Advances in methods for characterising dietary patterns: a scoping review
There is a growing focus on understanding the complexity of dietary patterns and how they relate to health and other factors. Approaches that have not traditionally been applied to characterise dietary patterns, such as latent class analysis and machine learning algorithms, may offer opportunities t...
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| Vydáno v: | British journal of nutrition Ročník 133; číslo 7; s. 987 - 1001 |
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| Hlavní autoři: | , , , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Cambridge, UK
Cambridge University Press
14.04.2025
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| ISSN: | 0007-1145, 1475-2662, 1475-2662 |
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| Abstract | There is a growing focus on understanding the complexity of dietary patterns and how they relate to health and other factors. Approaches that have not traditionally been applied to characterise dietary patterns, such as latent class analysis and machine learning algorithms, may offer opportunities to characterise dietary patterns in greater depth than previously considered. However, there has not been a formal examination of how this wide range of approaches has been applied to characterise dietary patterns. This scoping review synthesised literature from 2005 to 2022 applying methods not traditionally used to characterise dietary patterns, referred to as novel methods. MEDLINE, CINAHL and Scopus were searched using keywords including latent class analysis, machine learning and least absolute shrinkage and selection operator. Of 5274 records identified, 24 met the inclusion criteria. Twelve of twenty-four articles were published since 2020. Studies were conducted across seventeen countries. Nine studies used approaches with applications in machine learning, such as classification models, neural networks and probabilistic graphical models, to identify dietary patterns. The remaining studies applied methods such as latent class analysis, mutual information and treelet transform. Fourteen studies assessed associations between dietary patterns characterised using novel methods and health outcomes, including cancer, cardiovascular disease and asthma. There was wide variation in the methods applied to characterise dietary patterns and in how these methods were described. The extension of reporting guidelines and quality appraisal tools relevant to nutrition research to consider specific features of novel methods may facilitate consistent reporting and enable synthesis to inform policies and programs. |
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| AbstractList | There is a growing focus on understanding the complexity of dietary patterns and how they relate to health and other factors. Approaches that have not traditionally been applied to characterise dietary patterns, such as latent class analysis and machine learning algorithms, may offer opportunities to characterise dietary patterns in greater depth than previously considered. However, there has not been a formal examination of how this wide range of approaches has been applied to characterise dietary patterns. This scoping review synthesised literature from 2005 to 2022 applying methods not traditionally used to characterise dietary patterns, referred to as novel methods. MEDLINE, CINAHL and Scopus were searched using keywords including latent class analysis, machine learning and least absolute shrinkage and selection operator. Of 5274 records identified, 24 met the inclusion criteria. Twelve of twenty-four articles were published since 2020. Studies were conducted across seventeen countries. Nine studies used approaches with applications in machine learning, such as classification models, neural networks and probabilistic graphical models, to identify dietary patterns. The remaining studies applied methods such as latent class analysis, mutual information and treelet transform. Fourteen studies assessed associations between dietary patterns characterised using novel methods and health outcomes, including cancer, cardiovascular disease and asthma. There was wide variation in the methods applied to characterise dietary patterns and in how these methods were described. The extension of reporting guidelines and quality appraisal tools relevant to nutrition research to consider specific features of novel methods may facilitate consistent reporting and enable synthesis to inform policies and programs. There is a growing focus on understanding the complexity of dietary patterns and how they relate to health and other factors. Approaches that have not traditionally been applied to characterize dietary patterns, such as latent class analysis and machine learning algorithms, may offer opportunities to characterize dietary patterns in greater depth than previously considered. However, there has not been a formal examination of how this wide range of approaches has been applied to characterize dietary patterns. This scoping review synthesized literature from 2005-2022 applying methods not traditionally used to characterize dietary patterns, referred to as novel methods. MEDLINE, CINAHL, and Scopus were searched using keywords including latent class analysis, machine learning, and least absolute shrinkage and selection operator. Of 5274 records identified, 24 met the inclusion criteria. Twelve of 24 articles were published since 2020. Studies were conducted across 17 countries. Nine studies used approaches with applications in machine learning, such as classification models, neural networks, and probabilistic graphical models, to identify dietary patterns. The remaining studies applied methods such as latent class analysis, mutual information, and treelet transform. Fourteen studies assessed associations between dietary patterns characterized using novel methods and health outcomes, including cancer, cardiovascular disease, and asthma. There was wide variation in the methods applied to characterize dietary patterns and in how these methods were described. The extension of reporting guidelines and quality appraisal tools relevant to nutrition research to consider specific features of novel methods may facilitate consistent reporting and enable synthesis to inform policies and programs.There is a growing focus on understanding the complexity of