Faculty Bibliography

BACKGROUND:Relatively little is known about how global and regional brain volumes changes in myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) compare with Multiple Sclerosis (MS), Neuromyelitis optica spectrum disorder (NMOSD), and healthy controls (HC). OBJECTIVE:To compare global and regional brain volumes in MOGAD, MS, NMOSD, and HC cross-sectionally as well as longitudinally in a subset of patients. METHODS:We retrospectively reviewed all adult MOGAD and NMOSD patients with brain MRI performed in stable remission and compared them with MS patients and HC. Volumetric parameters were assessed using the FDA-approved icobrain software. adjusted for age and sex. RESULTS:Twenty-four MOGAD, 47 NMOSD, 40 MS patients, and 37 HC were included in the cross-sectional analyses. Relative to HC, the age-adjusted whole brain (WB) volume was significantly lower in patients with MOGAD (p=0.0002), NMOSD (p=0.042), and MS (p=0.01). Longitudinal analysis of a subset of 8 MOGAD, 22 NMOSD, and 34 MS patients showed a reduction in the WB and cortical gray matter (CGM) volumes over time in all three disease groups, without statistically significant differences between groups. The MOGAD group had a greater loss of thalamic volume compared to MS (p=0.028) and NMOSD (p=0.023) and a greater loss of hippocampal volumes compared to MS (p=0.007). CONCLUSIONS:Age-adjusted WB volume loss was evident in all neuroinflammatory conditions relative to HC in cross-sectional comparisons. In longitudinal analyses, MOGAD patients had a higher thalamic atrophy rate relative to MS and NMOSD, and a higher hippocampal atrophy rate relative to MS. Larger studies are needed to validate these findings and to investigate their clinical implications.

The classification of multiple sclerosis (MS) has been established by Lublin in 1996 and revised in 2013. The revision includes clinically isolated syndrome, relapsing-remitting, primary progressive and secondary progressive MS, and has added activity (i.e., formation of white matter lesions or clinical relapses) as a qualifier. This allows for the distinction between active and nonactive progression, which has been shown to be of clinical importance. We propose that a logical extension of this classification is the incorporation of additional key pathological processes, such as chronic perilesional inflammation, neuroaxonal degeneration, and remyelination. This will distinguish MS phenotypes that may present as clinically identical but are driven by different combinations of pathological processes. A more precise description of MS phenotypes will improve prognostication and personalized care as well as clinical trial design. Thus, our proposal provides an expanded framework for conceptualizing MS and for guiding development of biomarkers for monitoring activity along the main pathological axes in MS.

OBJECTIVE:To compare "hybrid immunity" (prior COVID-19 infection plus vaccination) and post-vaccination immunity to SARS CoV-2 in MS patients on different disease-modifying therapies (DMTs) and to assess the impact of vaccine product and race/ethnicity on post-vaccination immune responses. METHODS:Consecutive MS patients from NYU MS Care Center (New York, NY), aged 18-60, who completed primary COVID-19 vaccination series ≥6 weeks previously were evaluated for SARS CoV-2-specific antibody responses with electro-chemiluminescence and multiepitope bead-based immunoassays and, in a subset, live virus immunofluorescence-based microneutralization assay. SARS CoV-2-specific cellular responses were assessed with cellular stimulation TruCulture IFNγ and IL-2 assay and, in a subset, with IFNγ and IL-2 ELISpot assays. Multivariate analyses examined associations between immunologic responses and prior COVID-19 infection while controlling for age, sex, DMT at vaccination, time-to-vaccine, and vaccine product. RESULTS:Between 6/01/2021 and 11/11/2021, 370 MS patients were recruited (mean age 40.6 years; 76% female; 53% non-White; 22% with prior infection; common DMT classes: ocrelizumab 40%; natalizumab 15%, sphingosine-1-phosphate receptor modulators 13%; and no DMT 8%). Vaccine-to-collection time was 18.7 (±7.7) weeks and 95% of patients received mRNA vaccines. In multivariate analyses, patients with laboratory-confirmed prior COVID-19 infection had significantly increased antibody and cellular post-vaccination responses compared to those without prior infection. Vaccine product and DMT class were independent predictors of antibody and cellular responses, while race/ethnicity was not. INTERPRETATION/CONCLUSIONS:Prior COVID-19 infection is associated with enhanced antibody and cellular post-vaccine responses independent of DMT class and vaccine type. There were no differences in immune responses across race/ethnic groups.

