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Unveiling Dietary Complexity: A Scoping Review and Reporting Guidance for Network Analysis in Dietary Pattern Research

Rebecca M. J. Taylor, Jack A. Moore, Amy R. Griffiths, Alecia Cousins Orcid Logo, Hayley Young Orcid Logo

Nutrients, Volume: 17, Issue: 20, Start page: 3261

Swansea University Authors: Alecia Cousins Orcid Logo, Hayley Young Orcid Logo

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DOI (Published version): 10.3390/nu17203261

Abstract

Background/Objectives: Dietary patterns play a crucial role in health, yet most research examines foods individually, overlooking how they interact. This approach provides an incomplete picture of how diet influences health outcomes. Network analysis (e.g., Gaussian graphical models, mutual informat...

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Published in: Nutrients
ISSN: 2072-6643
Published: MDPI AG 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa70756
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This approach provides an incomplete picture of how diet influences health outcomes. Network analysis (e.g., Gaussian graphical models, mutual information networks, mixed graphical models) offers a more comprehensive way to study food co-consumption by capturing complex relationships between dietary components. However, while researchers have applied various network algorithms to explore food co-consumption, inconsistencies in methodology, incorrect application of algorithms, and varying results have made interpretation challenging. The objectives of this scoping review were to systematically map and synthesise studies that have applied network analysis to dietary data, and to establish guiding principles for future research in this area. Methods: Using PRISMA-ScR criteria, our scoping review identified 171 articles published from inception up to 7 March 2025, of which 18 studies met the inclusion criteria. Results: Gaussian graphical models were the most frequent approach, used in 61% of studies, and were often paired with regularisation techniques (e.g., graphical LASSO) to improve clarity (93%). The analysis revealed significant methodological challenges across the literature: 72% of studies employed centrality metrics without acknowledging their limitations, there was an overreliance on cross-sectional data limiting the ability to determine cause and effect, and difficulties in handling non-normal data. While most studies using GGM addressed the issue of non-normal data, either by using the nonparametric extension, Semiparametric Gaussian copula graphical model (SGCGM), or log-transforming the data, 36% did nothing to manage their non-normal data. Conclusions: To improve the reliability of network analysis in dietary research, this review proposes five guiding principles: model justification, design&#x2013;question alignment, transparent estimation, cautious metric interpretation, and robust handling of non-normal data. To facilitate their adoption, a CONSORT-style checklist is introduced&#x2014;the Minimal Reporting Standard for Dietary Networks (MRS-DN)&#x2014;to help guide future studies. 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spelling 2025-11-13T13:59:38.4643249 v2 70756 2025-10-22 Unveiling Dietary Complexity: A Scoping Review and Reporting Guidance for Network Analysis in Dietary Pattern Research d6a58b5cb0cef9e120b0f9d65a9aa015 0000-0001-8591-2508 Alecia Cousins Alecia Cousins true false 22748f1a953255d63cb6ab9a98c11d70 0000-0002-6954-3519 Hayley Young Hayley Young true false 2025-10-22 PSYS Background/Objectives: Dietary patterns play a crucial role in health, yet most research examines foods individually, overlooking how they interact. This approach provides an incomplete picture of how diet influences health outcomes. Network analysis (e.g., Gaussian graphical models, mutual information networks, mixed graphical models) offers a more comprehensive way to study food co-consumption by capturing complex relationships between dietary components. However, while researchers have applied various network algorithms to explore food co-consumption, inconsistencies in methodology, incorrect application of algorithms, and varying results have made interpretation challenging. The objectives of this scoping review were to systematically map and synthesise studies that have applied network analysis to dietary data, and to establish guiding principles for future research in this area. Methods: Using PRISMA-ScR criteria, our scoping review identified 171 articles published from inception up to 7 March 2025, of which 18 studies met the inclusion criteria. Results: Gaussian graphical models were the most frequent approach, used in 61% of studies, and were often paired with regularisation techniques (e.g., graphical LASSO) to improve clarity (93%). The analysis revealed significant methodological challenges across the literature: 72% of studies employed centrality metrics without acknowledging their limitations, there was an overreliance on cross-sectional data limiting the ability to determine cause and effect, and difficulties in handling non-normal data. While most studies using GGM addressed the issue of non-normal data, either by using the nonparametric extension, Semiparametric Gaussian copula graphical model (SGCGM), or log-transforming the data, 36% did nothing to manage their non-normal data. Conclusions: To improve