Research Article: Development and validation of a clinical model for preconception and early pregnancy risk prediction of gestational diabetes mellitus in nulliparous women

Date Published: April 12, 2019

Publisher: Public Library of Science

Author(s): Brittney M. Donovan, Patrick J. Breheny, Jennifer G. Robinson, Rebecca J. Baer, Audrey F. Saftlas, Wei Bao, Andrea L. Greiner, Knute D. Carter, Scott P. Oltman, Larry Rand, Laura L. Jelliffe-Pawlowski, Kelli K. Ryckman, Andreas Beyerlein.


Implementation of dietary and lifestyle interventions prior to and early in pregnancy in high risk women has been shown to reduce the risk of gestational diabetes mellitus (GDM) development later in pregnancy. Although numerous risk factors for GDM have been identified, the ability to accurately identify women before or early in pregnancy who could benefit most from these interventions remains limited. As nulliparous women are an under-screened population with risk profiles that differ from their multiparous counterparts, development of a prediction model tailored to nulliparous women may facilitate timely preventive intervention and improve maternal and infant outcomes. We aimed to develop and validate a model for preconception and early pregnancy prediction of gestational diabetes mellitus based on clinical risk factors for nulliparous women. A risk prediction model was built within a large California birth cohort including singleton live birth records from 2007–2012. Model accuracy was assessed both internally and externally, within a cohort of women who delivered at University of Iowa Hospitals and Clinics between 2009–2017, using discrimination and calibration. Differences in predictive accuracy of the model were assessed within specific racial/ethnic groups. The prediction model included five risk factors: race/ethnicity, age at delivery, pre-pregnancy body mass index, family history of diabetes, and pre-existing hypertension. The area under the curve (AUC) for the California internal validation cohort was 0.732 (95% confidence interval (CI) 0.728, 0.735), and 0.710 (95% CI 0.672, 0.749) for the Iowa external validation cohort. The model performed particularly well in Hispanic (AUC 0.739) and Black women (AUC 0.719). Our findings suggest that estimation of a woman’s risk for GDM through model-based incorporation of risk factors accurately identifies those at high risk (i.e., predicted risk >6%) who could benefit from preventive intervention encouraging prompt incorporation of this tool into preconception and prenatal care.

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Gestational diabetes mellitus (GDM) is the most common metabolic complication in pregnancy affecting 7% of pregnancies globally.[1,2] Women who develop GDM experience higher rates of hypertension and pre-eclampsia during pregnancy and are more likely to need intervention during labor and delivery by way of assisted vaginal delivery, induction of labor, and cesarean delivery for dystocia and fetal distress.[3] Intra-uterine exposure to metabolic alterations and epigenetic programming can lead to excessive growth of the fetus along with a myriad of medical complications after delivery, including infant respiratory distress syndrome, cardiomyopathy, and hypoglycemia.[4–6] Later in life, women and their offspring face an increased risk of developing metabolic syndrome, obesity, type 2 diabetes, and cardiovascular disease.[3,4,7] Due to increasing prevalence and the heightened risk of adverse outcomes as a consequence of exposure to hyperglycemia during pregnancy, universal glucose screening for GDM at 24–28 weeks gestation has become the standard practice in most developed nations.[8,9] However, there is accumulating evidence to suggest that implementing lifestyle interventions prior to or earlier in pregnancy (before the 20th week of gestation) in high risk women could limit gestational weight gain and reduce the risk of developing GDM later in pregnancy.[10–12]




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