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: 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
• 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

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.

人牛奶（HM）是一种“活组织”：其组成随时间变化，包括昼夜波动， 满足婴儿和编程婴儿的“生物钟”和生理学的不断发展的需求。尽管 它的重要性非常重要，对HM的年代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贡献的机制。 婴儿的输入（通过哺乳）如何影响HM生物学，这也是未知的，鉴于几乎是一个至关重要的问题 表达的饲料的普遍实践（即：泵送）HM。因此，我们的整理假设是HM是 生态系统具有不同的昼夜和纵向变化，这两种主要固定都受到影响 和可修改的因素以及婴儿输入，导致不同的HM生物学动力学，具体取决于是否是否 婴儿主要在表达的HM上喂养。我们的HM研究人员跨学科团队和 计算生物学家将收集每日纵向和昼夜HM样品，以及120的广泛元数据 母亲在产后1-4个月之间专门为健康婴儿喂HM：60个母亲主要 喂养ATB（ATB组）和60位母亲专门表达HM（Express Group）。多态分析 平台将用于表征HM马（胰岛素，瘦素，皮质醇），大量营养素，微生物群， 寡糖，细胞因子和免疫球蛋白作为生态系统研究HM。这个小说高级 分辨率抽样结合了最先进的分析和建模技术，这些目的是： 1）奇怪和纵向检测HM组成的暂时变化，并比较这些 ATB与Express组之间的轨迹。时间序列模型和最先进的分析 方法将用于推断潜在的HM动力学并首次识别临时轨迹 识别HM组成中的纵向和24小时临时模式。然后，我们将确定这些 通过比较之间的轨迹 ATB与Express组。 2）确定HM组成和临时模式的产妇决定剂。此外 社会人口统计学和健康因素，手机应用程序和可穿戴设备将用于捕获 有关HM表达模式，孕产妇饮食，压力，体育锻炼和睡眠模式的颗粒数据。这些 将通过贝叶斯的监督随着时间的流逝而与HM组件的“进化枝”联系在一起 机器学习算法。该算法将区分母校因素和 HM组成和ATB与快递组之间的动态差异。 这项工作将阐明HM对婴儿健康和发育的贡献的基础机制，从而允许 针对喂养ATB或表达的HM的婴儿的优化喂养习惯，并取决于早产儿 表示HM。这项研究还代表了科学界的变革性未来资源。