Statistical analyses
Continuous data are presented as mean ± standard deviation (SD) for
normally distributed variables and as medians and interquartile ranges
(IQRs) for non-normally distributed variables. Categorical data are
presented as frequencies and percentages. Student’s t-test,
Mann–Whitney U test, chi-square test, and Fisher’s exact test were used
to compare baseline characteristics between the normal and GDM groups.
Latent profile analysis (LPA) was performed to identify glucose patterns
in patients with GDM based on four measurements during the OGTT. This
method assumes that unobserved latent profiles generate patterns of
responses in a series of continuous variables. The optimal number of
clusters was determined by considering the Bayesian information
criterion (BIC) value, distribution of cluster membership probabilities,
cluster sizes, and interpretability of the identified patterns.19,20 A three-cluster model was selected because it
had a lower BIC value than the other models, and all cluster sizes were
>10% of the number of patients with GDM. To classify
individuals exclusively into three glucose patterns, we assigned
patients to the cluster with the highest cluster membership probability.
The individual area under the curve (AUC) for the OGTT was adopted to
evaluate the severity of maternal hyperglycemia by summing the area of
three trapezoids as follows: (0-h + 1-h glucose)/2, (1-h + 2-h
glucose)/2, and (2-h + 3-h glucose)/2. Binary logistic regression
analysis was performed to compare the prevalence of outcomes between the
normal and three latent glucose pattern groups, four groups by
classifying quartiles of individual AUCs, or three groups according to
the number of criteria in GDM patients. Two multiple logistic regression
models were used to control for the confounding factors. Model A
included age, preexisting hypertension, family history of diabetes
mellitus, family history of hypertension, pre-pregnancy body mass index
(BMI), parity, and gestational age before delivery as covariates. Model
B additionally included SBP, glucose level at 35 weeks, and insulin
treatment. The risk associated with the outcome was calculated and
presented as the OR and corresponding 95% CI. We also used a restricted
cubic spline (RCS) curve with four knots for the adjusted ORs to
graphically demonstrate the nonlinear relationship between the
individual AUC for OGTT and the risk of adverse pregnancy outcomes. All
reported p -values were two-sided, and statistical significance
was set at p <0.05. We used the Mclust function
in the mclust package (version 5.4.6) in R software (version 4.0.3; R
Foundation for Statistical Computing, Vienna, Austria) to conduct the
LPA. 21 All other statistical analyses were performed
using SAS software (version 9.4; SAS Institute Inc., Cary, NC, USA).