What is the best predictor of future type 2 diabetes?

Muhammad A. Abdul-Ghani, Ken Williams, Ralph A. DeFronzo, Michael Stern

Research output: Contribution to journalArticle

230 Scopus citations

Abstract

OBJECTIVE - We sought to assess insulin secretion/insulin resistance index in predicting the risk for future type 2 diabetes RESEARCH DESIGN AND METHODS- A total of 1,551 nondiabetic subjects from the San Antonio Heart Study received an oral glucose tolerance test (OGTT) with measurement of plasma glucose and insulin concentrations at 0, 30, 60, and 120 min at baseline and after 7-8 years of follow-up. Insulin secretion/insulin resistance index was calculated as the product of Matsuda index and ΔI0-30/ΔG0-30 or ΔI0-120/ΔG0-120. The discriminatory power of various prediction models for development of type 2 diabetes was tested with the area under the receiver-operating characteristic (ROC) curve. RESULTS - Insulin secretion/insulin resistance index (0- to 30- and 0- to 120-min time periods) had the greatest areas under the ROC curve (0.85 and 0.86, respectively), which were significantly greater than the 2-h plasma glucose concentration during the OGTT or the San Antonio Diabetes Prediction Model (SADPM) (P < 0.001 and P < 0.0001, respectively). A model based on the combination of the SADPM and a modified version of the insulin secretion/insulin resistance index or 1-h plasma glucose concentration had equal power to predict the risk for future type 2 diabetes compared with the insulin secretion/insulin resistance index. CONCLUSIONS - The insulin secretion/insulin resistance index is useful as a predictor of future development of type 2 diabetes. A model based on the combination of the SADPM and either a modified version of the insulin secretion/insulin resistance index or 1-h plasma glucose concentration can equally predict future type 2 diabetes.

Original languageEnglish (US)
Pages (from-to)1544-1548
Number of pages5
JournalDiabetes care
Volume30
Issue number6
DOIs
StatePublished - May 2007

ASJC Scopus subject areas

  • Internal Medicine
  • Endocrinology, Diabetes and Metabolism
  • Advanced and Specialized Nursing

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