Individual differences in compensatory vasodilation impact exercise performance
Bibliographic record
Abstract
INTRODUCTION It is often stated that oxygen delivery (O 2 D) demand matching is tightly coupled during submaximal exercise. Traditional research approaches have ignored the potential for unique individual response heterogeneity in this model. Previously when we challenged exercising muscle O 2 D by having participants perform progressive exercise to peak with exercising forearm perfusion pressure reduced, we found individuals inherently differed in their vasodilatory response to an O 2 D challenge, with some having compensatory vasodilation while others did not. PURPOSE To test the hypothesis that both compensatory and non‐compensatory vasodilation phenotypes are evident in the face of a sudden compromise to exercising muscle O 2 D. Furthermore, that non‐compensators suffer greater impacts on exercise performance as a result. METHODS 19 healthy male participants (21.8 ± 2.0 yrs) each completed 3 rhythmic isometric forearm exercise protocols separated by 24 hours. Day 1: Participants completed progressive exercise to peak. The intensity associated with 70% peak forearm vascular conductance (FVC: ml/min/100mmHg) was identified. Day 2: Participants performed steady state exercise at the 70% peak FVC intensity. This ensured that the vasodilatory reserve available to respond to a sudden challenge to O 2 D was the same across participants. A perfusion pressure‐induced challenge to O 2 D, which decreases local pressure by ~30 mmHg, was then introduced during the exercise. Day 3: Peak vasodilatory capacity was assessed, as well as perfusion and vasodilatory kinetics during exercise with a perfusion pressure challenge. Forearm blood flow (FBF: ml/min), mean arterial blood pressure (MAP: mmHg); and O 2 D (ml/O 2 /min) were measured throughout. RESULTS Day 2: 11 participants responded with compensatory vasodilation when steady state O 2 D was challenged (FVC RELAX : 660 ± 134 vs. 530 ± 124 ml/min/100mmHg, P<0.001) while 8 participants yielded no compensatory response (FVC RELAX : 667 ± 167 vs. 663 ± 165 ml/min/100mmHg, P=0.8). Steady state FBF, O 2 D, and oxygen consumption (VO 2 ) were all compromised in the non‐compensators (P<0.05), while MAP remained similar between vasodilator response groups (P>0.08). As a result of such compromises, exercise tolerance in a perfusion pressure challenged position was reduced to a greater extent in the non‐compensators compared to an unchallenged position (−92 ± 73 vs. −11 ± 37 N, P=0.01). Day 1: There was no difference in exercise performance (230 ± 26 vs. 245 ± 27 N, P=0.2) nor the intensity associated with 70% peak FVC (168 ± 33 vs. 158 ± 23 N, P=0.4) between non‐compensators and compensators. Day 3: Peak vasodilatory capacity was not different between compensatory and non‐compensatory vasodilators (956 ± 236 vs. 920 ± 364 ml/min/100mmHg, P=0.8). There was no difference in the FBF and FVC kinetic responses to an absolute intensity with a perfusion pressure challenge (all P>0.05). CONCLUSIONS Vasodilatory response phenotypes exist which determine inter‐individual differences in O 2 D and impact exercise performance. A non‐compensation response is not explained by differences in vasodilatory capacity, peak exercise capacity or work rate at which 70% peak vasodilation response occurred. Support or Funding Information NSERC
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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 | 0.001 | 0.001 |
| 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) | 0.004 | 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".