Translational and Clinical Research Institute, Newcastle University Medical School , Newcastle, UK.
Expert Rev Clin Immunol. 2020 Jun;16(6):621-630. doi: 10.1080/1744666X.2020.1771183. Epub 2020 May 27.
Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease. Early referral and treatment are key to the effective management of the disease. This makes imperative the identification of biomarkers and of pathobiological endotypes.
This review describes recent efforts to integrate large-scale datasets for the identification of disease endotypes for precision medicine in early, seropositive RA. We conducted a search for systems and multi-omics papers in early RA patients through to 1 January 2020. We reviewed investigations of multiple technologies such as transcriptomic, proteomic and metabolomic platforms as well as extensive clinical datasets. We outline progress made and describe some of the advantages and limitations of current computational and statistical methods.
The search for pathobiological endotypes in early RA is rapidly developing. While currently, studies tend to be small, reliant upon new technologies and unproven analytical tools, as the technology becomes cheaper and more reliable, and the properties of analytical tools for the integration of cross-platform biology become better understood, it seems likely that better biomarkers of disease, remission and response to individual therapies will emerge.
类风湿关节炎（RA）是一种慢性、系统性自身免疫性疾病。早期转介和治疗是有效管理疾病的关键。这使得识别生物标志物和病理生物学表型变得尤为重要。
本文描述了最近在整合大规模数据集以识别早期阳性 RA 患者精准医学疾病表型方面的努力。我们通过搜索系统和多组学论文，对早期 RA 患者进行了研究，时间截至 2020 年 1 月 1 日。我们回顾了转录组学、蛋白质组学和代谢组学等多种技术以及广泛的临床数据集的研究。我们概述了所取得的进展，并描述了当前计算和统计方法的一些优点和局限性。
在早期 RA 中寻找病理生物学表型的研究正在迅速发展。虽然目前的研究往往规模较小，依赖于新技术和未经证实的分析工具，但随着技术变得更便宜、更可靠，以及用于整合跨平台生物学的分析工具的特性得到更好的理解，似乎更有可能出现更好的疾病、缓解和对个体治疗反应的生物标志物。
