Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The newborn metabolome: associations with gestational diabetes, sex, gestation, birth mode, and birth weight

A Correction to this article was published on 29 October 2021

This article has been updated



Pathways towards many adult-onset conditions begin early in life, even in utero. Maternal health in pregnancy influences this process, but little is known how it affects neonatal metabolism. We investigated associations between pregnancy and birth factors and cord blood metabolomic profile in a large, population-derived cohort.


Metabolites were measured using nuclear magnetic resonance in maternal (28 weeks gestation) and cord serum from 912 mother–child pairs in the Barwon Infant Study pre-birth cohort. Associations between maternal (metabolites, age, BMI, smoking), pregnancy (pre-eclampsia, gestational diabetes (GDM)), and birth characteristics (delivery mode, gestational age, weight, infant sex) with 72 cord blood metabolites were examined by linear regression.


Delivery mode, sex, gestational age, and birth weight were associated with specific metabolite levels in cord blood, including amino acids, fatty acids, and cholesterols. GDM was associated with higher cord blood levels of acetoacetate and 3-hydroxybutyrate.


Neonatal factors, particularly delivery mode, were associated with many cord blood metabolite differences, including those implicated in later risk of cardiometabolic disease. Associations between GDM and higher offspring ketone levels at birth are consistent with maternal ketosis in diabetic pregnancies. Further work is needed to determine whether these neonatal metabolome differences associate with later health outcomes.


  • Variations in blood metabolomic profile have been linked to health status in adults and children, but corresponding data in neonates are scarce.

  • We report evidence that pregnancy complications, mode of delivery, and offspring characteristics, including sex, are independently associated with a range of circulating metabolites at birth, including ketone bodies, amino acids, cholesterols, and inflammatory markers.

  • Independent of birth weight, exposure to gestational diabetes is associated with higher cord blood ketone bodies and citrate.

  • These findings suggest that pregnancy complications, mode of delivery, gestational age, and measures of growth influence metabolic pathways prior to birth, potentially impacting later health and development.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Sex and cord blood serum NMR metabolomic profiles.
Fig. 2: Birth weight z-score and cord blood serum NMR metabolomic profiles.
Fig. 3: Gestational age and cord blood serum NMR metabolomic profiles.
Fig. 4: Mode of delivery and cord blood serum NMR metabolomic profiles.
Fig. 5: Binary maternal measures and cord blood serum NMR metabolomic profiles.

Change history


  1. Calkins, K. & Devaskar, S. U. Fetal origins of adult disease. Curr. Probl. Pediatr. Adolesc. Health Care 41, 158–176 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Floegel, A. et al. Serum metabolites and risk of myocardial infarction and ischemic stroke: a targeted metabolomic approach in two German prospective cohorts. Eur. J. Epidemiol. 33, 55–66 (2018).

    Article  CAS  PubMed  Google Scholar 

  3. Tzoulaki, I. et al. Serum metabolic signatures of coronary and carotid atherosclerosis and subsequent cardiovascular disease. Eur. Heart J. 40, 2883–2896 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Deelen, J. et al. A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals. Nat. Commun. 10, 1–8 (2019).

    Article  CAS  Google Scholar 

  5. Würtz, P. et al. High-throughput quantification of circulating metabolites improves prediction of subclinical atherosclerosis. Eur. Heart J. 33, 2307–2316 (2012).

    Article  PubMed  CAS  Google Scholar 

  6. Juonala, M. et al. Non-HDL cholesterol levels in childhood and carotid intima-media thickness in adulthood. Pediatrics 145, e20192114 (2020).

  7. Shah, P. K. Inflammation, infection and atherosclerosis. Trends Cardiovasc. Med. 29, 468–472 (2019).

    Article  CAS  PubMed  Google Scholar 

  8. Nakashima, Y., Wight, T. N. & Sueishi, K. Early atherosclerosis in humans: role of diffuse intimal thickening and extracellular matrix proteoglycans. Cardiovasc. Res. 79, 14–23 (2008).

    Article  CAS  PubMed  Google Scholar 

  9. Lowe, W. L. et al. Maternal BMI and glycemia impact the fetal metabolome. Diabetes Care 40, 902–910 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Desert, R., Canlet, C., Costet, N., Cordier, S. & Bonvallot, N. Impact of maternal obesity on the metabolic profiles of pregnant women and their offspring at birth. Metabolomics 11, 1896–1907 (2015).

