Characterizing metabolic variability during pregnancy to understand pathways of in-utero overnutrition: an integrative analysis of metabolomics and lifestyle data
表征妊娠期间的代谢变异性以了解子宫内营养过剩的途径:代谢组学和生活方式数据的综合分析
基本信息
- 批准号:10569564
- 负责人:
- 金额:$ 8.77万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-02-09 至 2023-06-30
- 项目状态:已结题
- 来源:
- 关键词:5 year old8 year oldAddressAmino AcidsBehavioralBiochemicalBiologicalBiological AssayBiological MarkersBiologyBirthBloodBlood PressureBody mass indexCardiometabolic DiseaseChildChildhoodCholesterolChorionic villiClinicalComplexDataDevelopmentDevelopmental BiologyDiabetes MellitusDietEnvironmentExposure toFRAP1 geneFastingFatty AcidsFatty acid glycerol estersFetal DevelopmentGestational DiabetesGlucoseHealthHeterogeneityHyperglycemiaInfantInstructionInsulinInsulin Signaling PathwayInterruptionKnowledgeLifeLife Cycle StagesLife StyleLife course epidemiologyLipidsMediatingMentorsMetabolicMetabolic DiseasesMetabolic PathwayMetabolismMothersObesityOvernutritionParticipantPathologyPathway interactionsPerinatalPhasePhenotypePhosphorylationPhysical activityPhysiological AdaptationPlacentaPlacental BiologyPlasmaPregnancyPregnant WomenPrevention strategyPrincipal Component AnalysisProcessProteinsResearchResearch PersonnelResource AllocationResourcesRiskRoleSamplingSerumSeveritiesSignal PathwaySignal TransductionSubgroupTechniquesTestingTissuesTrainingTriglyceridesWomanYouthadipokinescardiometabolic riskcareercirculating biomarkerscohortdetection of nutrientdisorder riskearly-onset obesityfetalfetal programminghigh dimensionalityimprovedin uterolifestyle datalifestyle interventionmaternal conditionmaternal obesitymetabolic profilemetabolomicsmodifiable behaviornovelobesity riskobstetrical complicationoffspringoffspring obesityphenotypic dataprenatalprepregnancyresponserisk stratificationsymposium
项目摘要
PROJECT SUMMARY/ABSTRACT
Maternal-fetal resource allocation is governed by complex maternal physiological adaptations; however,
conditions such as obesity and Gestational Diabetes Mellitus (GDM) are associated with a suboptimal adaptive
response to pregnancy resulting in under- or overnutrition in utero. In utero overnutrition (i.e., fetal over-
nourishment by excess exposure to maternal fuels) is a concern given the earlier onset of obesity and
cardiometabolic disease in youth. Moreover, higher glycemia and adiposity, even below thresholds of clinical
GDM and obesity, have detrimental effects on offspring health. Such metabolic heterogeneity is noteworthy as
it influences target tissues involved in fetal development, such as the placenta. Characterizing metabolic
variability in pregnant women and assessing associations with offspring metabolic health will reveal nuances in
maternal phenotypes that contribute to metabolic disease risk in offspring. However, the associations of maternal
metabolic heterogeneity with maternal fuels (e.g., glucose, insulin, lipids) and modifiable behaviors are unclear.
Moreover, it is unknown if placental signaling, which is implicated in fetal programming, reflects metabolic
heterogeneity assessed via circulating biomarkers during pregnancy. Dr. Ellen Francis’ proposal addresses
these knowledge gaps by integrating low- and high-dimensional observational data and applying advanced
statistical techniques to test the hypothesis that distinct maternal metabolic subgroups correspond to differences
in placental signaling pathways, and that the maternal subgroups and placental pathways are involved in
programming of offspring metabolic risk. This proposal will use existing samples of fasting maternal blood during
pregnancy (N=1410), placental villus tissue, and collect new metabolomics in offspring at 4-8 years of age.
