Feasibility of a clinical chemical analysis approach to predict misuse of growth promoting hormones in cattle.
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| Abstract |
A study was performed to determine if targeted metabolic profiling of cattle sera could be used to establish a predictive tool for identifying hormone misuse in cattle. Metabolites were assayed in heifers (n = 5) treated with nortestosterone decanoate (0.85 mg/kg body weight), untreated heifers (n = 5), steers (n = 5) treated with oestradiol benzoate (0.15 mg/kg body weight) and untreated steers (n = 5). Treatments were administered on days 0, 14, and 28 throughout a 42 day study period. Two support vector machines (SVMs) were trained, respectively, from heifer and steer data to identify hormone-treated animals. Performance of both SVM classifiers were evaluated by sensitivity and specificity of treatment prediction. The SVM trained on steer data achieved 97.33% sensitivity and 93.85% specificity while the one on heifer data achieved 94.67% sensitivity and 87.69% specificity. Solutions of SVM classifiers were further exploited to determine those days when classification accuracy of the SVM was most reliable. For heifers and steers, days 17-35 were determined to be the most selective. In summary, bioinformatics applied to targeted metabolic profiles generated from standard clinical chemistry analyses, has yielded an accurate, inexpensive, high-throughput test for predicting steroid abuse in cattle.
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| Authors | Rodat T Cunningham, Mark H Mooney, Xiao-Lei Xia, Steven Crooks, David Matthews, Michael O'Keeffe, Kang Li, Christopher T Elliott |
| Journal | Analytical chemistry (Anal Chem) Vol. 81 Issue 3 Pg. 977-83 (Feb 01 2009) ISSN: 1520-6882 [Electronic] United States |
| PMID | 19128143 (Publication Type: Comparative Study, Evaluation Study, Journal Article, Research Support, Non-U.S. Gov't) |
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