Exploring Relationships Between Maternal Characteristics, Continuous Glucose Monitoring Data, and Neonatal Hypoglycemia in Gestational Diabetes Pregnancies Using Probabilistic Modeling.

ABSTRACT:

Background

Gestational diabetes mellitus (GDM) is a frequent metabolic complication during pregnancy that significantly impacts both maternal and neonatal health outcomes regularly resulting in NH. Exploring the interactions between maternal characteristics, neonatal outcomes, and data collected from wearable technologies, such as continuous glucose monitoring (CGM) could potentially enable the development of predictive models and support personalized care.

Methods

This study employed probabilistic modeling, using Bayesian networks (BNs), to analyze data from the STEADY SUGAR clinical trial (N = 118 women with GDM) with the aim of discovering interactions between maternal characteristics, CGM-derived features calculated in the 90 days preceding delivery, and neonatal

SUBMITTER: Cappon G 

PROVIDER: S-EPMC12602297 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature