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Neurology(R) neuroimmunology & neuroinflammation · 2025

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

СтатьяНаблюдательное исследованиеНазвание и аннотация: Перевод в очереди
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Yeola Avani, Houston Samuel, Aggarwal Anupriya, Gamage Rashmi, Maltby Vicki E, Fabis-Pedrini Marzena J, Le-Kavanagh Linh, Merheb Vera, Nguyen Kristy, Lee Fiona X Z, Walters Susan, Taha Marinda, O'Connell Annmaree, Jokubaitis Vilija G, Roldan Angie, Monif Mastura, Butzkueven Helmut, Sampangi Sandeep, Rath Louise, Fazzolari Katherine, Hardy Todd A, Beadnall Heidi N, Barnett Michael H, Kermode Allan G, Dwyer Christopher, Kalincik Tomas, Broadley Simon A, Turville Stuart G, Reddel Stephen W, Ramanathan Sudarshini, Lechner-Scott Jeannette, Van Der Walt Anneke, Brilot Fabienne

Аннотация

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.

Предметные рубрики MeSH

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

Реквизиты и контакты