Hostname: page-component-7c8c6479df-5xszh Total loading time: 0 Render date: 2024-03-29T04:18:24.831Z Has data issue: false hasContentIssue false

Metabolomics in the developmental origins of obesity and its cardiometabolic consequences

Published online by Cambridge University Press:  29 January 2015

M. F. Hivert
Affiliation:
Obesity Prevention Program, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
W. Perng*
Affiliation:
Obesity Prevention Program, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
S. M. Watkins
Affiliation:
Metabolon Inc., West Sacramento, CA, USA
C. S. Newgard
Affiliation:
Nutrition and Metabolism Center, Duke University of Medicine, Durham, NC, USA
L. C. Kenny
Affiliation:
The Irish Center for Fetal and Neonatal Translational Research, University College Cork, Co. Cork, USA
B. S. Kristal
Affiliation:
Departments of Neurosurgery, Brigham & Women's Hospital and Harvard Medical School, Boston, MA, USA
M. E. Patti
Affiliation:
Research Division, Joslin Diabetes Center, Boston, MA, USA
E. Isganaitis
Affiliation:
Research Division, Joslin Diabetes Center, Boston, MA, USA
D. L. DeMeo
Affiliation:
Department of Medicine, Channing Division of Network Medicine and Division of Pulmonary and Critical Care, Brigham and Women's Hospital, Boston, MA, USA
E. Oken
Affiliation:
Obesity Prevention Program, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
M. W. Gillman
Affiliation:
Obesity Prevention Program, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
*
*Address for correspondence: W. Perng, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 133 Brookline Avenue, 3rd floor, Boston 02215, USA. (Email wei.perng@gmail.com)

Abstract

In this review, we discuss the potential role of metabolomics to enhance understanding of obesity-related developmental origins of health and disease (DOHaD). We first provide an overview of common techniques and analytical approaches to help interested investigators dive into this relatively novel field. Next, we describe how metabolomics may capture exposures that are notoriously difficult to quantify, and help to further refine phenotypes associated with excess adiposity and related metabolic sequelae over the life course. Together, these data can ultimately help to elucidate mechanisms that underlie fetal metabolic programming. Finally, we review current gaps in knowledge and identify areas where the field of metabolomics is likely to provide insights into mechanisms linked to DOHaD in human populations.

Type
Review
Copyright
© Cambridge University Press and the International Society for Developmental Origins of Health and Disease 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Contributed equally as first author

References

1. Newgard, CB, An, J, Bain, JR, et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 2009; 9, 311326.CrossRefGoogle ScholarPubMed
2. Barker, DJ, Winter, PD, Osmond, C, Margetts, B, Simmonds, SJ. Weight in infancy and death from ischaemic heart disease. Lancet. 1989; 2, 577580.Google Scholar
3. Barker, DJ, Osmond, C. Infant mortality, childhood nutrition, and ischaemic heart disease in England and Wales. Lancet. 1986; 1, 10771081.Google Scholar
4. Barker, DJ, Gluckman, PD, Godfrey, KM, et al. Fetal nutrition and cardiovascular disease in adult life. Lancet. 1993; 341, 938941.Google Scholar
5. Wishart, DS, Jewison, T, Guo, AC, et al. HMDB 3.0 – The Human Metabolome Database in 2013. Nucleic Acids Res. 2013; 41 (Database issue) D801D807.Google Scholar
