Human metabolome associates with dietary intake habits among African Americans in the atherosclerosis risk in communities study

Yan Zheng, Bing Yu, Danny Alexander, Lyn M. Steffen, Eric Boerwinkle

Research output: Contribution to journalArticlepeer-review

48 Scopus citations

Abstract

The human metabolome is a measurable outcome of interactions among an individual's inherited genome, microbiome, and dietary intake. We explored the relationship between dietary intake and serum untargeted metabolomic profiles in a subsample of 1,977 African Americans from the Atherosclerosis Risk in Communities (ARIC) Study in 1987-1989. For each metabolite, we conducted linear regression to estimate its relationships with each food group and food category. Potential confounding factors included age, sex, body mass index (weight (kg)/height (m)2), energy intake, kidney function, and food groups. We used a modified Bonferroni correction to determine statistical significance. In total, 48 pairs of diet-metabolite associations were identified, including multiple novel associations. The food group sugar-rich foods and beverages was inversely associated with 5 metabolites in the 2-hydroxybutyrate-related subpathway and positively associated with 5 γ-glutamyl dipeptides. The hypothesized mechanism of these associations may be through oxidative stress. Sugar-rich foods and beverages were also inversely associated with 7 unsaturated long-chain fatty acids. These findings suggest that the contribution of a sugar-rich dietary pattern to increased cardiovascular disease risk may be partially attributed to oxidative stress and disordered lipid profiles. Metabolomics may reveal novel metabolic biomarkers of dietary intake and provide insight into biochemical pathways underlying nutritional effects on disease development.

Original languageEnglish (US)
Pages (from-to)1424-1433
Number of pages10
JournalAmerican journal of epidemiology
Volume179
Issue number12
DOIs
StatePublished - Jun 15 2014

Keywords

  • African Americans
  • dietary habits
  • metabolomics

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