S. Mottillo, K. B. Filion, J. Genest, L. Joseph, L. Pilote et al., The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis, J Am Coll Cardiol, vol.56, issue.14, pp.1113-1145, 2010.

S. M. Grundy, J. I. Cleeman, S. R. Daniels, K. A. Donato, R. H. Eckel et al., Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement, Circulation, vol.112, issue.17, pp.2735-52, 2005.

K. G. Alberti and P. Z. Zimmet, Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation, Diabet Med, vol.15, issue.7, pp.539-53, 1998.

B. Balkau and M. A. Charles, Comment on the provisional report from the WHO consultation

, National Cholesterol Education Program Expert Panel on Detection E, Treatment of High Blood Cholesterol in A. Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report, Circulation, vol.16, issue.5, pp.3143-421, 1999.

D. Einhorn, G. M. Reaven, R. H. Cobin, E. Ford, O. P. Ganda et al., American College of Endocrinology position statement on the insulin resistance syndrome, Endocr Pract, vol.9, issue.3, pp.237-52, 2003.

K. G. Alberti, P. Zimmet, J. Shaw, and I. Group, The metabolic syndrome--a new worldwide definition, Lancet, vol.366, issue.9491, pp.1059-62, 2005.

E. S. Ford and W. H. Giles, A comparison of the prevalence of the metabolic syndrome using two proposed definitions, Diabetes Care, vol.26, issue.3, pp.575-81, 2003.

Y. Liao, S. Kwon, S. Shaughnessy, P. Wallace, A. Hutto et al., Critical evaluation of adult treatment panel III criteria in identifying insulin resistance with dyslipidemia, Diabetes Care, vol.27, issue.4, pp.978-83, 2004.

G. Marchesini, G. Forlani, F. Cerrelli, R. Manini, S. Natale et al., WHO and ATPIII proposals for the definition of the metabolic syndrome in patients with Type 2 diabetes, Diabet Med, vol.21, issue.4, pp.383-390, 2004.

A. Rodriguez, D. C. Muller, M. Engelhardt, and R. Andres, Contribution of impaired glucose tolerance in subjects with the metabolic syndrome: Baltimore Longitudinal Study of Aging, Metabolism, vol.54, issue.4, pp.542-549, 2005.

D. F. Wyszynski, D. M. Waterworth, P. J. Barter, J. Cohen, Y. A. Kesaniemi et al., Relation between atherogenic dyslipidemia and the Adult Treatment Program-III definition of metabolic syndrome (Genetic Epidemiology of Metabolic Syndrome Project), Am J Cardiol, vol.95, issue.2, pp.194-202, 2005.

C. E. Tan, S. Ma, D. Wai, S. K. Chew, and E. S. Tai, Can we apply the National Cholesterol Education Program Adult Treatment Panel definition of the metabolic syndrome to Asians? Diabetes Care, vol.27, pp.1182-1188, 2004.

B. Enkhmaa, K. Shiwaku, E. Anuurad, A. Nogi, K. Kitajima et al., Prevalence of the metabolic syndrome using the Third Report of the National Cholesterol Educational Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (ATP III) and the modified ATP III definitions for Japanese and Mongolians, Clin Chim Acta, vol.352, issue.1-2, pp.105-118, 2005.

K. J. Hunt, R. G. Resendez, K. Williams, S. M. Haffner, and M. P. Stern, San Antonio Heart S. National Cholesterol Education Program versus World Health Organization metabolic syndrome in relation to all-cause and cardiovascular mortality in the San Antonio Heart Study, Circulation, vol.110, issue.10, pp.1251-1258, 2004.

, American Heart Association

, World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity, Circulation, vol.120, issue.16, pp.1640-1645, 2009.

N. Kumar, M. A. Hoque, M. Shahjaman, S. M. Islam, M. N. Mollah et al., Data standards can boost metabolomics research, and if there is a will, there is a way, Nucleic Acids Res, vol.62, issue.D1, pp.20-26, 2016.

K. Haug, R. M. Salek, and C. Steinbeck, Global open data management in metabolomics, Curr Opin Chem Biol, vol.36, pp.58-63, 2017.

K. Haug, R. M. Salek, P. Conesa, J. Hastings, P. De-matos et al., MetaboLights--an openaccess general-purpose repository for metabolomics studies and associated meta-data, Nucleic Acids Res, vol.41, pp.781-787, 2013.

M. Sud, E. Fahy, D. Cotter, K. Azam, I. Vadivelu et al., Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools, Nucleic Acids Res, vol.44, issue.D1, p.104, 2011.

T. Ara, M. Enomoto, M. Arita, C. Ikeda, K. Kera et al., Metabolonote: a wiki-based database for managing hierarchical metadata of metabolome analyses, Front Bioeng Biotechnol, vol.3, p.38, 2015.

M. D. Wilkinson, M. Dumontier, I. J. Aalbersberg, G. Appleton, M. Axton et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, vol.3, p.160018, 2016.

P. Lampen, H. Hillig, A. Davies, and M. Linscheid, JCAMP-DX for mass-spectrometry, Applied Spectroscopy, 1994.

R. Rew and G. Davis, NetCDF: an interface for scientific data access, IEEE Computer Graphics and Applications, vol.10, issue.4, pp.76-82, 1990.

P. G. Pedrioli, J. K. Eng, R. Hubley, M. Vogelzang, E. W. Deutsch et al., A common open representation of mass spectrometry data and its application to proteomics research, Nat Biotechnol, vol.22, issue.11, pp.1459-66, 2004.

S. Orchard, C. Taylor, H. Hermjakob, W. Zhu, R. Julian et al., Current status of proteomic standards development, Expert Rev Proteomics, vol.1, issue.2, pp.179-83, 2004.

L. Martens, M. Chambers, M. Sturm, D. Kessner, F. Levander et al., mzML--a community standard for mass spectrometry data, Mol Cell Proteomics, vol.10, issue.1, 2011.

M. Wilhelm, M. Kirchner, J. A. Steen, and H. Steen, mz5: space-and time-efficient storage of mass spectrometry data sets, Mol Cell Proteomics, vol.11, issue.1, pp.111-011379, 2012.

J. C. Lindon, J. K. Nicholson, E. Holmes, H. C. Keun, A. Craig et al., Summary recommendations for standardization and reporting of metabolic analyses, Nat Biotechnol, vol.23, issue.7, pp.833-841, 2005.

L. W. Sumner, A. Amberg, D. Barrett, M. H. Beale, R. Beger et al., Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI), Metabolomics, vol.3, issue.3, pp.211-232, 2007.

M. Members, S. A. Sansone, T. Fan, R. Goodacre, J. L. Griffin et al., The metabolomics standards initiative, Nat Biotechnol, vol.25, issue.8, pp.846-854, 2007.

P. Rocca-serra, M. Brandizi, E. Maguire, N. Sklyar, C. Taylor et al., ISA software suite: supporting standards-compliant experimental annotation and enabling curation at the community level, Bioinformatics, vol.26, issue.18, pp.2354-2360, 2010.

F. Giacomoni, L. Corguille, G. Monsoor, M. Landi, M. Pericard et al., Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics, Bioinformatics, vol.31, issue.9, pp.1493-1498, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01214152

Y. Guitton, M. Tremblay-franco, L. Corguille, G. Martin, J. F. Petera et al., Create, run, share, publish, and reference your LC-MS
URL : https://hal.archives-ouvertes.fr/hal-01574351

Z. Lacroix and T. Critchlow, Bioinformatics: Managing Scientific Data, 2003.

J. J. Ellinger, R. A. Chylla, E. L. Ulrich, and J. L. Markley, Databases and Software for NMR-Based Metabolomics, Curr Metabolomics, vol.1, issue.1, 2013.

A. Fukushima and M. Kusano, Recent progress in the development of metabolome databases for plant systems biology, Front Plant Sci, vol.4, p.73, 2013.

S. Kim, J. Chen, T. Cheng, A. Gindulyte, J. He et al., PubChem 2019 update: improved access to chemical data, Nucleic Acids Res, vol.47, issue.D1, pp.1102-1111, 2019.

J. Hastings, P. De-matos, A. Dekker, M. Ennis, B. Harsha et al., The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013, Nucleic Acids Res, vol.41, pp.456-63, 2013.

H. Horai, M. Arita, S. Kanaya, Y. Nihei, T. Ikeda et al., MassBank: a public repository for sharing mass spectral data for life sciences, J Mass Spectrom, vol.45, issue.7, pp.703-717, 2010.

R. Tautenhahn, K. Cho, W. Uritboonthai, Z. Zhu, G. J. Patti et al., An accelerated workflow for untargeted metabolomics using the METLIN database, Nat Biotechnol, vol.30, issue.9, pp.826-834, 2012.

J. Kopka, N. Schauer, S. Krueger, C. Birkemeyer, B. Usadel et al., DB: the Golm Metabolome Database, Bioinformatics, vol.21, issue.8, pp.1635-1643, 2005.

