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Applying metabolomics to detect growth hormone administration in athletes: Proof of concept
Author(s) -
Narduzzi Luca,
Dervilly Gaud,
Marchand Alexandre,
Audran Michel,
Le Bizec Bruno,
Buisson Corinne
Publication year - 2020
Publication title -
drug testing and analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.065
H-Index - 54
eISSN - 1942-7611
pISSN - 1942-7603
DOI - 10.1002/dta.2798
Subject(s) - metabolomics , anabolism , metabolome , growth hormone , athletes , hormone , valine , medicine , endocrinology , catabolism , amino acid , metabolism , physiology , bioinformatics , biology , biochemistry , physical therapy
Abstract Growth hormone (GH), an endogenous peptide regulating anabolism and lipolysis in humans, is known to be abused by athletes to improve their performance. Despite the development of two distinct screening methods, few positive cases have been reported by the antidoping authorities, probably due to the quick turnover of GH 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‐acid uptake, and protein breakdown. It is reasonable to imagine observing its markers of effects through the leading tool on metabolism study, metabolomics. In this proof‐of‐concept study, a cohort of well‐trained volunteers was split in two equal groups and administered with micro‐doses of EPO or EPO + GH every second day for 2 weeks. Urine and plasma samples were collected before, during, and after treatment and analyzed using metabolomics and lipidomics approaches. The results show that, by 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 a higher difference was observed between the groups, including a perfect classification of the samples before and after the treatments.