Effect of leucine supplementation on fat free mass with prolonged hypoxic exposure during a 13-day trek to Everest Base Camp: a double-blind randomized study
Bibliographic record
Abstract
Loss of body weight and fat-free mass (FFM) are commonly noted with prolonged exposure to hypobaric hypoxia. Recent evidence suggests protein supplementation, specifically leucine, may potentially attenuate loss of FFM in subcaloric conditions during normoxia. The purpose of this study was to determine if leucine supplementation would prevent the loss of FFM in subcaloric conditions during prolonged hypoxia. Eighteen physically active male (n = 10) and female (n = 8) trekkers completed a 13-day trek in Nepal to Everest Base Camp with a mean altitude of 4140 m (range 2810-5364 m). In this double-blind study, participants were randomized to ingest either leucine (LEU) (7 g leucine, 93 kcal, 14.5 g whey-based protein) or an isocaloric isonitrogenous control (CON) (0.3 g LEU, 93 kcal, 11.3 g collagen protein) twice daily prior to meals. Body weight, body composition, and circumferences of bicep, thigh, and calf were measured pre- and post-trek. There was a significant time effect for body weight (-2.2% ± 1.7%), FFM (-1.7% ± 1.5%), fat mass (-4.0% ± 6.9%), and circumferences (p < 0.05). However, there was no treatment effect on body weight (CON -2.3 ± 2.0%; LEU -2.2 ± 1.5%), FFM (CON -2.1 ± 1.5%; LEU -1.2 ± 1.6%), fat mass (CON -2.9% ± 5.9%; LEU -5.4% ± 8.1%), or circumferences. Although a significant loss of body weight, FFM, and fat mass was noted in 13 days of high altitude exposure, FFM loss was not attenuated by leucine. Future studies are needed to determine if leucine attenuates loss of FFM with longer duration high altitude exposure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".