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Feasibility of a Clinical Chemical Analysis Approach To Predict Misuse of Growth Promoting Hormones in Cattle Rodat T. Cunningham,*,† Mark H. Mooney,† Xiao-Lei Xia,‡ Steven Crooks,§ David Matthews,| Michael O’Keeffe,⊥ Kang Li,‡ and Christopher T. Elliott† Institute of Agri-Food and Land Use, School of Biological Sciences, Queen’s University Belfast, Belfast BT9 5AG, U.K., School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Ashby Building, Stranmillis Road, Belfast BT9 5AH, U.K., Veterinary Sciences Division, Agri-Food and Biosciences Institute, Stormont, Stoney Road, Belfast, Biometrics and Information Systems, Agri-Food and Biosciences Institute (AFBI), AFBI Headquarters, 18a NewForge Lane Belfast BT9 5PX, U.K., and Ashtown Food Research Centre, Dunsinea, Ashtown, Dublin 15, Ireland 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 hormonetreated 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. Despite the European Union (EU) Directive 88/146/EEC, produced in the late 1980s, banning use of growth-promoting agents (GPAs) in beef production, illegal use still persists.1 At around the same time that GPAs were banned in Europe, the Joint * To whom correspondence should be addressed. Dr. R. T. Cunningham, Institute of Agri-Food and Land Use, School of Biological Sciences, Queen’s University Belfast, BT9 5AG, U.K. E-mail:
[email protected]. Fax: 02890976513. † Institute of Agri-Food and Land Use, School of Biological Sciences, Queen’s University Belfast. ‡ School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast. § Veterinary Sciences Division, Agri-Food and Biosciences Institute. | Agri-Food and Biosciences Institute, Biometrics and Information Systems. ⊥ Ashtown Food Research Centre. (1) Courtheyn, D.; Le Bizec, B.; Brambilla, G.; De Brabander, H. F.; Cobbaert, E.; Van De Wiele, M.; Vercammen, J.; De Wasch, K. Anal. Chim. Acta 2002, 473, 71–82. 10.1021/ac801966g CCC: $40.75 2009 American Chemical Society Published on Web 01/07/2009
Food and Agricultural Organization/ World Health Organization (FAO/WHO) expert committee on food additives (JECFA) along with the U.S. Food and Drug Administration (FDA) ruled that the use of sex steroids in cattle growth promotion was not dangerous.2,3 Consequently, hormones have continued to be used to promote growth in beef cattle both legally in the U.S. and elsewhere in the world and illegally within the EU.4 Use and abuse of GPAs is an extremely lucrative business with substantial economic benefits resulting from the pronounced increase in muscle mass obtained and resulting feed efficiency.5 Research into the mechanisms by which steroids establish maximum growth promotion continues unabated5-12 alongside developments in the methods used to detect their illicit use.13-16 Within the EU, mandatory monitoring of steroid abuse occurs in only approximately 0.05% of the total herd population17 and generally involves tests based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) and gas chromatography tandem (2) Andersson, A.-M.; Skakkebaek, N. E. Eur. J. Endocrinol. 1999, 140, 477– 485. (3) Brower, V. EMBO Rep. 2001, 2 (3), 173–174. (4) Stephany, R. W. APMIS 2001, 109 (103), S357–64. (5) White, M. E.; Johnson, B. J.; Hathaway, M. R.; Dayton, W. R. J. Anim. Sci. 2003, 81, 965–72. (6) Hendricks, D. M.; Brandt, R. T.; Titgemeyer, E. C.; Milton, C. T. J. Anim. Sci. 1997, 75, 2627–2633. (7) Meyer, D. L.; Kerley, M. S.; Walker, E. L.; Keisler, D. H.; Pierce, V. L.; Schmidt, T. B.; Stahl, C. A.; Linville, M. L.; Berg, E. P. J. Anim. Sci. 2005, 83, 2752–2761. (8) Meyer, H. H. D. APMIS 2001, 109, 1–8. (9) Smith, K. R.; Duckett, S. K.; Azain, M. J.; Sonon, R. N.; Pringle, T. D. J. Anim. Sci. 2007, 85, 430–440. (10) Faucitano, L.; Chouinard, P. Y.; Fortin, J.; Mandell, I. B.; Lafreniere, C.; Girard, C. L.; Berthiaume, R. J. Anim. Sci. 2008, 86, 1678–1689. (11) Johnson, B. J.; Chung, K. Y. Vet. Clin. Food Anim. 2007, 23, 321–332. (12) Reinhardt, C. Vet. Clin. Food Anim. 2007, 23, 309–319. (13) Odore, R.; Badino, P.; Pagliasso, S.; Nebbia, C.; Cuniberti, B.; Barbero, R.; Re, G. J. Vet. Pharmacol. Ther. 2006, 29, 91–97. (14) Noppe, H.; Le Bizec, B.; Verheyden, K.; De Brabander, H. F. Anal. Chim. Acta 2008, 611, 1-16. (15) Van den Hauwe, O.; Dumoulin, F.; Antignac, J. P.; Bouche, M. P.; Elliott, C.; Van Peteghem, C. Anal. Chim. Acta 2002, 473, 127–134. (16) Elliott, C. T.; Francis, K. S.; Shortt, H. D.; McCaughey, W. J. Analyst 1995, 120, 1827–1830. (17) O’Keeffe, M.; Rehmann, F.-J.; Coen, K. National Food Residue Database Report, 2006, http://nfrd.teagasc.ie/pdf/NFRD_Annual_Report_2006.pdf (accessed February 2008).
