Ketone monoester plus high‐dose glucose supplementation before exercise does not affect immediate post‐exercise erythropoietin concentrations versus glucose alone
Bibliographic record
Abstract
Abstract The objective of this study was to examine the effect of consuming ketone monoester plus a high dose of carbohydrate from glucose (KE + CHO) on the change in erythropoietin (EPO) concentrations during load carriage exercise compared with carbohydrate (CHO) alone. Using a randomized, crossover design, 12 males consumed KE + CHO (573 mg KE/kg body mass, 110 g glucose) or CHO (110 g glucose) 30 min before 4 miles of self‐paced treadmill exercise (KE + CHO:51 ± 13%, CHO: 52 ± 12% V̇O2peak) wearing a weighted vest (30% body mass; 25 ± 3 kg). Blood samples for analysis were obtained under resting fasted conditions before (Baseline) consuming the KE + CHO or CHO supplement and immediately after exercise (Post). βHB increased (p < 0.05) from Baseline to Post in KE + CHO, with no change in CHO. Glucose and glycerol increased (p < 0.05) from Baseline to Post in CHO, with no effect of time in KE + CHO. Insulin and lactate increased (p < 0.05) from Baseline to Post independent of treatment. EPO increased (p < 0.05) from Baseline to Post in KE + CHO and CHO with no difference between treatments. Although KE + CHO altered βHB, glucose, and glycerol concentrations, results from this study suggest that KE + CHO supplementation before load carriage exercise does not enhance immediate post‐exercise increases in EPO compared with CHO alone.
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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 | 0.001 |
| 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".