Michael Glicksman1, Shivraj Grewal1, Shrayus Sortur1, Brent S Abel1, Sungyoung Auh1, Trudy R Gaillard2, Kwame Osei3, Ranganath Muniyappa4. 1. Diabetes, Endocrinology, and Obesity Branch, NIDDK, Bethesda, MD, USA. 2. Nicole Wertheim College of Nursing and Health Sciences, Florida International University, Miami, FL, USA. 3. Division of Endocrinology, Diabetes and Metabolism, Wexner Medical Center, The Ohio State University, Columbus, OH, USA. 4. Diabetes, Endocrinology, and Obesity Branch, NIDDK, Bethesda, MD, USA. muniyapr@mail.nih.gov.
Abstract
PURPOSE: Current reference methods for measuring glucose effectiveness (GE) are the somatostatin pancreatic glucose clamp and minimal model analysis of frequently sampled intravenous glucose tolerance test (FSIVGTT), both of which are laborious and not feasible in large epidemiological studies. Consequently, surrogate indices derived from an oral glucose tolerance test (OGTT) to measure GE (oGE) have been proposed and used in many studies. However, the predictive accuracy of these surrogates has not been formally validated. In this study, we used a calibration model analysis to evaluate the accuracy of surrogate indices to predict GE from the reference FSIVGTT (SgMM). METHODS: Subjects (n = 123, mean age 48 ± 11 years; BMI 35.9 ± 7.3 kg/m2) with varying glucose tolerance (NGT, n = 37; IFG/IGT, n = 78; and T2DM, n = 8) underwent FSIVGTT and OGTT on two separate days. Predictive accuracy was assessed by both root mean squared error (RMSE) of prediction and leave-one-out cross-validation-type RMSE of prediction (CVPE). RESULTS: As expected, insulin sensitivity, SgMM, and oGE were reduced in subjects with T2DM and IFG/IGT when compared with NGT. Simple linear regression analyses revealed a modest but significant relationship between oGE and SgMM (r = 0.25, p < 0.001). However, using calibration model, measured SgMM and predicted SgMM derived from oGE were modestly correlated (r = 0.21, p < 0.05) with the best fit line suggesting poor predictive accuracy. There were no significant differences in CVPE and RMSE among the surrogates, suggesting similar predictive ability. CONCLUSIONS: Although OGTT-derived surrogate indices of GE are convenient and feasible, they have limited ability to robustly predict GE.
PURPOSE: Current reference methods for measuring glucose effectiveness (GE) are the somatostatin pancreatic glucose clamp and minimal model analysis of frequently sampled intravenous glucose tolerance test (FSIVGTT), both of which are laborious and not feasible in large epidemiological studies. Consequently, surrogate indices derived from an oral glucose tolerance test (OGTT) to measure GE (oGE) have been proposed and used in many studies. However, the predictive accuracy of these surrogates has not been formally validated. In this study, we used a calibration model analysis to evaluate the accuracy of surrogate indices to predict GE from the reference FSIVGTT (SgMM). METHODS: Subjects (n = 123, mean age 48 ± 11 years; BMI 35.9 ± 7.3 kg/m2) with varying glucose tolerance (NGT, n = 37; IFG/IGT, n = 78; and T2DM, n = 8) underwent FSIVGTT and OGTT on two separate days. Predictive accuracy was assessed by both root mean squared error (RMSE) of prediction and leave-one-out cross-validation-type RMSE of prediction (CVPE). RESULTS: As expected, insulin sensitivity, SgMM, and oGE were reduced in subjects with T2DM and IFG/IGT when compared with NGT. Simple linear regression analyses revealed a modest but significant relationship between oGE and SgMM (r = 0.25, p < 0.001). However, using calibration model, measured SgMM and predicted SgMM derived from oGE were modestly correlated (r = 0.21, p < 0.05) with the best fit line suggesting poor predictive accuracy. There were no significant differences in CVPE and RMSE among the surrogates, suggesting similar predictive ability. CONCLUSIONS: Although OGTT-derived surrogate indices of GE are convenient and feasible, they have limited ability to robustly predict GE.
Entities:
Keywords:
Accuracy; Diabetes; Glucose effectiveness; Surrogate index
Authors: Jan Erik Henriksen; Frank Alford; Glenn Ward; Peter Thye-Rønn; Klaus Levin; Ole Hother-Nielsen; Christian Rantzau; Ray Boston; Henning Beck-Nielsen Journal: Diabetes Metab Res Rev Date: 2010-10 Impact factor: 4.876