Markers of systemic inflammation are positively associated with influenza vaccine antibody responses with a possible role for ILT2(+)CD57(+) NK-cells
Bibliographic record
Abstract
BACKGROUND: With increasing age, overall health declines while systemic levels of inflammatory mediators tend to increase. Although the underlying mechanisms are poorly understood, there is a wealth of data suggesting that this so-called "inflammaging" contributes to the risk of adverse outcomes in older adults. We sought to determine whether markers of systemic inflammation were associated with antibody responses to the seasonal influenza vaccine. RESULTS: Over four seasons, hemagglutination inhibition antibody titres and ex vivo bulk peripheral blood mononuclear cell (PBMC) responses to live influenza viruses assessed via interferon (IFN)-γ/interleukin (IL)-10 production, were measured pre- and 4-weeks post-vaccination in young adults (n = 79) and older adults randomized to standard- or high-dose inactivated vaccine (n = 612). Circulating tumour necrosis factor (TNF), interleukin (IL)-6 and C-reactive protein (CRP) were also measured pre-vaccination. Post-vaccination antibody titres were significantly associated with systemic inflammatory levels; specifically, IL-6 was positively associated with A/H3N2 titres in young adults (Cohen's d = 0.36), and in older high-dose, but not standard-dose recipients, all systemic inflammatory mediators were positively associated with A/H1N1, A/H3N2 and B titres (d = 0.10-0.45). We further show that the frequency of ILT2(+)CD57(+) CD56-Dim natural killer (NK)-cells was positively associated with both plasma IL-6 and post-vaccination A/H3N2 titres in a follow-up cohort of older high-dose recipients (n = 63). Pathway analysis suggested that ILT2(+)CD57(+) Dim NK-cells mediated 40% of the association between IL-6 and A/H3N2 titres, which may be related to underlying participant frailty. CONCLUSIONS: In summary, our data suggest a complex relationship amongst influenza vaccine responses, systemic inflammation and NK-cell phenotype in older adults, which depends heavily on age, vaccine dose and possibly overall health status. While our results suggest that "inflammaging" may increase vaccine immunogenicity in older adults, it is yet to be determined whether this enhancement contributes to improved protection against influenza disease.
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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) | 0.005 | 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".