Applying metabolomics to detect growth hormone administration in athletes: proof of concept.

Growth hormone (GH), an endogenous peptide regulating anabolism and lipolysis in humans, is known to be abused by the athletes to improve their performances. Despite the development of two distinct screening methods, few positive cases have been reported by the anti-doping authorities, probably due to the GH quick turnover and the masking effects of age, ethnicity and sex. Apart from growth regulation, GH is known to affect several metabolic pathways in humans including ketosis, amino-acids uptake and proteins breakdown. It is reasonable to imagine to observe its markers of effects through the leading tool on metabolism study, metabolomics. In this proof-of-concept study, a cohort of well-trained volunteers has been split in two equal groups and administered with micro-doses of EPO or EPO + GH every second day for two weeks. Urine and plasma samples have been collected before, during and after the treatment and analyzed using metabolomics and lipidomics approaches. The results show that, applying a direct discriminant analysis on the treated groups, it is possible to distinguish the treatments, and to use this difference to classify them correctly. High intragroup variability is observed, due to the subject-specific effect of the hormones. Through time 0 centering the data, a longitudinally tracking of the group was performed and higher difference was observed between the groups, including a perfect classification of the samples before and after the treatments.

[1]  W. J. Dyer,et al.  A rapid method of total lipid extraction and purification. , 1959, Canadian journal of biochemistry and physiology.

[2]  Yrjö Vartia,et al.  Efficient Methods of Measuring Welfare Change and Compensated Income in Terms of Ordinary Demand Functions , 1983 .

[3]  M. Vance,et al.  Role of dopamine in the regulation of growth hormone secretion: dopamine and bromocriptine augment growth hormone (GH)-releasing hormone-stimulated GH secretion in normal man. , 1987, The Journal of clinical endocrinology and metabolism.

[4]  M. Köppen,et al.  The Curse of Dimensionality , 2010 .

[5]  M. Akçay,et al.  The effect of growth hormone on 24-h urinary creatinine levels in burned patients. , 2001, Burns : journal of the International Society for Burn Injuries.

[6]  M. Bidlingmaier,et al.  Changes in non-22-kilodalton (kDa) isoforms of growth hormone (GH) after administration of 22-kDa recombinant human GH in trained adult males. , 2001, The Journal of clinical endocrinology and metabolism.

[7]  H. Pijl,et al.  Growth hormone blunts protein oxidation and promotes protein turnover to a similar extent in abdominally obese and normal-weight women. , 2002, The Journal of clinical endocrinology and metabolism.

[8]  Anders Berglund,et al.  New and old trends in chemometrics. How to deal with the increasing data volumes in R&D&P (research, development and production)—with examples from pharmaceutical research and process modeling , 2002 .

[9]  Martin Vingron,et al.  Variance stabilization applied to microarray data calibration and to the quantification of differential expression , 2002, ISMB.

[10]  M. Ashenden,et al.  A strategy to deter blood doping in sport. , 2002, Haematologica.

[11]  N. Møller,et al.  Effects of growth hormone on lipid metabolism in humans. , 2003, Growth hormone & IGF research : official journal of the Growth Hormone Research Society and the International IGF Research Society.

[12]  R. Olney Regulation of bone mass by growth hormone. , 2003, Medical and pediatric oncology.

[13]  T. Ebbels,et al.  Geometric trajectory analysis of metabolic responses to toxicity can define treatment specific profiles. , 2004, Chemical research in toxicology.

[14]  E. Diamanti-Kandarakis,et al.  Hormones in sports: growth hormone abuse. , 2004, Hormones.

[15]  Age K. Smilde,et al.  ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data , 2005, Bioinform..

[16]  A. Smilde,et al.  Fusion of mass spectrometry-based metabolomics data. , 2005, Analytical chemistry.

[17]  E. Barrett,et al.  The regulation of body and skeletal muscle protein metabolism by hormones and amino acids. , 2006, The Journal of nutrition.

[18]  J. Merchant,et al.  Inhibition of growth hormone receptor gene expression by saturated fatty acids: role of Kruppel-like zinc finger factor, ZBP-89. , 2006, Molecular endocrinology.

[19]  R. A. van den Berg,et al.  Centering, scaling, and transformations: improving the biological information content of metabolomics data , 2006, BMC Genomics.

