Age-associated telomere attrition of lymphocytes<i>in vivo</i>is co-ordinated with changes in telomerase activity, composition of lymphocyte subsets and health conditions
Bibliographic record
Abstract
Telomeres are essential in maintaining chromosome integrity and in controlling cellular replication. Attrition of telomere length in peripheral blood mononuclear cells (PBMCs) with age is well documented from cross-sectional studies. But the actual in vivo changes in telomere lengths and its relationship with the contributing factors within the individuals with age have not been fully addressed. In the present paper, we report a longitudinal analysis of telomere length in the PBMCs, lymphocytes and monocytes of 216 human subjects aged from 20-90 years assessed at 0-, 5- and 12-year follow-up. For the 5- and 12-year follow-up, telomere length in the PBMCs decreased in 34% and 46%, exhibited no detectable change in 56% and 47% and increased in 10% and 7% of the subjects respectively. The rate of telomere change was distinct for T-cells, B-cells and monocytes for any given subject. Telomerase activity declined with age in the resting T-cells and B-cells and the activated T-cells. Finally, a significant portion of telomere attrition in T-cells with age was explained by a decline in the telomerase activity, decreased naïve cells and the change in physiological conditions such as elevated blood glucose and interleukin (IL)-6 levels. These findings show that changes in the telomere length of the PBMCs with age in vivo occur at different rates in different individuals and cell types and reveal that changes in the telomere length in the T-cells with age is influenced by the telomerase activity, naïve T-cell percentage and changes in health conditions.
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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 | 0.000 |
| 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) | 0.001 | 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".