Modeling of antibody responses to COVID-19 vaccination in patients with rheumatoid arthritis | Scientific Reports – Nature.com

Study design

The purpose of the study was to examine the antibody response in RA patients vaccinated against COVID-19 and to identify clinical factors affecting the antibody response in a real-world setting. In this study, the type of vaccination and the intervals between vaccinations were heterogeneous among the patients; this is because COVID-19 vaccinations were administered as part of routine clinical practice. The primary analysis involved measurement of antibody titers from RA patients that received the BNT162b2, mRNA-1273, ChAdOx1, or Ad26.COV2.S vaccines. All vaccines were monovalent as no bivalent vaccines were available at the time. A patient was censored if he/she was infected with COVID-19 during follow-up. Antibody response curves were constructed after the 1st, 2nd, 3rd, and 4th vaccinations; these were based on the anti-RBD antibody titers measured after each vaccination. To identify factors that contribute to a peak response, the antibody response for each patient was modeled based on their individual clinical factors.

The study was carried out in accordance with the Declaration of Helsinki, and was approved by the Institutional Review Boards of Seoul National University Hospital (IRB No. 2205-060-1322).

South Korea experienced two major peaks of COVID-19 infection: Feb 2022 and Sep 2022. Two groups of RA patients attending Seoul National University Hospital (SNUH), a nationwide tertiary referral center in South Korea, were enrolled before the COVID-19 peaks occurred. Group 1 comprised RA patients enrolled in an influenza vaccination study between October 6 and November 3, 2021 (IRB No. 2109-020-1252). The original study was a randomized controlled trial to evaluate the vaccination response by comparing a 1-week versus 2-week temporary discontinuation of MTX after influenza vaccination. According to the enrollment criteria, all RA patients in Group 1 had taken stable dose of methotrexate over the preceding 6weeks of influenza vaccination. Serial serum samples were obtained at 0, 4, and 16weeks after the influenza vaccination. Group 2 comprised patients who participated in the SNUH RA cohort study between January 1 and June 3, 2022 (IRB No. 2105-085-1219). The study was a cohort study to monitor disease activity and treatment response. For these patients, sera were obtained once at the start of participation. The samples analyzed in the study were all archived samples, not additional blood draws. Informed consent was obtained from all participants to use their samples for further study at the time of the enrollment.

Among the enrolled patients, only those with an available vaccination history and who were nave to COVID-19 infection were included in data analysis. The exclusion criteria were as follows: (1) self-reported or a Korea Disease Control and Prevention Agency (KDCA) record of COVID-19 infection before sampling; (2) positive for anti-nucleocapsid (anti-N) antibodies; (3) did not receive a COVID-19 vaccination before sampling.

Patient demographics, comorbidities, and concurrent immunosuppressive medications were obtained from electronic medical records. Comorbidities included diabetes mellitus, hypertension, chronic liver disease, chronic kidney disease, and history of tuberculosis. Concomitant medications were defined as those prescribed within 3months of blood sampling. These included glucocorticoids (GCs), methotrexate (MTX), hydroxychloroquine, sulfasalazine (SSZ), leflunomide, tacrolimus, tumor necrosis factor alpha inhibitors (TNFi), tocilizumab, abatacept (ABA), Janus kinase inhibitors (JAKi), and rituximab (RTX).

Since February, 2021 in South Korea, COVID-19 vaccinations have been mandatory in accordance with the national guidelines. The type of vaccine and intervals between vaccinations were decided by the government. The first approved vaccines were BNT162b2, mRNA-1273, ChAdOx1, and Ad26.COV2.S. With the exception of Ad26.COV2.S, all primary vaccinations required a follow-up 2nd dose after 312weeks. Cross-vaccination was allowed. In December 2021, the 3rd dose of vaccine was administered (i.e., an interval of 23months after the previous vaccination). In April 2022, a 4th dose was recommended (i.e., an interval of 4months from the previous vaccination). The vaccination history of each patient was listed by the KDCA. Any PCR-proved COVID-19 infection should be reported to the KDCA through the regional infection center or a local clinic.

Information on the dose, date and the type of COVID-19 vaccination, was obtained from the patients and cross-checked with the data from KDCA. Previous COVID-19 infection was reported by the patients, and confirmed by an infection certificate from KDCA and a positive anti-nucleocapsid (anti-N) antibody test. When the patient tested positive in the self-antigen test but did not undergo a formal diagnostic test for any reason, the patient was considered as a positive infection case and excluded.

The titer of IgG antibodies specific for the SARS-CoV-2 receptor binding domain of spike 1 protein (anti-RBD) was measured in stored serum samples using a chemiluminescence microparticle immunoassay (Abbott, USA). The anti-RBD ranged from 21 to 40,000AU/mL. A value<21 or>40,000AU/mL was documented as 20 or 44,000AU/mL, respectively. Anti-RBD antibodies represent the humoral response to COVID-19 vaccination10.

In addition, the titer of anti-N antibodies was measured using an electrochemiluminescence immunoassay (Roche, Germany). Anti-N antibody titers above the cut-off value of 1.00AU/mL denoted a previous natural COVID-19 infection11.

Since the two studies (influenza vaccination study and SNUH RA cohort study) used in this model were not specifically designed for modeling of antibody response to COVID-19 vaccination, vaccination and sampling schedules were heterogenous among individuals. Therefore, we performed several sensitivity analyses to reconfirm our results.

First, we estimated the change of the log anti-RBD titer over time for the subjects who received the same type of vaccination. Second, the group 1 (influenza vaccination study) and group 2 (SNUH RA cohort study) were separately analyzed to investigate the clinical factors affecting anti-RBD titer and to reveal time-course of anti-RBD antibodies following vaccination.

The characteristics of the subjects were expressed as mean (standard deviation) for continuous variables and numbers (percentage) for categorical variables. The anti-RBD titer was log-transformed to improve normality. The second-degree fractional polynomials which covers wide range of curve shapes were applied since the pattern of change in the log anti-RBD titer over time is unknown and may not be linear12. The change of the log anti-RBD titer over time was determined in the fractional polynomial regression while adjusting the vaccination dose. Robustness of the curve was confirmed with adjustment of clinical factors affecting log anti-RBD titer: age, ABA use JAKi use, SSZ use, and the vaccination dose. Then clinical variables related to humoral responses to COVID-19 vaccination were determined. A regression model using a generalized estimating equation was applied to account for the correlation among anti-RBD titers among the subjects. Since only 22% subjects (120 out of 550) have two or three antibody responses, we chose the population average model, instead of a subject specific estimate. The exchangeable variance structure was applied because the interval between repeated measurements were various from subject to subject, and only 20% of the subjects had three anti-RBD titer. However, the mixed effect model was used to estimate the change of the log anti-RBD for Group1 subjects (from whom serial samples were obtained). The time and vaccination dose were fixed effects and the subject was a random effect. The linear assumption of continuous variables was checked using scatter plot and locally weighted scatterplot smoothing with clinical knowledge of relationship with log anti-RBD titer. The significant variables at 0.1 significance levels in the univariable analysis were considered for the multivariable model. The forward variable selection method was used to detect significant clinical variables affecting the log anti-RBD titer. The all two-way interaction terms were tested in the multivariable model one by one at 0.01 level of significance. The goodness of fit for the multivariable model was measured using R-square.

Statistical analysis was performed using R (version 4.3.1; R Foundation for Statistical Computing) and SAS software (version 9.4; SAS Institute).

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Modeling of antibody responses to COVID-19 vaccination in patients with rheumatoid arthritis | Scientific Reports - Nature.com