[20]  E Eryl Bassett,et al.  Validation of the growth hormone (GH)-dependent marker method of detecting GH abuse in sport through the use of independent data sets. , 2007, Growth hormone & IGF research : official journal of the Growth Hormone Research Society and the International IGF Research Society.

[21]  P. Sönksen,et al.  Detection of growth hormone abuse in sport. , 2007, Growth hormone & IGF research : official journal of the Growth Hormone Research Society and the International IGF Research Society.

[22]  Joshua N. Adkins,et al.  Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition , 2009, Bioinform..

[23]  A. Bergmann,et al.  High-sensitivity chemiluminescence immunoassays for detection of growth hormone doping in sports. , 2009, Clinical chemistry.

[24]  Peter Filzmoser,et al.  An Object-Oriented Framework for Robust Multivariate Analysis , 2009 .

[25]  J. Antignac,et al.  Development of a metabonomic approach based on LC-ESI-HRMS measurements for profiling of metabolic changes induced by recombinant equine growth hormone in horse urine , 2009, Analytical and bioanalytical chemistry.

[26]  Niels Møller,et al.  Effects of growth hormone on glucose, lipid, and protein metabolism in human subjects. , 2009, Endocrine reviews.

[27]  P. Toutain,et al.  Generation and processing of urinary and plasmatic metabolomic fingerprints to reveal an illegal administration of recombinant equine growth hormone from LC-HRMS measurements , 2011, Metabolomics.

[28]  G. Hart,et al.  O-GlcNAc signaling: a metabolic link between diabetes and cancer? , 2010, Trends in biochemical sciences.

[29]  Martial Saugy,et al.  Endogenous steroid profiling in the athlete biological passport. , 2010, Endocrinology and metabolism clinics of North America.

[30]  Nele Friedrich,et al.  The Association Between IGF-I and Insulin Resistance , 2012, Diabetes Care.

[31]  G. Musumeci,et al.  Serotonin/growth hormone/insulin-like growth factors axis on pre- and post-natal development: a contemporary review , 2013 .

[32]  Philippe Schmitt-Kopplin,et al.  Doping Control Using High and Ultra-High Resolution Mass Spectrometry Based Non-Targeted Metabolomics-A Case Study of Salbutamol and Budesonide Abuse , 2013, PloS one.

[33]  Alan Saghatelian,et al.  Discovery of a Class of Endogenous Mammalian Lipids with Anti-Diabetic and Anti-inflammatory Effects , 2014, Cell.

[34]  Neil Robinson,et al.  Monitoring of biological markers indicative of doping: the athlete biological passport , 2014, British Journal of Sports Medicine.

[35]  Serge Rudaz,et al.  Harnessing the complexity of metabolomic data with chemometrics , 2014 .

[36]  Tu Bao Ho,et al.  A nucleosomal approach to inferring causal relationships of histone modifications , 2014, BMC Genomics.

[37]  J. Veuthey,et al.  Untargeted profiling of urinary steroid metabolites after testosterone ingestion: opening new perspectives for antidoping testing. , 2014, Bioanalysis.

[38]  B. Le Bizec,et al.  Global urine fingerprinting by LC-ESI(+)-HRMS for better characterization of metabolic pathway disruption upon anabolic practices in bovine , 2015, Metabolomics.

[39]  Hydrophilic interaction (HILIC) and reverse phase liquid chromatography (RPLC)–high resolution MS for characterizing lipids profile disruption in serum of anabolic implanted bovines , 2015, Metabolomics.

[40]  L. Narduzzi,et al.  Comparing Wild American Grapes with Vitis vinifera: A Metabolomics Study of Grape Composition. , 2015, Journal of agricultural and food chemistry.

[41]  D. Böhning,et al.  The development of decision limits for the GH-2000 detection methodology using additional insulin-like growth factor-I and amino-terminal pro-peptide of type III collagen assays. , 2015, Drug testing and analysis.

[42]  Dirk Walther,et al.  The Roles of Post-translational Modifications in the Context of Protein Interaction Networks , 2015, PLoS Comput. Biol..

[43]  Eric Fleury,et al.  Detailed Contact Data and the Dissemination of Staphylococcus aureus in Hospitals , 2015, PLoS Comput. Biol..

