Abstract
Introduction: The Dietary Patterns Methods Project reported higher scores of four selected dietary indices were all inversely associated with mortality risk. Diet quality components are generally weighted equally but previous meta-analyses and NHANES analyses suggest some may be more integral for mortality risk reduction. Thus, we examined if modified weight (vs. standard) Healthy Eating Index (HEI)-2015 scores were differently associated with all-cause and cardiovascular (CVD) mortality risks. Hypothesis: High modified weight (vs. standard) HEI-2015 scores will be associated with stronger mortality risk reduction. Methods: Data from adults (n=156,863) enrolled in the Multiethnic Cohort (MEC) Study recruited between 1993-96 from Hawaii and California with vital status and dietary data were analyzed. Baseline diet quality was assessed with the HEI-2015 using a food frequency questionnaire. Standard component weights of 5 or 10 points on 9 adequacy and 4 moderation components were reweighted creating two modified HEI scores, the Key Facets HEI with equally weighted fruits, vegetables, whole grains, and plant proteins and the Machine Learning (ML)-weighted HEI, calculated using weights reflecting relative component contributions to mortality risk from a prior analysis of NHANES III adults using LASSO models. All 3 HEI scores were assigned to deciles; sex-stratified, adjusted Cox models evaluated associations of HEI deciles with all-cause and CVD mortality risk. Results: Participants represented 5 ethnic groups (24% White, 16% African American, 23% Latino, 29% Japanese American, and 7% Native Hawaiian), and 55.3% were women. The mean age was 59.1 years, mean BMI was 26.4 kg/m 2 , and 27.3% were college graduates. For men and women, high scores (≥90th, vs. <10th percentile) on all HEIs were significantly associated (p<0.05) with 10% to 24% reduced all-cause and CVD mortality risks. Conclusions: Similar mortality risk reductions were observed for standard and modified weight diet quality scores in this sample.
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