044 Serum metabolomics fingerprinting during the dry off period identifies metabolite signatures that can predict the risk of metritis
Notice bibliographique
Résumé
The objective of this study was to screen transition dairy cows during the dry off period for identification of metabolite signatures in the serum that can be used for prediction of risk of metritis and give insights into the pathobiology of the disease. Blood samples were collected from coccygeal vein at 8 and 4 wk prepartum, disease diagnosis week, and 4 and 8 wk postpartum. Gas chromatography mass spectrometry was used to identify and quantify 29 metabolites in the serum of 20 healthy control cows (CON) and 6 cows that were diagnosed with metritis. Data were analyzed using univariate and multivariate analysis. Results showed that 16, 12, 14, 15, and 10 metabolites were significantly altered at −8 and −4 wk, at disease diagnosis, and at +4 and +8 wk around calving in cows diagnosed with metritis versus healthy CON. The multivariate analyses indicated consistent disease-dependent clustering with fairly similar set of metabolites distinguishing premetritis cows from healthy controls at −8 and −4 wk. The utility of these metabolites as biomarkers of risk of disease was assessed by the area under the curve (AUC), and AUC values of 1.0 and 0.969 were observed at −8 and −4 wk, respectively. Overall, results of this study indicated that selected metabolites can be used to early predict the risk of metritis in transition dairy cows. Results indicated significant (P < 0.05) metabolite alterations at −8 and −4 wk and at disease diagnosis in premitritis cows and those that developed metritis compared with healthy CON. The multivariate analyses also demonstrated consistent disease-dependent clustering with fairly similar set of metabolites distinguishing premetritis cows from healthy controls at −8 and −4 wk. Among the metabolites that distinguished premetritic cows from healthy CON at −8 wk, oxalate, ornithine, pyroglutamic acid, glutamic acid, and d-mannose were ranked as the top 5 in variable importance in the projection. A similar set of metabolites (except oxalate substituted by phosphoric acid) were ranked as the top 5 at −4 wk, indicating that those top 5 metabolites can be used as predictive biomarkers at −8 and −4 wk before the incidence of postpartum metritis. Intriguingly, multiple metabolites, such as galactose, phosphoric acid, oleic acid, urea, and oxalate, were identified to be different (P < 0.5) even at 4 and 8 wk after parturition. The significant alterations of serum metabolite concentrations and disease-dependent clustering around parturition indicate the potential of these metabolites to track the progression and development of metritis in dairy cattle.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».