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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 Division of Clinical Epidemiology, University of Texas Health Sciences Center, San Antonio, Texas
² Leadership Sinai Centre for Diabetes, Mt. Sinai Hospital, Toronto, Ontario, Canada
³ Centro de Estudios en Diabetes, American British Coudray Hospital, Endocrinology and Metabolism Service, Division of Internal Medicine, Mexican Social Security Institute, Mexico City, Mexico
⁴ Department of Public Health Sciences, Wake Forest University School of Medicine, Winston Salem, North Carolina

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 (ISI_0,120) 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 ISI_0,120 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.

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