Maternal glucose is the major determinant of fetal growth, predicting large for gestational age (LGA) infants and neonatal outcomes (1). However, maternal glucose is dynamic, with glucose tolerance and insulin sensitivity varying across the 24-h day with a circadian rhythmicity (2,3). Superimposed upon this, there are the peaks and troughs in glucose that are determined by the balance between insulin resistance and lifestyle/behavioral factors, including diet, physical activity, energy expenditure, stress, sleep, and shift work. Insulin sensitivity also varies across pregnancy, with insulin resistance increasing with gestation (4). It is this dynamic glucose signal to which the fetus is exposed in pregnancy. Continuous glucose monitoring (CGM) provides the most objective method of assessing this dynamic glucose signal in daily life (5). With up to 288 interstitial fluid glucose measurements per day, CGM accurately reflects blood glucose variations (5). Although standard summary metrics are recommended for the reporting of CGM (5,6), they do not give dynamic information about the timing of glucose excursions, thereby losing much of the detailed temporal glycemic information generated. We have pioneered the application of functional data analysis (FDA) to CGM data to extract shape information and to identify glucose dysregulation that is undetectable by summary statistical measures (7,8). We found that FDA is sensitive at detecting shorter periods of relative hyperglycemia that may not be detectable by summary metrics and enables accurate definition of time periods across the 24-h day where differences in temporal glucose control occurs between groups and in relation to clinical outcomes (7,8). Detecting this variation is particularly important in the context of pregnancy where even small increases in maternal glucose are related to poorer clinical outcomes (1).
The recent Continuous Glucose Monitoring in Women With Type 1 Diabetes in Pregnancy Trial (CONCEPTT) showed that use of real-time (RT)-CGM during pregnancy in women with type 1 diabetes was associated with improved neonatal outcomes, including a lower incidence of LGA, neonatal hypoglycemia, and neonatal intensive care unit admission (9) compared with women who used only self-monitored blood glucose (SMBG). While these improvements are likely to be attributable to improved glucose control, standard CGM metrics showed no differences in mean glucose, and they showed only that pregnant RT-CGM users spent more time in the pregnancy glucose target range (3.5–7.8 mmol/L or 63–140 mg/dL) and less time hyperglycemic (9). The effect of using pumps or multiple daily insulin injections (MDIs) was also explored and unexpectedly showed that women using pumps had poorer pregnancy outcomes, with significantly more neonatal hypoglycemia and neonatal intensive care admissions (10). Standard CGM metrics showed only that pump users spent 5% more time above the glucose target range at 24 weeks’ gestation and 5% less time in the range at 24 weeks than women on MDI (10). The lack of comprehensive differences in standard CGM metrics while showing differences in neonatal outcomes suggests that there may be differences in temporal glucose profiles that were not detected by the standard CGM metrics.
The objective of the current study was therefore to perform FDA on the CGM data obtained in the CONCEPTT trial to determine if temporal differences in 24-h glucose profiles occurred between 1) women who were randomized to RT-CGM or SMBG, 2) women who used insulin pumps or MDI, and 3) women whose infants had LGA or not.
University of Lincoln, College of Social Science Research
Eleanor M. Scott, University of Leeds, Department of Clinical and Population Science, Leeds Institute of Cardiovascular and Metabolic Medicine
Denice S. Feig, Department of Medicine, Sinai Health System, Toronto, Ontario
Helen R. Murphy, Kings College London, Division of Maternal Health, St Thomas’ Hospital
Graham R. Law, University of Lincoln, School of Health and Social Care on behalf of the CONCEPTT Collaborative Group