title: The metabolic syndrome as a tool for predicting future diabetes: the AusDiab study.
abstract: OBJECTIVE
  To compare the ability of the metabolic syndrome (MetS), a diabetes prediction model (DPM), a noninvasive risk questionnaire and individual glucose measurements to predict future diabetes.
  
  
  METHODS
  Five-year longitudinal cohort study. Tools tested included MetS definitions [World Health Organization, International Diabetes Federation, ATPIII and European Group for the study of Insulin Resistance (EGIR)], the FINnish Diabetes RIsk SCore risk questionnaire, the DPM, fasting and 2-h post load plasma glucose.
  
  
  METHODS
  Adult Australian population.
  
  
  METHODS
  A total of 5842 men and women without diabetes > or =25 years. Response 58%. A total of 224 incident cases of diabetes.
  
  
  RESULTS
  In receiver operating characteristic curve analysis, the MetS was not a better predictor of incident diabetes than the DPM or measurement of glucose. The risk for diabetes among those with prediabetes but not MetS was almost triple that of those with MetS but not prediabetes (9.0% vs. 3.4%). Adjusted for component parts, the MetS was not a significant predictor of incident diabetes, except for EGIR in men [OR 2.1 (95% CI 1.2-3.7)].
  
  
  CONCLUSIONS
  A single fasting glucose measurement may be more effective and efficient than published definitions of the MetS or other risk constructs in predicting incident diabetes. Diagnosis of the MetS did not confer increased risk for incident diabetes independent of its individual components, with an exception for EGIR in men. Given these results, debate surrounding the public health utility of a MetS diagnosis, at least for identification of incident diabetes, is required.
authors.affiliation: International Diabetes Institute, Caulfield, Vict., Australia. acameron@idi.org.au
authors.forename: A J
authors.initials: AJ
authors.lastname: Cameron
authors.forename: D J
authors.initials: DJ
authors.lastname: Magliano
authors.forename: P Z
authors.initials: PZ
authors.lastname: Zimmet
authors.forename: T A
authors.initials: TA
authors.lastname: Welborn
authors.forename: S
authors.initials: S
authors.lastname: Colagiuri
authors.forename: A M
authors.initials: AM
authors.lastname: Tonkin
authors.forename: J E
authors.initials: JE
authors.lastname: Shaw
mesh_terms: D001786:Blood Glucose / Q000378:metabolism*; D015992:Body Mass Index; D003920:Diabetes Mellitus / Q000453:epidemiology*; D005260:Female; D005544:Forecasting; D018149:Glucose Intolerance / Q000453:epidemiology; D005951:Glucose Tolerance Test / Q000379:methods; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D024821:Metabolic Syndrome / Q000175:diagnosis*; D008875:Middle Aged; D009765:Obesity / Q000150:complications; D011236:Prediabetic State / Q000175:diagnosis*; D011237:Predictive Value of Tests; D012372:ROC Curve; D012307:Risk Factors; D012680:Sensitivity and Specificity
chemical_list: D001786:Blood Glucose