Analyze Diet
Drug testing and analysis2021; 14(5); 915-928; doi: 10.1002/dta.3041

Establishment of a post-race biomarkers database and application of pathway analysis to identify potential biomarkers in post-race equine plasma.

Abstract: In the context of doping control, conventional direct chemical testing detects only a limited scope of target substances in equine biological samples. To expand the ability to detect doping agents and their detection windows, metabolomics has recently become a common approach for monitoring alteration of biomarkers caused by doping agents in relevant metabolic pathways. In horse racing, remarkable changes in metabolic profiles between the rest state and racing are likely to affect the identification of doping biomarkers. Previously, we reported a limited number of significantly upregulated metabolites after racing, based on a non-targeted metabolomics approach using out-of-competition and post-race equine plasma samples. In this study, we performed a more thorough analysis of the data set, using pathway analysis to establish a post-race biomarkers database (PBD) that includes upregulated and downregulated metabolites, their fold changes, and relevant pathways, with the main objective of improving our understanding of changes in physiological status related to horse racing. Statistical analysis of the PBD revealed that two peak combinations of pentadecanoyl carnitine/galactosylglycerol (P/G) and heptadecanoyl carnitine/galactosylglycerol (H/G) could be used as potential biomarkers for the discrimination of the rest and post-race groups. To demonstrate the applicability of the PBD, we validated the post-race biomarkers P/G and H/G (highly involved in lipid metabolism) by a single-blind test. This strategy, which combines establishment of a biomarker database with pathway analysis, represents a powerful tool for discovering potential doping biomarkers in the future.
Publication Date: 2021-05-17 PubMed ID: 33835667DOI: 10.1002/dta.3041Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research focuses on improving doping detection in horse racing by expanding the range of detectable substances and metabolites. The study included a thorough analysis of horse plasma samples at rest and post-race to develop a post-race biomarkers database (PBD) to better understand the physiological changes related to horse racing. The researchers found potential biomarkers that can distinguish between rest and post-race states, contributing to future efficacy in doping control.

About the Research

  • The researchers examined biomarkers for doping detection in horse racing by leveraging metabolomics, a biological technique that measures the quantities of metabolites in biological systems.
  • This study was premised on the fact that conventional chemical testing is restricting as it detects only a limited number of target substances in equine biological samples.
  • Making use of the metabolomics approach, the research expands the detectable substances and their detection periods. This approach helps monitor the alteration of biomarkers caused by the use of doping substances within metabolic pathways.

Process and Findings

  • The research engaged a comprehensive analysis of equine plasma samples taken at rest and after racing. This was with the intent of establishing a Post-race Biomarkers Database (PBD).
  • PBD includes upregulated and downregulated metabolites, their fold changes, and related pathways. This was created to enhance the understanding of the changes in physiological status linked to horse racing.
  • Resultantly, the researchers discovered that two peak combinations of pentadecanoyl carnitine/galactosylglycerol (P/G) and heptadecanoyl carnitine/galactosylglycerol (H/G) could potentially be used as biomarkers to distinguish between rest and post-race states.

Conclusion and Implications

  • The procedure of establishing a biomarkers database and applying pathway analysis offers a potent tool for identifying potential doping biomarkers in the future. By-evaluation of the biomarkers revealed in the study could enable better discrimination of doping cases.
  • Improvements in doping detection will not only ensure fairness in the sport but will also protect the welfare of the horses involved.

