ArticleJournal of health, population, and nutrition2025
Exploration of dietary patterns in southern China: application-based comparison of factor analysis and latent class analysis.
Article in Journal of health, population, and nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundIn 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.
methodsWe 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.
resultsBoth 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 P < 0.05) were the main factors influencing dietary patterns.
conclusionsThis study reveals previously uncharacterized dietary patterns in Southern China, offering novel insights for future research in this field.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.