Department of Arthritis and Clinical Immunology Research, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA; Department of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
Department of Arthritis and Clinical Immunology Research, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA.
Mult Scler Relat Disord. 2022 Jul;63:103922. doi: 10.1016/j.msard.2022.103922. Epub 2022 May 28.
For relapsing-remitting multiple sclerosis (RRMS), there is a need for biomarker development beyond clinical manifestations and MRI. Soluble neurofilament light chain (sNfL) has emerged as a biomarker for inflammatory activity in RRMS. However, there are limitations to the accuracy of sNfL in identifying relapses. Here, we sought to identify a panel of biomarkers that would increase the precision of distinguishing patients in relapse compared to sNfL alone.
We used a multiplex approach to measure levels of 724 blood proteins in two distinct RRMS cohorts. Multiple t-tests with covariate correction determined biomarkers that were differentially regulated in relapse and remission. Logistic regression models determined the accuracy of biomarkers to distinguish relapses from remission.
The discovery cohort identified 37 proteins differentially abundant in active RRMS relapse compared to remission. The verification cohort confirmed four proteins, including sNfL, were altered in active RRMS relapse compared to remission. Logistic regression showed that the 4-protein panel identified active relapse with higher accuracy (AUC = 0.87) than sNfL alone (AUC = 0.69).
Our studies confirmed that sNfL is elevated during relapses in RRMS patients. Furthermore, we identified three other blood proteins, uPA, hK8 and DSG3 that were altered during relapse. Together, these four biomarkers could be used to monitor disease activity in RRMS patients.
对于复发缓解型多发性硬化症(RRMS),除了临床表现和 MRI 之外,还需要开发生物标志物。可溶性神经丝轻链(sNfL)已成为 RRMS 炎症活动的生物标志物。然而,sNfL 在识别复发方面的准确性存在局限性。在这里,我们试图确定一组生物标志物,以提高与 sNfL 相比区分复发患者的精确性。
我们使用多重方法测量了两个不同的 RRMS 队列中 724 种血液蛋白的水平。具有协变量校正的多重 t 检验确定了在复发和缓解期间差异调节的生物标志物。逻辑回归模型确定了生物标志物区分复发和缓解的准确性。
发现队列确定了 37 种在活跃 RRMS 复发中与缓解相比差异丰富的蛋白质。验证队列证实了在活跃 RRMS 复发中与缓解相比,有四种蛋白质(包括 sNfL)发生了改变。逻辑回归表明,与单独使用 sNfL 相比,该 4 蛋白组合能更准确地识别活跃性复发(AUC=0.87)。
我们的研究证实 sNfL 在 RRMS 患者的复发期间升高。此外,我们还发现了另外三种在复发期间发生改变的血液蛋白,uPA、hK8 和 DSG3。这四种生物标志物可以一起用于监测 RRMS 患者的疾病活动。