Skip to main content Accessibility help

Metabolomics in the developmental origins of obesity and its cardiometabolic consequences

  • M. F. Hivert (a1) (a2), W. Perng (a1), S. M. Watkins (a3), C. S. Newgard (a4), L. C. Kenny (a5), B. S. Kristal (a6), M. E. Patti (a7), E. Isganaitis (a7), D. L. DeMeo (a8), E. Oken (a1) (a9) and M. W. Gillman (a1) (a9)...


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.


Corresponding author

*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


Hide All

Contributed equally as first author



Hide All
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.
2. Barker, DJ, Winter, PD, Osmond, C, Margetts, B, Simmonds, SJ. Weight in infancy and death from ischaemic heart disease. Lancet. 1989; 2, 577580.
3. Barker, DJ, Osmond, C. Infant mortality, childhood nutrition, and ischaemic heart disease in England and Wales. Lancet. 1986; 1, 10771081.
4. Barker, DJ, Gluckman, PD, Godfrey, KM, et al. Fetal nutrition and cardiovascular disease in adult life. Lancet. 1993; 341, 938941.
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.
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.
7. Holland, NT, Smith, MT, Eskenazi, B, Bastaki, M. Biological sample collection and processing for molecular epidemiological studies. Mutat Res. 2003; 543, 217234.
8. John, MW. ed. Metabolomics methods and protocols. In Methods in Molecular Biology (ed. Weckwerth W), 2007; pp. 37. Humana Press: Totowa, NJ.
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.
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.
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.
12. Saude, E, Sykes, B. Urine stability for metabolomic studies: effects of preparation and storage. Metabolomics. 2007; 3, 1927.
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.
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.
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.
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.
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.
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.
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.
20. Wu, N, Clausen, AM. Fundamental and practical aspects of ultrahigh pressure liquid chromatography for fast separations. J Sep Sci. 2007; 30, 11671182.
21. Dettmer, K, Aronov, PA, Hammock, BD. Mass spectrometry-based metabolomics. Mass Spectrom Rev. 2007; 26, 5178.
22. Dunn, WB. Current trends and future requirements for the mass spectrometric investigation of microbial, mammalian and plant metabolomes. Phys Biol. 2008; 5, 011001.
23. Robertson, DG and Lindaon, J. Metabonomics in Toxicity Assessment . 2005. CRC Press: Boca Raton, FL.
24. Edwards, J. Principles of NMR [online]. Retrieved 3 March 2013 from
25. Lenz, EM, Wilson, ID. Analytical strategies in metabonomics. J Proteome Res. 2007; 6, 443458.
26. Issaq, HJ, Van, QN, Waybright, TJ, Muschik, GM, Veenstra, TD. Analytical and statistical approaches to metabolomics research. J Sep Sci. 2009; 32, 21832199.
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.
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.
29. Katajamaa, M, Oresic, M. Processing methods for differential analysis of LC/MS profile data. BMC Bioinformatics. 2005; 6, 179.
30. Skov, T, van den Berg, F, Tomasi, G, Bro, R. Automated alignment of chromatographic data. J Chemom. 2006; 20, 484497.
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.
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.
33. Wishart, DS, Knox, C, Guo, AC, et al. HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res. 2009; 37 (Database issue) D603D610.
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.
35. Smith, CA, O’Maille, G, Want, EJ, et al. METLIN: a metabolite mass spectral database. Ther Drug Monit. 2005; 27, 747751.
36. Oba, S, Sato, MA, Takemasa, I, et al. A Bayesian missing value estimation method for gene expression profile data. Bioinformatics. 2003; 19, 20882096.
37. Ouyang, M, Welsh, WJ, Georgopoulos, P. Gaussian mixture clustering and imputation of microarray data. Bioinformatics. 2004; 20, 917923.
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.
39. Jolliffe, IT. Principal Component Analysis. 1986. Springer-Verlag: New York.
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.
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.
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.
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.
44. Shin, SY, Fauman, EB, Petersen, AK, et al. An atlas of genetic influences on human blood metabolites. Nat. Genet. 2014; 46, 543550.
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.
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.
47. Trygg, J, Wold, S. Orthogonal projections to latent structures (O-PLS). J Chemom. 2002; 16, 119128.
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.
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.
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.
51. Wang, TJ, Larson, MG, Vasan, RS, et al. Metabolite profiles and the risk of developing diabetes. Nat Med. 2011; 17, 448453.
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.
53. Kennedy, ET, Ohls, J, Carlson, S, Fleming, K. The Healthy Eating Index: design and applications. J Am Diet Assoc. 1995; 95, 11031108.
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.
55. Menni, C, Zhai, G, Macgregor, A, et al. Targeted metabolomics profiles are strongly correlated with nutritional patterns in women. Metabolomics. 2013; 9, 506514.
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.
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.
58. Steffen, LM, Zheng, Y, Steffen, BT. Metabolomic biomarkers reflect usual dietary pattern: a review. Curr Nutr Rep. 2014; 3, 6268.
59. Wang, Z, Klipfell, E, Bennett, BJ, et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature. 2011; 472, 5763.
60. Ridaura, VK, Faith, JJ, Rey, FE, et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science. 2013; 341, 1241214.
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.
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.
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.
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.
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.
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.
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.
68. Krug, S, Kastenmuller, G, Stuckler, F, et al. The dynamic range of the human metabolome revealed by challenges. FASEB J. 2012; 26, 26072619.
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.
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.
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.
72. Lu, J, Xie, G, Jia, W, Jia, W. Insulin resistance and the metabolism of branched-chain amino acids. Front Med. 2013; 7, 5359.
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.
74. Scheepers, PT. The use of biomarkers for improved retrospective exposure assessment in epidemiological studies: summary of an ECETOC workshop. Biomarkers. 2008; 13, 734748.
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.
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.
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.
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.
79. Lewis, GD, Farrell, L, Wood, MJ, et al. Metabolic signatures of exercise in human plasma. Sci Transl Med. 2010; 2, 33ra37.
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.
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.
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.
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.
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.
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.
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.
87. Karelis, AD. Metabolically healthy but obese individuals. Lancet. 2008; 372, 12811283.
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.
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.
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.
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.
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.
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.
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.
95. Favretto, D, Cosmi, E, Ragazzi, E, et al. Cord blood metabolomic profiling in intrauterine growth restriction. Anal Bioanal Chem. 2012; 402, 11091121.
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.
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.
98. Perng, WGM, Fleisch, AF, Michalek, RD, et al. Metabolomic profiles of childhood obesity. Early Nutrition Conference 2014.
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.
100. Yousri, NA, Kastenmuller, G, Gieger, C, et al. Long term conservation of human metabolic phenotypes and link to heritability. Metabolomics. 2014; 10, 10051017.
101. Luan, H, Meng, N, Liu, P, et al. Pregnancy-induced metabolic phenotype variations in maternal plasma. J Proteome Res. 2014; 13, 15271536.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.



Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed