title: Increasing the accuracy of oral glucose tolerance testing and extending its application to individuals with normal glucose tolerance for the prediction of type 1 diabetes: the Diabetes Prevention Trial-Type 1.
abstract: OBJECTIVE
  We assessed the extent to which both standard and alternative indexes from 2-h oral glucose tolerance testing predict type 1 diabetes and whether oral glucose tolerance tests (OGTTs) predict type 1 diabetes in individuals with normal glucose tolerance.
  
  
  METHODS
  The prediction of type 1 diabetes from baseline OGTTs was studied in 704 Diabetes Prevention Trial-Type 1 participants (islet-cell autoantibody [ICA]-positive relatives of type 1 diabetic patients). The maximum follow-up was 7.4 years. Analyses utilized receiver-operator curves (ROCs), proportional hazards models, and survival curves.
  
  
  RESULTS
  ROC areas under the curve (ROCAUCs) for both the AUC glucose (0.73 +/- 0.02) and an OGTT prediction index (0.78 +/- 0.02) were higher (P < 0.001) than those for the fasting (0.53 +/- 0.02) and 2-h glucose (0.66 +/- 0.02). ROCAUCs for the 60- and 90-min glucose (0.71 +/- 0.02 and 0.72 +/- 0.02, respectively) were also higher (P < 0.01) than those for the fasting and 2-h glucose. Among individuals with normal glucose tolerance, OGTTs were highly predictive, with 4th versus 1st quartile hazard ratios for the 2-h glucose, AUC glucose, and OGTT prediction index ranging from 3.77 to 5.30 (P < 0.001 for all).
  
  
  CONCLUSIONS
  Certain alternative OGTT indexes appear to better predict type 1 diabetes than standard OGTT indexes in ICA-positive relatives of type 1 diabetic patients. Moreover, even among those with normal glucose tolerance, OGTTs are strongly predictive. This suggests that subtle metabolic abnormalities are present several years before the diagnosis of type 1 diabetes.
authors.affiliation: Division of Endocrinology, University of Miami, P.O. Box 016960 (D110), Miami, FL 33101, USA. jsosenko@med.miami.edu
authors.forename: Jay M
authors.initials: JM
authors.lastname: Sosenko
authors.forename: Jerry P
authors.initials: JP
authors.lastname: Palmer
authors.forename: Carla J
authors.initials: CJ
authors.lastname: Greenbaum
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Mahon
authors.forename: Catherine
authors.initials: C
authors.lastname: Cowie
authors.forename: Jeffrey P
authors.initials: JP
authors.lastname: Krischer
authors.forename: H Peter
authors.initials: HP
authors.lastname: Chase
authors.forename: Neil H
authors.initials: NH
authors.lastname: White
authors.forename: Bruce
authors.initials: B
authors.lastname: Buckingham
authors.forename: Kevan C
authors.initials: KC
authors.lastname: Herold
authors.forename: David
authors.initials: D
authors.lastname: Cuthbertson
authors.forename: Jay S
authors.initials: JS
authors.lastname: Skyler
mesh_terms: D019540:Area Under Curve; D001786:Blood Glucose / Q000378:metabolism; D002096:C-Peptide / Q000097:blood; D003922:Diabetes Mellitus, Type 1 / Q000453:epidemiology* / Q000517:prevention & control*; D018149:Glucose Intolerance / Q000097:blood / Q000145:classification; D005951:Glucose Tolerance Test / Q000592:standards*; D006801:Humans; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D012372:ROC Curve; D012016:Reference Values; D015203:Reproducibility of Results; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D013997:Time Factors
chemical_list: D001786:Blood Glucose; D002096:C-Peptide