Tolerable amounts of amino acids for human supplementation: summary and lessons from published peer-reviewed studies
Bibliographic record
Abstract
Amino acid supplementation may be indicated to correct for insufficient amino acid intake in healthy individuals, and in specific physiological or pathophysiological situations. However, there is a concern to not supplement beyond the tolerable upper intake level (UL) by determining parameters of no-observed-adverse-effect level (NOAEL) or lowest-observed-adverse-effect level (LOAEL) for each amino acid. Since the NOAEL and LOAEL values are at least one order of magnitude different when comparing the values obtained in rats and humans, the aim of this review is to evaluate to what extent the amino acid UL measured in the rat model, when referenced to the dietary usual consumption (UC) and dietary requirement (RQ) for indispensable amino acids, may be used as an approximation of the UL in humans. This review then compares the ratios of the NOAEL or LOAEL over UC and RQ in the rat model with the same ratios calculated in humans for the nine amino acids (arginine, serine, glycine, histidine, leucine, lysine, methionine, phenylalanine, and tryptophan) for which this comparison can be done. From the calculations made, it appears that for these 9 amino acids, the calculated ratios for rats and humans, although rather different for several amino acids, remains for all of them in the same order of magnitude. For tryptophan, tyrosine, and valine, the ratios calculated in rats are markedly different according to the sex of animals, raising the view that it may be also the case in humans.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".