Aronia juice consumption prior to half-marathon race can acutely affect platelet activation in recreational runners
Bibliographic record
Abstract
Long-distance running, especially in non-professional runners, can increase cardiac arrest risk by enhancing platelet activation and aggregation. Polyphenols can exert cardioprotective effects by positively influencing platelet function. This study aimed to examine the acute effects of polyphenol-rich aronia juice consumption, before simulation of a half-marathon race, on platelet activation and aggregation with leukocytes in recreational runners. In this acute crossover study,10 healthy male runners (age 30.8 ± 2.3 years) consumed breakfast with 200 mL of aronia juice or 200 mL of placebo. They warmed-up and ran a simulated half-marathon race (21.1 km). Blood was collected at baseline, and at 15 min, 1 h, and 24 h after the run. All variables were analyzed with 4 (time) × 2 (group) ANOVA with repeated measures on both factors. Results revealed a significant effect of group on platelet activation parameters: P-selectin and GPIIb-IIIa expressions significantly decreased in the aronia group compared with the placebo group (F[1,9] = 10.282, p = 0.011 and F[1,9] = 7.860, p = 0.021, respectively). The effect of time was significant on both platelet aggregation markers: platelet-monocyte and platelet-neutrophil aggregates were significantly lower after the race (F[3,7] = 4.227, p = 0.014 and F[3,7] = 70.065, p = 0.000, respectively), with changes more pronounced in the later. All effects remained when platelets were exposed to an agonist. These results suggest that aronia consumption could counteract the half-marathon race-induced changes in platelet function. Novelty Aronia juice consumption significantly decreased the expression of platelet activation markers but did not affect platelet aggregation. The race itself did significantly reduce platelet-neutrophil aggregation. Aronia juice may serve as a supplement beverage for recreational runners to alleviate enhanced platelet reactivity caused by prolonged running.
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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 | 0.000 |
| 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".