Advances in methods for characterizing dietary patterns: A scoping review.

Autor:	Hutchinson JM; School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada., Raffoul A; Department of Nutritional Sciences, University of Toronto, Toronto, ON, Canada., Pepetone A; School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada., Andrade L; School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada., Williams TE; School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada., McNaughton SA; Health and Well-Being Centre for Research Innovation, School of Human Movement and Nutrition Sciences, University of Queensland, St. Lucia, QLD, Australia., Leech RM; Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Victoria, Geelong, Australia., Reedy J; National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Shams-White MM; Population Science Department, American Cancer Society, Washington DC, USA.; Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA., Vena JE; Alberta's Tomorrow Project, Alberta Health Services, Edmonton, AB, Canada., Dodd KW; Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, USA., Bodnar LM; School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA., Lamarche B; Centre Nutrition, santé et société (NUTRISS), Institut sur la nutrition et les aliments fonctionnels (INAF), Université Laval, Québec City, QC, Canada., Wallace MP; Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada., Deitchler M; Intake - Center for Dietary Assessment, FHI Solutions, Washington, DC, USA., Hussain S; School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada., Kirkpatrick SI; School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.
Jazyk:	angličtina
Zdroj:	MedRxiv : the preprint server for health sciences [medRxiv] 2024 Jul 08. Date of Electronic Publication: 2024 Jul 08.
DOI:	10.1101/2024.06.20.24309251
Abstrakt:	There is a growing focus on better 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 machine learning algorithms and latent class analysis methods, may offer opportunities to measure and 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 machine learning, latent class analysis, and least absolute shrinkage and selection operator (LASSO). 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 that have applications in machine learning to identify dietary patterns. Fourteen studies assessed associations between dietary patterns that were 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 complete and consistent reporting and enable evidence synthesis to inform policies and programs aimed at supporting healthy dietary patterns. Competing Interests: Conflict of Interest: RML is a statistical editor for the British Journal of Nutrition. Other authors have none to declare.
Databáze:	MEDLINE
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