Dynamics of metabolic characteristics in dairy cows and their impact on disease-free survival time

Abstract

Dairy cows are at a greater risk of disease due to increased energy demand during the transition period. Blood biomarkers including beta-hydroxybutyrate (BHBA)11BHBA: beta-hydroxy butyric acid. and non-esterified fatty acids (NEFA)22NEFA: non-esterified fatty acids are routinely used to identify animals in a state of negative energy balance (NEB)33NEB: Negative Energy Balance. Recent research demonstrates cattle have varied response to NEB, that requires multiple blood biomarkers to characterize. This research identified five subcategories (cowtypes) of metabolic responses in transition dairy cows: Healthy, Athlete, Clever, Hyperketonemia, and Poor Metabolic Adaptation Syndrome (PMAS)44PMAS: Poor metabolic adaptation syndrome. The data set used in this study was collected in Germany by VIT - Vereinigte Informationssysteme Tierhaltung w.V. from 2016 to 2020. Health issues with time of diagnostic were included in the dataset. Using previously reported prediction models for blood BHB and blood NEFA and milk Fourier-transform infrared spectroscopy (FTIR)55FTIR: Fourier-transform infrared spectroscopy data, the cowtypes in our dataset were predicted. The objective of this study is to evaluate the association of the cowtypes with the disease-free survival time in dairy cows during early post calving using an accelerated failure time regression model. Additionally, transition probabilities of the population dynamics between cowtypes are studied by means of a Markov chain model. Using Healthy cowtype as reference level, Athlete, Clever, and PMAS cowtypes were found to be significant for the disease-free survival probability (P < 0.01). Conversely, Hyperketonemia cowtype was not significant (P = 0.182). Compared to the Healthy cowtype, all other cowtypes had a negative effect on the survival probabilities, which was higher for PMAS cows. Furthermore, after computing the estimated population transition probabilities among cowtypes, the stationary distribution of the Markov chain, along with bootstrap confidence intervals were computed. The results showed 0.091 (95% CI:0.089,0.092), 0.077 (95 % CI:0.074,0.078), 0.684 (95 % CI:0.067,0.069), 0.138 (95 % CI:0.136,0.139), and 0.009 (95% CI:0.008,0.010) of probability of being in Healthy, Athlete, Clever, Hyperketonemia, and PMAS cowtype, respectively. These estimates represent the proportion of cows belonging to the different cowtypes in a herd; information which may prove useful for herd management. The application of blood biomarker predictions using milk FTIR allows us to investigate differences between predicted cowtype and movements between these states and the association with time to disease. Further research will improve our understanding of the dynamic nature of the transition period.