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Epidemiology and Population Health

A prospective study of maternal adiposity and glycemic traits across pregnancy and mid-childhood metabolomic profiles

International Journal of Obesity volume 45, pages 860–869 (2021)Cite this article

Subjects

Abstract

Background

Fetal exposure to maternal excess adiposity and hyperglycemia is risk factors for childhood adverse metabolic outcomes. Using data from a prospective pre-birth cohort, we aimed to further understand the prenatal determinants of fetal metabolic programming based on analyses of maternal adiposity and glycemic traits across pregnancy with childhood metabolomic profiles.

Methods

This study included 330 mother–child pairs from the Gen3G cohort with information on maternal adiposity and glycemic markers at 5–16 (visit 1) and 24–30 (visit 2) weeks of pregnancy. At mid-childhood (4.8–7.2 years old), we collected fasting plasma and measured 1116 metabolites using an untargeted approach. We constructed networks of interconnected metabolites using a weighted-correlation network analysis algorithm. We estimated Spearman’s partial correlation coefficients of maternal adiposity and glycemic traits across pregnancy with metabolite networks and individual metabolites, adjusting for maternal age, gravidity, race/ethnicity, history of smoking, and child’s sex and age at blood collection for metabolite measurement.

Results

We identified a network of 16 metabolites, primarily glycero-3-phosphoethanolamines (GPE) at mid-childhood that showed consistent negative correlations with maternal body mass index, waist circumference, and body-fat percentage at visits 1 and 2 (ρadjusted = −0.14 to −0.21) and post-challenge glucose levels at visit 2 (ρadjusted = −0.10 to −0.13), while positive correlations with Matsuda index (ρadjusted = 0.13). Within this identified network, 1-palmitoyl-2-decosahexaenoyl-GPE and 1-stearoyl-2-decosahexaenoyl-GPE appeared to be driving the associations. In addition, a network of 89 metabolites, primarily phosphatidylcholines, plasmalogens, sphingomyelins, and ceramides showed consistent negative correlations with insulin at visit 1 and post-challenge glucose at visit 2, while positive correlation with adiponectin at visit 2.

Conclusions

Prenatal exposure to maternal higher adiposity and hyperglycemic traits and lower insulin sensitivity markers were associated with a unique metabolomic pattern characterized by low serum phospho- and sphingolipids in mid-childhood.

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Fig. 1: Metabolite networks represented by clusters of correlated metabolites.
Fig. 2: Adjusted Spearman’s partial correlation coefficients (p values) of maternal adiposity and glycemic traits across pregnancy with metabolite network score at childhood 5–7 years of age.
Fig. 3: Graphic representation of the “Pink” network, as represented by the edges (connections) and nodes (metabolites).
Fig. 4: Manhattan plot presenting associations of the individual metabolites with maternal adiposity and glycemic traits across pregnancy.

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References

  1. Barker DJ. The origins of the developmental origins theory. J Intern Med. 2007;261:412–7.

    Article  CAS  PubMed  Google Scholar 

  2. Marsit CJ. Placental epigenetics in children’s environmental health. Semin Reprod Med. 2016;34:36–41.

    CAS  PubMed  Google Scholar 

  3. Hivert MF, Perng W, Watkins SM, Newgard CS, Kenny LC, Kristal BS, et al. Metabolomics in the developmental origins of obesity and its cardiometabolic consequences. J Dev Orig Health Dis. 2015;6(2):65–78.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Gautier JF, Wilson C, Weyer C, Mott D, Knowler WC, Cavaghan M, et al. Low acute insulin secretory responses in adult offspring of people with early onset type 2 diabetes. Diabetes. 2001;50:1828–33.

    Article  CAS  PubMed  Google Scholar 

  5. Krishnaveni GV, Veena SR, Hill JC, Kehoe S, Karat SC, Fall CH. Intrauterine exposure to maternal diabetes is associated with higher adiposity and insulin resistance and clustering of cardiovascular risk markers in Indian children. Diabetes Care. 2010;33:402–4.

    Article  CAS  PubMed  Google Scholar 

  6. Sauder KA, Hockett CW, Ringham BM, Glueck DH, Dabelea D. Fetal overnutrition and offspring insulin resistance and beta-cell function: the Exploring Perinatal Outcomes among Children (EPOCH) study. Diabetic Med J Br Diabet Assoc. 2017;34:1392–9.

    Article  CAS  Google Scholar 

  7. West NA, Crume TL, Maligie MA, Dabelea D. Cardiovascular risk factors in children exposed to maternal diabetes in utero. Diabetologia. 2011;54:504–7.

    Article  CAS  PubMed  Google Scholar 

  8. Lowe WL Jr., Scholtens DM, Lowe LP, Kuang A, Nodzenski M, Talbot O, et al. Association of gestational diabetes with maternal disorders of glucose metabolism and childhood adiposity. JAMA. 2018;320:1005–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Wright CS, Rifas-Shiman SL, Rich-Edwards JW, Taveras EM, Gillman MW, Oken E. Intrauterine exposure to gestational diabetes, child adiposity, and blood pressure. Am J Hypertens. 2009;22:215–20.