    Article  CAS  Google Scholar 

  11. Dani, C. et al. Metabolomic profile of term infants of gestational diabetic mothers. J. Matern. Fetal Neonatal Med. 27, 537–542 (2014).

    Article  CAS  PubMed  Google Scholar 

  12. Marchioro, L. et al. Caesarean section, but not induction of labour, is associated with major changes in cord blood metabolome. Sci. Rep. 9, 1–9 (2019).

    Article  CAS  Google Scholar 

  13. Shokry, E. et al. Investigation of the impact of birth by cesarean section on fetal and maternal metabolism. Arch. Gynecol. Obstet. 300, 589–600 (2019).

    Article  CAS  PubMed  Google Scholar 

  14. Birchenall, K. A., Welsh, G. I. & López Bernal, A. Metabolite changes in maternal and fetal plasma following spontaneous labour at term in humans using untargeted metabolomics analysis: a pilot study. Int. J. Environ. Res. Public Health 16, 1527 (2019).

    Article  CAS  PubMed Central  Google Scholar 

  15. Ruoppolo, M. et al. Female and male human babies have distinct blood metabolomic patterns. Mol. Biosyst. 11, 2483–2492 (2015).

    Article  CAS  PubMed  Google Scholar 

  16. Ellul, S. et al. Sex differences in infant blood metabolite profile in association with weight and adiposity measures. Pediatr. Res. 88, 473–483 (2020).

  17. Bell, J. A. et al. Sex differences in systemic metabolites at four life stages: cohort study with repeated metabolomics. BMC Med. 19, 58 (2021).

  18. Ellul, S. et al. Metabolomics: population epidemiology and concordance in Australian children aged 11–12 years and their parents. BMJ Open 9, 106–117 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Perng, W. et al. Associations of cord blood metabolites with perinatal characteristics, newborn anthropometry, and cord blood hormones in project viva. Metabolism 76, 11–22 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hellmuth, C. et al. Cord blood metabolome is highly associated with birth weight, but less predictive for later weight development. Obes. facts 10, 85–100 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Ho, J. E. et al. Metabolomic profiles of body mass index in the Framingham Heart Study reveal distinct cardiometabolic phenotypes. PLoS ONE 11, e0148361 (2016).

  22. Saner, C. et al. Sex and puberty-related differences in metabolomic profiles associated with adiposity measures in youth with obesity. Metabolomics 15, 75 (2019).

    Article  PubMed  CAS  Google Scholar 

  23. Rauschert, S., Uhl, O., Koletzko, B. & Hellmuth, C. Metabolomic biomarkers for obesity in humans: a short review. Ann. Nutr. Metab. 64, 314–324 (2014).

    Article  CAS  PubMed  Google Scholar 

  24. Vuillermin, P. et al. Cohort profile: the Barwon Infant Study. Int J. Epidemiol. 44, 1148–1160 (2015).

    Article  PubMed  Google Scholar 

  25. Nankervis, A., McIntyre, H. D., Moses, R. G., Ross, G. P. & Callaway, L. K. Testing for gestational diabetes mellitus in Australia. Diabetes Care 36, e64 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Tranquilli, A. et al. The classification, diagnosis and management of the hypertensive disorders of pregnancy: a revised statement from the ISSHP. Pregnancy Hypertens. 4, 97 (2014).

    Article  CAS  PubMed  Google Scholar 

  27. Pink, B. Technical Paper: Socio-Economic Indexes for Areas (SEIFA) (Australian Bureau of Statistics, 2011).

  28. Cole, T. J., Williams, A. F. & Wright, C. M. Revised birth centiles for weight, length and head circumference in the UK-WHO growth charts. Ann. Hum. Biol. 38, 7–11 (2011).

    Article  PubMed  Google Scholar 

  29. Hashimoto, F. et al. Metabolomics analysis of umbilical cord blood clarifies changes in saccharides associated with delivery method. Early Hum. Dev. 89, 315–320 (2013).

    Article  CAS  PubMed  Google Scholar 

  30. Kettunen, J. et al. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat. Genet 44, 269–276 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Würtz, P. et al. Quantitative serum nuclear magnetic resonance metabolomics in large-scale epidemiology: a primer on-omic technologies. Am. J. Epidemiol. 186, 1084–1096 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Auro, K. et al. A metabolic view on menopause and ageing. Nat. Commun. 5, 4708 (2014).

