Dietary Energy Source in Dairy Cows in Early Lactation: Metabolites and Metabolic Hormones
Bibliographic record
Abstract
Negative energy balance-related metabolic disorders suggest that the balance between available lipogenic and glucogenic nutrients is important. The objectives of this study were to compare the effects of a glucogenic or a lipogenic diet on liver triacylglycerides (TAG), metabolites, and metabolic hormones in dairy cows in early lactation and to relate metabolite concentrations to the determined energy retention in body mass (ER). Sixteen dairy cows were fed either a lipogenic or glucogenic diet from wk 3 prepartum to wk 9 postpartum (pp) and were housed in climate respiration chambers from wk 2 to 9 pp. Diets were isocaloric (net energy basis). Postpartum, cows fed a lipogenic diet tended to have higher nonesterified fatty acid concentration (NEFA; 0.46 +/- 0.04 vs. 0.37 +/- 0.04 mmol/L) and lower insulin concentration (4.0 +/- 0.5 vs. 5.5 +/- 0.6 microIU/mL). No difference was found in plasma glucose, beta-hydroxybutyrate, insulin-like growth factor-I, and thyroid hormones. Liver TAG was equal between both diets in wk -2 and 2 pp. In wk 4 pp cows fed the glucogenic diet had numerically lower TAG levels, although there was no significant dietary effect. Negative relationships were detected between ER and milk fat and between ER and NEFA. A positive relationship was detected between ER and insulin concentration. Overall, results suggest that insulin plays a regulating role in altering energy partitioning between milk and body tissue. Feeding lactating dairy cows a glucogenic diet decreased mobilization of body fat compared with a lipogenic diet. The relative abundance of lipogenic nutrients, when feeding a more lipogenic diet, is related to more secretion of lipogenic nutrients in milk, lower plasma insulin, and higher plasma NEFA concentration.
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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) | 0.000 | 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".