Serum Metabolites Associated with Healthy Dietary Patterns in Middle-Aged US Adults. (29th May 2020)

• Record Type: Journal Article
• Authors: Kim, Hyunju; Hu, Emily; Yu, Bing; Steffen, Lyn; Seidelmann, Sara; Boerwinkle, Eric; Coresh, Josef; Rebholz, Casey
• Is Part Of: Current developments in nutrition. Volume 4(2020)Supplement 2
• Page Start: 535
• Page End: 535
• Publication Date: 2020-05-29
• Subjects: Nutrition -- Periodicals; Nutritional Physiological Phenomena; Nutrition; Periodicals; Periodicals; Fulltext; Internet Resources; Periodicals; 612.3
• DOI: 10.1093/cdn/nzaa046_035
• Languages: English
• ISSNs: 2475-2991

Abstract

Abstract: Objectives: Healthy dietary patterns are recommended for health promotion. Metabolomics can be used to identify objective biomarkers of healthy dietary patterns, which has the potential to improve dietary assessment. We used metabolomics to identify serum metabolites associated with healthy dietary patterns and the components within these dietary patterns in middle-aged US adults. Methods: We evaluated known metabolites associated with 4 dietary patterns [Healthy Eating Index (HEI)-2015, Alternative Healthy Eating Index (AHEI)-2010, the Dietary Approaches to Stop Hypertension (DASH) diet, Mediterranean diet (aMED)] and their components using untargeted metabolomics in two subsamples (N1 = 1864; N2 = 2091) of the Atherosclerosis Risk in Communities Study. Dietary intakes were assessed using a food frequency questionnaire. We used multivariable linear regression models to examine associations between dietary patterns and individual serum metabolites in each sample, adjusting for sociodemographic factors, health behaviors, and clinical factors. Results: 21 out of 373 metabolites (HEI = 10; AHEI = 9; DASH = 15; aMED = 2) in sample 1 and 57 out of 758 metabolites (HEI = 32; AHEI = 22; DASH = 44; aMED = 22) in sample 2 were significantly associated with healthy dietary patterns after Bonferroni correction. More than half of the significant metabolites (n1 = 10; n2 = 35) were associated with more than one dietary pattern. The DASH diet had the highest number of unique metabolites (n1 = 7; n2 = 17), a majority of which were amino acids. Other diets had similar number of unique metabolites (range: 0–3), which were mostly lipids. Some of the unique metabolites were positively associated with components of every diet. For example, N-methylproline was associated with fruit and dairy intake in the DASH diet; docosahexaenoate (22:6n3) was associated with omega-3 fatty acid intake in AHEI, and 1-docosahexaenoylglycerophosphoethanolamine was associated with plant protein and saturated fat intake in HEI. Conclusions: An untargeted metabolomics approach identified many metabolites associated with healthy dietary patterns. A considerable overlap of metabolites associated with HEI, AHEI, DASH, and aMED reflects the similar food components within healthy diets. Funding Sources: NIDDK, NHLBI.