# Predicting Newborn and Childhood Adiposity:  An Integrated Omics Approach

> **NIH NIH R01** · NORTHWESTERN UNIVERSITY · 2020 · $608,926

## Abstract

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. T...

## Key facts

- **NIH application ID:** 9983682
- **Project number:** 5R01DK117491-03
- **Recipient organization:** NORTHWESTERN UNIVERSITY
- **Principal Investigator:** William L Lowe
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $608,926
- **Award type:** 5
- **Project period:** 2018-08-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9983682

## Citation

> US National Institutes of Health, RePORTER application 9983682, Predicting Newborn and Childhood Adiposity:  An Integrated Omics Approach (5R01DK117491-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9983682. Licensed CC0.

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