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.

项目概要 美国公共卫生服务部和美国儿科学会建议母乳喂养 优化婴儿营养和健康。接近 85% 的美国母亲开始母乳喂养，但这一比例仍显着 我们对母乳和哺乳作为一个生物系统的理解仍然存在差距。这些差距 削弱我们识别可能损害母乳喂养或降低乳汁质量的影响的能力。肥胖是 持续存在的公共卫生流行病影响了至少 29% 的孕妇和 19% 的儿童 青少年。产妇肥胖不仅影响妊娠，还会缩短母乳喂养时间， 排他性。反过来，在大多数研究中，母乳喂养的减少与儿童肥胖的风险增加有关， 尽管对残留混杂因素的担忧削弱了人们对这些发现的信心。此外，还有很多小 研究报告称，母亲肥胖与乳汁成分（尤其是瘦素、 炎症细胞因子、脂肪酸、寡糖和肽）与儿童肥胖相关。许多 这些转变可能是不健康的，并通过多种方式导致儿童肥胖。令人信服地定义 母亲肥胖及其相关炎症和合并症对母乳、哺乳期和哺乳期的影响 儿童健康，需要一种系统方法，利用下一代技术和大型 队列。在这里，我们提出了 MOM2CHild 研究，该研究利用系统生物学来发现 孕产妇肥胖、牛奶以及对儿童健康的影响。 MOM2CHild 将使用来自 PREVAIL 和 IMPRINT 出生队列由与 CDC 和 NIAID 的合作协议资助， 分别。这些队列招募了辛辛那提怀孕期的母亲，并跟踪孩子至 2 岁以上。胜出 已完成245对母婴的随访。 IMPRINT将完成1,370名母婴的登记 到 2023 年配对。这两个队列都是由同一团队设计和制定的，涉及全面的 调查问卷和健康数据库以及样本收集，包括牛奶和其他样本。标准化 在第 2 周和第 6 周时从研究母亲那里采集母乳。这两个队列最初都不是 资助广泛表征母乳成分，但样品已被仔细收集和 为此目的存入银行。在 MOM2CHild 下，我们将使用来自 PREVAIL 和 IMPRINT 队列，并应用代谢组学、脂肪酸分析、蛋白质组学、糖组学和微生物组 分析，由最先进的统计和机器学习支持：1）广泛表征 使用系统研究母亲肥胖、炎症和代谢失调对乳汁成分的影响 生物学方法； 2) 确定产妇肥胖、炎症和代谢失调对产妇的影响 哺乳成功； 3) 确定母乳喂养和母乳成分变化对 儿童肥胖/肥胖、炎症和代谢组学到 2 岁。我们的团队在科学领域拥有丰富的资质 成功实现我们目标所需的领域，这与 NICHD BEGIN 倡议和 RFA-HD-22-020 一致。