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
