4.7 Article

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

期刊

AMERICAN JOURNAL OF CLINICAL NUTRITION
卷 108, 期 2, 页码 243-255

出版社

OXFORD UNIV PRESS
DOI: 10.1093/ajcn/nqy099

关键词

dietary intake; biomarkers; blood pressure; metabolism; metabolomics

资金

  1. National Institute of Diabetes and Digestive and Kidney Diseases [K01 DK107782]
  2. Mid-Atlantic Nutrition and Obesity Research Center (NORC) - National Institute of Diabetes and Digestive and Kidney Diseases [P30 DK072488]
  3. Johns Hopkins Institute for Clinical and Translational Research (ICTR) - National Center for Advancing Translational Sciences (NCATS), a component of the NIH [UL1 TR001079]
  4. NIH Roadmap for Medical Research
  5. Chronic Kidney Disease Biomarkers Consortium - National Institute of Diabetes and Digestive and Kidney Diseases [U01 DK085689]

向作者/读者索取更多资源

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 x 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, beta-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.

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