Serum
GFAP
and Age Accurately Distinguish
AQP4
‐
IgG
Positive and Double Seronegative
Manon Rival, Fabien Rollot, Sabine Laurent‐Chabalier, Romain Casey, Illiasse El Bahi, Guillaume Brocard, Romain Marignier, Sophie Trouillet‐Assant, Sandra Vukusic, Agnès Fromont, David Axel Laplaud, Guillaume Mathey, Elisabeth Maillart, Laure Michel, Aurélie Ruet, Pierre Labauge, Xavier Ayrignac, Hélène Zephir, Jonathan Ciron, Jérôme de Sèze, Arnaud Kwiatkowski, Caroline Papeix, Pierre Branger, Christine Lebrun‐Frenay, Eric Berger, Pierre Clavelou, Olivier Heinzlef, Jean Pelletier, Abdullatif Al‐Khedr, Olivier Casez, Bertrand Bourre, Alain Créange, Thomas David, Laurent Magy, Jean‐Philippe Camdessanche, Inès Doghri, Amélie Dos Santos, Thibault Mura, Hanane Agherbi, Eric Thouvenot, ABSTRACT
Background
Serum neurofilament‐light chain (sNfL) and glial fibrillary acidic protein (sGFAP) are associated with multiple sclerosis (MS) activity. This study aimed to identify patient‐ and disease‐level determinants of sNfL and sGFAP and to evaluate their ability to support differential diagnosis in neuromyelitis optica (NMO).
Methods
sNFL and sGFAP levels were measured in 640 patients from the OFSEP cohort (88 NMO spectrum disorder (NMOSD) with anti‐aquaporin‐4 antibodies [AQP4‐NMOSD], 28 double‐seronegative NMOSD [DN‐NMOSD], 61 myelin oligodendrocyte glycoprotein antibody–associated disease [MOGAD], 64 clinically isolated syndrome [CIS], 237 relapsing–remitting MS [RRMS], and 162 primary progressive MS [PPMS]). The association between log[sNFL] and log[sGFAP] and age, sex, time since relapse, EDSS, oligoclonal bands, and MRI characteristics was analyzed by multiple linear regression. The diagnostic performance of sNfL and sGFAP in discriminating between NMOSD and MS subgroups was evaluated using multivariable logistic regression and area under the curve (AUC).
Results
For each group, specific associations of sNFL and sGFAP levels with patient characteristics were observed. Clearest associations were observed in AQP4‐NMOSD patients. After a recent attack (≤ 90 days), multivariate analysis revealed that sGFAP and age discriminated AQP4‐NMOSD from MOGAD (AUC = 0.939 [0.910;0.982]), AQP4‐NMOSD and DN‐NMOSD from MOGAD (AUC = 0.863 [0.776;0.977]), and AQP4‐NMOSD from all other groups (AUC = 0.950 [0.918;0.987]). Diagnostic probability heatmaps based on sGFAP and age illustrate the probability of each diagnosis relative to others for bedside interpretation.
Conclusion
After an acute attack of NMOSD, risk score based on sGFAP and age may guide the management of patients before autoantibody status is available.