Diabetologia

, Volume 60, Issue 3, pp 518–530 | Cite as

Associations of maternal BMI and insulin resistance with the maternal metabolome and newborn outcomes

  • Victoria Sandler
  • Anna C. Reisetter
  • James R. Bain
  • Michael J. Muehlbauer
  • Michael Nodzenski
  • Robert D. Stevens
  • Olga Ilkayeva
  • Lynn P. Lowe
  • Boyd E. Metzger
  • Christopher B. Newgard
  • Denise M. Scholtens
  • William L. LoweJr
  • for the HAPO Study Cooperative Research Group
Article

Abstract

Aims/hypothesis

Maternal obesity increases the risk for large-for-gestational-age birth and excess newborn adiposity, which are associated with adverse long-term metabolic outcomes in offspring, probably due to effects mediated through the intrauterine environment. We aimed to characterise the maternal metabolic milieu associated with maternal BMI and its relationship to newborn birthweight and adiposity.

Methods

Fasting and 1 h serum samples were collected from 400 European-ancestry mothers in the Hyperglycaemia and Adverse Pregnancy Outcome Study who underwent an OGTT at ∼28 weeks gestation and whose offspring had anthropometric measurements at birth. Metabolomics assays were performed using biochemical analyses of conventional clinical metabolites, targeted MS-based measurement of amino acids and acylcarnitines and non-targeted GC/MS.

Results

Per-metabolite analyses demonstrated broad associations with maternal BMI at fasting and 1 h for lipids, amino acids and their metabolites together with carbohydrates and organic acids. Similar metabolite classes were associated with insulin resistance with unique associations including branched-chain amino acids. Pathway analyses indicated overlapping and unique associations with maternal BMI and insulin resistance. Network analyses demonstrated collective associations of maternal metabolite subnetworks with maternal BMI and newborn size and adiposity, including communities of acylcarnitines, lipids and related metabolites, and carbohydrates and organic acids. Random forest analyses demonstrated contribution of lipids and lipid-related metabolites to the association of maternal BMI with newborn outcomes.

Conclusions/interpretation

Higher maternal BMI and insulin resistance are associated with broad-based changes in maternal metabolites, with lipids and lipid-related metabolites accounting, in part, for the association of maternal BMI with newborn size at birth.

Keywords

Fetal growth Maternal BMI Maternal insulin resistance Maternal metabolism Pregnancy 

Abbreviations

BCAA

Branched-chain amino acid

BW

Birthweight

FDR

False discovery rate

FPG

Fasting plasma glucose

GDM

Gestational diabetes mellitus

HAPO

Hyperglycaemia and Adverse Pregnancy Outcome

QC

Quality control

RTL

Retention-time-locked

SSF

Sum of skinfolds

Supplementary material

125_2016_4182_MOESM1_ESM.pdf (406 kb)
ESM(PDF 405 kb)

