Modeling changes in glucose and glycerol rates of appearance when true basal rates of appearance cannot be readily determined.

Advancing diabetes care requires accurate physiological assessments. Hyperinsulinemic clamps with stable isotope tracers can simultaneously measure insulin's ability to suppress lipolysis and hepatic glucose release. Traditionally, these methods require an assessment of basal glucose and glycerol rate of appearance (Ra). Basal Ra is challenging to measure in insulin-dependent diabetes, where exogenous insulin required to maintain normoglycemia can raise peripheral insulin concentrations sufficiently to suppress basal Ra. Thus we identified two alternative statistical approaches to describe changes in glucose and glycerol Ra that are less reliant on basal assessments. Sixteen youths (4 type 1 diabetic, 4 type 2 diabetic, 4 lean controls, and 4 obese nondiabetic) underwent a four-phase ("basal" and 10, 16, and 80 mU·m(2)·min(-1)) hyperinsulinemic euglycemic clamp with glucose and glycerol tracers. Glucose and glycerol Ra were calculated per phase. A statistical method, the standard two-stage (STS) algorithm, was applied to the individual log insulin vs. Ra curves to calculate a single predicted Ra value. A population-based mixed-effects model (MEM) compared the group average Ra with log insulin curves and described individual deviations from group means and was used to calculate individual predicted Ra. Both models were applied to the participant data, and predicted Ras at the mean insulin concentration per phase (10 for glycerol, 16 for glucose) were calculated, with good agreement between observed and predicted values. In our data set, the MEM was better able to detect group differences. Both STS and MEM can model lipolysis and endogenous glucose release in insulin-dependent states when basal Ra cannot be accurately measured.