title: Predicting glucose intolerance with normal fasting plasma glucose by the components of the metabolic syndrome.
abstract: BACKGROUND
  Surprisingly, it is estimated that about half of type 2 diabetics remain undetected. The possible causes may be partly attributable to people with normal fasting plasma glucose (FPG) but abnormal postprandial hyperglycemia. We attempted to develop an effective predictive model by using the metabolic syndrome (MeS) components as parameters to identify such persons.
  
  
  METHODS
  All participants received a standard 75-g oral glucose tolerance test, which showed that 106 had normal glucose tolerance, 61 had impaired glucose tolerance, and 6 had diabetes-on-isolated postchallenge hyperglycemia. We tested five models, which included various MeS components. Model 0: FPG; Model 1 (clinical history model): family history (FH), FPG, age and sex; Model 2 (MeS model): Model 1 plus triglycerides, high-density lipoprotein cholesterol, body mass index, systolic blood pressure and diastolic blood pressure; Model 3: Model 2 plus fasting plasma insulin (FPI); Model 4: Model 3 plus homeostasis model assessment of insulin resistance. A receiver-operating characteristic (ROC) curve was used to determine the predictive discrimination of these models.
  
  
  RESULTS
  The area under the ROC curve of the Model 0 was significantly larger than the area under the diagonal reference line. All the other 4 models had a larger area under the ROC curve than Model 0. Considering the simplicity and lower cost of Model 2, it would be the best model to use. Nevertheless, Model 3 had the largest area under the ROC curve.
  
  
  CONCLUSIONS
  We demonstrated that Model 2 and 3 have a significantly better predictive discrimination to identify persons with normal FPG at high risk for glucose intolerance.
authors.affiliation: Department of Internal Medicine, Cardinal Tien Hospital, College of Medicine, Fu Jen Catholic University, Xindian, Taiwan, ROC.
authors.forename: Dee
authors.initials: D
authors.lastname: Pei
authors.forename: Jiunn-Diann
authors.initials: JD
authors.lastname: Lin
authors.forename: Du-An
authors.initials: DA
authors.lastname: Wu
authors.forename: Chang-Hsun
authors.initials: CH
authors.lastname: Hsieh
authors.forename: Yi-Jen
authors.initials: YJ
authors.lastname: Hung
authors.forename: Shi-Wen
authors.initials: SW
authors.lastname: Kuo
authors.forename: Ko-Lin
authors.initials: KL
authors.lastname: Kuo
authors.forename: Chung-Ze
authors.initials: CZ
authors.lastname: Wu
authors.forename: Jer-Chuan
authors.initials: JC
authors.lastname: Li
mesh_terms: D000328:Adult; D001786:Blood Glucose / Q000032:analysis*; D015992:Body Mass Index; D002784:Cholesterol / Q000097:blood; D005215:Fasting / Q000502:physiology; D005260:Female; D018149:Glucose Intolerance / Q000097:blood / Q000175:diagnosis* / Q000453:epidemiology / Q000503:physiopathology; D005951:Glucose Tolerance Test; D006801:Humans; D008297:Male; D015233:Models, Statistical; D019518:Postprandial Period / Q000502:physiology*; D011237:Predictive Value of Tests; D012372:ROC Curve; D018570:Risk Assessment; D014280:Triglycerides / Q000097:blood
chemical_list: D001786:Blood Glucose; D014280:Triglycerides; D002784:Cholesterol