title: Using cluster analysis to examine dietary patterns: nutrient intakes, gender, and weight status differ across food pattern clusters.
abstract: OBJECTIVE
  This study explored the usefulness of cluster analysis in identifying food choice patterns of three groups of adults in relation to their energy intake.
  
  
  METHODS
  Food frequency data were converted to percentage of total energy from 38 food groups and entered into a cluster analysis procedure. Subjects in the emerging food group patterns were compared in terms of weight status, demographics, and the nutrition composition of their usual diet.
  
  
  METHODS
  Data were collected as part of three studies in two US metropolitan areas using identical protocols. Participants were university employees (103 women and 99 men) who volunteered for a reliability study of health behavior questionnaires and moderately obese volunteers (223 women and 101 men) to two weight-loss studies who were recruited by newspaper advertisements.
  
  
  METHODS
  Subjects were clustered according to food energy sources using the FASTCLUS procedure in the Statistical Analysis System. One-way analysis of variance and chi 2 analysis were then performed to compared the weight status, nutrient intakes, and demographics of the food patterns.
  
  
  RESULTS
  Six food pattern clusters were identified. Subjects in the two clusters associated with high consumption of pastry and meat had significantly higher fat intakes (P = .0001). Subjects in two other clusters, those associated with high intake of skim milk and a broad distribution of energy sources had significantly higher micronutrient levels (P = .0001). Body mass index and the distribution of gender were also significantly different across clusters.
  
  
  CONCLUSIONS
  The success of cluster analysis in identifying dietary exposure categories with unique demographic and nutritional correlates suggests that the approach may be useful in epidemiologic studies that examine conditions such as obesity, and in the design of nutrition interventions.
authors.affiliation: Malmö Diet Cancer Study, University of Lund, Sweden.
authors.forename: A K
authors.initials: AK
authors.lastname: Wirfält
authors.forename: R W
authors.initials: RW
authors.lastname: Jeffery
mesh_terms: D000328:Adult; D000704:Analysis of Variance; D015992:Body Mass Index; D016009:Chi-Square Distribution; D002791:Cholesterol, Dietary / Q000008:administration & dosage; D016000:Cluster Analysis; D004032:Diet / Q000592:standards*; D015930:Diet Records; D004040:Dietary Carbohydrates / Q000008:administration & dosage; D004041:Dietary Fats / Q000008:administration & dosage; D004043:Dietary Fiber / Q000008:administration & dosage; D004044:Dietary Proteins / Q000008:administration & dosage; D002149:Energy Intake; D004734:Energy Metabolism; D005260:Female; D005518:Food Preferences; D006801:Humans; D008297:Male; D008903:Minerals / Q000008:administration & dosage; D017678:Sex Distribution; D011795:Surveys and Questionnaires; D014815:Vitamins / Q000008:administration & dosage
chemical_list: D002791:Cholesterol, Dietary; D004040:Dietary Carbohydrates; D004041:Dietary Fats; D004043:Dietary Fiber; D004044:Dietary Proteins; D008903:Minerals; D014815:Vitamins