Cite This Article

APA
Ohnuma K, Uchida T, Leung GN, Ueda T, Obara T, Ishii H. (2021). Establishment of a post-race biomarkers database and application of pathway analysis to identify potential biomarkers in post-race equine plasma. Drug Test Anal, 14(5), 915-928. https://doi.org/10.1002/dta.3041

Publication

ISSN: 1942-7611
NlmUniqueID: 101483449
Country: England
Language: English
Volume: 14
Issue: 5
Pages: 915-928

Researcher Affiliations

Ohnuma, Kohei
  • Drug Analysis Department, Laboratory of Racing Chemistry, Utsunomiya, Japan.
Uchida, Taiga
  • Drug Analysis Department, Laboratory of Racing Chemistry, Utsunomiya, Japan.
Leung, Gary Ngai-Wa
  • Drug Analysis Department, Laboratory of Racing Chemistry, Utsunomiya, Japan.
Ueda, Toshiki
  • Drug Analysis Department, Laboratory of Racing Chemistry, Utsunomiya, Japan.
  • Bioinformatics Team, Research Laboratory, H. U. Group Research Institute G.K., Hachioji, Japan.
Obara, Taku
  • Department of Pharmaceutical Sciences, Tohoku University Hospital, Sendai, Japan.
Ishii, Hideaki
  • Drug Analysis Department, Laboratory of Racing Chemistry, Utsunomiya, Japan.
  • Department of Pharmaceutical Sciences, Tohoku University Hospital, Sendai, Japan.

MeSH Terms

  • Animals
  • Biomarkers
  • Carnitine
  • Doping in Sports
  • Horses
  • Metabolomics
  • Plasma
  • Single-Blind Method

