title: Identifying dietary patterns using a normal mixture model: application to the EPIC study.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Biostatistics Unit, Medical Research Council, Cambridge, UK. michael.fahey@csiro.au
authors.forename: Michael T
authors.initials: MT
authors.lastname: Fahey
authors.forename: Pietro
authors.initials: P
authors.lastname: Ferrari
authors.forename: Nadia
authors.initials: N
authors.lastname: Slimani
authors.forename: Jeroen K
authors.initials: JK
authors.lastname: Vermunt
authors.forename: Ian R
authors.initials: IR
authors.lastname: White
authors.forename: Kurt
authors.initials: K
authors.lastname: Hoffmann
authors.forename: Elisabet
authors.initials: E
authors.lastname: Wirfält
authors.forename: Christina
authors.initials: C
authors.lastname: Bamia
authors.forename: Mathilde
authors.initials: M
authors.lastname: Touvier
authors.forename: Jakob
authors.initials: J
authors.lastname: Linseisen
authors.forename: Miguel
authors.initials: M
authors.lastname: Rodríguez-Barranco
authors.forename: Rosario
authors.initials: R
authors.lastname: Tumino
authors.forename: Eiliv
authors.initials: E
authors.lastname: Lund
authors.forename: Kim
authors.initials: K
authors.lastname: Overvad
authors.forename: Bas
authors.initials: B
authors.lastname: Bueno de Mesquita
authors.forename: Sheila
authors.initials: S
authors.lastname: Bingham
authors.forename: Elio
authors.initials: E
authors.lastname: Riboli
mesh_terms: D000328:Adult; D000368:Aged; D016000:Cluster Analysis; D004032:Diet / Q000706:statistics & numerical data*; D005060:Europe; D005247:Feeding Behavior; D005260:Female; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D009752:Nutritional Status; D011336:Probability; D057566:Self Report