T. Kind, G. Wohlgemuth, D. Y. Lee, Y. Lu, M. Palazoglu et al., FiehnLib: mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry, Anal Chem, vol.81, issue.24, pp.10038-10086, 2009.

D. S. Wishart, T. Jewison, A. C. Guo, M. Wilson, C. Knox et al., HMDB 3.0--The Human Metabolome Database in 2013, Nucleic Acids Res, vol.41, pp.801-808, 2013.

G. Wohlgemuth, P. K. Haldiya, E. Willighagen, T. Kind, and O. Fiehn, The Chemical Translation Service--a web-based tool to improve standardization of metabolomic reports, Bioinformatics, vol.26, issue.20, pp.2647-2655, 2010.

X. Domingo-almenara, J. Brezmes, M. Vinaixa, S. Samino, N. Ramirez et al., eRah: A Computational Tool Integrating Spectral Deconvolution and Alignment with Quantification and Identification of Metabolites in GC/MS-Based Metabolomics, Anal Chem, vol.88, pp.9821-9830, 2016.

K. Hiller, J. Hangebrauk, C. Jäger, K. Schreiber, and D. Schomburg, MetaboliteDetector: comprehensive analysis tool for targeted and nontargeted GC/MS based metabolome analysis, Anal Chem, vol.81, issue.9, pp.3429-3468, 2009.

D. Tulpan, S. Leger, L. Belliveau, A. Culf, and M. Cuperlovic-culf, MetaboHunter: an automatic approach for identification of metabolites from 1H-NMR spectra of complex mixtures, BMC Bioinformatics, vol.12, p.400, 2011.

D. Jacob, C. Deborde, and A. Moing, An efficient spectra processing method for metabolite identification from 1H-NMR metabolomics data, Anal Bioanal Chem, vol.405, issue.15, pp.5049-61, 2013.
URL : https://hal.archives-ouvertes.fr/hal-02648547

P. Mercier, M. J. Lewis, D. Chang, D. Baker, and D. S. Wishart, Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra, J Biomol NMR, vol.49, issue.3-4, pp.307-330, 2011.

M. Kanehisa, S. Goto, Y. Sato, M. Furumichi, and M. Tanabe, KEGG for integration and interpretation of large-scale molecular data sets, Nucleic Acids Res, vol.40, pp.109-123, 2012.

T. Jewison, Y. Su, F. M. Disfany, Y. Liang, C. Knox et al., SMPDB 2.0: big improvements to the Small Molecule Pathway Database, Nucleic Acids Res, vol.42, pp.478-84, 2014.

T. Kelder, M. P. Van-iersel, K. Hanspers, M. Kutmon, B. R. Conklin et al., WikiPathways: building research communities on biological pathways, Nucleic Acids Res, vol.40, pp.1301-1308, 2012.

R. Caspi, H. Foerster, C. A. Fulcher, P. Kaipa, M. Krummenacker et al., The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases, Nucleic Acids Res, vol.36, pp.623-654, 2008.

F. Garcia-alcalde, F. Garcia-lopez, J. Dopazo, and A. Conesa, Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data, REFERENCES REFERENCES DU CHAPITRE, vol.27, issue.1, pp.137-146, 2011.

D. Moher, A. Liberati, J. Tetzlaff, D. G. Altman, and P. Group, Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement, J Clin Epidemiol, vol.62, issue.10, pp.1006-1018, 2009.

A. Liberati, D. G. Altman, J. Tetzlaff, C. Mulrow, P. C. Gotzsche et al., The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration, J Clin Epidemiol, vol.62, issue.10, pp.1-34, 2009.

A. Panevska, M. Skocaj, I. Krizaj, P. Macek, and K. Sepcic, Ceramide phosphoethanolamine, an enigmatic cellular membrane sphingolipid, Biochim Biophys Acta Biomembr, vol.1861, issue.7, pp.1284-92, 2019.

T. Fall, S. Salihovic, S. Brandmaier, C. Nowak, A. Ganna et al., Non-targeted metabolomics combined with genetic analyses identifies bile acid synthesis and phospholipid metabolism as being associated with incident type 2 diabetes, Diabetologia, vol.59, issue.10, 2016.

P. Gaudreau, J. A. Morais, B. Shatenstein, K. Gray-donald, A. Khalil et al., Nutrition as a determinant of successful aging: description of the Quebec longitudinal study Nuage and results from cross-sectional pilot studies, Rejuvenation Res, vol.10, issue.3, pp.377-86, 2007.

E. Blaak, Gender differences in fat metabolism, Curr Opin Clin Nutr Metab Care, vol.4, issue.6, pp.499-502, 2001.

, Statistique Canada a . Proportion des personnes de 12 ans et plus ayant reçu un diagnostic d'hypertension, selon le groupe d'âge et selon le sexe, pp.2013-2014

S. Canada-b,

, Statistique Canada c . Proportion des personnes de 12 ans et plus ayant reçu un diagnostic d'hypertension, selon le groupe d'âge et selon le sexe, pp.2013-2014

K. G. Alberti, R. H. Eckel, S. M. Grundy, P. Z. Zimmet, J. I. Cleeman et al., Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute, Circulation, vol.120, issue.16, pp.1640-1645, 2009.

S. Tan, G. Avalos, B. Dineen, A. Burke, J. Gavin et al., Traveller health: prevalence of diabetes, pre diabetes and the metabolic syndrome, Ir Med J, vol.102, issue.6, pp.176-184, 2009.

T. Cajka and O. Fiehn, Comprehensive analysis of lipids in biological systems by liquid chromatography-mass spectrometry, Trends Analyt Chem, vol.61, pp.192-206, 2014.

I. F. Duarte, S. O. Diaz, and A. M. Gil, NMR metabolomics of human blood and urine in disease research, J Pharm Biomed Anal, vol.93, pp.17-26, 2014.

S. Forcisi, F. Moritz, B. Kanawati, D. Tziotis, R. Lehmann et al., Liquid chromatography-mass spectrometry in metabolomics research: mass analyzers in ultra high pressure liquid chromatography coupling, J Chromatogr A, vol.1292, pp.51-65, 2013.

J. K. Nicholson, J. C. Lindon, and E. Holmes, Metabonomics': Understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data, Xenobiotica, vol.29, pp.1181-1189, 1999.

R. Ramautar, R. Berger, J. Van-der-greef, and T. Hankemeier, Human metabolomics: Strategies to understand biology, Curr. Opin. Chem. Biol, vol.17, pp.841-846, 2013.

S. Forcisi, F. Moritz, B. Kanawati, D. Tziotis, R. Lehmann et al., Liquid chromatography-mass spectrometry in metabolomics research: Mass analyzers in ultra-high pressure liquid chromatography coupling, J. Chromatogr. A, vol.1292, pp.51-65, 2013.

N. L. Kuehnbaum and P. Britz-mckibbin, New advances in separation science for metabolomics: Resolving chemical diversity in a post-genomic era, Chem. Rev, vol.113, pp.2437-2468, 2013.

T. Fuhrer and N. Zamboni, High-throughput discovery metabolomics, Curr. Opin. Biotechnol, vol.31, pp.73-78, 2015.

A. Alonso, S. Marsal, and A. Julia, Analytical methods in untargeted metabolomics: State of the art in 2015, Front. Bioeng. Biotechnol, vol.3, p.23, 2015.

C. Kuhl, R. Tautenhahn, C. Bottcher, T. R. Larson, and S. Neumann, CAMERA: An integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets, Anal. Chem, vol.84, pp.283-289, 2012.

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, Anal. Chem, vol.78, pp.779-787, 2006.

A. Alonso, A. Julia, A. Beltran, M. Vinaixa, M. Diaz et al., AStream: An R package for annotating LC/MS metabolomic data, Bioinformatics, vol.27, pp.1339-1340, 2011.

O. Senan, A. Aguilar-mogas, M. Navarro, J. Capellades, L. Noon et al., CliqueMS: A computational tool for annotating in-source metabolite ions from LC-MS untargeted metabolomics data based on a coelution similarity network, Bioinformatics, vol.35, pp.4089-4097, 2019.

K. Uppal, D. I. Walker, and D. P. Jones, xMSannotator: An R package for network-based annotation of high-resolution metabolomics data, Anal. Chem, vol.89, pp.1063-1067, 2017.

M. D. Wilkinson, M. Dumontier, I. J. Aalbersberg, G. Appleton, M. Axton et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, vol.3, p.160018, 2016.

Y. Guitton, M. Tremblay-franco, G. Le-corguillé, J. F. Martin, M. Pétéra 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, Int. J. Biochem. Cell Biol, vol.93, pp.89-101, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01574351

F. Giacomoni, G. Le-corguillé, M. Monsoor, M. Landi, P. Pericard et al., Workflow4Metabolomics: A collaborative research infrastructure for computational metabolomics, vol.31, pp.1493-1495, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01214152

A. Carusi, Reimer, T. Virtual Research Environment Collaborative Landscape Study

. Uk's, Joint Information Systems Committee, 2010.

