Genetic and environmental influences on covariation in reproducible diet–metabolite associations. Issue 5 (8th May 2021)

• Record Type: Journal Article
• Authors: Bermingham, Kate M; Brennan, Lorraine; Segurado, Ricardo; Barron, Rebecca E; Gibney, Eileen R; Ryan, Miriam F; Gibney, Michael J; O'Sullivan, Aifric M
• Is Part Of: American journal of clinical nutrition. Volume 113:Issue 5(2021)
• Page Start: 1232
• Page End: 1240
• Publication Date: 2021-05-08
• Subjects: twins -- diet quality -- healthy eating index -- dietary biomarkers -- metabolomics -- urinary metabolomic profile -- metabotype -- genes and environment -- Cholesky modeling; Diet therapy -- Periodicals; Nutrition -- Periodicals; Dietetics -- Periodicals; 613.205
• DOI: 10.1093/ajcn/nqaa378
• Languages: English
• ISSNs: 0002-9165

Abstract

ABSTRACT: Background: Early applications of metabolomics in nutrition and health research identified associations between dietary patterns and metabolomic profiles. Twin studies show that diet-related phenotypes and diet-associated metabolites are influenced by genes. However, studies have not examined whether diet–metabolite associations are explained by genetic or environmental factors and whether these associations are reproducible over multiple time points. Objective: This research aims to examine the genetic and environmental factors influencing covariation in diet–metabolite associations that are reproducible over time in healthy twins. Methods: The UCD Twin Study is a semi-longitudinal classic twin study that collected repeated dietary, anthropometric, and urinary data over 2 months. Correlation analysis identified associations between diet quality measured using the Healthy Eating Index (HEI) and urinary metabolomic profiles at 3 time points. Diet-associated metabolites were examined using linear regression to identify those significantly influenced by familial factors between twins and those significantly influenced by unique factors. Cholesky decomposition modeling quantified the genetic and environmental path coefficients through associated dietary components onto the metabolites. Results: The HEI was associated with 14 urinary metabolites across 3 metabolomic profiles (r: ±0.15–0.49). For 8 diet–metabolite associations, genetic or shared environmental factors influencing HEI component scores significantly influenced variation in metabolites (β: 0.40–0.52). A significant relation was observed between dietary intakes of whole grain and acetoacetate (β: −0.50, P < 0.001) and β-hydroxybutyrate (β: −0.46, P < 0.001), as well as intakes of saturated fat and acetoacetate (β: 0.47, P < 0.001) and β-hydroxybutyrate (β: 0.52, P < 0.001). For these diet–metabolite associations a common shared environmental factor explained 66–69% of variance in the metabolites. Conclusions: This study shows that diet–metabolite associations are reproducible in 3 urinary metabolomic profiles. Components of the HEI covary with metabolites, and covariation is largely due to the shared environment.