Dietary Nitrate improves Cerebral Perfusion, in Young Adults during Exercise: Relationship to Cognitive Performance
Bibliographic record
Abstract
Dietary nitrate increases middle cerebral artery mean velocity (MCA Vmean) during aerobic exercise but it remains unclear if this is true with the simultaneous inclusion of a cognitively demanding task. Therefore, our aim was to investigate whether nitrate supplementation would influence exercise‐induced changes in MCA Vmean, and cognitive performance. In a double blind randomized crossover design, 12 healthy adults consumed two 70 ml doses of either beetroot juice or placebo two hours prior to exercise. Subjects then completed four 8‐min workloads of exercise set to elicit 30%, 50%, 70%, and 85% of heart rate reserve (HRR) on a cycle ergometer. Reaction time and accuracy was assessed during a modified version of the color Stroop task, with simple and complex (incongruent) response types, in the last 3 min of each workload. MCA Vmean (Transcranial Doppler) was monitored throughout. MCA Vmean displayed a typical relationship with exercise intensity, but was significantly elevated with beetroot, especially during the complex Stroop task, at 70% (p=0.001, d=0.47) and 85% HRR (p=0.027, d=0.74). Reaction time was not statistically different between conditions regardless of the difficulty level. However, beetroot may have contributed to increased accuracy in the complex task (p=0.059) whilst cycling. These preliminary results suggest that dietary nitrate elevates MCA Vmean during a combined exercise and cognitive challenge, which may be related to improved accuracy during the most demanding tasks.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| 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".