339 1H-NMR–based metabolomics identifies new predictive urinary biomarkers and highlights the pathobiology of ketosis in periparturient dairy cows
Bibliographic record
Abstract
The objective of this study was to investigate metabolic fingerprints in the urine of preketotic cows as well as during and after the occurrence of disease and to identify newly predictive and diagnostic urine biomarkers that can be used to distinguish cows with ketosis from healthy controls (CON). In this study, proton nuclear magnetic resonance–based metabolomics was performed to analyze urine samples from 6 cows with ketosis and 20 CON cows at −8 wk, −4 wk, disease diagnosis, +4 wk, and +8 wk relative to parturition. Univariate (t-test or Wilcoxon–Mann–Whitney test; significance, P < 0.05) and multivariate analyses (permutation test; P < 0.05) and biomarker analysis (empirical; P < 0.05) were used to select metabolite sets for the noninvasive prediction and diagnosis of ketosis. All data analyses were performed using MetaboAnalyst 3.0. A total of 14, 21, 14, 2, and 2 differential metabolites between the 2 groups were identified at −8 wk, −4 wk, disease diagnosis, +4 wk, and +8 wk, respectively. VIP plots ranked the most significant differential metabolites that contributed to the onset and progression of ketosis. Specifically, concentrations of pantothenic acid (i.e., 3.42 vs. 1.44 μM/mM creatinine at −8 wk [P = 0.04], 2.9 vs. 1.26 μM/mM creatinine at −4 wk [P = 0.01], and 3.04 vs. 1.54 μM/mM creatinine at the disease week [P = 0.03]) and myo-inositol (i.e., 31.89 vs. 10.73 at −8 wk [P < 0.01], 26.35 vs. 9.17 at −4 wk [P < 0.01], and 21.85 vs. 13.26 at the disease week [P = 0.04]) were persistently greater in the urine of both preketotic and ketotic cows when compared with CON cows. Urinary concentrations of urea were lower in preketotic and ketotic cows versus the CON group at −8 wk (41.79 vs. 146.55; P < 0.01), −4 wk (44.51 vs. 144.3; P = 0.04), and the disease week (43.77 vs. 194.57; P < 0.01). Moreover, 2 promising biomarker models were identified for prediction of ketosis with an excellent level of sensitivity and specificity. Overall, multiple urine metabolite alterations were identified in preketotic, ketotic, and postketotic cows, which could be used as potential screening biomarkers as well as to better understand the pathobiology of disease and to develop new preventive treatments in the future.
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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 | 0.000 |
| 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) | 0.001 | 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".