A statistical focus on doping using a metabolomics approach
- Publisher:
- Elsevier
- Publication Type:
- Journal Article
- Citation:
- Toxicologie Analytique et Clinique, 2022, 34, (3), pp. s58
- Issue Date:
- 2022-09
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Aim Identify potential biomarkers that are indicative of doping in equine plasma through statistical analysis of metabolomics data. Introduction The purpose of metabolomics is to detect endogenous changes in molecular entities, such as metabolites and adducts, that are indicative of challenges to the system (Fiehn et al., Metabolomics, 2015, 11, 1036–1040). These challenges to the system may include things such as doping, disease and environmental changes. Doping is an ever-growing field due to the changing nature of new and popular agents. For the racing industry, any performance altering agents are prohibited to maintain the welfare of athletes involved and the integrity of the sport and breeding industry (Cawley et al., Drug Testing and Analysis, 2017, 9, 1441–1447). Therefore, an alternative approach to conventional detection methods is essential for the decreasing the bottleneck that is the introduction of new compounds into routine screening efforts. Method Using only 100μL of equine plasma, a rapid protein precipitation method was developed for the analysis of endogenous compounds. The IMTAKT Intrada Amino Acid column (100mm×2mm, 3μm) was able to separate dopamine-related compounds. The method used positive and negative ionisation mode with an 11-minute gradient method on the Agilent 1290 Infinity II LC system coupled to an Agilent 6545 QTOF mass spectrometer. A substantial reference population study was completed to assess basal concentrations of endogenous compounds of interest. A 12-horse administration study of Stalevo® (800mg levodopa, 200mg carbidopa, 1600mg entacapone) was analysed. Agilent Technologies’ Profinder was used to complete a batch recursive feature extraction of the data. The statistical analysis included longitudinal profiling and identification of significant entities through volcano plot analysis, principal component analysis and heatmap visualisation. Biomarker quality was determined using a ‘best biomarker quality (BBQ)’ assessment. Re
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