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This Article Full Text Full Text (PDF) All Versions of this Article: 291/1/E167    most recent 00328.2004v1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via ISI Web of Science (1) Citing Articles via Google Scholar Google Scholar Articles by Godsland, I. F. Articles by Hovorka, R. Search for Related Content PubMed PubMed Citation Articles by Godsland, I. F. Articles by Hovorka, R.

Evaluation of nonlinear regression approaches to estimation of insulin sensitivity by the minimal model with reference to Bayesian hierarchical analysis

¹Endocrinology and Metabolic Medicine, Faculty of Medicine, Imperial College London; and ²Diabetes Modelling Group, Department of Paediatrics, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom

Submitted 26 July 2004 ; accepted in final form 5 February 2006

Minimal model analysis of intravenous glucose tolerance test (IVGTT) glucose and insulin concentrations offers a validated approach to measuring insulin sensitivity, but model identification is not always successful. Improvements may be achieved by using alternative settings in the modeling process, although results may differ according to setting, and care must be exercised in combining results. IVGTT data (12 samples, regular test) from 533 men without diabetes was modeled by the traditional nonlinear regression (NLR) approach, using five different permutations of settings. Results were evaluated with reference to the more robust Bayesian hierarchical (BH) approach to model identification and to the proportion of variance they explained in known correlates of insulin sensitivity (age, BMI, blood pressure, fasting glucose and insulin, serum triglyceride, HDL cholesterol, and uric acid concentration). BH analysis was successful in all cases. With NLR analysis, between 17 and 35 IVGTTs were associated with parameter coefficients of variation (PCVs) for minimal model parameters S_I (insulin sensitivity) and S_G (glucose effectiveness) of >100%. Systematic use of each different approach in combination reduced this number to five. Mean (interquartile range) S_I^NLR was then 3.14 (2.29–4.63) min^–1·mU^–1·l x 10^–4 and 2.56 (1.74–3.83) min^–1·mU^–1·l x 10^–4 for S_I^BH (correlation 0.86, P < 0.0001). S_I^NLR explained, on average, 10.6% of the variance in known correlates of insulin sensitivity, whereas S_I^BH explained 8.5%. In a large body of data, which BH analysis demonstrated could be fully identified, use of alternative modeling settings in NLR analysis could substantially reduce the number of analyses with PCVs >100%. S_I^NLR compared favorably with S_I^BH in the proportion of variance explained in known correlates of insulin sensitivity.

parameter estimation; population modeling; metabolic syndrome