Combining total and differential somatic cell count to screen for mastitis

Tania Bobbo, Ali Zidi, Martino Cassandro

Abstract


Submitted 2020-06-30 | Accepted 2020-07-23 | Available 2020-12-01

https://doi.org/10.15414/afz.2020.23.mi-fpap.88-96

Somatic cell count (SCC) has been extensively used as indicator of udder health and milk quality. Recent developments in milk-testing technology have led to cell differentiation in milk in a high throughput manner. Information on the proportion of the different cell types in milk would represent a valuable asset for a more precise definition of udder health status. The aim of the present study was to apply receiver-operating characteristic curve analysis to define the most accurate thresholds of milk differential somatic cell count (DSCC), which represents the percentage of neutrophils plus lymphocytes in the total SCC. The dataset accounted for 117,482 test-day records of 60,009 Holstein Friesian, Brown Swiss and Simmental cows. Different thresholds were defined so that DSCC trends were analysed throughout the lactation, considering also the classification factors of breed and parity. Finally, cows were classified as healthy, susceptible, mastitic or chronic on the basis of their health status, which was defined combining the information of SCC (below or above 200,000 cells/mL) and DSCC (below or above the specific cut-off). Our findings offered new insights for a practical use of DSCC to screen for mastitis, in order to help farmers make decisions to reduce the use of antimicrobials in the herd.

Keywords: differential somatic cell count; receiver-operating characteristic (ROC) curve; cut-off; mastitis; cattle

References

Adkins, P.R.F. and Middleton, J.R. (2018) Methods for diagnosing mastitis. Veterinary Clinics of North America: Food Animal Practice, 34, 479–491. https://doi.org/10.1016/j.cvfa.2018.07.003

Bobbo, T. et al. (2016) The nonlinear effect of somatic cell count on milk composition, coagulation properties, curd firmness modeling, cheese yield, and curd nutrient recovery. Journal of Dairy Science, 99, 5104-5119. https://doi.org/10.3168/jds.2015-10512

Bobbo, T. et al. (2019) Short communication: Genetic aspects of milk differential somatic cell count in Holstein cows: A preliminary analysis. Journal of Dairy Science, 102, 4275–4279. https://doi.org/10.3168/jds.2018-16092

Bobbo, T. et al. (2018) Alternative somatic cell count traits exploitable in genetic selection for mastitis resistance in Italian Holsteins. Journal of Dairy Science, 101, 10001–10010. https://doi.org/10.3168/jds.2018-14827

Cecchinato, A. et al. (2018) Genetic variation in serum protein pattern and blood β-hydroxybutyrate and their relationships with udder health traits, protein profile, and cheese-making properties in Holstein cows. Journal of Dairy Science, 101, 11108-11119. https://doi.org/10.3168/jds.2018-14907

Cassandro, M. et al. (2008) Genetic parameters of milk coagulation properties and their relationships with milk yield and quality traits in Italian Holstein cows. Journal of Dairy Science, 91, 371-376. https://10.3168/jds.2007-0308.

Damm, M. et al. (2017) Differential somatic cell count - A novel method for routine mastitis screening in the frame of Dairy Herd Improvement testing programs. Journal of Dairy Science, 100, 4926–4940. https://doi.org/10.3168/jds.2016-12409

Dohoo, I.R. and Leslie, K.E. (1991) Evaluation of changes in somatic cell counts as indicators of new intra-mammary infections. Journal of Preventive Veterinary Medicine, 10, 225-237. https://doi.org/10.1016/0167-5877(91)90006-N.