6. Tuck, MK, Chan, DW, Chia, D, et al. Standard operating procedures for serum and plasma collection: early detection research network consensus statement standard operating procedure integration working group. J Proteome Res. 2009; 8, 113117.CrossRefGoogle Scholar
7. Holland, NT, Smith, MT, Eskenazi, B, Bastaki, M. Biological sample collection and processing for molecular epidemiological studies. Mutat Res. 2003; 543, 217234.Google Scholar
8. John, MW. ed. Metabolomics methods and protocols. In Methods in Molecular Biology (ed. Weckwerth W), 2007; pp. 37. Humana Press: Totowa, NJ.Google Scholar
9. Dunn, WB, Broadhurst, D, Begley, P, et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc. 2011; 6, 10601083.Google Scholar
10. Dietmair, S, Timmins, NE, Gray, PP, Nielsen, LK, Kromer, JO. Towards quantitative metabolomics of mammalian cells: development of a metabolite extraction protocol. Anal Biochem. 2010; 404, 155164.Google Scholar
11. Teahan, O, Gamble, S, Holmes, E, et al. Impact of analytical bias in metabonomic studies of human blood serum and plasma. Anal Chem. 2006; 78, 43074318.Google Scholar
12. Saude, E, Sykes, B. Urine stability for metabolomic studies: effects of preparation and storage. Metabolomics. 2007; 3, 1927.CrossRefGoogle Scholar
13. Yin, P, Peter, A, Franken, H, et al. Preanalytical aspects and sample quality assessment in metabolomics studies of human blood. Clin Chem. 2013; 59, 833845.CrossRefGoogle ScholarPubMed
14. Dunn, WB, Broadhurst, DI, Atherton, HJ, Goodacre, R, Griffin, JL. Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chem Soc Rev. 2011; 40, 387426.Google Scholar
15. Wu, H, Southam, AD, Hines, A, Viant, MR. High-throughput tissue extraction protocol for NMR- and MS-based metabolomics. Anal Biochem. 2008; 372, 204212.CrossRefGoogle ScholarPubMed
16. Beckonert, O, Keun, HC, Ebbels, TM, et al. Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat Protoc. 2007; 2, 26922703.CrossRefGoogle ScholarPubMed
17. Want, EJ, O’Maille, G, Smith, CA, et al. Solvent-dependent metabolite distribution, clustering, and protein extraction for serum profiling with mass spectrometry. Anal Chem. 2006; 78, 743752.CrossRefGoogle ScholarPubMed
18. Bruce, SJ, Tavazzi, I, Parisod, V, et al. Investigation of human blood plasma sample preparation for performing metabolomics using ultrahigh performance liquid chromatography/mass spectrometry. Anal Chem. 2009; 81, 32853296.Google Scholar
19. Gika, HG, Theodoridis, G, Extance, J, Edge, AM, Wilson, ID. High temperature-ultra performance liquid chromatography-mass spectrometry for the metabonomic analysis of Zucker rat urine. J Chromatogr B Analyt Technol Biomed Life Sci. 2008; 871, 279287.CrossRefGoogle ScholarPubMed
20. Wu, N, Clausen, AM. Fundamental and practical aspects of ultrahigh pressure liquid chromatography for fast separations. J Sep Sci. 2007; 30, 11671182.Google Scholar
21. Dettmer, K, Aronov, PA, Hammock, BD. Mass spectrometry-based metabolomics. Mass Spectrom Rev. 2007; 26, 5178.CrossRefGoogle ScholarPubMed
22. Dunn, WB. Current trends and future requirements for the mass spectrometric investigation of microbial, mammalian and plant metabolomes. Phys Biol. 2008; 5, 011001.Google Scholar
23. Robertson, DG and Lindaon, J. Metabonomics in Toxicity Assessment . 2005. CRC Press: Boca Raton, FL.Google Scholar
