Negative animal health and performance outcomes are associated with disease incidences that can be labor-intensive, costly, and cumbersome for many farms. Amelioration of unfavorable outcomes through early detection and treatment of disease has emphasized the value of improving health monitoring. Although the value is recognized, detecting hyperketonemia (HYK) is still difficult for many farms to do practically and efficiently. Increasing data streams available to farms presents opportunities to use data to better monitor cow and herd health; however, challenges remain with regard to validating, integrating, and interpreting data. During the transition to lactation period, useful data are presented in the form of milk production and composition, milk Fourier-transform infrared (FTIR) wavelength absorbance, cow management records, and genomics, which have been employed to monitor postpartum onset of HYK. Attempts to predict postpartum HYK from test-day milk and performance variables incorporated into multiple linear regression models have demonstrated sufficient accuracy to monitor monthly herd prevalence; however, they lacked the sensitivity and specificity for individual cow diagnostics. Subsequent artificial neural network prediction models employing FTIR data and milk composition variables achieved 83 and 81% sensitivity and specificity for individual cow diagnostics. Although these results fail to reach the diagnostic goals of 90%, they are achieved without individual cow blood samples, which may justify acceptance of lower performance. The caveat is that these models depend on milk analysis, which is traditionally done every 4 weeks. This infrequent sampling allows for a single diagnostic sample for about half of the fresh cows. Benefits to farms are greatly improved if postpartum cows can be milk-tested weekly. Additionally, this allows for close monitoring of somatic cell count and may open the door for use of other herd health monitoring tools. Future improvements in these models may be achievable by maximizing sensitivity at the expense of specificity and may be most economical in disorders for which the cost of treatment is less than that of mistreating (e.g., HYK). Genomic predictions for HYK may be improved by incorporating genome-wide associated SNP and further utilized for precision management of HYK risk groups. Development and validation of HYK prediction models may provide producers with individual cow and herd-level management tools.

中文翻译：

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专题讨论会回顾：大数据，大预测：利用牛奶的傅立叶变换红外光谱和基因组学来改善高钾血症的管理。

不利的动物健康和生产绩效与疾病发生率相关，对于许多农场来说，这种疾病的发生可能是劳动密集型，昂贵且麻烦的事情。通过早期发现和治疗疾病来改善不良结果，已强调了改善健康监测的价值。尽管已认识到该价值，但对于许多农场来说，实际检测高酮血症（HYK）仍然很困难。农场可用的数据流不断增加，提供了使用数据更好地监测牛群和牛群健康的机会。但是，在验证，集成和解释数据方面仍然存在挑战。在过渡到哺乳期的过程中，有用的数据以牛奶的生产和成分，牛奶的傅立叶变换红外（FTIR）波长吸收率，奶牛管理记录和基因组学的形式提供，已经被用来监测HYK的产后发作。尝试从测试日的牛奶和纳入多个线性回归模型的性能变量预测产后HYK的方法已显示出足够的准确性来监测每月的牛群流行率。但是，他们缺乏对单个母牛诊断的敏感性和特异性。随后的采用FTIR数据和牛奶成分变量的人工神经网络预测模型对单个母牛的诊断获得了83％和81％的灵敏度和特异性。尽管这些结果未能达到90％的诊断目标，但没有单独的牛血样就可以实现，这可能证明可接受较低的性能。需要注意的是，这些模型依赖于牛奶分析，传统上每4周进行一次。这种不频繁的采样可以为大约一半的新鲜奶牛提供单个诊断样本。如果产后母牛可以每周进行牛奶测试，对农场的好处将大大改善。另外，这允许紧密监测体细胞计数，并且可以为使用其他畜群健康监测工具打开大门。这些模型的未来改进可能是通过以牺牲特异性为代价来最大化灵敏度来实现的，并且对于那些治疗成本比治疗失误更轻的疾病（例如HYK）可能是最经济的。HYK的基因组预测可以通过整合全基因组相关的SNP进行改进，并进一步用于HYK危险人群的精确管理。HYK预测模型的开发和验证可以为生产者提供个体的奶牛和畜群管理工具。