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mass spectrometry (GC/MS/MS).18,19 These MS-based technology platforms have evolved as the analytical method of choice based on their ability to detect a wide range of different steroid compounds and their metabolites with high sensitivity. The same rationale applies to samples taken from animals and humans as part of antidoping control programs.20 Though a number of additional tests are performed as part of industry-led farm quality assurance schemes in countries which export meat, the costs associated with such monitoring are prohibitive. Issues relating to both the cost of sampling and the complexity and cost of analysis are common to both food production and sports related drug misuse monitoring programs. As a result, particularly in the food production arena, relatively few samples are taken from farm animals for scrutiny for the presence of illicit substances. This has led to the belief that widespread abuse of hormonal substances by unscrupulous farmers has become the norm in some parts of Europe.4 A further complicating factor in steroid analysis is the design and synthesis of new drugs which are not included within existing testing programmes. This mirrors the situation in sports medicine where the misuse of tetrahydrogestrinone (THG) by athletes only became known when a quantity of the substance was sent to the authorities for analysis.21 Moreover the trend for steroid abuse during beef production in Europe has been associated with the use of “steroid cocktails” of multiple agents all used at concentrations below the levels of reliable MS detection.4,14 A growing school of thought has pointed toward the need for alternative means of detecting drug abuse, both in food production and in sport. The “omic” technologies have found an ever widening range of applications in human health and life science research areas. Such techniques offer the potential for application in profiling abuse of growth enhancing substances. Through the use of targeted proteomic profiling of blood samples obtained from animals treated with steroids, clear patterns of up- and downregulation of selected proteins, e.g., aminoterminal propeptide of type III procollagen (PIIINP) have been observed in response to administrations.22,23 The potential to use the measurement of the concentrations of such proteins as steroid abuse “biomarkers” offers possibilities for the future. However, for such methods to be capable of reliably detecting steroid administration, large numbers of biomarkers will have to be identified. This is in part due to the large fluctuations in the levels of measured protein levels due to differences arising from such variables as age, sex, breed, health, and feeding regimes employed. An alternative means of identifying illegal GPA use is to analyze blood samples for a wide range of general serum biochemical analytes, e.g., creatinine, glucose, total bilirubin, etc., which have been altered in concentration as a consequence of drug admin(18) Le Bizec, B.; Van Hoof, N.; Courtheyn, N.; Gaudin, I.; Van De Wiele, M.; Bichon, E.; De Brabander, H.; Andre, F. J. Steroid Biochem. Mol. Biol. 2006, 28, 78–89. (19) Blasco, C.; Van Poucke, C.; Van Peteghem, C. V. J. Chromatogr., A 2007, 1154 (1-2), 230–239. (20) Borrey, D.; Moerman, E.; Cockx, A.; Engelrelst, V.; Langlois, M. R. Clin. Chim. Acta 2007, 382, 134–137. (21) Catlin, D. H.; Sekera, M. H.; Aherns, B. D.; Starcevic, B.; Chang, Y.-C.; Hatton, C. K. Rapid Commun. Mass Spectrom. 2004, 18, 1245–1249. (22) Mooney, M. H.; Situ, C.; Cacciatore, G.; Hutchinson, T.; Elliott, C.; Bergwerff, A. Biomarkers 2008, 13 (3), 246–256. (23) Mooney, M. H.; Bergwerff, A. A.; van Meeuwen, J. A.; Luppa, P. B.; Elliott, C. T. Anal. Chim. Acta, in press.
