Introduction
Maternal metabolic risk factors, including high pre-pregnancy
body-mass-index(BMI), excessive gestational weight gain(GWG), and
gestational hyperglycaemia, are known to induce adverse pregnancy
outcomes. These include stillbirth, pre-term delivery, low or high
birthweight, pre-eclampsia, and maternal postnatal
diabetes/cardiovascular disease(1, 2). The prevalence of gestational
diabetes mellitus(GDM) varies from 2 to 25 percent worldwide, and has
increased over the last decades in parallel with the increasing obesity
prevalence of women in child-bearing age (3, 4).
A variety of strategies targeting metabolic management during pregnancy
have been developed to prevent endocrine-related adverse pregnancy
outcomes. However, a recent individual patient data meta-analysis
suggested that diet and lifestyle interventions in pregnancy only
achieved modest success in reducing GWG, but have no effects on
composite maternal and foetal outcomes, such as pre-term birth,
macrosomia, and foetal adiposity(5). It might be partly attributed to
the alterations of maternal/placental function and metabolic programming
in preconception and early pregnancy, which occurs prior to when most
interventions are initiated. Other than maternal weight and glucose
level, maternal lipid levels during pregnancy are recognised as ignored
risk factors for adverse pregnancy outcomes recently(6).
The mechanism of how maternal metabolic dysfunction is linked with
neonatal health remains uncertain, however, most researchers believe it
may be explained by the unfavourable intrauterine nutritional
environment(7, 8). Previous studies mainly focused on one specific
metabolic trait or on the number of metabolic disorders, without
assessing the underlying interacted effect of the natural metabolic
network (9, 10). This is due to the limited analytical ability of
classical statistical methods to analyse multidimensional data.
Understanding how the metabolic network influences neonatal health is
crucial to future interventional studies and potentially to
antenatal/prenatal advice.
In this study, we investigated the independent association of maternal
metabolic traits with birthweight and cord blood insulin in the Born in
Guangzhou Cohort Study(BIGCS). The inter-dependency of maternal
metabolic risk factors and their association with birthweight and cord
blood insulin was then mapped by Additive Bayesian Network(ABN)
analysis, a robust data-driven structure discovery model, which has been
widely used in other disciplines(11, 12).