Patient subtyping analysis of baseline multi-omic data reveals distinct pre-immune states associated with antibody response to seasonal influenza vaccination
Bibliographic record
Abstract
Understanding the molecular mechanisms underpinning diverse vaccination responses is critical for developing efficient vaccines. Molecular subtyping can offer insights into heterogeneous nature of responses and aid in vaccine design. We analyzed multi-omic data from 62 haemagglutinin seasonal influenza vaccine recipients (2019-2020), including transcriptomics, proteomics, glycomics, and metabolomics data collected pre-vaccination. We performed a subtyping analysis on the integrated data revealing five subtypes with distinct molecular signatures. These subtypes differed in the expression of pre-existing adaptive or innate immunity signatures, which were linked to significant variation in baseline immunoglobulin A (IgA) and hemagglutination inhibition (HAI) titer levels. It is worth noting that these differences persisted through day 28 post-vaccination, indicating the effect of initial immune state on vaccination response. These findings highlight the significance of interpersonal variation in baseline immune status as a crucial factor in determining the effectiveness of seasonal vaccines. Ultimately, incorporating molecular profiling could enable personalized vaccine optimization.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".