References

  1. 1.
    HAPO Study Cooperative Research Group (2010) Hyperglycaemia and Adverse Pregnancy Outcome (HAPO) study: associations with maternal body mass index. BJOG 117:575–584CrossRefGoogle Scholar
  2. 2.
    Desert R, Canlet C, Costet N, Cordier S, Baonvallot N (2015) Impact of maternal obesity on the metabolic profiles of pregnant women and their offspring at birth. Metabolomics 11:1896–1907CrossRefGoogle Scholar
  3. 3.
    Flegal KM, Kruszon-Moran D, Carroll MD, Fryar CD, Ogden CL (2016) Trends in obesity among adults in the United States, 2005 to 2014. JAMA 315:2284–2291CrossRefPubMedGoogle Scholar
  4. 4.
    Catalano PM, McIntyre HD, Cruickshank JK et al (2012) The hyperglycemia and adverse pregnancy outcome study: associations of GDM and obesity with pregnancy outcomes. Diabetes Care 35:780–786CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Cnattingius S, Reilly M, Pawitan Y, Lichtenstein P (2004) Maternal and fetal genetic factors account for most of familial aggregation of preeclampsia: a population-based Swedish cohort study. Am J Med Genet A 130:365–371CrossRefGoogle Scholar
  6. 6.
    Nelson SM, Matthews P, Poston L (2010) Maternal metabolism and obesity: modifiable determinants of pregnancy outcome. Hum Reprod Update 16:255–275CrossRefPubMedGoogle Scholar
  7. 7.
    Schellong K, Schulz S, Harder T, Plagemann A (2012) Birthweight and long-term overweight risk: systematic review and a meta-analysis including 643,902 persons from 66 studies and 26 countries globally. PLoS One 7, e47776CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Black MH, Sacks DA, Xiang AH, Lawrence JM (2013) The relative contribution of prepregnancy overweight and obesity, gestational weight gain, and IADPSG-defined gestational diabetes mellitus to fetal overgrowth. Diabetes Care 36:56–62CrossRefPubMedGoogle Scholar
  9. 9.
    Stuebe AM, Landon MB, Lai Y et al (2012) Maternal BMI, glucose tolerance, and adverse pregnancy outcomes. Am J Obstet Gynecol 207:62.e1–62.e7CrossRefGoogle Scholar
  10. 10.
    Tyrrell J, Richmond RC, Palmer TM et al (2016) Genetic evidence for causal relationships between maternal obesity-related traits and birthweight. JAMA 315:1129–1140CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Metzger BE, Lowe LP, Dyer AR et al (2008) Hyperglycemia and adverse pregnancy outcomes. N Engl J Med 358:1991–2002CrossRefPubMedGoogle Scholar
  12. 12.
    Hayes MG, Urbanek M, Hivert MF et al (2013) Identification of HKDC1 and BACE2 as genes influencing glycemic traits during pregnancy through genome-wide association studies. Diabetes 62:3282–3291CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Scholtens DM, Bain JR, Reisetter AC et al (2016) Metabolic networks and metabolites underlie associations between maternal glucose during pregnancy and newborn size at birth. Diabetes 65:2039–2050CrossRefPubMedGoogle Scholar
  14. 14.
    Urbanek M, Hayes MG, Armstrong LL et al (2013) The chromosome 3q25 genomic region is associated with measures of adiposity in newborns in a multi-ethnic genome-wide association study. Hum Mol Genet 22:3583–3596CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    HAPO Study Cooperative Research Group (2002) The Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study. Int J Gynaecol Obstet 78:69–77CrossRefGoogle Scholar
  16. 16.
    Radaelli T, Farrell KA, Huston-Presley L et al (2010) Estimates of insulin sensitivity using glucose and C-peptide from the hyperglycemia and adverse pregnancy outcome glucose tolerance test. Diabetes Care 33:490–494CrossRefPubMedGoogle Scholar
  17. 17.
    Scholtens DM, Muehlbauer MJ, Daya NR et al (2014) Metabolomics reveals broad-scale metabolic perturbations in hyperglycemic mothers during pregnancy. Diabetes Care 37:158–166CrossRefPubMedGoogle Scholar
  18. 18.
    Kind T, Wohlgemuth G, Lee do Y (2009) FiehnLib: mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Anal Chem 81:10,038–10,048CrossRefGoogle Scholar
  19. 19.
    Halket JM, Przyborowska A, Stein SE, Mallard WG, Down S, Chalmers RA (1999) Deconvolution gas chromatography/mass spectrometry of urinary organic acids – potential for pattern recognition and automated identification of metabolic disorders. Rapid Commun Mass Spectrom 13:279–284CrossRefPubMedGoogle Scholar
  20. 20.
    Nodzenski M, Muehlbauer MJ, Bain JR, Reisetter AC, Lowe WL Jr, Scholtens DM (2014) Metabomxtr: an R package for mixture-model analysis of non-targeted metabolomics data. Bioinformatics 30:3287–3288CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Benjamini Y, Hochberg Y (2000) On the adaptive control of the false discovery rate in multiple testing with independent statistics. J Educ Behav Stat 25:60–83CrossRefGoogle Scholar
  22. 22.
    Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M (2016) KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res 44:D457–D462CrossRefPubMedGoogle Scholar
  23. 23.
    Goeman JJ, van de Geer SA, de Kort F, van Houwelingen HC (2004) A global test for groups of genes: testing association with a clinical outcome. Bioinformatics 20:93–99CrossRefPubMedGoogle Scholar
  24. 24.
    Beisser D, Klau GW, Dandekar T, Muller T, Dittrich MT (2010) BioNet: an R-package for the functional analysis of biological networks. Bioinformatics 26:1129–1130CrossRefPubMedGoogle Scholar
  25. 25.
