Skeletal muscle mechanisms contributing to improved glycemic control following intense interval exercise and training
Bibliographic record
Abstract
High-intensity and sprint interval training (HIIT and SIT, respectively) enhance insulin sensitivity and glycemic control in both healthy adults and those with cardiometabolic diseases. The beneficial effects of intense interval training on glycemic control include both improvements seen in the hours to days following a single session of HIIT/SIT and those which accrue with chronic training. Skeletal muscle is the largest site of insulin-stimulated glucose uptake and plays an integral role in the beneficial effects of exercise on glycemic control. Here we summarize the skeletal muscle responses that contribute to improved glycemic control during and following a single session of interval exercise and evaluate the relationship between skeletal muscle remodelling and improved insulin sensitivity following HIIT/SIT training interventions. Recent evidence suggests that targeting skeletal muscle mechanisms via nutritional interventions around exercise, particularly with carbohydrate manipulation, can enhance the acute glycemic benefits of HIIT. There is also some evidence of sex-based differences in the glycemic benefits of intense interval exercise, with blunted responses observed after training in females relative to males. Differences in skeletal muscle metabolism between males and females may contribute to sex differences in insulin sensitivity following HIIT/SIT, but well-controlled studies evaluating purported muscle mechanisms alongside measurement of insulin sensitivity are needed. Given the greater representation of males in muscle physiology literature, there is also a need for more research involving female-only cohorts to enhance our basic understanding of how intense interval training influences muscle insulin sensitivity in females across the lifespan.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".