Dataset Information

Prediction of birth weight in pregnancy with gestational diabetes mellitus using an artificial neural network.

ABSTRACT: Gestational diabetes mellitus (GDM) is common during pregnancy, with the prevalence reaching as high as 31.0% in some European regions (McIntyre et al., 2019). Dysfunction of the glucose metabolism in pregnancy can influence fetal growth via alteration of the intrauterine environment, resulting in an increased risk of abnormal offspring birth weight (McIntyre et al., 2019). Infants with abnormal birth weight will be faced with increased risks of neonatal complications in the perinatal period and chronic non-communicable diseases in childhood and adulthood (Mitanchez et al., 2015; McIntyre et al., 2019). Therefore, accurate estimation of birth weight for neonates from women with GDM is crucial for more sensible perinatal decision-making and improvement of perinatal outcomes. Timely antenatal intervention, with reference to accurately estimated fetal weight, may also decrease the risks of adverse long-term diseases.

SUBMITTER: Zhou M

PROVIDER: S-EPMC9110320 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Prediction of birth weight in pregnancy with gestational diabetes mellitus using an artificial neural network.

Zhou Menglin M Ji Jiansheng J Xie Ni N Chen Danqing D

Journal of Zhejiang University. Science. B 20220501 5

Gestational diabetes mellitus (GDM) is common during pregnancy, with the prevalence reaching as high as 31.0% in some European regions (McIntyre et al., 2019). Dysfunction of the glucose metabolism in pregnancy can influence fetal growth via alteration of the intrauterine environment, resulting in an increased risk of abnormal offspring birth weight (McIntyre et al., 2019). Infants with abnormal birth weight will be faced with increased risks of neonatal complications in the perinatal period and ...[more]

PMID: 35557043

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Project description:Excessive pre-pregnancy weight is a known risk factor of pregnancy complications. The purpose of this analysis was to assess the relationship between several categories of maternal weight and the risk of developing hypertension and diabetes in pregnancy, and the relationship of these complications with the results of the newborn. It was carried out in a common cohort of pregnant women and taking into account the influence of disturbing factors. Our analysis was conducted in a prospective cohort of 912 Polish pregnant women, recruited during 2015-2016. We evaluated the women who subsequently developed diabetes with dietary modification (GDM-1) (n = 125) and with insulin therapy (GDM-2) (n = 21), as well as the women who developed gestational hypertension (GH) (n = 113) and preeclampsia (PE) (n = 24), compared to the healthy controls. Odds ratios of the complications (and confidence intervals (95%)) were calculated in the multivariate logistic regression. In the cohort, 10.8% of the women had pre-pregnancy obesity (body mass index (BMI) ≥ 30 kg/m2), and 36.8% had gestational weight gain (GWG) above the range of the Institute of Medicine recommendation. After correction for excessive GWG and other confounders, pre-pregnancy obesity (vs. normal BMI) was associated with a higher odds ratio of GH (AOR = 4.94; p < 0.001), PE (AOR = 8.61; p < 0.001), GDM-1 (AOR = 2.99; p < 0.001), and GDM-2 (AOR = 11.88; p <0.001). The threshold risk of development of GDM-2 occurred at lower BMI values (26.9 kg/m2), compared to GDM-1 (29.1 kg/m2). The threshold point for GH was 24.3 kg/m2, and for PE 23.1 kg/m2. For GWG above the range (vs. GWG in the range), the adjusted odds ratios of GH, PE, GDM-1, and GDM-2 were AOR = 1.71 (p = 0.045), AOR = 1.14 (p = 0.803), AOR = 0.74 (p = 0.245), and AOR = 0.76 (p = 0.672), respectively. The effect of maternal edema on all the results was negligible. In our cohort, hypertension and diabetes were associated with incorrect birth weight and gestational age at delivery. Conclusions: This study highlights the importance and influence of excessive pre-pregnancy maternal weight on the risk of pregnancy complications such as diabetes and hypertension which can impact fetal outcomes.

Dataset Information

Prediction of birth weight in pregnancy with gestational diabetes mellitus using an artificial neural network.

Publications

Prediction of birth weight in pregnancy with gestational diabetes mellitus using an artificial neural network.

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