾病预测和预防。
