Assessing the predictive accuracy of QUICKI as a surrogate index for insulin sensitivity using a calibration model

Abstract

The quantitative insulin-sensitivity check index (QUICKI) has an excellent linear correlation with the glucose clamp index of insulin sensitivity (SI Clamp) that is better than that of many other surrogate indexes. However, correlation between a surrogate and reference standard may improve as variability between subjects in a cohort increases (i.e., with an increased range of values). Correlation may be excellent even when prediction of reference values by the surrogate is poor. Thus, it is important to evaluate the ability of QUICKI to accurately predict insulin sensitivity as determined by the reference glucose clamp method. In the present study, we used a calibration model to compare the ability of QUICKI and other simple surrogates to predict SIClamp. Predictive accuracy was evaluated by both root mean squared error of prediction as well as a more robust leave-one-out cross-validation-type root mean squared error of prediction (CVPE). Based on data from 116 glucose clamps obtained from nonobese, obese, type 2 diabetic, and hypertensive subjects, we found that QUICKI and log (homeostasis model assessment [HOMA]) were both excellent at predicting SIClamp (CVPE = 1.45 and 1.51, respectively) and significantly better than HOMA, 1/HOMA, and fasting insulin (CVPE = 3.17, P < 0.001; 1.67, P < 0.02; and 2.85, P < 0.001, respectively). QUICKI and log(HOMA) also had the narrowest distribution of residuals (measured SIClamp - predicted SIClamp). In a subset of subjects (n = 78) who also underwent a frequently sampled intravenous glucose tolerance test with minimal model analysis, QUICKI was significantly better than the minimal model index of insulin sensitivity (SIMM) at predicting SIClamp (CVPE = 1.54 vs. 1.98, P = 0.001). We conclude that QUICKI and log(HOMA) are among the most accurate surrogate indexes for determining insulin sensitivity in humans. © 2005 by the American Diabetes Association.