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Erschienen in: Diabetologia 3/2017

16.12.2016 | Article

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

verfasst von: 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. Lowe Jr, for the HAPO Study Cooperative Research Group

Erschienen in: Diabetologia | Ausgabe 3/2017

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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.
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Literatur
1.
Zurück zum Zitat HAPO Study Cooperative Research Group (2010) Hyperglycaemia and Adverse Pregnancy Outcome (HAPO) study: associations with maternal body mass index. BJOG 117:575–584CrossRef HAPO Study Cooperative Research Group (2010) Hyperglycaemia and Adverse Pregnancy Outcome (HAPO) study: associations with maternal body mass index. BJOG 117:575–584CrossRef
2.
Zurück zum Zitat 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 11:1896–1907CrossRef 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 11:1896–1907CrossRef
3.
Zurück zum Zitat 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 315:2284–2291CrossRefPubMed 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 315:2284–2291CrossRefPubMed
4.
Zurück zum Zitat 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 35:780–786CrossRefPubMedPubMedCentral 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 35:780–786CrossRefPubMedPubMedCentral
5.
Zurück zum Zitat 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 130:365–371CrossRef 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 130:365–371CrossRef
6.
Zurück zum Zitat Nelson SM, Matthews P, Poston L (2010) Maternal metabolism and obesity: modifiable determinants of pregnancy outcome. Hum Reprod Update 16:255–275CrossRefPubMed Nelson SM, Matthews P, Poston L (2010) Maternal metabolism and obesity: modifiable determinants of pregnancy outcome. Hum Reprod Update 16:255–275CrossRefPubMed
7.
Zurück zum Zitat 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, e47776CrossRefPubMedPubMedCentral 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, e47776CrossRefPubMedPubMedCentral
8.
Zurück zum Zitat 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 36:56–62CrossRefPubMed 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 36:56–62CrossRefPubMed
9.
Zurück zum Zitat 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.e7CrossRef 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.e7CrossRef
10.
Zurück zum Zitat Tyrrell J, Richmond RC, Palmer TM et al (2016) Genetic evidence for causal relationships between maternal obesity-related traits and birthweight. JAMA 315:1129–1140CrossRefPubMedPubMedCentral Tyrrell J, Richmond RC, Palmer TM et al (2016) Genetic evidence for causal relationships between maternal obesity-related traits and birthweight. JAMA 315:1129–1140CrossRefPubMedPubMedCentral
11.
Zurück zum Zitat Metzger BE, Lowe LP, Dyer AR et al (2008) Hyperglycemia and adverse pregnancy outcomes. N Engl J Med 358:1991–2002CrossRefPubMed Metzger BE, Lowe LP, Dyer AR et al (2008) Hyperglycemia and adverse pregnancy outcomes. N Engl J Med 358:1991–2002CrossRefPubMed
12.
Zurück zum Zitat 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 62:3282–3291CrossRefPubMedPubMedCentral 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 62:3282–3291CrossRefPubMedPubMedCentral
13.
Zurück zum Zitat 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 65:2039–2050CrossRefPubMed 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 65:2039–2050CrossRefPubMed
14.
Zurück zum Zitat 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 22:3583–3596CrossRefPubMedPubMedCentral 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 22:3583–3596CrossRefPubMedPubMedCentral
15.
Zurück zum Zitat HAPO Study Cooperative Research Group (2002) The Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study. Int J Gynaecol Obstet 78:69–77CrossRef HAPO Study Cooperative Research Group (2002) The Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study. Int J Gynaecol Obstet 78:69–77CrossRef
16.
Zurück zum Zitat 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 33:490–494CrossRefPubMed 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 33:490–494CrossRefPubMed
