title: Simple modeling allows prediction of steady-state glucose disposal rate from early data in hyperinsulinemic glucose clamps.
abstract: After a constant insulin infusion is initiated, determination of steady-state conditions for glucose infusion rates (GIR) typically requires >or=3 h. The glucose infusion follows a simple time-dependent rise, reaching a plateau at steady state. We hypothesized that nonlinear fitting of abbreviated data sets consisting of only the early portion of the clamp study can provide accurate estimates of steady-state GIR. Data sets from two independent laboratories were used to develop and validate this approach. Accuracy of the predicted steady-state GDR was assessed using regression analysis and Altman-Bland plots, and precision was compared by applying a calibration model. In the development data set (n = 88 glucose clamp studies), fitting the full data set with a simple monoexponential model predicted reference GDR values with good accuracy (difference between the 2 methods -0.37 mg x kg(-1) x min(-1)) and precision [root mean square error (RMSE) = 1.11], validating the modeling procedure. Fitting data from the first 180 or 120 min predicted final GDRs with comparable accuracy but with progressively reduced precision [fitGDR-180 RMSE = 1.27 (P = NS vs. fitGDR-full); fitGDR-120 RMSE = 1.56 (P < 0.001)]. Similar results were obtained with the validation data set (n = 183 glucose clamp studies), confirming the generalizability of this approach. The modeling approach also derives kinetic parameters that are not available from standard approaches to clamp data analysis. We conclude that fitting a monoexponential curve to abbreviated clamp data produces steady-state GDR values that accurately predict the GDR values obtained from the full data sets, albeit with reduced precision. This approach may help reduce the resources required for undertaking clamp studies.
authors.affiliation: Indiana University School of Medicine, Indianapolis, USA.
authors.forename: Pooja
authors.initials: P
authors.lastname: Singal
authors.forename: Ranganath
authors.initials: R
authors.lastname: Muniyappa
authors.forename: Robin
authors.initials: R
authors.lastname: Chisholm
authors.forename: Gail
authors.initials: G
authors.lastname: Hall
authors.forename: Hui
authors.initials: H
authors.lastname: Chen
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Quon
authors.forename: Kieren J
authors.initials: KJ
authors.lastname: Mather
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D000704:Analysis of Variance; D001786:Blood Glucose / Q000378:metabolism*; D004305:Dose-Response Relationship, Drug; D005260:Female; D015309:Glucose Clamp Technique / Q000379:methods*; D006706:Homeostasis; D006801:Humans; D006946:Hyperinsulinism / Q000139:chemically induced / Q000378:metabolism*; D007262:Infusions, Intravenous; D007328:Insulin / Q000008:administration & dosage / Q000097:blood*; D007700:Kinetics; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D013997:Time Factors; D055815:Young Adult
chemical_list: D001786:Blood Glucose; D007328:Insulin