J. Goecks, A. Nekrutenko, J. Taylor, and T. Galaxy, Galaxy: A comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences, Genome Biol, p.86, 2010.

P. Shannon, A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang et al., Cytoscape: A software environment for integrated models of biomolecular interaction networks, Genome Res, vol.13, pp.2498-2504, 2003.

E. Pujos-guillot, M. Brandolini, M. Pétéra, D. Grissa, C. Joly et al., Systems metabolomics for prediction of metabolic syndrome, J. Proteome Res, vol.16, pp.2262-2272, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01722032

. References and . Chapitre,

D. A. Morrow and J. A. De-lemos, Benchmarks for the assessment of novel cardiovascular biomarkers, Circulation, vol.115, issue.8, pp.949-52, 2007.

J. Goecks, A. Nekrutenko, J. Taylor, and T. Galaxy, Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences, Genome Biol, vol.11, issue.8, p.86, 2010.

L. Cottret, D. Wildridge, F. Vinson, M. P. Barrett, H. Charles et al., MetExplore: a web server to link metabolomic experiments and genome-scale metabolic networks, Nucleic Acids Res, vol.38, pp.132-139, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00690651

M. Kanehisa and S. Goto, KEGG: kyoto encyclopedia of genes and genomes, Nucleic Acids Res, vol.28, issue.1, pp.27-30, 2000.

R. Caspi, R. Billington, L. Ferrer, H. Foerster, C. A. Fulcher et al., The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases, Nucleic Acids Res, vol.44, issue.D1, pp.471-80, 2016.

I. Thiele, N. Swainston, R. M. Fleming, A. Hoppe, S. Sahoo et al., A community-driven global reconstruction of human metabolism, Nat Biotechnol, vol.31, issue.5, pp.419-444, 2013.

D. R. Schlegel, A. Ruttenberg, and P. L. , Ontologies in Metabolomics. Metabolomics, vol.5, p.137, 2015.

P. S. Gromski, H. Muhamadali, D. I. Ellis, Y. Xu, E. Correa et al., A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding, Anal Chim Acta, vol.879, pp.10-23, 2015.

I. Guyon and A. Elisseeff, An Introduction to Variable and Feature Selection, Journal of Machine Learning Research, vol.3, 2003.

H. Liu and H. Motoda, Feature selection for knowledge discovery and data mining: The Springer International Series in Engineering and Computer Science, 1998.

D. Grissa, M. Petera, M. Brandolini, A. Napoli, B. Comte et al., Feature Selection Methods for Early Predictive Biomarker Discovery Using Untargeted Metabolomic Data, Front Mol Biosci, vol.3, p.30, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01581591

P. Rinaudo, S. Boudah, C. Junot, and E. A. Thevenot, biosigner: A New Method for the Discovery of Significant Molecular Signatures from Omics Data, Front Mol Biosci, vol.3, p.26, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01869335

S. Wold, M. Sjöström, and L. Eriksson, PLS-regression: a basic tool of chemometrics. Chemometrics and intelligent laboratory systems, vol.58, pp.109-139, 2001.

L. Breiman, Random Forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.

I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, Gene Selection for Cancer Classification usingSupport Vector Machines, Machine Learning, vol.46, pp.389-422, 2002.

N. Alcaraz, H. Kücük, J. Weil, A. Wipat, J. Baumbach et al., Detecting Case-Specific Biological Pathways Using Expression Data, REFERENCES CONCLUSIONS ET PERSPECTIVES, vol.7, issue.4, pp.299-313, 2011.

O. Dammann, P. Gray, P. Gressens, O. Wolkenhauer, and A. Leviton, Systems Epidemiology: What's in a Name?, Online J Public Health Inform, vol.6, issue.3, p.198, 2014.

. Valorisation and . De-these,

M. Pétéra, P. Gaudreau, B. Comte, and E. Pujos-guillot, Analytic correlation filtration: a new tool to reduce analytical complexity of metabolomic datasets, Ce travail de thèse a fait l'objet de plusieurs valorisations scientifiques citées ci-dessous : Publications scientifiques : 1. Publié ? Monnerie S

S. En-révision-?-monnerie, B. Comte, D. Ziegler, J. A. Morais, E. Pujos-guillot et al., Metabolomic and lipidomic signatures of metabolic syndrome and its physiological components in aging: a systematic review

B. En-cours-?-comte, S. Monnerie, M. Brandolini, C. Canlet, C. Castelli et al., Integrative multiplatform metabolomics for metabolic syndrome exploration

N. Lenuzza, M. Pétéra, S. Monnerie, J. A. Morais, H. Payette et al., The importance of an optimized selection process for biomarker discovery in epidemiological studies

S. ?-monnerie, D. Ziegler, J. A. Morais, H. Payette, B. Comte et al., Biomarqueurs du syndrome métabolique et signatures métaboliques comme outil de caractérisation d'un vieillissement en santé : une revue systématique. 11 ème Congrès International Francophone de Gérontologie et Gériatrie (CIFGG), 2018.

E. ?-pujos-guillot, S. Monnerie, E. Thévenot, C. Junot, J. A. Morais et al., Apport d'un phénotypage multidimensionnel dans la stratification du syndrome métabolique. 11 ème Congrès International Francophone de Gérontologie et Gériatrie (CIFGG), 152018-06-13.

S. Flash-poster-?-monnerie, M. Pétéra, B. Comte, and E. Pujos-guillot, A new tool to reduce analytical complexity of metabolomic dataset. 12 èmes journées scientifiques du Réseau Francophone de Métabolomique et Fluxomique (RFMF), 2019.

S. ?-monnerie, M. Pétéra, B. Comte, and E. Pujos-guillot, A new tool to reduce analytical complexity of metabolomic dataset. MetaboMeeting, 2018.

S. ?-monnerie, M. Pétéra, B. Comte, and E. Pujos-guillot, A new tool to reduce analytical complexity of metabolomic dataset. 12 èmes journées scientifiques du RFMF, 2019.

S. ?-monnerie, M. Pétéra, B. Comte, and E. Pujos-guillot, A new tool to reduce analytical complexity of metabolomic dataset. 15 th Annual Conference of the Metabolomics Society -Metabolomics, 2019.

N. Lenuzza, T. E. Pétéra, M. Monnerie, S. Morais, J. A. Payette et al., Optimized selection process to identify a metabolic syndrome metabolomic/lipidomic signature in older adults of the NuAge cohort, 13 th International Conference of the European Union Geriatric Medicine Society, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02736369

N. Lenuzza, E. Thévenot, M. Pétéra, S. Monnerie, J. A. Morais et al., Optimized selection process to identify a metabolic syndrome metabolomic/lipidomic signature in older adults of the NuAge cohort. 46 th Annual Scientific and Educational Meeting, Evidence for Action in an Aging World, Canadian Association of Gerontology, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02736369

E. Pujos-guillot, S. Monnerie, E. Thévenot, C. Junot, J. A. Morais et al., Input of multidimensional phenotyping in the metabolic syndrome stratification, 13 th International Conference of the European Union Geriatric Medicine Society, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01809596

E. ?-pujos-guillot, S. Monnerie, E. Thévenot, C. Junot, J. A. Morais et al., Input of multidimensional phenotyping in the metabolic syndrome stratification. 46 th Annual Scientific and Educational Meeting, Evidence for Action in an Aging World, Canadian Association of Gerontology, 2017.

E. ?-pujos-guillot, S. Monnerie, E. Thévenot, C. Junot, J. A. Morais et al., Input of multidimensional phenotyping in the metabolic syndrome stratification. 16 th annual scientific sessions of the Society for Heart and Vascular Metabolism, 2018.

B. ?-comte, S. Monnerie, C. Canlet, F. Castelli, B. Colsch et al., Metabolic characterization of metabolic syndrome: evidence from cross-sectional and longitudinal data, th International Conference on Systems Biology, 2018.

E. ?-pujos-guillot, S. Monnerie, D. Ziegler, M. Pétéra, J. A. Morais et al., Far from the FAIR principle? Results from a systematic review on metabolomic / lipidomic signatures of metabolic syndrome, 2018.

M. Pétéra, S. Monnerie, C. Joly, J. Morais, H. Payette et al., Evaluation de méthodes statistiques pour l'intégration de données métabolomiques, cliniques et alimentaires. Congrès de Spectrométrie de Masse, Métabolomique et Analyse Protéomique, 2017.

S. Monnerie, M. Pétéra, B. Comte, and E. Pujos-guillot, A new tool to reduce analytical complexity of metabolomic dataset, Autres valorisations 1. Prix et récompenses ? "Poster presentation Award, 2018.