Grant Funding

  • Japan Racing Association

References

This article includes 70 references
  1. Fragkaki AG, Kioukia-Fougia N, Kiousi P, Kioussi M, Tsivou M. Challenges in detecting substances for equine anti-doping. Drug Test Anal 2017;9(9):1291-1303.
    doi: 10.1002/dta.2162google scholar: lookup
  2. Guan F, Fay S, Li X, You Y, Robinson MA. Identification of ex vivo catabolites of peptides with doping potential in equine plasma by HILIC-HRMS. Drug Test Anal 2020;12(6):771-784.
    doi: 10.1002/dta.2781google scholar: lookup
  3. Wong JKY, Wan TSM. Doping control analyses in horseracing: a clinician's guide. Vet J 2014;200(1):8-16.
  4. Kwok WH, Ho ENM, Lau MY, Leung GNW, Wong ASY, Wan TSM. Doping control analysis of seven bioactive peptides in horse plasma by liquid chromatography-mass spectrometry. Anal Bioanal Chem 2013;405(8):2595-2606.
    doi: 10.1007/s00216-012-6697-9google scholar: lookup
  5. Teale P, Barton C, Driver PM, Kay RG. Biomarkers: unrealized potential in sports doping analysis. Bioanalysis 2009;1(6):1103-1118.
    doi: 10.4155/bio.09.87google scholar: lookup
  6. Sieckmann T, Elmongy H, Ericsson M, Bhuiyan H, Lehtihet M, Ekström L. Longitudinal studies of putative growth hormone (GH) biomarkers and hematological and steroidal parameters in relation to 2 weeks administration of human recombinant GH. Drug Test Anal 2020;12(6):711-719.
    doi: 10.1002/dta.2787google scholar: lookup
  7. Wang G, Karanikolou A, Verdouka I, Friedmann T, Pitsiladis Y. Next generation “omics” approaches in the “fight” against blood doping. Med Sport Sci 2017;62:119-128.
    doi: 10.1159/000470919google scholar: lookup
  8. Salamin O, Kuuranne T, Saugy M, Leuenberger N. Erythropoietin as a performance-enhancing drug: its mechanistic basis, detection, and potential adverse effects. Mol Cell Endocrinol 2018;464:75-87.
    doi: 10.1016/j.mce.2017.01.033google scholar: lookup
  9. Salamin O, De Angelis S, Tissot JD, Saugy M, Leuenberger N. Autologous blood transfusion in sports: emerging biomarkers. Transfus Med Rev 2016;30(3):109-115.
  10. Pottgiesser T, Schumacher YO. Current strategies of blood doping detection. Anal Bioanal Chem 2013;405(30):9625-9639.
    doi: 10.1007/s00216-013-7270-xgoogle scholar: lookup
  11. Pitsiladis YP, Durussel J, Rabin O. An integrative “Omics” solution to the detection of recombinant human erythropoietin and blood doping. Br J Sports Med 2014;48(10):856-861.
  12. Reichel C. OMICS-strategies and methods in the fight against doping. Forensic Sci Int 2011;213(1-3):20-34.
  13. Sottas PE, Robinson N, Fischetto G, Dollé G, Alonso JM, Saugy M. Prevalence of blood doping in samples collected from elite track and field athletes. Clin Chem 2011;57(5):762-769.
  14. Viljanto M, Scarth J, Hincks P. Application of testosterone to epitestosterone ratio to horse urine-a complementary approach to detect the administrations of testosterone and its pro-drugs in Thoroughbred geldings. Drug Test Anal 2017;9(9):1328-1336.
    doi: 10.1002/dta.2109google scholar: lookup
  15. van de Kerkhof DH, de Boer D, Thijssen JHH, Maes RAA. Evaluation of testosterone/epitestosterone ratio influential factors as determined in doping analysis. J Anal Toxicol 2000;24(2):102-115.
    doi: 10.1093/jat/24.2.102google scholar: lookup
  16. Dehennin L. On the origin of physiologically high ratios of urinary testosterone to epitestosterone: consequences for reliable detection of testosterone administration by male athletes. J Endocrinol 1994;142(2):353-360.
    doi: 10.1677/joe.0.1420353google scholar: lookup
  17. Holt RIG, Erotokritou-Mulligan I, McHugh C. The GH-2004 project: the response of IGF1 and type III pro-collagen to the administration of exogenous GH in non-Caucasian amateur athletes. Eur J Endocrinol 2010;163(1):45-54.
    doi: 10.1530/eje-09-0978google scholar: lookup
  18. Powrie JK, Bassett EE, Rosen T. Detection of growth hormone abuse in sport. Growth Horm IGF Res 2007;17(3):220-226.
  19. Erotokritou-Mulligan I, Bassett EE, Cowan DA. Influence of ethnicity on IGF-I and procollagen III peptide (P-III-P) in elite athletes and its effect on the ability to detect GH abuse. Clin Endocrinol (Oxf) 2009;70(1):161-168.
  20. Erotokritou-Mulligan I, Bassett EE, Bartlett C. The effect of sports injury on insulin-like growth factor-I and type 3 procollagen: implications for detection of growth hormone abuse in athletes. J Clin Endocrinol Metab 2008;93(7):2760-2763.
    doi: 10.1210/jc.2007-2801google scholar: lookup
  21. Aikin R, Baume N, Equey T, Rabin O. Biomarkers of doping: uses, discovery and validation. Bioanalysis 2020;12(11):791-800.
    doi: 10.4155/bio-2020-0035google scholar: lookup
  22. Simon P, Neuberger EW, Wang G, Pitsiladis YP. Antidoping science: important lessons from the medical sciences. Curr Sports Med Rep 2018;17(10):326-331.