Kamarudin, A.N. et al. (2017) Time-dependent ROC curve analysis in medical research: current methods and applications. BMC Medical Research and Methodology, 17, 53. https://doi.org/10.1186/s12874-017-0332-6

Kehrli, M.E. and Shuster, D.E. (1994) Factors affecting milk somatic cells and their role in health of the bovine mammary gland. Journal of Dairy Science, 77, 619–627. https://doi.org/10.3168/jds.S0022-0302(94)76992-7

Kirkeby, C. et al. (2019) Differential somatic cell count as an additional indicator for intramammary infections in dairy cows. Journal of Dairy Science, 103, 1759-1775. https://doi.org/10.3168/jds.2019-16523

Lee, C.S. et al. (1980) Identification properties and differential counts of cell populations using electron microscopy of dry cow secretions, colostrum and milk from normal cows. Journal of Dairy Research, 47, 39–50. https://doi.org/10.1017/S0022029900020860

Leitner, G. et al. (2008) Milk leucocyte population patterns in bovine udder infection of different aetiology. Journal of Veterinary Medicine B, 47, 581–589. https://doi.org/10.1046/j.1439-0450.2000.00388.x

Liu, H. and Wu, T. (2003) Estimating the area under a receiver operating characteristic (ROC) curve for repeated measures design. Journal of Statistical Software, 8, 1–18. https://doi.org/10.18637/jss.v008.i12.

Lopez-Raton, M. et al. (2014) OptimalCutpoints: An R package for selecting optimal cutpoints in diagnostic tests. Journal of Statistical Software, 61, 1-36. https://doi.org/10.18637/jss.v061.i08

Michael, H. et al. (2019) The ROC curve for regularly measured longitudinal biomarkers. Biostatistics, 20, 433-451. https://doi.org/10.1093/biostatistics/kxy010

Pilla, R. et al. (2012) Microscopic differential cell counting to identify inflammatory reactions in dairy cow quarter milk samples. Journal of Dairy Science, 95, 4410–4420. https://doi.org/10.3168/jds.2012-5331

Pilla, R. et al. (2013) Differential cell count as an alternative method to diagnose dairy cow mastitis. Journal of Dairy Science, 96, 1653–1660. https://doi.org/10.3168/jds.2012-6298

Pillai, S.R. et al. (2001) Application of differential inflammatory cell count as a tool to monitor udder health. Journal of Dairy Science, 84, 1413–1420. https://doi.org/10.3168/jds.S0022-0302(01)70173-7

R Core Team (2018) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

Ruegg, P.L. and Pantoja, J.C.F. (2013) Understanding and using somatic cell counts to improve milk quality. Irish Journal of Agricultural and Food Research, 52, 101-117.

Schukken, Y.H. et al. (2003) Monitoring udder health and milk quality using somatic cell counts. Veterinary Research, 34, 579-596. https://doi.org/10.1051/vetres:2003028

Schwarz, D. et al. (2011) Microscopic differential cell counts in milk for the evaluation of inflammatory reactions in clinically healthy and subclinically infected bovine mammary glands. Journal of Dairy Research, 78, 448–455. https://doi.org/10.1017/S0022029911000574

Schwarz, D. et al. (2019) Investigation of differential somatic cell count as a potential new supplementary indicator to somatic cell count for identification of intramammary infection in dairy cows at the end of the lactation period. Preventive Veterinary Medicine, 172, 104803. https://doi.org/10.1016/j.prevetmed.2019.104803

Viale, E. et al. (2017) Association of candidate gene polymorphisms with milk technological traits, yield, composition, and somatic cell score in Italian Holstein-Friesian sires. Journal of Dairy Science, 100, 7271–7281. https://doi.org/10.3168/jds.2017-12666

Wall, S. K. et al. (2018) Differential somatic cell count in milk before, during, and after artificially induced immune reactions of the mammary gland. Journal of Dairy Science, 101, 5362–5373. https://doi.org/10.3168/jds.2017-14152

Youden, W.J. (1950) An index for rating diagnostic tests. Cancer, 3, 32–35. https://doi.org/10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3

Zecconi, A. et al. (2019) Assessment of subclinical mastitis diagnostic accuracy by differential cell count in individual cow milk. Italian Journal of Animal Science, 18, 460-465. https://doi.org/10.1080/1828051X.2018.1533391

 


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