Immune response after two doses of the BNT162b2 COVID-19 vaccine and risk of SARS-CoV-2 breakthrough infection in Tyrol, Austria: an open-label, observational phase 4 trial

Abstract

Background: Correlates of protection could help to assess the extent to which a person is protected from SARS-CoV-2 infection after vaccination (so-called breakthrough infection). We aimed to clarify associations of antibody and T-cell responses after vaccination against COVID-19 with risk of a SARS-CoV-2 breakthrough infection and whether measurement of these responses enhances risk prediction.

Methods: We did an open-label, phase 4 trial in two community centres in the Schwaz district of the Federal State of Tyrol, Austria, before the emergence of the omicron (B.1.1.529) variant of SARS-CoV-2. We included individuals (aged ≥16 years) a mean of 35 days (range 27-43) after they had received a second dose of the BNT162b2 (Pfizer-BioNTech) COVID-19 vaccine. We quantified associations between immunological parameters and breakthrough infection and assessed whether information on these parameters improves risk discrimination. The study is registered with the European Union Drug Regulating Authorities Clinical Trials Database, 2021-002030-16.

Findings: 2760 individuals (1682 [60·9%] female, 1078 [39·1%] male, mean age 47·4 years [SD 14·5]) were enrolled into this study between May 15 and May 21, 2021, 712 (25·8%) of whom had a previous SARS-CoV-2 infection. Over a median follow-up of 5·9 months, 68 (2·5%) participants had a breakthrough infection. In models adjusted for age, sex, and previous infection, hazard ratios for breakthrough infection for having twice the immunological parameter level at baseline were 0·72 (95% CI 0·60-0·86) for anti-spike IgG, 0·80 (0·70-0·92) for neutralising antibodies in a surrogate virus neutralisation assay, 0·84 (0·58-1·21) for T-cell response after stimulation with a CD4 peptide pool, and 0·77 (0·54-1·08) for T-cell response after stimulation with a combined CD4 and CD8 peptide pool. For neutralising antibodies measured in a nested case-control sample using a pseudotyped virus neutralisation assay, the corresponding odds ratio was 0·78 (0·62-1·00). Among participants with previous infection, the corresponding hazard ratio was 0·73 (0·61-0·88) for anti-nucleocapsid Ig. Addition of anti-spike IgG information to a model containing information on age and sex improved the C-index by 0·085 (0·027-0·143).

Interpretation: In contrast to T-cell response, higher levels of binding and neutralising antibodies were associated with a reduced risk of breakthrough SARS-CoV-2 infection. The assessment of anti-spike IgG enhances the prediction of incident breakthrough SARS-CoV-2 infection and could therefore be a suitable correlate of protection in practice. Our phase 4 trial measured both humoral and cellular immunity and had a 6-month follow-up period; however, the longer-term protection against emerging variants of SARS-CoV-2 remains unclear.

Funding: None.

Figure 1

Study profile

Figure 2

Correlation coefficients and scatter plots of baseline levels of immunological parameters For all analyses we used log₂-transformed values of immunological parameters. The upper part of the matrix shows unadjusted Pearson's correlation coefficients (95% CIs) and numbers of participants. Areas are shaded according to the magnitude of the point estimates of Pearson's correlation coefficients, with darker shading indicating values closer to 1. The lower part of the matrix depicts scatter plots of different immunological parameters, with both axes presented on a log scale. Anti-N Ig=anti-nucleocapsid Ig. Anti-S IgG=anti-spike IgG. BAU=binding antibody units. COI=cutoff index. IU=international unit. pVNT=pseudotyped SARS-CoV-2 virus neutralisation test. SI=stimulation index. sVNT=surrogate SARS-CoV-2 virus neutralisation test. *The analyses of pVNT values were restricted to the nested case-control sample. †The analyses of anti-N Ig values were restricted to participants with previous SARS-CoV-2 infection.

Figure 3

Associations of baseline levels of immunological parameters with incident breakthrough SARS-CoV-2 infection Symptomatic SARS-CoV-2 infection was defined as having one or more symptoms of fever or chills, cough, breathing difficulties, muscle or limb pain, loss of sense of smell or taste, sore throat, diarrhoea, or vomiting. Previous SARS-CoV-2 infection was based on self-report or seropositivity of anti-N Ig at the time of enrolment. For additional information on participants with previous SARS-CoV-2 infection and with incident breakthrough SARS-CoV-2 infection, see the appendix (p 5). Cox regression was applied for anti-S IgG, sVNT, CD4 peptide pool, CD4 and CD8 peptide pool, and anti-N Ig and conditional logistic regression for pVNT. Immunological parameters were entered as log₂-transformed continuous terms. Anti-N Ig=anti-nucleocapsid Ig. Anti-S IgG=anti-spike IgG. HR=hazard ratio. OR=odds ratio. pVNT=pseudotyped SARS-CoV-2 virus neutralisation test. sVNT=surrogate SARS-CoV-2 virus neutralisation test. *HRs and ORs were adjusted for age, sex, and previous SARS-CoV-2 infection. †The analysis of anti-N Ig was restricted to participants with previous SARS-CoV-2 infection. ‡pVNT was measured in a subset of 68 participants infected with SARS-CoV-2 and 204 individual-matched controls. §pVNT was measured in a subset of 53 participants with symptomatic SARS-CoV-2 infection and 159 individual-matched controls.

Figure 4

Associations of categorised baseline levels of binding and neutralising antibody levels with incident breakthrough SARS-CoV-2 infection Cox regression was applied for anti-S IgG, sVNT, and anti-N Ig, and conditional logistic regression was applied for pVNT. For anti-N Ig, the regression model was adjusted for age and sex (and not for previous SARS-CoV-2 infection due to collinearity) and for anti-S IgG, sVNT, and pVNT the model was additionally adjusted for previous SARS-CoV-2 infection as established by the seropositivity of anti-N Ig at the time of enrolment or self-report. p_trend indicates the p value of the likelihood ratio test comparing regression models including categories of antibody concentrations as a continuous variable and without antibody information. For additional information on participants with previous SARS-CoV-2 infection and with incident breakthrough SARS-CoV-2 infection, see the appendix (p 5). Anti-N Ig=anti-nucleocapsid Ig. Anti-S IgG=anti-spike IgG. BAU=binding antibody units. COI=cutoff index. HR=hazard ratio. IU=international unit. OR=odds ratio. pVNT=pseudotyped SARS-CoV-2 virus neutralisation test. sVNT=surrogate SARS-CoV-2 virus neutralisation test. *pVNT was measured in a nested case-control sample of 68 participants and 204 individual-matched controls.

Figure 5

Improvement in prediction of incident breakthrough SARS-CoV-2 infection when including information on anti-SARS-CoV-2 antibodies and previous SARS-CoV-2 infection Participants with complete data for all variables are included in analyses (2760 participants; 68 incident SARS-CoV-2 breakthrough infections). A C-index of 1·0 indicates perfect prediction of the order of failure; a C-index of 0·5 is achieved purely by chance. Immunological parameters were entered as log₂-transformed continuous terms. sVNT=surrogate SARS-CoV-2 virus neutralisation test. *Refers to a SARS-CoV-2 infection before study entry established by self-report.