Age and diet modulate the insulin-sensitizing effects of exercise: a tracer-based oral glucose tolerance test
Bibliographic record
Abstract
Diet modulates the development of insulin resistance during aging. This includes tissue-specific alterations in insulin signaling and mitochondrial function, which ultimately affect glucose homeostasis. Exercise stimulates glucose clearance, mitochondrial lipid oxidation and enhances insulin sensitivity. It is not well known how exercise interacts with age and diet in the development of insulin resistance. To investigate this, oral glucose tolerance tests (OGTT) with a tracer were conducted in mice ranging from 4 to 21 months of age, fed a low- (LFD) or high-fat diet (HFD), with or without life-long voluntary access to a running wheel (RW). We developed a computational model to derive glucose fluxes, which were commensurate with independent values from steady-state tracer infusions. Both insulin sensitivity indices derived for peripheral tissues and liver (IS-P and IS-L, respectively) were steeply decreased by aging and a HFD. This preceded the age-dependent decline in the mitochondrial capacity to oxidize lipids. In LFD young animals, RW access enhanced the IS-P concomitantly with the muscle β- oxidation capacity. Surprisingly, RW access completely prevented the age-dependent IS-L decrease, but only in LFD animals. This study indicates, therefore, that endurance exercise can improve the age-dependent decline in organ-specific IS mostly in the context of a healthy diet.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".