Nuclear magnetic resonance spectroscopy reveals variations in the milk metabolome associated with total and differential somatic cell count in dairy cows

Udder health is a key determinant of milk quality, animal welfare, and economic sustainability in dairy production systems. This study evaluated the ability of proton nuclear magnetic resonance spectroscopy (1H NMR) to discriminate milk according to udder health status and to investigate the effect of udder health on the milk metabolomic profile. Individual milk samples (n = 410) were repeatedly collected from 62 lactating cows and classified as healthy, susceptible, mastitic, and chronic based on SCC and differential somatic cell count (DSCC) thresholds. A total of 33 milk metabolites were identified through 1H NMR. Canonical discriminant analysis based on 1H NMR spectral data showed a higher classification performance for the binary model (healthy cows vs. the pooled susceptible, mastitic, and chronic cows) compared with the multiclass approach (healthy, susceptible, mastitic, and chronic), with overall accuracy reaching 0.85 and 0.81 in training and cross-validation, respectively. ANOVA revealed significant differences in metabolite abundance among the 4 udder health classes. In particular, chronic cows exhibited a distinctive metabolomic profile, characterized by higher concentrations of acetone, O-acetylcarnitine, acetate, lecithin, and choline, and lower concentrations of glutamate, dimethylsulfone, and glucose compared with cows in at least one of the other classes. Concentrations of mannose, galactose-1-phosphate, and orotate were higher in healthy and susceptible cows than in mastitic and chronic cows, whereas lactose concentrations decreased from the healthy to the chronic class. Overall, these findings indicate that milk metabolomic profiling by 1H NMR is a valuable approach for characterizing udder health status and identifying potential metabolic biomarkers. The integration of metabolomic data with traditional indicators such as SCC and DSCC may contribute to the development of improved tools for monitoring udder health and enhancing milk quality in dairy production systems.