Laboratory of Systems and Synthetic Biology, Wageningen University and Research, Stippeneng 4, 6708 WE, Wageningen, the Netherlands; Adaptation Physiology group, Department of Animal Sciences, Wageningen University and Research, PO Box 338, 6700 AH, Wageningen, the Netherlands.
Adaptation Physiology group, Department of Animal Sciences, Wageningen University and Research, PO Box 338, 6700 AH, Wageningen, the Netherlands.
J Dairy Sci. 2022 May;105(5):4565-4580. doi: 10.3168/jds.2021-21518. Epub 2022 Mar 26.
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.
由于摄入的能量相对较低，而产奶所需的能量需求较高，奶牛在泌乳初期会经历能量负平衡（EB）。这种能量不足会导致体重减轻，并增加患代谢疾病的风险。奶牛的负 EB 严重程度和持续时间可能存在差异。奶牛围产期 EB 的时间分布曲线在文献中尚未描述。创建具有相应代谢状态和疾病治疗的 EB 衍生时间分布曲线，可以提高对 EB 与代谢状态之间关系的理解，以及增强对代谢状态受损风险奶牛的识别。在这项研究中，我们提出了一种新的方法来聚类 EB 时间序列，并检查围产期奶牛的相关代谢状态和疾病治疗情况。在这项研究中，合并并检查了三个早期实验的数据。通过全局对齐核算法，针对产后 -3 周到 +7 周的 EB 时间序列，生成了四个奶牛聚类。对于每个聚类，产前体重的平均值是可区分的，这表明这可能是围产期 EB 曲线的一个潜在的农场生物标志物。此外，EB 严重下降的奶牛在产后更易患乳热，且在泌乳的前 7 周内，血浆中非酯化脂肪酸和β-羟丁酸浓度较高，IGF-1、胰岛素和血糖浓度较低。总的来说，这项研究表明，奶牛可以根据 EB 时间分布曲线进行聚类，而产前体重以及产后非酯化脂肪酸和血糖浓度等特征是识别 EB 时间分布曲线和潜在代谢疾病风险的有前途的生物标志物。