    Article  PubMed  Google Scholar 

  10. Perng W, Gillman MW, Fleisch AF, Michalek RD, Watkins SM, Isganaitis E, et al. Metabolomic profiles and childhood obesity. Obesity. 2014;22:2570–8.

    Article  CAS  PubMed  Google Scholar 

  11. Bunt JC, Tataranni PA, Salbe AD. Intrauterine exposure to diabetes is a determinant of hemoglobin A(1)c and systolic blood pressure in pima Indian children. J Clin Endocrinol Metab. 2005;90:3225–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Razquin C, Liang L, Toledo E, Clish CB, Ruiz-Canela M, Zheng Y, et al. Plasma lipidome patterns associated with cardiovascular risk in the PREDIMED trial: a case-cohort study. Int J Cardiol. 2018;253:126–32.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Rhee EP, Cheng S, Larson MG, Walford GA, Lewis GD, McCabe E, et al. Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. J Clin Invest. 2011;121:1402–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Alshehry ZH, Mundra PA, Barlow CK, Mellett NA, Wong G, McConville MJ, et al. Plasma lipidomic profiles improve on traditional risk factors for the prediction of cardiovascular events in type 2 diabetes mellitus. Circulation. 2016;134:1637–50.

    Article  CAS  PubMed  Google Scholar 

  15. Guillemette L, Allard C, Lacroix M, Patenaude J, Battista MC, Doyon M, et al. Genetics of glucose regulation in gestation and growth (Gen3G): a prospective prebirth cohort of mother-child pairs in Sherbrooke, Canada. BMJ Open. 2016;6:e010031.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Nunez C, Gallagher D, Visser M, Pi-Sunyer FX, Wang Z, Heymsfield SB. Bioimpedance analysis: evaluation of leg-to-leg system based on pressure contact footpad electrodes. Med Sci Sports Exerc. 1997;29:524–31.

    Article  CAS  PubMed  Google Scholar 

  17. Matsuda M, DeFronzo RA. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care. 1999;22:1462–70.

    Article  CAS  PubMed  Google Scholar 

  18. Kirwan JP, Huston-Presley L, Kalhan SC, Catalano PM. Clinically useful estimates of insulin sensitivity during pregnancy: validation studies in women with normal glucose tolerance and gestational diabetes mellitus. Diabetes Care. 2001;24:1602–7.

    Article  CAS  PubMed  Google Scholar 

  19. Zaghlool SB, Mook-Kanamori DO, Kader S, Stephan N, Halama A, Engelke R, et al. Deep molecular phenotypes link complex disorders and physiological insult to CpG methylation. Human Mol Genet. 2018;27:1106–21.

    Article  CAS  Google Scholar 

  20. Evans AM, DeHaven CD, Barrett T, Mitchell M, Milgram E. Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems. Anal Chem. 2009;81:6656–67.

    Article  CAS  PubMed  Google Scholar 

  21. Dehaven CD, Evans AM, Dai H, Lawton KA. Organization of GC/MS and LC/MS metabolomics data into chemical libraries. J Cheminform. 2010;2:9.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Hagenbeek FA, Pool R, van Dongen J, Draisma HHM, Jan Hottenga J, Willemsen G, et al. Heritability estimates for 361 blood metabolites across 40 genome-wide association studies. Nat Commun. 2020;11:39.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Wei R, Wang J, Su M, Jia E, Chen S, Chen T, et al. Missing value imputation approach for mass spectrometry-based metabolomics data. Sci Rep. 2018;8:663.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Yang J, Zhao X, Lu X, Lin X, Xu G. A data preprocessing strategy for metabolomics to reduce the mask effect in data analysis. Front Mol Biosci. 2015;2:4.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Perng W, Rifas-Shiman SL, Sordillo J, Hivert M-F, Oken E. Metabolomic profiles of overweight/obesity phenotypes during adolescence: a cross-sectional study in project viva. Obesity. 2020;28:379–87.

    Article  CAS  PubMed  Google Scholar 

  26. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 2008;9:559.

    Article  Google Scholar 

  27. Bareinboim E, Barbosa VC. Descents and nodal load in scale-free networks. Phys Rev E Stat Nonlin Soft Matter Phys. 2008;77:046111.

    Article  PubMed  Google Scholar 

  28. Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, et al. Integration of biological networks and gene expression data using Cytoscape. Nat Protoc. 2007;2:2366.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Catalano PM, Presley L, Minium J, Hauguel-de Mouzon S. Fetuses of obese mothers develop insulin resistance in utero. Diabetes Care. 2009;32:1076–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Catalano PM, Thomas A, Huston-Presley L, Amini SB. Increased fetal adiposity: a very sensitive marker of abnormal in utero development. Am J Obstet Gynecol. 2003;189:1698–704.

