Treadmill Running Induces a Detrimental Metabolic Adaptation in the Diabetic Mouse
Bibliographic record
Abstract
Background: Regular exercise is generally recommended for the treatment of type 2 diabetes. Exercise reduces body weight, increases insulin sensitivity, and improves glycemic control. The present study was designed to determine the impact of voluntary wheel and forced treadmill running on the metabolic state in the db/db mouse, a model of type 2 diabetes. Our hypothesis is that exercise training improves the metabolic status such that a reduction in body weight, blood glucose and insulin are observed, resulting in improved insulin sensitivity. Methods: Male diabetic db/db mice were assigned to sedentary (DS), voluntary wheel running (DV), and treadmill running (DT) running groups for 12 weeks. Nondiabetic heterozygote littermates served as control (CN). Results: After 12 weeks of training, DV and DT mice ran a total of 4.24 ± 0.18 km and 11.8 km, respectively. Data are expressed as mean ± SEM for 10-12 mice in each group. * P < 0.05 vs CN, † P < 0.05 vs DS *** Table in Full Text PDF. *** Conclusions: Voluntary exercise training is beneficial in reducing body weight and blood glucose in the db/db mouse, but this effect is minor. Forced treadmill running, however, did not improve body weight, blood glucose and the hyperinsulinemic state remained. These results suggest that forced treadmill exercise training may actually worsen the metabolic state in this model of diabetes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".