Anti-Spike Antibodies Protect Against Covid-19 Infection in Immune-Mediated Inflammatory Diseases: Findings from the SUCCEED Study
Bibliographic record
Abstract
Objectives People with immune-mediated inflammatory diseases (IMIDs) may be more vulnerable to severe COVID-19 outcomes. COVID-19 vaccination is a key element in mitigating this risk. Anti-SARS-CoV-2 antibodies (Ab), including anti-spike (S) and anti-receptor binding domain (RBD) Ab, are metrics of seroconversion following COVID-19 vaccination in the general population. We assessed if anti-S and anti-RBD antibodies were negatively correlated with COVID-19 infection in IMID. Methods SUCCEED, a prospective Canada-wide study, was conducted in 2 phases. First, between Feb 2021-Jul 2023, adult IMID participants provided dried blood spot samples for anti-S and anti-RBD ELISA testing at intervals of 1, 3, 6, and 12 months following each COVID-19 vaccine dose. Second, between Sep 2022-Aug 2023, consenting participants from 4 academic centers in British Columbia, Ontario and Quebec (2) also provided monthly saliva samples for PCR detection of SARS-CoV-2. We studied subjects receiving at least their primary series (3+ doses) of a COVID-19 vaccine. Multivariable general estimating equation (GEE) models (accounting for repeated measures) evaluated PCR SARS-CoV-2 detection in saliva, assessing the effects of anti-S or anti-RBD levels (in separate models) within the 6 months preceding a given saliva sample. We controlled for recent COVID-19 infection, sex, age, medications (conventional immunosuppressives, biologics, and prednisone), and time since last COVID vaccine. Results 366 participants contributed 1,266 saliva samples. Participants were 79.8% female and 85.5% White, with median age 56.7 (standard deviation: 13.8) years. Most participants were taking immunosuppressants (N=252, 68.9%). The majority (N=356, 97.3%) of participants displayed seroconversion at the first saliva sample, defined as ≥11.3 Binding Antibody Units (BAU)/ml for anti-S or ≥31 BAU/ml for anti-RBD. In the GEE models of positive saliva PCR for SARS-CoV-2, (Table 1) a 1000 BAU/ml increase in anti-S was associated with an adjusted odds ratio (aOR) of 0.66 (95% confidence interval [CI] 0.45-0.97). Anti-RBD Ab levels had a similar effect (aOR 0.91, 95% CI 0.81-1.02). Table 1: Odds Ratios, OR (95% confidence intervals, CI) for Having a Positive COVID-19 Saliva Test in Univariable and Multivariable GEE Models Conclusion In this large, multi-center sample of COVID-19-vaccinated individuals with IMIDs, most of whom were immunosuppressed, we demonstrated that anti-S Ab levels were associated with lower odds of positive saliva PCR test for SARS-CoV-2, with a similar trend for anti-RBD Ab. This highlights clear benefits for vaccination against SARS-CoV-2 in IMID.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".