Insulin and aging – a disappointing relationship
Bibliographic record
Abstract
Experimental studies in animal models of aging such as nematodes, fruit flies or mice have observed that decreased levels of insulin or insulin signaling promotes longevity. In humans, hyperinsulinemia and concomitant insulin resistance are associated with an elevated risk of age-related diseases suggestive of a shortened healthspan. Age-related disorders include neurodegenerative diseases, hypertension, cardiovascular disease, and type 2 diabetes. High ambient insulin concentrations promote increased lipogenesis and fat storage, heightened protein synthesis and accumulation of non-functional polypeptides due to limited turnover capacity. Moreover, there is impaired autophagy activity, and less endothelial NO synthase activity. These changes are associated with mitochondrial dysfunction and oxidative stress. The cellular stress induced by anabolic activity of insulin initiates an adaptive response aiming at maintaining homeostasis, characterized by activation of the transcription factor Nrf2, of AMP activated kinase, and an unfolded protein response. This protective response is more potent in the long-lived human species than in short-lived models of aging research resulting in a stronger pro-aging impact of insulin in nematodes and fruit flies. In humans, resistance to insulin-induced cell stress decreases with age, because of an increase of insulin and insulin resistance levels but less Nrf2 activation. These detrimental changes might be contained by adopting a lifestyle that promotes low insulin/insulin resistance levels and enhances an adaptive response to cellular stress, as observed with dietary restriction or exercise.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".