Caffeine ingestion does not alter carbohydrate or fat metabolism in human skeletal muscle during exercise
Bibliographic record
Abstract
This study examined the effect of ingesting caffeine (6 mg kg-1) on muscle carbohydrate and fat metabolism during steady-state exercise in humans. Young male subjects (n = 10) performed 1 h of exercise (70% maximal oxygen consumption (VO2,max)) on two occasions (after ingestion of placebo and caffeine) and leg metabolism was quantified by the combination of direct Fick measures and muscle biopsies. Following caffeine ingestion serum fatty acid and glycerol concentration increased (P< or =0.05) at rest, suggesting enhanced adipose tissue lipolysis. In addition circulating adrenaline concentration was increased (P< or =0.05) at rest following caffeine ingestion and this, as well as leg noradrenaline spillover, was elevated (P< or =0.05) above placebo values during exercise. Caffeine resulted in a modest increase (P< or =0.05) in leg vascular resistance, but no difference was found in leg blood flow. Arterial lactate and glucose concentrations were increased (P< or =0.05) by caffeine, while the rise in plasma potassium was dampened (P< or =0.05). There were no differences in respiratory exchange ratio or in leg glucose uptake, net muscle glycogenolysis, leg lactate release or muscle lactate, or glucose 6-phosphate concentration. Similarly there were no differences between treatments in leg fatty acid uptake, glycerol release or muscle acetyl CoA concentration. These findings indicate that caffeine ingestion stimulated the sympathetic nervous system but did not alter the carbohydrate or fat metabolism in the monitored leg. Other tissues must have been involved in the changes in circulating potassium, fatty acids, glucose and lactate.
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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 | 0.000 | 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".