Effects of pre-pregnancy weight on incidence of large for gestational age newborn in pregnant women with gestational diabetes mellitus

Abstract

The aim of this study is to compare the incidence of large for gestational age (LGA) infants between gestational diabetes mellitus (GDM) women whose pre-pregnancy weight was in normal and overweight/obese category. Possible associated factors for LGA were evaluated and pregnancy outcomes were compared. A total of 272 singleton pregnant women with GDM were enrolled, 136 overweight/obese women (BMI ≥25 kg/m 2 ) were in study group and another 136 normal weight women (BMI <25 kg/m 2 ) were served as comparison group. Data were retrieved from medical records, including demographic data, GDM diagnosis and risks, labor and delivery data, and pregnancy outcomes. Baseline characteristics and incidence of LGA were compared between groups, and logistic regression analysis was performed in order to determine independent risk factors for LGA, adjusted for potential confounders. Baseline demographic data were comparable between groups, except that study group was significantly more likely to have excessive weight gain and more likely to need insulin treatment than control group. The rate of LGA and macrosomic infants was significantly higher in study group than in control group (p=0.038 and 0.024, respectively). Logistic regression analysis showed that only gestational weight gain was significantly associated with LGA. Gestational weight gain less than recommendation significantly decreased the risk of LGA by 60 % (adjusted odds ratio (OR) 0.39, 95 % confidence interval (CI) 0.17-0.96, p=0.04). On the other hand, gestational weight gain greater than recommendation significantly doubled the risk of LGA (adjusted OR 2.03, 95 % CI 1.11-3.71, p=0.022). Gestational weight gain, but not pre-pregnancy BMI was independently associated with LGA in GDM pregnant women.

Diabetes and data in many forms

“…A review of personalized drug use in diabetes is published in this issue [28]. Although genetic variants and metabolomics studies using omics technologies have the potential to be used as biomarkers [29], translatable results are scarce.…”