title: Food patterns and cardiovascular disease risk factors: the Swedish INTERGENE research program.
abstract: BACKGROUND
  Analyzing the impact of the intake of many foods simultaneously provides additional knowledge about analyses of nutrients and might make it easier to implement recommendations for the public.
  
  
  OBJECTIVE
  The objective was to examine food patterns in a Swedish population and determine how they are related to metabolic risk factors for cardiovascular disease.
  
  
  METHODS
  The study is based on data from the INTERGENE population study of women and men aged 25-74 y in western Sweden. Dietary patterns were identified with cluster analysis of 93 food frequencies reported by 3452 participants. Associations with features of the metabolic syndrome, including blood lipids, blood pressure, and anthropometric measures, were analyzed.
  
  
  RESULTS
  Five distinct food patterns were identified, of which one was interpreted as a "healthy" reference pattern. This healthy cluster was distinguished by more frequent consumption of high-fiber and low-fat foods and lower consumption of products rich in fat and sugar. The 4 other clusters differed significantly from the reference cluster with respect to prevalence of cardiovascular disease risk factors and the metabolic syndrome. For example, body mass index and waist-to-hip ratio were significantly higher in a cluster characterized by high consumption of energy-dense drinks and white bread and low consumption of fruit and vegetables (P < 0.0001 and P = 0.004, respectively).
  
  
  CONCLUSIONS
  It is possible to distinguish food patterns that are related to obesity and obesity-related cardiovascular disease risk factors in contrast with a more healthy pattern conforming with current dietary guidelines. Thus, the results indicate no reason for questioning the current recommendations.
authors.affiliation: Department of Food Health and Environment, University of Gothenburg, Göteborg, Sweden. christina.berg@ped.gu.se
authors.forename: Christina M
authors.initials: CM
authors.lastname: Berg
authors.forename: Georgios
authors.initials: G
authors.lastname: Lappas
authors.forename: Elisabeth
authors.initials: E
authors.lastname: Strandhagen
authors.forename: Alicja
authors.initials: A
authors.lastname: Wolk
authors.forename: Kjell
authors.initials: K
authors.lastname: Torén
authors.forename: Annika
authors.initials: A
authors.lastname: Rosengren
authors.forename: Nibia
authors.initials: N
authors.lastname: Aires
authors.forename: Dag S
authors.initials: DS
authors.lastname: Thelle
authors.forename: Lauren
authors.initials: L
authors.lastname: Lissner
mesh_terms: D000328:Adult; D000368:Aged; D000886:Anthropometry; D001786:Blood Glucose / Q000378:metabolism; D001794:Blood Pressure / Q000502:physiology; D002318:Cardiovascular Diseases / Q000097:blood / Q000453:epidemiology* / Q000209:etiology; D016000:Cluster Analysis; D003430:Cross-Sectional Studies; D004032:Diet; D004041:Dietary Fats / Q000008:administration & dosage*; D002149:Energy Intake; D005247:Feeding Behavior; D005260:Female; D005638:Fruit; D006306:Health Surveys; D006801:Humans; D008055:Lipids / Q000097:blood; D008297:Male; D008875:Middle Aged; D009749:Nutrition Surveys; D009765:Obesity / Q000097:blood / Q000453:epidemiology* / Q000209:etiology; D012307:Risk Factors; D011795:Surveys and Questionnaires; D013548:Sweden / Q000453:epidemiology; D014675:Vegetables
chemical_list: D001786:Blood Glucose; D004041:Dietary Fats; D008055:Lipids