24. Edwards, J. Principles of NMR [online]. Retrieved 3 March 2013 from http://www.process-nmr.com/nmr1.htmCrossRefGoogle Scholar
25. Lenz, EM, Wilson, ID. Analytical strategies in metabonomics. J Proteome Res. 2007; 6, 443458.CrossRefGoogle ScholarPubMed
26. Issaq, HJ, Van, QN, Waybright, TJ, Muschik, GM, Veenstra, TD. Analytical and statistical approaches to metabolomics research. J Sep Sci. 2009; 32, 21832199.CrossRefGoogle ScholarPubMed
27. Dumas, ME, Maibaum, EC, Teague, C, et al. Assessment of analytical reproducibility of 1H NMR spectroscopy based metabonomics for large-scale epidemiological research: the INTERMAP Study. Anal Chem. 2006; 78, 21992208.Google Scholar
28. Scheltema, R, Decuypere, S, Dujardin, J, et al. Simple data-reduction method for high-resolution LC-MS data in metabolomics. Bioanalysis. 2009; 1, 15511557.Google Scholar
29. Katajamaa, M, Oresic, M. Processing methods for differential analysis of LC/MS profile data. BMC Bioinformatics. 2005; 6, 179.Google Scholar
30. Skov, T, van den Berg, F, Tomasi, G, Bro, R. Automated alignment of chromatographic data. J Chemom. 2006; 20, 484497.Google Scholar
31. Forshed, J, Torgrip, RJ, Aberg, KM, et al. A comparison of methods for alignment of NMR peaks in the context of cluster analysis. J Pharm Biomed Anal. 2005; 38, 824832.Google Scholar
32. Evans, AM, Mitchell, MW, Dai, H and DeHaven, C. Categorizing ion – features in liquid chromatography/mass spectrometry metobolomics data. J Postgenom. 2012; 2:3.Google Scholar
33. Wishart, DS, Knox, C, Guo, AC, et al. HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res. 2009; 37 (Database issue) D603D610.CrossRefGoogle ScholarPubMed
34. Fahy, E, Subramaniam, S, Murphy, RC, et al. Update of the LIPID MAPS comprehensive classification system for lipids. J Lipid Res. 2009; 50(Suppl.), S9S14.Google Scholar
35. Smith, CA, O’Maille, G, Want, EJ, et al. METLIN: a metabolite mass spectral database. Ther Drug Monit. 2005; 27, 747751.Google Scholar
36. Oba, S, Sato, MA, Takemasa, I, et al. A Bayesian missing value estimation method for gene expression profile data. Bioinformatics. 2003; 19, 20882096.Google Scholar
37. Ouyang, M, Welsh, WJ, Georgopoulos, P. Gaussian mixture clustering and imputation of microarray data. Bioinformatics. 2004; 20, 917923.CrossRefGoogle ScholarPubMed
38. Sehgal, MS, Gondal, I, Dooley, LS. Collateral missing value imputation: a new robust missing value estimation algorithm for microarray data. Bioinformatics. 2005; 21, 24172423.Google Scholar
39. Jolliffe, IT. Principal Component Analysis. 1986. Springer-Verlag: New York.Google Scholar
40. Smilde, AK, Jansen, JJ, Hoefsloot, HC, et al. ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data. Bioinformatics. 2005; 21, 30433048.Google Scholar
41. Beckonert, O, Bollard, ME, Ebbels, T, et al. NMR-based metabonomic toxicity classification: hierarchical cluster analysis and k-nearest-neighbour approaches. Analytica Chimica Acta. 2003; 490, 315.Google Scholar
42. Krumsiek, J, Suhre, K, Illig, T, Adamski, J, Theis, FJ. Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data. BMC Syst Biol. 2011; 5, 21.Google Scholar
43. Krumsiek, J, Suhre, K, Evans, AM, et al. Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information. PLoS Genet. 2012; 8, e1003005.Google Scholar
44. Shin, SY, Fauman, EB, Petersen, AK, et al. An atlas of genetic influences on human blood metabolites. Nat. Genet. 2014; 46, 543550.CrossRefGoogle ScholarPubMed
