Evidence of a limb- and shear stress stimulus profile-dependent impact of high-intensity cycling training on flow-mediated dilation
Bibliographic record
Abstract
Lower limb endurance training can improve conduit artery flow-mediated dilation (FMD) in response to transient increases in shear stress (reactive hyperemia; RH-FMD) in both the upper and lower limbs. Sustained increases in shear stress recruit a partially distinct transduction pathway and elicit a physiologically relevant FMD response (SS-FMD) that provides distinct information regarding endothelial function. However, the impact of training on SS-FMD is not well understood. The purpose of this study was to determine the impact of cycling training on handgrip exercise-induced brachial artery (BA) FMD (BA SS-FMD) and calf plantar-flexion-induced superficial femoral artery (SFA) FMD (SFA SS-FMD). RH-FMD was also assessed in both arteries. Twenty-eight young males were randomized to control (n = 12) or training (n = 16) groups. The training group cycled 30 min/day, 3 days/week for 4 weeks at 80% heart rate reserve. FMD was assessed in the BA and SFA before and after the intervention via Duplex ultrasound. Results are means ± SD. Training did not impact SS-FMD in either artery, and SFA RH-FMD was also unchanged (p > 0.05). When controlling for the shear rate stimulus via covariate analysis, BA RH-FMD improved in the training group (p = 0.05) (control – pre-intervention: 5.7% ± 2.4%, post-intervention: 5.3% ± 2.4%; training – pre-intervention: 5.4% ± 2.5%, post-intervention: 7.2% ± 2.4%). Thus, endurance training resulted in nonuniform adaptations to endothelial function, with an isolated impact on the BA’s ability to transduce a transient increase in shear stress. Novelty Training did not alter SS-FMD in the arm or leg. RH-FMD was augmented in the arm only. Thus training adaptations were limb- and shear stress profile-specific.
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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) | 0.000 | 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) | 0.003 | 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".