Prediction of metabolic clusters in early lactation dairy cows using models based on 2 milk biomarkers

The aim of this study was to describe metabolism of early lactation dairy cows by clustering cows based on glucose, insulin like growth factor I (IGF-I), free fatty acid (FFA), and β- hydroxybutyrate (BHB) using the k-means method. Predictive models for metabolic clusters were created and validated using three sets of milk biomarkers (milk metabolites and enzymes,glycans on the immuno-gamma globulin (IgG) fraction of milk, and Fourier transformed mid-infrared spectra (FT-MIR) of milk). Metabolic clusters are used to identify dairy cows with a(n) (im)balanced metabolic profile. Around 14 and 35 days in milk, serum or plasma concentrations of BHB, FFA, glucose and IGF-I, were determined. Cows with a favorable metabolic profile were grouped in BALANCED (n=43) and compared with OTHERBAL (n=64). Cows with an unfavorable metabolic profile were grouped in IMBALANCED (n=19) and compared with OTHERIMBAL (n=88). Glucose and IGF-I were higher in BALANCED compared with OTHERBAL. FFA and BHB were lower in BALANCED compared with OTHERBAL. Glucose and IGF-I were lower in IMBALANCED compared with OTHERIMBAL. FFA and BHB were higher in IMBALANCED. Metabolic clusters were related to production parameters. There was a trend for a higher daily increase in fat and protein corrected milk yield (FPCM) in BALANCED while FPCM of IMBALANCED was higher. Dry matter intake (DMI) and the daily increase in DMI were higher in BALANCED and lower in IMBALANCED. Energy balance was continuously higher in BALANCED and lower in IMBALANCED. Weekly or bi- weekly milk samples were taken and milk metabolites and enzymes (milk glucose, glucose-6- phosphate, BHB, lactate dehydrogenase, N-acetyl-β-D-glucosaminidase, isocitrate), IgGglycans (19 peaks) and FT-MIR (1,060 wavelengths reduced to 15 principal components) determined. Milk biomarkers with or without additional cow information (DIM, parity, milk yield features) were used to create predictive models for the metabolic clusters. Accuracy for prediction of BALANCED (80%) and IMBALANCED (88%) was highest using milk metabolites and enzymes combined with DIM and parity. The results and models of the present study are part of the GplusE project and identify novel milk based phenotypes that may be used as predictors for metabolic and performance traits in early lactation dairy cows.