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istration. A number of reports have studied the profiles of such metabolites in cattle blood, both in relation to disease processes and to establish normal ranges. As in humans, these may act as indicators of nutrition, wellbeing, and clinical status and can be of immense value in arriving at opinions relating to the health of the animal. However, such diagnostics are not without their own complications and it has been found that breed, age, reproductive status, and stage of lactation can all influence blood analytes.24 Therefore, when considering abnormal levels, it is essential to look at age-specific control animals also.25 The use and misuse of anabolic steroids in cattle can be thought of as being closely related to the abuse of the same compounds by athletes and bodybuilders, as the biological effect being sought, i.e., increased anabolism, is identical. In both animals and humans, steroid hormones are being used to manipulate metabolic processes and influence how energy is directed and transformed into body mass. The blood biochemistry of individuals engaged in long-term steroid abuse has been investigated previously with Stimac et al.26 linking high doses of androgens and anabolic steroids in bodybuilders to increased levels of bilirubin, liver enzymes, and albumin, with levels returning to normal once drug use discontinued. Groot et al.27 recorded similar liver pathology in cattle given the anabolic steroid stanozolol to those effects seen in anabolic steroid abuse in bodybuilders. Consequently, the use of these compounds may potentially be determined by comparing the normal circulating levels of a panel of serum biochemical parameters with levels detected postadministration of GPAs. In the present study, a detailed comparison was made of clinical chemistry profiles in matched control and treated male (castrated) animals (steers) receiving oestradiol benzoate and matched control and treated females (heifers) receiving nortestosterone decanoate. Nortestosterone decanoate and 17β-oestradiol benzoate were chosen as the administered agents due to the fact that they are commonly abused steroids and are difficult to detect using conventional analytical approaches.2,22 The doses of compounds used and the duration of the treatment period are in line with previous experimental studies where animals have been administered growth promoting agents.11,12,22 The aim was to determine if the profiles of routinely measured metabolites present in cattle serum could be used as a screening test to detect hormone misuse. MATERIALS AND EXPERIMENTS Reagents. Nortestosterone decanoate was from Organon Laboratories Ltd., Cambridge, U.K., and 17β-oestradiol benzoate was from Intervet International B.V. (Boxmeer, The Netherlands). Animals. Continental crossbred heifers (n ) 10) and steers (n ) 10) were obtained from farms regularly tested for the presence of anabolic compounds and consequently regarded as hormone-free. At approximately 16-18 months of age and with an average bodyweight of 357 kg, they were randomly placed into control heifer, treated heifer, control steer, and treated steer (24) Doornenbal, H.; Tong, A. K. W.; Murray, N. L. Can. J. Vet. Res. 1988, 52, 99–105. (25) Mohri, M.; Sharifi, K.; Eidi, S. Res. Vet. Sci. 2007, 83, 30–39. (26) Stimac, D.; Milic, S.; Dintinjana, R. D.; Kovac, D.; Ristic, S. J. Clin. Gastroenterol. 2002, 35 (4), 350–352. (27) Groot, M. J. J. Vet. Med. 2002, 49, 466–469.
