Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Sociodemographic and Genetic Influences on Dietary Patterns and Their Influence on Health Outcomes in the Atlanta Center for Health Discovery and Well Being Cohort

Version 1 : Received: 6 July 2020 / Approved: 7 July 2020 / Online: 7 July 2020 (02:36:11 CEST)

How to cite: Chen, J.; Huang, X.; Ziegler, T.R.; Jones, D.P.; Lampl, M.; Quyyumi, A.A.; Clark, J.; Martin, G.S.; Brigham, K.L.; Gibson, G. Sociodemographic and Genetic Influences on Dietary Patterns and Their Influence on Health Outcomes in the Atlanta Center for Health Discovery and Well Being Cohort. Preprints 2020, 2020070106 (doi: 10.20944/preprints202007.0106.v1). Chen, J.; Huang, X.; Ziegler, T.R.; Jones, D.P.; Lampl, M.; Quyyumi, A.A.; Clark, J.; Martin, G.S.; Brigham, K.L.; Gibson, G. Sociodemographic and Genetic Influences on Dietary Patterns and Their Influence on Health Outcomes in the Atlanta Center for Health Discovery and Well Being Cohort. Preprints 2020, 2020070106 (doi: 10.20944/preprints202007.0106.v1).

Abstract

Diet influences, and is influenced by, a wide range of socioeconomic, cultural, geographic, and genetic variables. Here we survey a matrix of such interactions as well as their connection to a variety of health outcomes, in a cohort of 689 diverse adults employed at Emory University and enrolled in the Center for Health Discovery and Well-Being (CHDWB) study. Principal component analysis (PCA) of the Block Food Frequency Questionnaire revealed seven PC cumulatively explaining 25.8% and each individually at least 2% of the proportional consumption of 110 food items. PC1 is strongly correlated with the Healthy Eating Index-2015 measure, and accordingly healthier scores associate with multiple measures of physical and mental health. It, as well as PC2 (likely a measure of food expense) and PC3 (carbohydrate versus protein consumption) show significant geographic structure across the Atlanta metropolitan area, correlating with race and ethnicity, income level, age and sex. Notably, a polygenic score for body mass index (BMI) consisting of 281 SNPs explains 2.8% of the variance in PC5, which is as strong as its association with BMI itself. PC5 appears to differentiate participants with respect to conscious eating behavior related to the choice of diet or comfort foods. Our analysis adds to the growing literature on factor analysis of socio-demographic influences on nutrition and health.

Subject Areas

polygenic risk; wellness; food frequency; principal component analysis; healthy eating index; obesity; food desert

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