, ? Premier prix du Jury lors de la finale régionale de Ma Thèse en 180 secondes à Clermont-Ferrand le 7 mars 2019. Participation à la demi-finale nationale à, 2019.

?. Bourse-d'échange-dans-le, Open MultiMed) pour un séjour de 5 semaines à Munich au sein du laboratoire de bio-informatique expérimentale de l'Université Technique de, 2019.

, ? Bourse de voyage du Réseau Francophone de Métabolomique et Fluxomique pour assister à la conférence de la Métabolomique Society à La Haye du 23 au 27 juin, 2019.

, Secondes ? Participation à l'émission de radio « Les décodeurs » sur France Bleu Pays d'Auvergne, animée par Jean-Luc Guillet, 2019.

, ? Présentation ma thèse en 180 secondes lors des journées scientifiques de l'Unité de Nutrition Humaine, 2019.

, ? Présentation ma thèse en 180 secondes lors Rencontres Régionales de l'Enseignement Supérieur, de la Recherche et de l'Innovation, 21 juin, 2019.

. Autres-?-membre, École internationale de recherche d'Agreenium (EIR-A) et obtention du label Agreenium suite à la réalisation de 2 séjours à l'étranger de 5 semaines chacun : ? Préparation de la revue systématique : équipe du Professeur Pierrette GAUDREAU, 2017.

, ? Etude des sous-phénotypes du SMet par approche réseau : équipe du Professeur Jan Baumbach, mars et avril, 2019.

, Estelle Pujos-Guillot, vol.1, p.3

U. Inra, ;. Auvergne, U. Inra, and C. Auvergne, , vol.3, p.63000

, *corresponding author: estelle.pujos-guillot@inra.fr Abstract (100 mots) Introduction: Metabolomic data reflects variations from a very large number of sources. Thus, designing metabolomics studies require special attention

, Objectives: Within a context of biomarker discovery using cohorts, the objective was to highlight important points to consider for an optimal experimental design

, Using a 2-timepoint case-control study on metabolic syndrome, subject selection and experimental design were investigated and discussed, Methods

S. Mottillo, The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis, J Am Coll Cardiol, vol.56, issue.14, pp.1113-1145, 2010.

S. M. Grundy, Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation, vol.112, pp.2735-52, 2005.

B. Balkau and M. A. Charles, National Cholesterol Education Program Expert Panel on Detection, E. and A. Treatment of High Blood Cholesterol in, Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report, Comment on the provisional report from the WHO consultation, vol.16, pp.3143-421, 1999.

D. Einhorn, American College of Endocrinology position statement on the insulin resistance syndrome, Endocr Pract, vol.9, issue.3, pp.237-52, 2003.

K. G. Alberti, The metabolic syndrome--a new worldwide definition, Lancet, vol.366, issue.9491, pp.1059-62, 2005.

E. S. Ford and W. H. Giles, A comparison of the prevalence of the metabolic syndrome using two proposed definitions. Diabetes Care, vol.26, pp.575-81, 2003.

Y. Liao, Critical evaluation of adult treatment panel III criteria in identifying insulin resistance with dyslipidemia. Diabetes Care, vol.27, pp.978-83, 2004.

G. Marchesini, WHO and ATPIII proposals for the definition of the metabolic syndrome in patients with Type 2 diabetes, Diabet Med, vol.21, issue.4, pp.383-390, 2004.

A. Rodriguez, Contribution of impaired glucose tolerance in subjects with the metabolic syndrome: Baltimore Longitudinal Study of Aging, Metabolism, vol.54, issue.4, pp.542-549, 2005.

D. F. Wyszynski, Relation between atherogenic dyslipidemia and the Adult Treatment Program-III definition of metabolic syndrome (Genetic Epidemiology of Metabolic Syndrome Project), Am J Cardiol, vol.95, issue.2, pp.194-202, 2005.

C. E. Tan, Can we apply the National Cholesterol Education Program Adult Treatment Panel definition of the metabolic syndrome to Asians? Diabetes Care, vol.27, pp.1182-1188, 2004.

B. Enkhmaa, Prevalence of the metabolic syndrome using the Third Report of the National Cholesterol Educational Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (ATP III) and the modified ATP III definitions for Japanese and Mongolians, Clin Chim Acta, vol.352, issue.1-2, pp.105-118, 2005.

K. J. Hunt, National Cholesterol Education Program versus World Health Organization metabolic syndrome in relation to all-cause and cardiovascular mortality in the San Antonio Heart Study, Circulation, vol.110, issue.10, pp.1251-1258, 2004.

K. G. Alberti, Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention

, World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity, American Heart Association, vol.120, issue.16, pp.1640-1645, 2009.

A. J. Cameron, J. E. Shaw, and P. Z. Zimmet, The metabolic syndrome: prevalence in worldwide populations, Endocrinol Metab Clin North Am, vol.33, issue.2, pp.351-75, 2004.

J. B. Meigs, Prevalence and characteristics of the metabolic syndrome in the San Antonio Heart and Framingham Offspring Studies, Diabetes, vol.52, issue.8, pp.2160-2167, 2003.

A. J. Cameron, S. J. Zimmet, P. Z. Chitson, P. Alberti, K. Tuomilehto et al., Comparison of WHO and NCEP metabolic syndrome definitions over 5 years in Mauritius, Diabetologia, vol.46, p.3068, 2003.

D. E. Laaksonen, Metabolic syndrome and development of diabetes mellitus: application and validation of recently suggested definitions of the metabolic syndrome in a prospective cohort study, Am J Epidemiol, vol.156, issue.11, pp.1070-1077, 2002.

E. S. Ford, W. H. Giles, and W. H. Dietz, Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey, Jama, vol.287, issue.3, pp.356-359, 2002.

H. E. Resnick and I. , Strong Heart Study, Metabolic syndrome in American Indians, Diabetes Care, vol.25, issue.7, pp.1246-1253, 2002.

C. A. Aguilar-salinas, Analysis of the agreement between the World Health Organization criteria and the National Cholesterol Education Program-III definition of the metabolic syndrome: results from a population-based survey, Diabetes Care, vol.26, issue.5, p.1635, 2003.

M. R. Araneta, D. L. Wingard, and E. Barrett-connor, Type 2 diabetes and metabolic syndrome in Filipina-American women : a high-risk nonobese population, Diabetes Care, vol.25, issue.3, pp.494-503, 2002.

B. Balkau, The incidence and persistence of the NCEP (National Cholesterol Education Program) metabolic syndrome. The French, Diabetes Metab, vol.29, issue.5, pp.526-558, 2003.

F. Azizi, Prevalence of metabolic syndrome in an urban population: Tehran Lipid and Glucose Study, Diabetes Res Clin Pract, vol.61, issue.1, pp.29-37, 2003.

A. Onat, Metabolic syndrome: major impact on coronary risk in a population with low cholesterol levels--a prospective and cross-sectional evaluation, Atherosclerosis, vol.165, issue.2, pp.285-92, 2002.

J. A. Al-lawati, Prevalence of the metabolic syndrome among Omani adults. Diabetes Care, vol.26, pp.1781-1786, 2003.

R. Deepa, Prevalence of insulin resistance syndrome in a selected south Indian population--the Chennai urban population study-7

, Indian J Med Res, vol.115, pp.118-145, 2002.

A. Gupta, Prevalence of diabetes, impaired fasting glucose and insulin resistance syndrome in an urban Indian population, Diabetes Res Clin Pract, vol.61, issue.1, pp.69-76, 2003.

R. Villegas, Prevalence and lifestyle determinants of the metabolic syndrome, Ir Med J, vol.97, issue.10, pp.300-303, 2004.

R. B. Ervin, Prevalence of metabolic syndrome among adults 20 years of age and over, by sex, age, race and ethnicity, and body mass index: United States, Natl Health Stat Report, issue.13, pp.1-7, 2003.

R. Li, Prevalence of metabolic syndrome in Mainland China: a meta-analysis of published studies, vol.16, p.296, 2016.

E. Suliga, D. Koziel, and S. Gluszek, Prevalence of metabolic syndrome in normal weight individuals, Ann Agric Environ Med, vol.23, issue.4, pp.631-635, 2016.

L. Geetha, Prevalence and clinical profile of metabolic obesity and phenotypic obesity in Asian Indians, J Diabetes Sci Technol, vol.5, issue.2, pp.439-485, 2011.

F. De-carvalho-vidigal, The prevalence of metabolic syndrome and metabolically healthy obesity in Europe: a collaborative analysis of ten large cohort studies, BMC Public Health, vol.13, p.9, 2013.

A. Friend, L. Craig, and S. Turner, The prevalence of metabolic syndrome in children: a systematic review of the literature, Metab Syndr Relat Disord, vol.11, issue.2, pp.71-80, 2013.

