Beetroot Juice Supplementation Increases High Density Lipoprotein-Cholesterol and Reduces Oxidative Stress in Physically Active Individuals
Bibliographic record
Abstract
Beetroot juice contains a high level of biologically accessible antioxidants, beneficial phytochemicals and dietary nitrate, which seem to exert beneficial effects in human health. Dietary nitrate, from beetroot has been reported to lower blood pressure. However the impact of beetroot on lipid profile and oxidative stress is unknown. In present study, the effect of supplementation with beetroot juice for 15 days was investigated. Plasma lipid profile, antioxidant status, oxidative stress and body composition changes were evaluated at baseline and after 15 days of beetroot juice supplementation. Beetroot juice supplementation beneficially influenced the lipid profile by significantly increasing the levels of high-density lipoprotein cholesterol (HDL-C) from 42.9 ± 8.3 mg/dl to 50.2 ± 9.8 mg/dl and decreasing low-density lipoprotein cholesterol (LDL-C) from 129.7 ± 82.3 mg/dl to 119.5 ± 79.2 mg/dl compared with baseline values. Beetroot juice supplementation increased (P < 0.05) plasma nitrite level and guanosine 3’, 5’-cyclic monophosphate (c-GMP) levels. A significant increase in plasma total antioxidant capacity and vitamin C levels was observed after beetroot juice intake for 15 days. There was no significant change in the body fat mass and lean body mass of participants with the beetroot juice supplementation. Beetroot juice supplementation significantly decreased the stress markers plasma hydroperoxides and cortisol levels. Beetroot juice acts as a potent vasodilator by increasing plasma c-GMP levels and nitrite levels. Beetroot juice consumption improves plasma lipid profile and antioxidant status, encouraging further evaluation on a population with higher cardiovascular disease risk.
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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 | 0.000 |
| 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 | 0.000 | 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".