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

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 极低出生体重儿（VLBW）占所有长期神经系统疾病的50%。 北美儿童的发病率;他们通常有次优的增长和生命威胁 坏死性小肠结肠炎和败血症等疾病。现在人们普遍认为，母乳（HM） 喂养是预防极低出生体重儿严重发病的最佳策略， 在当前的一刀切的喂养方式下，常常保持次优。人们越来越关注 “精确营养”方法，但目前还不清楚哪些HM组分需要个性化滴定。 以前的努力集中在常量营养素，但HM也含有必需的微量营养素以及非微量营养素。 塑造肠道微生物组的营养生物活性成分。此外，目前还不清楚父母因素是否或如何 (e.g.压力、体重指数、饮食）和婴儿因素（如遗传、肠道微生物群、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 复杂的关系对于为极低出生体重制定有效的个性化人乳喂养策略至关重要 婴儿。这是拟议的优化营养和牛奶（Opti-NuM）项目的总体目标。 方法：我们将利用两个已建立的研究平台，由本资助的PI领导：1）最大化 母乳喂养计划及其新生儿喂养试验网络; 2）国际母乳喂养计划 牛奶成分（IMiC）协会。这种伙伴关系将全面的营养和临床 数据（每日饲料体积和组成）和从MaxiMoM（n=1105）中初步采集的生物标本 IMiC Consortium的系统生物学和机器学习管道。我们的目标是定义最佳 营养素摄入量范围（目标1）和微生物相关的非营养素摄入量概况（目标2） 不同临床亚群中的最佳生长和神经发育以及严重发病率的低风险 HM喂养的极低出生体重婴儿。此外，我们将探讨婴儿肠道微生物群，婴儿遗传学和 父母压力与早期营养和生长、神经发育和发病率之间的关系（目标3）。 创新：MaxiMoM平台在规模、营养数据范围、 生物库样本和纵向随访数据。IMiC联盟研究HM作为一种 使用复杂建模和机器学习方法的生物系统正在推动 HM研究结合起来，这些平台提供了一个无与伦比的机会，破译HM如何支持 VLBW婴儿的生长发育，并加速开发新型精准营养 为这些弱势群体提供帮助。