Greater organ involution in highly proliferative tissues associated with the early onset and acceleration of ageing in humans
Bibliographic record
Abstract
Domination of cell proliferation over cell death is a driving force for carcinogenesis, whereas reduced cell proliferation and increased cell death are characteristic of ageing. We employed published data to estimate representative mean values of cell turnover times for 31 different organs and tissues in adult humans and animals (when data in humans were lacking) as well as functional mass loss for 5 organs, accounting for actual mass loss and tissue conversion to fat, in humans over the adult period, age 25 to 70. We found that greater actual and functional mass loss was significantly associated (P=0.001 and P<0.0001, respectively) with the log of shorter cell turnover times. We propose that this is characteristic of stem cell exhaustion and replicative senescence. In addition, we provide quantitative evidence that, in many organs, involution is evident even in young adults. On the basis of published mass measurements of major organs, by analysis of covariance, we identified examples of significant (P≤0.05), accelerated actual or functional mass loss and ageing from early to late adulthood. We hypothesise and quantitatively demonstrate that functional mass loss accelerates with ageing by incorporating the contribution of actual mass loss, tissue conversion to fatty or fibrous tissue, and the presence of apoptotic, necrotic and senescent cells. We propose that mass loss, linked to replicative senescence, is an evolutionary adaptation that effectively limits cancer in young adults, as mass loss is first apparent soon after the end of the growth period, accelerating in the more elderly as biological conditions deviate away from those prevailing in youth, when the selective pressure on pleiotropic genes is greatest.
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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 | 0.001 | 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 | 0.000 |
| 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".