L-Arginine Improves Endurance to High-Intensity Interval Exercises in Overweight Men
Bibliographic record
Abstract
The effects of acute consumption of L-Arginine (L-Arg) in healthy young individuals are not clearly defined, and no studies on the effects of L-Arg in individuals with abnormal body mass index undertaking strenuous exercise exist. Thus, we examined whether supplementation with L-Arg diminishes cardiopulmonary exercise testing responses, such as ventilation (VE), VE/VCO2, oxygen uptake (VO2), and heart rate, in response to an acute session of high-intensity interval exercise (HIIE) in overweight men. A double-blind, randomized crossover design was used to study 30 overweight men (age, 26.5 ± 2.2 years; body weight, 88.2 ± 5.3 kilogram; body mass index, 28.0 ± 1.4 kg/m2). Participants first completed a ramped-treadmill exercise protocol to determine VO2max velocity (vVO2max), after which they participated in two sessions of HIIE. Participants were randomly assigned to receive either 6 g of L-Arg or placebo supplements. The HIIE treadmill running protocol consisted of 12 trials, including exercise at 100% of vVO2max for 1 min interspersed with recovery intervals of 40% of vVO2max for 2 min. Measurements of VO2 (ml·kg-1·min-1), VE (L/min), heart rate (beat per min), and VE/VCO2 were obtained. Supplementation with L-Arg significantly decreased all cardiorespiratory responses during HIIE (placebo+HIIE vs. L-Arg+HIIE for each measurement: VE [80.9 ± 4.3 L/min vs. 74.6 ± 3.5 L/min, p < .05, ES = 1.61], VE/VCO2 [26.4 ± 1.3 vs. 24.4 ± 1.0, p < .05, ES = 1.8], VO2 [26.4 ± 0.8 ml·kg-1·min-1 vs. 24.4 ± 0.9 ml·kg-1·min-1, p < .05, ES = 2.2], and heart rate [159.7 ± 6.3 beats/min vs. 155.0 ± 3.7 beats/min, p < .05, d = 0.89]). The authors conclude consuming L-Arg before HIIE can alleviate the excessive physiological strain resulting from HIIE and help to increase exercise tolerance in participants with a higher body mass index who may need to exercise on a regular basis for extended periods to improve their health.
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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) | 0.001 | 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) | 0.001 | 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".