Inflammatory biomarkers in milk for monitoring udder health status in Murciano-Granadina dairy goats

The aim of this study was to investigate the potential use of some inflammatory biomarkers (somatic cell count, milk amyloid A and lactoferrin) as a tool to monitor the udder health status in dairy goats. Individual milk samples from 582 Murciano-Granadina goats belonging to six commercial herds were used in this study: half-udder milk samples for subclinical mastitis diagnosis and composite milk samples to determine gross composition, pH, somatic cell count (SCC), milk amyloid A and lactoferrin concentrations. Factors affecting the udder health traits analysed in milk (farm, parity, days in milk and subclinical mastitis) were statistically analysed using a General Lineal Model procedure. A logistic regression analysis was also applied to develop predictive models to detect subclinical mastitis in dairy goats. Results indicate that milk from goats with subclinical intramammary infections (n= 216) showed higher log SCC (6.31±0.05 vs 5.95±0.04 cells/ml; p< 0.001) and milk amyloid A (40.93±3.56 vs 31.62±2.83 µg/ml; p< 0.05) concentrations than that from uninfected animals. Higher log SCC (p< 0.05) and lactoferrin (p< 0.05) concentrations were also detected in milk from goats with subclinical mastitis caused by major pathogens such as Staphylococcus aureus and gram-negative bacteria. However, the non-infectious factors considered significantly affected the concentrations of the three inflammatory biomarkers in milk, compromising their suitability to be used as indicators of mastitis. Thus, the logistic regression model based on the complementary use of the SCC, milk amyloid A and lactoferrin for subclinical mastitis diagnosis presented a predictive performance too low to be used for practical purposes (area under ROC curve= 0.673±0.046; performance= 64.27 %). However, the use of the lactoferrin and lactose concentrations in milk could be a very sensitive tool to detect intramammary infections caused by major pathogens with higher inflammatory responses (area under ROC curve= 0.941±0.028; performance= 87.16 %). Therefore, further research is needed to investigate other indicators present in milk in order to establish a more accurate and effective methodology to detect subclinical mastitis in dairy goats.