Chrome Extension
WeChat Mini Program
Use on ChatGLM

Predictors for reactogenicity and humoral immunity to SARS-CoV-2 following infection and mRNA vaccination: A regularized, mixed-effects modelling approach

Erin C. Williams, Alexander Kizhner, Valerie S. Stark, Aria Nawab, Daniel D. Muniz, Felipe Echeverri Tribin, Juan Manuel Carreno, Dominika Bielak, Gagandeep Singh, Michael E. Hoffer, Florian Krammer, Suresh Pallikkuth, Savita Pahwa

Frontiers in immunology(2023)

Cited 2|Views23
No score
Abstract
IntroductionThe influence of pre-existing humoral immunity, inter-individual demographic factors, and vaccine-associated reactogenicity on immunogenicity following COVID vaccination remains poorly understood. MethodsTen-fold cross-validated least absolute shrinkage and selection operator (LASSO) and linear mixed effects models were used to evaluate symptoms experienced by COVID+ participants during natural infection and following SARS-CoV-2 mRNA vaccination along with demographics as predictors for antibody (AB) responses to recombinant spike protein in a longitudinal cohort study. ResultsIn previously infected individuals (n=33), AB were more durable and robust following primary vaccination when compared to natural infection alone. Higher AB were associated with experiencing dyspnea during natural infection, as was the total number of symptoms reported during the COVID-19 disease course. Both local and systemic symptoms following 1(st) and 2(nd) dose (n=49 and 48, respectively) of SARS-CoV-2 mRNA vaccines were predictive of higher AB after vaccination. Lastly, there was a significant temporal relationship between AB and days since infection or vaccination, suggesting that vaccination in COVID+ individuals is associated with a more robust immune response. DiscussionExperiencing systemic and local symptoms post-vaccine was suggestive of higher AB, which may confer greater protection.
More
Translated text
Key words
vaccine reactogenicity,infection,protective antibodies,COVID-19,SARS-CoV-2
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined