Metabolic Clusters of Early-Lactating Dairy Cows Based on Blood β-hydroxybutyrate Trajectories and Predicted from Milk Compounds.

Fatty acids Metabolic status Trajectories β-hydroxybutyrate

Journal

Journal of dairy science
ISSN: 1525-3198
Titre abrégé: J Dairy Sci
Pays: United States
ID NLM: 2985126R

Informations de publication

Date de publication:
28 Jun 2024
Historique:
received: 20 02 2024
accepted: 01 06 2024
medline: 1 7 2024
pubmed: 1 7 2024
entrez: 30 6 2024
Statut: aheadofprint

Résumé

High-yielding dairy cows encounter metabolic challenges in early lactation. Typically, β-hydroxybutyrate (BHB), measured at a specific time point is employed to diagnose the metabolic status of cows based on a predetermined threshold. However, in early lactation, BHB is highly dynamic, and there is high interindividual variability in its time profile. This could limit the effectiveness of the single measurement and threshold-based diagnosis probably contributing to the disparities in reports linking metabolic status with productive and reproductive outcomes. This research delves into the examination of the trajectories of BHB to unveil inter-cow variations and identify latent metabolic groups. We compiled a data set from 2 observational studies involving a total of 195 lactations from multiparous Holstein Friesian cows. The data set encompasses measurements of BHB, NEFA, and insulin from blood samples collected at 3, 6, 9, and 21 d in milk (DIM), along with weekly determinations of milk composition and fatty acids (FA) proportions in milk fat. In both experiments, milk yield (MY) and feed intake were recorded daily during the first month of lactation. We explored interindividual and intraindividual variations in metabolic responses using the trajectories of blood BHB and evaluated the presence of distinct metabolic groups based on such variations. For this purpose, we employed the growth mixture model (GMM), a trajectory clustering technique. Our findings unveil novel insights into the diverse metabolic responses among cows, encompassing both trajectory patterns and the magnitude of blood BHB concentrations. Specifically, we identified 3 latent metabolic groups: the "QuiBHB" cluster (≈10%) exhibited a higher initial BHB concentration than other clusters, peaking on d 9 (average maximum BHB of 2.4 mM) and then declining by d 21; the "SloBHB" cluster (≈23%) started with a lower BHB concentration, gradually increasing until d 9, and at the highest BHB concentration at d 21 (1.6 mM serum BHB at the end of the experimental period); and the "LoBHB" cluster (≈67%) began with the lowest serum BHB concentration (serum BHB <0.75 mM), remaining relatively stable throughout the sampling period. Notably, the 3 metabolic groups exhibited significant physiological disparities, evident in blood NEFA and insulin concentrations. The QuiBHB and SloBHB cows exhibited higher NEFA and lower insulin concentrations as compared with the LoBHB cows. Interestingly, these metabolic differences extended to MY and DMI during the first month of lactation. The elevated BHB concentrations observed in QuiBHB cows were linked with lower DMI and MY as compared with SloBHB and LoBHB cows. Accordingly, these animals were considered metabolically impaired. Conversely, SloBHB cows displayed higher MY along with increased DMI, and thus the elevated BHB might be indicative of an adaptive response for these cows. The QuiBHB cows also displayed higher proportions of unsaturated FA (UFA), monounsaturated FA (MUFA), and total C18:1 FA in milk during the first week of lactation. Prediction of the QuiBHB cows using these FA and test day variables resulted in moderate predictive accuracy (ROC

Identifiants

pubmed: 38945267
pii: S0022-0302(24)00967-6
doi: 10.3168/jds.2024-24762
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Auteurs

Muluken Girma (M)

Laboratory for Animal Nutrition and Animal Product Quality, Department of Animal Sciences and Aquatic Ecology, Ghent University, Coupure Links 653, 9000, Gent, Belgium. Electronic address: mulukengirma.mulat@ugent.be.

S Heirbaut (S)

Laboratory for Animal Nutrition and Animal Product Quality, Department of Animal Sciences and Aquatic Ecology, Ghent University, Coupure Links 653, 9000, Gent, Belgium.

K Hertogs (K)

Inagro, Ieperseweg 87, 8800 Rumbeke-Beitem, Belgium.

X P Jing (XP)

State Key Laboratory of Grassland and Agro-Ecosystems, International Centre for Tibetan Plateau Ecosystem Management, School of Life Sciences, Lanzhou University, Lanzhou 730000, China.

M Q Zhang (MQ)

Laboratory for Animal Nutrition and Animal Product Quality, Department of Animal Sciences and Aquatic Ecology, Ghent University, Coupure Links 653, 9000, Gent, Belgium.

P Lutakome (P)

Laboratory for Animal Nutrition and Animal Product Quality, Department of Animal Sciences and Aquatic Ecology, Ghent University, Coupure Links 653, 9000, Gent, Belgium; Department of Agricultureal Production, College of Agricultureal and Environmental Science, Makerere University, Kampala, Uganda.

K Geerinckx (K)

Hooibeekhoeve, Hooibeeksedijk 1, 2440 Geel, Belgium.

S Els (S)

Hooibeekhoeve, Hooibeeksedijk 1, 2440 Geel, Belgium.

B Aernouts (B)

Hooibeekhoeve, Hooibeeksedijk 1, 2440 Geel, Belgium.

L Vandaele (L)

ILVO, Scheldeweg 68, 9090 Melle, Belgium.

V Fievez (V)

Laboratory for Animal Nutrition and Animal Product Quality, Department of Animal Sciences and Aquatic Ecology, Ghent University, Coupure Links 653, 9000, Gent, Belgium. Electronic address: veerle.fievez@ugent.be.

Classifications MeSH