Improving growth and neurodevelopment of very low birth weight infants through precision nutrition: The Optimizing Nutrition and Milk (Opti-NuM) Project.

通过精准营养改善极低出生体重婴儿的生长和神经发育：优化营养和牛奶 (Opti-NuM) 项目。

• 批准号: 10708940
• 负责人: Meghan Brianne Azad
• 金额: $ 45.48万
• 依托单位: HOSPITAL FOR SICK CHLDRN (TORONTO)
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-22 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10708940
• 关键词: Acceleration; Address; American; Bacteria; Bifidobacterium; Birth; Blood; Body mass index; Butyrates; Cells; Child; Clinical; Clinical Data; Clinical Nutrition; Cognition; Collaborations; Complex; Consumption; Country; DNA; Data; Data Scientist; Data Set; Databases; Development; Diet; Doctor of Philosophy; Enterocolitis; Feces; Genetic; Goals; Grant; Growth; Growth Factor; Growth and Development function; Health; Hospitalization; Hour; Human Milk; Hydrocortisone; Immunologic Factors; Infant; Infant Health; Intake; International; Life; Machine Learning; Macronutrients Nutrition; Measures; Micronutrients; Milk; Milk Substitutes; Modeling; Morbidity - disease rate; Neonatal; Neurologic; Nutrient; Nutritional; Oligosaccharides; Outcome; Parents; Plasma; Population; Protocols documentation; Questionnaires; Research; Research Personnel; Resources; Risk; Role; Sampling; Sepsis; Shapes; Single Nucleotide Polymorphism; Source; Stress; Subgroup; Systems Biology; Technology; Time; Titrations; Very Low Birth Weight Infant; Vulnerable Populations; Weight; adverse outcome; biobank; biological systems; donor milk; enteric pathogen; evidence base; feeding; follow-up; gut microbiome; gut microbiota; high risk infant; improved; innovation; interest; machine learning method; machine learning pipeline; microbial; microbial composition; microbiota; multidisciplinary; multiple omics; neurodevelopment; novel; nutrition; polygenic risk score; precision nutrition; predictive modeling; prevent; programs; sex; Acceleration; Address; American; Bacteria; Bifidobacterium; Birth; Blood; Body mass index; Butyrates; Cells; Child; Clinical; Clinical Data; Clinical Nutrition; Cognition; Collaborations; Complex; Consumption; Country; DNA; Data; Data Scientist; Data Set; Databases; Development; Diet; Doctor of Philosophy; Enterocolitis; Feces; Genetic; Goals; Grant; Growth; Growth Factor; Growth and Development function; Health; Hospitalization; Hour; Human Milk; Hydrocortisone; Immunologic Factors; Infant; Infant Health; Intake; International; Life; Machine Learning; Macronutrients Nutrition; Measures; Micronutrients; Milk; Milk Substitutes; Modeling; Morbidity - disease rate; Neonatal; Neurologic; Nutrient; Nutritional; Oligosaccharides; Outcome; Parents; Plasma; Population; Protocols documentation; Questionnaires; Research; Research Personnel; Resources; Risk; Role; Sampling; Sepsis; Shapes; Single Nucleotide Polymorphism; Source; Stress; Subgroup; Systems Biology; Technology; Time; Titrations; Very Low Birth Weight Infant; Vulnerable Populations; Weight; adverse outcome; biobank; biological systems; donor milk; enteric pathogen; evidence base; feeding; follow-up; gut microbiome; gut microbiota; high risk infant; improved; innovation; interest; machine learning method; machine learning pipeline; microbial; microbial composition; microbiota; multidisciplinary; multiple omics; neurodevelopment; novel; nutrition; polygenic risk score; precision nutrition; predictive modeling; prevent; programs; sex

