Differential acute effects of carbohydrate- and protein-rich drinks compared with water on cardiac output during rest and exercise in healthy young men
Bibliographic record
Abstract
The acute effects of drinks rich in protein (PRO) versus carbohydrate (CHO) on cardiovascular hemodynamics and reactivity are uncertain. A randomized crossover design was used to compare 400-mL isoenergetic (1.1 MJ) drinks containing whey protein (PRO; 44 g) or carbohydrate (CHO; 57 g) versus 400 mL of water in 14 healthy men. The primary and secondary outcomes were changes in cardiac output, blood pressure, systemic vascular resistance (SVR) and digital volume pulse measured prior to and 30 min following consumption at rest, during 12 min of multi-stage bicycle ergometry, and 15 min postexercise. The mean change (95% confidence interval (CI)) in resting cardiac output at 30 min was greater for CHO than for PRO or water: 0.7 (0.4 to 1.0), 0.1 (-0.2 to 0.40), and 0.0 (-0.3 to 0.3) L/min (P < 0.001), respectively; the higher cardiac output following CHO was accompanied by an increase in stroke volume and a lower SVR. The mean increments (95% CI) in cardiac output during exercise were CHO 4.7 (4.4 to 5.0), PRO 4.9 (4.6 to 5.2), and water 4.6 (4.3 to 4.9) L/min with the difference between PRO versus water being significant (P < 0.025). There were no other statistically significant differences. In summary, a CHO-rich drink increased cardiac output and lowered SVR in the resting state compared with a PRO-rich drink or water but the effect size of changes in these variables did not differ during or after exercise between CHO and PRO. Neither protein nor carbohydrate affected blood pressure reactivity to exercise.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".