Annual Research & Review in Biology

The transition period, extending approximately from three weeks before to three weeks after calving, is characterised by profound endocrine, metabolic and immune remodelling. The onset of lactation sharply increases nutrient demand while dry-matter intake often lags behind, producing negative energy balance, adipose-tissue mobilisation and changes in insulin, growth hormone, insulin-like growth factor-1, cortisol, leptin and adiponectin signalling. At the same time, parturition, colostrogenesis, lactogenesis, calcium disturbances, oxidative stress and inflammatory remodelling can transiently impair innate immune competence. These changes are highly relevant to subclinical mastitis (SCM), an intramammary inflammatory condition that lacks obvious clinical signs but commonly manifests as increased milk somatic cell count (SCC), altered milk composition and reduced production. Evidence indicates that excessive lipid mobilisation, elevated non-esterified fatty acids (NEFA), hyperketonaemia, hypocalcaemia and dysregulated inflammatory signalling can interact with impaired neutrophil and lymphocyte function to increase susceptibility to intramammary infection. However, metabolic abnormalities should not be interpreted as specific diagnostic tests for SCM; rather, they may identify cows with reduced resilience or increased risk. Established udder-health indicators include SCC, bacteriological culture and cow-side tests such as the California Mastitis Test and milk electrical conductivity. More recently, milk acute-phase proteins, host-defence peptides, enzymes, oxidative-stress markers and metabolomic signatures have been investigated as more biologically proximate biomarkers. Untargeted metabolomics has identified blood, milk and urinary metabolite changes before or around SCM diagnosis, suggesting that predictive screening may be feasible weeks before conventional detection. Emerging technologies—including mid-infrared spectroscopy, infrared thermography, automated milking sensors, biosensors and machine-learning models—may allow continuous or high-frequency udder-health surveillance. This review integrates endocrine and metabolic physiology with mammary immune biology and evaluates established and emerging biomarkers and diagnostics for SCM. Particular emphasis is placed on the distinction between risk prediction, screening and diagnosis, and on the need for external validation before biomarker panels are translated into routine herd-level practice.