0149 DI/LC-MS/MS-based metabolomics identifies early predictive serum biomarkers for ketosis in dairy cows
Bibliographic record
Abstract
Subclinical ketosis is a prevalent metabolic disease in transition dairy cows that affects 30 to 40% of the cows during early lactation. Cows with ketosis have lower milk yield and reproductive performance, greater risk of other periparturient diseases, and higher culling rate. The objectives of this study were to retrospectively evaluate alterations of metabolites in the serum of dairy cows with ketosis before, during, and after the diagnosis of disease and identify monitoring and diagnostic serum metabolite biomarkers for ketosis. One hundred transition dairy cows, 20 healthy cows (CON), and six cows with ketosis were sampled during d –8, –4, at disease diagnosis, and wk +4 and +8 relative to parturition. One hundred and twenty-eight serum metabolites were quantitatively profiled in CON and ketosis cows using a targeted metabolomics approach based on DI/LC-MS/MS at all time points. Univariate and multivariate data analyses were conducted at each time point to examine alterations of serum metabolites throughout the progress of ketosis. Significant changes were detected in the concentrations of several molecular species of amino acids, glycerophospholipids, sphingolipids, acylcarnitines, biogenic amines, and hexose in the serum of cows with ketosis during the entire experimental period. Multivariate analysis (i.e., PCA and PLS-DA) also showed clear distinctions between the two groups on the basis of the measured 128 serum metabolites at five time points. Furthermore, several metabolic pathways including Lys degradation, biotin metabolism, Try metabolism, urea cycle, Arg-Pro metabolism, protein biosynthesis, Met metabolism, phospholipid biosynthesis, Val-Leu-Ile degradation, betaine metabolism, Asp metabolism, His metabolism, and β-Ala metabolism were perturbed in cows with ketosis during the onset and progression of disease. These new findings give insights into further understanding of the pathobiology of ketosis in dairy cows. Biomarker analysis showed that AUCs for ROC curves were 0.996 (95% CI, 0.969–1) at –8 wks, 0.995 (95% CI, 0.938–1) at –4 wks, 0.99 (95% CI, 0.882–1) at disease wk, 1 (95% CI: 1–1) at +4 wks and 0.985 (95% CI: 0.806–1) at +8 wks, respectively, which suggest that serum biomarkers identified have pretty accurate predictive, diagnostic, and prognostic abilities for ketosis in transition 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".