[44]  M. Ibáñez,et al.  Untargeted metabolomics in doping control: detection of new markers of testosterone misuse by ultrahigh performance liquid chromatography coupled to high-resolution mass spectrometry. , 2015, Analytical chemistry.

[45]  R. Wanders,et al.  Transient decrease of hepatic NAD+ and amino acid alterations during treatment with valproate: new insights on drug-induced effects in vivo using targeted MS-based metabolomics , 2016, Metabolomics.

[46]  Emma L. Schymanski,et al.  MetFrag relaunched: incorporating strategies beyond in silico fragmentation , 2016, Journal of Cheminformatics.

[47]  Luca Scrucca,et al.  mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models , 2016, R J..

[48]  Ron Wehrens,et al.  Improved batch correction in untargeted MS-based metabolomics , 2016, Metabolomics.

[49]  M. Cecchini,et al.  Ultrastructural Characterization of the Lower Motor System in a Mouse Model of Krabbe Disease , 2016, Scientific Reports.

[50]  Shin-Hye Kim,et al.  Effects of growth hormone on glucose metabolism and insulin resistance in human , 2017, Annals of pediatric endocrinology & metabolism.

[51]  T. Friedmann,et al.  Next Generation "Omics" Approaches in the "Fight" against Blood Doping. , 2017, Medicine and sport science.

[52]  Bo Li,et al.  NOREVA: normalization and evaluation of MS-based metabolomics data , 2017, Nucleic Acids Res..

[53]  Jeremy P. Koelmel,et al.  LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data , 2017, BMC Bioinformatics.

[54]  Nicola Zamboni,et al.  Non-targeted LC-MS based metabolomics analysis of the urinary steroidal profile. , 2017, Analytica chimica acta.

[55]  Yann Guitton,et al.  Create, run, share, publish, and reference your LC-MS, FIA-MS, GC-MS, and NMR data analysis workflows with the Workflow4Metabolomics 3.0 Galaxy online infrastructure for metabolomics. , 2017, The international journal of biochemistry & cell biology.

[56]  B. Le Bizec,et al.  Serum-based metabolomics characterization of pigs treated with ractopamine , 2017, Metabolomics.

[57]  P. Sottas,et al.  Validation of whole-blood transcriptome signature during microdose recombinant human erythropoietin (rHuEpo) administration , 2017, BMC Genomics.

[58]  Pietro Franceschi,et al.  The Compound Characteristics Comparison (CCC) approach: a tool for improving confidence in natural compound identification , 2018, Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment.

[59]  Yann Guitton,et al.  A multidimensional 1H NMR lipidomics workflow to address chemical food safety issues , 2018, Metabolomics.

[60]  G. Siuzdak,et al.  METLIN: A Technology Platform for Identifying Knowns and Unknowns. , 2018, Analytical chemistry.

[61]  F. Pedrotti,et al.  Applying novel approaches for GC × GC-TOF-MS data cleaning and trends clustering in VOCs time-series analysis: Following the volatiles fate in grass baths through passive diffusion sampling. , 2018, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[62]  Abraham Otero,et al.  Metabolite Annotation and Identification , 2018 .

[63]  A. Badawy Modulation of Tryptophan and Serotonin Metabolism as a Biochemical Basis of the Behavioral Effects of Use and Withdrawal of Androgenic-Anabolic Steroids and Other Image- and Performance-Enhancing Agents , 2018, International journal of tryptophan research : IJTR.

[64]  L. Ekström,et al.  Longitudinally monitoring of P-III-NP, IGF-I, and GH-2000 score increases the probability of detecting two weeks' administration of low-dose recombinant growth hormone compared to GH-2000 decision limit and GH isoform test and micro RNA markers. , 2018, Drug testing and analysis.

[65]  P. Sottas,et al.  Combined administration of microdoses of growth hormone and erythropoietin: effects on performance and evaluation of GH detection capability using anti-doping methods. , 2019, Drug testing and analysis.

[66]  Juho Rousu,et al.  SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information , 2019, Nature Methods.

[67]  B. Le Bizec,et al.  A role for metabolomics in the anti-doping toolbox? , 2020, Drug testing and analysis.