Identifying dietary patterns using a normal mixture model: application to the EPIC study.

BACKGROUND: Finite mixture models posit the existence of a latent categorical variable and can be used for probabilistic classification. The authors illustrate the use of mixture models for dietary pattern analysis. An advantage of this approach is taking classification uncertainty into account.
METHODS: Participants were a random sample of women from the European Prospective Investigation into Cancer. Food consumption was measured using dietary questionnaires. Mixture models identified latent classes in food consumption data, which were interpreted as dietary patterns.
RESULTS: Among various assumptions examined, models allowing the variance of foods to vary within and between classes fit better than alternatives assuming constant variance (the K-means method of cluster analysis also makes the latter assumption). An eight-class model was best fitting and five patterns validated well in a second random sample. Patterns with lower classification uncertainty tended to be better validated. One pattern showed low consumption of foods despite being associated with moderate body mass index.
CONCLUSION: Mixture modelling for dietary pattern analysis has advantages over both factor and cluster analysis. In contrast to these other methods, it is easy to estimate pattern prevalence, to describe patterns and to use patterns to predict disease taking classification uncertainty into account. Owing to substantial error in food consumptions, any analysis will usually find some patterns that cannot be well validated. While knowledge of classification uncertainty may aid pattern evaluation, any method will better identify patterns from food consumptions measured with less error. Mixture models may be useful to identify individuals who under-report food consumption.