title: Gaussian Graphical Models Identify Networks of Dietary Intake in a German Adult Population.
abstract: BACKGROUND
  Data-reduction methods such as principal component analysis are often used to derive dietary patterns. However, such methods do not assess how foods are consumed in relation to each other. Gaussian graphical models (GGMs) are a set of novel methods that can address this issue.
  
  
  OBJECTIVE
  We sought to apply GGMs to derive sex-specific dietary intake networks representing consumption patterns in a German adult population.
  
  
  METHODS
  Dietary intake data from 10,780 men and 16,340 women of the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort were cross-sectionally analyzed to construct dietary intake networks. Food intake for each participant was estimated using a 148-item food-frequency questionnaire that captured the intake of 49 food groups. GGMs were applied to log-transformed intakes (grams per day) of 49 food groups to construct sex-specific food networks. Semiparametric Gaussian copula graphical models (SGCGMs) were used to confirm GGM results.
  
  
  RESULTS
  In men, GGMs identified 1 major dietary network that consisted of intakes of red meat, processed meat, cooked vegetables, sauces, potatoes, cabbage, poultry, legumes, mushrooms, soup, and whole-grain and refined breads. For women, a similar network was identified with the addition of fried potatoes. Other identified networks consisted of dairy products and sweet food groups. SGCGMs yielded results comparable to those of GGMs.
  
  
  CONCLUSIONS
  GGMs are a powerful exploratory method that can be used to construct dietary networks representing dietary intake patterns that reveal how foods are consumed in relation to each other. GGMs indicated an apparent major role of red meat intake in a consumption pattern in the studied population. In the future, identified networks might be transformed into pattern scores for investigating their associations with health outcomes.
authors.affiliation: Departments of Epidemiology and khalid.iqbal@dife.de.
authors.forename: Khalid
authors.identifier: http://orcid.org/0000-0002-3312-4259
authors.initials: K
authors.lastname: Iqbal
authors.affiliation: Departments of Epidemiology and.
authors.forename: Brian
authors.initials: B
authors.lastname: Buijsse
authors.affiliation: Departments of Epidemiology and.
authors.forename: Janine
authors.initials: J
authors.lastname: Wirth
authors.affiliation: Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; and German Center for Diabetes Research, Neuherberg, Germany.
authors.forename: Matthias B
authors.initials: MB
authors.lastname: Schulze
authors.affiliation: Departments of Epidemiology and.
authors.forename: Anna
authors.initials: A
authors.lastname: Floegel
authors.affiliation: Departments of Epidemiology and.
authors.forename: Heiner
authors.initials: H
authors.lastname: Boeing
mesh_terms: D000328:Adult; D000363:Agaricales; D000368:Aged; D000818:Animals; D003430:Cross-Sectional Studies; D004032:Diet; D004041:Dietary Fats; D002149:Energy Intake; D007887:Fabaceae; D005260:Female; D005858:Germany; D006801:Humans; D008297:Male; D008461:Meat Products; D008875:Middle Aged; D016011:Normal Distribution; D015596:Nutrition Assessment; D011200:Poultry; D011446:Prospective Studies; D011795:Surveys and Questionnaires; D014675:Vegetables; D044465:White People; D000067348:Whole Grains
chemical_list: D004041:Dietary Fats
keywords: GGMs; Gaussian graphical models; consumption pattern; dietary networks; dietary pattern analysis