A. Z. Iqbal, Prevalence of the metabolic syndrome and its component abnormalities among school age Pakistani children, J Ayub Med Coll Abbottabad, vol.26, issue.2, pp.194-203, 2014.

J. K. Kelleher, Probing metabolic pathways with isotopic tracers: insights from mammalian metabolic physiology, Metab Eng, vol.6, issue.1, pp.1-5, 2004.

D. S. Wishart, HMDB 3.0--The Human Metabolome Database in 2013, Nucleic Acids Res, issue.41, pp.801-808, 2013.

D. S. Wishart, HMDB: a knowledgebase for the human metabolome, Nucleic Acids Res, vol.37, pp.603-613, 2009.

J. K. Nicholson, J. C. Lindon, and E. Holmes, Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data, Xenobiotica, vol.29, issue.11, pp.1181-1190, 1999.

T. Cajka and O. Fiehn, Comprehensive analysis of lipids in biological systems by liquid chromatography-mass spectrometry, Trends Analyt Chem, vol.61, pp.192-206, 2014.

K. Strimbu and J. A. Tavel, What are biomarkers?, Curr Opin HIV AIDS, vol.5, issue.6, pp.463-469, 2010.

W. B. Dunn, Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy, Chem Soc Rev, vol.40, issue.1, pp.387-426, 2011.

J. L. Griffin, Metabolic profiles to define the genome: can we hear the phenotypes?, Philos Trans R Soc Lond B Biol Sci, vol.359, pp.857-71, 1446.

J. K. Nicholson and J. C. Lindon, Systems biology: Metabonomics. Nature, vol.455, pp.1054-1060, 2008.

J. H. Bothwell and J. L. Griffin, An introduction to biological nuclear magnetic resonance spectroscopy, Biol Rev Camb Philos Soc, vol.86, issue.2, pp.493-510, 2011.

B. Blümich and P. T. Callaghan, Principle of nuclear magnetic resonance microscopy, vol.492, 1993.

J. L. Ward, J. M. Baker, and M. H. Beale, Recent applications of NMR spectroscopy in plant metabolomics, FEBS J, vol.274, issue.5, pp.1126-1157, 2007.

C. Brunius, L. Shi, and R. Landberg, Large-scale untargeted LC-MS metabolomics data correction using between-batch feature alignment and cluster-based within-batch signal intensity drift correction, Metabolomics, vol.12, issue.11, p.173, 2016.

W. B. Dunn, The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans, Bioanalysis, vol.4, issue.18, pp.2249-64, 2012.

T. Sangster, A pragmatic and readily implemented quality control strategy for HPLC-MS and GC-MS-based metabonomic analysis, Analyst, vol.131, issue.10, pp.1075-1083, 2006.

M. Hilario, Processing and classification of protein mass spectra, Mass Spectrom Rev, vol.25, issue.3, pp.409-458, 2006.

J. Boccard, J. L. Veuthey, and S. Rudaz, Knowledge discovery in metabolomics: an overview of MS data handling, J Sep Sci, vol.33, issue.3, pp.290-304, 2010.

T. Pluskal, MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data, BMC Bioinformatics, vol.11, p.395, 2010.

A. Lommen and H. J. Kools, MetAlign 3.0: performance enhancement by efficient use of advances in computer hardware, Metabolomics, vol.8, issue.4, pp.719-726, 2012.

C. A. Smith, XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification, Anal Chem, vol.78, issue.3, pp.779-87, 2006.

M. M. Ulaszewska, Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies, Mol Nutr Food Res, vol.63, issue.1, p.1800384, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01906543

P. S. Gromski, Influence of missing values substitutes on multivariate analysis of metabolomics data. Metabolites, vol.4, pp.433-52, 2014.

R. Wei, Missing Value Imputation Approach for Mass Spectrometry-based, Metabolomics Data. Sci Rep, vol.8, issue.1, p.663, 2018.

N. Kumar, Metabolomic Biomarker Identification in Presence of Outliers and Missing Values, Biomed Res Int, p.2437608, 2017.

P. Rocca-serra, Data standards can boost metabolomics research, and if there is a will, there is a way, Metabolomics, vol.12, p.14, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01232411

C. E. Cook, The European Bioinformatics Institute in 2016: Data growth and integration, Nucleic Acids Res, vol.44, issue.D1, pp.20-26, 2016.

K. Haug, R. M. Salek, and C. Steinbeck, Global open data management in metabolomics, Curr Opin Chem Biol, vol.36, pp.58-63, 2017.

K. Haug, MetaboLights--an open-access general-purpose repository for metabolomics studies and associated meta-data, Nucleic Acids Res, issue.41, pp.781-787, 2013.

M. Sud, Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools, Nucleic Acids Res, vol.44, issue.D1, pp.463-70, 2016.

H. Ferry-dumazet, MeRy-B: a web knowledgebase for the storage, visualization, analysis and annotation of plant NMR metabolomic profiles, BMC Plant Biol, vol.11, p.104, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00645944

T. Ara, Metabolonote: a wiki-based database for managing hierarchical metadata of metabolome analyses, Front Bioeng Biotechnol, vol.3, p.38, 2015.

M. D. Wilkinson, The FAIR Guiding Principles for scientific data management and stewardship. Sci Data, vol.3, p.160018, 2016.

P. Lampen, JCAMP-DX for mass-spectrometry, Applied Spectroscopy, 1994.

R. Rew and G. Davis, NetCDF: an interface for scientific data access, IEEE Computer Graphics and Applications, vol.10, issue.4, pp.76-82, 1990.

P. G. Pedrioli, A common open representation of mass spectrometry data and its application to proteomics research, Nat Biotechnol, vol.22, issue.11, pp.1459-66, 2004.

S. Orchard, Current status of proteomic standards development, Expert Rev Proteomics, vol.1, issue.2, pp.179-83, 2004.

L. Martens, mzML--a community standard for mass spectrometry data, Mol Cell Proteomics, vol.10, issue.1, pp.110-000133, 2011.

M. Wilhelm, mz5: space-and time-efficient storage of mass spectrometry data sets, Mol Cell Proteomics, vol.11, issue.1, pp.111-011379, 2012.

J. C. Lindon, Summary recommendations for standardization and reporting of metabolic analyses, Nat Biotechnol, vol.23, issue.7, pp.833-841, 2005.

L. W. Sumner, Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics, vol.3, pp.211-221, 2007.

M. S. Members, The metabolomics standards initiative, Nat Biotechnol, vol.25, issue.8, pp.846-854, 2007.

P. Rocca-serra, ISA software suite: supporting standards-compliant experimental annotation and enabling curation at the community level, Bioinformatics, vol.26, issue.18, pp.2354-2360, 2010.

F. Giacomoni, Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics, Bioinformatics, vol.31, issue.9, pp.1493-1498, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01214152

Y. Guitton, 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, Int J Biochem Cell Biol, vol.93, pp.89-101, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01574351

D. I. Broadhurst and D. B. Kell, Statistical strategies for avoiding false discoveries in metabolomics and related experiments, Metabolomics, 2002.

E. Holmes and H. Antti, Chemometric contributions to the evolution of metabonomics: mathematical solutions to characterising and interpreting complex biological NMR spectra, Analyst, vol.127, issue.12, pp.1549-57, 2002.

N. J. Serkova, T. J. Standiford, and K. A. Stringer, The emerging field of quantitative blood metabolomics for biomarker discovery in critical illnesses, Am J Respir Crit Care Med, vol.184, issue.6, pp.647-55, 2011.

D. A. Morrow and J. A. De-lemos, Benchmarks for the assessment of novel cardiovascular biomarkers. Circulation, vol.115, pp.949-52, 2007.

L. Rasmussen, Standardization of factors that influence humain urine metabolomics, Metabolomics, issue.7, 2011.

J. H. Winnike, Effects of a prolonged standardized diet on normalizing the human metabolome, Am J Clin Nutr, vol.90, issue.6, pp.1496-501, 2009.

M. K. Townsend, Reproducibility of metabolomic profiles among men and women in 2 large cohort studies, Clin Chem, vol.59, issue.11, pp.1657-67, 2013.

M. Vinaixa, A Guideline to Univariate Statistical Analysis for LC/MS-Based Untargeted Metabolomics-Derived Data, Metabolites, vol.2, issue.4, pp.775-95, 2012.

Y. Benjamini and Y. Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the royal statistical society, vol.57, pp.289-300, 1995.

R. Bro and A. K. Smilde, Principal component analysis, Analytical methods, issue.9, p.2014

S. World, K. Esbensen, and P. Geladi, Principal component analysis. Chemometrics and intelligent laboratory systems, vol.2, pp.37-52, 1987.

H. Hotelling, Analysis of a complex of statistical variables into principal components, Journal of educational psychology, vol.24, issue.6, pp.417-441, 1933.

M. B. Eisen, Cluster analysis and display of genome-wide expression patterns, Proc Natl Acad Sci, vol.95, issue.25, pp.14863-14871, 1998.

