Lipidomic Profiling of Milk Enables Identification of Novel Biomarkers for Subclinical Ketosis in Dairy Cows.

The current clinical test for ketosis in dairy cows, which is based on measuring blood β-hydroxybutyrate (BHB) concentrations, is an invasive procedure that impacts herd welfare. Alternative biomarkers, especially non-invasive ones, are urgently needed for subclinical ketosis (SCK) diagnosis. To address this, we examined the milk lipidomic profiles of healthy and SCK cows from a research dairy farm over two consecutive years. A total of 125 polar lipid species were quantified in milk samples using a targeted liquid chromatography-mass spectrometry (LC-MS) approach. Chemometric analysis identified four potential biomarkers (PC 28:0, PC 29:0, PC 30:1 and PC 31:0), which met the thresholds for p value (<0.0004) and fold change (FC > 2). Global fatty acid (FA) profiling by gas chromatography-flame ionization detection (GC-FID) after transesterification of all lipids revealed that the levels of C8:0, C10:0, C12:0, C14:0, C15:0 and C18:1t11 were significantly suppressed in the SCK group (15-52% lower compared to the control group, p < 0.01), and C15:0 and C18:1t11 were the most promising markers for SCK in dairy cows at the FA level (43-50% reduction compared to the control group, p < 0.0001). Tandem LC-MS and FA data generated by GC demonstrated that SCK also caused significant changes in regio-isomer and double bond isomer composition of a model phospholipid molecule (PC 34:1) and a model triglyceride molecule TAG 52:2; these fine-structural alterations could also be explored for discovering novel SCK biomarkers in dairy cows. The PLS-DA predictive model built from lipidomic data showed excellent accuracy (>95%) in predicting cows with SCK. All of the promising biomarkers identified and the predictive model built in this study need further validation using larger SCK cohorts from different herds.