Center for Autoimmune Diseases Research (CREA) School of Medicine and Health Sciences, Universidad del Rosario, Carrera 26 # 63B-51, Bogota, Colombia.
J Transl Med. 2017 Nov 25;15(1):239. doi: 10.1186/s12967-017-1345-y.
Evidence supports the existence of different subphenotypes in systemic lupus erythematosus (SLE) and the pivotal role of cytokines and autoantibodies, which interact in a highly complex network. Thus, understanding how these complex nonlinear processes are connected and observed in real-life settings is a major challenge. Cluster approaches may assist in the identification of these subphenotypes, which represent such a phenomenon, and may contribute to the development of personalized medicine. Therefore, the relationship between autoantibody and cytokine clusters in SLE was analyzed.
This was an exploratory study in which 67 consecutive women with established SLE were assessed. Clinical characteristics including disease activity, a 14-autoantibody profile, and a panel of 15 serum cytokines were measured simultaneously. Mixed-cluster methodology and bivariate analyses were used to define autoantibody and cytokine clusters and to identify associations between them and related variables.
First, three clusters of autoantibodies were defined: (1) neutral, (2) antiphospholipid antibodies (APLA)-dominant, and (3) anti-dsDNA/ENA-dominant. Second, eight cytokines showed levels above the threshold thus making possible to find 4 clusters: (1) neutral, (2) chemotactic, (3) G-CSF dominant, and (4) IFNα/Pro-inflammatory. Furthermore, the disease activity was associated with cytokine clusters, which, in turn, were associated with autoantibody clusters. Finally, when all biomarkers were included, three clusters were found: (1) neutral, (2) chemotactic/APLA, and (3) IFN/dsDNA, which were also associated with disease activity.
These results support the existence of three SLE cytokine-autoantibody driven subphenotypes. They encourage the practice of personalized medicine, and support proof-of-concept studies.
有证据表明系统性红斑狼疮(SLE)存在不同的亚表型,细胞因子和自身抗体起着关键作用,它们在一个高度复杂的网络中相互作用。因此,了解这些复杂的非线性过程是如何相互关联的,并在现实环境中观察到这些过程,是一个主要的挑战。聚类方法可以帮助识别这些亚表型,这些亚表型代表了一种现象,并可能有助于个性化医学的发展。因此,分析了 SLE 中的自身抗体和细胞因子聚类之间的关系。
这是一项探索性研究,纳入了 67 名确诊为 SLE 的连续女性患者。同时评估了临床特征,包括疾病活动度、14 种自身抗体谱和 15 种血清细胞因子谱。使用混合聚类方法和双变量分析来定义自身抗体和细胞因子聚类,并确定它们与相关变量之间的关联。
首先,定义了三个自身抗体聚类:(1)中性,(2)抗磷脂抗体(APLA)主导型,(3)抗 dsDNA/ENA 主导型。其次,有 8 种细胞因子的水平超过了阈值,因此可以找到 4 个聚类:(1)中性,(2)趋化因子,(3)G-CSF 主导型,和(4)IFNα/促炎细胞因子。此外,疾病活动度与细胞因子聚类相关,而细胞因子聚类又与自身抗体聚类相关。最后,当所有生物标志物都包括在内时,发现了三个聚类:(1)中性,(2)趋化因子/APLA,和(3)IFN/dsDNA,这三个聚类也与疾病活动度相关。
这些结果支持 SLE 存在三种由细胞因子-自身抗体驱动的亚表型。它们鼓励实践个性化医学,并支持概念验证研究。