J. Xia, Translational biomarker discovery in clinical metabolomics: an introductory tutorial, Metabolomics, issue.9, pp.280-299, 2013.

J. M. Fonville, The evolution of partial least squares models and related chemometric approaches in metabonomics and metabolic phenotyping, Journal of chemometrics, vol.24, pp.636-349, 2010.

J. Trygg and S. Wold, Orthogonal projections to latent structures (O-PLS), Journal of chemometrics, vol.16, issue.3, pp.119-128, 2002.

H. Tapp and E. K. Kemsley, Notes on the practical utility of OPLS, Trends in Analytical Chemistry, vol.28, issue.11, pp.1322-1327, 2009.

P. S. Gromski, A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding, Anal Chim Acta, vol.879, pp.10-23, 2015.

Y. Kim, Multivariate classification of urine metabolome profiles for breast cancer diagnosis, BMC Bioinformatics, vol.11, issue.2, p.4, 2010.

J. Luts, A tutorial on support vector machine-based methods for classification problems in chemometrics, Anal Chim Acta, vol.665, issue.2, pp.129-174, 2010.

S. Mahadevan, Analysis of metabolomic data using support vector machines, Anal Chem, vol.80, pp.7562-70, 2008.

T. Sing, ROCR: visualizing classifier performance in R, Bioinformatics, pp.3940-3941, 1920.

X. Robin, pROC: an open-source package for R and S+ to analyze and compare ROC curves, BMC Bioinformatics, vol.12, p.77, 2011.

Z. Lacroix and T. Critchlow, Bioinformatics: Managing Scientific Data, 2003.

J. J. Ellinger, Databases and Software for NMR-Based Metabolomics, Curr Metabolomics, issue.1, 2013.

A. Fukushima and M. Kusano, Recent progress in the development of metabolome databases for plant systems biology, Front Plant Sci, vol.4, p.73, 2013.

S. Kim, PubChem 2019 update: improved access to chemical data, Nucleic Acids Res, vol.47, issue.D1, pp.1102-1109, 2019.

J. Hastings, The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013, Nucleic Acids Res, issue.41, pp.456-63, 2013.

H. Horai, MassBank: a public repository for sharing mass spectral data for life sciences, J Mass Spectrom, vol.45, issue.7, pp.703-717, 2010.

R. Tautenhahn, An accelerated workflow for untargeted metabolomics using the METLIN database, Nat Biotechnol, vol.30, issue.9, pp.826-834, 2012.

J. Kopka, DB: the Golm Metabolome Database, Bioinformatics, vol.21, issue.8, pp.1635-1643, 2005.

T. Kind, FiehnLib: mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry, Anal Chem, vol.81, issue.24, pp.10038-10086, 2009.

G. Wohlgemuth, The Chemical Translation Service--a web-based tool to improve standardization of metabolomic reports, Bioinformatics, vol.26, pp.2647-2655, 1920.

X. Domingo-almenara, eRah: A Computational Tool Integrating Spectral Deconvolution and Alignment with Quantification and Identification of Metabolites in GC/MS-Based Metabolomics, Anal Chem, vol.88, pp.9821-9829, 2016.

K. Hiller, MetaboliteDetector: comprehensive analysis tool for targeted and nontargeted GC/MS based metabolome analysis, Anal Chem, vol.81, issue.9, pp.3429-3468, 2009.

D. Tulpan, MetaboHunter: an automatic approach for identification of metabolites from 1H-NMR spectra of complex mixtures, BMC Bioinformatics, vol.12, p.400, 2011.

P. Mercier, Towards automatic metabolomic profiling of high-resolution onedimensional proton NMR spectra, J Biomol NMR, vol.49, pp.307-330, 2011.

M. Kanehisa, KEGG for integration and interpretation of large-scale molecular data sets, Nucleic Acids Res, vol.40, pp.109-123, 2012.

T. Jewison, SMPDB 2.0: big improvements to the Small Molecule Pathway Database, Nucleic Acids Res, vol.42, pp.478-84, 2014.

T. Kelder, WikiPathways: building research communities on biological pathways, Nucleic Acids Res, vol.40, pp.1301-1308, 2012.

R. Caspi, The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases, Nucleic Acids Res, vol.36, pp.623-654, 2008.

F. Garcia-alcalde, Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data, Bioinformatics, vol.27, issue.1, pp.137-146, 2011.

H. Rohn, VANTED v2: a framework for systems biology applications, BMC Syst Biol, vol.6, p.139, 2012.

M. E. Smoot, Cytoscape 2.8: new features for data integration and network visualization, Bioinformatics, vol.27, issue.3, pp.431-433, 2011.

J. Xia, MetaboAnalyst 2.0--a comprehensive server for metabolomic data analysis, Nucleic Acids Res, vol.40, pp.127-160, 2012.

J. Kim, Network rewiring is an important mechanism of gene essentiality change, vol.2, p.900, 2012.

I. Thiele, A community-driven global reconstruction of human metabolism, Nat Biotechnol, vol.31, issue.5, pp.419-444, 2013.

P. Langfelder and S. Horvath, WGCNA: an R package for weighted correlation network analysis, BMC Bioinformatics, vol.9, p.559, 2008.

J. Krumsiek, Gaussian graphical modeling reconstructs pathway reactions from highthroughput metabolomics data, BMC Syst Biol, vol.5, p.21, 2011.

B. Valcarcel, A differential network approach to exploring differences between biological states: an application to prediabetes, PLoS One, vol.6, issue.9, p.24702, 2011.

L. Cottret, MetExplore: collaborative edition and exploration of metabolic networks, Nucleic Acids Res, vol.46, issue.W1, pp.495-502, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01886470

J. Gao, Metscape: a Cytoscape plug-in for visualizing and interpreting metabolomic data in the context of human metabolic networks, Bioinformatics, vol.26, issue.7, pp.971-974, 2010.

M. Latendresse and P. D. Karp, Web-based metabolic network visualization with a zooming user interface, BMC Bioinformatics, vol.12, p.176, 2011.

O. Dammann, Systems Epidemiology: What's in a Name?, Online J Public Health Inform, vol.6, issue.3, p.198, 2014.

D. Moher, Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement, J Clin Epidemiol, vol.62, issue.10, pp.1006-1018, 2009.

A. Liberati, The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration, J Clin Epidemiol, vol.62, issue.10, pp.1-34, 2009.

K. G. Alberti, P. Zimmet, and J. Shaw, Metabolic syndrome--a new world-wide definition. A Consensus Statement from the International Diabetes Federation, Diabet Med, vol.23, issue.5, pp.469-80, 2006.

C. Day, Metabolic syndrome, or What you will: definitions and epidemiology. Diab Vasc Dis Res, vol.4, pp.32-40, 2007.

D. W. Lam, D. Leroith, and M. Syndrome, , 2000.

E. S. Ford, Prospective association between lung function and the incidence of diabetes: findings from the National Health and Nutrition Examination Survey Epidemiologic Follow-up Study, Diabetes Care, vol.27, issue.12, pp.2966-70, 2004.

H. Beltran-sanchez, Prevalence and trends of metabolic syndrome in the adult U.S. population, 1999-2010, J Am Coll Cardiol, vol.62, issue.8, pp.697-703, 2013.

M. G. Saklayen, The Global Epidemic of the Metabolic Syndrome, Curr Hypertens Rep, vol.20, issue.2, p.12, 2018.

P. B. Nolan, Prevalence of metabolic syndrome and metabolic syndrome components in young adults: A pooled analysis, Prev Med Rep, vol.7, pp.211-215, 2017.

E. S. Ford, W. H. Giles, and W. H. Dietz, Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey, JAMA, vol.287, issue.3, pp.356-365, 2002.

J. Kaur, A comprehensive review on metabolic syndrome, Cardiol Res Pract, p.943162, 2014.

P. Ranasinghe, Prevalence and trends of metabolic syndrome among adults in the asiapacific region: a systematic review, vol.17, p.101, 2017.

P. W. Wilson, Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus. Circulation, vol.112, pp.3066-72, 1920.

M. P. Stern, Does the metabolic syndrome improve identification of individuals at risk of type 2 diabetes and/or cardiovascular disease? Diabetes Care, vol.27, pp.2676-81, 2004.

C. Li and E. S. Ford, Definition of the Metabolic Syndrome: What's New and What Predicts Risk?, Metab Syndr Relat Disord, vol.4, issue.4, pp.237-51, 2006.

S. M. Grundy, Pre-diabetes, metabolic syndrome, and cardiovascular risk, J Am Coll Cardiol, vol.59, issue.7, pp.635-678, 2012.

V. T. Poon, J. L. Kuk, and C. I. Ardern, Trajectories of metabolic syndrome development in young adults, PLoS One, vol.9, issue.11, p.111647, 2014.

