Circulating Angiogenic and Senescent T Lymphocytes in Ageing and Frailty
Bibliographic record
Abstract
BACKGROUND: There is a need to identify vascular and geroscience-relevant markers and mediators that can physiologically link ageing to vascular disease. There is evidence of specific T cell subsets, all influenced by age, that exert positive and negative effects on vascular health. CD31+, termed angiogenic T cells, have been linked to vascular repair whereas CD28null, termed senescent T cells, display pro-inflammatory and cytotoxic effector functions. OBJECTIVE: This study sought to determine the combined influence of increasing age and frailty status on these circulating CD31+ and CD28null T cell subsets. METHODS: This cross-sectional study recruited four different cohorts of men and women; young (20-30 years, n=22), older (65-75 years, n=17), robust non-frail (76+ years, n=17), and frail (76+ years, n=15) adults. Frailty was determined using the Fried Frailty method. T cell subsets were determined by whole blood flow cytometry based on the expression of CD3, CD4, CD8, CD31 and CD28. Cognitive impairment (CI) was measured via the Montreal Cognitive Assessment test. RESULTS: Whether expressed as circulating counts or as a % of total T cells, there was a progressive decrease (p<0.05) in CD31+ T cells with increasing age but paradoxically higher values (p<0.05) in the frail compared to the robust non-frail group. These changes were similar in the CD4+ and CD8+ fractions. CD28null T cells were considerably higher (p<0.05) in the frail compared to the robust non-frail group, including in the CD8+ (47% vs 29%, p<0.05) and CD4+ (4% vs 1%, p<0.05) fractions. CD28null T cell percentage was also higher (p<0.05) in those with moderate CI compared to mild CI and normal function. CONCLUSION: CD8+CD28null T cells are considerably elevated in frailty and with cognitive impairment and may serve as a useful target for intervention. Currently, the utility of CD31+ T cells as an ageing biomarker may be confined to healthy ageing cohorts.
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".