Natural variation in biomarkers indicating mastitis in healthy cows

Author:

Åkerstedt Maria,Forsbäck Linda,Larsen Torben,Svennersten-Sjaunja Kerstin

Abstract

Dairy herds are expanding and, with increasing numbers of animals in each herd, there is a need for automatic recording of indicators in milk in order to detect mastitis, inflammation of the udder. A number of biomarkers for mastitis have been suggested over the years. Mastitis usually occurs in one of the four udder quarters and since it is now possible to milk each udder quarter separately in automated milking systems, it is important to evaluate the normal variation in the biomarkers at udder quarter level. This study evaluated the normal variations between milkings for some biomarkers in clinically healthy cows, determined by repeated somatic cell count and bacteriological analysis. The biomarkers studied were serum amyloid A (SAA), haptoglobin (Hp), lactate dehydrogenase (LDH), N-acetyl-β-d-glucosaminidase (NAGase) and alkaline phosphatase (AP), parameters that have been suggested as markers for mastitis. Ten cows were monitored on 42 consecutive milking occasions through collection of udder quarter milk samples and representative cow composite milk samples, giving a total of 2100 individual milk samples. Each cow had its individual profile for the concentrations and variations in the parameters analysed. Although there was relatively large variation between cows for the biomarkers analysed, the variation between milkings in clinically healthy quarters within cows was often below 10%. The biomarker with the lowest variation in this study was LDH. The results suggest that comparing quarters within an individual cow can identify deviations from the natural variations between milkings. This could be a valuable tool instead of, or in combination with, a cut-off value for each parameter in order to detect changes in the milk indicating mastitis.

Publisher

Cambridge University Press (CUP)

Subject

Animal Science and Zoology,General Medicine,Food Science

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