Exploration of dietary patterns in southern China: application-based comparison of factor analysis and latent class analysis.

BACKGROUND

In recent years, compared with traditional dietary analysis (simply focused on individual nutrients or foods), the analysis of dietary patterns has emerged as a comprehensive approach. This study aims to explore the dietary patterns of residents in a certain region of southern China through factor and latent class analysis (LCA), and compare the advantages and disadvantages of the two methods, providing data support for future research.

METHODS

We conducted a cross-sectional study using random stratified cluster sampling in the Gaozhou County, Maoming, Guangdong Province, China. Overall, 12,212 participants were recruited for the study, and data were collected using a general questionnaire consisting of two parts focusing on sociodemographic characteristics and residents’ dietary behaviors. Factor and latent class analysis (LCA) were then performed to identify patterns of dietary behaviors, and logistic regression was used to explore the associations between sociodemographic characteristics and dietary behavior classes.

RESULTS

Both factor analysis and LCA were useful when assessing the classification of residents’ dietary patterns. However, unlike prior models, the LCA identified emergent dietary behavior, highlighting previously unrecognized variations. Five latent classes (the balanced diet: 10.75%, tending-to-be-balanced diet: 8.03%, meat-loving diet: 22.19%, traditional diet: 45.00%, and unbalanced diet: 14.03%) were identified. The results showed that sex, age, marital status, education level, monthly income, and chronic disease status (all  < 0.05) were the main factors influencing dietary patterns.

CONCLUSIONS

This study reveals previously uncharacterized dietary patterns in Southern China, offering novel insights for future research in this field.