Postprandial metabolomics: A pilot mass spectrometry and NMR study of the human plasma metabolome in response to a challenge meal

Analytica Chimica Acta

Volumn 908, Issue , 2016, Pages 121-131

  Karimpour, Masoumeh ^a   Surowiec, Izabella ^a   Wu, Junfang ^a   Gouveia Figueira, Sandra ^a   Pinto, Rui ^a,b   Trygg, Johan ^a   Zivkovic, Angela M ^c   Nording, Malin L ^a  

^a UMEÅ UNIVERSITY   (Sweden)

^b Bioinformatics Infrastructure for Life Sciences ^*  (Sweden)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Data analysis; Mass spectrometry; Metabolomics; Multi platform analysis; Nucleic magnetic resonance; Postprandial

Indexed keywords

AMINO ACIDS; BIOMOLECULES; CHROMATOGRAPHY; DATA HANDLING; DATA REDUCTION; FATTY ACIDS; GAS CHROMATOGRAPHY; HYDROPHOBICITY; INFORMATION ANALYSIS; LIQUID CHROMATOGRAPHY; MASS SPECTROMETRY; METABOLISM; METABOLITES; MULTIVARIANT ANALYSIS; SPECTROMETRY;

3-HYDROXYBUTANOIC ACID; COEFFICIENTS OF VARIATIONS; GAS CHROMATOGRAPHY-MASS SPECTROMETRIES (GC-MS); LIQUID CHROMATOGRAPHY-MASS SPECTROMETRY; METABOLOMICS; MULTI-PLATFORM; NUCLEAR MAGNETIC RESONANCE(NMR); POSTPRANDIAL;

NUCLEAR MAGNETIC RESONANCE;

AMINO ACID; ERYTHRITOL; FATTY ACID; GLYCERIC ACID; HYDROXYBUTYRIC ACID; INOSITOL; MALEIC ACID; PALMITIC ACID; RIBOSE; SACCHARIC ACID; SUGAR; URIC ACID;

ARTICLE; BLOOD SAMPLING; CHEMICAL ANALYSIS; CONTROLLED STUDY; DIETARY INTAKE; HUMAN; HYDROPHOBICITY; IONIZATION; LIQUID CHROMATOGRAPHY; MASS FRAGMENTOGRAPHY; MASS SPECTROMETRY; METABOLOMICS; NUCLEAR MAGNETIC RESONANCE; PHENOTYPE; POSTPRANDIAL STATE; PRIORITY JOURNAL; TEST MEAL; UPREGULATION; VALIDITY; BLOOD; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; PILOT STUDY; VALIDATION STUDY;

BLOOD; CHROMATOGRAPHY, LIQUID; GAS CHROMATOGRAPHY-MASS SPECTROMETRY; HUMANS; MAGNETIC RESONANCE SPECTROSCOPY; METABOLOMICS; PILOT PROJECTS; POSTPRANDIAL PERIOD;

EID: 84955606106     PISSN: 00032670     EISSN: 18734324     Source Type: Journal    
DOI: 10.1016/j.aca.2015.12.009     Document Type: Article

References (29)

