Classifying Individuals Into a Dietary Pattern Based on Metabolomic Data

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Title: Classifying Individuals Into a Dietary Pattern Based on Metabolomic Data
Authors: Prendiville, OrlaWalton, JanetteFlynn, AlbertNugent, Anne P.McNulty, Breige A.Brennan, Lorraine
Permanent link: http://hdl.handle.net/10197/13016
Date: Jun-2021
Online since: 2022-07-25T15:47:04Z
Abstract: Scope: The objectives are to develop a metabolomic-based model capable of classifying individuals into dietary patterns and to investigate the reproducibility of the model. Methods and Results: K-means cluster analysis is employed to derive dietary patterns using metabolomic data. Differences across the dietary patterns are examined using nutrient biomarkers. The model is used to assign individuals to a dietary pattern in an independent cohort, A-DIET Confirm (n = 175) at four time points. The stability of participants to a dietary pattern is assessed. Four dietary patterns are derived: moderately unhealthy, convenience, moderately healthy, and prudent. The moderately unhealthy and convenience patterns has lower adherence to the alternative healthy eating index (AHEI) and the alternative mediterranean diet score (AMDS) compared to the moderately healthy and prudent patterns (AHEI = 24.5 and 22.9 vs 26.7 and 28.4, p < 0.001). The dietary patterns are replicated in A-DIET Confirm, with good reproducibility across four time points. The stability of participants’ dietary pattern membership ranged from 25.0% to 61.5%. Conclusion: The multivariate model classifies individuals into dietary patterns based on metabolomic data. In an independent cohort, the model classifies individuals into dietary patterns at multiple time points furthering the potential of such an approach for nutrition research.
Funding Details: European Research Council
Type of material: Journal Article
Publisher: Wiley
Journal: Molecular Nutrition and Food Research
Volume: 65
Issue: 11
Copyright (published version): 2021 the Authors
Keywords: BiomarkersDietary patternsMetabolomicsReproducibility
DOI: 10.1002/mnfr.202001183
Language: en
Status of Item: Peer reviewed
ISSN: 1613-4125
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Conway Institute Research Collection
Institute of Food and Health Research Collection
Agriculture and Food Science Research Collection

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