Milk fever in dairy cows is preceded by activation of innate immunity and alterations in carbohydrate metabolism prior to disease occurrence

Abstract

The objective of this study was to search for potential alterations in innate immunity reactants and carbohydrate and lipid metabolism in the blood of transition dairy cows before, during, and after clinical occurrence of milk fever (MF) and identify potential predictive biomarkers of disease. One hundred pregnant multiparous Holstein dairy cows were involved in the study starting from −8wks before the expected day of parturition until +8wks postpartum as part of a large retrospective longitudinal study. Health status, DMI, milk yield, and milk composition were monitored during the whole experimental period. Six healthy cows (CON) and 6 cows that showed clinical signs of MF were selected for blood analyses. Serum concentrations of lactate, non-esterified fatty acids (NEFA), β-hydroxybutyric acid (BHBA), interleukin-1 (IL-1), interleukin-6 (IL-6), tumor necrosis factor (TNF), haptoglobin (Hp), and serum amyloid A (SAA) were determined. Results indicated that concentrations of serum lactate, IL-6, TNF, SAA, and Hp were greater in cows with MF than those in the CON group at different time points. Moreover, serum lactate, TNF, SAA, and Hp were greater in cows with MF starting at −8 and −4wks prior to parturition. Both principal component analysis (PCA) and partial least squares - discriminant analysis (PLS-DA) showed separated clusters between MF and CON cows at −8, −4, and disease diagnosis weeks. Overall DMI and milk production were lower in MF-affected cows. Additionally milk fat and fat:protein ratio were greater in MF. In conclusion, cows affected by MF showed alterations in some of the innate immunity reactants and metabolites related to carbohydrate metabolism several weeks prior to appearance of clinical signs of MF. Variable importance in projection plots demonstrated that TNF and SAA in the serum were the strongest discriminators between MF cows and CON ones, which might be useful as predictive biomarkers of the disease.