    Dittrich MT, Klau GW, Rosenwald A, Dandekar T, Muller T (2008) Identifying functional modules in protein–protein interaction networks: an integrated exact approach. Bioinformatics 24:i223–i231CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Csardi G, Nepusz T (2006) The igraph software package for complex network research. Interjournal - Complex Systems: 1695Google Scholar
  27. 27.
    Reichardt J, Bornholdt S (2006) Statistical mechanics of community detection. Phys Rev E 74:016110CrossRefGoogle Scholar
  28. 28.
    Breiman L (2001) Random forests. Mach Learn 45:5–32CrossRefGoogle Scholar
  29. 29.
    Strobl C, Boulesteix AL, Kneib T, Augustin T, Zeileis A (2008) Conditional variable importance for random forests. BMC Bioinforma 9:307CrossRefGoogle Scholar
  30. 30.
    Newgard CB, An J, Bain JR et al (2009) A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab 9:311–326CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Schooneman MG, Vaz FM, Houten SM, Soeters MR (2013) Acylcarnitines: reflecting or inflicting insulin resistance? Diabetes 62:1–8CrossRefPubMedGoogle Scholar
  32. 32.
    Huynh J, Xiong G, Bentley-Lewis R (2014) A systematic review of metabolite profiling in gestational diabetes mellitus. Diabetologia 57:2453–2464CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Dudzik D, Zorawski M, Skotnicki M et al (2014) Metabolic fingerprint of gestational diabetes mellitus. J Proteomics 103:57–71CrossRefPubMedGoogle Scholar
  34. 34.
    Lowe WL Jr, Karban J (2014) Genetics, genomics and metabolomics: new insights into maternal metabolism during pregnancy. Diabet Med 31:254–262CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Hajduk J, Klupczynska A, Derezinski P et al (2015) A combined metabolomic and proteomic analysis of gestational diabetes mellitus. Int J Mol Sci 16:30,034–30,045CrossRefGoogle Scholar
  36. 36.
    Lindsay KL, Hellmuth C, Uhl O et al (2015) Longitudinal metabolomic profiling of amino acids and lipids across healthy pregnancy. PLoS One 10, e0145794CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Luan H, Meng N, Liu P et al (2014) Pregnancy-induced metabolic phenotype variations in maternal plasma. J Proteome Res 13:1527–1536CrossRefPubMedGoogle Scholar
  38. 38.
    Pinto J, Barros AS, Domingues MR et al (2015) Following healthy pregnancy by NMR metabolomics of plasma and correlation to urine. J Proteome Res 14:1263–1274CrossRefPubMedGoogle Scholar
  39. 39.
    Kim JY, Park JY, Kim OY et al (2010) Metabolic profiling of plasma in overweight/obese and lean men using ultra performance liquid chromatography and Q-TOF mass spectrometry (UPLC-Q-TOF MS). J Proteome Res 9:4368–4375CrossRefPubMedGoogle Scholar
  40. 40.
    Valcarcel B, Ebbels TM, Kangas AJ et al (2014) Genome metabolome integrated network analysis to uncover connections between genetic variants and complex traits: an application to obesity. J R Soc Interface 11:20130908CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Cheng S, Rhee EP, Larson MG et al (2012) Metabolite profiling identifies pathways associated with metabolic risk in humans. Circulation 125:2222–2231CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Ho JE, Larson MG, Ghorbani A et al (2016) Metabolomic profiles of body mass index in the Framingham Heart Study reveal distinct cardiometabolic phenotypes. PLoS One 11, e0148361CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Vidakovic AJ, Jaddoe VW, Gishti O et al (2015) Body mass index, gestational weight gain and fatty acid concentrations during pregnancy: the Generation R Study. Eur J Epidemiol 30:1175–1185CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Haggarty P (2010) Fatty acid supply to the human fetus. Annu Rev Nutr 30:237–255CrossRefPubMedGoogle Scholar
  45. 45.
    Tai ES, Tan ML, Stevens RD et al (2010) Insulin resistance is associated with a metabolic profile of altered protein metabolism in Chinese and Asian-Indian men. Diabetologia 53:757–767CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Catalano PM, Hauguel-De Mouzon S (2011) Is it time to revisit the Pedersen hypothesis in the face of the obesity epidemic? Am J Obstet Gynecol 204:479–487CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Jang C, Oh SF, Wada S et al (2016) A branched-chain amino acid metabolite drives vascular fatty acid transport and causes insulin resistance. Nat Med 22:421–426CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Tremblay F, Krebs M, Dombrowski L et al (2005) Overactivation of S6 kinase 1 as a cause of human insulin resistance during increased amino acid availability. Diabetes 54:2674–2684CrossRefPubMedGoogle Scholar
  49. 49.
    Herrera E, Amusquivar E, Lopez-Soldado I, Ortega H (2006) Maternal lipid metabolism and placental lipid transfer. Horm Res 65(Suppl 3):59–64PubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Victoria Sandler
    • 1
  • Anna C. Reisetter
    • 1
  • James R. Bain
    • 2
    • 3
    • 4
  • Michael J. Muehlbauer
    • 2
    • 3
    • 4
  • Michael Nodzenski
    • 1
  • Robert D. Stevens
    • 2
    • 3
    • 4
  • Olga Ilkayeva
    • 2
    • 3
    • 4
  • Lynn P. Lowe
    • 1
  • Boyd E. Metzger
    • 1
  • Christopher B. Newgard
    • 2
    • 3
    • 4
  • Denise M. Scholtens
    • 1
  • William L. LoweJr
    • 1
  • for the HAPO Study Cooperative Research Group
  1. 1.Feinberg School of MedicineNorthwestern UniversityChicagoUSA
  2. 2.Sarah W. Stedman Nutrition and Metabolism CenterDuke University Medical CenterDurhamUSA
  3. 3.Duke Molecular Physiology InstituteDurhamUSA
  4. 4.Duke University School of MedicineDurhamUSA

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