45. Truong, Y, Lin, X, Beecher, C. Learning a complex metabolomic dataset using random forests and support vector machines. Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004; Seattle, WA, USA.Google Scholar
46. Fonville, JM, Richards, SE, Barton, RH, et al. The evolution of partial least squares models and related chemometric approaches in metabonomics and metabolic phenotyping. J Chemom. 2010; 24, 636649.Google Scholar
47. Trygg, J, Wold, S. Orthogonal projections to latent structures (O-PLS). J Chemom. 2002; 16, 119128.CrossRefGoogle Scholar
48. Shi, H, Vigneau-Callahan, KE, Shestopalov, AI, et al. Characterization of diet-dependent metabolic serotypes: primary validation of male and female serotypes in independent cohorts of rats. J Nutr. 2002; 132, 10391046.Google Scholar
49. Shi, H, Vigneau-Callahan, KE, Shestopalov, AI, et al. Characterization of diet-dependent metabolic serotypes: proof of principle in female and male rats. J Nutr. 2002; 132, 10311038.CrossRefGoogle ScholarPubMed
50. Skol, AD, Scott, LJ, Abecasis, GR, Boehnke, M. Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet. 2006; 38, 209213.Google Scholar
51. Wang, TJ, Larson, MG, Vasan, RS, et al. Metabolite profiles and the risk of developing diabetes. Nat Med. 2011; 17, 448453.Google Scholar
52. Cox, J, Williams, S, Grove, K, Lane, RH, Aagaard-Tillery, KM. A maternal high-fat diet is accompanied by alterations in the fetal primate metabolome. Am J Obstet Gynecol. 2009; 201, 281.e281281.e289.CrossRefGoogle ScholarPubMed
53. Kennedy, ET, Ohls, J, Carlson, S, Fleming, K. The Healthy Eating Index: design and applications. J Am Diet Assoc. 1995; 95, 11031108.Google Scholar
54. Guertin, KA, Moore, SC, Sampson, JN, et al. Metabolomics in nutritional epidemiology: identifying metabolites associated with diet and quantifying their potential to uncover diet-disease relations in populations. Nat Genet. 2014; 100, 208217.Google Scholar
55. Menni, C, Zhai, G, Macgregor, A, et al. Targeted metabolomics profiles are strongly correlated with nutritional patterns in women. Metabolomics. 2013; 9, 506514.CrossRefGoogle ScholarPubMed
56. Altmaier, E, Kastenmuller, G, Romisch-Margl, W, et al. Questionnaire-based self-reported nutrition habits associate with serum metabolism as revealed by quantitative targeted metabolomics. Eur J Epidemiol. 2011; 26, 145156.CrossRefGoogle ScholarPubMed
57. Floegel, A, von Ruesten, A, Drogan, D, et al. Variation of serum metabolites related to habitual diet: a targeted metabolomic approach in EPIC-Potsdam. Eur J Clin Nutr. 2013; 67, 11001108.Google Scholar
58. Steffen, LM, Zheng, Y, Steffen, BT. Metabolomic biomarkers reflect usual dietary pattern: a review. Curr Nutr Rep. 2014; 3, 6268.Google Scholar
59. Wang, Z, Klipfell, E, Bennett, BJ, et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature. 2011; 472, 5763.Google Scholar
60. Ridaura, VK, Faith, JJ, Rey, FE, et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science. 2013; 341, 1241214.CrossRefGoogle ScholarPubMed
61. Vrieze, A, Van Nood, E, Holleman, F, et al. Transfer of intestinal microbiota from lean donors increases insulin sensitivity in individuals with metabolic syndrome. Gastroenterology. 2012; 143, 913916, e917.Google Scholar
62. Llorach, R, Urpi-Sarda, M, Jauregui, O, Monagas, M, Andres-Lacueva, C. An LC-MS-based metabolomics approach for exploring urinary metabolome modifications after cocoa consumption. J Proteome Res. 2009; 8, 50605068.Google Scholar