    Article  CAS  PubMed  Google Scholar 

  33. Xie, G. et al. The metabolite profiles of the obese population are gender-dependent. J. Proteome Res. 13, 4062–4073 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Torloni, M. et al. Prepregnancy BMI and the risk of gestational diabetes: a systematic review of the literature with meta‐analysis. Obes. Rev. 10, 194–203 (2009).

    Article  CAS  PubMed  Google Scholar 

  35. O’Brien, T. E., Ray, J. G. & Chan, W.-S. Maternal body mass index and the risk of preeclampsia: a systematic overview. Epidemiology 14, 368–374 (2003).

  36. Toohill, J., Soong, B. & Flenady, V. Interventions for ketosis during labour. Cochrane Database Syst. Rev. CD004230 (2008).

  37. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodol.) 57, 289–300 (1995).

    Google Scholar 

  38. Hawkes, C. P. et al. Gender-and gestational age–specific body fat percentage at birth. Pediatrics 128, e645–e651 (2011).

    Article  PubMed  Google Scholar 

  39. Wang, X., Magkos, F. & Mittendorfer, B. Sex differences in lipid and lipoprotein metabolism: it’s not just about sex hormones. J. Clin. Endocrinol. Metab. 96, 885–893 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Comitato, R., Saba, A., Turrini, A., Arganini, C. & Virgili, F. Sex hormones and macronutrient metabolism. Crit. Rev. Food Sci. Nutr. 55, 227–241 (2015).

    Article  CAS  PubMed  Google Scholar 

  41. Schaefer-Graf, U. M. et al. Maternal lipids as strong determinants of fetal environment and growth in pregnancies with gestational diabetes mellitus. Diabetes Care 31, 1858–1863 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Ramadhani, M. K. et al. Lower birth weight predicts metabolic syndrome in young adults: the Atherosclerosis Risk in Young Adults (ARYA)-study. Atherosclerosis 184, 21–27 (2006).

    Article  CAS  PubMed  Google Scholar 

  43. Duprez, D. A. et al. Comparison of the predictive value of GlycA and other biomarkers of inflammation for total death, incident cardiovascular events, noncardiovascular and noncancer inflammatory-related events, and total cancer events. Clin. Chem. 62, 1020–1031 (2016).

    Article  CAS  PubMed  Google Scholar 

  44. Leviton, A. et al. Inflammation-related proteins in the blood of extremely low gestational age newborns. The contribution of inflammation to the appearance of developmental regulation. Cytokine 53, 66–73 (2011).

    Article  CAS  PubMed  Google Scholar 

  45. Cappelletti, M., Della Bella, S., Ferrazzi, E., Mavilio, D. & Divanovic, S. Inflammation and preterm birth. J. Leukoc. Biol. 99, 67–78 (2016).

    Article  CAS  PubMed  Google Scholar 

  46. Muhlestein, J. B. et al. GlycA and hsCRP are independent and additive predictors of future cardiovascular events among patients undergoing angiography: the intermountain heart collaborative study. Am. Heart J. 202, 27–32 (2018).

    Article  CAS  PubMed  Google Scholar 

  47. Collier, F. et al. Naïve regulatory T cells in infancy: associations with perinatal factors and development of food allergy. Allergy 74, 1760–1768 (2019).

    Article  CAS  PubMed  Google Scholar 

  48. Ortiz, R. et al. The association of morning serum cortisol with glucose metabolism and diabetes: the Jackson Heart Study. Psychoneuroendocrinology 103, 25–32 (2019).

    Article  CAS  PubMed  Google Scholar 

  49. John, K., Marino, J. S., Sanchez, E. R. & Hinds, T. D. Jr The glucocorticoid receptor: cause of or cure for obesity? Am. J. Physiol. Endocrinol. Metab. 310, E249–E257 (2016).

    Article  PubMed  Google Scholar 

  50. Lawson, E. A., Olszewski, P. K., Weller, A. & Blevins, J. E. The role of oxytocin in regulation of appetitive behaviour, body weight and glucose homeostasis. J. Neuroendocrinol. 32, e12805 (2020).

    Article  CAS  PubMed  Google Scholar 

  51. Ding, C., Leow, M. S. & Magkos, F. Oxytocin in metabolic homeostasis: implications for obesity and diabetes management. Obes. Rev. 20, 22–40 (2019).

