Association of Immune Cell Subsets With Longevity: The Cardiovascular Health Study
Bibliographic record
Abstract
BACKGROUND: Changes in the immune system are a potential biological mechanism of aging. We investigated the association of circulating immune cell subsets with age at death and survival to age 90. METHODS: Immune cell phenotypes were measured at baseline in 1 625 adults, aged 70-85 years, in the Cardiovascular Health Study. We selected 5 primary immune cell subsets: gamma-delta T-cells, natural killer cells, CD8+ T effector memory CD45RA expressing cells(TEMRA) cells, ratio of CD4+ to CD8+ cells, and ratio of naïve to memory CD8+ cells. We used linear regression and Poisson models, adjusting for demographics and clinical factors; and tested for effect modification by sex and race. In a secondary analysis, we investigated 23 additional immune cell subsets, using the Holm-Bonferroni method to adjust for multiple comparisons. RESULTS: No primary immune cell subsets were significantly associated with longevity. Two secondary subsets were significantly associated with age at death. Each SD higher proportion of CD4+CD57+ cells was associated with a 0.64-year earlier death (95% CI: -0.99, -0.30) and each SD higher proportion of CD4+CD28-CD57+ cells was associated with a 0.54-year earlier death (95% CI: -0.87, -0.21). Several subsets had significant interactions with sex and race in the fully adjusted model of age at death. A higher proportion of CD4+CD57+ T-cells was significantly associated with lower likelihood of survival to age 90 (relative risk: 0.79) and 1.07-year earlier age at death in males, but not in females. CONCLUSIONS: Our results suggest that CD4+CD57+ cells are associated with earlier death and this relationship was stronger in males than females.
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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 | 0.001 |
| 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".