63. van Velzen, EJ, Westerhuis, JA, van Duynhoven, JP, et al. Phenotyping tea consumers by nutrikinetic analysis of polyphenolic end-metabolites. J Proteome Res. 2009; 8, 33173330.Google Scholar
64. Johansson-Persson, A, Barri, T, Ulmius, M, Onning, G, Dragsted, LO. LC-QTOF/MS metabolomic profiles in human plasma after a 5-week high dietary fiber intake. Anal Bioanal Chem. 2013; 405, 47994809.Google Scholar
65. Gurdeniz, G, Rago, D, Bendsen, NT, et al. Effect of trans fatty acid intake on LC-MS and NMR plasma profiles. PLoS One. 2013; 8, e69589.Google Scholar
66. Schmidt, MD, Dwyer, T, Magnussen, CG, Venn, AJ. Predictive associations between alternative measures of childhood adiposity and adult cardio-metabolic health. Int J Obes (Lond). 2011; 35, 3845.Google Scholar
67. Bondia-Pons, I, Nordlund, E, Mattila, I, et al. Postprandial differences in the plasma metabolome of healthy Finnish subjects after intake of a sourdough fermented endosperm rye bread versus white wheat bread. Nutr J. 2011; 10, 116.Google Scholar
68. Krug, S, Kastenmuller, G, Stuckler, F, et al. The dynamic range of the human metabolome revealed by challenges. FASEB J. 2012; 26, 26072619.CrossRefGoogle ScholarPubMed
69. Socha, P, Grote, V, Gruszfeld, D, et al. Milk protein intake, the metabolic-endocrine response, and growth in infancy: data from a randomized clinical trial. Am J Clin Nutr. 2011; 94(6 Suppl.), 1776s1784s.Google Scholar
70. O’Sullivan, A, He, X, McNiven, EM, et al. Early diet impacts infant rhesus gut microbiome, immunity, and metabolism. J Proteome Res. 2013; 12, 28332845.Google Scholar
71. Herman, MA, She, P, Peroni, OD, Lynch, CJ, Kahn, BB. Adipose tissue branched chain amino acid (BCAA) metabolism modulates circulating BCAA levels. J Biol Chem. 2010; 285, 1134811356.Google Scholar
72. Lu, J, Xie, G, Jia, W, Jia, W. Insulin resistance and the metabolism of branched-chain amino acids. Front Med. 2013; 7, 5359.Google Scholar
73. Bertram, HC, Hoppe, C, Petersen, BO, et al. An NMR-based metabonomic investigation on effects of milk and meat protein diets given to 8-year-old boys. Br J Nutr. 2007; 97, 758763.Google Scholar
74. Scheepers, PT. The use of biomarkers for improved retrospective exposure assessment in epidemiological studies: summary of an ECETOC workshop. Biomarkers. 2008; 13, 734748.Google Scholar
75. Scholtens, DM, Muehlbauer, MJ, Daya, NR, et al. Metabolomics reveals broad-scale metabolic perturbations in hyperglycemic mothers during pregnancy. Diabetes Care. 2014; 37, 158166.CrossRefGoogle ScholarPubMed
76. Xu, T, Holzapfel, C, Dong, X, et al. Effects of smoking and smoking cessation on human serum metabolite profile: results from the KORA cohort study. BMC Med. 2013; 11, 60.Google Scholar
77. Oken, E, Levitan, EB, Gillman, MW. Maternal smoking during pregnancy and child overweight: systematic review and meta-analysis. Int J Obes (Lond). 2008; 32, 201210.Google Scholar
78. Enea, C, Seguin, F, Petitpas-Mulliez, J, et al. (1)H NMR-based metabolomics approach for exploring urinary metabolome modifications after acute and chronic physical exercise. Anal Bioanal Chem. 2010; 396, 11671176.Google Scholar
79. Lewis, GD, Farrell, L, Wood, MJ, et al. Metabolic signatures of exercise in human plasma. Sci Transl Med. 2010; 2, 33ra37.Google Scholar
80. Netzer, M, Weinberger, KM, Handler, M, et al. Profiling the human response to physical exercise: a computational strategy for the identification and kinetic analysis of metabolic biomarkers. J Clin Bioinforma. 2011; 1, 34.Google Scholar
