Antibody responses to BNT162b2 mRNA COVID-19 vaccine and their predictors among healthcare workers in a tertiary referral hospital in Japan

Abstract

Objectives: This study aimed to determine antibody responses in healthcare workers who receive the BNT162b2 mRNA COVID-19 vaccine and identify factors that predict the response.

Methods: We recruited healthcare workers receiving the BNT162b2 mRNA COVID-19 vaccine at the Chiba University Hospital COVID-19 Vaccine Center. Blood samples were obtained before the 1st dose and after the 2nd dose vaccination, and serum antibody titers were determined using Elecsys® Anti-SARS-CoV-2S, an electrochemiluminescence immunoassay. We established a model to identify the baseline factors predicting post-vaccine antibody titers using univariate and multivariate linear regression analyses.

Results: Two thousand fifteen individuals (median age 37-year-old, 64.3% female) were enrolled in this study, of which 10 had a history of COVID-19. Before vaccination, 21 participants (1.1%) had a detectable antibody titer (≥0.4 U/mL) with a median titer of 35.9 U/mL (interquartile range [IQR] 7.8 - 65.7). After vaccination, serum anti-SARS-CoV-2S antibodies (≥0.4 U/mL) were detected in all 1774 participants who received the 2nd dose with a median titer of 2060.0 U/mL (IQR 1250.0 - 2650.0). Immunosuppressive medication (p < 0.001), age (p < 0.001), time from 2nd dose to sample collection (p < 0.001), glucocorticoids (p = 0.020), and drinking alcohol (p = 0.037) were identified as factors predicting lower antibody titers after vaccination, whereas previous COVID-19 (p < 0.001), female (p < 0.001), time between 2 doses (p < 0.001), and medication for allergy (p = 0.024) were identified as factors predicting higher serum antibody titers.

Conclusions: Our data demonstrate that healthcare workers universally have good antibody responses to the BNT162b2 mRNA COVID-19 vaccine. The predictive factors identified in our study may help optimize the vaccination strategy.

Keywords: COVID-19; Healthcare worker; Immunogenicity; SARS-CoV-2; Vaccine.

Fig. 1

Multivariate linear regression model to predict anti-SARS-CoV-2S antibody titers after vaccination. Shown are the variables retained in the final multivariate linear regression model to explain anti-SARS-CoV-2S antibody titers after vaccination. A dot and bar represent standardized coefficient β and 95% confidence interval for the variable.

Fig. S1

Distribution of antibody titers: A, B. Boxplots for the distribution of anti-SARS-CoV-2S antibody titers. A box represents interquartile range and a horizontal line in a box represents median. A vertical line represents a range excluding outliers. A circle and an asterisk represent outlier and extreme outlier, respectively. A. Before and after vaccination, in subjects whose data are available (n = 1774). Undetectable titers (<0.4 U/mL) are imputed with a value of 0.4 U/mL before log-transformed. The age range is 21-72-year-old. B. After vaccination, according to age groups and sex.