The Multi-Omic Milk (MuMi) Study: Leveraging the IMiC Platform and the CHILD Cohort to study human milk as a biological system and understand its composition, determinants and impacts on child health

多组学牛奶 (MuMi) 研究：利用 IMiC 平台和儿童队列研究母乳作为一个生物系统，并了解其成分、决定因素以及对儿童健康的影响

• 批准号: 10676907
• 负责人: Meghan Brianne Azad
• 金额: $ 49.21万
• 依托单位: UNIVERSITY OF MANITOBA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-12 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10676907
• 关键词: Address; Affect; Anthropometry; Artificial Intelligence; Bacteria; Big Data; Breast Feeding; Caring; Categories; Characteristics; Child; Child Development; Child Health; Childhood; Chronic Disease; Cohort Studies; Collaborations; Data; Data Scientist; Data Set; Development; Ensure; Environmental Exposure; Environmental Risk Factor; Fatty Acids; Future Generations; Genetic; Grant; Growth; Growth and Development function; Health; Human; Human Milk; Hypersensitivity; Individual; Infant; Infant Development; Infant Health; Infection; Infrastructure; International; Lactation; Life; Life Style; Macronutrients Nutrition; Metadata; Micronutrients; Milk; Milk Substitutes; Mothers; Nutrient; Nutritional Study; Oligosaccharides; Parents; Phenotype; Positioning Attribute; Pregnancy; Proteins; Research; Research Personnel; Resolution; Resources; Sampling; Scientist; System; Systems Biology; Systems Development; Variant; Wheezing; Work; atopy; biobank; biological systems; cohort; complex biological systems; design; disorder prevention; early childhood; feeding; improved; infancy; infant nutrition; innovation; lifestyle factors; machine learning method; method development; microbiome; microbiota; multiple omics; novel; nutrition; programs; unsupervised learning

PROJECT SUMMARY Significance: Human milk (HM) has evolved over millions of years to nourish and protect human infants - yet we know surprisingly little about its composition, variation, and function. Traditionally, HM research has focused on individual HM components, yet HM is a complex biological system comprising thousands of components that interact and function in combination. Moreover, while HM composition is known to be affected by maternal, infant, and environmental factors, these are poorly understood and rarely examined simultaneously. To address these gaps, our team is championing a multi-omics systems biology approach to study HM as a “system within a system”, reflecting that milk itself is a system embedded within the “mother-milk-infant” triad. Approach: This grant will leverage and unite two established HM research platforms to investigate HM and its determinants and health impacts among 1600 mother-infant dyads using a novel multi-omic approach. The International Milk Composition (IMiC) Consortium is a network of HM researchers and data scientists with an established infrastructure for multi-omic HM research. CHILD is an ongoing national pregnancy cohort of 3600 children born in 2009-12. Our team has already analyzed 1600 CHILD HM samples for 19 oligosaccharides, 28 fatty acids, and hundreds of bacteria. We will now enhance the rich CHILD dataset with new multi-omic HM analyses (20 nutrients, 15 non-nutritive bioactive proteins and thousands of metabolites) and apply unsupervised machine learning methods to identify discrete ‘lactotypes’ (Aim 1). Next, we will leverage the rich CHILD data to identify maternal, infant and environmental factors associated with lactotype membership and/or individual HM components (Aim 2). Finally, we will use machine learning methods to understand how HM composition influences microbiome development, growth, wheezing and allergies during infancy and childhood (Aim 3). Innovation: Integrating the CHILD and IMiC platforms will facilitate unprecedented research on HM as a system-within-a-system and generate the world’s largest and most deeply-phenotyped mother-milk-infant dataset (n=1600 triads with multi-omic milk profiles and rich longitudinal maternal and infant metadata). This project will unite expert HM scientists, renowned pediatric researchers and data scientists at the forefront of multi-omic methods development, placing the interdisciplinary MuMi team in an unrivaled position to make novel discoveries in this space and revolutionize the way HM is studied and understood.

项目概要 意义：母乳（HM）已经进化了数百万年，以滋养和保护人类婴儿 - 然而 令人惊讶的是，我们对它的组成、变异和功能知之甚少。传统上，HM 研究的重点是。 HM 是一个由数千个组件组成的复杂生物系统 此外，虽然已知 HM 成分会受到母体的影响， 婴儿和环境因素，人们对这些因素知之甚少，也很少同时进行研究。 针对这些差距，我们的团队正在倡导一种多组学系统生物学方法，将 HM 作为“内部系统”进行研究 一个系统”，反映出牛奶本身就是一个嵌入“母乳-婴儿”三位一体的系统。 方法：这笔赠款将利用并联合两个已建立的 HM 研究平台来研究 HM 及其 使用新颖的多组学方法研究 1600 名母婴二人的决定因素和健康影响。 国际牛奶成分 (IMiC) 联盟是一个由 HM 研究人员和数据科学家组成的网络， 已建立多组学 HM 研究基础设施 CHILD 是一个正在进行的全国妊娠队列，共有 3600 人。 我们的团队已经分析了 2009 年 12 月出生的儿童 HM 样本中的 19 种寡糖、28 种。 我们现在将使用新的多组学 HM 来增强丰富的 CHILD 数据集。 分析（20 种营养素、15 种非营养性生物活性蛋白质和数千种代谢物）并应用 接下来，我们将利用无监督的机器学习方法来识别离散的“乳型”（目标 1）。 儿童数据，用于识别与乳型成员相关的母亲、婴儿和环境因素和/或 最后，我们将使用机器学习方法来理解 HM 组件（目标 2）。 成分影响婴儿期和儿童期微生物群的发育、生长、喘息和过敏 （目标 3）。 创新：CHILD 和 IMiC 平台的集成将促进对 HM 作为一种前所未有的研究 系统中的系统并产生世界上最大、表型最深的母乳婴儿 数据集（n=1600 个三联体，具有多组学牛奶谱和丰富的纵向母婴元数据）。 该项目将联合英国皇家医学专家、著名儿科研究人员和处于最前沿的数据科学家 多组学方法开发，使跨学科 MuMi 团队处于无与伦比的地位 该领域的新发现彻底改变了 HM 的研究和理解方式。