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Prediction of Type 2 Diabetes Using Simple Measures of Insulin Resistance

Combined Results From the San Antonio Heart Study, the Mexico City Diabetes Study, and the Insulin Resistance Atherosclerosis Study

  1. Anthony J.G. Hanley12,
  2. Ken Williams1,
  3. Clicerio Gonzalez3,
  4. Ralph B. D’Agostino, Jr4,
  5. Lynne E. Wagenknecht4,
  6. Michael P. Stern1 and
  7. Steven M. Haffner1
  1. 1Division of Clinical Epidemiology, University of Texas Health Sciences Center, San Antonio, Texas
  2. 2Leadership Sinai Centre for Diabetes, Mt. Sinai Hospital, Toronto, Ontario, Canada
  3. 3Centro de Estudios en Diabetes, American British Coudray Hospital, Endocrinology and Metabolism Service, Division of Internal Medicine, Mexican Social Security Institute, Mexico City, Mexico
  4. 4Department of Public Health Sciences, Wake Forest University School of Medicine, Winston Salem, North Carolina

    Abstract

    To determine and formally compare the ability of simple indexes of insulin resistance (IR) to predict type 2 diabetes, we used combined prospective data from the San Antonio Heart Study, the Mexico City Diabetes Study, and the Insulin Resistance Atherosclerosis Study, which include well-characterized cohorts of non-Hispanic white, African-American, Hispanic American, and Mexican subjects with 5–8 years of follow-up. Poisson regression was used to assess the ability of each candidate index to predict incident diabetes at the follow-up examination (343 of 3,574 subjects developed diabetes). The areas under the receiver operator characteristic (AROC) curves for each index were calculated and statistically compared. In pooled analysis, Gutt et al.’s insulin sensitivity index at 0 and 120 min (ISI0,120) displayed the largest AROC (78.5%). This index was significantly more predictive (P < 0.0001) than a large group of indexes (including those by Belfiore, Avignon, Katz, and Stumvoll) that had AROC curves between 66 and 74%. These findings were essentially similar both after adjustment for covariates and when analyses were conducted separately by glucose tolerance status and ethnicity/study subgroups. In conclusion, we found substantial differences between published IR indexes in the prediction of diabetes, with ISI0,120 consistently showing the strongest prediction. This index may reflect other aspects of diabetes pathogenesis in addition to IR, which might explain its strong predictive abilities despite its moderate correlation with direct measures of IR.

    Footnotes

    • Address correspondence and reprint requests to Dr. Steven Haffner, Division of Clinical Epidemiology, University of Texas Health Science Center at San Antonio, Mail Code 7873, 7703 Floyd Curl Dr., San Antonio, TX, 78229-3900. E-mail: haffner{at}uthscsa.edu.

      Received for publication 18 September 2002 and accepted in revised form 29 October 2002.

      AROC; area under the receiver operator characteristic; FIRI, Duncan’s fasting insulin resistance index; FSIGT, frequently sampled intravenous glucose tolerance test; HOMA, homeostasis model assessment; IGT, impaired glucose tolerance; IR, insulin resistance; IRAS, Insulin Resistance Atherosclerosis Study; ISI, insulin sensitivity index; ISI0,120, insulin sensitivity index at 0 and 120 min; MCDS, Mexico City Diabetes Study; NGT, normal glucose tolerance; OGTT, oral glucose tolerance test; QUICKI, Quantitative Insulin Sensitivity Check Index; SAHS, San Antonio Heart Study; SI, insulin sensitivity; SiM, Avignon’s insulin sensitivity index; Stum_nodem, Stumvoll index without demographic variables.

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