Light-intensity and high-intensity interval training improve cardiometabolic health in rats
Bibliographic record
Abstract
Physical activity has the potential to reduce cardiometabolic risk factors but evaluation of different intensities of physical activity and the mechanisms behind their health effects still need to be fully established. This study examined the effects of sedentary behaviour, light-intensity training, and high-intensity interval training on biometric indices, glucose and lipid metabolism, inflammatory and oxidative stress markers, and vascular and cardiac function in adult rats. Rats (12 weeks old) were randomly assigned to 1 of 4 groups: control (CTL; no exercise), sedentary (SED; no exercise and housed in small cages to reduce activity), light-intensity trained (LIT; four 30-min exercise bouts/day at 8 m/min separated by 2-h rest period, 5 days/week), and high-intensity interval trained (HIIT, four 2.5-min work bouts/day at 50 m/min separated by 3-min rest periods, 5 days/week). After 12 weeks of intervention, SED had greater visceral fat accumulation (p < 0.01) and slower cardiac conduction (p = 0.04) compared with the CTL group. LIT and HIIT demonstrated beneficial changes in body weight, visceral and epididymal fat weight, glucose regulation, low-density lipoprotein cholesterol, total cholesterol, and mesenteric vessel contractile response compared with the CTL group (p < 0.05). LIT had significant improvements in insulin sensitivity and cardiac conduction compared with the CTL and SED groups whilst HIIT had significant improvements in systolic blood pressure and endothelium-independent vasodilation to aorta and mesenteric artery compared with the CTL group (p < 0.05). LIT and HIIT induce health benefits by improving traditional cardiometabolic risk factors. LIT improves cardiac health while HIIT promotes improvements in vascular health.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".