Class switching toward IgG4 six months after primary mRNA-based COVID-19 vaccination in kidney patients
Bibliographic record
Abstract
Abstract Background Class switching toward spike (S)-binding IgG4 antibodies after mRNA-based COVID-19 vaccination has been observed, an antibody subclass with strong neutralizing but limited effector activity. While this has been reported in healthy individuals, subclass dynamics in immunocompromised kidney patients are unclear. We assessed IgG subclass patterns and S-specific B-cell phenotypes up to 6 months after a two-dose mRNA-1273 vaccination schedule in kidney transplant recipients (KTRs), dialysis patients, and patients with chronic kidney disease (CKD). Methods In this exploratory study, KTRs (n=11), dialysis patients (n=5), CKD stage G4–5 patients (eGFR <30 ml/min/1.73m2, n=5), and controls without known kidney disease (eGFR >45 ml/min/1.73m2, n=8) received two mRNA-1273 doses 28 days apart. Blood was collected pre-vaccination (V1), and at 28 days (V3) and 6 months (V4) after the second dose. S1-specific IgG antibodies were measured by a validated fluorescent bead-based multiplex-immunoassay, and participants seronegative at V1 and seropositive at V3 were included. B cells were phenotyped by flow cytometry. Results Five of 11 KTRs had no detectable S-binding B cells, whereas all other groups mounted responses. Across responders, the frequency of S-binding B cells increased from V1 (median 0.08%) to 0.49% at V3 and to 0.84% at V4 (both p<0.0001). S-binding B cells mainly comprised IgG+ plasmablasts. The IgG4:IgG1 log-ratio increased significantly from V3 to V4 (p<0.001), indicating a relative shift toward IgG4; absolute frequencies were comparable across the groups. Conclusions Approximately half of KTRs lacked detectable S-binding B cells after two mRNA-1273 doses, despite antibody formation. Among responders, S-binding B cells persisted up to 6 months after vaccination with a relative shift toward IgG4, a pattern also observed in dialysis patients, CKD patients and controls. The clinical significance of this subclass skewing requires confirmation in larger cohorts with functional antibody readouts.
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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".