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

Joy M Hutchinson1, Amanda Raffoul2, Alexandra Pepetone1

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Summary
This summary is machine-generated.

Novel methods like machine learning offer deeper insights into dietary patterns and health. Consistent reporting guidelines are needed for these advanced approaches to inform public health policies.

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Area of Science:

  • Nutritional Epidemiology
  • Computational Biology
  • Public Health

Background:

  • Understanding complex dietary patterns and their health impacts is crucial.
  • Traditional methods may not fully capture dietary complexity.
  • Novel analytical approaches offer new possibilities for dietary pattern characterization.

Purpose of the Study:

  • To conduct a scoping review of literature (2005-2022) on novel methods for characterizing dietary patterns.
  • To synthesize the application of non-traditional approaches, including machine learning and latent class analysis.
  • To identify trends and variations in the use of these novel methods.

Main Methods:

  • Scoping review of MEDLINE, CINAHL, and Scopus databases.
  • Keywords included machine learning, latent class analysis, and LASSO.
  • Inclusion criteria applied to 5274 identified records, with 24 meeting criteria.

Main Results:

  • 24 studies met inclusion criteria, with 12 published since 2020.
  • Studies spanned 17 countries, with 9 using machine learning applications.
  • 14 studies linked novel dietary patterns to health outcomes (cancer, cardiovascular disease, asthma).

Conclusions:

  • Novel methods are increasingly used to characterize dietary patterns, offering deeper insights.
  • Significant variation exists in methods and reporting, hindering evidence synthesis.
  • Enhanced reporting guidelines and appraisal tools are needed to standardize the use of novel methods in nutrition research.