Latent Class Analysis Is Useful to Classify Pregnant Women into Dietary Patterns

Empirical dietary patterns are derived predominantly using principal components exploratory factor analysis (EFA) or cluster analysis Interestingly latent variable models are less used despite their being more flexible to accommodate important characteristics of dietary data and despite dietary patterns being recognized as latent variables Latent class analysis (LCA) has been shown empirically to be more appropriate to derive dietary patterns than k-means clustering but has not been compared yet to confirmatory factor analysis (CFA) In this article we derived dietary patterns using EFA CFA and LCA on food items tested how well the classes from LCA were characterized by the factors from CFA and compared participants direct classification from LCA on food items compared with 2 a posteriori classifications from factor scores Methods were illustrated with the Pregnancy Infection and Nutrition Study North Carolina 2000-2005 (n = 1285 women) From EFA and CFA we found that food items were grouped into 4 factors Prudent Prudent with coffee and alcohol Western and Southern From LCA pregnant women were classified into 3 classes Prudent Hard core Western and Health conscious Western There was high agreement between the direct classification from LCA on food items and the classification from the 2 step LCA on factor scores [kappa=0 70(95% Cl = 0 66 0 73)] despite factors explaining only 25% of the total variance We suggest LCA on food items to study the effect for mutually exclusive classes and CFA to understand which foods are eaten in combination When interested in both benefits the 2-step classification using LCA on previously derived factor scores seems promising J Nutr 140 2253-2259 2010