The minimal model method is widely used to estimate glucose effectiveness (S(G)) and insulin sensitivity (S(I)) from intravenous glucose tolerance test (IVGTT) data. In the standard protocol (sIVGTT, 0.33 g/kg glucose bolus given at time 0), which allows the simultaneous assessment of beta-cell function, the precision of the individualized estimates often degrades and particularly so in the ... [Show full abstract] presence of reduced sampling schedules. Here, we investigated the use of a population approach, the iterative two-stage (ITS) approach, to analyze 16 sIVGTTs in healthy subjects and to obtain refined estimates of S(G) and S(I) in the population and in the individual subjects. The ITS is based on calculation of the population mean and standard deviation of the parameters at each iteration and then use of them as prior information for the individual analyses. Theoretically, the use of a prior in the ITS should improve the precision of the individual estimates. The customary approach (standard two stage, STS), where modeling is performed separately for each individual subject, does not take the population knowledge into account. We used both frequent (FSS, 30 samples) and (quasi-optimally) reduced (RSS, 14 samples) sampling schedules. For the FSS, STS gave estimates (mean +/- SD) for S(G) = 2.66 +/- 1.09 x 10(-2). min(-1) and S(I) = 6.46 +/- 6.99 10(-4). min(-1). microU(-1). ml, with an average precision of 51 (range 5-176) and 33% (3-91), respectively. RSS radically worsened the precision of both S(G) and S(I). However, RSS and ITS gave S(G) = 2.59 +/- 0.73 and S(I) = 6.06 +/- 7.28, with an average precision of 23 (12-42) and 27% (), respectively. In conclusion, population minimal modeling of sIVGTT data improves the precision of individual estimates of glucose effectiveness and insulin sensitivity, as the theory predicts, and, even with reduced sampling, the improvement is substantial.