Pathway‐linked milk and serum metabolites predict cheesemaking traits in Holstein cows: Glycerophospholipid, nicotinamide, amino acid and purine signatures

Background/Context/Rationale Milk yield and cheesemaking traits arise from metabolic networks governing nutrient partitioning and mammary biosynthesis, yet biochemical predictors remain underexplored in Holsteins. Aim(s) To identify milk/serum metabolites and pathways predicting cheese yield, rennet coagulation and recovery via untargeted LC–MS/MS metabolomics. Methods Untargeted LC–MS/MS profiling was conducted on milk and serum from 40 multiparous Holstein cows, stratified by yield [high‐yielding (HY) ≥35 kg/day, n = 20; low‐yielding (LY) ≤25 kg/day, n = 20]. The principal component analysis /PERMANOVA separated the profiles ( P < 0.001), while partial least squares discriminant analysis predicted cheese yield (RY = 0.78, Q = 0.71), recovery (RY = 0.82) and protein retention (RY = 0.79); biomarkers were prioritised by VIP >1.5, followed by pathway enrichment testing. Major Findings Glycerophospholipids [lysoPC(16:0), SM(d18:1/16:0)] dominated PC1 (42.3% variance); nicotinamide, glycine and xanthine elevated in HY ( P < 0.01). Pathways included glycerophospholipid ( P = 0.002), purine ( P = 0.003) and amino acid metabolism. Pilot cheesemaking validated correlations ( r = 0.81–0.89, P < 0.01). Scientific/Industrial Implications This study establishes precision dairy biomarkers for early milk selection, nutritional optimisation and cheesemaking efficiency in Holstein herds.