groups. They were housed throughout the project and fed as normal. After a 1 week acclimatization period, the treated heifer group received nortestosterone decanoate (0.85 mg/kg body weight) and the treated steer group received oestradiol benzoate (0.15 mg/kg body weight) by intramuscular injection. This treatment was repeated on days 14 and 28 in the treated heifer and treated steer groups. Blood samples were collected on days 0, 1, 4, 7, 11, 14, 17, 21, 25, 28, 31, 35, 39, and 42 from the anterior jugular vein. Blood collection commenced at the same time each day (0900) to avoid potential interferences caused by diurnal rhythms. Serum was removed from all samples by centrifugation at 2500 rpm for 10 min at 4 °C. Samples were stored frozen at -20 °C. Independent blood samples (n ) 25) were obtained from experimental herds located on government controlled and thus hormone-free facilities. Sampled herds consisted of animals of various breeds and constituted steer (n ) 7, 17-21 months old), cow (n ) 5, 43-45 months old), and heifer (n ) 13, 17-22 months old) animals. Care of all animals used in the study was in accordance with institutional guidelines. Analysis of Metabolic Markers. All analytes were tested and results calculated using a Roche Modular Analyzer, West Sussex, U.K. The analytes measured and used in subsequent predictive analysis constitute a profile routinely adopted within standard blood chemistry analysis to monitor normal health and wellbeing and comprised analysis of total protein, potassium, phosphate, creatinine, cholesterol, albumin, alkaline phosphatase (ALP), aspartate transaminase (AST), alanine transaminase (ALT), gamma glutamyltransferase (GGT), lactate dehydrodenase (LDH), sodium, chloride, carbon dioxide (CO2), urea, glucose, calcium, total bilirubin, urate, and corrected calcium. Bioinformatic Analysis. For basic statistical analysis, unpaired t tests were used to compare treated and control animals while paired t tests were used to compare day 0 sample data with that obtained over subsequent sampling points. For the classification of control cattle and treated cattle, Support Vector Machines (SVMs), which are a family of popular supervised machine learning methods, were used.28-31 To initiate data analysis, clinical chemistry profiles of all heifers (or steers) across 14 observation days were transformed into a training set for an SVM. Based on the metabolite concentrations present, an SVM learns this training set and can then discriminate between control and treated cattle. Therefore, the SVM is able to predict whether animals have been treated or not according to their clinical chemistry profiles. Two SVM classifiers, one for steers the other for heifers, were established. Also investigated by exploitation of information revealed from SVM solutions were the study days most suitable for sampling in terms of those providing the greatest prediction accuracy. Support Vector Machines. Described later is the establishment of the training data set for heifer SVM, which was similarly applied to the establishment of the steer SVM. The measurements of 20 clinical chemistry markers across 14 days for the 10 heifers (28) Vapnik, V. Statistical Learning Theory; Wiley: New York, 1998. (29) Burges, C. J. Data Min. Knowledge Discovery 1998, 2, 121–167. (30) Scholkopf, B.; Burges, C. J.; Smola, A. J. In Advances in Kernel MethodsSupport Vector Learning MIT Press: Cambridge, MA, 1999. (31) Kohavi, R. In Proceedings of the Fourteenth International Conference on Artificial Intelligence (IJCAI), San Mateo, CA, Aug 20-25, 1995, pp 11371143.
form a matrix of 140 rows of vectors composed of 20 elements. Each vector, denoted by Xi ) (x1, x2, ... x20), is in fact a single training sample. Of the 140 training samples, 75 are of control group and 65 treated group; the imbalance is due to the absence of actual treated heifer data on day 0. Each vector Xi can be regarded as a data point in the 20-dimensional space with the control group assigned a class label of “+1” and the treated group “-1”. Theoretically, it is feasible to find a separating hyperplane in this space with which the training data are correctly arranged into their intended class and the distance between boundaries of the two classes, which is known as the margin, is maximized. However, in practice, most reallife problems are not trivial so that the separating hyperplane cannot be located. This problem is solved by mapping the data to a space of higher dimensions where they become separable. The new space is termed as feature space, as opposed to the input space which contains the training data. However, one obvious drawback is that the data mapping from the input space to the feature space imposes more computational cost. Nonetheless, in the case that the feature space is infinite in dimensions, great difficulties arise in implementing the data transformation. Fortunately, in SVM algorithms, a favorable fact is that its optimal hyperplane requires only the dot product between training data in the feature space. By introduction of the kernel technique which performs the dot products between vectors of feature space, the search for the optimal hyperplane can be formulated even without explicit knowledge of the feature space. This feature of kernel function spares SVMs the computation of the exact representation of the training data in the feature space. For illposed classification problems, it is not practical to have a hyperplane of the optimal margin with which each training sample falls into the supposed group. SVMs then settle for a separating hyperplane which allows classification errors on training errors. A parameter is to be chosen to impose penalties on errors, which actually acts as the tradeoff between the training errors and the margin. Therefore, the establishment of a support vector machine is started with proper settings of the penalty constant and also parameter(s) of the chosen kernel function, which depend heavily upon the specific training data set. Experiments in this paper all opted for Gaussian radial basis function (RBF) which is the most widely used one and takes the form of K(X, Y) ) exp(-λ||X - Y||2)
(1)
where Xi is the vector representations for the ith heifer sample and Xj for the jth one, where X and Y are two points in n-dimensional Eucleidan space, and λ is a constant. In this paper, the parameter λ as well as the penalty constant, denoted as C, for an SVM is tuned by implementing grid-search and is set as the values which produce the best leave-one-out cross validation (LOOCV) accuracy. The decision function of an SVM evaluated on a test sample Z is f(Z) )
∑ R y exp(-λ||X - Z|| ) 2
i i
i
(2)
i
where Xi is an input training vector with a positive coefficient Ri as its weight and bears a class label of yi which is either +1 Analytical Chemistry, Vol. 81, No. 3, February 1, 2009
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Figure 1. Percentage increase in bodyweight of control and treated (a) heifer and (b) steer animals over the 42 day study period. Values shown are mean ( SEM (n ) 5 per group).
