Resistance-based interval exercise acutely improves endothelial function in type 2 diabetes
Bibliographic record
Abstract
Different modes of exercise, disease, and training status can modify endothelial shear stress and result in distinct effects on endothelial function. To date, no study has examined the influence of type 2 diabetes (T2D) and training status on the acute endothelial response to different modes of interval exercise (INT). We examined the effect of a single session of resistance- and cardio-based INT compared with a time-matched control on endothelial function in 12 age-matched T2D participants, 12 untrained, and 11 trained adults (aged 56 ± 7 yr). Flow-mediated dilation (%FMD) of the brachial artery was assessed at baseline and immediately, 1, and 2 h after an acute bout of cardio interval (C-INT), resistance interval (R-INT), and seated control (CTL); these interventions were randomized and separated by >2 days. C-INT involved seven 1-min cycling intervals at 85% of peak power with 1-min recovery between. R-INT involved the same pattern of seven 1-min intervals using leg resistance exercises. Endothelial function (%FMD) was improved after R-INT in all groups (Condition × Time interaction, P < 0.01), an effect that was most robust in T2D where %FMD was higher immediately (+4.0 ± 2.8%), 1 h (+2.5 ± 2.5%), and 2 h (+1.9 ± 1.9%) after R-INT compared with CTL (P < 0.01 for all). C-INT improved %FMD in T2D at 1-h postexercise (+1.6 ± 2.2%, P = 0.03) compared with CTL. In conclusion, R-INT acutely improves endothelial function throughout the 2-h postexercise period in T2D patients. The long-term impact of resistance exercise performed in an interval pattern is warranted.
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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) | 0.001 | 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 | 0.001 |
| 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".