Metabolaging: a new geroscience perspective linking aging pathologies and metabolic dysfunction
Bibliographic record
Abstract
With age, our metabolic systems undergo significant alterations, which can lead to a cascade of adverse effects that are implicated in both metabolic disorders, such as diabetes, and in the body's ability to respond to acute stress and trauma. To elucidate the metabolic imbalances arising from aging, we introduce the concept of "metabolaging." This framework encompasses the broad spectrum of metabolic disruptions associated with the hallmarks of aging, including the functional decline of key metabolically active organs, like the adipose tissue. By examining how these organs interact with essential nutrient-sensing pathways, "metabolaging" provides a more comprehensive view of the systemic metabolic imbalances that occur with age. This concept extends to understanding how age-related metabolic disturbances can influence the response to acute stressors, like burn injuries, highlighting the interplay between metabolic dysfunction and the ability to handle severe physiological challenges. Finally, we propose potential interventions that hold promise in mitigating the effects of metabolaging and its downstream consequences.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".