or -1. Input vectors Xi as employed in eq 2 are called “support vectors” (SV) for they make an actual contribution to the construction of the optimal hyperplane. b is the bias term of the hyperplane. For all the experiments in this paper, support vector machines (SVMs) were implemented by LIBSVM.32
Figure 2. Serum creatinine levels in control and treated (a) heifers and (b) steers. Values shown are mean ( SEM (n ) 5 per group). // p < 0.01, / p < 0.05 vs control group.
RESULTS AND DISCUSSION Bodyweight. Figure 1 illustrates the percentage increase in bodyweight of control and treated (a) heifer and (b) steer animals over the 42 day study period. Animals undergoing treatment were found to demonstrate an increase in bodyweight, but this increase was not significantly different from the observed increase in matching control groups. This may have been due to the relatively small study cohort size and the fact that only one hormone was administered per animal. This finding is in agreement with previous reports9 in heifers and steers implanted with a combination of oestradiol benzoate and trenbolone acetate (TBA), where bodyweight in both groups increased but no significant difference was noted between treated and control heifers until day 80 and until after reimplantation (day 73) in steers, though average daily gain for both groups did increase. Individual Markers. In the present study, the potential of biochemical analytes to screen for the administration of banned steroids in cattle was investigated. The reasons for choosing analytes were numerous. Creatinine, urea, and total protein would be expected to be altered by GPAs since these affect protein accretion. Diethylstilbestrol (DES), when implanted in steers, causes a decrease in plasma amino acids and urea and this may be due to its effects on protein metabolism. TBA, an androgen, is also associated with decreased plasma urea when implanted in
ruminants and in female rats, increases growth rate, nitrogen retention, feed conversion ratio, but decreases the rate of skeletal muscle protein synthesis and degradation.33 The overall increase in growth rate is due to a more dramatic reduction in protein degradation relative to synthesis. However, not all androgens are believed to act in a similar mode. Testosterone and its derivatives may also have an impact on glucocorticoid action by blocking glucocorticoid-receptor complex formation.33 In bodybuilders, anabolic steroids have also been linked to changes in cholesterol levels34 or liver enzymes, with improved levels being recorded within 12 weeks of discontinuing the drugs.26 In this study, no single analyte could be used as an absolute marker of anabolic steroid abuse over the complete course of the study period although creatinine showed statistically significant differences between treated and control animals at various time points (Figure 2). In heifers, a statistically significant difference in creatinine was observed between treated and control animals at day 42. Significant differences were also recorded in steers at days 17, 21, and 28 (poststeroid treatment 2) and at day 31 (after the third treatment). Creatinine levels increased in all animals from levels observed at day 0. Creatinine is released from the catabolysis of creatine, stored in muscle, and is an indicator of muscle mass, hence the increase over time in both sexes. It is reported that the best growth promotion is observed with a cocktail of estrogen and androgen.6 Males receiving estrogen might be expected to increase food intake and gain weight, as indicated by increased creatinine levels (Figure 2b). The combination of androgen (nortestosterone) against a background of estrogen in heifers may
(32) Chih-Chung, C.; Chih-Jen, L. LIBSVM: a Library for Support Vector Machines, 2001. Software available at http://www.csie.ntu.edu.tw/∼cjlin/libsvm (accessed November 2008).
(33) Buttery, P. J.; Vernon, B. G. Vet. Res. Commun. 1983, 7, 11–17. (34) Dickerman, R. D.; McConathy, W. J.; Zachariah, N. Y. J. Cardiovas. Risk 1997, 4 (5-6), 363–366.
