Boris Labrador1, François-Xavier Lejeune1, Alain Paris2, Cécile Canlet3, Jérôme Molina3,4, Michel Guinot5,6,7, Armand Mégret8, Michel Rieu9, Jean-Christophe Thalabard10, Yves Le Bouc11. 1. Institut du Cerveau et de la Moelle épiniere (ICM), Sorbonne Université, Inserm U 1127, CNRS UMR 7225, Hôpital Pitié Salpêtrière, Paris, France. 2. Unité Molécules de Communication et Adaptation des Microorganismes (MCAM), Muséum national d'Histoire naturelle, CNRS, Paris, France. alain.paris@mnhn.fr. 3. Axiom, Toxalim, INRAE, ENVT, INPT-EI Purpan, Université Paul Sabatier, Toulouse, France. 4. Dynamiques et écologie des paysages agriforestiers (DYNAFOR), INRAE, INPT-ENSAT, INPT-EI Purpan, Auzeville, Castanet-Tolosan Cedex, France. 5. CHU Grenoble-Alpes, UM Sports et Pathologies, Grenoble, France. 6. Hypoxia and Pathophysiology Unit, INSERM U 1042, Université Grenoble-Alpes, Grenoble, France. 7. UM Sports et Pathologies, CHU Sud, Echirolles, France. 8. Fédération française de Cyclisme, 1 rue Laurent Fignon, Montigny le Bretonneux, France. 9. Agence Française de Lutte contre le Dopage (AFLD), Paris, France. 10. MAP5, Université de Paris, UMR CNRS 8145, Paris, France. 11. Sorbonne Université, INSERM, UMR S 938, Centre de Recherche Saint-Antoine (CRSA), Paris, France.
Abstract
INTRODUCTION: Serum phenotyping of elite cyclists regarding cortisol, IGF1 and testosterone is a way to detect endocrine disruptions possibly explained by exercise overload, non-balanced diet or by doping. This latter disruption-driven approach is supported by fundamental physiology although without any evidence of any metabolic markers. OBJECTIVES: Serum samples were distributed through Low, High or Normal endocrine classes according to hormone concentration. A 1H NMR metabolomic study of 655 serum obtained in the context of the longitudinal medical follow-up of 253 subjects was performed to discriminate the three classes for every endocrine phenotype. METHODS: An original processing algorithm was built which combined a partial-least squares-based orthogonal correction of metabolomic signals and a shrinkage discriminant analysis (SDA) to get satisfying classifications. An extended validation procedure was used to plan in larger size cohorts a minimal size to get a global prediction rate (GPR), i.e. the product of the three class prediction rates, higher than 99.9%. RESULTS: Considering the 200 most SDA-informative variables, a sigmoidal fitting of the GPR gave estimates of a minimal sample size to 929, 2346 and 1408 for cortisol, IGF1 and testosterone, respectively. Analysis of outliers from cortisol and testosterone Normal classes outside the 97.5%-confidence limit of score prediction revealed possibly (i) an inadequate protein intake for outliers or (ii) an intake of dietary ergogenics, glycine or glutamine, which might explain the significant presence of heterogeneous metabolic profiles in a supposedly normal cyclists subgroup. CONCLUSION: In a next validation metabolomics study of a so-sized cohort, anthropological, clinical and dietary metadata should be recorded in priority at the blood collection time to confirm these functional hypotheses.
INTRODUCTION: Serum phenotyping of elite cyclists regarding cortisol, IGF1 and testosterone is a way to detect endocrine disruptions possibly explained by exercise overload, non-balanced diet or by doping. This latter disruption-driven approach is supported by fundamental physiology although without any evidence of any metabolic markers. OBJECTIVES: Serum samples were distributed through Low, High or Normal endocrine classes according to hormone concentration. A 1H NMR metabolomic study of 655 serum obtained in the context of the longitudinal medical follow-up of 253 subjects was performed to discriminate the three classes for every endocrine phenotype. METHODS: An original processing algorithm was built which combined a partial-least squares-based orthogonal correction of metabolomic signals and a shrinkage discriminant analysis (SDA) to get satisfying classifications. An extended validation procedure was used to plan in larger size cohorts a minimal size to get a global prediction rate (GPR), i.e. the product of the three class prediction rates, higher than 99.9%. RESULTS: Considering the 200 most SDA-informative variables, a sigmoidal fitting of the GPR gave estimates of a minimal sample size to 929, 2346 and 1408 for cortisol, IGF1 and testosterone, respectively. Analysis of outliers from cortisol and testosterone Normal classes outside the 97.5%-confidence limit of score prediction revealed possibly (i) an inadequate protein intake for outliers or (ii) an intake of dietary ergogenics, glycine or glutamine, which might explain the significant presence of heterogeneous metabolic profiles in a supposedly normal cyclists subgroup. CONCLUSION: In a next validation metabolomics study of a so-sized cohort, anthropological, clinical and dietary metadata should be recorded in priority at the blood collection time to confirm these functional hypotheses.
Authors: Leonie H A Broersen; Alberto M Pereira; Jens Otto L Jørgensen; Olaf M Dekkers Journal: J Clin Endocrinol Metab Date: 2015-04-06 Impact factor: 5.958
Authors: Sheri R Colberg; Ronald J Sigal; Jane E Yardley; Michael C Riddell; David W Dunstan; Paddy C Dempsey; Edward S Horton; Kristin Castorino; Deborah F Tate Journal: Diabetes Care Date: 2016-11 Impact factor: 19.112