The Impact of Pre-Exercise Carbohydrate Meal on the Effects of Yerba Mate Drink on Metabolism, Performance, and Antioxidant Status in Trained Male Cyclists
Bibliographic record
Abstract
Abstract Introduction The consumption of yerba mate (YM), a source of antioxidants, in a fasted state increases fatty acid oxidation (FATox) during low–moderate-intensity exercise and improves performance in high-intensity exercise. However, the impact of a pre-exercise carbohydrate (CHO) meal on YM effects during exercise is unknown. Objective We investigated the effects of yerba mate drink (YMD) consumed in the fasted state (YMD-F) or after a CHO meal (YMD-CHO) on measurements of metabolism, performance, and blood oxidative stress markers in cycling exercise. Methods In a randomized, repeated-measures, crossover design, eight trained male cyclists ingested (i) YMD-CHO, (ii) YMD-F, or (iii) control-water and CHO meal (Control-CHO). The YMD (an infusion of 5 g of ultrarefined leaves in 250 mL of water) was taken for 7 days and 40 min before exercise. CHO meal (1 g/kg body mass) was consumed 60 min before exercise. The cycling protocol included a 40-min low-intensity (~ 53%V̇O2peak) constant load test (CLT); a 20-min time trial (TT); and 4 × 10-sall-outsprints. Blood samples and respiratory gases were collected before, during, and/or after tests. Results During CLT, YMD-CHO increased FATox ~ 13%vs. YMD-F (P = 0.041) and ~ 27%vs. Control-CHO (P < 0.001). During TT, YMD-CHO increased FATox ~ 160%vs. YMD-F (P < 0.001) and ~ 150%vs. Control-CHO (P < 0.001). Power output during TT improved ~ 3% (P = 0.022) in YMD-CHOvs. Control-CHO and was strongly correlated with changes in serum total antioxidant capacity (r = −0.87) and oxidative stress index (r = 0.76) at post-exercise in YMD-CHO. Performance in sprints was not affected by YMD. Conclusion CHO intake did not negate the effect of YMD on FAToxor TT performance. Instead, a synergism between the two dietary strategies may be present. Clinical Trial RegistrationNCT04642144. November 18, 2020. Retrospectively registered.
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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".