Materials and Methods
Participants
The design and methods of BIGCS have been described previously(13). In
brief, eligible women with Chinese nationality, living in Guangzhou who
are <20 weeks gestation and who intend to deliver at one of
the two Guangzhou Women and Children’s Medical Centre(GWCMC) campuses
were recruited into BIGCS. This study was conducted in a planned
subgroup of BIGCS in whom maternal and cord blood were analysed for
metabolic parameters separately. Pregnant women attending BIGCS with a
singleton pregnancy who delivered at GWCMC between Jan 2015 and Jun 2016
and had umbilical cord blood retained are eligible for this study. Women
were excluded if: 1) maternal blood samples unavailable at 14-27
gestation week; 2) no records of maternal fasting glucose at 20-28
gestation week; 3) lacking maternal demographic information; 4)
diagnosed with health condition prior to pregnancy, including type 1 or
type 2 diabetes, thyroid dysfunction, hypertension, virus hepatitis, and
renal diseases. The study was powered for the association between
maternal triglycerides (the potential weakest risk factors among
maternal metabolic traits) with birthweight according to
literature(Supplementary file S1). The eligible mother-child pairs were
then selected into this study by computer generated randomization.
Ethical permission for the study was granted by the GWCMC Ethics
Committee.
Study Procedures
Maternal demographic data, including anthropometric measures,
socioeconomic status, family and personal medical history, were
collected through a semi-structured questionnaire(Q1) at recruitment.
Maternal overnight fasting blood samples were collected during second
trimester. At 22-28 weeks gestation, women attending their second
prenatal visit underwent a standard 2h 75g oral glucose tolerance
test(OGTT). Women with OGTT results which met or exceeded at least one
threshold of the International Association of Diabetes and Pregnancy
Study Groups(IADPSG) criteria(FPG≥5.1 mmol/L, 1h glucose ≥10.0 mmol/l,
and 2h glucose≥8.5 mmol/L) were diagnosed as having gestational diabetes
mellitus(GDM)(14). For participating children, birth information,
including birth characteristics, delivery mode, and perinatal outcomes
were obtained from routine medical records. Umbilical cord blood samples
were collected by midwives at birth.
Demographic Data
Maternal demographic information(age, height, pre-pregnancy weight,
parity, date of last menstrual period, monthly income, education levels,
and ethnicity) were collected through Q1 questionnaire. BMI was
calculated by dividing weight in kilograms by height in meters squared.
Based on the recommendations of the China Obesity Task Force of the
Chinese Ministry of Health, maternal pre-pregnancy BMI is classified
into two groups: lean group(<24 kg/m2) and
overweight group(≥24 kg/m2) (15). Maternal second
trimester weight was measured to the nearest 0.1 kg using an electronic
scale. Maternal early gestational weight gain(GWG) was calculated by
subtracting pre-pregnancy weight from maternal second trimester weight,
with documentation of the gestational age at measurement. Maternal
fasting glucose concentration was obtained from OGTT test zero-time
value in hospital records.
Biochemical Test
Sample collection, delivery, pre-treatment, and measurements were
blinded. All blood samples were stored and delivered to pre-treatment
laboratory centre. Blood samples were then separated to serum and plasma
by immediate centrifugation, and were stored in EDTA tube in the
bio-bank at -80℃ until analysis. Plasma lipids(TC, HDL-C, LDL-C, and TG)
and insulin levels were measured using commercial kits in fully
automated clinical analyser(Roche Diagnostics, Mannheim, Germany).
Intra- and inter-day coefficients of variation(CVs) were consistently
less than 2 percent for all assays.
Neonatal anthropometry
Gestational age was estimated from ultrasound examination during the
first- or second-trimester. Birthweight and other information, including
gestational age at delivery, mode of delivery, neonatal sex, and
pregnancy complications were obtained from hospital records. Birthweight
was measured to the nearest 50g using an electronic scale by midwives
immediately after delivery. Birthweight Z-Score and percentile(adjusted
for gestational age at delivery and neonatal sex) were calculated using
Intergrowth 21st Newborn Size Standard and Tools(16).
Large for gestational age(LGA) was defined as a birthweight larger than
the 90th percentile for gestational age by sex, while
Small for gestational age(SGA) was defined as a birthweight smaller than
the 10th percentile based on the same birthweight
reference.
Statistical Analysis
Classic statistical
methods
For the baseline table data are summarized as mean ± Standard
Deviation(SD), median(Inter Quartile Range, IQR), or counts with
percentages. Pearson correlation was used to assess the impact of the
long-term -80 °C storage on insulin concentrations in EDTA tube.
Adjustments were then made to account for any degradation by correcting
the initial value using linear regression methods(Supplementary file
S2). Similarly, maternal lipid levels were adjusted for gestational age
using regression model to account for timing of blood
sampling(Supplementary file S3)(17).
Initially, linear and logistic regression were used to estimate the
association between maternal metabolic parameters and neonatal
continuous and binary outcomes, respectively. Further analyses using
linear regression model were performed after all exposures were
transformed to Z-Scores. This was to enable comparison of the effect
size each maternal metabolic parameter had on birthweight Z-Score and
CBI Z-Score. CBI and maternal triglycerides were log-transformed prior
to standardization. Multiple imputation was used to handle missing data.
Subgroup analyses were conducted in boys and girls respectively.
Sensitivity analyses were conducted to compare the estimate differences
between GDM and non-GDM participants, fasting blood samples and
non-fasting samples, primiparous women and non-primiparous women, lean
and overweight group, as well as before and after multiple
imputation(Supplementary file S4). All statistical tests were two-tailed
and a P <0.05 was considered statistically significant.
Statistical analyses were performed in Stata version 14.0(College
Station, Texas, USA).
Additive Bayesian Networks (ABN)
analysis
To further assess the inter-dependency between maternal metabolic risk
factors and their association with birthweight and CBI, Additive
Bayesian Network(ABN) model - an unsupervised machine learning method -
was conducted. Bayesian network analysis is a form of structure
discovery statistical modelling that derives, from empirical data, a
graphical network describing the dependency structure between variables,
shown as directed acyclic graphs(DAGs)(12). ABNs comprise of DAGs where
each node in the graph comprises a generalized linear model(GLM) or a
generalized linear mixed model(GLMM). ABN model is suitable for
analysing highly complex epidemiological data comprising many
inter-dependent variables(11).
Ten variables were chosen for ABN based on prior knowledge gained from
literature and findings of the classical statistical analyses. These ten
variables were maternal age, maternal pre-pregnancy BMI, maternal
fasting glycaemia in OGTT, early GWG, maternal fasting plasma HDL-C and
triglycerides in the second trimester, birthweight Z-Score, cord blood
insulin, gestational age at delivery, and neonatal sex. GWG was adjusted
for gestational age at weight measurement in mid-pregnancy. Cord blood
insulin was adjusted for sample storage duration. All continuous
variables were standardized to Z-Scores to eliminate the influence of
different measurement units. Mother-child pairs with missing data were
excluded(n=93/1522, 6%).
Firstly, an optimal DAG with the best goodness of fit(highest log
marginal likelihood) was identified. Next, parametric
bootstrapping(12800 samples) was performed to address the potential
overfitting. Full technical details are provided in the supplementary
file(S5). ABN analysis was conducted in R 3.4.4(The R Foundation for
Statistical Computing) using ‘abn’ package(11).