  23. Loziuk P, Meier F, Johnson C, Ghashghaei HT, Muddiman DC. TransOmic analysis of forebrain sections in Sp2 conditional knockout embryonic mice using IR-MALDESI imaging of lipids and LC-MS/MS label-free proteomics. Anal Bioanal Chem 2016;408(13):3453-3474.
    doi: 10.1007/s00216-016-9421-3google scholar: lookup
  24. Vernec AR. The athlete biological passport: an integral element of innovative strategies in antidoping. Br J Sports Med 2014;48(10):817-819.
  25. Zhao J, Wang Y, Zhao D, Zhang L, Chen P, Xu X. Integration of metabolomics and proteomics to reveal the metabolic characteristics of high-intensity interval training. Analyst 2020;145(20):6500-6510.
    doi: 10.1039/d0an01287dgoogle scholar: lookup
  26. Bonilauri B, Dallagiovanna B. Linking long noncoding RNAs (lncRNAs) and doping detection. Drug Test Anal 2020:1-4.
    doi: 10.1002/dta.2952google scholar: lookup
  27. Semenova EA, Miyamoto-Mikami E, Akimov EB. The association of HFE gene H63D polymorphism with endurance athlete status and aerobic capacity: novel findings and a meta-analysis. Eur J Appl Physiol 2020;120:665-673.
  28. Tanaka M, Wang G, Pitsiladis YP. Advancing sports and exercise genomics: moving from hypothesis-driven single study approaches to large multi-omics collaborative science. Physiol Genomics 2016;48(3):173-174.
  29. Klont F, Jahn S, Grivet C, König S, Bonner R, Hopfgartner G. SWATH data independent acquisition mass spectrometry for screening of xenobiotics in biological fluids: opportunities and challenges for data processing. Talanta 2020;211:120747.
  30. Keen B, Cawley A, Fouracre C, Pyke J, Fu S. Towards an untargeted mass spectrometric approach for improved screening in equine antidoping. Drug Test Anal 2021:1-7.
    doi: 10.1002/dta.3021google scholar: lookup
  31. Ueda T, Tozaki T, Nozawa S, Kinoshita K, Gawahara H. Identification of metabolomic changes in horse plasma after racing by liquid chromatography-high resolution mass spectrometry as a strategy for doping testing. J Equine Sci 2019;30(3):55-61.
    doi: 10.1294/jes.30.55google scholar: lookup
  32. Chong J, Xia J. Using MetaboAnalyst 4.0 for Metabolomics Data Analysis, Interpretation, and Integration with Other Omics Data. Methods Mol Biol 2020;2104:337-360.
  33. Li S, Park Y, Duraisingham S. Predicting network activity from high throughput metabolomics. PLoS Comput Biol 2013;9(7):e1003123.
  34. Li S, Pozhitkov A, Ryan RA, Manning CS, Brown-Peterson N, Brouwer M. Constructing a fish metabolic network model. Genome Biol 2010;11:1-15.
  35. Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol 2010;5(9):1315-1316.
  36. . Sensitivity, Specificity, Accuracy, Associated Confidence Interval and ROC Analysis with Practical SAS ® Implementations. 2010.
  37. Ntoumou E, Tzetis M, Braoudaki M. Serum microRNA array analysis identifies miR-140-3p, miR-33b-3p and miR-671-3p as potential osteoarthritis biomarkers involved in metabolic processes. Clin Epigenetics 2017;9(127).
    doi: 10.1186/s13148-017-0428-1google scholar: lookup
  38. Wray NR, Yang J, Goddard ME, Visscher PM. The genetic interpretation of area under the ROC curve in genomic profiling. PLoS Genet 2010;6(2):e1000864.
  39. Budoff M. Triglycerides and triglyceride-rich lipoproteins in the causal pathway of cardiovascular disease. Am J Cardiol 2016;118(1):138-145.
  40. Schranner D, Kastenmüller G, Schönfelder M, Römisch-Margl W, Wackerhage H. Metabolite concentration changes in humans after a bout of exercise: a systematic review of exercise metabolomics studies. Sport Med - Open 2020;6(1):11.
    doi: 10.1186/s40798-020-0238-4google scholar: lookup
  41. Lippi G, Sanchis-Gomar F. Epidemiological, biological and clinical update on exercise-induced hemolysis. Ann Transl Med 2019;7(12):270-270.
    doi: 10.21037/atm.2019.05.41google scholar: lookup
  42. Pellegrini Masini A, Tedeschi D, Baragli P, Sighieri C, Lubas G. Exercise-induced intravascular haemolysis in standardbred horses. Comp Clin Path 2003;12(1):45-48.
  43. Klein DJ, McKeever KH, Mirek ET, Anthony TG. Metabolomic response of equine skeletal muscle to acute fatiguing exercise and training. Front Physiol 2020;11:110.
    doi: 10.3389/fphys.2020.00110google scholar: lookup
  44. Aizawa K, Iemitsu M, Maeda S, Mesaki N, Ushida T, Akimoto T. Endurance exercise training enhances local sex steroidogenesis in skeletal muscle. Med Sci Sport Exerc 2011;43(11):2072-2080.
  45. Lee BS, Choi E-J, So W-Y. Cytochrome expression in breast cancer Xenograft Mice after 12 weeks of treadmill exercise. Iran J Public Health 2018;47(5):759-761.
  46. Ishikura K, Ra S-G, Ohmori H. Exercise-induced changes in amino acid levels in skeletal muscle and plasma. J Phys Fit Sport Med 2013;2(3):301-310.
    doi: 10.7600/jpfsm.2.301google scholar: lookup