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account for the increase in creatinine levels in treated relative to control animals, which reaches significance at day 42 in this group (Figure 2a). Sex hormones have been linked to moderate increases in blood bilirubin. Severe androgen abuse in athletes can cause toxic effects on the liver leading to hyperbilirubinaemia26 though levels of sex hormones administered in this study were not as high.26 Here, a significant difference in total bilirubin serum levels was observed between treated and control heifers at day 1 (p < 0.05), with lower levels observed in animals given nortestosterone decanoate. A significant difference in serum total bilirubin levels was noted at day 17 in steers treated with oestradiol benzoate (p < 0.05) with higher levels recorded in treated animals. At all other time points there was no significant difference between control and treated animals. Urea production is linked to protein catabolism since this is the process by which ammonium is excreted. Decreased plasma urea has been observed in steers treated with DES and in ruminants treated with TBA.33 Here, serum urea levels increased from day 0 to day 42 both in treated and control heifers (p ) 0.0005 for treated animals and p ) 0.001 for controls, paired t test) and in treated and control steers (p < 0.0001 for treated animals and p < 0.01 for controls, paired t-test). No significant difference was recorded between treated and control animals during the experiment. Albumin synthesis is very sensitive to protein and nitrogen loss so an increase may be an indication of increased nitrogen gain accompanying estrogen-treatment in males. However, pretreatment albumin levels differed from controls in both steers and heifers so albumin cannot be reliably used as a marker since influences other than steroid treatment affect levels. Of the other parameters measured only a limited number were found to be demonstratively altered during the course of the study period. At day 39 in treated steers serum glucose was elevated above controls (p < 0.05, unpaired t test) while CO2 was also significantly lower in treated steers at day 31 relative to control animals. Serum calcium was increased significantly in treated heifers at day 42 only p < 0.05, unpaired t test). Total protein did show a statistically significant difference between treated and control heifers at day 28 (p < 0.05) and steers at day 31 (p < 0.05) with higher measured levels in control heifers and lower levels in control steers. Sodium levels were significantly different at days 17 and 25 (p < 0.05) in heifers with higher levels in control animals. In steers, significant differences in sodium levels were noted at days 3, 14, and 17 (p < 0.05) with levels higher in treated animals. Significant differences in chloride levels were only noted in steers at days 3, 7, and 17 (p < 0.05). Levels were higher in treated steers in each case. Levels of other measured parameters including cholesterol, potassium, phosphate, AST, ALT, GGT, LDH, ALP, and urate were found not to be statistically different in treated and untreated steers or heifers. In the case of the liver enzymes, AST, ALT, GGT, and ALP, substantial liver damage is required to see an elevation. The levels of sex hormones used did not affect cholesterol, potassium, phosphate, LDH, or urate levels. Classification Results. First, training samples were scaled to [-1, +1] for each classification problem. The test samples were then transformed with the same scaling parameters. For heifer
Figure 3. (a) The support vector machine (SVM) trained from heifer data and its decision values on the 140 training heifers across 14 different days. (b) The support vector machine trained from steer data and its decision values on the 140 training steers across 14 different days.
data, the parameter setting of the SVM was the penalty constant C ) 16 and λ ) 0.25. The number of support vectors (SV) is 78. Figure 3a illustrates the decision values given by the SVM trained from heifer data on the 10 heifers across 14 different days. It shows that for each day, the SVM has categorized heifers correctly by assigning control heifers positive values and treated ones negative values. Aside from achieving overlapping-free between the two regions, their boundary lines were also pulled as far apart as possible in an effort to ensure satisfactory classification accuracy on testing examples. For steer data, the parameter setting of the SVM was C ) 256 and λ ) 0.125. The number of support vectors is 36. Figure 3b illustrates the decision values given by the SVM trained from steer data on the 10 heifers across 14 different days. Again on each day, the SVM successfully marked the treated steers from the control one, by producing positive values for control heifers and negative values for treated ones. The SVM trained from steers also demonstrated no overlapping between the two classes whose boundary lines were also pulled as far apart as possible to obtain good generalization abilities. Analytical Chemistry, Vol. 81, No. 3, February 1, 2009
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Table 1. Classification Table of Samples Subjected to SVM Classifiers Trained, Respectively, on Steer and Heifer Data
steers heifers total
true positive
false positive
true negative
false negative
73 71 144
4 8 12
61 57 118
2 4 6
