Serum untargeted metabolomic profile of the Dietary Approaches to Stop Hypertension (DASH) dietary pattern.

Pubmed ID: 29917038

Pubmed Central ID: PMC6669331

Journal: The American journal of clinical nutrition

Publication Date: Aug. 1, 2018

Affiliation: Division of General Internal Medicine, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD.

MeSH Terms: Humans, Male, Adult, Female, Aged, Adolescent, Middle Aged, Hypertension, Young Adult, Fruit, Vegetables, Dietary Approaches To Stop Hypertension, Metabolomics

Grants: UL1 TR001079, P30 DK072488, U01 DK085689, K01 DK107782

Authors: Appel LJ, Lichtenstein AH, Coresh J, Rebholz CM, Zheng Z

Cite As: Rebholz CM, Lichtenstein AH, Zheng Z, Appel LJ, Coresh J. Serum untargeted metabolomic profile of the Dietary Approaches to Stop Hypertension (DASH) dietary pattern. Am J Clin Nutr 2018 Aug 1;108(2):243-255.

Studies:

Abstract

BACKGROUND: The Dietary Approaches to Stop Hypertension (DASH) dietary pattern is recommended for cardiovascular disease risk reduction. Assessment of dietary intake has been limited to subjective measures and a few biomarkers from 24-h urine collections. OBJECTIVE: The aim of the study was to use metabolomics to identify serum compounds that are associated with adherence to the DASH dietary pattern. DESIGN: We conducted untargeted metabolomic profiling in serum specimens collected at the end of 8 wk following the DASH diet (n = 110), the fruit and vegetables diet (n = 111), or a control diet (n = 108) in a multicenter, randomized clinical feeding study (n = 329). Multivariable linear regression was used to determine the associations between the randomized diets and individual log-transformed metabolites after adjustment for age, sex, race, education, body mass index, and hypertension. Partial least-squares discriminant analysis (PLS-DA) was used to identify a panel of compounds that discriminated between the dietary patterns. The area under the curve (C statistic) was calculated as the cumulative ability to distinguish between dietary patterns. We accounted for multiple comparisons with the use of the Bonferroni method (0.05 of 818 metabolites = 6.11 × 10-5). RESULTS: Serum concentrations of 44 known metabolites differed significantly between participants randomly assigned to the DASH diet compared with both the control diet and the fruit and vegetables diet, which included an amino acid, 2 cofactors and vitamins (n = 2), and lipids (n = 41). With the use of PLS-DA, component 1 explained 29.4% of the variance and component 2 explained 12.6% of the variance. The 10 most influential metabolites for discriminating between the DASH and control dietary patterns were N-methylproline, stachydrine, tryptophan betaine, theobromine, 7-methylurate, chiro-inositol, 3-methylxanthine, methyl glucopyranoside, β-cryptoxanthin, and 7-methylxanthine (C statistic = 0.986). CONCLUSIONS: An untargeted metabolomic platform identified a broad array of serum metabolites that differed between the DASH diet and 2 other dietary patterns. This newly identified metabolite panel may be used to assess adherence to the DASH dietary pattern. This trial was registered at http://www.clinicaltrials.gov as NCT03403166.