Phenotyping metabolic status of dairy cows using clustering of time profiles of energy balance peripartum.

Due to a combination of a relatively low energy intake and a high demand of energy required for milk production, dairy cows experience a negative energy balance (EB) at the start of lactation. This energy deficit causes body weight reduction and an increased risk for metabolic diseases. Severity and length of negative EB can differ among cows. Peripartum time profiles of EB for dairy cows are not described yet in the literature. Creating EB-derived time profiles with corresponding metabolic status and disease treatments could improve understanding the relationship between EB and metabolic status, as well as enhance identification of cows at risk for compromised metabolic status. In this research we propose a novel method to cluster EB time series and examine associated metabolic status and disease treatments of dairy cows in the peripartum period. In this study, data of 3 earlier experiments were merged and examined. Four dairy cow clusters for time profiles of EB from wk -3 until +7 relative to calving were generated by the global alignment kernel algorithm. For each cluster, mean of body weight prepartum was distinguishable, indicating this might be a possible on-farm biomarker for the peripartum EB profile. Moreover, cows with severe EB drop postpartum were more treated for milk fever and had high plasma nonesterified fatty acids and β-hydroxybutyrate concentration, and low IGF-1, insulin, and glucose concentration in the first 7 wk of lactation. Overall, this study demonstrated that cows can be clustered based on EB time profiles and that characteristics such as prepartum body weight, and postpartum nonesterified fatty acids and glucose concentration are promising biomarkers to identify the time profile of EB and potentially the risk for metabolic diseases.