Is beetroot juice more effective than sodium nitrate? The effects of equimolar nitrate dosages of nitrate-rich beetroot juice and sodium nitrate on oxygen consumption during exercise
Bibliographic record
Abstract
Dietary nitrate has been reported to lower oxygen consumption in moderate- and severe-intensity exercise. To date, it is unproven that sodium nitrate (NaNO3(-); NIT) and nitrate-rich beetroot juice (BR) have the same effects on oxygen consumption, blood pressure, and plasma nitrate and nitrite concentrations or not. The aim of this study was to compare the effects of different dosages of NIT and BR on oxygen consumption in male athletes. Twelve healthy, well-trained men (median [minimum; maximum]; peak oxygen consumption: 59.4 mL·min(-1)·kg(-1) [40.5; 67.0]) performed 7 trials on different days, ingesting different nitrate dosages and placebo (PLC). Dosages were 3, 6, and 12 mmol nitrate as concentrated BR or NIT dissolved in plain water. Plasma nitrate and nitrite concentrations were measured before, 3 h after ingestion, and postexercise. Participants cycled for 5 min at moderate intensity and further 8 min at severe intensity. End-exercise oxygen consumption at moderate intensity was not significantly different between the 7 trials (p = 0.08). At severe-intensity exercise, end-exercise oxygen consumption was ~4% lower in the 6-mmol BR trial compared with the 6-mmol NIT (p = 0.003) trial as well as compared with PLC (p = 0.010). Plasma nitrite and nitrate concentrations were significantly increased after the ingestion of BR and NIT with the highest concentrations in the 12-mmol trials. Plasma nitrite concentration between NIT and BR did not significantly differ in the 6-mmol (p = 0.27) and in the 12-mmol (p = 0.75) trials. In conclusion, BR might reduce oxygen consumption to a greater extent compared with NIT.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".