Short communication: Protease activity measurement in milk as a diagnostic test for clinical mastitis in dairy cows

Due to the increasing use of automated milking systems, automated detection of clinical mastitis is becoming more important. Various in- or on-line diagnostic tests are in use, but generally suffer from false mastitis alerts. In this study, we explored a new diagnostic approach based on measurement of protease activity using fluorogenic protease substrates, which can be performed on site, at high speed, and at low costs. Samples from cows with clinical mastitis submitted for bacteriological culture at the University Farm Animal Practice were collected during several months and kept at -20 degrees C until protease activity measurement. A reference set of milk samples from clinically healthy cows were collected on 9 different farms and were tested for protease activity directly and after freezing at -20 degrees C to allow for comparison with the samples from clinical cases. The protease activity in mastitic milk samples was significantly higher than in samples from healthy animals. Based on 71 clinical mastitis samples and 180 milk samples from clinically healthy quarters, the area under the receiver-operating characteristic curve was estimated to be between 0.88 and 0.90, and at a threshold of 38 fluorescence per minute the test had a specificity of 0.99 at a sensitivity of 0.58. Protease activity measured in fresh milk from clinically healthy cows was significantly associated with somatic cell count and parity, but not with electrical conductivity, whereas protease activity in milk that had been frozen was statistically significantly associated with all 3 parameters. This study indicates that protease activity measurement as a stand-alone test can be used for detecting mastitis samples, using milk samples that have been frozen. Because protease activity acts in part on a different biological mechanism than somatic cell count or electrical conductivity, this test may increase the accuracy of mastitis diagnosis in combination with currently available in- or on-line tests in automated milking systems.