Precision Biomarkers for Early Detection and Prevention of Disease in Dairy Cows

Ketosis is one of the most economically significant metabolic disorders affecting periparturient dairy cows, causing production losses and predisposing animals to secondary complications. Current blood-based diagnostics are invasive and provide limited insight into the underlying metabolic perturbations. This study employed an integrated three-platform metabolomics approach to characterize milk metabolite alterations in ketotic Holstein dairy cows and to evaluate milk-based biomarker panels for early ketosis detection. Milk samples from 20 healthy control (CON) cows and 6 ketotic cows were collected at 2 weeks postpartum and analyzed by direct injection/liquid chromatography–tandem mass spectrometry (DI/LC-MS/MS), proton nuclear magnetic resonance (¹H-NMR) spectroscopy, and inductively coupled plasma mass spectrometry (ICP-MS). Ketosis was confirmed by serum β-hydroxybutyrate concentrations ≥ 1400 μmol/L. Principal component analysis, partial least squares-discriminant analysis, and receiver operating characteristic (ROC) curve analyses were applied. All three platforms discriminated ketotic cows from healthy cows, with clear cluster separation validated by 2000 permutation tests (p < 0.05). DI/LC-MS/MS identified 16 significantly altered metabolites (p < 0.05), with butyrylcarnitine (C4), phosphatidylcholine 30:0 (PC 30:0), ether-linked phosphatidylcholine O-38:3 (PC O-38:3), and citrulline identified as the top discriminatory biomarkers (AUC = 0.920; 95% CI: 0.85–0.98; sensitivity = 91.7%; specificity = 93.3%). ICP-MS revealed significantly reduced selenium (Se, p = 0.017), manganese (Mn, p = 0.045), and chromium (Cr, p = 0.037), as well as elevated cobalt (Co, p = 0.014) in ketotic milk (AUC = 0.870). ¹H-NMR detected no individually significant metabolites; however, multivariate analysis distinguished groups (AUC = 0.890), with succinate (numerical fold change: +5.77×; p = 0.059), methanol (−1.94×; not significant), and acetate (+2.88×; not significant) as top VIP contributors. The combined multi-platform biomarker panel (joint classification using top VIP features from all three platforms, without formal data fusion) achieved superior diagnostic performance (AUC = 0.970; 95% CI: 0.93–1.00; sensitivity = 95.0%; specificity = 96.7%). These findings identify coordinated perturbations in glycerophospholipid metabolism, acylcarnitine profiles, amino acid homeostasis, antioxidant mineral status, and energy metabolism during early ketosis, and suggest that milk metabolomics is a promising non-invasive approach for precision dairy health monitoring, pending validation in independent cohorts. We acknowledge the small ketotic group size (n = 6) as a limitation; therefore, these findings should be considered discovery cohort observations requiring prospective validation before clinical translation. Full article