The effect of acute dark chocolate consumption on carbohydrate metabolism and performance during rest and exercise
Bibliographic record
Abstract
Consumption of cocoa-enriched dark chocolate (DC) has been shown to alter glucose and insulin concentration during rest and exercise compared with cocoa-depleted control (CON). However, the impact of DC consumption on exercise metabolism and performance is uncertain. Therefore, we investigated carbohydrate metabolism via stable isotope tracer techniques during exercise after subjects ingested either DC or CON. Sixteen overnight-fasted male cyclists performed a single-blinded, randomized, crossover design trial, after consuming either DC or CON at 2 h prior to 2.5 h of steady-state (SS) exercise (∼45% peak oxygen uptake). This was followed by an ∼15-min time-trial (TT) and 60 min of recovery. [6,6-(2)H2]Glucose and [U-(13)C]glucose were infused during SS to assess glucose rate of appearance (Ra) and disappearance (Rd). After DC consumption, plasma (-)-glucose and insulin concentrations were significantly (p < 0.001) elevated throughout vs. CON. During SS, there was no difference in [6,6-(2)H2]glucose Ra between treatments, but towards the end of SS (last 60 min) there was a ∼16% decrease in Rd in DC vs. CON (p < 0.05). Accordingly, after DC there was an ∼18% significant decrease in plasma glucose oxidation (trial effect; p = 0.032), and an ∼15% increase in tracer-derived muscle glycogen utilization (p = 0.045) late during SS exercise. The higher blood glucose concentrations during exercise and recovery after DC consumption coincided with high concentrations of epicatechin and (or) theobromine. In summary, DC consumption altered muscle carbohydrate partitioning, between muscle glucose uptake and glycogen oxidation, but did not effect cycling TT performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".