  47. Rederstorff M, Krol A, Lescure A. Understanding the importance of selenium and selenoproteins in muscle function. Cell Mol Life Sci 2006;63(1):52-59.
    doi: 10.1007/s00018-005-5313-ygoogle scholar: lookup
  48. Evans LW, Omaye ST. Use of Saliva Biomarkers to Monitor Efficacy of Vitamin C in Exercise-Induced Oxidative Stress. MDPI AG 2017;6(1):5.
    doi: 10.3390/antiox6010005google scholar: lookup
  49. Rebouche CJ. Ascorbic acid and carnitine biosynthesis. Am J Clin Nutr 1991;54(6):1147S-1152S.
    doi: 10.1093/ajcn/54.6.1147sgoogle scholar: lookup
  50. Ferrández MD, Maynar M, De La Fuente M. Effects of a long-term training program of increasing intensity on the immune function of indoor Olympic cyclists. Int J Sports Med 1996;17(08):592-596.
    doi: 10.1055/s-2007-972900google scholar: lookup
  51. Jing L, Chengji W. GC/MS-based metabolomics strategy to analyze the effect of exercise intervention in diabetic rats. Endocr Connect 2019;8(6):654-660.
    doi: 10.1530/ec-19-0012google scholar: lookup
  52. Gleeson M, Robertson JD, Maughan RJ. Influence of exercise on ascorbic acid status in man. Clin Sci 1987;73(5):501-505.
    doi: 10.1042/cs0730501google scholar: lookup
  53. Maxwell SRJ, Jakeman P, Thomason H, Leguen C, Thorpe GHG. Changes in plasma antioxidant status during eccentric exercise and the effect of vitamin supplementation. Free Radic Res 1993;19(3):191-202.
    doi: 10.3109/10715769309111602google scholar: lookup
  54. Petersen EW, Ostrowski K, Ibfelt T. Effect of vitamin supplementation on cytokine response and on muscle damage after strenuous exercise. Am J Physiol-Cell Physiol 2001;280(6):1570-1575.
  55. Zhou W, Zeng G, Lyu C, Kou F, Zhang S, Wei H. The effect of exhaustive exercise on plasma metabolic profiles of male and female rats. J Sport Sci Med 2019;18:253-263.
  56. Hickson JF, Wolinsky I. Nutrition in exercise and sport. Med Sci Sport Exerc 1994;23(3):136-143.
  57. Guilland JC, Penaranda T, Gallet C, Boggio V, Fuchs F, Klepping J. Vitamin status of young athletes including the effects of supplementation. Med Sci Sports Exerc 1989;21(4):441-449.
  58. Soares MJ, Satyanarayana K, Bamjlt MS, Jacob CM, Venkata Ramana Y, Sudhakar Rao S. The effect of exercise on the riboflavin status of adult men. Br J Nutr 1993;69(2):541-551.
    doi: 10.1079/bjn19930054google scholar: lookup
  59. Manore MM. Effect of physical activity on thiamine, riboflavin, and vitamin B-6 requirements. Am J Clin Nutr 2000;72(2):598S-606S.
    doi: 10.1093/ajcn/72.2.598sgoogle scholar: lookup
  60. Manore MM. Vitamin B6 and exercise. Int J Sport Nutr 1994;4(2):89-103.
    doi: 10.1123/ijsn.4.2.89google scholar: lookup
  61. Crozier PG, Cordain L, Sampson DA. Exercise-induced changes in plasma vitamin B−6 concentrations do not vary with exercise intensity. Am J Clin Nutr 1994;60(4):552-558.
    doi: 10.1093/ajcn/60.4.552google scholar: lookup
  62. Virk RS, Dunton NJ, Young JC, Leklem JE. Effect of vitamin B-6 supplementation on fuels, catecholamines, and amino acids during exercise in men. Med Sci Sports Exerc 1999;31(3):400-408.
  63. Morville T, Sahl RE, Trammell SA. Divergent effects of resistance and endurance exercise on plasma bile acids, FGF19, and FGF21 in humans. JCI Insight 2018;3(15):e122737.
  64. Ryan KK, Tremaroli V, Clemmensen C. FXR is a molecular target for the effects of vertical sleeve gastrectomy. Nature 2014;8:183-188.
    doi: 10.1038/nature13135google scholar: lookup
  65. Worthmann A, John C, Rühlemann MC. Cold-induced conversion of cholesterol to bile acids in mice shapes the gut microbiome and promotes adaptive thermogenesis. Nat Med 2017;23(7):839-849.
    doi: 10.1038/nm.4357google scholar: lookup
  66. Kçdzierski W. The effect of training on plasma L-carnitine metabolism in purebred Arabian horses. J Anim Feed Sci 2010;19(3):398-407.
    doi: 10.22358/jafs/66302/2010google scholar: lookup
  67. English PA, Williams JA, Martini J-F, Motzer RJ, Valota O, Buller RE. A case for the use of receiver operating characteristic analysis of potential clinical efficacy biomarkers in advanced renal cell carcinoma. Future Oncol 2016;12(2):175-182.
    doi: 10.2217/fon.15.290google scholar: lookup
  68. De Clercq N, Vanden Bussche J, Meulebroek LV. Metabolic fingerprinting reveals a novel candidate biomarker for prednisolone treatment in cattle. Metabolomics 2016;12:1.
    doi: 10.1007/s11306-015-0887-3google scholar: lookup
  69. Marchand CR, Farshidfar F, Rattner J, Bathe OF. A framework for development of useful metabolomic biomarkers and their effective knowledge translation. Metabolites 2018;8(4):59.
    doi: 10.3390/metabo8040059google scholar: lookup
  70. Koulman A, Lane GA, Harrison SJ, Volmer DA. From differentiating metabolites to biomarkers. Anal Bioanal Chem 2009;394(3):663-670.
    doi: 10.1007/s00216-009-2690-3google scholar: lookup

