1165. Longitudinal Evaluation of SARS-CoV-2 Antibody Response Using Dried Blood Spot Samples Following Vaccination with Three and Four Doses of mRNA-1273, BNT162b2 and/or ChAdOx1-S in Adults Aged 50 and Above: Interim Analysis from the PREVENT-COVID Study
Bibliographic record
Abstract
Abstract Background Multiple combinations of COVID-19 vaccine regimens have been used in Canada throughout the SARS-CoV-2 immunization campaign. Studies evaluating the humoral immune response following COVID-19 vaccination in community dwelling older adults remain limited. This study assessed COVID-19 vaccine elicited antibody responses in older adult populations, alongside factors that influence antibody responses. Methods Community dwelling adults aged 50 to 87 years (mean=65) were enrolled (n=612). Detection of index SARS-CoV-2 anti-spike IgG (anti-S-IgG) concentration and surrogate neutralization were performed on dried blood spot samples via two multiplex assays (Meso Scale Diagnostics). Anti-S-IgG concentration and surrogate neutralization were quantified following mRNA (mRNA-1273 [m-1273], BNT162b2 [BNT]) or viral vector (ChAdOx1-S [ChAd]) vaccination. Vaccine groups were compared using one-way ANOVA and Tukey-Kramer multiple comparisons tests. Multivariable regression analyses evaluated influences of demographic and clinical factors on humoral immune responses. Results Three doses of m-1273 resulted in significantly higher anti-S-IgG compared with three BNT doses at four months (geometric mean concentration; 10167 AU/mL vs. 5412 AU/mL, P=0.009) post dose three. Three dose mixed vaccination with ChAd, m-1273 and BNT resulted in comparable anti-S-IgG concentration to three dose m-1273 at four months post dose three. Three doses of either m-1273 or mixed mRNA containing vaccines was associated with significantly higher surrogate neutralization compared with three BNT doses at four months (46% & 43% vs. 34%, P=0.002) post dose three. No significant difference in anti-S-IgG concentration was observed in four dose vaccination regimens. SARS-CoV-2 infection, health status of excellent or very good, and m-1273 containing vaccine regimens positively influenced the antibody response. Conclusion Immunization schedules including a minimum of one m-1273 dose elicited the strongest and most durable antibody responses compared with BNT only containing regimens. There is no established correlate of protection for COVID-19, and as such this data should be interpreted alongside vaccine effectiveness studies. Omicron and XBB specific antibody responses will be compared. Disclosures Sofia R. Bartlett, PhD, Abbvie: Advisor/Consultant|Abbvie: Grant/Research Support|Cepheid: Advisor/Consultant|Gilead: Advisor/Consultant|Gilead: Grant/Research Support Theodore Steiner, MD, FRCPC, Edesa: Grant/Research Support|Ferring: Advisor/Consultant|Ferring: Grant/Research Support|Qu Biologics: Advisor/Consultant|Qu Biologics: Stocks/Bonds|Seres: Grant/Research Support Manish Sadarangani, BM BCh, FRCPC, DPhil, GlaxoSmithKline: Grant/Research Support|Merck: Grant/Research Support|Moderna: Grant/Research Support|Pfizer: Grant/Research Support|Sanofi Pasteur: Grant/Research Support|Seqirus: Grant/Research Support|Symvivo: Grant/Research Support|VBI Vaccines: Grant/Research Support
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".