Population approaches to estimate minimal model indexes of insulin sensitivity and glucose effectiveness using full and reduced sampling schedules

Abstract

The intravenous glucose tolerance test (IVGTT) interpreted with the minimal model provides individual indexes of insulin sensitivity (S(I)) and glucose effectiveness (S(G)). In population studies, the traditional approach, the standard two-stage (STS) method, fails to account for uncertainty in individual estimates, resulting in an overestimation of between-subject variability. Furthermore, in the presence of reduced sampling and/or insulin resistance, individual estimates may be unobtainable, biasing population information. Therefore, we investigated the use of two population approaches, the iterative two-stage (ITS) method and nonlinear mixed-effects modeling (NM), in a population (n = 235) of insulin-sensitive and insulin-resistant subjects under full (FSS, 33 samples) and reduced [RSS(240-min), 13 samples and RSS(180-min), 12 samples] IVGTT sampling schedules. All three population methods gave similar results with the FSS. Using RSS(240), the three methods gave similar results for S(I), but S(G) population means were overestimated. With RSS(180), S(I) and S(G) population means were higher for all three methods compared with their FSS counterparts. NM estimated similar between-subject variability (19% S(G), 53% S(I)) with RSS(180), whereas ITS showed regression to the mean for S(G) (0.01% S(G), 56% S(I)) and STS provided larger population variability in S(I) (29% S(G), 91% S(I)). NM provided individual estimates for all subjects, whereas the two-stage methods failed in 16-18% of the subjects using RSS(180) and 6-14% using RSS(240). We conclude that population approaches, specifically NM, are useful in studies with a sparsely sampled IVGTT ( approximately 12 samples) of short duration ( approximately 3 h) and when individual parameter estimates in all subjects are desired.