Diagnostic value of intereye difference metrics for optic neuritis in aquaporin-4 antibody seropositive neuromyelitis optica spectrum disorders

Retinal Changes in Double-Antibody Seronegative Neuromyelitis Optica Spectrum Disorders

BACKGROUND AND OBJECTIVES

To systematically describe the clinical picture of double-antibody seronegative neuromyelitis optica spectrum disorders (DN-NMOSD) with specific emphasis on retinal involvement.

METHODS

Cross-sectional data of 25 people with DN-NMOSD (48 eyes) with and without a history of optic neuritis (ON) were included in this study along with data from 25 people with aquaporin-4 antibody seropositive neuromyelitis optica spectrum disorder (AQP4-NMOSD, 46 eyes) and from 25 healthy controls (HCs, 49 eyes) for comparison. All groups were matched for age and sex and included from the collaborative retrospective study of retinal optical coherence tomography (OCT) in neuromyelitis optica (CROCTINO). Participants underwent OCT with central postprocessing and local neurologic examination and antibody testing. Retinal neurodegeneration was quantified as peripapillary retinal nerve fiber layer thickness (pRNFL) and combined ganglion cell and inner plexiform layer thickness (GCIPL).

RESULTS

This DN-NMOSD cohort had a history of [median (inter-quartile range)] 6 (5; 9) attacks within their 5 ± 4 years since onset. Myelitis and ON were the most common attack types. In DN-NMOSD eyes after ON, pRNFL (p < 0.001) and GCIPL (p = 0.023) were thinner compared with eyes of HCs. Even after only one ON episode, DN-NMOSD eyes already had considerable neuroaxonal loss compared with HCs. In DN-NMOSD eyes without a history of ON, pRNFL (p = 0.027) and GCIPL (p = 0.022) were also reduced compared with eyes of HCs. However, there was no difference in pRNFL and GCIPL between DN-NMOSD and AQP4-NMOSD for the whole group and for subsets with a history of ON and without a history of ON-as well as between variances of retinal layer thicknesses.

DISCUSSION

DN-NMOSD is characterized by severe retinal damage after ON and attack-independent retinal neurodegeneration. Most of the damage occurs during the first ON episode, which highlights the need for better diagnostic markers in DN-NMOSD to facilitate an earlier diagnosis as well as for effective and early treatments. In this study, people with DN-NMOSD presented with homogeneous clinical and imaging findings potentially suggesting a common retinal pathology in these patients.

Optic Nerve Imaging in Multiple Sclerosis and Related Disorders

Optic neuritis is a common feature in multiple sclerosis and in 2 other autoimmune demyelinating disorders such as aquaporin-4 IgG antibody-associated neuromyelitis optica spectrum disorder and myelin oligodendrocyte glycoprotein antibody-associated disease. Although serologic testing is critical for differentiating these different autoimmune-mediated disorders, MR imaging, which is the preferred imaging modality for assessing the optic nerve, can provide valuable information, suggesting a specific diagnosis and guiding the appropriate serologic testing.

Delimiting MOGAD as a disease entity using translational imaging

The first formal consensus diagnostic criteria for myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) were recently proposed. Yet, the distinction of MOGAD-defining characteristics from characteristics of its important differential diagnoses such as multiple sclerosis (MS) and aquaporin-4 antibody seropositive neuromyelitis optica spectrum disorder (NMOSD) is still obstructed. In preclinical research, MOG antibody-based animal models were used for decades to derive knowledge about MS. In clinical research, people with MOGAD have been combined into cohorts with other diagnoses. Thus, it remains unclear to which extent the generated knowledge is specifically applicable to MOGAD. Translational research can contribute to identifying MOGAD characteristic features by establishing imaging methods and outcome parameters on proven pathophysiological grounds. This article reviews suitable animal models for translational MOGAD research and the current state and prospect of translational imaging in MOGAD.