Adenosine Monophosphate-Activated Protein Kinase Activation and Mammalian Target of Rapamycin Complex 1 Inhibition: A Mechanistic Rationale for Anti-Aging Therapy in Type 2 Diabetes
Bibliographic record
Abstract
Aging is a complicated biological process that induces a decline in the human organs' structure and function and elevates the risks of aging-related diseases such as Alzheimer's disease (AD) and type 2 diabetes. Type 2 diabetes accelerates all clinical manifestations of aging. Metabolic disorders in type 2 diabetes are unfavorably associated with all hallmarks of aging, such as inflammation and mitochondrial dysfunction. Adenosine monophosphate-activated protein kinase (AMPK) and the mammalian target of rapamycin complex 1 (mTORC1) are key players in cellular metabolism, and AMPK activation and mTORC1 inhibition improve all hallmarks of aging. AMPK activation and mTORC1 inhibition are favorably associated with diabetic complications. Nutritional interventions, such as caloric restriction, resveratrol, and astaxanthin, have AMPK-activating and mTORC1-inhibitory effects and improve metabolic abnormalities in type 2 diabetes. Anti-diabetic drugs, metformin, sodium-glucose cotransporter-2 inhibitors, and glucagon-like peptide 1 receptor agonists have been reported to have AMPK-activating and mTORC1-inhibiting effects and show prevention of aging-related diseases such as cardiovascular disease. The therapeutic interventions that activate AMPK and inhibit mTORC1 may be optimal treatments for type 2 diabetes from the perspective of anti-aging medicine. Furthermore, senolytics may be a promising, direct anti-aging therapeutic strategy specifically for type 2 diabetes and its complications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".