Tracking main environmental factors masking a minor steroidal doping effect using metabolomic analysis of horse urine by liquid chromatography–high-resolution mass spectrometry

Abstract

There is an urgent need to implement holistic and untargeted doping control protocols with improved discriminatory power, compared to conventional methods that only target doping agents. Metabolomics, which aims to characterize all metabolites present in biological matrices, could fulfill this need. In this context, the aim of this study was to evaluate the impact of environmental factors on the ability to obtain a metabolic signature of stanozolol administration in horse doping situation. Urine samples from 16 horses breeded in two different places were collected over a one-year period, before, during and seven months after the administration of stanozolol, a horse doping agent. Metabolomic analysis was performed using ultra-high pressure reverse phase liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry (MS). Results showed a major impact of the nutritional regimen, drug administration (for de-worming purpose) and breeding place on the metabolite profiles of horse urines, which hampered the detection of metabolic perturbations induced by stanozolol administration. After having used MS/MS experiments to characterize some MS features related to these environmental factors, we showed that highlighting and then removing the features impacted by these confounding factors before performing supervised multivariate statistical analyses could address this issue. In conclusion, adequate consideration should be given to environmental and physiological factors; otherwise, they can emerge as confounding factors and conceal doping administration.

From a non‐targeted metabolomics approach to a targeted biomarkers strategy to highlight testosterone abuse in equine. Illustration of a methodological transfer between platforms and laboratories

“…17 Proofs of concept of the relevance of such approaches in an anti-doping context are available. [18][19][20] Despite the great breakthrough of metabolomics strategies and the tremendous momentum of the scientific community, some aspects of these approaches still need to be further developed in relation to data mining and processing or potential biomarkers identification. 21 Particularly, to anticipate the implementation of these approaches, it is important to address aspects related to method robustness and then transferability.…”

“…Investigating the corresponding metabolome was notably enabled by both improved liquid chromatography coupled to high resolution mass spectrometry (LC‐HRMS) workflows and by large data processing and analysis tools 17 . Proofs of concept of the relevance of such approaches in an anti‐doping context are available 18–20 . Despite the great breakthrough of metabolomics strategies and the tremendous momentum of the scientific community, some aspects of these approaches still need to be further developed in relation to data mining and processing or potential biomarkers identification 21 .…”

Comparison of muscle metabolomics between two Chinese horse breeds

“…Furthermore, the metabolomics of blood (6, 7) and muscle (8) for horses during endurance exercise reasons out the energetic demands for locomotion. Undoubtedly, metabolomics analysis has practical use for tracking stimulants from equine urine (9)(10)(11), and the analysis can also reflect exercise fatigue (12, 13) during exercise and training. Nevertheless, its utility is being increasingly acknowledged between different species [e.g., dogs (14, 15), chicken (16,17), and cattle (18)].…”

Novel Algorithms for Comprehensive Untargeted Detection of Doping Agents in Biological Samples

“…Metabolomics techniques are a promising strategy to achieve such a goal. Indeed, metabolomics methods have been reported that indirectly detect abuse of growth hormone in horses, misuse of steroidal aromatase inhibitors, illicit use of continuous erythropoiesis receptor activator (CERA), and the steroidal profile changes induced by drugs and environmental factors. , Additionally, a metabolomics data analysis tool was successfully employed to detect a class of “unknown” psychoactive substances in equine urine . However, the limitation of all reported methods to date is that only one class of drugs can be detected by each method.…”