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A Case-Control Study on the Usefulness of Serum Lecithin: Cholesterol Acyltransferase Activity as a Predictor of
Hiroki Satoh1, Kyoko Chisato1, Rika Fukumori1
1Veterinary Herd Health, Department of Veterinary Medicine, School of Veterinary Medicine, Rakuno Gakuen University, Ebetsu 069-8501, Hokkaido, Japan.
Abstract:
The purpose of this study was to investigate the usefulness of the activity of lecithin:cholesterol acyltransferase (LCAT), the enzyme responsible for esterification of cholesterol in plasma, as a predictor of retained placenta (RP) in close-up cows, compared with the non-esterified fatty acids (NEFA) concentration. This study was conducted as a case-control study between February 2010 and February 2016, on a single farm with approximately 200 Holstein parous cows in Hokkaido, Japan. Of the 1187 dairy cattle that calved, 835 dairy cattle were enrolled that underwent routine regular health examinations including blood sampling, body condition score (BCS) and the rumen fill score (RFS) at the close-up stage between 2 and 21 days before their expected calving dates. Of these, 27 cows that were multiparous and had RP were designated as the RP group. The controls were 60 clinically healthy cows that did not develop RP and were matched for the sampling period and parity with the RP group. The LCAT activity and NEFA concentration were significantly (p < 0.01) lower and higher, respectively, in the RP group than in controls. There was no significant difference in cholesteryl esters, free cholesterol concentrations and BCS between the two groups. However, RFS was significantly (p < 0.01) lower in the RP group than in the controls. Cows with LCAT activity of <450 U were 3.6 times more likely to develop RP than those with higher values, whereas those with NEFA levels above 0.4 mEq/L were 5.4 times more likely to. The area under the curve of receiver operator characteristic curves showed that LCAT activity was as efficient as the NEFA concentration in the diagnostic prediction of RP, suggesting it to be a useful predictor. Logistic regression analysis with LCAT or NEFA and RFS as explanatory variables resulted in a model with higher predictive accuracy than with each alone, indicating RFS to be a possible factor in predicting RP.
