Advances in methods for characterising dietary patterns: a scoping review.

Area of Science:

• Nutrition Science
• Data Science
• Epidemiology

Background:

• Understanding dietary patterns is crucial for public health.
• Traditional methods may not fully capture dietary complexity.
• Novel analytical approaches are emerging for dietary pattern characterization.

Purpose of the Study:

• To conduct a scoping review of literature from 2005-2022.
• To synthesize the application of non-traditional methods for characterizing dietary patterns.
• To examine the association between novel method-derived dietary patterns and health outcomes.

Main Methods:

• Searched MEDLINE, CINAHL, and Scopus databases.
• Included studies published between 2005 and 2022.
• Focused on methods like latent class analysis and machine learning algorithms.

Main Results:

• 24 studies met inclusion criteria, with 12 published since 2020.
• Nine studies utilized machine learning applications; others used latent class analysis and similar methods.
• Fourteen studies linked novel dietary patterns to health outcomes (cancer, cardiovascular disease, asthma).

Conclusions:

• Novel methods provide advanced characterization of dietary patterns.
• There is significant variation in the application and reporting of these methods.
• Enhanced reporting guidelines and quality appraisal tools are needed for consistent synthesis and policy development.