Abstract

A major problem with dietary assessments is their subjective nature. Untargeted metabolomics and new technologies can shed light on this issue and provide a more complete picture of dietary intake by measuring the profile of metabolites in biological samples. Oranges are one of the most consumed fruits in the world, and therefore one of the most studied for their properties. The aim of this work was the application of untargeted metabolomics approach with the novel combination of ion mobility separation coupled to high resolution mass spectrometry (IMS-HRMS) and study the advantages that this technique can bring to the area of dietary biomarker discovery, with the specific case of biomarkers associated with orange consumption (Citrus reticulata) in plasma samples taken during an acute intervention study (consisting of a randomized, controlled crossover trial in healthy individuals). A total of six markers of acute orange consumption, including betonicines and conjugated flavonoids, were identified with the experimental data and previous literature, demonstrating the advantages of ion mobility in the identification of dietary biomarkers and the benefits that an additional structural descriptor, as the collision cross section value (CCS), can provide in this area.

Highlights

  • One of the main limitations of nutritional epidemiology is the difficulty involved in measuring dietary intake [1]

  • Metabolomics has opened up new opportunities for food intake biomarker discovery through metabolic profiling of biological samples, following the intake of specific foods, meals, or diets [10,11]

  • The main aim of this study was to perform an exploratory untargeted metabolomic study using Ultra-high performance liquid chromatography (UHPLC)-ion mobility separation (IMS)-high resolution accurate mass spectrometry (HRMS) to find out short-term plasma biomarkers of orange consumption, as well as to explore the promising improvements that IMS in combination with LC coupled to high-resolution mass spectrometry (LC-HRMS) could achieve in the untargeted metabolomic field in an acute crossover intervention trial using multivariate analysis (PCA, Partial Least Square–Discriminant Analysis (PLS-DA) and Orthogonal PLS-DA (OPLS-DA)) to highlight the most relevant short-term markers of orange intake

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Summary

Introduction

One of the main limitations of nutritional epidemiology is the difficulty involved in measuring dietary intake [1]. In observational studies carried out on a large number of participants, the most commonly applied tools for estimating dietary intake are mainly based on self-reporting, including food frequency questionnaires (FFQs) for the assessment of regular consumption (usually one-year), or 24-h recalls for one-day assessment. Such methodologies for data collection may contain substantial recall bias and other systematic or random errors that may have a large effect on the estimated food intake and, on subsequent associations between food intake and the diseases studied [2,3].

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