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

• 批准号: 10531705
• 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
• 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/10531705
• 关键词: Address; Affect; Age; Algorithms; Biofeedback; Biological; Biological Clocks; Biology; Biometry; Body mass index; Breast; Breast Feeding; Car Phone; 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; Play; Postpartum Period; Premature Infant; Process; Public Health; Pump; Research; Research Personnel; Resolution; Resources; Role; 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; infant nutrition; infant outcome; insight; inter-individual variation; lifestyle factors; machine learning algorithm; microbiota; milk expression; mother nutrition; multiple omics; novel; parity; sleep pattern; sociodemographics; suckling; supervised learning; tool; virtual; wearable device

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.

人乳（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，并为婴儿的“生物钟”和生理学编程。尽管 由于其非凡的重要性，人们对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作用机制。 婴儿的输入（通过吮吸）如何影响人乳生物学也是未知的，这是一个关键问题，因为几乎所有的哺乳动物都是如此。 普遍的做法，饲料表达（即：泵）HM。因此，我们的过度假设是，HM是一个 具有明显的昼夜和纵向变化的生态系统，受母体固定的影响 和可修改的因素，并由婴儿输入，导致不同的人乳生物动力学取决于是否 婴儿主要是母乳喂养与挤出的人乳喂养。我们的跨学科团队HM研究人员和 计算生物学家将收集每日纵向和昼夜HM样本，并从120个 母亲在产后1-4个月期间专门给健康婴儿喂养人乳：60名母亲主要 喂养ATB（ATB组）和60只仅表达HM的母亲（Express组）。多组学分析 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作为生态系统。这部小说高- 分辨率采样与尖端分析和建模技术相结合，为这些目标提供动力： 1)每日和纵向检测HM成分的时间变化，并比较这些变化 ATB与express组之间的轨迹。时间序列模型和最先进的分析 方法将被用来推断潜在的HM动态和识别时间轨迹，第一次， 确定HM组成的纵向和24小时时间模式。然后我们将确定这些 当婴儿缺乏进入HM系统的“生物反馈”时， ATB vs Express Group 2)确定母亲的HM组成和时间模式的决定因素。除了 社会人口和健康因素，移动的手机应用程序和可穿戴设备将被用来捕捉 关于HM表达模式、母体饮食、压力、体力活动和睡眠模式的颗粒数据。这些 将与HM组分的“进化枝”联系起来，这些组分通过贝叶斯监督随时间共同变化 机器学习算法。该算法将区分母体因素和 ATB组与Express组的HM组成和动力学不同。 这项工作将阐明人乳对婴儿健康和发育的作用机制， ATB喂养或表达HM的婴儿以及依赖于 ”HM表示。这项研究也代表了科学界未来的变革资源。