Circulating serum metabolites as biomarkers and predictors of residual feed intake in lactating dairy cows
Bibliographic record
Abstract
This study explored the potential of circulatory serum metabolite profiles to increase understanding of the physiology of feed efficiency and identify biomarkers to predict residual feed intake (RFI) in lactating Holsteins. Serum metabolite profiles were compared in high (n = 20) and low RFI (n = 20) cows at early, mid, and late lactation stages. The low RFI cows had decreased (P < 0.05) concentrations of dodecanoylcarnitine, dodecenoylcarnitine, dodecanedioylcarnitine, tetradecanoylcarnitine, succinic acid, trimethylamine N-oxide, betaine, and increased concentrations of p-Hydroxyhippuric acid, hydroxysphingomyeline C16:1, phosphatidylcholine diacyl C40:6, and glutarylcarnitine at early lactation. A similar comparison at mid lactation stage showed altered serum concentrations of 26 metabolites that fall into the categories of acyl carnitines, glycerophospholipids, biogenic amines, amino acids, and organic acids. At late lactation, fewer sets of metabolites were significantly affected by RFI grouping. Receiver operator curve analyses identified p-Hydroxyhippuric acid as the top biomarker at early lactation and acetylornithine at mid and late lactation. Models based on sets of serum metabolites in early, mid, and late lactation stages predicted RFI with a validation coefficient of determination of 0.54, 0.68, and 0.64, respectively. This study demonstrated the potential of circulatory serum metabolites as biomarkers and predictors of RFI in lactating dairy cows.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".