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1 Department of Bioengineering, University of Washington, Seattle, Washington 98195; and 2 Department of Electronics and Informatics, University of Padova, Padua, Italy 35123
The minimal model
method is widely used to estimate glucose effectiveness
(SG) and insulin sensitivity (SI) 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 
-cell function, the
precision of the individualized estimates often degrades and
particularly so in the 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 SG and SI 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
SG = 2.66 ± 1.09 × 10
2 · min1 and SI = 6.46 ± 6.99 104 · min1 · µU1 · ml,
with an average precision of 51 (range 5-176) and 33%
(3-91), respectively. RSS radically worsened the
precision of both SG and SI. However, RSS and
ITS gave SG = 2.59 ± 0.73 and
SI = 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.
glucose effectiveness; insulin sensitivity; parameter estimation
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