1. Kussmann M., Raymond F., Affolter M. OMICS-driven biomarker discovery in nutrition and health. J. Biotechnol. 2006, 124:758-787.
2. Gibney M.J., Walsh M., Brennan L., Roche H.M., German B., van Ommen B. Metabolomics in human nutrition: opportunities and challenges. Am. J. Clin. Nutr. 2005, 82:497-503.
3. Lundman P., Boquist S., Samnegård A., Bennermo M., Held C., Ericsson C.-G., Silveira A., Hamsten A., Tornvall P. A high-fat meal is accompanied by increased plasma interleukin-6 concentrations. Nutr. Metab. Cardiovasc. Dis. 2007, 17:195-202.
4. Cianflone K., Paglialunga S., Roy C. Intestinally-derived cholesterol and lipids: metabolic regulation and consequences-an overview. Atheroscler. Suppl. 2008, 9:102.
5. Lopez-Miranda J., Williams C., Lairon D. Dietary, physiological, genetic and pathological influences on postprandial lipid metabolism. Br. J. Nutr. 2007, 98:458-473.
6. Paglialunga S., Cianflone K. Regulation of postprandial lipemia: an update on current trends. Appl. Physiol. Nutr. Metab. 2007, 32:61-75.
7. Zivkovic A., Wiest M., Nguyen U., Nording M., Watkins S., German J.B. Assessing individual metabolic responsiveness to a lipid challenge using a targeted metabolomic approach. Metabolomics 2009, 5:209-218.
8. Dutta T., Chai H.S., Ward L.E., Ghosh A., Persson X.-M.T., Ford G.C., Kudva Y.C., Sun Z., Asmann Y.W., Kocher J.-P.A., Nair K.S. Concordance of changes in metabolic pathways based on plasma metabolomics and skeletal muscle transcriptomics in type 1 diabetes. Diabetes 2012, 61:1004-1016.
9. Moazzami A.A., Shrestha A., Morrison D.A., Poutanen K., Mykkänen H. Metabolomics reveals differences in postprandial responses to breads and fasting metabolic characteristics associated with postprandial insulin demand in postmenopausal women. J. Nutr. 2014, 144:807-814.
10. Pellis L., van Erk M.J., van Ommen B., Bakker G.C.M., Hendriks H.F.J., Cnubben N.H.P., Kleemann R., van Someren E.P., Bobeldijk I., Rubingh C.M., Wopereis S. Plasma metabolomics and proteomics profiling after a postprandial challenge reveal subtle diet effects on human metabolic status. Metabolomics 2012, 8:347-359.
11. Tredwell G.D., Miller J.A., Chow H.H.S., Thompson P.A., Keun H.C. Metabolomic Characterization of Nipple Aspirate Fluid by 1H NMR Spectroscopy and GC-MS. J. Proteome Res. 2013, 13:883-889.
12. Rochfort S. Metabolomics reviewed:A a new "omics" platform technology for systems biology and implications for natural products research. J. Nat. Prod. 2005, 68:1813-1820.
13. Griffin J., Bollard M. Metabonomics: its potential as a tool in toxicology for safety assessment and data integration. Curr. Drug Metab. 2004, 5:389-398.
14. Serkova N.J., Spratlin Jl Fau-Eckhardt S.G., Eckhardt S.G. NMR-based metabolomics: translational application and treatment of cancer. Curr. Opin. Mol. Ther. 2007, 9:572-585.
15. Ellis D.I., Dunn W.B., Griffin J.L., Allwood J.W., Goodacre R. Metabolic fingerprinting as a diagnostic tool. Pharmacogenomics 2007, 8:1243-1266.
16. Griffin J.L., Atherton H., Shockcor J., Atzori L. Metabolomics as a tool for cardiac research. Nat. Rev. Cardiol. 2011, 8:630-643.
17. Wishart D.S. Metabolomics: applications to food science and nutrition research. Trends Food Sci. Technol. 2008, 19:482-493.
18. O'Gorman A., Gibbons H., Brennan L. Metabolomics in the identification of biomarkers of dietary intake. Comput. Struct. Biotechnol. J. 2013, 4:1-7.
19. Underwood B.R., Broadhurst D., Dunn W.B., Ellis D.I., Michell A.W., Vacher C., Mosedale D.E., Kell D.B., Barker R.A., Grainger D.J., Rubinsztein D.C. Huntington disease patients and transgenic mice have similar pro-catabolic serum metabolite profiles. Brain 2006, 129:877-886.
20. Feng B., Wu S., Lv S., Liu F., Chen H., Yan X., Li Y., Dong F., Wei L. Metabolic profiling analysis of a d-galactosamine/lipopolysaccharide-induced mouse model of fulminant hepatic failure. J. Proteome Res. 2007, 6:2161-2167.
21. Smilowitz J.T., Zivkovic A.M., Wan Y.-J.Y., Watkins S.M., Nording M.L., Hammock B.D., German J.B. Nutritional lipidomics: molecular metabolism, analytics, and diagnostics. Mol. Nutr. Food Res. 2013, 57:1319-1335.
22. Trygg J.A.J., Gullberg J., Johansson A.I., Jonsson P., Antti H., Marklund S.L., Moritz T. Extraction and GC/MS analysis of the human blood plasma metabolome. Anal. Chem. 2005, 77:8086-8094.
23. Schicho R., Shaykhutdinov R., Ngo J., Nazyrova A., Schneider C., Panaccione R., Kaplan G.G., Vogel H.J., Storr M. Quantitative metabolomic profiling of serum, plasma, and urine by 1H NMR spectroscopy discriminates between patients with inflammatory bowel disease and healthy individuals. J. Proteome Res. 2012, 11:3344-3357.
24. Weljie A.M., Newton J., Mercier P., Carlson E., Slupsky C.M. Targeted profiling: quantitative analysis of 1H NMR metabolomics data. Anal. Chem. 2006, 78:4430-4442.
25. Trygg J., Wold S. Orthogonal projections to latent structures (O-PLS). J. Chemom. 2002, 16:119-128.
26. Eriksson L. Multi- and Megavariate Data Analysis 2006, Umetrics AB.
27. ® models. J. Chemom. 2008, 22:594-600.
28. Efron B., Gong G. A leisurely look at the bootstrap, the jackknife, and cross-validation. Am. Stat. 1983, 37:36-48.
29. Wiklund S., Johansson E., Sjöström L., Mellerowicz E.J., Edlund U., Shockcor J.P., Gottfries J., Moritz T., Trygg J. Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models. Anal. Chem. 2008, 80:115-122.