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COVID-19 Vaccine Boosters in People With Multiple Sclerosis: Improved SARS-CoV-2 Cross-Variant Antibody Response and Prediction of Protection
Journal article   Open access   Peer reviewed

COVID-19 Vaccine Boosters in People With Multiple Sclerosis: Improved SARS-CoV-2 Cross-Variant Antibody Response and Prediction of Protection

Avani Yeola, Samuel Houston, Anupriya Aggarwal, Rashmi Gamage, Vicki E Maltby, Marzena J Fabis-Pedrini, Linh Le-Kavanagh, Vera Merheb, Kristy Nguyen, Fiona X Z Lee, …
Neurology : neuroimmunology & neuroinflammation, Vol.12(5), e200443
2025
DOI: https://doi.org/10.1212/NXI.0000000000200443
PMID: 40694731
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Published1.38 MBDownloadView
CC BY-NC-ND V4.0,  Open Access

Abstract

Adult Antibodies, Neutralizing - immunology Antibodies, Viral - blood Antibodies, Viral - immunology COVID-19 - immunology COVID-19 - prevention & control COVID-19 Vaccines - administration & dosage COVID-19 Vaccines - immunology Female Humans Immunoglobulin G - blood Immunoglobulin G - immunology Machine Learning Male Middle Aged Multiple Sclerosis - drug therapy Multiple Sclerosis - immunology Prospective Studies SARS-CoV-2 - immunology Spike Glycoprotein, Coronavirus - immunology
Background and Objectives Although disease-modifying therapies (DMTs) may suppress coronavirus disease 2019 (COVID-19) vaccine responses in people with multiple sclerosis (pwMS), limited data are available on the cumulative effect of additional boosters. Maturation of Spike immunoglobulin G (IgG) to target a greater diversity of SARS-CoV-2 variants, especially past the BA.1 variant, has not been reported. In addition, the prediction of variant-specific protection, given that Spike antibody testing is not performed routinely, remains a challenge. We, therefore, evaluated whether additional vaccine doses improved the breadth of cross-variant recognition to target emerging SARS-CoV-2 variants. Machine learning–based models were designed to predict variant-specific protection status. Methods In a prospective observational cohort (n = 442), Spike IgG titers and live virus neutralization against D614, BA.1, BA.2, BA.5, XBB.1.1, XBB.1.5, and EG.5.1 variants were determined in 1,011 serum samples (0–12 months after 2–4 doses). Predictive protection models were developed by K-fold cross-validation on training and test data sets (random split 70:30). Results After primary vaccination, pwMS on immunosuppressive disease-modifying therapy (IMM-DMT) had 10-fold and 7.2-fold lower D614 Spike IgG titers than pwMS on low-efficacy (LE)-DMT and cladribine (p < 0.01). After 4 doses, pwMS on IMM-DMT had significantly lower Spike IgG titers, compared with pwMS on low-efficacy disease-modifying therapy, for D614 (p < 0.05), as well as BA.1, BA.2, BA.5, XBB.1, XBB.1.5, and EG.5.1(p < 0.01). The breadth of Spike IgG to recognize variants other than the cognate antigen increased after 4 doses of all DMTs. Although pwMS on IMM-DMT displayed reduced cross-variant recognition, a fourth dose resulted in a 2–4-fold increase in protection against newer variants and a reduction in two-thirds of pwMS without protective Spike IgG (p < 0.0001). Tixagevimab and cilgavimab did not induce additional cross-variant protection. Variant-specific predictive models of vaccine protection were influenced by treatment, time since primary vaccination, and age, with high sensitivity (99.4%, 95% CI 96.8–99.99) and specificity (72.0%, 95% CI 50.6–87.9) for XBB.1.5/EG.5.1 variants. Discussion Despite not eliciting adequate antibody response in pwMS on IMM-DMT, COVID-19 boosters improve the breadth of the humoral response against SARS-CoV-2 emerging variants. Vaccine protection can be predicted by statistical modeling.

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Collaboration types
Domestic collaboration
Citation topics
1 Clinical & Life Sciences
1.203 Neuromuscular Disorders
1.203.147 Multiple Sclerosis
Web Of Science research areas
Clinical Neurology
Neurosciences
ESI research areas
Neuroscience & Behavior
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