Advances in methods for characterizing dietary patterns: A scoping review

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 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.