206 Meta-analysis of the response of piglets to dietary valine: impact of other branched chain amino acids
Bibliographic record
Abstract
Abstract Branched chain amino acids (BCAA), valine (Val), isoleucine (Ile) and leucine (Leu) share the same metabolic pathways. An excess of Leu stimulates the catabolism of Val and Ile that may lead to a decrease in piglet growth performance. The objective of this study is to quantify the response of piglets to dietary Val and the influence of Leu and Ile on this response through a meta-analysis approach. A total of 16 articles published between 2001 and 2018, including 23 experiments and 126 treatments were used. Multiple regression models were fitted with the MIXED procedure of Minitab software with the random effect of the experiment. The Y variables were Average Daily Gain (ADG), Average Daily Feed Intake (ADFI) and Feed Conversion Ratio (FCR). The main X variable was the Standardized Ileal Digestible Val (ValSID) and the other ones were Leu (LeuSID) and Ile (IleSID). The response of ADG, ADFI and FCR to ValSID was curvilinear (P< 0.001: ADG, R2 = 0.93%; ADFI, R2= 0.97%; FCR, R2=0.93%). Results showed that increasing dietary LeuSID reduced ADG and ADFI (P< 0.05) but also that the response of piglets to ValSID was stronger in high LeuSID diet (P < 0.05; Interaction ValSIDxLeuSID; ADG and ADFI). Based on these models, increasing dietary ValSID from 7 to 8.5g/kg generates in wheat-based diets (10 g /kg of LeuSID) an improvement of ADG of 4.7% and ADFI of 2.5% compared to 7.4% for ADG and 5.2% for ADFI in corn-based diets (14 g/ kg of LeuSID). The response of ADG, ADFI and FCR to ValSID was not modified by IleSID. This study showed that ADG, ADFI and FCR are improved with increasing dietary Val and this effect was modulated by dietary Leu content except for FCR. Results can help piglet nutritionists to optimize dietary Val levels based on other BCAA content.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.046 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".