Effect of high nitrate vegetable juice supplementation on plasma nitrate and blood pressure in healthy adults: a pilot randomized crossover intervention in healthy volunteers
Bibliographic record
Abstract
Abstract Background: Beetroot juice has been demonstrated to decrease blood pressure due to the high inorganic nitrate content, but few studies have tested the effect of other high nitrate vegetable juices on blood pressure. Methods : This pilot randomized crossover trial aimed to investigate the effect of two different high nitrate vegetable juices on plasma nitrate concentrations and blood pressure in healthy adults. Eighteen healthy volunteers were randomized to receive 115 ml of beetroot juice or 250 ml of green leafy vegetable juice for seven days. Blood samples were collected, and clinic blood pressure measured at baseline and at the end of each juice consumption. Daily home blood pressure assessment was conducted two hours after juice consumption. Nitrate and nitrite concentrations were analysed using a commercially available kit on a Triturus automated ELISA analyser. Hills and Armitage analysis was used for the two- period crossover design and paired sample t- tests were performed to compare within-group changes. Results : Plasma nitrate and nitrite concentration significantly increased and there was significant reduction in clinic and home SBP mean during the beetroot juice period (P-values 0.004 and 0.002, respectively). Home DBP reduced significantly during green leafy vegetable juice consumption week (P-value 0.03). The difference between groups did not reach statistical significance during the formal crossover analysis adjusted for period effects. Conclusion : Beetroot juice and green leafy vegetable juice may reduce systolic or diastolic blood pressure but there was no statistically significant difference between the two juices, although this was only a pilot study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".