Use of a Doubly Robust Machine-Learning–Based Approach to Evaluate Body Mass Index as a Modifier of the Association Between Fruit and Vegetable Intake and Preeclampsia

Abstract The Dietary Guidelines for Americans rely on summaries of the effect of dietary pattern on disease risk, independent of other population characteristics. We explored the modifying effect of prepregnancy body mass index (BMI; weight (kg)/height (m)2) on the relationship between fruit and veg...

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Bibliographic Details
Published in:American journal of epidemiology 2022-07, Vol.191 (8), p.1396-1406
Main Authors: Bodnar, Lisa M, Cartus, Abigail R, Kennedy, Edward H, Kirkpatrick, Sharon I, Parisi, Sara M, Himes, Katherine P, Parker, Corette B, Grobman, William A, Simhan, Hyagriv N, Silver, Robert M, Wing, Deborah A, Perry, Samuel, Naimi, Ashley I
Format: Article
Language:English
Subjects:
Body mass
Body Mass Index
Body size
Cohort Studies
Cups
Density
Diet
Dietary intake
Female
Food intake
Fruit
Fruits
Health care facilities
Health risks
Humans
Machine Learning
Obesity
Original Contribution
Population characteristics
Pre-eclampsia
Pre-Eclampsia - epidemiology
Preeclampsia
Pregnancy
Robustness
Vegetables
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Summary:Abstract The Dietary Guidelines for Americans rely on summaries of the effect of dietary pattern on disease risk, independent of other population characteristics. We explored the modifying effect of prepregnancy body mass index (BMI; weight (kg)/height (m)2) on the relationship between fruit and vegetable density (cup-equivalents/1,000 kcal) and preeclampsia using data from a pregnancy cohort study conducted at 8 US medical centers (n = 9,412; 2010–2013). Usual daily periconceptional intake of total fruits and total vegetables was estimated from a food frequency questionnaire. We quantified the effects of diets with a high density of fruits (≥1.2 cups/1,000 kcal/day vs.
ISSN:0002-9262
1476-6256
DOI:10.1093/aje/kwac062