    Article  PubMed  Google Scholar 

  31. Lowe WL Jr., Bain JR, Nodzenski M, Reisetter AC, Muehlbauer MJ, Stevens RD, et al. Maternal BMI and glycemia impact the fetal metabolome. Diabetes Care. 2017;40:902–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, et al. PubChem 2019 update: improved access to chemical data. Nucleic Acids Res. 2019;47:D1102–9.

    Article  PubMed  Google Scholar 

  33. Wassall SR, Leng X, Canner SW, Pennington R, Kinnun JJ, Cavazos A, et al. Docosahexaenoic acid regulates the formation of lipid rafts: a unified view from experiment and simulation. Biochimica et Biophysica Acta (BBA) Biomembr. 2018;1860:1985–93.

  34. Avallone R, Vitale G, Bertolotti M. Omega 3 fatty acids and neurodegenerative diseases: new evidence in clinical trials. Int J Mol Sci. 2019;20:4256.

  35. Gianfrancesco MA, Paquot N, Piette J, Legrand-Poels S. Lipid bilayer stress in obesity-linked inflammatory and metabolic disorders. Biochem Pharmacol. 2018;153:168–83.

    Article  CAS  PubMed  Google Scholar 

  36. Wishart DS, Feunang YD, Marcu A, Guo AC, Liang K, Vazquez-Fresno R, et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 2018;46:D608–17.

    Article  CAS  PubMed  Google Scholar 

  37. National Center for Biotechnology Information. PubChem Database. 2020. CID=53478105. 2020.

  38. Palmer ND, Okut H, Hsu FC, Ng MCY, Chen YI, Goodarzi MO, et al. Metabolomics identifies distinctive metabolite signatures for measures of glucose homeostasis: the Insulin Resistance Atherosclerosis Family Study (IRAS-FS). J Clin Endocrinol Metab. 2018;103:1877–88.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Wahl S, Holzapfel C, Yu Z, Michaela B, Kondofersky I, Christiane F, et al. Metabolomics reveals determinants of weight loss during lifestyle intervention in obese children. Metabolomics. 2013;9:1157–67.

    Article  CAS  Google Scholar 

  40. Viner RM, Ross D, Hardy R, Kuh D, Power C, Johnson A, et al. Life course epidemiology: recognising the importance of adolescence. J Epidemiol Community Health. 2015;69:719–20.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Pirhaji L, Milani P, Leidl M, Curran T, Avila-Pacheco J, Clish CB, et al. Revealing disease-associated pathways by network integration of untargeted metabolomics. Nat Methods. 2016;13:770–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Molenaar PCM. A manifesto on psychology as idiographic science: bringing the person back into scientific psychology, this time forever. Meas: Interdiscip Res Perspect. 2004;2:201–18.

    Google Scholar 

  43. Lof M, Forsum E. Evaluation of bioimpedance spectroscopy for measurements of body water distribution in healthy women before, during, and after pregnancy. J Appl Physiol (1985). 2004;96:967–73.

    Article  Google Scholar 

  44. Ceyhan ST, Safer U, Cintosun U. Bioelectric impedance analysis in pregnant women. American J Obstet Gynecol. 2015;212:120.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by American Diabetes Association accelerator award #1–15-ACE-26 (M-FH), Fonds de recherche du Québec en santé (FRSQ) #20697 (M-FH); Canadian Institute of Health Research #MOP 115071 (M-FH), and Diabète Québec Grants (PP and LB). LB is a senior research scholar from the FRSQ. LB, M-FH, and PP are members of the FRSQ-funded CRCHUS.

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Authors and Affiliations

  1. Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA

    Mohammad L. Rahman & Marie-France Hivert

  2. Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke (CHUS), Sherbrooke, QC, Canada

    Myriam Doyon, Melina Arguin, Patrice Perron, Luigi Bouchard & Marie-France Hivert

  3. Faculty of Medicine and Life Sciences, Department of Medicine, Université de Sherbrooke, Sherbrooke, QC, Canada

    Patrice Perron

  4. Faculty of Medicine and Life Sciences, Department of Biochemistry, Université de Sherbrooke, Sherbrooke, QC, Canada

    Luigi Bouchard

  5. Department of Medical Biology, CIUSSS du Saguenay-Lac-Saint-Jean, Saguenay, QC, Canada

    Luigi Bouchard

  6. Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA

    Marie-France Hivert

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Correspondence to Marie-France Hivert.

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Rahman, M.L., Doyon, M., Arguin, M. et al. A prospective study of maternal adiposity and glycemic traits across pregnancy and mid-childhood metabolomic profiles. Int J Obes 45, 860–869 (2021). https://doi.org/10.1038/s41366-021-00750-4

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