From Single Conventional Regression to Ensemble Modeling: Relative Importance of the Healthy Eating Index-2015 Components in Relation to Adverse Pregnancy Outcomes

The Healthy Eating Index (HEI) is widely used to assess diet quality, but certain contexts (e.g., pregnancy) may benefit from tailored versions. We evaluated whether the HEI's current approach of assigning approximately equal weights to all components to compute the total score is appropriate when studying diet quality around conception. Data were from a U.S. prospective cohort of individuals who had not delivered a previous pregnancy past 20 weeks' gestation (2010-2013, n=7882). Usual dietary intake around conception was estimated from food frequency questionnaires. Select adverse pregnancy outcomes (gestational diabetes, preeclampsia, preterm delivery, and small-for-gestational age birth) were abstracted from the medical record. We regressed each outcome on the 13 HEI-2015 component scores using SuperLearner, an ensemble machine learning method that combines predictions from multiple algorithms and avoids relying on parametric assumptions that characterize standard regression. We assessed the relative importance of each component using two permutation-based metrics: change in negative log likelihood (global influence) and absolute difference in the predicted probabilities (individual-level influence). Six of the 13 components (Greens and Beans, Saturated Fats, Total Protein Foods, Seafood and Plant Proteins, Fatty Acids, and Added Sugars) were important according to at least one metric for at least two of the four outcomes. In contrast, the Refined Grains component was not appreciably important for any outcome. These findings suggest that equal weighting of the HEI components may not be appropriate when evaluating diet quality for studies of pregnancy.