Classifying the antibody-negative NMO syndromes

Abstract

ObjectiveTo determine whether unsupervised principal component analysis (PCA) of comprehensive clinico-radiologic data can identify phenotypic subgroups within antibody-negative patients with overlapping features of multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSDs), and to validate the phenotypic classifications using high-resolution nuclear magnetic resonance (NMR) plasma metabolomics with inference to underlying pathologies.MethodsForty-one antibody-negative patients were recruited from the Oxford NMO Service. Thirty-six clinico-radiologic parameters, focusing on features known to distinguish NMOSD and MS, were collected to build an unbiased PCA model identifying phenotypic subgroups within antibody-negative patients. Metabolomics data from patients with relapsing-remitting MS (RRMS) (n = 34) and antibody-positive NMOSD (Ab-NMOSD) (aquaporin-4 antibody n = 54, myelin oligodendrocyte glycoprotein antibody n = 20) were used to identify discriminatory plasma metabolites separating RRMS and Ab-NMOSD.ResultsPCA of the 36 clinico-radiologic parameters revealed 3 phenotypic subgroups within antibody-negative patients: an MS-like subgroup, an NMOSD-like subgroup, and a low brain lesion subgroup. Supervised multivariate analysis of metabolomics data from patients with RRMS and Ab-NMOSD identified myoinositol and formate as the most discriminatory metabolites (both higher in RRMS). Within antibody-negative patients, myoinositol and formate were significantly higher in the MS-like vs NMOSD-like subgroup; myoinositol (mean [SD], 0.0023 [0.0002] vs 0.0019 [0.0003] arbitrary units [AU]; p = 0.041); formate (0.0027 [0.0006] vs 0.0019 [0.0006] AU; p = 0.010) (AU).ConclusionsPCA identifies 3 phenotypic subgroups within antibody-negative patients and that the metabolite discriminators of RRMS and Ab-NMOSD suggest that these groupings have some pathogenic meaning. Thus, the identified clinico-radiologic discriminators may provide useful diagnostic clues when seeing antibody-negative patients in the clinic.

The Importance of Optical Coherence Tomography in the Diagnosis of Atypical or Subclinical Optic Neuritis: A Case Series Study

“…On suspect diagnosis of seronegative NMOSD, he was treated with monoclonal antibody targeting CD20 B cells for five years without relapse. It took only 18 months without the treatment for the patient to relapse with both myelitis and bilateral ON, which confirmed the diagnosis [ 18 , 19 ]. During the first five years’ follow-up with OCT, the patient showed bilateral normal RNFL and GCIPL thickness.…”

“…His visual acuity was preserved bilaterally and color vision was slightly affected, and the VF test showed inferior bitemporal quadrantanopia ( Figure 2 j,k). Thus, subclinical bilateral ON was confirmed and diagnosis of seronegative NMOSD was fulfilled [ 19 ].…”

Granulocyte activation markers in cerebrospinal fluid differentiate acute neuromyelitis spectrum disorder from multiple sclerosis

“…5 Accordingly, metabolomic approaches have allowed to differentiate with high accuracy aAQP4 − NMOSD versus MS based on increased plasma levels of myoinositol and formate in the latter disease; different from our study cohort these results were derived from an out of relapse population and it is not known whether the differentiation between the two disease would apply as well in acute disease. 28 The correlations of GAM levels with CSF granulocytosis and acute disability scores strengthen the concept of a pathogenetic link between recruitment and activation of granulocytes, neural tissue damage and development of disability in NMOSD. In this context, it is notable that a significant number of patients with acute and s/c NMOSD had markedly increased GAM concentrations, despite corticosteroid or immunomodulating therapy prior to sampling.…”

Double-negative neuromyelitis optica spectrum disorder

“… 34 The metabolic differentiators measured in the patient serum: myoinositol and formate, previously shown to differentiate MS from the two antibody conditions together, were also shown to validate principal component analysis spontaneously separating DN NMOSD-like groups into “MS like” and “NMOSD like” groups. 45 …”