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  5. Classifying Individuals Into a Dietary Pattern Based on Metabolomic Data
 
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Classifying Individuals Into a Dietary Pattern Based on Metabolomic Data

Author(s)
Prendiville, Orla  
Walton, Janette  
Flynn, Albert  
Nugent, Anne P.  
McNulty, Breige A.  
Brennan, Lorraine  
Uri
http://hdl.handle.net/10197/13016
Date Issued
2021-06
Date Available
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.
Sponsorship
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
Subjects

Biomarkers

Dietary patterns

Metabolomics

Reproducibility

DOI
10.1002/mnfr.202001183
Language
English
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/
File(s)
No Thumbnail Available
Name

Prendiville et al 2021, final.pdf

Size

754.65 KB

Format

Adobe PDF

Checksum (MD5)

8e73016f02afc8eb693b50431249f6f4

Owning collection
Agriculture and Food Science Research Collection
Mapped collections
Conway Institute Research Collection•
Institute of Food and Health Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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