The prevalence of childhood obesity and related metabolic disorders is increasing. Early identification of offspring at risk for childhood obesity is critical to initiate early preventive interventions. Childhood obesity is determined by a complex mix of genetic and environmental factors. Important among these is the intrauterine environment as it impacts fetal adiposity, which our preliminary data show is highly associated with childhood adiposity. Thus, identifying factors important for fetal fat accretion a key challenge. We propose to address the hypothesis that maternal metabolites and metabolic networks during pregnancy impact newborn adiposity with varying degree depending upon the genetic susceptibility of the fetus and, ultimately, impact childhood adiposity and metabolic health. Our goal is to identify metabolites and metabolic pathways associated with fetal and childhood adiposity and determine whether these associations are impacted by fetal genetic variants. These data then will be used to develop a model for early prediction of fetal and childhood adiposity. We will accomplish this using phenotypic data, serum samples, and DNA from mothers and their offspring enrolled in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study and phenotype data from the HAPO Follow-Up Study (FUS). HAPO showed that hyperglycemia in pregnancy, less severe than overt diabetes, is independently associated with increased risk of adverse maternal and neonatal outcomes. The HAPO FUS examined HAPO mother-child pairs ~10-14 years after delivery to address the hypothesis that hyperglycemia in pregnancy, less severe than overt diabetes, is independently associated with increased risk of adverse childhood and maternal outcomes.
The specific aims for this study are as follows. (1) Leverage existing and new metabolomic data to identify maternal metabolites and metabolic networks at ~28 weeks gestation associated with higher newborn and childhood adiposity. Targeted assays for key metabolites will be developed to quantify metabolite levels in additional HAPO mothers and validate the identified associations. (2) Use new and existing genomic data to identify maternal genetic variation associated with levels of key metabolites identified in Aim 1 for use in predictive models in Aim 4. (3) Address the hypothesis that the impact of maternal metabolites on fetal adiposity is modulated through an interaction with fetal genotype by: (a) using existing fetal GWAS data to test for interaction between maternal metabolites or metabolite networks and fetal genotype in determining fetal and childhood phenotype; and (b) fine mapping genetic loci important in mediating the effect of maternal metabolites or networks on fetal and childhood phenotype and establishing the functional consequences of identified variants. (4) Use maternal phenotypic, environmental and genetic data together with fetal genetic data to establish predictive models for newborn and childhood adiposity. These studies will allow development of a model for pre-gestational prediction of higher newborn and childhood adiposity with a long-term goal of developing early interventions to alter the trajectory of in utero fat accretion.
Newborns with excess fat at birth are at risk for short- (shoulder dystocia, hypoglycemia, asphyxia, and brachial plexus and skeletal injuries) and long- (obesity, type 2 diabetes, lipid abnormalities and hypertension) term adverse outcomes. We propose to integrate data generated using metabolomics and genomics to identify maternal metabolites and metabolic networks important for newborn and childhood adiposity. We will use these data to develop a predictive model for higher fat at birth and in childhood to allow for prediction of these outcomes prior to and early in pregnancy and, thus, facilitate implementation of early interventions.
