Using milk components to estimate the risk of energy imbalance in Holstein cows by means of receiver operating characteristic (ROC) analysis

Negative energy balance (NEB) indairy cows during early lactation significantly contributes tometabolic and infectious diseases, traditionally diagnosed via costly and time-consuming serum non-esterified fatty acids (NEFA) analysis. This study aimed todevelop apractical and cost-effective diagnostic test for NEB based onmilk components analysed routinely. Data from 692 Holstein cows (5–35days inmilk) located atfive Czech dairy farms were analysed using multiple logistic regression and receiver operating characteristic (ROC) analysis. Results showed that 99cows (14.3%) were classified asNEB+ (NEFA> 0.6mmol/l). Cows inthe NEB+ group exhibited asignificantly higher milk fat content (P<0.001) and milk fat-to-protein ratio (P<0.001), and lower lactose concentrations (P<0.001) compared toNEB− cows. Key indicators oflipomobilisation, such asC18:1, C18:0, and monounsaturated fatty acids (FA), were significantly higher (P<0.001) inNEB+ cows, while saturated, short-chain, and medium-chain FA were lower (P<0.001). The developed prediction models, incorporating milk fat and specific FA (e.g. C18:1, C18:0, C14:0), demonstrated high diagnostic efficacy. The area under the ROC curve (AUC) values ranged from 0.84 to0.92 for individual farms and reached 0.83 for the combined dataset. Using the Index ofUnion method, optimal cut-off points yielded sensitivities between 0.72 and 0.86, and specificities between 0.72 and 0.85. For the overall model, both sensitivity and specificity were 0.76. Inconclusion, the proposed diagnostic test, leveraging milk components, offers areliable and practical tool for early NEB detection indairy cows. This facilitates timely intervention, thereby mitigating adverse health and economic impacts. Further validation with larger and more diverse datasets isrecommended.