Vol 94, No 2 (2023)
Research paper
Published online: 2022-03-04

open access

Page views 2943
Article views/downloads 631
Get Citation

Connect on Social Media

Connect on Social Media

Impact of gestational diabetes and other maternal factors on neonatal body composition in the first week of life: a case-control study

Karolina Karcz1, Matylda Czosnykowska-Lukacka1, Barbara Krolak-Olejnik1
Pubmed: 35315018
Ginekol Pol 2023;94(2):119-128.


Objectives: Newborns of diabetic mothers are at increased risk of abnormal nutritional status at birth, thus developing metabolic disorders. The aim of this study was to evaluate the anthropometric measurements and body composition of newborns born to mothers with gestational diabetes in comparison to newborns born to mothers with normal glucose tolerance in pregnancy, in the first week of their life. Maternal factors affecting the gestational period were also evaluated. Material and methods: The study included 70 participants: neonates born to mothers with gestational diabetes (GDM) and neonates born to healthy mothers (non-GDM). A set of statistical methods (e.g., ANOVA, Kruskal-Wallis test, Chi-square test, regression, cluster analysis) was used to compare data between the study groups and to find their association with maternal factors. Results: Our approach resulted in statistically significant classification (p < 0.05) by maternal history of hypothyroidism, weight gain during pregnancy and diagnosis of GDM. Newborns of mothers diagnosed with both GDM and hypothyroidism had lower birth weight and fat mass than newborns of mothers without GDM nor hypothyroidism (p < 0.05), however this finding might be associated with high incidence of excessive gestational weight gain among healthy mothers. No differences in body composition were found between the study groups on account of maternal GDM only (p > 0.05). Conclusions: Thus, well-controlled gestational diabetes mellitus as an individual factor does not significantly affect neonatal anthropometric measurements and body composition.

