Advanced Dietary Patterns Analysis Using Sparse Latent Factor Models in Young Adults.

ABSTRACT:

Background

Principal components analysis (PCA) has been the most widely used method for deriving dietary patterns to date. However, PCA requires arbitrary ad hoc decisions for selecting food variables in interpreting dietary patterns and does not easily accommodate covariates. Sparse latent factor models can be utilized to address these issues.

Objective

The objective of this study was to compare Bayesian sparse latent factor models with PCA for identifying dietary patterns among young adults.

Methods

Habitual food intake was estimated in 2730 sedentary young adults from the Training Interventions and Genetics of Exercise Response (TIGER) Study [aged 18-35 y; body mass index (BMI; in kg/m2): 26.5 ± 6.1] who exercised <30 min/wk during the previous 30 d without restric

SUBMITTER: Joo J 

PROVIDER: S-EPMC6280002 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature