Empirical prediction of net splanchnic release of ketogenic nutrients, acetate, butyrate and β-hydroxybutyrate in ruminants: a meta-analysis
Bibliographic record
Abstract
For energy feeding systems for ruminants to evolve towards a nutrient-based system, dietary energy supply has to be determined in terms of amount and nature of nutrients. The objective of this study was to establish response equations of the net hepatic flux and net splanchnic release of acetate, butyrate and β-hydroxybutyrate to changes in diet and animal profiles. A meta-analysis was applied on published data compiled from the FLuxes of nutrients across Organs and tissues in Ruminant Animals database, which pools the results from international publications on net splanchnic nutrient fluxes measured in multi-catheterized ruminants. Prediction variables were identified from current knowledge on digestion, hepatic and other tissue metabolism. Subsequently, physiological and other, more integrative, predictors were obtained. Models were established for intakes up to 41 g dry matter per kg BW per day and diets containing up to 70 g concentrate per 100 g dry matter. Models predicted the net hepatic fluxes or net splanchnic release of each nutrient from its net portal appearance and the animal profile. Corrections were applied to account for incomplete hepatic recovery of the blood flow marker, para-aminohippuric acid. Changes in net splanchnic release (mmol/kg BW per hour) could then be predicted by combining the previously published net portal appearance models and the present net hepatic fluxes models. The net splanchnic release of acetate and butyrate were thus predicted from the intake of ruminally fermented organic matter (RfOM) and the nature of RfOM (acetate: residual mean square error (RMSE)=0.18; butyrate: RMSE=0.01). The net splanchnic release of β-hydroxybutyrate was predicted from RfOM intake and the energy balance of the animals (RMSE=0.035), or from the net portal appearance of butyrate and the energy balance of the animals (RMSE=0.050). Models obtained were independent of ruminant species, and presented low interfering factors on the residuals, least square means or individual slopes. The model equations highlighted the importance of considering the physiological state of animals when predicting splanchnic metabolism. This work showed that it is possible to use simple predictors to accurately predict the amount and nature of ketogenic nutrients released towards peripheral tissues in both sheep and cattle at different physiological status. These results provide deeper insight into biological processes and will contribute to the development of improved tools for dietary formulation.
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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 | 0.030 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.042 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 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".