Through didactic instruction and mentored training, Dr. Francis will obtain training in the analysis of ‘omics data
within a lifecourse epidemiological framework to characterize metabolic variability in pregnancy and generate
metabolic subgroups related to adiposity and maternal fuels. She will advance her understanding of placental
and developmental biology (fetal programing) by receiving extensive hands-on-training (inclusion in lab and
bench training, and conference and seminar attendance) from mentors with expertise in placental and perinatal
biology. In the independent phase, she will use training gained in the mentored phase to assess if maternal
metabolic subgroups are reflected in placental nutrient sensing pathways, generate metabolomics data in
offspring and create offspring metabolic profiles. She will then assess whether maternal metabolic subgroups
are associated with offspring metabolic profiles in childhood, and the extent to which these associations are
mediated by placental nutrient sensing pathways. Findings from these complementary studies will improve our
understanding of metabolic pathways involved in fetal development, contribute to knowledge of how maternal
metabolic variability influences metabolic risk in offspring, and could reveal key cellular and behavioral targets
for prevention strategies, while assisting Dr. Francis’ to establish a career as an independent investigator.
项目概要/摘要
然而,母婴资源分配受到复杂的母体生理适应的控制;
肥胖和妊娠期糖尿病 (GDM) 等疾病与适应性欠佳有关
对妊娠的反应导致子宫内营养不足或过度(即胎儿营养过剩)。
由于肥胖发生较早,并且由于过度接触母体燃料而导致的营养不良,
此外,年轻人的心脏代谢疾病更高,甚至低于临床阈值。
GDM 和肥胖对后代健康产生不良影响,这种代谢异质性值得注意。
它影响参与胎儿发育的靶组织,例如胎盘的代谢特征。
孕妇的变异性以及评估与后代代谢健康的关联将揭示以下方面的细微差别
影响后代代谢疾病风险的母亲表型然而,母亲的关联。
母体燃料(例如葡萄糖、胰岛素、脂质)的代谢异质性和可改变的行为尚不清楚。
此外,尚不清楚与胎儿编程有关的胎盘信号传导是否会影响新陈代谢。
艾伦·弗朗西斯博士的提案提出了通过怀孕期间循环生物标志物评估的异质性。
通过整合低维和高维观测数据并应用先进的技术来弥补这些知识差距
检验不同母体代谢亚组与差异相对应的假设的统计技术
胎盘信号通路,并且母体亚组和胎盘通路参与
该提案将使用现有的母体空腹血液样本。
妊娠(N=1410)、胎盘绒毛组织,并收集 4-8 岁后代的新代谢组学。
通过教学指导和指导培训,弗朗西斯博士将获得“组学数据分析”方面的培训
在生命全程流行病学框架内描述妊娠期代谢变异性并生成
她将加深对胎盘的了解。
和发育生物学(胎儿编程),通过接受广泛的实践培训(包括在实验室和
由具有胎盘和围产期专业知识的导师提供的台架培训以及参加会议和研讨会
在独立阶段,她将利用在指导阶段获得的培训来评估是否是母亲。
代谢亚组反映在胎盘营养传感途径中,生成代谢组学数据
然后,她将评估母体代谢亚组是否存在。
与后代童年时期的代谢特征相关,以及这些关联的程度
这些补充研究的结果将改善我们的胎盘营养感应途径。
了解胎儿发育所涉及的代谢途径,有助于了解母亲如何
代谢变异性影响后代的代谢风险,并可以揭示关键的细胞和行为目标
制定预防策略,同时协助弗朗西斯博士建立独立调查员的职业生涯。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Ellen Cross Francis其他文献
Ellen Cross Francis的其他文献
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{{ truncateString('Ellen Cross Francis', 18)}}的其他基金
Characterizing metabolic variability during pregnancy to understand pathways of in-utero overnutrition: an integrative analysis of metabolomics and lifestyle data
表征妊娠期间的代谢变异性以了解子宫内营养过剩的途径:代谢组学和生活方式数据的综合分析
- 批准号:
10913646 - 财政年份:2023
- 资助金额:
$ 8.77万 - 项目类别:
Characterizing metabolic variability during pregnancy to understand pathways of in-utero overnutrition: an integrative analysis of metabolomics and lifestyle data
表征妊娠期间的代谢变异性以了解子宫内营养过剩的途径:代谢组学和生活方式数据的综合分析
- 批准号:
10425090 - 财政年份:2022
- 资助金额:
$ 8.77万 - 项目类别:
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Characterizing metabolic variability during pregnancy to understand pathways of in-utero overnutrition: an integrative analysis of metabolomics and lifestyle data
表征妊娠期间的代谢变异性以了解子宫内营养过剩的途径:代谢组学和生活方式数据的综合分析
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