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.

总结 美国育龄妇女的饮食质量比过去50年来的任何时候都要差。不良饮食 质量与导致婴儿死亡的不良妊娠结局有关， 巨大的社会负担。然而，关于促进健康的饮食模式的正式建议 妊娠结局不佳。美国国会最近规定，怀孕的饮食建议， 包括在下一版的《美国人膳食指南》中，这是一份主要的营养政策文件， 提供饮食建议以促进健康。包括PI丽莎在内的USDA/HHS妊娠工作组 博德纳尔，负责总结现有的知识，饮食模式，支持健康的怀孕 为具体的妊娠指导方针提供信息。他们确定了一个证据基础， 这一结论不足以得出经验性建议，并呼吁开展研究，填补这一关键的知识空白。 我们的目标是产生经验证据，将告知国家饮食指南的饮食模式 促进健康的怀孕结果。我们假设我们的研究结果将表明饮食 孕妇的营养建议将不同于流行的营养建议。我们预计这一 分歧，因为我们的创新方法将适应食品之间的复杂协同作用， 饮食.使用一个大型的，前瞻性队列的7995名美国妇女参加了8个美国学术中心，我们将 量化饮食模式对不良妊娠结局（早产）风险变化的影响 <37周、小于胎龄儿、妊娠期糖尿病和先兆子痫）。我们将使用机器 学习允许饮食成分之间复杂相互作用的技术。然后，我们将概括 推荐的饮食模式，在我们的样本，以美国人口的孕妇使用先进的 “可移植性”方法在因果推理文献中发展起来。最后，我们将开发机器 学习算法，将确定哪些小组将从饮食模式建议中受益最多。 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局的理想饮食模式的临床数据。我们 创新的方法将作为其他健康领域营养流行病学家的模板， 应用于他们的数据，导致对膳食指南的广泛影响。开发实用的数据驱动 优化妊娠结局的饮食建议将有助于降低高经济和社会成本。 这将有助于减轻不良妊娠结果的负担，并改善母亲及其子女的健康。