81. Pechlivanis, A, Kostidis, S, Saraslanidis, P, et al. (1)H NMR-based metabonomic investigation of the effect of two different exercise sessions on the metabolic fingerprint of human urine. J Proteome Res. 2010; 9, 64056416.Google Scholar
82. Pechlivanis, A, Kostidis, S, Saraslanidis, P, et al. 1H NMR study on the short- and long-term impact of two training programs of sprint running on the metabolic fingerprint of human serum. J Proteome Res. 2013; 12, 470480.Google Scholar
83. Roberts, LD, Bostrom, P, O'Sullivan, JF, et al. β-Aminoisobutyric acid induces browning of white fat and hepatic β-oxidation and is inversely correlated with cardiometabolic risk factors. Cell Metab. 2014; 19, 96108.Google Scholar
84. Huffman, KM, Slentz, CA, Bateman, LA, et al. Exercise-induced changes in metabolic intermediates, hormones, and inflammatory markers associated with improvements in insulin sensitivity. Diabetes Care. 2011; 34, 174176.Google Scholar
85. Yan, B, , AJ, Wang, G, et al. Metabolomic investigation into variation of endogenous metabolites in professional athletes subject to strength-endurance training. J Appl Physiol (1985). 2009; 106, 531538.Google Scholar
86. Brochu, M, Tchernof, A, Dionne, IJ, et al. What are the physical characteristics associated with a normal metabolic profile despite a high level of obesity in postmenopausal women? J Clin Endocrinol Metab. 2001; 86, 10201025.Google ScholarPubMed
87. Karelis, AD. Metabolically healthy but obese individuals. Lancet. 2008; 372, 12811283.Google Scholar
88. Karelis, AD, Faraj, M, Bastard, JP, et al. The metabolically healthy but obese individual presents a favorable inflammation profile. J Clin Endocrinol Metab. 2005; 90, 41454150.Google Scholar
89. Thomas, EL, Parkinson, JR, Frost, GS, et al. The missing risk: MRI and MRS phenotyping of abdominal adiposity and ectopic fat. Obesity (Silver Spring). 2012; 20, 7687.Google Scholar
90. Oken, E, Kleinman, KP, Rich-Edwards, J, Gillman, MW. A nearly continuous measure of birth weight for gestational age using a United States national reference. BMC Pediatr. 2003; 3, 6.Google Scholar
91. Horgan, RP, Broadhurst, DI, Walsh, SK, et al. Metabolic profiling uncovers a phenotypic signature of small for gestational age in early pregnancy. J Proteome Res. 2011; 10, 36603673.Google Scholar
92. Ivorra, C, Garcia-Vicent, C, Chaves, FJ, et al. Metabolomic profiling in blood from umbilical cords of low birth weight newborns. J Transl Med. 2012; 10, 142.Google Scholar
93. Alexandre-Gouabau, MC, Courant, F, Moyon, T, et al. Maternal and cord blood LC-HRMS metabolomics reveal alterations in energy and polyamine metabolism, and oxidative stress in very-low birth weight infants. J Proteome Res. 2013; 12, 27642778.Google Scholar
94. Tea, I, Le Gall, G, Kuster, A, et al. 1H-NMR-based metabolic profiling of maternal and umbilical cord blood indicates altered materno-foetal nutrient exchange in preterm infants. PLoS One. 2012; 7, e29947.Google Scholar
95. Favretto, D, Cosmi, E, Ragazzi, E, et al. Cord blood metabolomic profiling in intrauterine growth restriction. Anal Bioanal Chem. 2012; 402, 11091121.Google Scholar
96. Morris, C, O’Grada, C, Ryan, M, et al. The relationship between BMI and metabolomic profiles: a focus on amino acids. Proc Nutr Soc. 2012; 71, 634638.Google Scholar
