Short‐term exercise training reduces glycaemic variability and lowers circulating endothelial microparticles in overweight and obese women at elevated risk of type 2 diabetes
Bibliographic record
Abstract
Abstract Exercise is recognized as a frontline therapy for the prevention and treatment of type 2 diabetes (T2D) but the optimal type of exercise is not yet determined. We compared the effects of high‐intensity interval training (HIIT) with moderate‐intensity continuous training (MICT) for improvement of continuous glucose monitoring (CGM)‐derived markers of glycaemic variability, and biomarkers of endothelial cell damage (CD31+ and CD62+ endothelial microparticles (EMPs)) within a population at elevated risk of developing T2D. Fifteen inactive overweight or obese women were randomized to 2 weeks (10‐sessions) of progressive HIIT ( n = 8, 4–10X 1‐min @ ∼ 90% peak heart rate, 1‐min rest periods) or MICT ( n = 7, 20–50 min of continuous activity at ∼65% peak heart rate). Prior and three days post‐training, fasting blood samples were collected. Both HIIT and MICT improved glycaemic variability as measured by CGM standard deviation (HIIT: 0.82 ± 0.39 vs. 0.72 ± 0.33 mmol/L; MICT: 0.82 ± 0.19 vs. 0.62 ± 0.16 mmol/L, pre vs. post) and mean amplitude of glycaemic excursions (MAGE; HIIT: 1.98 ± 0.81 vs. 1.41 ± 0.90; MICT; 1.98 ± 0.43 vs. 1.65 ± 0.48, pre vs. post) with no difference between groups. CD62+ EMPs were lower following HIIT (187.7 ± 65 vs. 174.9 ± 55, pre vs. post) and MICT (170 ± 60 vs. 160.3 ± 59, pre vs. post) with no difference between groups. There was no change in 24‐h mean glucose or CD31+ EMPs. Two weeks of both HIIT or MICT similarly decreased glycaemic variability and CD62+ EMPs in overweight/obese women at elevated risk of T2D.
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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 | 0.004 | 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) | 0.000 | 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".