mRNA versus inactivated virus COVID-19 vaccines in multiple sclerosis: Humoral responses and protectivity-Does it matter?

BACKGROUND: COVID-19 vaccines are recommended for people with multiple sclerosis (pwMS). Adequate humoral responses are obtained in pwMS receiving disease-modifying therapies (DMTs) after vaccination, with the exception of those receiving B-cell-depleting therapies and non-selective S1P modulators. However, most of the reported studies on the immunity of COVID-19 vaccinations have included mRNA vaccines, and information on inactivated virus vaccine responses, long-term protectivity, and comparative studies with mRNA vaccines are very limited. Here, we aimed to investigate the association between humoral vaccine responses and COVID-19 infection outcomes following mRNA and inactivated virus vaccines in a large national cohort of pwMS receiving DMTs. METHODS: This is a cross-sectional and prospective multicenter study on COVID-19-vaccinated pwMS. Blood samples of pwMS with or without DMTs and healthy controls were collected after two doses of inactivated virus (Sinovac) or mRNA (Pfizer-BioNTech) vaccines. PwMS were sub-grouped according to the mode of action of the DMTs that they were receiving. SARS-CoV-2 IgG titers were evaluated by chemiluminescent microparticle immunoassay. A representative sample of this study cohort was followed up for a year. COVID-19 infection status and clinical outcomes were compared between the mRNA and inactivated virus groups as well as among pwMS subgroups. RESULTS: A total of 1484 pwMS (1387 treated, 97 untreated) and 185 healthy controls were included in the analyses (male/female: 544/1125). Of those, 852 (51.05%) received BioNTech, and 817 (48.95%) received Sinovac. mRNA and inactivated virus vaccines result in similar seropositivity; however, the BioNTech vaccination group had significantly higher antibody titers (7.175±10.074) compared with the Sinovac vaccination group (823±1.774) (p<0.001). PwMS under ocrelizumab, fingolimod, and cladribine treatments had lower humoral responses compared with the healthy controls in both vaccine types. After a mean of 327±16 days, 246/704 (34.9%) of pwMS who were contacted had COVID-19 infection, among whom 83% had asymptomatic or mild disease. There was no significant difference in infection rates of COVID-19 between participants vaccinated with BioNTech or Sinovac vaccines. Furthermore, regression analyses show that no association was found regarding age, sex, Expanded Disability Status Scale score (EDSS), the number of vaccination, DMT type, or humoral antibody responses with COVID-19 infection rate and disease severity, except BMI Body mass index (BMI). CONCLUSION: mRNA and inactivated virus vaccines had similar seropositivity; however, mRNA vaccines appeared to be more effective in producing SARS-CoV-2 IgG antibodies. B-cell-depleting therapies fingolimod and cladribine were associated with attenuated antibody titer. mRNA and inactive virus vaccines had equal long-term protectivity against COVID-19 infection regardless of the antibody status.

A multiparametric score for assessing the individual risk of severe Covid-19 among patients with Multiple Sclerosis.

BACKGROUND: Many risk factors for the development of severe forms of Covid-19 have been identified, some applying to the general population and others specific to Multiple Sclerosis (MS) patients. However, a score for quantifying the individual risk of severe Covid-19 in patients with MS is not available. The aim of this study was to construct such score and to evaluate its performance. METHODS: Data on patients with MS infected with Covid-19 in Italy, Turkey and South America were extracted from the Musc-19 platform. After imputation of missing values, data were separated into training data set (70%) and validation data set (30%). Univariable logistic regression models were performed in the training dataset to identify the main risk factors to be included in the multivariable logistic regression analyses. To select the most relevant variables we applied three different approaches: (1) multivariable stepwise, (2) Lasso regression, (3) Bayesian model averaging. Three scores were defined as the linear combination of the coefficients estimated in the models multiplied by the corresponding value of the variables and higher scores were associated to higher risk of severe Covid-19 course. The performances of the three scores were compared in the validation dataset based on the area under the ROC curve (AUC) and an optimal cut-off was calculated in the training dataset for the score with the best performance. The probability of showing a severe Covid-19 course was calculated based on the score with the best performance. RESULTS: 3852 patients were included in the study (2696 in the training dataset and 1156 in the validation data set). 17% of the patients required hospitalization and risk factors for severe Covid-19 course were older age, male sex, living in Turkey or South America instead of living in Italy, presence of comorbidities, progressive MS, longer disease duration, higher Expanded Disability Status Scale, Methylprednisolone use and anti-CD20 treatment. The score with the best performance was the one derived using the Lasso selection approach (AUC= 0.72) and it was built with the following variables: age, sex, country, BMI, presence of comorbidities, EDSS, methylprednisolone use, treatment. An excel spreadsheet to calculate the score and the probability of severe Covid-19 is available at the following link: https://osf.io/ac47u/?view_only=691814d57b564a34b3596e4fcdcf8580. CONCLUSIONS: The originality of this study consists in building a useful tool to quantify the individual risk for Covid-19 severity based on patient's characteristics. Due to the modest predictive ability and to the need of external validation, this tool is not ready for being fully used in clinical practice to make important decisions or interventions. However, it can be used as an additional instrument to identify high-risk patients and persuade them to take important measures to prevent Covid-19 infection (i.e. getting vaccinated against Covid-19, adhering to social distancing, and using of personal protection equipment).

The Turkish experience of COVID-19 infection in people with NMOSD and MOGAD: A milder course?

BACKGROUND: COVID-19 is a multisystemic infection with variables consequences depending on individual and comorbid conditions. The course and outcomes of COVID-19 during neuromyelitis optica spectrum disorders (NMOSD) and myelin oligodendrocyte glycoprotein antibody-associated disorders (MOGAD) are not clearly known. OBJECTIVE/METHODS: The aim of this study was to examine the features and outcomes of COVID-19 infection in NMOSD and MOGAD patients. The patients' demographic and clinical factors, disease modifying treatment (DMT) used and disease information of COVID-19 infection were recorded. Conditions leading to hospitalization and severe exposure to COVID-19 infection were also analyzed. RESULTS: The study included 63 patients from 25 centers. Thirty-two patients (50.8%) belong to AQP-4 seropositive group, 13 (20.6%) and 18 (28.6%) were in MOG-positive and double-seronegative groups, respectively. Risk factors for severe COVID-19 infection and hospitalization were advanced age, high disability level and the presence of comorbid disease. Disease severity was found to be high in double-seronegative NMOSD and low in MOGAD patients. No statistically significant effect of DMTs on disease severity and hospitalization was found. CONCLUSION: In NMOSD and MOGAD patients, advanced age, high disability and presence of comorbid disease pose risks for severe COVID-19 infection. There was no direct significant effect of DMTs for COVID-19 infection.