Effects of COVID-19 mRNA vaccination on HIV viremia and reservoir size
Bibliographic record
Abstract
OBJECTIVE: The immunogenic nature of coronavirus disease 2019 (COVID-19) mRNA vaccines led to some initial concern that these could stimulate the HIV reservoir. We analyzed changes in plasma HIV loads (pVL) and reservoir size following COVID-19 mRNA vaccination in 62 people with HIV (PWH) receiving antiretroviral therapy (ART), and analyzed province-wide trends in pVL before and after the mass vaccination campaign. DESIGN: Longitudinal observational cohort and province-wide analysis. METHODS: Sixty-two participants were sampled prevaccination, and one month after their first and second COVID-19 immunizations. Vaccine-induced anti-SARS-CoV-2-Spike antibodies in serum were measured using the Roche Elecsys Anti-S assay. HIV reservoirs were quantified using the intact proviral DNA assay; pVL were measured using the cobas 6800 (lower limit of quantification: 20 copies/ml). The province-wide analysis included all 290 401 pVL performed in British Columbia, Canada between 2012 and 2022. RESULTS: Prevaccination, the median intact reservoir size was 77 [interquartile range (IQR): 20-204] HIV copies/million CD4 + T-cells, compared to 74 (IQR: 27-212) and 65 (IQR: 22-174) postfirst and -second dose, respectively (all comparisons P > 0.07). Prevaccination, 82% of participants had pVL <20 copies/ml (max: 110 copies/ml), compared to 79% postfirst dose (max: 183 copies/ml) and 85% postsecond dose (max: 79 copies/ml) ( P > 0.4). There was no evidence that the magnitude of the vaccine-elicited anti-SARS-CoV-2-Spike immune response influenced pVL nor changes in reservoir size ( P > 0.6). We found no evidence linking the COVID-19 mass vaccination campaign to population-level increases in detectable pVL frequency among all PWH in the province, nor among those who maintained pVL suppression on ART. CONCLUSION: We found no evidence that COVID-19 mRNA vaccines induced changes in HIV reservoir size nor plasma viremia.
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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 | 0.000 | 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) | 0.001 | 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".