0150 Targeted metabolomics reveals multiple metabolite alterations in the urine of transition dairy cows preceding the incidence of lameness
Bibliographic record
Abstract
Lameness (Lam) is a major issue of transition dairy cows consuming high grain diets affecting 25–35% of the herd. It is associated with decreased milk production, fertility problems, and high culling rates and treatment costs. Various hypotheses have been forwarded during the years with regards to the causes of lameness including rumen histamine, endotoxin, or biogenic amines. Although much is known about non-mechanical lameness, the precise pathobiology is not known. The objectives of this study were to evaluate weekly metabolite composition of urine in dairy cows starting from the beginning of dry off until 8 wks postpartum. Urine samples were collected from 100 cows at −8, −4, disease week, +4, and +8 wk around calving and stored at −80 C until analyzes. DI/LC-MS/MS analyzes were conducted on samples collected from 20 healthy control cows (CON) and 6 cows diagnosed only with lameness (no other periparturient diseases). A total of 154 metabolites including 41 carnitines, 9 lysophosphatidylcholines, 74 phosphatidylcholines, 15 sphingomyelines, 11 amino acids, 2 biogenic amines, hexose, and carnosine were identified and quantified. Data were processed statistically by MetaboAnalyst and univariate analyses. Results showed that 41, 29, 59, 26, and 40 metabolites were identified and measured to be different between the two groups on −8, −4, disease week, +4, and +8 wks around calving, respectively. The highest number of altered metabolites was identified during the week of diagnosis of Lam. Several metabolic pathways were found to be associated with the disease including amino acid metabolism, catecholamine biosynthesis, protein biosynthesis and urea cycle at −8 wks precalving; cysteine, glutamate, tyrosine, and glutathione metabolism at −4 wks precalving; and β-alanine metabolism during the week of disease. ROC analyses, for determination of specificity and sensitivity of potential biomarkers, identified several metabolites with AUCs for ROC curves 0.96 (95% CI, 075–1.0) at −8 wks; 0.971 (95% CI, 0.905–1.0) at −4wks; 1.0 (95% CI, 1.0–1.0) at disease week; 1.0 (95% CI, 1.0–1.0) at +4wks; and 1.0 (95% CI, 1.0–1.0) at +8wks postpartum. PLS-DA analysis also showed clear separation between the two groups of cows with regards to altered metabolites. In conclusion, targeted metabolomics can be used to identify metabolic alterations in the urine of dairy cows before, during, and after diagnosis of Lam; it can also help to better understand the pathobiology of disease, identify cows more susceptible to Lam, and develop new preventive strategies.
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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) | 0.001 | 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".