Urine Fingerprints of Stanozolol Treated Horses by Liquid Chromatography High Resolution Mass Spectrometry

Natali Stojiljkovic, S. Bubanj, M. Djordjevic, S. Mazić, C. Junot, M. Popot, Y. Bonnaire, J. Tabet
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引用次数: 4

Abstract

The current detection methods for stanozolol are all based on targeted approach. The present study aimed to assess the global biological effect of stanozolol-treatment by means of chemometric models, after generating and comparing horse urine LC-HRMS fingerprints collected from control and stanozolol-treated horses. The animal study was conducted according to an ethically approved protocol at two different places in France: Chamberet and Coye la Foret. The total duration of the animal phase was seven months and only females were selected to partake. The sixteen mares in this study were not actively racing horses, but were in good physical condition. SIMCA-P+ software and R free software environment were used for multivariate data analysis. Principal Component Analysis (PCA) and Orthogonal Projections to Latent Structures-Discriminant Analysis (OPLS-DA) were applied to build some descriptive and predictive models. The analyzed horse urine fingerprints based on the 220 features selected after suppression of confounding factors show changes in metabolic states after chronic stanozolol treatment. This proof of concept study confirms the power of untargeted approach in doping control since the changes are present over seven months after anabolic administration.
用液相色谱-高分辨质谱法测定施他诺唑尔处理过的马的尿液指纹图谱
目前斯坦诺唑尔的检测方法都是基于靶向法。本研究旨在通过化学计量模型,生成并比较对照马和施塔诺唑尔处理马尿液的LC-HRMS指纹图谱,评估施塔诺唑尔处理的整体生物学效应。这项动物研究是根据一项经伦理批准的协议在法国两个不同的地方进行的:Chamberet和Coye la Foret。动物期的总持续时间为7个月,只选择雌性参与。这项研究中的16匹母马并不活跃,但身体状况良好。采用SIMCA-P+软件和R自由软件环境进行多变量数据分析。应用主成分分析(PCA)和潜在结构正交投影判别分析(OPLS-DA)建立了一些描述和预测模型。抑制混杂因素后选取的220个特征分析马尿指纹,显示慢性司他诺唑尔治疗后代谢状态的变化。这一概念验证研究证实了非目标方法在兴奋剂控制方面的力量,因为这些变化在合成代谢管理后的7个月内就出现了。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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