Longitudinal Patterns of Glycemic Control and Blood Pressure in Pregnant Women with Type 1 Diabetes Mellitus: Phenotypes from Functional Data Analysis

Abstract

Objective To identify phenotypes of type 1 diabetes control and associations with maternal/neonatal characteristics based on blood pressure (BP), glucose, and insulin curves during gestation, using a novel functional data analysis approach that accounts for sparse longitudinal patterns of medical monitoring during pregnancy.

Methods We performed a retrospective longitudinal cohort study of women with type 1 diabetes whose BP, glucose, and insulin requirements were monitored throughout gestation as part of a program-project grant. Scores from sparse functional principal component analysis (fPCA) were used to classify gestational profiles according to the degree of control for each monitored measure. Phenotypes created using fPCA were compared with respect to maternal and neonatal characteristics and outcome.

Results Most of the gestational profile variation in the monitored measures was explained by the first principal component (82–94%). Profiles clustered into three subgroups of high, moderate, or low heterogeneity, relative to the overall mean response. Phenotypes were associated with baseline characteristics, longitudinal changes in glycohemoglobin A1 and weight, and to pregnancy-related outcomes.

Conclusion Three distinct longitudinal patterns of glucose, insulin, and BP control were found. By identifying these phenotypes, interventions can be targeted for subgroups at highest risk for compromised outcome, to optimize diabetes management during pregnancy.

Keywords

blood pressure variability - curve shape - functional data analysis - functional principal component analysis - glucose control - glucose variability - insulin variability - medical monitoring - sparse longitudinal data

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