Impact of polyphenols on physiological stress and cardiac burden in marathon runners – results from a substudy of the BeMaGIC study
Bibliographic record
Abstract
Both physiologic stress and chronic heart disease are associated with increased systemic levels of chromogranin A (CGA) and NT-proBNP. Marathon running causes physiological stress and imposes a significant cardiac burden. Polyphenol-rich Mediterranean and Asian diets have been demonstrated to exert beneficial effects on the cardiovascular system. In this study we investigated whether pretreatment with a polyphenol beverage could attenuate the physiological and cardiac stress associated with a marathon. In the BeMaGIC trial, 277 athletes were randomized into 2 groups in a double-blinded fashion, receiving 1-1.5 L/day of the same beverages either with (study beverage) or without (placebo) polyphenol enrichment (approximately 400 mg of gallic acid equivalents per day of a complex mixture of polyphenols). Blood samples were taken 3 weeks and 1 day before, and immediately, 24 h, and 72 h after running a marathon. In our current substudy, CGA and NT-proBNP levels were analyzed by ELISA in the fastest 18 and the slowest 22 runners. CGA and NT-proBNP levels increased significantly immediately after the marathon and returned to baseline at 72 h after the marathon. Neither CGA nor NT-proBNP differed significantly between athletes receiving study beverage versus placebo. Separating our cohort into fast and slow runners did not reveal any significant difference regarding CGA or NT-proBNP levels between groups. Our study provides no evidence that polyphenol supplementation attenuates marathon running-induced physiological stress and cardiac burden in fast or slow runners.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".