Predictors of the antibody response to influenza vaccination in older adults with type 2 diabetes
Bibliographic record
Abstract
OBJECTIVE: Type 2 diabetes mellitus (T2DM) is one of the most prevalent chronic inflammatory diseases of the elderly. Its development is related to the alteration of the immune system with aging characterized by immunosenescence and inflamm-aging. In turn, T2DM also alters the immune response. As a consequence, older people with T2DM are more susceptible to influenza and to its complications as compared with healthy controls. Vaccination against influenza has shown poor efficacy in the older population and even less efficacy in patients with diabetes. We studied here the antibody response to vaccination in healthy and diabetic elderly participants. RESEARCH DESIGN AND METHODS: In 2 groups of elderly participants (healthy N=119 and T2DM N=102), we measured the immunogenicity of influenza vaccine by hemagglutination inhibition assays. We assessed several blood and functional parameters as potential predictors of the vaccine efficacy. RESULTS: We found no difference between antibody responses in diabetic elderly compared with healthy elderly. Among the biological and functional determinants, the cytomegalovirus (CMV) serostatus played a more prominent role in determining the magnitude of response. We concluded that in addition to age and diabetic status, immunological history such as CMV status should be taken into account. None of the other biological or functional parameters studied could be reliably linked to the vaccine antibody response in older adults who are not frail including those with well-controlled diabetes. CONCLUSIONS: Our data strongly suggest that influenza vaccine should be administered to elderly patients with T2DM; however, the immune determinants of the antibody response to influenza vaccination should be further investigated.
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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 | 0.002 |
| 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".