97. McCormack, SE, Shaham, O, McCarthy, MA, et al. Circulating branched-chain amino acid concentrations are associated with obesity and future insulin resistance in children and adolescents. Pediatr Obes. 2013; 8, 5261.Google Scholar
98. Perng, WGM, Fleisch, AF, Michalek, RD, et al. Metabolomic profiles of childhood obesity. Early Nutrition Conference 2014.Google Scholar
99. Sumner, LW, Amberg, A, Barrett, D, et al. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics. 2007; 3, 211221.Google Scholar
100. Yousri, NA, Kastenmuller, G, Gieger, C, et al. Long term conservation of human metabolic phenotypes and link to heritability. Metabolomics. 2014; 10, 10051017.Google Scholar
101. Luan, H, Meng, N, Liu, P, et al. Pregnancy-induced metabolic phenotype variations in maternal plasma. J Proteome Res. 2014; 13, 15271536.Google Scholar
102. Graca, G, Goodfellow, BJ, Barros, AS, et al. UPLC-MS metabolic profiling of second trimester amniotic fluid and maternal urine and comparison with NMR spectral profiling for the identification of pregnancy disorder biomarkers. Mol Biosyst. 2012; 8, 12431254.CrossRefGoogle ScholarPubMed
103. Graca, G, Duarte, IF, Barros, AS, et al. Impact of prenatal disorders on the metabolic profile of second trimester amniotic fluid: a nuclear magnetic resonance metabonomic study. J Proteome Res. 2010; 9, 60166024.Google Scholar
104. Horgan, RP, Broadhurst, DI, Dunn, WB, et al. Changes in the metabolic footprint of placental explant-conditioned medium cultured in different oxygen tensions from placentas of small for gestational age and normal pregnancies. Placenta. 2010; 31, 893901.Google Scholar
105. Heazell, AE, Brown, M, Dunn, WB, et al. Analysis of the metabolic footprint and tissue metabolome of placental villous explants cultured at different oxygen tensions reveals novel redox biomarkers. Placenta. 2008; 29, 691698.Google Scholar
106. Dunn, WB, Brown, M, Worton, SA, et al. Changes in the metabolic footprint of placental explant-conditioned culture medium identifies metabolic disturbances related to hypoxia and pre-eclampsia. Placenta. 2009; 30, 974980.Google Scholar
107. Tissot van Patot, MC, Murray, AJ, Beckey, V, et al. Human placental metabolic adaptation to chronic hypoxia, high altitude: hypoxic preconditioning. Am J Physiol Regul Integr Comp Physiol. 2010; 298, R166R172.Google Scholar
108. Kurland, IJ, Accili, D, Burant, C, et al. Application of combined omics platforms to accelerate biomedical discovery in diabesity. Ann NY Acad Sci. 2013; 1287, 116.Google Scholar
109. Putignani, L, Del Chierico, F, Petrucca, A, Vernocchi, P, Dallapiccola, B. The human gut microbiota: a dynamic interplay with the host from birth to senescence settled during childhood. Pediatr Res. 2014; 76, 210.Google Scholar
110. Wurtz, P, Kangas, AJ, Soininen, P, et al. Lipoprotein subclass profiling reveals pleiotropy in the genetic variants of lipid risk factors for coronary heart disease: a note on Mendelian randomization studies. J Am Coll Cardiol. 2013; 62, 19061908.Google Scholar
111. Timpson, NJ, Nordestgaard, BG, Harbord, RM, et al. C-reactive protein levels and body mass index: elucidating direction of causation through reciprocal Mendelian randomization. Int J Obes (Lond). 2011; 35, 300308.Google Scholar
112. Prentice, KJ, Luu, L, Allister, EM, et al. The furan fatty acid metabolite CMPF is elevated in diabetes and induces beta cell dysfunction. Cell Metab. 2014; 19, 653666.CrossRefGoogle ScholarPubMed