Feasibility of a Clinical Chemical Analysis Approach To Predict Misuse of Growth Promoting Hormones in Cattle

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.

Use of NMR metabolomic plasma profiling methodologies to identify illicit growth-promoting administrations

“…Plasma glutamate also plays a central role in mammalian nitrogen flow and protein synthesis acting as a substrate for glutamine production in peripheral tissues such as skeletal muscle. Observed alterations to creatinine and creatine levels also signify effects on protein accretion and nitrogen retention and have previously been shown to be perturbed in other studies also investigating blood [30] and urine [15,20] parameters affected by growth promoters.…”

“…Direct comparison of metabolite panels identified in this study with previous metabolomic studies investigating biological responses to growth promoters [12,15,20,30] is complicated by variations in utilised treatment regimes, in sample analysis methods, and in the type of matrices analysed, i.e. plasma versus urine.…”

Set-up of a multivariate approach based on serum biomarkers as an alternative strategy for the screening evaluation of the potential abuse of growth promoters in veal calves

“…In addition, there is increasing evidence that rather than looking at deviations from the ‘normal’ range values of a single biomarker, the combination of multiple biomarkers and the application of multivariate class modelling or discriminant classification techniques may improve the diagnostic potential of screening assays. Either approach has been successfully applied not only in humans for doping detection (Pottgiesser & Schumacher 2013 ), alcohol abuse (Oliveri & Downey 2012 ; Pirro et al 2013 ), or food origin control (Marini, Bucci, et al 2006 ; Marini, Magrì, et al 2006 ), but also in cattle to predict misuse of growth-promoting hormones (Cunningham et al 2009 ). Among the supervised pattern recognition methods, multivariate class modelling represents a suitable mean for data analysis, whenever a class of interest (i.e., untreated veal calves) has to be mathematically described, for example, on the basis of several biomarkers’ values and with no bias from any other classes in the computation of the model.…”

Structure elucidation and HPLC-MS/MS determination of a potential biomarker for estradiol administration in cattle

“…Pattern recognition of steroid profiles has risen also as a promising approach for tracing/detecting the abuse of natural hormones and their esters administered to cattle [15][16][17]. Other approaches also include protein biomarker profiles [18] or blood chemistry [19]. Metabolomics is based on detecting small molecules and excluding big biopolymers such as proteins, generating this way a large set of descriptors characteristic of the biological matrix under investigation in different experimental groups [20].…”

“…However, given the large amount of data generated during metabolomics acquisitions, specifically dedicated bioinformatics tools such as XCMS software are required for processing and analyzing this huge volume of complex data. Finally, the use of multivariate statistical techniques are required to analyze such data set and finally to point out potential signals (i.e., biomarkers) of interest [19].…”