the reliability of network analysis in dietary research, this review proposes five guiding principles: model justification, design–question alignment, transparent estimation, cautious metric interpretation, and robust handling of non-normal data. To facilitate their adoption, a CONSORT-style checklist is introduced—the Minimal Reporting Standard for Dietary Networks (MRS-DN)—to help guide future studies. This review was preregistered on Open Science Framework. Journal Article Nutrients 17 20 3261 MDPI AG 2072-6643 dietary patterns; models; statistical 17 10 2025 2025-10-17 10.3390/nu17203261 COLLEGE NANME Psychology School COLLEGE CODE PSYS Swansea University Other None 2025-11-13T13:59:38.4643249 2025-10-22T18:45:35.3321249 Faculty of Medicine, Health and Life Sciences School of Psychology Rebecca M. J. Taylor 1 Jack A. Moore 2 Amy R. Griffiths 3 Alecia Cousins 0000-0001-8591-2508 4 Hayley Young 0000-0002-6954-3519 5 70756__35623__3f055bbf6a6a4deba08ed69983ca4820.pdf 70756.VoR.pdf 2025-11-13T13:57:37.8791016 Output 1119441 application/pdf Version of Record true © 2025 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. true eng https://creativecommons.org/licenses/by/4.0/
title Unveiling Dietary Complexity: A Scoping Review and Reporting Guidance for Network Analysis in Dietary Pattern Research
spellingShingle Unveiling Dietary Complexity: A Scoping Review and Reporting Guidance for Network Analysis in Dietary Pattern Research
Alecia Cousins
Hayley Young
title_short Unveiling Dietary Complexity: A Scoping Review and Reporting Guidance for Network Analysis in Dietary Pattern Research
title_full Unveiling Dietary Complexity: A Scoping Review and Reporting Guidance for Network Analysis in Dietary Pattern Research
title_fullStr Unveiling Dietary Complexity: A Scoping Review and Reporting Guidance for Network Analysis in Dietary Pattern Research
title_full_unstemmed Unveiling Dietary Complexity: A Scoping Review and Reporting Guidance for Network Analysis in Dietary Pattern Research
title_sort Unveiling Dietary Complexity: A Scoping Review and Reporting Guidance for Network Analysis in Dietary Pattern Research
author_id_str_mv d6a58b5cb0cef9e120b0f9d65a9aa015
22748f1a953255d63cb6ab9a98c11d70
author_id_fullname_str_mv d6a58b5cb0cef9e120b0f9d65a9aa015_***_Alecia Cousins
22748f1a953255d63cb6ab9a98c11d70_***_Hayley Young
author Alecia Cousins
Hayley Young
author2 Rebecca M. J. Taylor
Jack A. Moore
Amy R. Griffiths
Alecia Cousins
Hayley Young
format Journal article
container_title Nutrients
container_volume 17
container_issue 20
container_start_page 3261
publishDate 2025
institution Swansea University
issn 2072-6643
doi_str_mv 10.3390/nu17203261
publisher MDPI AG
college_str Faculty of Medicine, Health and Life Sciences
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hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str School of Psychology{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}School of Psychology
document_store_str 1
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description Background/Objectives: Dietary patterns play a crucial role in health, yet most research examines foods individually, overlooking how they interact. This approach provides an incomplete picture of how diet influences health outcomes. Network analysis (e.g., Gaussian graphical models, mutual information networks, mixed graphical models) offers a more comprehensive way to study food co-consumption by capturing complex relationships between dietary components. However, while researchers have applied various network algorithms to explore food co-consumption, inconsistencies in methodology, incorrect application of algorithms, and varying results have made interpretation challenging. The objectives of this scoping review were to systematically map and synthesise studies that have applied network analysis to dietary data, and to establish guiding principles for future research in this area. Methods: Using PRISMA-ScR criteria, our scoping review identified 171 articles published from inception up to 7 March 2025, of which 18 studies met the inclusion criteria. Results: Gaussian graphical models were the most frequent approach, used in 61% of studies, and were often paired with regularisation techniques (e.g., graphical LASSO) to improve clarity (93%). The analysis revealed significant methodological challenges across the literature: 72% of studies employed centrality metrics without acknowledging their limitations, there was an overreliance on cross-sectional data limiting the ability to determine cause and effect, and difficulties in handling non-normal data. While most studies using GGM addressed the issue of non-normal data, either by using the nonparametric extension, Semiparametric Gaussian copula graphical model (SGCGM), or log-transforming the data, 36% did nothing to manage their non-normal data. Conclusions: To improve the reliability of network analysis in dietary research, this review proposes five guiding principles: model justification, design–question alignment, transparent estimation, cautious metric interpretation, and robust handling of non-normal data. To facilitate their adoption, a CONSORT-style checklist is introduced—the Minimal Reporting Standard for Dietary Networks (MRS-DN)—to help guide future studies. This review was preregistered on Open Science Framework.
published_date 2025-10-17T05:33:39Z
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