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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 0.001 |
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 teacher head, 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".