The Role of Selection for Function in Aging and Chronic Diseases: A Novel Evolutionary Perspective
Bibliographic record
Abstract
Aging, and by extension age-related diseases, has traditionally been understood through classical evolutionary genetic models, such as the mutation accumulation and antagonistic pleiotropy theories. However, these frameworks primarily focus on the declining efficacy of organismal-level selection against mutations with deleterious effects in late life. Here, we propose a novel hypothesis: many chronic diseases associated with aging may emerge, at least in part, as a result of selection acting at lower organizational levels, including non-replicative biological entities, enabled by the relaxation of selective pressures that constrained within-organism evolutionary processes in early life. This hypothesis is built on the recently proposed concept of selection for function that extends the evolutionary process to non-replicative entities. While Darwinian selection acting at the organismal level strongly constrains within-organism evolution during an organism's reproductive lifespan, these constraints weaken with age. As a consequence, lower-level non-replicative entities, such as benign and malignant tumors, atherosclerotic plaques, and neurodegenerative aggregates, may experience a form of selection that favors those with increased stability, organization, and long-term persistence, sometimes at the cost to host fitness. These entities do not evolve via long-term differential reproduction, but rather certain configurations of their structure persist preferentially over others due to environmental constraints, microenvironmental selection, and internal stabilization mechanisms. Understanding aging through the lens of selection for function at the level of internal non-replicative entities provides new insights into the evolution of chronic diseases and opens novel therapeutic avenues aimed at disrupting internal functional organization, rather than merely targeting cellular proliferation/abnormalities or disease symptoms.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".