Determination of the Tolerable Upper Limit (UL) of Leucine Intake in Adult Humans
Bibliographic record
Abstract
The branched-chain amino acids (BCAA) include leucine and are popular as dietary supplements among strength training athletes. Leucine has been implicated to improve athletic performance, although experimental evidence is inconclusive, and raises concerns regarding adverse effects. Our objective was to determine the ‘metabolic limit’ to oxidize leucine in vivo. Four, healthy adults (20-35 y) each received graded stepwise increases in leucine intakes of 50, 150, 250, 500, 750, 1000 and 1250 mg/kg/d corresponding to the Estimated Average Requirement (EAR), EARx3, x5, x10, x15, x20 and x25 to a total of 25 studies. The safe upper limit (UL) of leucine intake was determined by measuring the oxidation of L-[1-13C]-Leucine to 13CO2 (F13CO2). Breath samples were collected at baseline and isotopic steady state. Linear regression crossover analysis identified a breakpoint (UL) in F13CO2. The mean upper metabolic limit to oxidize leucine was determined to be 555.8 mg/kg/d (Figure 1). These results correspond to 10 X the EAR for leucine. This study is the first to directly estimate the safe upper limit of leucine intake in humans and raises concerns that intakes greater than 555 mg/kg/d may be a risk to health because this is the upper limit of oxidative capacity in humans. (Supported by ICAAS). Figure 1Open in figure viewerPowerPoint Leucine oxidation (F13CO2, μmol/kg/hr) in response to graded excess leucine intakes
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".