Predicting Newborn and Childhood Adiposity: An Integrated Omics Approach

预测新生儿和儿童肥胖：综合组学方法

基本信息

批准号：
10452488
负责人：
William L Lowe
金额：
$ 57.71万
依托单位：
NORTHWESTERN UNIVERSITY AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-01 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10452488
关键词：
Address Asphyxia Biochemical Pathway Biological Biological Assay Birth Body mass index Brachial plexus structure Child Childhood Chromosome Mapping Complex DNA Data Data Collection Development Diabetes Mellitus Dystocia Early Intervention Early identification Enrollment Environment Environmental Risk Factor Fatty acid glycerol esters Fetus Follow-Up Studies Frequencies Genetic Genetic Predisposition to Disease Genetic Variation Genomics Genotype Glucose Goals Health Human Resources Hyperglycemia Hypertension Hypoglycemia Laboratories Lipids Measures Mediating Metabolic Metabolic Diseases Metabolic Pathway Metabolism Modeling Mothers Newborn Infant Non-Insulin-Dependent Diabetes Mellitus OGTT Obesity Observational Study Outcome Outcome Study Phenotype Pregnancy Prevalence Protocols documentation Resources Risk Sampling Serum Shoulder Testing Third Pregnancy Trimester Training Variant Woman adverse maternal outcomes adverse outcome adverse pregnancy outcome base cohort fetal fetal adiposity genetic variant genome wide association study genome-wide genomic data genomic locus glucose tolerance high risk implementation facilitation in utero instrument intrauterine environment maternal obesity maternal outcome metabolomics model development neonatal outcome newborn adiposity obesity in children obesity risk offspring outcome prediction phenotypic data predictive modeling prevent preventive intervention racial diversity skeletal injury

项目摘要

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.

儿童肥胖症和相关代谢障碍的患病率正在增加。早期识别儿童肥胖症有风险的后代对于开始早期预防干预措施至关重要。儿童肥胖是由遗传和环境因素的复杂组合确定。其中重要的是宫内环境会影响胎儿肥胖，我们的初步数据显示，这与童年高度相关肥胖。因此，确定对胎儿脂肪积聚重要的因素是关键挑战。我们建议解决孕妇在怀孕期间的代谢产物和代谢网络影响新生儿肥胖的假设程度不同，取决于胎儿的遗传敏感性，并最终影响童年肥胖和代谢健康。我们的目标是确定与之相关的代谢物和代谢途径胎儿和儿童期肥胖，并确定这些关联是否受到胎儿遗传变异的影响。然后，这些数据将用于开发一个模型，用于早期预测胎儿和儿童肥胖。我们将使用表型数据，血清样品和母亲的DNA及其后代的DNA来完成此操作 HAPO的高血糖和不良怀孕结局（HAPO）研究和表型数据后续研究（FUS）。 Hapo表明，怀孕的高血糖症，不如明显的糖尿病严重，是与不良母体和新生儿结局的风险增加有关。 Hapo Fus 分娩后检查了〜10 - 14年的HAPO母子对，以解决高血糖的假设在怀孕中，不如明显的糖尿病严重，与不良风险增加有关童年和产妇的结果。这项研究的具体目的如下。（1）利用现有和新的代谢组数据以识别孕产妇代谢产物和代谢网络，妊娠28周与较高的新生儿和童年肥胖有关。关键代谢物的针对性测定将是开发以量化其他HAPO母亲的代谢产物水平并验证已确定的关联。（2）使用新的和现有的基因组数据来识别与关键水平相关的母体遗传变异 AIM 1中确定的代谢物在AIM 4中用于预测模型。（3）解决了以下假设。母体代谢物对胎儿肥胖的影响通过与胎儿基因型的相互作用来调节：（a）使用现有的胎儿GWAS数据测试母体代谢产物或代谢物网络之间的相互作用胎儿基因型在确定胎儿和儿童期表型方面；（b）精细的映射遗传基因座在介导孕产妇代谢物或网络对胎儿和儿童期表型的影响并建立已识别变体的功能后果。（4）使用母体表型，环境和遗传数据以及胎儿遗传数据，以建立新生儿和儿童肥胖的预测模型。这些研究将允许开发一个模型，以预先预测更高的新生儿和童年肥胖的长期目标是开发早期干预措施以改变子宫脂肪积聚的轨迹。