Biomarkers of individual foods, and separation of diets using untargeted LC-MS-based plasma metabolomics in a randomized controlled trial

Evrim Acar, Gözde Gürdeniz, Bekzod Khakimov, Francesco Savorani, Sanne Kellebjerg Korndal, Thomas Meinert Larsen, Søren Balling Engelsen, Arne Astrup, Lars Ove Dragsted

Research output: Contribution to journalJournal articleResearchpeer-review

34 Citations (Scopus)

Abstract

Scope: Self-reported dietary intake does not represent an objective unbiased assessment. We investigate the effect of the New Nordic Diet (NND) versus Average Danish Diet (ADD) on plasma metabolic profiles to identify biomarkers of compliance and metabolic effects.

Methods and Results: In a 26-week controlled dietary intervention study, 146 subjects followed either NND, a predominantly organic diet high in fruit, vegetables, whole grains, and fish, or ADD, a diet higher in imported and processed foods. Fasting plasma samples were analyzed with untargeted UPLC-QTOF. We demonstrate that supervised machine learning with feature selection can separate NND and ADD samples with an average test set performance of up to 0.88 Area Under the Curve. The NND plasma metabolome was characterized by diet related metabolites such as pipecolic acid betaine (whole grain), trimethylamine oxide and prolyl hydroxyproline (both fish intake) while theobromine (chocolate) and proline betaine (citrus) were associated with ADD. Amino acid (i.e., indolelactic acid and hydroxy-3-methylbutyrate) and fat metabolism (butyryl carnitine) characterized ADD while NND was associated with higher concentrations of polyunsaturated phosphatidylcholines.

Conclusions: The plasma metabolite profiles were predictive of dietary patterns and reflected good compliance while indicating effects of potential health benefit, including changes in fat metabolism and glucose utilization. This article is protected by copyright. All rights reserved.

Original languageEnglish
Article number1800215
JournalMolecular Nutrition & Food Research
Volume63
Issue number1
Number of pages10
ISSN1613-4125
DOIs
Publication statusPublished - 2019

Keywords

  • Faculty of Science
  • Untargeted metabolomics
  • LC-MS
  • Plasma
  • Biomarker patterns
  • Compliance

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