Acute Ketone Salts–Caffeine–Taurine–Leucine Supplementation but not Ketone Salts–Taurine–Leucine, Improves Endurance Cycling Performance
Bibliographic record
Abstract
Coingestion of ketone salts, caffeine and the amino acids, taurine, and leucine improves endurance exercise performance. However, there is no study comparing this coingestion to the same nutrients without caffeine. We assessed whether ketone salts-caffeine-taurine-leucine (KCT) supplementation was superior to caffeine-free ketone salts-taurine-leucine supplementation (KT), or to an isoenergetic carbohydrate placebo (CHO-PLAC). Thirteen recreationally active men (mean ± SD: 177.5 ± 6.1 cm, 75.9 ± 4.6 kg, 23 ± 3 years, 12.0 ± 5.1% body fat) completed a best effort 20-km cycling time-trial, followed 15 min later by a Wingate power cycle test, after supplementing with either KCT (approximately 7 g of beta-hydroxybutyrate, approximately 120 mg of caffeine, 2.1 g of leucine, and 2.7 g of taurine), KT (i.e., same supplement without caffeine), or isoenergetic CHO-PLAC (11 g of dextrose). Blood ketones were elevated (p < .001) after ingestion of both KCT (0.65 ± 0.12 mmol/L) and KT (0.72 ± 0.31 mmol/L) relative to CHO-PLAC (0.06 ± 0.05 mmol/L). Moreover, KCT improved (p < .003) 20-km cycling time-trial performance (37.80 ± 2.28 min), compared with CHO-PLAC (39.40 ± 3.33 min) but not versus KT (38.75 ± 2.87 min; p < .09). 20-km cycling time-trial average power output was greater with KCT (power output = 180.5 ± 28.7 W) versus both KT (170.9 ± 31.7 W; p = .049) and CHO-PLAC (164.8 ± 34.7 W; p = .001). Wingate peak power output was also greater for both KCT (1,134 ± 137 W; p = .031) and KT (1,132 ± 128 W; p = .039) versus CHO-PLAC (1,068 ± 127 W). These data suggest that the observed improved exercise performance effects of this multi-ingredient supplement containing beta-hydroxybutyrate salts, taurine, and leucine are attributed partially to the addition of caffeine.
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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".