KU Leuven, Department of Biosystems, Biosystems Technology Cluster, Campus Geel, Kleinhoefstraat 4, 2440 Geel, Belgium; KU Leuven, Department of Biosystems, Mechatronics, Biostatistics and Sensors Division, Kasteelpark Arenberg 30, 3001 Leuven, Belgium; Natural Resources Institute of Finland (Luke), Maarintie 6, 02150 Espoo, Finland.
KU Leuven, Department of Biosystems, Biosystems Technology Cluster, Campus Geel, Kleinhoefstraat 4, 2440 Geel, Belgium; KU Leuven, Department of Biosystems, Mechatronics, Biostatistics and Sensors Division, Kasteelpark Arenberg 30, 3001 Leuven, Belgium.
J Dairy Sci. 2020 Jul;103(7):6422-6438. doi: 10.3168/jds.2019-17952. Epub 2020 May 7.
In high-yielding dairy cattle, severe postpartum negative energy balance is often associated with metabolic and infectious disorders that negatively affect production, fertility, and welfare. Mobilization of adipose tissue associated with negative energy balance is reflected through an increased level of nonesterified fatty acids (NEFA) in the blood plasma. Earlier, identification of negative energy balance through detection of increased blood plasma NEFA concentration required laborious and stressful blood sampling. More recently, attempts have been made to predict blood NEFA concentration from milk samples. In this study, we aimed to develop and validate a model to predict blood plasma NEFA concentration using the milk mid-infrared (MIR) spectra that are routinely measured in the context of milk recording. To this end, blood plasma and milk samples were collected in wk 2, 3, and 20 postpartum for 192 lactations in 3 herds. The blood plasma samples were taken in the morning, and representative milk samples were collected during the morning and evening milk sessions on the same day. To predict plasma NEFA concentration from the milk MIR spectra, partial least squares regression models were trained on part of the observations from the first herd. The models were then thoroughly validated on all other observations of the first herd and on the observations of the 2 independent herds to explore their robustness and wide applicability. The final model could accurately predict blood plasma NEFA concentrations <0.6 mmol/L with a root mean square error of prediction of <0.143 mmol/L. However, for blood plasma with >1.2 mmol/L NEFA, the model clearly underestimated the true level. Additionally, we found that morning blood plasma NEFA levels were predicted with significantly higher accuracy using MIR spectra of evening milk samples compared with MIR spectra of morning samples, with root mean square error of prediction values of, respectively, 0.182 and 0.197 mmol/L, and R values of 0.613 and 0.502. These results suggest a time delay between variations in blood plasma NEFA and related milk biomarkers. Based on the MIR spectra of evening milk samples, cows at risk for negative energy status, indicated by detrimental morning blood plasma NEFA levels (>0.6 mmol/L), could be identified with a sensitivity and specificity of, respectively, 0.831 and 0.800. As this model can be applied to millions of historical and future milk MIR spectra, it opens an opportunity for regular metabolic screening and improved resilience phenotyping.
在高产奶牛中，产后严重的负能量平衡通常与代谢和传染性疾病有关，这些疾病会对生产、繁殖力和福利产生负面影响。与负能量平衡相关的脂肪组织动员反映在血浆中非酯化脂肪酸（NEFA）水平的增加。早些时候，通过检测血浆中 NEFA 浓度的增加来识别负能量平衡需要进行费力且有压力的采血。最近，人们尝试从奶样中预测血 NEFA 浓度。在这项研究中，我们旨在开发和验证一种使用中红外（MIR）光谱预测血浆 NEFA 浓度的模型，该模型通常在奶记录的背景下进行测量。为此，在 3 个牛群的 192 个泌乳期中，在产后 2、3 和 20 周采集血浆和奶样。血浆样本在早上采集，当天早上和晚上的奶样采集时采集代表性的奶样。为了从奶 MIR 光谱中预测血浆 NEFA 浓度，我们在第一个牛群的部分观测数据上训练偏最小二乘回归模型。然后，我们在第一个牛群的所有其他观测数据以及两个独立牛群的观测数据上对模型进行了彻底验证，以探索其稳健性和广泛适用性。最终模型可以准确预测<0.6mmol/L 的血浆 NEFA 浓度，预测值的均方根误差<0.143mmol/L。然而，对于 NEFA>1.2mmol/L 的血浆，模型明显低估了真实水平。此外，我们发现使用晚奶样本的 MIR 光谱可以更准确地预测早上的血浆 NEFA 水平，预测值的均方根误差分别为 0.182mmol/L 和 0.197mmol/L，R 值分别为 0.613 和 0.502。这些结果表明，血浆 NEFA 与相关乳生物标志物之间存在时间延迟。基于晚奶样本的 MIR 光谱，可以识别出处于负能量状态风险的奶牛，其特征是早上的血浆 NEFA 水平（>0.6mmol/L）不利，敏感性和特异性分别为 0.831 和 0.800。由于该模型可应用于数百万个历史和未来的奶 MIR 光谱，因此为定期代谢筛选和提高弹性表型提供了机会。
