MOM2CHild Study: Leveraging systems biology toward discoveries in Maternal Obesity, Milk, and Translation To Child Health

MOM2CHild 研究：利用系统生物学发现孕产妇肥胖、乳汁及其对儿童健康的影响

• 批准号: 10532603
• 负责人: ARDYTHE L MORROW
• 金额: $ 78.95万
• 依托单位: UNIVERSITY OF CINCINNATI
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-23 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10532603
• 关键词: Academy; Address; Adolescent; Affect; Age; Aliquot; American; Award; Bar Codes; Biological Markers; Biological Process; Birth; Blood; Body mass index; Breast Feeding; Centers for Disease Control and Prevention (U.S.); Child; Child Health; Collection; Complex; Data; Data Analyses; Data Collection; Data Science; Data Set; Databases; Diet; Discipline; Discipline of obstetrics; Ecology; Enrollment; Environment; Epidemic; Exclusive Breastfeeding; Fatty Acids; Funding; Funding Mechanisms; Funding Opportunities; Goals; Growth; Health; Human Milk; Immunity; Impairment; Infant; Infant Health; Infection; Inflammation; Inflammatory; Investigation; Lactation; Leptin; Life; Machine Learning; Maternal Health; Measures; Metabolic; Metabolic Marker; Milk; Modeling; Mothers; National Institute of Allergy and Infectious Disease; National Institute of Child Health and Human Development; Obesity; Obesity Epidemic; Oligosaccharides; Outcome; Pediatrics; Peptides; Phenotype; Population Heterogeneity; Pregnancy; Pregnant Women; Proteins; Proteomics; Public Health; Publishing; Questionnaires; Records; Reporting; Research; Research Design; Research Personnel; Research Priority; Residual state; Risk; Sampling; Scientist; Serum; Standardization; System; Systems Biology; Techniques; Technology; Testing; Third Pregnancy Trimester; Translations; U-Series Cooperative Agreements; United States Public Health Service; Variant; biological systems; cohort; comorbidity; cryogenics; cytokine; data repository; design; epidemiology study; follow-up; health knowledge; hormone regulation; immunoregulation; infant nutrition; inflammatory marker; maternal obesity; metabolome; metabolomics; microbiome; microbiome analysis; milk production; mother nutrition; multiple omics; next generation; obesity in children; offspring; predictive marker; prenatal; prepregnancy; sample collection; statistical and machine learning; success

PROJECT SUMMARY Breastfeeding is recommended by the U.S. Public Health Service and American Academy of Pediatrics to optimize infant nutrition and health. Breastfeeding initiation is approaching 85% of U.S. mothers, yet significant gaps remain regarding our understanding of human milk and lactation as a biologic system. These gaps undermine our ability to identify influences that may impair breastfeeding or reduce quality of milk. Obesity is an ongoing public health epidemic that affects at least 29% of pregnant women and 19% of children and adolescents. Maternal obesity influences not only pregnancy but also reduces breastfeeding duration and exclusivity. In turn, reduced breastfeeding is associated with greater risk of childhood obesity in most studies, though concerns about residual confounding undermine confidence in these findings. In addition, many small studies have reported that maternal obesity is associated with shifts in milk components (notably, leptin, inflammatory cytokines, fatty acids, oligosaccharides, and peptides) associated child adiposity or obesity. Many of these shifts could be unhealthy and contribute to child adiposity by various means. To convincingly define the impact of maternal obesity and its associated inflammation and co-morbidities on human milk, lactation and child health, a systems approach is needed, drawing on the power of next generation technologies and large cohorts. Here, we propose the MOM2CHild Study, which leverages systems biology towards discoveries in maternal obesity, milk, and translation to child health. MOM2CHild will use data and samples from the PREVAIL and IMPRINT birth cohorts, which are funded under cooperative agreements with CDC and NIAID, respectively. These cohorts enroll Cincinnati mothers in pregnancy and follow children to >2 years. PREVAIL has completed follow-up of 245 mother-infant pairs. IMPRINT will complete enrollment of 1,370 mother-infant pairs by 2023. Both cohorts were designed and enacted by the same team, involve comprehensive questionnaire and health databases and sample collection, including milk and other samples. Standardized human milk collections from study mothers are undertaken at weeks 2 and 6. Neither cohort was originally funded to extensively characterize human milk components, but samples have been carefully collected and banked for that purpose. Under MOM2CHild, we will use the wealth of data and samples from PREVAIL and IMPRINT cohorts, and apply metabolomics, fatty acid profiling, proteomics, glycomics, and microbiome analysis, supported by state-of-the-art statistical and machine learning to: 1) Extensively characterize the impact of maternal obesity, inflammation and metabolic dysregulation on milk composition using a systems biology approach; 2) Identify the impact of maternal obesity, inflammation and metabolic dysregulation on lactation success; and 3) Determine the impact of breastfeeding and variation in human milk composition on child adiposity/obesity, inflammation, and metabolome to age 2. Our team is well-qualified in the scientific domains needed to succeed in our aims, which align with the NICHD BEGIN initiative and RFA-HD-22-020.

项目摘要 美国公共卫生服务局和美国儿科学会建议母乳喂养 优化婴儿营养和健康。母乳喂养开始接近85％的美国母亲，但很重要 关于我们对人类牛奶的理解和作为生物系统的泌乳的理解仍然存在。这些差距 破坏了我们识别可能损害母乳喂养或降低牛奶质量的影响的能力。肥胖是 正在进行的公共卫生流行病，至少影响29％的孕妇和19％的儿童和 青少年。孕产妇肥胖不仅影响怀孕，而且还会减少母乳喂养的持续时间和 排他性。反过 尽管担心残留的混淆破坏了对这些发现的信心。此外，许多小 研究报告说，母性肥胖与牛奶成分的转移有关（尤其是瘦素，， 炎性细胞因子，脂肪酸，寡糖和肽）相关的儿童肥胖或肥胖。许多 这些转变可能是不健康的，并通过各种方式促进了儿童肥胖。令人信服地定义 孕产妇肥胖及其相关的炎症和合并症对人乳，泌乳和 需要儿童健康，需要采用系统方法，借鉴下一代技术和大型技术的力量 同伙。在这里，我们提出了MOM2Child研究，该研究利用系统生物学的发现 孕产妇肥胖，牛奶和儿童健康的翻译。 Mom2Child将使用数据和样本 盛行和烙印出生队列，是根据与CDC和NIAID的合作协议资助的 分别。这些队列在怀孕中招募辛辛那提母亲，并跟随儿童至2岁。盛行 已完成245对母亲的随访。烙印将完成1,370个母亲的入学 到2023年对成对。这两个队列都是由同一团队设计和制定的，涉及全面 问卷和健康数据库和样本收集，包括牛奶和其他样品。标准化 在第2周和第6周进行研究母亲的人乳收集。 资助以广泛的特征为人类乳的成分，但已仔细收集样品，并 为此目的而存放。在Mom2Child的领导下，我们将使用Prevail of Pricail和 烙印队列并应用代谢组学，脂肪酸分析，蛋白质组学，糖基因和微生物组 在最先进的统计和机器学习的支持下，分析的分析：1）广泛表征 使用系统的孕产妇肥胖，炎症和代谢失调对牛奶成分的影响 生物学方法； 2）确定孕产妇肥胖，炎症和代谢失调的影响 哺乳成功； 3）确定母乳组成对母乳和变异的影响 儿童肥胖/肥胖，炎症和代谢组为2岁。我们的团队在科学方面非常合格 在我们的目标中需要取得成功的领域，该目标与NICHD BEGIN INIPATIVE和RFA-HD-22-020保持一致。