Functional rather than immunoreactive levels of IgG<sub>4</sub> correlate closely with clinical response to grass pollen immunotherapy
Bibliographic record
Abstract
BACKGROUND: Induction of allergen-specific IgG(4) antibodies is the most consistent immunological finding in immunotherapy trials. However, quantitative assessments of IgG(4) antibodies have not proven beneficial in evaluating clinical changes during or after immunotherapy. In the current study, we investigated the relationship between clinical outcome and allergen-specific IgG(4) titres or functional antibody responses following immunotherapy. We hypothesized that functional assays of serum IgG-associated inhibitory activity such as inhibition of IgE-allergen interactions (IgE-blocking factor) and inhibition of CD23-dependent IgE-facilitated allergen binding (IgE-FAB) correlate more closely with clinical outcome and may be biomarkers of clinical response. METHODS: In an 8-month dose-response randomized double-blind placebo-controlled study, 221 polysensitized subjects with severe seasonal rhinitis received Alutard SQ, Phleum pratense 100,000 SQ-U, 10,000 SQ-U or placebo injections. Serum specimens were collected before treatment, after up-dosing, during the peak season and at the end of the study. Allergen-specific IgG(4) titres and IgG-associated inhibitory activity were evaluated. RESULTS: A time- and dose-dependent increase in serum inhibitory activity for both the IgE-blocking factor and IgE-FAB was observed, which paralleled increases in grass pollen-specific IgG(4) antibodies. A modest but significant inverse relationship was demonstrated between postimmunotherapy serum inhibitory activity and combined symptom-rescue medication scores (IgE-FAB: r = -0.25, P = 0.0002; IgE-blocking factor: r = -0.28, P < 0.0001), whereas this was not observed for immunoreactive IgG(4) levels (r = -0.11, P = 0.12). CONCLUSIONS: Functional assays of inhibitory IgG(4) and IgE-blocking factor may be more useful surrogates of clinical response than IgG(4). Whether these antibody effects may serve as predictive biomarkers of clinical efficacy in individual patients requires further investigation.
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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 | 0.001 |
| 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".