OBJECTIVE:The objective of this study was to determine the impact of multiple sclerosis (MS) disease-modifying therapies (DMTs) on the development of cellular and humoral immunity to severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) infection. METHODS:Patients with MS aged 18 to 60 years were evaluated for anti-nucleocapsid and anti-Spike receptor-binding domain (RBD) antibody with electro-chemiluminescence immunoassay; antibody responses to Spike protein, RBD, N-terminal domain with multiepitope bead-based immunoassays (MBI); live virus immunofluorescence-based microneutralization assay; T-cell responses to SARS-CoV-2 Spike using TruCulture enzyme-linked immunosorbent assay (ELISA); and IL-2 and IFNγ ELISpot assays. Assay results were compared by DMT class. Spearman correlation and multivariate analyses were performed to examine associations between immunologic responses and infection severity. RESULTS:Between January 6, 2021, and July 21, 2021, 389 patients with MS were recruited (mean age 40.3 years; 74% women; 62% non-White). Most common DMTs were ocrelizumab (OCR)-40%; natalizumab -17%, Sphingosine 1-phosphate receptor (S1P) modulators -12%; and 15% untreated. One hundred seventy-seven patients (46%) had laboratory evidence of SARS-CoV-2 infection; 130 had symptomatic infection, and 47 were asymptomatic. Antibody responses were markedly attenuated in OCR compared with other groups (p ≤0.0001). T-cell responses (IFNγ) were decreased in S1P (p = 0.03), increased in natalizumab (p <0.001), and similar in other DMTs, including OCR. Cellular and humoral responses were moderately correlated in both OCR (r = 0.45, p = 0.0002) and non-OCR (r = 0.64, p <0.0001). Immune responses did not differ by race/ethnicity. Coronavirus disease 2019 (COVID-19) clinical course was mostly non-severe and similar across DMTs; 7% (9/130) were hospitalized. INTERPRETATION/CONCLUSIONS:DMTs had differential effects on humoral and cellular immune responses to SARS-CoV-2 infection. Immune responses did not correlate with COVID-19 clinical severity in this relatively young and nondisabled group of patients with MS. ANN NEUROL 2022.

There is an unmet need to develop practical methods for differentiating multiple sclerosis (MS) from other neuroinflammatory disorders using standard brain MRI. To develop a practical approach for differentiating MS from neuromyelitis optica spectrum disorder (NMOSD) and MOG antibody-associated disorder (MOGAD) with brain MRI, we first identified lesion locations in the brain that are suggestive of MS-associated demyelination ("MS Lesion Checklist") and compared frequencies of brain lesions in the "MS Lesion Checklist" locations in a development sample of patients (n = 82) with clinically definite MS, NMOSD, and MOGAD. Patients with MS were more likely than patients with non-MS to have lesions in 3 locations only: anterior temporal horn (p < 0.0001), periventricular ("Dawson's finger") (p < 0.0001), and cerebellar hemisphere (p = 0.02). These three lesion locations were used as predictor variables in a multivariable regression model for discriminating MS from non-MS. The model had area under the curve (AUC) of 0.853 (95% confidence interval: 0.76-0.945), sensitivity of 87.1%, and specificity of 72.5%. We then used an independent validation sample with equal representation of MS and NMOSD/MOGAD cases (n = 97) to validate our prediction model. In the validation sample, the model was 76.3% accurate in discriminating MS from non-MS. Our simple method for predicting MS versus NMOSD/MOGAD only requires a neuroradiologist or clinician to ascertain the presence of lesions in three locations on conventional MRI sequences. It can therefore be readily applied in the real-world setting for training and clinical practice.