Citations

This article has been cited 3 times.
  1. Ishii H, Shibuya M, Kusano K, Sone Y, Kamiya T, Wakuno A, Ito H, Miyata K, Sato F, Kuroda T, Yamada M, Leung GN. Generic approach for the discovery of drug metabolites in horses based on data-dependent acquisition by liquid chromatography high-resolution mass spectrometry and its applications to pharmacokinetic study of daprodustat. Anal Bioanal Chem 2022 Nov;414(28):8125-8142.
    doi: 10.1007/s00216-022-04347-2pubmed: 36181513google scholar: lookup
  2. Wang J, Ren W, Li Z, Li L, Wang R, Ma S, Zeng Y, Meng J, Yao X. Plasma Lipidomics and Proteomics Analyses Pre- and Post-5000 m Race in Yili Horses. Animals (Basel) 2025 Mar 30;15(7).
    doi: 10.3390/ani15070994pubmed: 40218387google scholar: lookup
  3. Ishii H, Shigematsu R, Takemoto S, Ishikawa Y, Mizobe F, Nomura M, Arima D, Kunii H, Yuasa R, Yamanaka T, Tanabe S, Nagata SI, Yamada M, Leung GN. Quantification of osilodrostat in horse urine using LC/ESI-HRMS to establish an elimination profile for doping control. Bioanalysis 2024;16(17-18):947-958.
    doi: 10.1080/17576180.2024.2385848pubmed: 39235065google scholar: lookup