Cluster analysis to evaluate disease risk in periparturient dairy cattle. (26th August 2020)

• Record Type: Journal Article
• Authors: Ishikawa, Sho; Ikuta, Kentaro; Obara, Yoshiaki; Oka, Akio; Otani, Yoshihisa; Takahashi, Yuji; Bai, Hanako; Terada, Fuminori; Kushibiki, Shiro
• Is Part Of: Animal science journal. Volume 91:Number 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-08-26
• Subjects: cluster analysis -- dairy cattle -- hypocalcemia -- ketosis -- periparturient disease risk; Animal culture -- Periodicals; Livestock -- Periodicals; 636
• DOI: 10.1111/asj.13442
• Languages: English
• ISSNs: 1344-3941

Abstract

Abstract: Predicting periparturient disease risk is of immense value to the dairy industry. Periparturient diseases are interrelated with each other; however, predicting the onset risk of these diseases has predominantly been based on a single blood parameter for a single disease. This study examined a new diagnostic method to predict the risk of periparturient diseases. We conducted cluster analysis of multiple blood constituents from 20 Holstein cattle at 1 week post‐partum, and the cattle were divided into two groups, A or B. We then compared the periparturient and early‐lactation blood constituents of these groups. Group B had significantly higher 3‐hydroxybutyric acid concentrations and were suspected to have subclinical ketosis. Group B also had significantly lower calcium concentrations, with a tendency for subclinical hypocalcemia. We also performed discriminant analysis using blood parameters at 1 week post‐partum, which grouped the population into the same two groups as the cluster analysis based on three variables: inorganic phosphorus, calcium, and either phospholipids or total cholesterol. We further showed that these discriminant functions could be used to predict the risk of periparturient disease even before parturition. Our results indicate that cluster analysis with multiple blood constituents is useful for predicting periparturient disease risks.