University of Nottingham, School of Veterinary Medicine and Science, Sutton Bonington Campus, Sutton Bonington, Leicestershire, LE12 5RD, United Kingdom.
Centre for Analytical Bioscience, Advanced Materials & Healthcare Technologies Division, School of Pharmacy, University of Nottingham, School of Pharmacy, Nottingham, NG7 2RD, United Kingdom.
J Dairy Sci. 2023 Oct;106(10):7033-7042. doi: 10.3168/jds.2022-23118. Epub 2023 Jul 26.
Lameness in dairy cattle is a highly prevalent condition that impacts on the health and welfare of dairy cows. Prompt detection and implementation of effective treatment is important for managing lameness. However, major limitations are associated with visual assessment of lameness, which is the most commonly used method to detect lameness. The aims of this study were to investigate the use of metabolomics and machine learning to develop novel methods to detect lameness. Untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) alongside machine learning models and a stability selection method were utilized to evaluate the predictive accuracy of differences in the metabolomics profile of first-lactation dairy cows before (during the transition period) and at the time of lameness (based on visual assessment using the 0-3 scale of the Agriculture and Horticulture Development Board). Urine samples were collected from 2 cohorts of dairy heifers and stored at -86°C before analysis using LC-MS. Cohort 1 (n = 90) cows were recruited as current first-lactation cows with weekly mobility scores recorded over a 4-mo timeframe, from which newly lame and nonlame cows were identified. Cohort 2 (n = 30) cows were recruited within 3 wk before calving, and lameness events (based on mobility score) were recorded through lactation until a minimum of 70 d in milk (DIM). All cows were matched paired by DIM ± 14 d. The median DIM at lameness identification was 187.5 and 28.5 for cohort 1 and 2, respectively. The best performing machine learning models predicted lameness at the time of lameness with an accuracy of between 81 and 82%. Using stability selection, the prediction accuracy at the time of lameness was 80 to 81%. For samples collected before and after calving, the best performing machine learning model predicted lameness with an accuracy of 71 and 75%, respectively. The findings from this study demonstrate that untargeted LC-MS profiling combined with machine learning methods can be used to predict lameness as early as before calving and before observable changes in gait in first-lactation dairy cows. The methods also provide accuracies for detecting lameness at the time of observable changes in gait of up to 82%. The findings demonstrate that these methods could provide substantial advancements in the early prediction and prevention of lameness risk. Further external validation work is required to confirm these findings are generalizable; however, this study provides the basis from which future work can be conducted.
奶牛跛行是一种高发疾病，会影响奶牛的健康和福利。及时发现并采取有效的治疗措施对于管理跛行非常重要。然而，视觉评估跛行存在主要限制，这是最常用的检测跛行的方法。本研究的目的是探讨代谢组学和机器学习在开发新的跛行检测方法中的应用。本研究采用非靶向代谢组学结合液相色谱-质谱联用技术（LC-MS）和机器学习模型以及稳定性选择方法，评估了初产奶牛跛行前（在过渡期）和跛行时（根据农业和园艺发展委员会的 0-3 级视觉评估）代谢组学特征的差异。采集了两组奶牛的尿液样本，并在 -86°C 下储存，然后使用 LC-MS 进行分析。第 1 组（n=90）奶牛为当前初产奶牛，在 4 个月的时间内每周记录移动评分，从中确定新出现的跛行和非跛行奶牛。第 2 组（n=30）奶牛在分娩前 3 周内招募，通过泌乳期记录跛行事件（基于移动评分），直到至少 70 天产奶（DIM）。所有奶牛均通过 DIM ± 14 d 进行配对。第 1 组和第 2 组跛行识别的中位数 DIM 分别为 187.5 和 28.5。最佳的机器学习模型在跛行时预测跛行的准确率在 81%到 82%之间。使用稳定性选择，跛行时的预测准确率为 80%至 81%。对于分娩前后采集的样本，表现最佳的机器学习模型预测跛行的准确率分别为 71%和 75%。本研究结果表明，非靶向 LC-MS 分析与机器学习方法相结合，可用于预测初产奶牛早在分娩前和可见步态变化之前的跛行。这些方法还可以达到高达 82%的可见步态变化时检测跛行的准确率。这些发现表明，这些方法可以在早期预测和预防跛行风险方面取得重大进展。需要进一步的外部验证工作来确认这些发现是否具有普遍性；然而，本研究为未来的工作提供了基础。