17.
Zurück zum Zitat Scholtens DM, Muehlbauer MJ, Daya NR et al (2014) Metabolomics reveals broad-scale metabolic perturbations in hyperglycemic mothers during pregnancy. Diabetes Care 37:158–166CrossRefPubMed Scholtens DM, Muehlbauer MJ, Daya NR et al (2014) Metabolomics reveals broad-scale metabolic perturbations in hyperglycemic mothers during pregnancy. Diabetes Care 37:158–166CrossRefPubMed
18.
Zurück zum Zitat 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 81:10,038–10,048CrossRef 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 81:10,038–10,048CrossRef
19.
Zurück zum Zitat 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 13:279–284CrossRefPubMed 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 13:279–284CrossRefPubMed
20.
Zurück zum Zitat 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 30:3287–3288CrossRefPubMedPubMedCentral 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 30:3287–3288CrossRefPubMedPubMedCentral
21.
Zurück zum Zitat Benjamini Y, Hochberg Y (2000) On the adaptive control of the false discovery rate in multiple testing with independent statistics. J Educ Behav Stat 25:60–83CrossRef Benjamini Y, Hochberg Y (2000) On the adaptive control of the false discovery rate in multiple testing with independent statistics. J Educ Behav Stat 25:60–83CrossRef
22.
Zurück zum Zitat 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–D462CrossRefPubMed 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–D462CrossRefPubMed
23.
Zurück zum Zitat 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 20:93–99CrossRefPubMed 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 20:93–99CrossRefPubMed
24.
Zurück zum Zitat Beisser D, Klau GW, Dandekar T, Muller T, Dittrich MT (2010) BioNet: an R-package for the functional analysis of biological networks. Bioinformatics 26:1129–1130CrossRefPubMed Beisser D, Klau GW, Dandekar T, Muller T, Dittrich MT (2010) BioNet: an R-package for the functional analysis of biological networks. Bioinformatics 26:1129–1130CrossRefPubMed
25.
Zurück zum Zitat 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–i231CrossRefPubMedPubMedCentral 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–i231CrossRefPubMedPubMedCentral
26.
Zurück zum Zitat Csardi G, Nepusz T (2006) The igraph software package for complex network research. Interjournal - Complex Systems: 1695 Csardi G, Nepusz T (2006) The igraph software package for complex network research. Interjournal - Complex Systems: 1695
27.
Zurück zum Zitat Reichardt J, Bornholdt S (2006) Statistical mechanics of community detection. Phys Rev E 74:016110CrossRef Reichardt J, Bornholdt S (2006) Statistical mechanics of community detection. Phys Rev E 74:016110CrossRef
29.
Zurück zum Zitat Strobl C, Boulesteix AL, Kneib T, Augustin T, Zeileis A (2008) Conditional variable importance for random forests. BMC Bioinforma 9:307CrossRef Strobl C, Boulesteix AL, Kneib T, Augustin T, Zeileis A (2008) Conditional variable importance for random forests. BMC Bioinforma 9:307CrossRef
30.
Zurück zum Zitat 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 9:311–326CrossRefPubMedPubMedCentral 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 9:311–326CrossRefPubMedPubMedCentral
31.
Zurück zum Zitat Schooneman MG, Vaz FM, Houten SM, Soeters MR (2013) Acylcarnitines: reflecting or inflicting insulin resistance? Diabetes 62:1–8CrossRefPubMed Schooneman MG, Vaz FM, Houten SM, Soeters MR (2013) Acylcarnitines: reflecting or inflicting insulin resistance? Diabetes 62:1–8CrossRefPubMed
32.
Zurück zum Zitat Huynh J, Xiong G, Bentley-Lewis R (2014) A systematic review of metabolite profiling in gestational diabetes mellitus. Diabetologia 57:2453–2464CrossRefPubMedPubMedCentral Huynh J, Xiong G, Bentley-Lewis R (2014) A systematic review of metabolite profiling in gestational diabetes mellitus. Diabetologia 57:2453–2464CrossRefPubMedPubMedCentral
33.
Zurück zum Zitat Dudzik D, Zorawski M, Skotnicki M et al (2014) Metabolic fingerprint of gestational diabetes mellitus. J Proteomics 103:57–71CrossRefPubMed Dudzik D, Zorawski M, Skotnicki M et al (2014) Metabolic fingerprint of gestational diabetes mellitus. J Proteomics 103:57–71CrossRefPubMed
