The Influence of Dietary Habits and Meat Consumption on Plasma 3‐Methylhistidine—A Potential Marker for Muscle Protein Turnover
Bibliographic record
Abstract
SCOPE: 3-Methylhistidine (3-MH) as a potential biomarker for muscle protein turnover is influenced by meat intake but data on the impact of meat on plasma 3-MH are scarce. We determined the association of plasma 3-MH, 1-methylhistidine (1-MH), and creatinine with dietary habits and assessed the impact of a single white meat intervention during a meat-free period. METHODS AND RESULTS: Plasma 3-MH, 1-MH, and creatinine concentrations of healthy young omnivores (n = 19) and vegetarians (n = 16) were analyzed together with data on anthropometry, body composition, grip strength, and nutrition. After baseline measurements omnivores adhered to a meat-free diet for 6 days and received a defined administration of chicken breast on day four. At baseline, omnivores had higher plasma 3-MH and 1-MH concentrations than vegetarians. White meat administration led to a slight increase in plasma 3-MH in omnivores. The elevated 3-MH concentrations significantly declined within 24 h after white meat intake. CONCLUSION: 1-MH concentrations in plasma seem to be suitable to display (white) meat consumption and its influence on 3-MH plasma concentration. 3-MH in plasma may be used as a biomarker for muscle protein turnover if subjects have not consumed meat in the previous 24 h.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".