LOOCV is the technique used to evaluate the prediction performance of SVMs. It starts by removing one of the training points, retraining, and then testing on the removed point. The process is then repeated for all training points. Each test sample can be categorized into one of the following four groups: true positive (TP) or true negative (TN) if the SVM classifier assignment is in agreement with known animal status or alternatively false positive (FP) or false negative (FN) if test samples are incorrectly assigned by the SVM classifier. The sensitivity and specificity of SVM classifier prediction were measured as sensitivity ) TP/(TP + FN); specificity ) TN/(TN + FP). Findings from classification analysis are shown in Table 1 with the SVM trained on heifer data achieving 94.67% sensitivity and 87.69% specificity, while the SVM for steer data reached 97.33% sensitivity and 93.85% specificity. As an additional investigation of the accuracy of the devised strategy, data from the clinical chemistry profiles from independent hormone-free animals of various breed, sex, and age were also subjected to classification. Through the use of constructed SVMs, all samples (n ) 25) were found to be correctly classified as being derived from nontreated animals. Day Significance. The significance of a sampling day refers to the possibility that the SVM successfully identifies between treated heifers (or steers) among study animals on that specific day— the better classification accuracy of the SVM on a day, the more significant that day is regarded as. The analysis of day significance is of major importance to determine optimum sampling day ranges to allow for greater detection rates of hormone misuse. Fortunately, the solutions of SVMs are very informative for determining day significance. Addressed below is the issue of the ranking of sampling days by the solution of an SVM. On the basis of the aforementioned LOOCV, an upper bound of the possibility that an SVM classifier commits error on a test sample is given by35 E[P(error)] e
E[number of support vectors] n
(3)
where P(error) is the classification error for an SVM trained on n - 1 examples, E[P(error)] is the expectation of the error over all possible training sets of size n - 1. As mentioned earlier, the solution of an SVM is constructed by a portion of, rather than all, training samples, which were referred to as “support vectors”. E[number of support vectors] in eq 3, is the expectation of the number of SVs for the SVM on n samples. From eq 3, a conclusion can be reached that the larger the number of SVs is, the more likely the associated SVM classifier fails to deliver. As a result, misclassifications tend to occur more often on those sampling days (35) Vapnik, V. The Nature of Statistical Learning Theory; Springer-Verlag: New York, 1995.
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Figure 4. (a) Significance levels of the 14 sampling days for heifer data. (b) Significance levels of the 14 sampling days for steer data.
that feature more SVs. This fact makes the daily number of SVs an indicator of the prediction accuracy of the SVM on the exact sampling day. Thus, the significance of a sampling day can be estimated by
significance of day i ) 1 -
number of SVs on day i 10
(4)
It can then be inferred that over the 42-day observation period the most significant day range(s) can be identified, i.e., the day range with greater possibility of a sound judgment about whether an animal has been treated or not. Figure 4a is a bar graph illustrating the relative significance level of the 14 sampling days for heifer data. The approximation of the day significance trend for heifer data over the observation period of 42 days can be inferred from Figure 4a. It can be seen that sampling during days 17-35 of the study period is more likely to accurately predict whether heifer animals have been receiving hormone administrations or not. Figure 4b is a bar graph of significance level of the 14 sampling days for steer animals. It can be concluded that days 17-35 should be recommended as
the sampling period for steers offering more accurate identification rates of treated steers. CONCLUSIONS In this study we have outlined an inexpensive, accurate, feasible, high-throughput test for screening for steroid abuse in cattle using a clinical chemistry profile coupled to a bioinformatic interpretation of the data generated (SVM classification methods). This is particularly important because regardless of the political debate regarding GPAs3 within the EU, their illegal use persists. If the EU Directive is to be enforced, methods of screening, confirmatory detection, and policing must exist. With regard to detection, it is estimated that of the 0.05% of the total herd population that are tested, only 0.2% of these are returned positive for hormone abuse,17 this is in contrast to suggestions that an estimated 10% of cattle in Europe should be testing positive for steroids.4 This would imply that there are serious problems with the existing control systems, at least one of which is the infrequency of testing. Under EU directive 96/23/EC, it is incumbent on all member states to monitor for illegal steroids under a national residue surveillance plan. Many of the steroid hormones being abused occur naturally, and LC-MS/MS analysis may require that a quantitative decision limit or ratio to another