D. El-maouche, W. Arlt, and D. P. Merke, Congenital adrenal hyperplasia, Lancet, vol.390, issue.17, pp.31431-31440, 2017.

A. Bachelot, V. Grouthier, C. Courtillot, J. Dulon, P. Touraine et al., Congenital adrenal hyperplasia due to 21-hydroxylase deficiency: update on the management of adult patients and prenatal treatment, European journal of endocrinology, vol.176, pp.167-181, 2017.

L. Fardet and B. Fève, Systemic glucocorticoid therapy: a review of its metabolic and cardiovascular adverse events, Drugs, vol.74, pp.1731-1745, 2014.

M. Oray, K. Abu-samra, N. Ebrahimiadib, H. Meese, and C. S. Foster, Long-term side effects of glucocorticoids, Expert Opin Drug Saf, vol.15, pp.457-465, 2016.

H. Falhammar, Increased Mortality in Patients With Congenital Adrenal Hyperplasia Due to 21-Hydroxylase Deficiency, The Journal of Clinical Endocrinology & Metabolism, vol.99, pp.2715-2721, 2014.

H. Falhammar, Increased Cardiovascular and Metabolic Morbidity in Patients With 21-Hydroxylase Deficiency: A Swedish Population-Based National Cohort Study, The Journal of clinical endocrinology and metabolism, vol.100, pp.3520-3528, 2015.

N. Reisch, Substitution therapy in adult patients with congenital adrenal hyperplasia, Best Pract Res Clin Endocrinol Metab, vol.29, pp.33-45, 2015.

R. Kaddurah-daouk and R. M. Weinshilboum, Pharmacometabolomics: implications for clinical pharmacology and systems pharmacology, Clinical pharmacology and therapeutics, vol.95, pp.154-167, 2014.

R. Kaddurah-daouk and R. Weinshilboum, Metabolomic Signatures for Drug Response Phenotypes: Pharmacometabolomics Enables Precision Medicine, Clinical pharmacology and therapeutics, vol.98, pp.71-75, 2015.

M. A. Alwashih, Plasma metabolomic profile varies with glucocorticoid dose in patients with congenital adrenal hyperplasia, 2017.

E. Prifti, Interpretable and accurate prediction models for metagenomics data, GigaScience, vol.9, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02520519

A. Mock, MetaboDiff: an R package for differential metabolomic analysis, Bioinformatics, vol.34, pp.3417-3418, 2018.

J. E. Salem, Complex Association of Sex Hormones on Left Ventricular Systolic Function: Insight into Sexual Dimorphism, e231, vol.31, pp.231-240, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02384979

N. Bordag, Glucocorticoid (dexamethasone)-induced metabolome changes in healthy males suggest prediction of response and side effects, 2015.

J. Geng and A. Liu, Heme-dependent dioxygenases in tryptophan oxidation, Archives of biochemistry and biophysics, vol.544, pp.18-26, 2014.

I. Davis and A. Liu, What is the tryptophan kynurenine pathway and why is it important to neurotherapeutics?, Expert review of neurotherapeutics, vol.15, pp.719-721, 2015.

C. E. Fluck and W. L. Miller, P450 oxidoreductase deficiency: a new form of congenital adrenal hyperplasia, Current opinion in pediatrics, vol.18, pp.435-441, 2006.

M. Pizzichini, A. Di-stefano, and E. Marinello, The regulation of purine ribonucleotide biosynthesis by glucocorticoid hormones, The Italian journal of biochemistry, vol.34, pp.305-312, 1985.

L. S. Nguyen, Influence of hormones on the immunotolerogenic molecule HLA-G: a cross-sectional study in patients with congenital adrenal hyperplasia, European journal of endocrinology, vol.181, pp.481-488, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02374057

D. Rosenbaum, Early central blood pressure elevation in adult patients with 21-hydroxylase deficiency, J Hypertens, vol.37, pp.175-181, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02152829

I. Garali, A strategy for multimodal data integration: application to biomarkers identification in spinocerebellar ataxia, Briefings in bioinformatics, vol.19, pp.1356-1369, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01630727

F. Giacomoni, Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics, Bioinformatics, vol.31, pp.1493-1495, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01123263

C. A. Smith, E. J. Want, G. O'maille, R. Abagyan, and G. Siuzdak, XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification, Analytical chemistry, vol.78, pp.779-787, 2006.

R. Tautenhahn, G. J. Patti, D. Rinehart, G. Siuzdak, and . Xcms, Online: a web-based platform to process untargeted metabolomic data, Analytical chemistry, vol.84, pp.5035-5039, 2012.

E. J. Want, Global metabolic profiling procedures for urine using UPLC-MS, Nature protocols, vol.5, pp.1005-1018, 2010.

W. B. Dunn, Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry, Nature protocols, vol.6, pp.1060-1083, 2011.

W. B. Dunn, I. D. Wilson, A. W. Nicholls, and D. Broadhurst, The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans, Bioanalysis, vol.4, pp.2249-2264, 2012.

K. A. Veselkov, Optimized preprocessing of ultra-performance liquid chromatography/mass spectrometry urinary metabolic profiles for improved information recovery, Analytical chemistry, vol.83, pp.5864-5872, 2011.

A. Mock, MetaboDiff: an R package for differential metabolomic analysis, Bioinformatics, vol.34, pp.3417-3418, 2018.

W. Huber, A. Von-heydebreck, H. Sultmann, A. Poustka, and M. Vingron, Variance stabilization applied to microarray data calibration and to the quantification of differential expression, Bioinformatics, vol.18, issue.1, pp.96-104, 2002.

Y. Benjamini and Y. Hochberg, Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing, Journal of the Royal Statistical Society. Series B (Methodological), vol.57, pp.289-300, 1995.