Article available in PDF format

View PDF Download PDF file


  1. Schwarzenberg SJ, Georgieff MK. COMMITTEE ON NUTRITION. Advocacy for Improving Nutrition in the First 1000 Days to Support Childhood Development and Adult Health. Pediatrics. 2018; 141(2).
  2. Calkins K, Devaskar SU. Fetal origins of adult disease. Curr Probl Pediatr Adolesc Health Care. 2011; 41(6): 158–176.
  3. Wender-Ożegowska E, Bomba-Opoń D, Brązert J, et al. Standards of Polish Society of Gynecologists and Obstetricians in management of women with diabetes. Ginekol Pol. 2018; 89(6): 341–350.
  4. Manerkar K, Harding J, Conlon C, et al. Maternal gestational diabetes and infant feeding, nutrition and growth: a systematic review and meta-analysis. Br J Nutr. 2020; 123(11): 1201–1215.
  5. Kallem VR, Pandita A, Pillai A. Infant of diabetic mother: what one needs to know? J Matern Fetal Neonatal Med. 2020; 33(3): 482–492.
  6. Delisle H. World Health Organization. Programming of chronic disease by impaired fetal nutrition. Evidence and implications for policy and intervention strategies. WHO/NHD/02.3, WHO/NPH/02.1. https://apps.who.int/iris/bitstream/handle/10665/67126/WHO_NHD_02.3.pdf (10.06.2021).
  7. Marciniak A, Patro-Małysza J, Kimber-Trojnar Ż, et al. Fetal programming of the metabolic syndrome. Taiwan J Obstet Gynecol. 2017; 56(2): 133–138.
  8. Marken Lichtenbelt WD, Fogelholm M. Increased extracellular water compartment, relative to intracellular water compartment, after weight reduction. J Appl Physiol (1985). 1999; 87(1): 294–298.
  9. Sergi G, Lupoli L, Busetto L, et al. Changes in fluid compartments and body composition in obese women after weight loss induced by gastric banding. Ann Nutr Metab. 2003; 47(3-4): 152–157.
  10. American College of Obstetricians and Gynecologists. ACOG Committee opinion no. 548: weight gain during pregnancy. Obstet Gynecol. 2013; 121(1): 210–212.
  11. , et alAmerican Diabetes Association. 2. Classification and Diagnosis of Diabetes: . Diabetes Care. 2020; 43(Suppl 1): S14–S31.
  12. Metzger BE, Gabbe SG, Persson B, et al. International Association of Diabetes and Pregnancy Study Groups Consensus Panel. International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care. 2010; 33(3): 676–682.
  13. Hubalewska-Dydejczyk A, Lewiński A, Milewicz A, et al. Postępowanie w chorobach tarczycy u kobiet w ciąży [Management of thyroid diseases during pregnancy]. Endokrynol Pol. 2011; 62(4): 362–81.
  14. Thyroid Disease in Pregnancy: ACOG Practice Bulletin, Number 223. Obstet Gynecol. 2020; 135(6): e261–e274.
  15. Williams B, Mancia G, Spiering W, et al. ESC Scientific Document Group. 2018 ESC/ESH Guidelines for the management of arterial hypertension. Eur Heart J. 2018; 39(33): 3021–3104.
  16. Marra M, Sammarco R, De Lorenzo A, et al. Assessment of Body Composition in Health and Disease Using Bioelectrical Impedance Analysis (BIA) and Dual Energy X-Ray Absorptiometry (DXA): A Critical Overview. Contrast Media Mol Imaging. 2019; 2019: 3548284.
  17. Lyons-Reid J, Derraik JGB, Ward LC, et al. Bioelectrical impedance analysis for assessment of body composition in infants and young children-A systematic literature review. Clin Obes. 2021; 11(3): e12441.
  19. Lingwood BE. Bioelectrical impedance analysis for assessment of fluid status and body composition in neonates--the good, the bad and the unknown. Eur J Clin Nutr. 2013; 67 Suppl 1: S28–S33.
  20. Core R Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. 2020. http://www.r-project.org/ (10.06.2021).
  21. Maechler M. Package ‘cluster’. Cluster Analysis, extended original from Peter Rousseeuw, Anja Struyf and Mia Hubert, version 1.14.3. 2012. https://cran.r-project.org/web/packages/cluster/index.html (10.06.2021).
  22. Fraley C, Raftery A, Scrucca L. Package ‘mclust’. Normal mixture modeling for model-based clustering, classification, and density estimation, version 4.0. 2012. https://cran.r-project.org/web/packages/mclust/index.html (10.06.2021).
  23. Marczewski E, Steinhaus H. On a certain distance of sets and the corresponding distance of functions Available online: http://matwbn.icm.edu.pl/ksiazki/cm/cm6/cm6141.pdf. Accessed: 10th June 2021. Colloq Math. 1958; 6(1): 319–327.
  24. Zhai, C.X. A Note on the expectation-maximization (EM) algorithm. Chicago, IL: Department of Computer Science, University of Illinois at Urbana-Champaign. 2007. http://citeseerx.ist.psu.edu/viewdoc/download?doi= (10.06.2021).
  25. Maayan-Metzger A, Schushan-Eisen I, Strauss T, et al. Gestational weight gain and body mass indexes have an impact on the outcomes of diabetic mothers and infants. Acta Paediatr. 2015; 104(11): 1150–1155.
  26. Wang Na, Ding Y, Wu J. Effects of pre-pregnancy body mass index and gestational weight gain on neonatal birth weight in women with gestational diabetes mellitus. Early Hum Dev. 2018; 124: 17–21.
  27. Abreu LRS, Shirley MK, Castro NP, et al. Gestational diabetes mellitus, pre-pregnancy body mass index, and gestational weight gain as risk factors for increased fat mass in Brazilian newborns. PLoS One. 2019; 14(8): e0221971.
  28. Zhang C, Yang Xi, Zhang Y, et al. Association Between Maternal Thyroid Hormones and Birth Weight at Early and Late Pregnancy. J Clin Endocrinol Metab. 2019; 104(12): 5853–5863.
  29. Turunen S, Vääräsmäki M, Männistö T, et al. Pregnancy and Perinatal Outcome Among Hypothyroid Mothers: A Population-Based Cohort Study. Thyroid. 2019; 29(1): 135–141.