marker be set36 or that a discriminant metabolite is used as proof of abuse.37 Steroid esters, the form in which many GPAs are given to animals, are not naturally occurring and may accumulate in hair.38 While analysis of hair for steroid esters by LC-MS/MS may provide confirmatory testing, suitable methodology for screening for the abuse of GPAs is required. However, it is of concern that there are currently no high-throughput, convenient, and economically feasible methods of screening herds to detect illegal administration of steroids. Emerging designer drugs and the use of drug cocktails have exacerbated the testing dilemma, reflecting a similar position in sports medicine. Biochemical markers have been investigated in athletes to see if the biological effect of hormones might provide an indirect method of detection.39 The probability that doping has occurred is increased if more parameters are positive and where there is a greater deviation from normal. Kneiss et al.40 studied a small group (n ) 15) of noncompetitive athletes given human growth hormone(GH) and assayed for insulin growth factor-1 (IGF-1), IGF binding protein-3 (IGFBP-3), propeptides of type I and II procollagen (PINP, PIINP), osteocalcin, and leptin. They found that no one parameter could prove GH abuse but used a discriminant function incorporating a number of protein markers to distinguish treated individuals from controls with no false positives, three false negatives, and very few undecided cases. Recently the World AntiDoping Agency (WADA) backed the Union Cycliste Internationale (36) Hebestreit, M.; Flenker, U.; Buisson, C.; Andre, F.; Le Bizec, B.; Fry, H.; Lang, M.; Weigert, A. P.; Heinrich, K.; Hird, S.; Schanzer, W. J. Agric. Food Chem. 2006, 54, 2850–2858. (37) Le Bizec, B.; Courant, F.; Gaudin, I.; Bichon, E; Destrez, B.; Schilt, R.; Draisci, R.; Monteau, F.; Andre, F. Steroids 2006, 71, 1078–87. (38) Grataco´s-Cubarsıj, M.; Castellari, M.; Valero, A.; Garcıja-Regueiro, J. A. J. Chromatogr., B 2006, 834, 14–25. (39) Minuto, F.; Barreca, A.; Melioli, G. J. Endocrinol. Invest. 2003, 26, 919– 923. (40) Kneiss, A.; Ziegler, E.; Kratzsch, J.; Thieme, D.; Mu˝ller, R. K. Anal. Bioanal. Chem. 2003, 376, 696–700.
in proposals to establish biological passports for all riders. The passport will be an electronic document of each cyclist’s blood and urine test results collated over a period of time with individuals acting as their own reference level. At present, tests include hematological parameters and steroid results. A statistical model produced by the Laboratoire Suisse d’Analyze de Dopage, Lausanne, is then applied to determine deviation from the normal profile, possibly due to steroidal enhancement. Here, we have successfully investigated a number of clinical chemistry parameters and using bioinformatics have formulated a discriminant function which may be used to predict nortestosterone treatment in heifers with a success rate of 91.43 (sensitivity, 94.67%, specificity, 87.69%) and oestradiol treatment in steers with a success rate of 95.71% (sensitivity, 97.33%, specificity, 93.85%). Analysis of data derived from a set of independent bovine samples demonstrated the potential of such an approach with all animals being correctly assigned by SVM classifiers. In addition, those days when classification accuracy of the SVM was most accurate were identified. For both heifers and steers, days 17-35 were significant testing days. We have therefore demonstrated that bioinformatics, when applied to a clinical chemistry profile in cattle can identify steroid-treated animals. The power and effectiveness of this novel strategy to reveal growth promoter abuse among larger cohorts of animals which are representative of the wider herd population will require further work and development of new models of validation which can be applied to such screening tests. With regards to the future implementation of this technique, current EC legislation requires unequivocal proof of steroid administration and this can only be provided by use of hyphenated chromatographic procedures and this requirement is likely to persist in the medium to long-term. However, the main purpose of the screening approach envisaged here is the ability to target the mass spectrometric analysis toward samples which have been taken from cattle identified as being “highly suspicious” due to their SVM scores. This approach is fundamentally different to the status quo where samples are selected at random for analysis and in over 99.8% of cases are shown to be negative for illegal steroid content. The introduction of low cost, herd-based clinical chemistry profiling may prove to be a highly valuable weapon in the armory of those wishing to stamp out hormone abuse in Europe. ACKNOWLEDGMENT We are grateful to Margaret McDonnell and the staff of the Biochemistry Laboratory, Belfast City Hospital. This work was supported by grants from the European Commission Project, “New technologies to screen multiple chemical contaminants in foods” (Contract FOOD-CT-2005-006988 BIOCOP) and Safefood Ireland (Project 04CR/05). SUPPORTING INFORMATION AVAILABLE All data used for the SVMs outlined here. This material is available free of charge via the Internet at http://pubs.acs.org. Received for review September 17, 2008. Accepted December 1, 2008. AC801966G
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