The Acute Effects of L-arginine on Hormonal and Metabolic Responses During Submaximal Exercise in Trained Cyclists
Bibliographic record
Abstract
L-arginine may enhance endurance performance mediated by two primary mechanisms including enhanced secretion of endogenous growth hormone (GH) and as a precursor of nitric oxide (NO); however, research in trained participants has been equivocal. The purpose was to investigate the effect of acute L-arginine ingestion on the hormonal and metabolic response during submaximal exercise in trained cyclists. Fifteen aerobically trained men (age: 28 ± 5 y; body mass: 77.4 ± 9.5 kg; height: 180.9 ± 7.9 cm; VO2max: 59.6 ± 5.9 ml·kg- 1·min-1) participated in a randomized, double-blind, crossover study. Subjects consumed L-arginine (ARG; 0. 075 g·kg-1 body mass) or a placebo (PLA) before performing an acute bout of submaximal exercise (60 min at 80% of power output achieved at ventilatory threshold). The ARG condition significantly increased plasma L-arginine concentrations (~146%), while no change was detected in the PLA condition. There were no differences between conditions for GH, nonesterified fatty acids (NEFA), lactate, glucose, VO2, VCO2, RER, CHO oxidation, and NOx. There was reduced fat oxidation at the start of exercise (ARG: 0.36 ± 0.25 vs. PLA: 0.42 ± 0.23 g·min-1, p < .05) and an elevated plasma glycerol concentrations at the 45-min time point (ARG: 340.3 vs. PLA: 288.5 μmol·L-1, p < .05) after L-arginine consumption. In conclusion, the acute ingestion of L-arginine did not alter any hormonal, metabolic, or cardio-respiratory responses during submaximal exercise except for a small but significant increase in glycerol at the 45-min time point and a reduction in fat oxidation at the start of 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 | 0.000 | 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 | 0.000 | 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".