A. Steinbrecher and T. Pischon, The potential use of biomarkers in the prevention of Type 2 diabetes, Expert Review of Endocrinology and Metabolism, vol.8, issue.3, pp.217-219, 2013.

R. Ramautar, Human metabolomics: Strategies to understand biology, 2013.

S. Liggi and J. L. Griffin, Metabolomics applied to diabetes-lessons from human population studies, International Journal of Biochemistry & Cell Biology, vol.93, pp.136-147, 2017.

A. H. Zhang, Metabolomics in diabetes, Clin Chim Acta, vol.429, pp.106-116, 2014.

S. Park, K. C. Sadanala, and E. K. Kim, A Metabolomic Approach to Understanding the Metabolic Link between Obesity and Diabetes, Mol Cells, vol.38, issue.7, pp.587-96, 2015.

J. R. Bain, Metabolomics applied to diabetes research: moving from information to knowledge, Diabetes, vol.58, issue.11, pp.2429-2443, 2009.

T. Cajka and O. Fiehn, Toward Merging Untargeted and Targeted Methods in Mass Spectrometry-Based Metabolomics and Lipidomics, vol.88, pp.524-569, 2016.

I. F. Duarte, S. O. Diaz, and A. M. Gil, NMR metabolomics of human blood and urine in disease research, J Pharm Biomed Anal, vol.93, pp.17-26, 2014.

S. Forcisi, Liquid chromatography-mass spectrometry in metabolomics research: mass analyzers in ultra high pressure liquid chromatography coupling, J Chromatogr A, vol.1292, pp.51-65, 2013.

F. Capel, Metabolomics reveals plausible interactive effects between dairy product consumption and metabolic syndrome in humans, Clin Nutr, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02178453

I. Surowiec, Metabolomic and lipidomic assessment of the metabolic syndrome in Dutch middle-aged individuals reveals novel biological signatures separating health and disease, Metabolomics, vol.15, issue.2, p.23, 2019.

S. Becker, LC-MS-based metabolomics in the clinical laboratory, Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences

A. Alonso, S. Marsal, and A. Julia, Analytical methods in untargeted metabolomics: state of the art in 2015, Front Bioeng Biotechnol, vol.3, p.23, 2015.

K. M. Sas, Metabolomics and diabetes: analytical and computational approaches, Diabetes, vol.64, issue.3, pp.718-732, 2015.

G. Liebisch, Shorthand notation for lipid structures derived from mass spectrometry, J Lipid Res, vol.54, issue.6, pp.1523-1553, 2013.

M. Sud, LMSD: LIPID MAPS structure database, Nucleic Acids Res, vol.35, pp.527-559, 2007.

A. Gonzalez-franquesa, What Have Metabolomics Approaches Taught Us About Type 2 Diabetes?, Curr Diab Rep, vol.16, issue.8, p.74, 2016.

M. Palau-rodriguez, Metabolomic insights into the intricate gut microbial-host interaction in the development of obesity and type 2 diabetes. Front Microbiol, vol.6, p.1151, 2015.

H. Shapiro, J. Suez, and E. Elinav, Personalized microbiome-based approaches to metabolic syndrome management and prevention, J Diabetes, vol.9, issue.3, pp.226-236, 2017.

N. G. Forouhi, Differences in the prospective association between individual plasma phospholipid saturated fatty acids and incident type 2 diabetes: the EPIC-InterAct case-cohort study. The lancet, Diabetes & endocrinology, vol.2, issue.10, pp.810-818, 2014.

N. S. Kale, MetaboLights: An Open-Access Database Repository for Metabolomics Data, Curr Protoc Bioinformatics, vol.53, pp.14-27, 2016.

N. W. Hardy and C. Taylor, A roadmap for the establishment of standard data exchange structures for metabolomics, Metabolomics, vol.3, pp.243-248, 2007.

R. Lehmann, Diabetes subphenotypes and metabolomics: The key to discovering laboratory markers for personalized medicine?, 2013.

D. Grissa, Feature Selection Methods for Early Predictive Biomarker Discovery Using Untargeted Metabolomic Data, Front Mol Biosci, vol.3, p.30, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01581591

D. I. Broadhurst and D. B. Kell, Statistical strategies for avoiding false discoveries in metabolomics and related experiments, Metabolomics, vol.2, issue.4, pp.171-196, 2006.

E. Saccenti, Reflections on univariate and multivariate analysis of metabolomics data. metabolomics, vol.10, pp.361-374, 2014.

M. Vinaixa, Mass spectral databases for LC/MS and GC/MS-based metabolomics: state of the field and future prospects, Trends in Analytical Chemistry, 2015.

J. C. Lindon and J. K. Nicholson, The emergent role of metabolic phenotyping in dynamic patient stratification, Expert Opin Drug Metab Toxicol, vol.10, issue.7, pp.915-924, 2014.

M. E. Dumas, J. Kinross, and J. K. Nicholson, Metabolic phenotyping and systems biology approaches to understanding metabolic syndrome and fatty liver disease, Gastroenterology, vol.146, issue.1, pp.46-62, 2014.

P. K. Wiklund, Serum metabolic profiles in overweight and obese women with and without metabolic syndrome, Diabetol Metab Syndr, vol.6, issue.1, p.40, 2014.

E. Pujos-guillot, Systems Metabolomics for Prediction of Metabolic Syndrome, J Proteome Res, vol.16, issue.6, pp.2262-2272, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01722032

L. S. Sperling, The CardioMetabolic Health Alliance: Working Toward a New Care Model for the Metabolic Syndrome, J Am Coll Cardiol, vol.66, issue.9, pp.1050-67, 2015.

P. Bardou, jvenn: an interactive Venn diagram viewer, BMC Bioinformatics, vol.15, p.293, 2014.
URL : https://hal.archives-ouvertes.fr/hal-02640484

G. Caimi, Evaluation of nitric oxide metabolites in a group of subjects with metabolic syndrome, Diabetes Metab Syndr, vol.6, issue.3, pp.132-137, 2012.

T. M. James-todd, The association between phthalates and metabolic syndrome: the National Health and Nutrition Examination Survey, Environ Health, vol.15, p.52, 2001.

H. Kulkarni, Variability in associations of phosphatidylcholine molecular species with metabolic syndrome in Mexican-American families, Lipids, vol.48, issue.5, pp.497-503, 2013.

A. Ntzouvani, Amino acid profile and metabolic syndrome in a male Mediterranean population: A cross-sectional study, Nutr Metab Cardiovasc Dis, 2017.

A. Olszanecka, K. Kawecka-jaszcz, and D. Czarnecka, Association of free testosterone and sex hormone binding globulin with metabolic syndrome and subclinical atherosclerosis but not blood pressure in hypertensive perimenopausal women, Arch Med Sci, vol.12, issue.3, pp.521-529, 2016.

N. Ramakrishanan, Exploratory lipidomics in patients with nascent Metabolic Syndrome, Journal of Diabetes and its Complications, vol.32, issue.8, pp.791-794, 2018.

K. Shim, R. Gulhar, and I. , Exploratory metabolomics of nascent metabolic syndrome, Journal of Diabetes and its Complications, vol.33, issue.3, pp.212-216, 2019.

M. Tremblay-franco, Effect of obesity and metabolic syndrome on plasma oxysterols and fatty acids in human, Steroids, issue.99, pp.287-92, 2015.
URL : https://hal.archives-ouvertes.fr/hal-02639945

L. Antonio, Associations between sex steroids and the development of metabolic syndrome: A longitudinal study in European men, Journal of Clinical Endocrinology and Metabolism, vol.100, issue.4, pp.1396-1404, 2015.

L. Barrea, Trimethylamine-N-oxide (TMAO) as novel potential biomarker of early predictors of metabolic syndrome, Nutrients, issue.12, p.10, 2018.

K. Blouin, Contribution of age and declining androgen levels to features of the metabolic syndrome in men, Metabolism, vol.54, issue.8, pp.1034-1074, 2005.

S. Cheng, Metabolite profiling identifies pathways associated with metabolic risk in humans, Circulation, vol.125, issue.18, pp.2222-2253, 2012.

M. Favennec, The kynurenine pathway is activated in human obesity and shifted toward kynurenine monooxygenase activation, Obesity (Silver Spring), issue.23, pp.2066-74, 2015.

X. Gao, Unfavorable associations between serum trimethylamine N-oxide and Lcarnitine levels with components of metabolic syndrome in the Newfoundland population, Frontiers in Endocrinology, issue.10, 2019.

J. E. Ho, Metabolomic Profiles of Body Mass Index in the Framingham Heart Study Reveal Distinct Cardiometabolic Phenotypes, PLoS One, vol.11, issue.2, p.148361, 2016.

K. Huynh, High-Throughput Plasma Lipidomics: Detailed Mapping of the Associations with Cardiometabolic Risk Factors, Cell Chemical Biology, vol.26, issue.1, pp.71-84, 2019.

