Biomarker panel increases accuracy for identification of an MS relapse beyond sNfL

Saurabh Gawde, Agnieshka Agasing, Neal Bhatt, Mackenzie Toliver, Gaurav Kumar, Kaylea Massey, Andrew Nguyen, Yang Mao-Draayer, Susan Macwana, Wade DeJager, Joel M. Guthridge, Gabriel Pardo, Jeffrey Dunn, Robert C. Axtell

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Background: 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. Methods: 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. Results: 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). Conclusion: 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.

Original languageEnglish
Article number103922
JournalMultiple Sclerosis and Related Disorders
Volume63
DOIs
StatePublished - Jul 2022
Externally publishedYes

Keywords

  • Biomarkers
  • Multiple sclerosis
  • Proteomics
  • Relapses

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