Informing national guidelines on diet patterns that promote healthy pregnancy outcomes

通报有关促进健康妊娠结局的饮食模式的国家指南

• 批准号: 10455712
• 负责人: Lisa M Bodnar
• 金额: $ 59.72万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-07 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10455712
• 关键词: Accounting; Advisory Committees; American; Area; Birth; Characteristics; Charge; Child; Complex; Conceptions; Congresses; Consumption; Data; Diet; Dietary Component; Dietary Fiber; Dietary Practices; Dietary intake; Economics; Enrollment; Epidemiologist; Female of child bearing age; Fetal Growth Retardation; Food; Frequencies; General Population; Gestational Diabetes; Guidelines; Health; Health Insurance; Health Promotion; Heterogeneity; Incentives; Infant Mortality; Knowledge; Link; Literature; Machine Learning; Maternal and Child Health; Medical; Methodology; Methods; Modeling; Monitor; Mothers; Nulliparity; Nutrition Policy; Nutritional; Nutritional Requirements; Nutritional Support; Nutritional status; Oils; Outcome; Outcome Study; Pattern; Planned Pregnancy; Policies; Population; Pre-Eclampsia; Pregnancy; Pregnancy Outcome; Pregnant Women; Premature Birth; Prospective cohort; Prospective cohort study; Public Health; Questionnaires; Recommendation; Research; Risk; Risk Factors; Sampling; Small for Gestational Age Infant; Special Supplemental Nutrition Program for Women, Infants, and Children; Specific qualifier value; Subgroup; Techniques; Time; Unhealthy Diet; United States Department of Agriculture; Variant; Weight; Woman; Work; adverse pregnancy outcome; child bearing; cohort; dietary; dietary control; dietary guidelines; disorder risk; evidence base; flexibility; food environment; fruits and vegetables; health of the mother; healthy pregnancy; improved; infant death; innovation; lifestyle intervention; machine learning algorithm; machine learning method; modifiable risk; nutrition; nutrition education; pregnant; programs; synergism; working group

SUMMARY The diet quality of U.S. childbearing aged women is worse now than any time in the last 50 years. Poor diet quality has been linked with adverse pregnancy outcomes that contribute to infant mortality and pose a tremendous societal burden. Nevertheless, formal recommendations on the diet patterns that promote healthy pregnancy outcomes are lacking. The US Congress recently mandated that dietary advice for pregnancy be included in the next edition of the Dietary Guidelines for Americans—the major nutrition policy document that provides dietary advice for health promotion. The USDA/HHS Pregnancy Work Group, which included PI Lisa Bodnar, was charged with summarizing existing knowledge on diet patterns that support healthy pregnancy outcomes to inform the pregnancy-specific guidelines. They identified an evidence base that was entirely insufficient for deriving empirical recommendations and called for research to fill this critical knowledge gap. Our objective is to generate empirical evidence that will inform national dietary guidance on the diet patterns that promote healthy pregnancy outcomes. We hypothesize that our results will suggest dietary recommendations for pregnant women that will diverge from prevailing nutrition advice. We expect this divergence because our innovative approaches will accommodate the complex synergy among foods in the diet. Using a large, prospective cohort of 7995 U.S. women enrolled at 8 U.S. academic centers, we will quantify the contribution of dietary patterns to variation in risk of adverse pregnancy outcomes (preterm birth <37 weeks, small-for-gestational-age birth, gestational diabetes, and preeclampsia). We will use machine learning techniques that allow for complex interactions among dietary components. Then, we will generalize recommended dietary patterns in our sample to the U.S. population of pregnant women using cutting edge “transportability” methods developed in the causal inference literature. Finally, we will develop machine learning algorithms that will identify subgroups who will benefit most from dietary pattern recommendations. The successful completion of this project will provide the Dietary Guidelines Scientific Advisory Committee with empirically-derived data on the ideal dietary patterns for promoting healthy pregnancy outcomes. Our innovative methodologies will serve as a template for nutritional epidemiologists in other areas of health to apply to their data, leading to a broad impact on the Dietary Guidelines. Developing practical data-driven dietary recommendations to optimize pregnancy outcomes will help to reduce the high economic and societal burden of adverse pregnancy outcomes and improve the health of mothers and their children.

概括 美国育龄妇女的饮食质量现在比过去 50 年来的任何时候都要差。 质量与不良妊娠结局有关，不良妊娠结局会导致婴儿死亡率并造成 然而，关于促进健康的饮食模式的正式建议。 美国国会最近规定，怀孕期间的饮食建议必须是完整的。 包含在下一版《美国人膳食指南》中——这是一份重要的营养政策文件， 美国农业部/美国卫生与公众服务部怀孕工作组（其中包括 PI Lisa）提供饮食建议。 博德纳尔负责总结支持健康怀孕的饮食模式的现有知识 他们确定了一个完全基于妊娠结果的证据基础。 不足以得出实证建议，并呼吁进行研究来填补这一关键的知识空白。 我们的目标是生成经验证据，为国家膳食指南提供有关饮食模式的信息 我们勇敢地说，我们的结果将建议饮食。 我们预计，针对孕妇的建议将与现行的营养建议有所不同。 分歧是因为我们的创新方法将适应食品之间复杂的协同作用 通过对在 8 个美国学术中心注册的 7995 名美国女性进行的大型前瞻性研究，我们将 量化饮食模式对不良妊娠结局（早产）风险变化的贡献 <37周、小于胎龄儿、妊娠期糖尿病、先兆子痫）我们将使用机器。 然后，我们将概括允许饮食成分之间复杂相互作用的学习技术。 我们的样本中使用尖端技术向美国孕妇推荐的饮食模式 因果推理文献中开发的“可移植性”方法最后，我们将开发机器。 学习算法将识别将从饮食模式建议中受益最多的亚组。 该项目的成功完成将为膳食指南科学咨询委员会提供 关于促进健康妊娠结果的理想饮食模式的经验数据。 创新方法将作为其他健康领域营养流行病学家的模板 应用他们的数据，对制定实用的数据驱动的膳食指南产生广泛的影响。 优化妊娠结局的饮食建议将有助于减少高经济和社会负担 减轻不良妊娠结局的负担并改善母亲及其子女的健康。