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.

项目总结