Discrete glucose profiles identified using continuous glucose monitoring data and their association with adverse pregnancy outcomes

ABSTRACT

Background

Continuous glucose monitoring (CGM) has facilitated the evaluation of dynamic changes in glucose throughout the day and their effect on fetal growth abnormalities in pregnancy. However, studies of multiple CGM metrics combined and their association with other adverse pregnancy outcomes are limited.

Objective

Our goals were to 1) use machine-learning techniques to identify discrete glucose profiles based on weekly CGM metrics in pregnant individuals with pregestational diabetes and, 2) investigate their association with adverse pregnancy outcomes.

Study Design

We analyzed data from a retrospective cohort study of pregnant patients with type 1 or type 2 diabetes who used Dexcom G6 CGM and delivered a non-anomalous, singleton gestation at a tertiary center between 2019 and 2023. CGM data were collapsed into 39 weekly glycemic measures related to centrality, spread, excursions, and circadian cycle patterns. Principal component analysis and k-means clustering were used to identify 4 discrete groups, and patients were assigned to the group that best represented their CGM patterns during pregnancy. Finally, the association between glucose profile groups and outcomes (preterm birth, cesarean delivery, preeclampsia, large-for-gestational-age (LGA) neonate, neonatal hypoglycemia, and neonatal intensive care unit (NICU) admission) was estimated using multivariate logistic regression adjusted for diabetes type, maternal age, insurance, CGM use before pregnancy, and parity.

Results

Of 177 included patients, 90 (50.8%) had type 1 diabetes and 85 (48.3%) had type 2 diabetes. We identified 4 glucose profiles: 1) well-controlled, 2) suboptimally controlled with high variability, fasting hypoglycemia and daytime hyperglycemia, 3) suboptimally controlled with minimal circadian variation, and 4) poorly controlled with peak hyperglycemia overnight. Compared to the well-controlled profile, the suboptimally controlled with high variability had higher odds of LGA neonate (aOR 3.34, 95% CI 1.15-9.89). The suboptimally controlled with minimal circadian variation profile had higher odds of preterm birth (aOR 2.59, 95% CI 1.10-6.24), cesarean delivery (aOR 2.76, 95% CI 1.09-7.46), and NICU admission (aOR 4.08, 95% CI 1.58-11.4). The poorly controlled with peak hyperglycemia overnight profile had higher odds of preeclampsia (aOR 2.54, 95% CI 1.02-6.52), LGA neonate (aOR 3.72, 95% CI 1.37-10.4), neonatal hypoglycemia (aOR 3.53, 95% CI 1.37-9.71), and NICU admission (aOR 3.15, 95% CI 1.20, 9.09).

Conclusion

Discrete glucose profiles of pregnant individuals with pregestational diabetes were identified through joint consideration of multiple CGM metrics. Prolonged exposure to maternal hyperglycemia may be associated with higher risk of adverse pregnancy outcomes compared to suboptimal glycemic control characterized by high glucose variability and intermittent hyperglycemia.