High-Intensity Interval Training Improves Markers of Oxidative Metabolism in Skeletal Muscle of Individuals With Obesity and Insulin Resistance
Bibliographic record
Abstract
Background: The excess body fat characteristic of obesity is related to various metabolic alterations, which includes insulin resistance. Among the non-pharmacological measures used to improve insulin sensitivity are aerobic physical training, such as high intensity interval training (HIIT). This study investigated the effects of 8 weeks of HIIT on blood and skeletal muscle markers related to insulin resistance (IR) and oxidative metabolism in physically inactive individuals with obesity and compared the changes between insulin resistant and non-insulin resistant phenotypes. Methods: Nine non-insulin (OB) and eight insulin-resistant (OBR) volunteers with obesity completed 8 weeks of HIIT in a cycle ergometer. Venous blood and vastus lateralis muscle samples were obtained before and after the HIIT. Body composition and peak oxygen consumption (VO2peak) were estimated before and after HIIT. Results: HIIT reduced IR assessed by the homeostatic model assessment of insulin resistance (HOMA-IR) in OBR (4.4 ± 1.4 versus 4.1 ± 2.2 (mmol) (μU)/l2), but not in OB (HOMA-IR 1.8 ± 0.5 versus 2.3 ± 1.0 (mmol) (μU)/l2) volunteers. HIIT increased VO2peak with no change in body fat in both groups. In skeletal muscle, HIIT increased the phosphorylation of IRS (Tyr612), Akt (Ser473), and increased protein content of β-HAD and COX-IV in both groups. There was a reduction in ERK1/2 phosphorylation in OBR after HIIT. Conclusion: Eight weeks of HIIT improved markers of the insulin signaling pathway and increased the content of proteins related to oxidative metabolism in skeletal muscle of individuals with obesity, independent of changes in insulin resistance or total body fat.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".