title: Metabolic syndrome model definitions predicting type 2 diabetes and cardiovascular disease.
abstract: OBJECTIVE
  Metabolic syndrome (MetS) is a cluster of abdominal obesity, hyperglycemia, hypertension, and dyslipidemia, which increases the risk for type 2 diabetes and cardiovascular diseases (CVDs). Some argue that MetS is not a single disorder because the traditional MetS features do not represent one entity, and they would like to exclude features from MetS. Others would like to add additional features in order to increase predictive ability of MetS. The aim of this study was to identify a MetS model that optimally predicts type 2 diabetes and CVD while still representing a single entity.
  
  
  METHODS
  In a random sample (n = 1,928) of the EPIC-NL cohort and a subset of the EPIC-NL MORGEN study (n = 1,333), we tested the model fit of several one-factor MetS models using confirmatory factor analysis. We compared predictive ability for type 2 diabetes and CVD of these models within the EPIC-NL case-cohort study of 545 incident type 2 diabetic subjects, 1,312 incident CVD case subjects, and the random sample, using survival analyses and reclassification.
  
  
  RESULTS
  The standard model, representing the current MetS definition (EPIC-NL comparative fit index [CFI] = 0.95; MORGEN CFI = 0.98); the standard model excluding blood pressure (EPIC-NL CFI = 0.95; MORGEN CFI = 1.00); and the standard model extended with hsCRP (EPIC-NL CFI = 0.95) had an acceptable model fit. The model extended with hsCRP predicted type 2 diabetes (integral discrimination index [IDI]: 0.34) and CVD (IDI: 0.07) slightly better than did the standard model.
  
  
  CONCLUSIONS
  It seems valid to represent the traditional MetS features by a single entity. Extension of this entity with hsCRP slightly improves predictive ability for type 2 diabetes and CVD.
authors.affiliation: National Institute for Public Health and the Environment, Bilthoven, The Netherlands. cecile.povel@wur.nl
authors.forename: Cécile M
authors.initials: CM
authors.lastname: Povel
authors.forename: Joline W
authors.initials: JW
authors.lastname: Beulens
authors.forename: Yvonne T
authors.initials: YT
authors.lastname: van der Schouw
authors.forename: Martijn E T
authors.initials: ME
authors.lastname: Dollé
authors.forename: Annemieke M W
authors.initials: AM
authors.lastname: Spijkerman
authors.forename: W M Monique
authors.initials: WM
authors.lastname: Verschuren
authors.forename: Edith J M
authors.initials: EJ
authors.lastname: Feskens
authors.forename: Jolanda M A
authors.initials: JM
authors.lastname: Boer
mesh_terms: D000368:Aged; D002318:Cardiovascular Diseases / Q000453:epidemiology*; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology*; D005260:Female; D006801:Humans; D024821:Metabolic Syndrome / Q000453:epidemiology*; D008875:Middle Aged; D008962:Models, Theoretical; D011446:Prospective Studies