The prediction of a relapsing course in MOG antibody-associated disease

Abstract

Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is a recently classified autoimmune demyelinating disorder that has recently gained recognition as a distinct entity. While progress has been made in diagnosing MOGAD, predicting its disease course remains a challenge. The purpose of this body of work was to investigate the prediction of relapse in adult MOGAD patients with biomarkers present in patient sera.

First, we examined whether MOG-immunoglobulin G (IgG) epitopes are associated with a disease course. Our findings revealed that patients with a non-P42 MOG-IgG epitope had 3.4x higher odds of a relapsing disease course compared to those with a P42 MOG-IgG epitope. This association was particularly evident in patients with unilateral optic neuritis (UON). Subsequently, we aimed to translate the research-based assay used for epitope testing into a diagnostic tool. Results from the MOG-IgG epitope analysis using the diagnostic assay matched those from the research-based assay for cases with clear P42-IgG levels while further refinement is needed for patients with borderline P42-IgG levels. Finally, we explored the serum cytokine profile of MOGAD patients to identify potential biomarkers. For the first time in MOGAD, we observed a positive correlation between MCP-1 levels and time since the onset attack. These findings suggest that MCP-1 levels dynamically fluctuate with disease activity, making it a potential predictive marker for the timing of relapse.

This research addresses a crucial challenge in the rapidly evolving field of MOGAD: the accurate prediction of relapse. The non-P42 MOG-IgG epitope discovery offers potential for revolutionising serum-based diagnostics with a live cell-based assay, and serum MCP-1 dynamics suggest a novel avenue for predicting relapse timing. These findings collectively aiding in identifying MOGAD patients at risk of relapse, enhancing clinical prognostication, and improving patient outcomes.