ArticleAmerican journal of epidemiology2022
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.
Article in American journal of epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 14 citations in OpenAlex.
- Association between a Periconception Healthy Plant-Based Diet and Adverse Pregnancy Outcomes.The Journal of nutrition · 2026Article
- From single conventional regression to ensemble modelling: relative importance of the Healthy Eating Index-2015 components in relation to adverse pregnancy outcomes.The British journal of nutrition · 2026Article
- Pulling back the curtain: the road from statistical estimand to machine-learning-based estimator for epidemiologists (no wizard required).American journal of epidemiology · 2025Article
- Heterogeneity in the association between a dietary pattern high in fat, sugar, and sodium and adverse pregnancy outcomes by maternal characteristics: a United States pregnancy cohort study.The American journal of clinical nutrition · 2025Article
- Survey of practices of handling exposure measurement errors in modern epidemiology: are the best practices in statistics being adopted by epidemiologists?BMC medical research methodology · 2025Article
- The effect of maternal pre-pregnancy body mass index on hypertensive disorders of pregnancy (HDP): a systematic review and dose-response meta-analysis of cohort studies involving 50 million pregnancies.EClinicalMedicine · 2025Article
- Association of Periconceptional Dietary Inflammatory Index and Hypertensive Disorders of Pregnancy and Its Subtype.American journal of reproductive immunology (New York, N.Y. : 1989) · 2025Observational
- Preeclampsia prediction via machine learning: a systematic literature review.Health systems (Basingstoke, England) · 2025Review
- Does heterogeneity underlie differences in treatment effects estimated from SuperLearner versus logistic regression? An application in nutritional epidemiology.Annals of epidemiology · 2023Article
- Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences.Science advances · 2022Review
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Authors and funding
13 authors.
Funding
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. <1.2 cups/1,000 kcal/day) and vegetables (≥1.3 cups/1,000 kcal/day vs. <1.3 cups/1,000 kcal/day) on preeclampsia risk, conditional on BMI, using a doubly robust estimator implemented in 2 stages. We found that the protective association of higher fruit density declined approximately linearly from a BMI of 20 to a BMI of 32, by 0.25 cases per 100 women per each BMI unit, and then flattened. The protective association of higher vegetable density strengthened in a linear fashion, by 0.3 cases per 100 women for every unit increase in BMI, up to a BMI of 30, where it plateaued. Dietary patterns with a high periconceptional density of fruits and vegetables appear more protective against preeclampsia for women with higher BMI than for leaner women.
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