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: 10597958
• 负责人: Meghan Brianne Azad
• 金额: $ 46.53万
• 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/10597958
• 关键词: Address; American; Bacteria; Bifidobacterium; Birth; Blood; Body mass index; Butyrates; Cells; Child; Clinical; Clinical Data; Clinical Nutrition; Cognition; 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.

项目概要 意义：极低出生体重 (VLBW) 婴儿占所有长期神经系统疾病患者的 50% 北美儿童的发病率较高；他们通常生长发育不佳且危及生命 诸如坏死性小肠结肠炎和败血症等疾病现在已被广泛认识到母乳（HM）。 喂养是预防 VLBW 婴儿严重发病的最佳策略，但生长和神经发育 目前的一刀切的喂养方式往往仍然不够理想，人们对这种喂养方式越来越感兴趣。 “精准营养”即将到来，但尚不清楚哪些HM成分需要个性化滴定。 以前的努力主要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必需营养素。 此外，尚不清楚父母是否或如何影响肠道微生物组。 （例如压力、体重指数、饮食）和婴儿因素（例如遗传、肠道微生物群、性别、敏锐度）的影响 早期营养与生长、神经发育和发病率之间的关系。 复杂的关系对于制定有效的 VLBW 个性化 HM 喂养策略至关重要 这是拟议的优化营养和牛奶 (Opti-NuM) 项目的总体目标。 方法：我们将利用由本次资助的 PI 领导的两个已建立的研究平台：1）最大化 母乳 (MaxiMoM) 计划及其新生儿喂养试验网络和 2) 国际 牛奶成分 (IMiC) 联盟将综合营养和临床结合起来。 数据（每日饲料量和成分）和从 MaxiMoM 原始收集的生物样本 (n=1105) 借助 IMiC 联盟的系统生物学和机器学习管道，我们的目标是定义最佳方案。 营养摄入范围（目标 1）和与微生物相关的非营养摄入概况（目标 2） 不同临床亚群的最佳生长和神经发育以及低严重发病风险 此外，我们将探讨婴儿肠道微生物群、婴儿遗传学和健康的作用。 父母压力与早期营养与生长、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、 IMiC 联盟将 HM 作为研究方法的生物库样本和纵向随访数据。 使用复杂的建模和机器学习方法的生物系统正在突破 HM 研究相结合，这些平台提供了无与伦比的机会来破译 HM 如何支持 极低出生体重儿的生长发育，加速新型精准营养的研发 针对这一弱势群体的方法。