PROJECT SUMMARY Significance: Infants born of very low birth weight (VLBW) account for 50% of all long-term neurological morbidity among North American children; they commonly have sub-optimal growth and life threatening morbidities such as necrotising enterocolitis and sepsis. It is now widely recognized that human milk (HM) feeding is the best strategy to prevent serious morbidity in VLBW infants, yet growth and neurodevelopment often remain sub-optimal with current one-size-fits-all feeding regimes. There is increasing interest in “precision nutrition” approaches, but it is unclear which HM components require personalized titration. Previous efforts have focused on macronutrients, but HM also contains essential micronutrients as well as non- nutrient bioactive components that shape the gut microbiome. Further, it is unclear if or how parental factors (e.g. stress, body mass index, diet) and infant factors (e.g. genetics, gut microbiota, sex, acuity) influence relationships between early nutrition and growth, neurodevelopment and morbidity. Understanding these complex relationships is paramount to developing effective personalized HM feeding strategies for VLBW infants. This is the overarching goal of the proposed Optimizing Nutrition and Milk (Opti-NuM) Project. Approach: We will leverage two established research platforms led by PIs of this grant: 1) the Maximizing Mother’s Milk (MaxiMoM) Program with its neonatal feeding trial network and 2) the International Milk Composition (IMiC) Consortium. This partnership unites the comprehensive nutrition and clinical data (daily feed volumes and composition) and pristinely collected biospecimens from MaxiMoM (n=1105) with the systems biology and machine learning pipelines from IMiC Consortium. We aim to define optimal nutrient intake ranges (Aim 1) and microbially-relevant non-nutrient intake profiles (Aim 2) associated with optimal growth and neurodevelopment and low risk of serious morbidity in different clinical sub-populations of HM-fed VLBW infants. Additionally, we will explore the role of infant gut microbiota, infant genetics and parent stress in associations between early nutrition and growth, neurodevelopment and morbidity (Aim 3). Innovation: The MaxiMoM platform is unique in the world in terms of size, scope of nutritional data, biobanked samples and longitudinal follow up data. The IMiC Consortium approach to studying HM as a biological system using sophisticated modelling and machine learning approaches is pushing the boundaries of HM research. Combined, these platforms offer an unparalleled opportunity to decipher how HM supports the growth and development of VLBW infants, and to accelerate the development of novel precision nutrition approaches for this vulnerable population.

项目摘要 意义：出生体重（VLBW）出生的婴儿占所有长期神经功能的50％ 北美儿童的发病率；他们通常具有次优的增长和威胁生命 坏死性小肠结肠炎和败血症等病态。现在已广泛认识到人牛奶（HM） 喂养是预防VLBW婴儿严重发病率的最佳策略，但生长和神经发育 通常与当前的一定尺寸的喂养方案保持不最佳状态。对 “精确营养”的方法，但尚不清楚哪种HM组件需要个性化滴定。 以前的努力集中在大量营养素上，但HM还包含必需的微量营养素以及非 - 塑造肠道微生物组的营养生物活性成分。此外，目前尚不清楚父母的因素是如何或如何 （例如，压力，体重指数，饮食）和婴儿因素（例如遗传学，肠道菌群，性别，敏锐度）影响 早期营养与生长，神经发育和发病率之间的关系。了解这些 复杂的关系对于为VLBW制定有效的个性化HM喂养策略至关重要 婴儿。这是提议优化营养和牛奶（Opti-num）项目的总体目标。 方法：我们将利用由这笔赠款的PI领导的两个既定的研究平台：1）最大化 母亲的牛奶（Maximom）计划及其新生儿喂养试验网络和2）国际 牛奶成分（IMIC）财团。该伙伴关系将全面的营养和临床团结在一起 数据（每日饲料量和组成）和最大值收集的生物测量（n = 1105） 使用系统生物学和机器学习管道，来自IMIC财团。我们的目标是定义最佳 营养摄入范围（AIM 1）和与微生物相关的非营养摄入率（AIM 2） 在不同临床亚群中，最佳生长和神经发育以及严重发病率的低风险 HM喂VLBW婴儿的。此外，我们将探讨婴儿肠道菌群，婴儿遗传学和 父母早期营养与生长，神经发育和发病率之间的关联中的父母压力（AIM 3）。 创新：最大值平台在世界上是独一无二的，就营养数据的范围而言， 生物循环的样品和纵向后续数据。 IMIC财团的方法研究HM 使用复杂的建模和机器学习方法的生物系统正在推动 HM研究。这些平台结合在一起，提供了一个无与伦比的机会来破译HM如何支持 VLBW婴儿的生长和发育，并加快新颖的精确营养的发展 对这个脆弱人群的方法。