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

Objectives: This study evaluates whether continuous glucose monitoring (CGM) metrics predict adverse neonatal outcomes among individuals undergoing gestational diabetes screening. Building on findings by Fishel Bartal et al, who reported an association between ≥10% time above 140 mg/dL and a composite neonatal outcome, this analysis examines 13 additional CGM-derived measures reflecting glycemic variability (GV) and glycemic extremes. Methods: Eighty-four pregnant individuals wore CGM devices for 10 days following a 1-hour 50 g glucose challenge test. Generalized estimating equation logistic regression models assessed associations between CGM metrics and a composite neonatal outcome consisting of large-for-gestational-age birth, need for intravenous glucose treatment, or shoulder dystocia. Model discrimination was quantified using the area under the receiver operating characteristic curve (AUROC). Results: Several CGM measures—including mean glucose, standard deviation (SD), maximum glucose, %Time >140 mg/dL, %Time >120 mg/dL, glucose management indicator (GMI), mean of daily differences (MODD), mean amplitude of glycemic excursions (MAGE), area under the CGM curve (AUC-CGM), and high blood glucose index (HBGI)—were significantly associated with adverse neonatal outcomes ( P < .05). Metrics demonstrating higher discrimination than the original ≥10% time-above-range measure (AUROC = 0.643) included SD (0.656), MAGE (0.665), mean glucose (0.676), GMI (0.676), maximum glucose (0.683), %Time >120 mg/dL (0.683), AUC-CGM (0.692), HBGI (0.701), and %Time >140 mg/dL (0.703). Conclusions: Continuous glucose monitoring metrics reflecting sustained or high-level hyperglycemia, rather than GV-focused metrics, more strongly predicted adverse neonatal outcomes. These findings suggest that persistent or extreme maternal glucose elevations, rather than variability alone, may drive neonatal risk and support refining CGM-based screening approaches in pregnancy.