High-intensity Interval Training Increases Muscle GLUT4 and Mitochondrial Capacity in Prediabetes and Type 2 Diabetes
Bibliographic record
Abstract
Traditional endurance exercise training (END) is an effective strategy to prevent and treat type 2 diabetes (T2D). Recent evidence indicates that high-intensity interval training (HIT) induces many adaptations associated with END despite a reduced exercise volume and time commitment. However, the effect of HIT in persons with, and at risk for, T2D has not been studied. PURPOSE: To examine the effects of low-volume HIT on skeletal muscle metabolic adaptations and blood glucose concentration in persons with T2D and prediabetes. METHODS: Four patients with T2D and three prediabetics (fasting blood glucose >6.1 mmol/L) performed six sessions of HIT over 2 wk. Each session consisted of 10 × 1-min intervals at an intensity of ∼90% maximal heart rate, with 1 min recovery between intervals (upright or recumbent cycling). Skeletal muscle biopsies were obtained before and after training to assess GLUT4 content and markers of mitochondrial capacity. Changes in blood glucose control were assessed using 24-hr continuous glucose monitoring (CGM). RESULTS: Training increased total GLUT4 protein content by ∼230% (p<0.05). An increase in mitochondrial capacity following training was demonstrated by increases in cytochrome c oxidase protein content (∼50%; subunit IV) and maximal activity (∼30%) (both p<0.05). Mean 24 h blood glucose concentration was reduced after training [6.0±1.0 vs 6.7±1.2 mmol/L; p<0.05]. CONCLUSIONS: Two weeks of low-volume HIT is effective for increasing GLUT4 content, inducing mitochondrial biogenesis and reducing 24-hr mean blood glucose concentration in individuals with T2D and prediabetes. These findings provide preliminary evidence that low-volume HIT may be an alternative exercise strategy with potential health benefits for individuals with T2D and prediabetes. Supported by the Canadian Diabetes Association.
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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".