Continuous Glucose Monitoring Metrics for Predicting Adverse Neonatal Outcomes in Individuals Undergoing Gestational

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Caleb Griffiths1, Rafael Bravo Santos2, Sarah A Wernimont3

  • 1Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, USA.

Journal of Diabetes Science and Technology
|July 1, 2026
PubMed

Insights

Continuous glucose monitoring (CGM) metrics indicating sustained high blood sugar, not just variability, better predict adverse neonatal outcomes in gestational diabetes screening. These findings can refine pregnancy glucose monitoring strategies.

Area of Science:

  • Obstetrics and Gynecology
  • Endocrinology
  • Neonatal Medicine

Background:

  • Gestational diabetes screening is crucial for maternal and neonatal health.
  • Continuous glucose monitoring (CGM) offers detailed glycemic insights.
  • Previous research linked elevated time above range (≥10% time >140 mg/dL) to adverse neonatal outcomes.

Purpose of the Study:

  • To evaluate if various CGM metrics, including those for glycemic variability (GV) and extremes, predict adverse neonatal outcomes.
  • To compare the predictive power of new CGM metrics against existing thresholds.

Main Methods:

  • Eighty-four pregnant individuals used CGM for 10 days post-glucose challenge test.
  • Generalized estimating equation logistic regression analyzed associations between CGM metrics and a composite neonatal outcome (large-for-gestational-age, intravenous glucose, or shoulder dystocia).
  • Model discrimination was assessed using the area under the receiver operating characteristic curve (AUROC).

Main Results:

  • Multiple CGM metrics, including mean glucose, SD, %Time >140 mg/dL, GMI, MAGE, and HBGI, were significantly associated with adverse neonatal outcomes (P < .05).
  • Several metrics showed superior predictive discrimination compared to the ≥10% time-above-range threshold (AUROC=0.643).
  • Notably, %Time >140 mg/dL (AUROC=0.703) and HBGI (AUROC=0.701) demonstrated the highest predictive power.

Conclusions:

  • CGM metrics reflecting sustained hyperglycemia or high glucose levels are stronger predictors of adverse neonatal outcomes than GV-focused metrics.
  • Persistent or extreme maternal hyperglycemia, rather than glycemic variability alone, appears to be the primary driver of neonatal risk.
  • These findings support refining CGM-based screening and management strategies for gestational diabetes.
Abstract

Keywords:
continuous glucose monitoringgestational diabetes screeningglycemic variabilityhyperglycemialarge for gestational ageneonatal

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