J. Liu, A Mendelian Randomization Study of Metabolite Profiles, Fasting Glucose, and Type 2 Diabetes. Diabetes, vol.66, pp.2915-2926, 2017.

G. B. Marchand, Increased body fat mass explains the positive association between circulating estradiol and insulin resistance in postmenopausal women, American journal of physiology, vol.314, issue.5, pp.448-456, 2018.

I. J. Neeland, Relation of plasma ceramides to visceral adiposity, insulin resistance and the development of type 2 diabetes mellitus: the Dallas Heart Study, Diabetologia, vol.61, issue.12, pp.2570-2579, 2018.

F. Ottosson, Altered asparagine and glutamate homeostasis precede coronary artery disease and type 2 diabetes, Journal of Clinical Endocrinology and Metabolism, vol.103, issue.8, pp.3060-3069, 2018.

R. Wang-sattler, Novel biomarkers for pre-diabetes identified by metabolomics, Mol Syst Biol, vol.8, p.615, 2012.

P. M. Lind, B. Zethelius, and L. Lind, Circulating levels of phthalate metabolites are associated with prevalent diabetes in the elderly, Diabetes Care, vol.35, issue.7, pp.1519-1543, 2012.

J. Liu, Metabolomics based markers predict type 2 diabetes in a 14-year follow-up study, 2017.

P. J. Meikle, Plasma Lipid Profiling Shows Similar Associations with Prediabetes and Type 2 Diabetes, PLoS ONE, vol.8, issue.9, 2013.

G. Peddinti, Early metabolic markers identify potential targets for the prevention of type 2 diabetes, 2017.

T. Suvitaival, Lipidome as a predictive tool in progression to type 2 diabetes in Finnish men, 2017.

L. Yengo, Impact of statistical models on the prediction of type 2 diabetes using nontargeted metabolomics profiling, 2016.

A. Panevska, Ceramide phosphoethanolamine, an enigmatic cellular membrane sphingolipid, Biochim Biophys Acta Biomembr, vol.1861, issue.7, pp.1284-1292, 2019.

T. Fall, Non-targeted metabolomics combined with genetic analyses identifies bile acid synthesis and phospholipid metabolism as being associated with incident type 2 diabetes, Diabetologia, vol.59, issue.10, pp.2114-2138, 2016.

P. Gaudreau, Nutrition as a determinant of successful aging: description of the Quebec longitudinal study Nuage and results from cross-sectional pilot studies, Rejuvenation Res, vol.10, issue.3, pp.377-86, 2007.

E. Blaak, Gender differences in fat metabolism, Curr Opin Clin Nutr Metab Care, vol.4, issue.6, pp.499-502, 2001.

S. Canada, Proportion des personnes de 12 ans et plus ayant reçu un diagnostic d'hypertension, selon le groupe d'âge et selon le sexe, pp.2013-2014

S. Canada, Tableau 13-10-0096-20 Indice de masse corporelle

S. Canada, Proportion des personnes de 12 ans et plus ayant reçu un diagnostic d'hypertension, selon le groupe d'âge et selon le sexe, pp.2013-2014

S. Tan, Traveller health: prevalence of diabetes, pre diabetes and the metabolic syndrome, Ir Med J, vol.102, issue.6, pp.176-184, 2009.

J. Goecks, Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences, Genome Biol, vol.11, issue.8, p.86, 2010.

R. Ramautar, Human metabolomics: strategies to understand biology, Curr Opin Chem Biol, vol.17, issue.5, pp.841-847, 2013.

N. L. Kuehnbaum and P. Britz-mckibbin, New advances in separation science for metabolomics: resolving chemical diversity in a post-genomic era, Chem Rev, vol.113, issue.4, pp.2437-68, 2013.

T. Fuhrer and N. Zamboni, High-throughput discovery metabolomics, Curr Opin Biotechnol, vol.31, pp.73-81, 2015.

C. Kuhl, CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets, Anal Chem, vol.84, issue.1, pp.283-292, 2012.

A. Alonso, AStream: an R package for annotating LC/MS metabolomic data, Bioinformatics, vol.27, issue.9, pp.1339-1379, 2011.

O. Senan, CliqueMS: A computational tool for annotating in-source metabolite ions from LC-MS untargeted metabolomics data based on a coelution similarity network, Bioinformatics, 2019.

K. Uppal, D. I. Walker, and D. P. Jones, xMSannotator: An R Package for Network-Based Annotation of High-Resolution Metabolomics Data, Anal Chem, vol.89, issue.2, pp.1063-1067, 2017.

A. Carusi and T. Reimer, Virtual Research Environment Collaborative Landscape Study: A JISC funded project, 2010.

P. Shannon, Cytoscape: a software environment for integrated models of biomolecular interaction networks, Genome Res, vol.13, issue.11, pp.2498-504, 2003.

L. Cottret, MetExplore: a web server to link metabolomic experiments and genome-scale metabolic networks, Nucleic Acids Res, vol.38, pp.132-139, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00690651

M. Kanehisa and S. Goto, KEGG: kyoto encyclopedia of genes and genomes, Nucleic Acids Res, vol.28, issue.1, pp.27-30, 2000.

R. Caspi, The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases, Nucleic Acids Res, vol.44, issue.D1, pp.471-80, 2016.

D. R. Schlegel, A. Ruttenberg, and E. P. , Ontologies in Metabolomics, vol.5, p.137, 2015.

I. Guyon and A. Elisseeff, An Introduction to Variable and Feature Selection, Journal of Machine Learning Research, vol.3, 2003.

H. Liu and H. Motoda, Feature selection for knowledge discovery and data mining, The Springer International Series in Engineering and Computer Science, 1998.

P. Rinaudo, biosigner: A New Method for the Discovery of Significant Molecular Signatures from Omics Data, Front Mol Biosci, vol.3, p.26, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01869335

S. Wold, M. Sjöström, and L. Eriksson, PLS-regression: a basic tool of chemometrics. Chemometrics and intelligent laboratory systems, vol.58, pp.109-130, 2001.

L. Breiman, Random Forests. Machine Learning, vol.45, pp.5-32, 2001.

I. Guyon, Gene Selection for Cancer Classification usingSupport Vector Machines, Machine Learning, vol.46, pp.389-422, 2002.

N. Alcaraz, KeyPathwayMiner: Detecting Case-Specific Biological Pathways Using Expression Data, Internet Mathematics, vol.7, issue.4, pp.299-313, 2011.

M. Brown, A metabolome pipeline: from concept to data to knowledge, Metabolomics, vol.1, issue.1, pp.39-51, 2005.

M. Haznadar, Navigating the road ahead: addressing challenges for use of metabolomics in epidemiology studies, Metabolomics, vol.10, issue.2, pp.176-178, 2014.

W. B. Dunn, Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry, Nat Protoc, vol.6, issue.7, pp.1060-83, 2011.

A. Alyass, M. Turcotte, and D. Meyre, From big data analysis to personalized medicine for all: challenges and opportunities, BMC Med Genomics, vol.8, p.33, 2015.

J. H. Lubin and M. H. Gail, Biased selection of controls for case-control analyses of cohort studies, Biometrics, vol.40, issue.1, pp.63-75, 1984.

E. S. Ford, C. Li, and N. Sattar, Metabolic syndrome and incident diabetes: current state of the evidence, Diabetes Care, vol.31, issue.9, pp.1898-904, 2008.

A. Fages, Investigating sources of variability in metabolomic data in the EPIC study: the Principal Component Partial R-square (PC-PR2) method, Metabolomics, vol.10, issue.6, pp.1074-1083, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00977571

J. N. Sampson, Metabolomics in epidemiology: sources of variability in metabolite measurements and implications, Cancer Epidemiol Biomarkers Prev, vol.22, issue.4, pp.631-671, 2013.

B. J. Blaise, Power Analysis and Sample Size Determination in Metabolic Phenotyping, Anal Chem, vol.88, issue.10, pp.5179-88, 2016.

D. Trutschel, Experiment design beyond gut feeling: statistical tests and power to detect differential metabolites in mass spectrometry data, Metabolomics, vol.11, issue.4, pp.851-860, 2015.

D. Broadhurst, Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies, Metabolomics, vol.14, issue.6, p.72, 2018.

, Statistique Canada a , Proportion des personnes de 12 ans et plus ayant reçu un diagnostic d'hypertension, selon le groupe d'âge et selon le sexe, pp.2013-2014

S. Canada-b,

, Statistique Canada c , Proportion des personnes de 12 ans et plus ayant reçu un diagnostic d'hypertension, selon le groupe d'âge et selon le sexe, pp.2013-2014

, Les variables métabolomiques ayant un score VIP > 1,25 (projection de l'importance de la variable) pour chacune des PLS réalisé sur les clusters 2 à 2 sont présentées ci-dessous

, A : PLS des clusters 1 et 2