    Article  CAS  PubMed  Google Scholar 

  52. Holm, M. B. et al. Uptake and release of amino acids in the fetal-placental unit in human pregnancies. PLoS ONE 12, e0185760 (2017).

  53. Monasta, L. et al. Early‐life determinants of overweight and obesity: a review of systematic reviews. Obes. Rev. 11, 695–708 (2010).

    Article  CAS  PubMed  Google Scholar 

  54. Santos Ferreira, D. L. et al. Association of pre-pregnancy body mass index with offspring metabolic profile: analyses of 3 European prospective birth cohorts. PLoS Med. 14, e1002376 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Laffel, L. Ketone bodies: a review of physiology, pathophysiology and application of monitoring to diabetes. Diabetes Metab. Res. Rev. 15, 412–426 (1999).

    Article  CAS  PubMed  Google Scholar 

  56. White, S. L. et al. Metabolic profiling of gestational diabetes in obese women during pregnancy. Diabetologia 60, 1903–1912 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Herrera, E. & Lasunción, M. A. in Fetal and Neonatal Physiology (eds Polin, R. A., Abman, S. H., Rowitch, D. H., Benitz, W. E. & Fox, W. W.) 342.e4–353.e4 (Elsevier, 2017).

  58. Perng, W. et al. A prospective study of associations between in utero exposure to gestational diabetes mellitus and metabolomic profiles during late childhood and adolescence. Diabetologia 63, 296–312 (2020).

    Article  CAS  PubMed  Google Scholar 

  59. Friedman, J. E. et al. Impaired glucose transport and insulin receptor tyrosine phosphorylation in skeletal muscle from obese women with gestational diabetes. Diabetes 48, 1807–1814 (1999).

    Article  CAS  PubMed  Google Scholar 

  60. Aalipour, S., Hantoushzadeh, S., Shariat, M., Sahraian, S. & Sheikh, M. Umbilical cord blood acidosis in term pregnancies with gestational diabetes mellitus and its relations to maternal factors and neonatal outcomes. Iran. Red Crescent Med. J. (2018).

  61. Malin, G. L., Morris, R. K. & Khan, K. S. Strength of association between umbilical cord pH and perinatal and long term outcomes: systematic review and meta-analysis. BMJ 340, c1471 (2010).

  62. Galea, S. & Tracy, M. Participation rates in epidemiologic studies. Ann. Epidemiol. 17, 643–653 (2007).

    Article  PubMed  Google Scholar 

Download references


We thank the BIS participants for the generous contribution they have made to this project. We also thank current and past staff for their efforts in recruiting and maintaining the cohort and in obtaining and processing the data and biospecimens.


The establishment work and infrastructure for the BIS was provided by the Murdoch Children’s Research Institute, Deakin University, and Barwon Health. Subsequent funding was secured from the National Health and Medical Research Council of Australia, The Jack Brockhoff Foundation, the Scobie Trust, the Shane O’Brien Memorial Asthma Foundation, the Our Women’s Our Children’s Fund Raising Committee Barwon Health, The Shepherd Foundation, the Rotary Club of Geelong, the Ilhan Food Allergy Foundation, GMHBA Limited, the Percy Baxter Charitable Trust, and Perpetual Trustees. In-kind support was provided by the Cotton On Foundation and CreativeForce. Research at Murdoch Children’s Research Institute is supported by the Victorian Government’s Operational Infrastructure Support Program. This work was also supported by NHMRC Senior Research Fellowships (1008396 to A.-L.P.; 1064629 to D.B.; 1045161 to R.S.) and NHMRC Investigator Grants to A.-L.P. (1110200) and D.B. (1175744). Funders did not participate in the work or writing of this manuscript.

Author information

Authors and Affiliations




T.M., D.B., F.C., and R.S. conceptualised and developed this study. T.M. undertook all aspects of data analysis. F.C. managed collection of samples and coordinated sample shipping and measurement collation and quality control. T.M., D.B., and R.S. drafted the manuscript. All authors provided critical expert advice and critical review of the manuscript and approved the final version.

Corresponding author

Correspondence to Richard Saffery.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

Informed participant consent was required and attained for this study.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original online version of this article was revised: The legend of Figure 2 has been corrected.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mansell, T., Vlahos, A., Collier, F. et al. The newborn metabolome: associations with gestational diabetes, sex, gestation, birth mode, and birth weight. Pediatr Res 91, 1864–1873 (2022).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links