Informing national guidelines on diet patterns that promote healthy pregnancy outcomes

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.

Grant Number: 5R01HD102313-05
NIH Institute/Center: NIH
Principal Investigator: Lisa Bodnar