dietary patterns and how they relate to health and other factors. Approaches that have not traditionally been applied to characterize dietary patterns, such as latent class analysis and machine learning algorithms, may offer opportunities to characterize dietary patterns in greater depth than previously considered. However, there has not been a formal examination of how this wide range of approaches has been applied to characterize dietary patterns. This scoping review synthesized literature from 2005-2022 applying methods not traditionally used to characterize dietary patterns, referred to as novel methods. MEDLINE, CINAHL, and Scopus were searched using keywords including latent class analysis, machine learning, and least absolute shrinkage and selection operator. Of 5274 records identified, 24 met the inclusion criteria. Twelve of 24 articles were published since 2020. Studies were conducted across 17 countries. Nine studies used approaches with applications in machine learning, such as classification models, neural networks, and probabilistic graphical models, to identify dietary patterns. The remaining studies applied methods such as latent class analysis, mutual information, and treelet transform. Fourteen studies assessed associations between dietary patterns characterized using novel methods and health outcomes, including cancer, cardiovascular disease, and asthma. There was wide variation in the methods applied to characterize dietary patterns and in how these methods were described. The extension of reporting guidelines and quality appraisal tools relevant to nutrition research to consider specific features of novel methods may facilitate consistent reporting and enable synthesis to inform policies and programs. |
| Author | Wallace, Michael P. Reedy, Jill Vena, Jennifer E. Williams, Tabitha E. Hussain, Sanaa McNaughton, Sarah A. Kirkpatrick, Sharon I. Lamarche, Benoît Dodd, Kevin W. Bodnar, Lisa M. Andrade, Lesley Hutchinson, Joy M. Shams-White, Marissa M. Raffoul, Amanda Leech, Rebecca M. Pepetone, Alexandra Deitchler, Megan |
| Author_xml | – sequence: 1 givenname: Joy M. orcidid: 0000-0002-1615-7964 surname: Hutchinson fullname: Hutchinson, Joy M. organization: School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada – sequence: 2 givenname: Amanda surname: Raffoul fullname: Raffoul, Amanda organization: Department of Nutritional Sciences, University of Toronto, Toronto, ON, Canada – sequence: 3 givenname: Alexandra surname: Pepetone fullname: Pepetone, Alexandra organization: School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada – sequence: 4 givenname: Lesley surname: Andrade fullname: Andrade, Lesley organization: School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada – sequence: 5 givenname: Tabitha E. surname: Williams fullname: Williams, Tabitha E. organization: School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada – sequence: 6 givenname: Sarah A. surname: McNaughton fullname: McNaughton, Sarah A. organization: Health and Well-Being Centre for Research Innovation, School of Human Movement and Nutrition Sciences, University of Queensland, St. Lucia, QLD, Australia – sequence: 7 givenname: Rebecca M. surname: Leech fullname: Leech, Rebecca M. organization: Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC, Australia – sequence: 8 givenname: Jill surname: Reedy fullname: Reedy, Jill organization: National Cancer Institute, National Institutes of Health, Bethesda, MD, USA – sequence: 9 givenname: Marissa M. surname: Shams-White fullname: Shams-White, Marissa M. organization: Population Science Department, American Cancer Society, Washington, DC, USA – sequence: 10 givenname: Jennifer E. surname: Vena fullname: Vena, Jennifer E. organization: Alberta’s Tomorrow Project, Alberta Health Services, Edmonton, AB, Canada – sequence: 11 givenname: Kevin W. surname: Dodd fullname: Dodd, Kevin W. organization: Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, USA – sequence: 12 givenname: Lisa M. surname: Bodnar fullname: Bodnar, Lisa M. organization: 0 School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA – sequence: 13 givenname: Benoît orcidid: 0000-0002-4443-5378 surname: Lamarche fullname: Lamarche, Benoît organization: 1 Centre Nutrition, santé et société (NUTRISS), Institut sur la nutrition et les aliments fonctionnels (INAF), Université Laval, Québec City, QC, Canada – sequence: 14 givenname: Michael P. surname: Wallace fullname: Wallace, Michael P. organization: 2 Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada – sequence: 15 givenname: Megan surname: Deitchler fullname: Deitchler, Megan organization: 3 Intake – Center for Dietary Assessment, FHI Solutions, Washington, DC, USA – sequence: 16 givenname: Sanaa surname: Hussain fullname: Hussain, Sanaa organization: School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada – sequence: 17 givenname: Sharon I. orcidid: 0000-0001-9896-5975 surname: Kirkpatrick fullname: Kirkpatrick, Sharon I. email: sharon.kirkpatrick@uwaterloo.ca organization: School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada |
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| Issue | 7 |
| Keywords | Novel methods Dietary patterns Health outcomes Machine learning Scoping review Latent class analysis Diet quality |
| Language | English |
| License | https://creativecommons.org/licenses/by/4.0/ This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. To the extent this is a work of the US Government, it is not subject to copyright protection within the United States. |
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| References | 2018; 361 2004; 62 2021; 23 2023; 33 2019; 10 2002; 13 2018; 169 2016; 146 2008; 36 2019; 19 2020; 12 2020; 10 2001; 45 2008; 2 2012; 11 2011; 111 2017; 117 2020; 19 2020; 7 2023; 63 1990; 43 2021; 75 2022; 161 1997; 55 2007; 170 2020; 94 2015; 132 2019; 28 2021; 151 2013; 113 2014; 17 2019; 393 2008; 61 2022; 128 2021; 190 2017; 20 2007; 19 2021; 4 2015; 16 2015; 3 2019; 5 2023; 123 2022; 47 2020; 147 2021; 50 2011; 173 2016; 13 2021; 13 2015; 27 2021; 12 2009; 31 2021 2018; 118 2021; 18 2013; 72 2005; 8 2022; 13 2020; 26 2020; 111 2008; 88 2020; 112 2021; 133 2005; 50 2020; 21 2023; 118 2021; 60 2014; 100 2018; 10 2005; 18 2007; 45 2018; 13 2012; 40 38947003 - medRxiv. 2024 Jul 08:2024.06.20.24309251. doi: 10.1101/2024.06.20.24309251. |
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