34.
35.
Zurück zum Zitat Hajduk J, Klupczynska A, Derezinski P et al (2015) A combined metabolomic and proteomic analysis of gestational diabetes mellitus. Int J Mol Sci 16:30,034–30,045CrossRef Hajduk J, Klupczynska A, Derezinski P et al (2015) A combined metabolomic and proteomic analysis of gestational diabetes mellitus. Int J Mol Sci 16:30,034–30,045CrossRef
36.
Zurück zum Zitat Lindsay KL, Hellmuth C, Uhl O et al (2015) Longitudinal metabolomic profiling of amino acids and lipids across healthy pregnancy. PLoS One 10, e0145794CrossRefPubMedPubMedCentral Lindsay KL, Hellmuth C, Uhl O et al (2015) Longitudinal metabolomic profiling of amino acids and lipids across healthy pregnancy. PLoS One 10, e0145794CrossRefPubMedPubMedCentral
37.
Zurück zum Zitat Luan H, Meng N, Liu P et al (2014) Pregnancy-induced metabolic phenotype variations in maternal plasma. J Proteome Res 13:1527–1536CrossRefPubMed Luan H, Meng N, Liu P et al (2014) Pregnancy-induced metabolic phenotype variations in maternal plasma. J Proteome Res 13:1527–1536CrossRefPubMed
38.
Zurück zum Zitat Pinto J, Barros AS, Domingues MR et al (2015) Following healthy pregnancy by NMR metabolomics of plasma and correlation to urine. J Proteome Res 14:1263–1274CrossRefPubMed Pinto J, Barros AS, Domingues MR et al (2015) Following healthy pregnancy by NMR metabolomics of plasma and correlation to urine. J Proteome Res 14:1263–1274CrossRefPubMed
39.
Zurück zum Zitat 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 9:4368–4375CrossRefPubMed 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 9:4368–4375CrossRefPubMed
40.
Zurück zum Zitat 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 11:20130908CrossRefPubMedPubMedCentral 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 11:20130908CrossRefPubMedPubMedCentral
41.
Zurück zum Zitat Cheng S, Rhee EP, Larson MG et al (2012) Metabolite profiling identifies pathways associated with metabolic risk in humans. Circulation 125:2222–2231CrossRefPubMedPubMedCentral Cheng S, Rhee EP, Larson MG et al (2012) Metabolite profiling identifies pathways associated with metabolic risk in humans. Circulation 125:2222–2231CrossRefPubMedPubMedCentral
42.
Zurück zum Zitat 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, e0148361CrossRefPubMedPubMedCentral 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, e0148361CrossRefPubMedPubMedCentral
43.
Zurück zum Zitat 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 30:1175–1185CrossRefPubMedPubMedCentral 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 30:1175–1185CrossRefPubMedPubMedCentral
44.
45.
Zurück zum Zitat 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 53:757–767CrossRefPubMedPubMedCentral 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 53:757–767CrossRefPubMedPubMedCentral
46.
Zurück zum Zitat 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 204:479–487CrossRefPubMedPubMedCentral 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 204:479–487CrossRefPubMedPubMedCentral
47.
Zurück zum Zitat 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 22:421–426CrossRefPubMedPubMedCentral 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 22:421–426CrossRefPubMedPubMedCentral
48.
Zurück zum Zitat 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 54:2674–2684CrossRefPubMed 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 54:2674–2684CrossRefPubMed
49.
Zurück zum Zitat Herrera E, Amusquivar E, Lopez-Soldado I, Ortega H (2006) Maternal lipid metabolism and placental lipid transfer. Horm Res 65(Suppl 3):59–64PubMed Herrera E, Amusquivar E, Lopez-Soldado I, Ortega H (2006) Maternal lipid metabolism and placental lipid transfer. Horm Res 65(Suppl 3):59–64PubMed
Metadaten
Titel
Associations of maternal BMI and insulin resistance with the maternal metabolome and newborn outcomes
verfasst von
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. Lowe Jr
for the HAPO Study Cooperative Research Group
Publikationsdatum
16.12.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Diabetologia / Ausgabe 3/2017
Print ISSN: 0012-186X
Elektronische ISSN: 1432-0428
DOI
https://doi.org/10.1007/s00125-016-4182-2

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