Myocardial blood flow and adenosine A<sub>2A</sub>receptor density in endurance athletes and untrained men
Bibliographic record
Abstract
Previous human studies have shown divergent results concerning the effects of exercise training on myocardial blood flow (MBF) at rest or during adenosine-induced hyperaemia in humans. We studied whether these responses are related to alterations in adenosine A2A receptor (A2AR) density in the left-ventricular (LV) myocardium, size and work output of the athlete's heart, or to fitness level. MBF at baseline and during intravenous adenosine infusion, and A2AR density at baseline were measured using positron emission tomography, and by a novel A(2A)R tracer in 10 healthy male endurance athletes (ET) and 10 healthy untrained (UT) men. Structural LV parameters were measured with echocardiography. LV mass index was 71% higher in ET than UT (193 +/- 18 g m(-2) versus 114 +/- 13 g m(-2), respectively). MBF per gram of tissue was significantly lower in the ET than UT at baseline, but this was only partly explained by reduced LV work load since MBF corrected for LV work was higher in ET than UT, as well as total MBF. The MBF during adenosine-induced hyperaemia was reduced in ET compared to UT, and the fitter the athlete was, the lower was adenosine-induced MBF. A2AR density was not different between the groups and was not coupled to resting or adenosine-mediated MBF. The novel findings of the present study show that the adaptations in the heart of highly trained endurance athletes lead to relative myocardial 'overperfusion' at rest. On the other hand hyperaemic perfusion is reduced, but is not explained by A2AR density.
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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 | 0.001 |
| 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) | 0.002 | 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".