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

项目摘要 美国公共卫生署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，但意义重大 我们对人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 一种持续的公共卫生流行病，影响至少29%的孕妇和19%的儿童， 青少年。母亲肥胖不仅影响怀孕，而且减少母乳喂养时间， 排他性反过来，在大多数研究中，减少母乳喂养与儿童肥胖的风险增加有关， 尽管对残余混杂的担忧削弱了对这些发现的信心。此外，许多小 研究已经报道母亲肥胖与乳汁成分（特别是，瘦素， 炎性细胞因子、脂肪酸、寡糖和肽）与儿童肥胖症或肥胖症相关。许多 这些变化可能是不健康的，并通过各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、泌乳和 儿童健康，需要一个系统的方法，利用下一代技术的力量， 同伙在这里，我们提出了MOM 2CHild研究，该研究利用系统生物学来发现 产妇肥胖，牛奶，并转化为儿童健康。MOM 2CHild将使用来自 PREVAIL和IMPRINT出生队列，根据与CDC和NIAID的合作协议提供资金， 分别这些队列招募了辛辛那提的怀孕母亲，并对儿童进行了2年以上的随访。为准 已完成245对母婴的随访。IMPRINT将完成1,370例母婴入组 到2023年双。两个队列都是由同一个团队设计和制定的，涉及全面的 调查表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周从研究母亲收集人乳。这两个队列最初都不是 资助，以广泛表征母乳成分，但样本已仔细收集， 银行为此目的。在MOM 2CHild下，我们将使用PREVAIL和 IMPRINT队列，并应用代谢组学、脂肪酸分析、蛋白质组学、糖组学和微生物组学 分析，由最先进的统计和机器学习支持，以：1）广泛表征 使用系统研究母体肥胖、炎症和代谢失调对乳汁成分影响 生物学方法; 2）确定母体肥胖、炎症和代谢失调对 哺乳成功率; 3）确定母乳喂养和母乳成分变化对 儿童肥胖症/肥胖症、炎症和代谢组。我们的团队在科学方面很有资格 我们的目标与NICHD开始倡议和RFA-HD-22-020保持一致。

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.