83 The acute effects of feeding a grain-free diet with or without methionine, taurine or methyl donor/acceptor supplementation on plasma and whole blood amino acid concentrations in dogs
Bibliographic record
Abstract
Abstract Grain-free pet foods have been sold for over a decade and comprise more than 40% of dog and cat diets sold today. Grain-free diets replace grain ingredients with pulse ingredients, which are high in lysine but low in methionine and cysteine, the precursor amino acids to taurine synthesis in the dog. The objective of this study was to evaluate the postprandial response of plasma methionine and taurine and whole blood taurine concentrations of dogs fed a grain-free diet without supplementation (CON) or with methionine (MET), taurine (TAU) or creatine, carnitine and choline (CCC) supplementation. Eight Beagles were pair housed and fed one of the four experimental diets for seven days in a 4x4 Latin Square Design. On the morning of d 7, cephalic catheters were placed and one fasted sample (0 min) and nine post-meal blood samples (15, 30, 60, 90, 120, 180, 240, 300 and 360 min) were collected. Data were analyzed as repeated measures using the PROC GLIMMIX function in SAS (Version 9.4). Dogs supplemented with MET had significantly higher plasma methionine concentrations from 30 to 360 minutes post-meal compared to dogs on CON, TAU and CCC treatments (P < 0.05). However, no differences were observed in plasma methionine concentrations between CON, TAU and CCC treatments at any time point (P > 0.05). Plasma taurine concentrations were significantly higher across time points in all treatment groups compared to CON (P < 0.05). Whole blood taurine concentrations tended to be higher across time points in MET and TAU treatment groups compared to CCC (P = 0.0513). Overall, MET, TAU and CCC supplementation increased plasma taurine concentrations compared to CON, but only MET supplementation increased plasma methionine concentrations from 30 to 360 minutes post-meal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".