Modulation of Plasma and Milk Sphingolipids in Dairy Cows Fed High-Starch Diets
Bibliographic record
Abstract
Bovine milk is a significant source of sphingolipids, dietary compounds that can exert anti-inflammatory actions, and which can modulate the host’s microbiome. Because sphingolipid synthesis can be modified by diet, we hypothesized that dietary conditions which reduced FFA availability may result in reduced sphingolipid synthesis. Twelve ruminally cannulated cows (120 ± 52 DIM; 35.5 ± 8.9 kg of milk/d; mean ± SD) were randomly assigned to treatment in a crossover design with 21-d periods. Treatments were (1) High starch (HS), (2) Control. The HS diet contained 29% starch, 24% NDF, and 2.8% fatty acids (FA), whereas the Control diet contained 20% starch, 31% NDF, and 2.3% FA. Plasma and milk samples were obtained on d 21 of each period and sphingolipids were quantified using targeted metabolomics. Univariate and multivariate analyses of generalized log-transformed and Pareto-scaled data included ANOVA (fixed effects of treatment) and discriminant analysis. The lipidomics analysis detected 71 sphingolipids across plasma and milk fat, including sphinganines (n = 3), dihydro-ceramides (n = 8), ceramides (Cer; n = 15), sphingomyelins (SM; n = 17), and glycosylated ceramides (n = 28). Followed by Cer, SM were the most abundant sphingolipids detected in milk and plasma, with a preponderance of 16:0-, 23:0-, and 24:0-carbon sidechains. Although no effects of HS diets were observed on plasma sphingolipids, we detected consistent reductions in the concentrations of several milk Cer (e.g., 22:0- and 24:0-Cer) and SM (17:0- and 23:0-SM) in response to HS. Discriminant analysis revealed distinct metabolite separation of HS and Control groups, with several Cer and SM being distinctively predictive of dietary treatment. We conclude that HS diets can reduce the secretion of milk Cer and SM, even in the absence of changes in circulating sphingolipids.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".