Modelling the effect of insulin on the disposal of meal-attributable glucose in type 1 diabetes

Abstract

The management of postprandial glucose excursions in type 1 diabetes has a major impact on overall glycaemic control. In this work, we propose and evaluate various mechanistic models to characterize the disposal of meal-attributable glucose. Sixteen young volunteers with type 1 diabetes were subject to a variable-target clamp which replicated glucose profiles observed after a highglycaemic-load (n = 8) or a low-glycaemic-load (n = 8) evening meal. [6,6-2 H 2 ] and [U-13 C;1,2,3,4,5,6,6-2 H 7 ] glucose tracers were infused to, respectively, mimic: (a) the expected post-meal suppression of endogenous glucose production and (b) the appearance of glucose due to a standard meal. Six compartmental models (all a priori identifiable) were proposed to investigate the remote effect of circulating plasma insulin on the disposal of those glucose tracers from the non-accessible compartments, representing e.g. interstitium. An iterative population-based parameter fitting was employed. Models were evaluated attending to physiological plausibility, posterior identifiability of their parameter estimates, accuracy-via weighted fitting residuals-and information criteria (i.e. parsimony). The most plausible model, best representing our experimental data, comprised: (1) a remote effect x of insulin active above a threshold x c = 1.74 (0.81-2.50) • 10~2 min -1 [median (inter-quartile range)], with parameter XQ having a satisfactory support: coefficient of variation CV = 42.33 (31.34-65.34) %, and (2) steady-state conditions at the onset of the experiment (t = 0) for the compartment representing the remote effect, but not for the masses of the tracer that mimicked endogenous glucose production. Consequently, our mechanistic model suggests non-homogeneous changes in the disposal rates for meal-attributable glucose in relation to plasma insulin. The model can be applied to the in silico simulation of meals for the optimization of postprandial insulin infusion regimes in type 1 diabetes.

A hybrid dynamic wavelet-based modeling method for blood glucose concentration prediction in type 1 diabetes

“…[ 37 ] In previous works, prior knowledge, correlation analysis,[ 21 ] and principal component analysis[ 29 ] have been used to select the main effective model inputs concerning BGC prediction, while the effect of severity of each input has somehow varied from person to person. [ 24 38 ] Therefore, important regressors should be selected from the input dynamic regressor space. The input dynamic regressor space is a set that includes input variables with varying time lags.…”

Identifiability of Control-Oriented Glucose-Insulin Linear Models: Review and Analysis

“…Based on the maximal model in [52], the authors got rid of one of the insulin compartments to avoid identifiability issues. In addition, in [72] 6 models were proposed (3 of them are linear) to characterize exogenous insulin's influence on postprandial glucose kinetics. However, this time, the accessible compartments represented plasma, and the non-accessible compartments corresponded to other tissues as the interstitium.…”

Integrating Economic Model Predictive Control and Event-Triggered Control: Application to Bi-Hormonal Artificial Pancreas System

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