Chronology, Maternal Determinants, and Impact of Feeding Mode on Human Milk: A Systems Biology and Ecological Approach

时间顺序、母亲决定因素以及喂养模式对母乳的影响：系统生物学和生态学方法

基本信息

批准号：
10701079
负责人：
Bridget Victoria Young
金额：
$ 7.7万
依托单位：
UNIVERSITY OF ROCHESTER
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：
2022-09-08 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10701079
关键词：
Address Affect Age Algorithms Biofeedback Biological Biological Clocks Biology Biometry Body mass index Breast Breast Feeding Caring Characteristics Chronobiology Chronology Collection Communities Complex Computing Methodologies Data Diet Ecosystem Exhibits Exposure to Family Feedback Formulation Future Growth and Development function Guidelines Health Health Insurance Hormones Hour Human Milk Hydrocortisone Immunoglobulins Individual Infant Infant Development Infant Health Insulin Leptin Link Longitudinal cohort Macronutrients Nutrition Metadata Milk Substitutes Modeling Mothers Nutrient Nutritional Oligosaccharides Pattern Phenotype Physical activity Physiology Postpartum Period Premature Infant Process Public Health Pump Recommendation Research Research Personnel Resolution Resources Sampling Series Signal Transduction Sleep Source Stress Structure System Systems Biology Techniques Testing Time Tissues Variant Work breast pump cost cytokine design evidence base experience feeding improved infant nutrition infant outcome insight inter-individual variation lifestyle factors machine learning algorithm microbiota milk expression mother nutrition multiple omics novel parity sleep pattern smartphone application sociodemographics suckling supervised learning tool virtual wearable device

项目摘要

Human milk (HM) is a “live tissue”: its composition changes over time including diurnal fluctuations, answering the evolving needs of the infant and programming infants' “biological clock” and physiology. Despite its extraordinary importance, little is known about the chronobiology and system biology of HM, the maternal factors that impact this biology, and the mechanisms that underlie its contribution to normal infant development. It is also unknown how infant input (via suckling) influences HM biology, a crucial question given the almost universal practice of feeding expressed (ie: pumped) HM. As such, our over-arching hypothesis is that HM is an ecological system that harbors distinct diurnal and longitudinal variation that is impacted by both maternal fixed and modifiable factors, and by infant input, resulting in different HM biological dynamics depending on if the infant is primarily fed at-the-breast vs expressed HM. Our interdisciplinary team of HM researchers and computational biologists will collect daily longitudinal and diurnal HM samples, and extensive metadata from 120 mothers exclusively feeding HM to their healthy infants between 1-4 months postpartum: 60 mothers primarily feeding ATB (ATB group) and 60 mothers exclusively expressing HM (Express group). Multi-omics analytical platforms will be used to characterize HM hormones (insulin, leptin, cortisol), macronutrients, microbiota, oligosaccharides, cytokines, and immunoglobulins to study HM as an ecological system. This novel high- resolution sampling combined with cutting-edge analytical and modeling techniques power these aims: 1) Detect temporal changes in HM composition diurnally and longitudinally and compare these trajectories between ATB vs express groups. Time-series models and state-of-the-art analytic approaches will be utilized to infer latent HM dynamics and identify temporal trajectories, for the first time, identifying longitudinal and 24-hour temporal patterns in HM composition. We will then determine how these trajectories differ when infant “biofeedback” into the HM system is lacking by comparing trajectories between ATB vs Express Groups. 2) Identify maternal determinants of HM composition and temporal patterns. In addition to sociodemographic and health factors, mobile phone apps and wearable devices will be utilized to capture granular data on HM expression patterns, maternal diet, stress, physical activity and sleep patterns. These will be linked with “clades” of HM components that co-vary together over time via Bayesian supervised machine learning algorithms. The algorithm will distinguish if relationships between maternal factors and HM composition and dynamics differs between ATB vs Express groups. This work will elucidate mechanisms underlying HM contribution to infant health and development, allowing for optimized feeding practices for infants fed ATB or expressed HM, and premature infants dependent on expressed HM. This study also represents a transformative future resource for the scientific community.

母乳 (HM) 是一种“活组织”：其成分随时间变化，包括昼夜波动、满足婴儿不断变化的需求并规划婴儿的“生物钟”和生理机能。其非凡的重要性，人们对母体 HM 的时间生物学和系统生物学知之甚少。影响这种生物学的因素，以及其对正常婴儿发育的贡献的机制。我们还不清楚婴儿的输入（通过吸吮）如何影响 HM 生物学，鉴于几乎喂养表达（即：泵送）HM 的普遍做法因此，我们的首要假设是 HM 是一种生态系统具有明显的昼夜和纵向变化，受到母体固定的影响和可修改的因素，以及婴儿的输入，导致不同的 HM 生物动力学，具体取决于是否婴儿主要采用母乳喂养，而不是表达母乳喂养。我们的跨学科母乳研究团队和计算生物学家将收集每日纵向和昼夜 HM 样本，以及来自 120 个国家的广泛元数据产后 1-4 个月内仅用 HM 喂养健康婴儿的母亲：主要有 60 名母亲喂养 ATB（ATB 组）和 60 名仅表达 HM（Express 组）的母亲。平台将用于表征 HM 激素（胰岛素、瘦素、皮质醇）、常量营养素、微生物群、寡糖、细胞因子和免疫球蛋白来研究 HM 作为一个生态系统。分辨率采样与尖端分析和建模技术相结合，可实现以下目标： 1) 检测 HM 成分的日间和纵向变化并进行比较 ATB 与 Express 组之间的轨迹和最先进的分析。方法将首次用于推断潜在的 HM 动态并识别时间轨迹，识别 HM 组成的纵向和 24 小时时间模式，然后我们将确定这些模式。通过比较婴儿与 HM 系统之间的轨迹，当缺乏对 HM 系统的“生物反馈”时，轨迹会有所不同 ATB 与 Express Group。 2) 确定 HM 组成和时间模式的母体决定因素。将利用社会人口统计和健康因素、手机应用程序和可穿戴设备来捕捉有关 HM 表达模式、母亲饮食、压力、体力活动和睡眠模式的详细数据。将与 HM 组件的“分支”联系起来，这些组件通过贝叶斯监督随时间共同变化机器学习算法将区分母亲因素和因素之间的关系。 ATB 组与 Express 组之间的 HM 组成和动态有所不同。这项工作将阐明 HM 对婴儿健康和发育的贡献机制，从而针对喂养 ATB 或表达 HM 的婴儿以及依赖于 HM 表示，这项研究也代表了科学界的变革性未来资源。