Increase in hepatic and decrease in peripheral insulin clearance characterize abnormal temporal patterns of serum insulin in diabetic subjects

Abstract

Insulin plays a central role in glucose homeostasis, and impairment of insulin action causes glucose intolerance and leads to type 2 diabetes mellitus (T2DM). A decrease in the transient peak and sustained increase of circulating insulin following an infusion of glucose accompany T2DM pathogenesis. However, the mechanism underlying this abnormal temporal pattern of circulating insulin concentration remains unknown. Here we show that changes in opposite direction of hepatic and peripheral insulin clearance characterize this abnormal temporal pattern of circulating insulin concentration observed in T2DM. We developed a mathematical model using a hyperglycemic and hyperinsulinemic-euglycemic clamp in 111 subjects, including healthy normoglycemic and diabetic subjects. The hepatic and peripheral insulin clearance significantly increase and decrease, respectively, from healthy to borderline type and T2DM. The increased hepatic insulin clearance reduces the amplitude of circulating insulin concentration, whereas the decreased peripheral insulin clearance changes the temporal patterns of circulating insulin concentration from transient to sustained. These results provide further insight into the pathogenesis of T2DM, and thus may contribute to develop better treatment of this condition.

Fig. 1

Concentrations of plasma glucose, serum insulin, and C-peptide during consecutive hyperglycemic and hyperinsulinemic-euglycemic clamps. The mean ± SD among the subjects for NGT (green, n = 50), borderline type (red, n = 18), and T2DM (blue, n = 53) of experimental (upper 3 panels) and simulation with Model VI (lower 2 panels) time courses are shown. Hyperglycemic clamp (HGC) was performed for 90 min and hyperinsulinemic-euglycemic clamp (HEC) for 120 min with a 10-min interval. The plasma glucose level is the average value calculated every 5 min of the measurements made every 1 min, and the serum insulin and C-peptide levels are measured values at sampling time (Methods). Simulation time courses are plotted every 10 min. Supplementary Figure S1 and Supplementary Table S1 illustrate the significant difference of concentrations at each time point among the three groups

Fig. 2

Mathematical model of serum insulin and C-peptide. a The structure of the model (see also Eqs. 1–4 and Model VI in Supplementary Figure S2). I and CP are serum insulin and C-peptide concentration, respectively. X is the amount of stored insulin and C-peptide, and Y is the provision rate controlled by plasma glucose concentration, G. Arrows indicate fluxes with corresponding parameters (red). b The estimated parameters for the NGT (green), borderline type (red), and T2DM (blue) subjects. Each dot corresponds to the indicated parameter for an individual subject. c The parameters of k_Iout and (1 − k_ratio), corresponding to peripheral and hepatic insulin clearance, respectively. *P < 0.05, **P < 0.01, NS not significant (two-sided Wilcoxon rank sum test with FDR-correction). Post-hoc statistical power analysis is shown in Supplementary Table S5. The bar and error bar show the median and lower and upper quartiles, respectively. Each dot corresponds to the indicated parameter for an individual subject

Fig. 3

Model parameters showing the highest correlation with clinical indices. a Scatter plots for the indicated measured clinical indices versus the highest correlated model parameters (Supplementary Table S6). ISI insulin sensitivity index, MCR metabolic clearance rate, AUC_IRI10 amount of insulin secretion during the first 10 min of hyperglycemic clamp. Each dot indicates the value of an individual subject. The correlation coefficient, r, and the P value for testing the hypothesis of no correlation are shown. The partial correlation coefficients among k_Iout, ISI, and MCR are shown in Supplementary Figure S5. b Summary of the model parameters k_Iout and k_ratio showing the highest correlation with the indicated clinical indices

Fig. 4

The roles of k_ratio and k_Iout in the amplitude and temporal patterns of serum insulin concentration. a Simulated time course of serum insulin concentration I during hyperglycemic clamp of subject #3 by changing k_ratio or k_Iout or both by scaling the fitted parameter value with 2^−1.0, 2^−0.5, 1, 2^0.5, and 2^1.0 (see Methods). Dotted arrows indicate the direction of the change in the temporal pattern as the parameter increases. b The definition of ipeak (incremental peak) and iTPI (incremental transient peak index), reflecting the peak amplitude and the temporal pattern of serum insulin concentration I. c ipeak and iTPI of I of subject #3 by changing k_ratio or k_Iout or both

Fig. 5

Overview of our study and main results. Mathematical modeling based on hyperglycemic and hyperinsulinemic-euglycemic clamp (glucose and insulin clamp) data in subjects showed changes in opposite direction of hepatic and peripheral insulin clearance from NGT to T2DM. Hepatic insulin clearance (1−k_ratio) increases and peripheral insulin clearance k_Iout decreases, characterizing the decrease in peak amplitude and the change in the temporal pattern of serum insulin concentration from transient to sustained, respectively