Tart cherry consumption with or without prior exercise increases antioxidant capacity and decreases triglyceride levels following a high-fat meal
Bibliographic record
Abstract
Exercise and high-phytonutrient foods may lower oxidative stress and increase antioxidant levels, which could combat the negative effects associated with a high-fat (HF) meal. The objective of this study is to test the effects of Montmorency tart cherry (Prunus cerasus L.) consumption, with or without aerobic exercise, on antioxidant responses to an HF meal. Twelve normal-weight men (aged 22 ± 3 years), participated in a randomized crossover design comprising 4 trials: (i) HF meal with Montmorency tart cherry consumption (MC), (ii) HF meal with placebo (P), (iii) exercise prior to HF meal with MC (E+MC), and (iv) exercise prior to HF meal with P (E+P). The HF meal contained 60 g of fat and was consumed with MC or P. For exercise trials, a 30-min bout of submaximal treadmill exercise was performed the afternoon prior to HF meal consumption. Antioxidant capacity and triglycerides (TG) levels were measured at baseline and at 1, 2, and 3 h postprandially. Postprandial antioxidant capacity as assessed by oxygen radical absorbance capacity was significantly higher after MC and E+MC compared with E+P (incremental area under the curve (iAUC): 2.95 ± 2.19 and 4.87 ± 1.45 vs. −1.02 ± 1.72 mmol Trolox equivalents/L for MC and E+MC vs. E+P, respectively; p < 0.01). Postprandial TG levels were significantly lower after E+MC compared with P (iAUC: 58.99 ± 19.46 vs. 107.46 ± 22.66 mmol Trolox equivalents/L for E+MC vs. P, respectively; p < 0.05). These results indicate that MC consumption alone, and in combination with prior exercise, leads to greater antioxidant capacity following an HF meal compared with prior exercise with placebo. Further, MC consumption with prior exercise led to more favorable postprandial TG levels compared with placebo.
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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 | 0.001 |
| 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 | 0.001 |
| 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".