AUTHOR=Cao Shang , Liu Linchen , Zhu Qianrang , Zhu Zheng , Zhou Jinyi , Wei Pingmin , Wu Ming 

TITLE=Association Between Dietary Patterns and Plasma Lipid Biomarker and Female Breast Cancer Risk: Comparison of Latent Class Analysis (LCA) and Factor Analysis (FA)

JOURNAL=Frontiers in Nutrition

VOLUME=Volume 8 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2021.645398

DOI=10.3389/fnut.2021.645398

ISSN=2296-861X

ABSTRACT=Background:
Diet research focuses on the characteristic of "dietary patterns" regardless of the statistical methods used to derive them. However, the solutions to these methods are different, both conceptually and statistically.
Methods: 
We compared Factor analysis (FA) and Latent class analysis (LCA) methods to identify dietary patterns of participants in the Chinese Wuxi Exposure and Breast Cancer Study, a population-based case-control study that included 818 patients and 935 healthy controls. We examined the association between dietary patterns and plasma lipids markers and breast cancer risk.
Results: 
FA grouped correlated food items into 5 factors while LCA classified the subjects into 4 mutually exclusive classes. For FA, we found the Prudent-factor was associated with a lower risk of breast cancer (4th vs. 1st quartile: OR for 0.70, 95%CI=0.52, 0.95), whereas the Picky-factor was associated with a higher risk (4th vs. 1st quartile: OR for 1.35, 95%CI=1.00, 1.81). For LCA, using the Prudent-class as the reference, the Picky-class has a positive association with the risk of breast cancer (OR for 1.42, 95%CI=1.06, 1.90). The multivariate-adjusted model containing all of the factors was better than that containing all of the classes in predicting HDL cholesterol (P=0.04), triacylglycerols (P=0.03), blood glucose (P=0.04), apolipoprotein A1 (P=0.02), high-sensitivity C-reactive Protein (P=0.02), but was weaker than predicting breast cancer risk (P=0.03).
Conclusion: 
FA is useful for understanding which foods are consumed in combination and for studying the associations with biomarkers, while LCA is useful for classifying individuals into mutually exclusive subgroups and compare disease risk between groups.