Markers of T-cell senescence and physical frailty: insights from Singapore Longitudinal Ageing Studies
Bibliographic record
Abstract
Abstract Background: Elderly individuals have an eroded immune system but whether immune senescence is implicated with the development of frailty is unknown. The underlying immune mechanisms and the link between markers of senescence and physical frailty is not well established. Methods: We explored the association of specific T-cell subset markers of immune differentiation and senescence on CD4+ and CD8+ cells (CD28−, CD27− and CD57+) and the immune risk profile (inverted CD4/CD8 ratio <1) with physical frailty among 421 participants who were frail (N=32), prefrail (N=187) and robust (N=202) in the Singapore Longitudinal Ageing Study cohort. Results: In ordinal logistic regression models relating tertile category rank scores of immune biomarker with frailty status (robust, prefrail and frail), CD8+CD28−CD27+ (odds ratio (OR)=1.35, P=0.013), CD4+CD28−CD27+ (OR=1.29, P=0.025), CD8+CD28− (OR=1.31, P=0.022), and CD4/CD8 ratio (OR=1.27, P=0.026) were positively associated with frailty, controlling for age, sex and multimorbidity. CD4/CD8 ratio less than one was not associated with frailty (OR=0.84, P=0.64). In stepwise multinomial logistic regression controlling for age, sex and comorbidity, only CD8+CD28−CD27+ was the independent predictor of prefrailty: highest tertile of the immune marker significantly predicted prefrailty (versus low tertile, OR=1.72, P=0.037) and frailty (OR=2.56, P=0.06). Conclusion: The study supports the hypothetical role of immune senescence in physical frailty, particularly in regard to the observed loss of CD28 expression from both CD8+ cells and CD4+ cells, but not for CD27 or CD4/CD8 ratio as a marker of senescence. The potential of CD8+CD28−CD27+ as a biological marker of frailty should be further investigated in prospective studies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".