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Cluster analysis to evaluate disease risk in periparturient dairy cattle
Author(s) -
Ishikawa Sho,
Ikuta Kentaro,
Obara Yoshiaki,
Oka Akio,
Otani Yoshihisa,
Takahashi Yuji,
Bai Hanako,
Terada Fuminori,
Kushibiki Shiro
Publication year - 2020
Publication title -
animal science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.606
H-Index - 38
eISSN - 1740-0929
pISSN - 1344-3941
DOI - 10.1111/asj.13442
Subject(s) - subclinical infection , cluster (spacecraft) , disease , ketosis , population , dairy cattle , biology , medicine , lactation , zoology , physiology , endocrinology , pregnancy , environmental health , diabetes mellitus , programming language , computer science , genetics
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.