Beetroot juice is more beneficial than sodium nitrate for attenuating muscle pain after strenuous eccentric-bias exercise
Bibliographic record
Abstract
The aim of this study was to compare the effects of beetroot juice (BTJ) and a nitrate only drink (sodium nitrate; SN) on indices of exercise-induced muscle damage (EIMD). Thirty recreationally active males consumed either BTJ (n = 10), a nitrate-matched SN drink (n = 10), or an isocaloric placebo (PLA; n = 10) immediately and at 24 and 48 h after performing 100 drop jumps. To assess muscle damage, maximal isometric voluntary contractions (MIVCs), countermovement jumps (CMJs), pressure-pain threshold (PPT), creatine kinase (CK), and high-sensitivity C-reactive protein (hs-CRP) were measured before, immediately after and at 24, 48, and 72 h following the drop jumps. BTJ and SN increased serum nitric oxide, which peaked at 2 h post-ingestion (136 ± 78 and 189 ± 79 μmol·L−1, respectively). PPT decreased in all groups postexercise (P = 0.001), but was attenuated with BTJ compared with SN and PLA (P = 0.043). PPT was 104% ± 26% of baseline values at 72 h after BTJ, 94% ± 16% after SN, and 91% ± 19% after PLA. MIVC and CMJ were reduced following exercise (−15% to 25%) and did not recover to baseline by 72 h in all groups; however, no group differences were observed (P > 0.05). Serum CK increased after exercise but no group differences were present (P > 0.05). hsCRP levels were unaltered by the exercise protocol (P > 0.05). These data suggest that BTJ supplementation is more effective than SN for attenuating muscle pain associated with EIMD, and that any analgesic effects are likely due to phytonutrients in BTJ other than nitrate, or interactions between them.
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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 | 0.001 | 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".