Distinct age-specific SARS-CoV-2 IgG decay kinetics following natural infection
Bibliographic record
Abstract
Abstract Background Antibody responses to SARS-CoV-2 can be observed as early as 14 days post-infection, but little is known about the stability of antibody levels over time. Here we evaluate the long-term stability of anti-SARS-CoV-2 IgG antibodies following infection with SARS-CoV-2 in 402 adult donors. Methods We performed a multi-center study carried out at Plasma Donor Centers in the city of Heidelberg (Plasmazentrum Heidelberg, Germany) and Munich (Plasmazentrum München, Germany). We present anti-S/N and anti-N IgG antibody levels in prospective serum samples collected up to 403 days post recovery from SARS-CoV-2 infected individuals. Results The cohort includes 402 adult donors (185 female, 217 male; 17 - 68 years of age) where anti-SARS-CoV-2 IgG levels were measured in plasma samples collected between 18- and 403-days post SARS-CoV-2 infection. A linear mixed effects model demonstrated IgG decay rates that decrease over time (χ 2 =176.8, p<0.00001) and an interaction of time*age χ (χ 2 =10.0, p<0.005)), with those over 60+ years showing the highest baseline IgG levels and the fastest rate of IgG decay. Baseline viral neutralization assays demonstrated that serum IgG levels correlated with in vitro neutralization capacity in 91% of our cohort. Conclusion Long-term antibody levels and age-specific antibody decay rates suggest the potential need for age-specific vaccine booster guidelines to ensure long term vaccine protection against SARS-CoV-2 infection.
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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 | 0.001 | 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".