The acute effects of dietary nitrate supplementation on postmenopausal endothelial resistance to ischemia reperfusion injury: a randomized, placebo-controlled, double blind, crossover clinical trial
Bibliographic record
Abstract
Postmenopausal cardiovascular health is a critical determinant of longevity. Consumption of beetroot juice (BR) and other nitrate-rich foods is a safe, effective non-pharmaceutical intervention to increase systemic bioavailability of the vasoprotective molecule, nitric oxide, through the exogenous nitrate (NO3 −)–nitrite (NO2 −)–nitric oxide (NO) pathway. We hypothesized that a single dose of nitrate-rich beetroot juice (BRnitrate 600 mg NO3 −/140 mL, BRplacebo ∼ 0 mg/140 mL) would improve resting endothelial function and resistance to ischemia–reperfusion (IR) injury to a greater extent in early-postmenopausal (1–6 years following their final menstrual period (FMP), n = 12) compared to late-postmenopausal (6+ years after FMP, n = 12) women. Analyses with general linear models revealed a significant ( p < 0.05) time*treatment interaction effect for brachial artery adjusted flow-mediated dilation (FMD). Pairwise comparisons revealed that adjusted FMD was significantly lower following IR-injury in comparison to all other time points with BRplacebo (early FMD 2.51 ± 1.18%, late FMD 1.30 ± 1.10, p < 0.001) and was lower than post-IR with BRnitrate (early FMD 3.84 ± 1.21%, late FMD 3.21 ± 1.13%, p = 0.014). A single dose of BRnitrate significantly increased resting macrovascular function in the late postmenopausal group only ( p = 0.005). Considering the postmenopausal stage-dependent variations in endothelial responsiveness to dietary nitrate, we predict differing mechanisms underpin macrovascular protection against IR injury.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".