DISTq: An Iterative Analysis of Glucose Data for Low-Cost, Real-Time and Accurate Estimation of Insulin Sensitivity

Abstract

Insulin sensitivity (SI) estimation has numerous uses in medical and clinical situations. However, highresolution tests that are useful for clinical diagnosis and monitoring are often too intensive, long and costly for regular use. Simpler tests that mitigate these issues are not accurate enough for many clinical diagnostic or monitoring scenarios. The gap between these tests presents an opportunity for new approaches.The quick dynamic insulin sensitivity test (DISTq) utilises the model-based DIST test protocol and a series of population estimates to eliminate the need for insulin or C-peptide assays to enable a high resolution, low-intensity, real-time evaluation of SI. The method predicts patient specific insulin responses to the DIST test protocol with enough accuracy to yield a useful clinical insulin sensitivity metric for monitoring of diabetes therapy.The DISTq method replicated the findings of the fully sampled DIST test without the use of insulin or C-peptide assays. Correlations of the resulting SI values was R=0.91. The method was also compared to the euglycaemic hyperinsulinaemic clamp (EIC) in an in-silico Monte-Carlo analysis and showed a good ability to re-evaluate SI EIC (R=0.89), compared to the fully sampled DIST (R=0.98) Population-derived parameter estimates using a-posteriori population-based functions derived from DIST test data enables the simulation of insulin profiles that are sufficiently accurate to estimate SI to a relatively high precision. Thus, costly insulin and C-peptide assays are not necessary to obtain an accurate, but inexpensive, real-time estimate of insulin sensitivity. This estimate has enough resolution for SI prediction and monitoring of response to therapy. In borderline cases, re-evaluation of stored (frozen) blood samples for insulin and C-peptide would enable greater accuracy where necessary, enabling a hierarchy of tests in an economical fashion.

Understanding insulin and nutrition administration in glycemic control with the right side of the brain

“…2.1 Fundamental Dynamics: Figure 1 shows the fundamental metabolic physiological dynamics considered [45,46], including primary routes of glucose appearance and delivery, insulin appearance and delivery, and their uptake and use leading to a net blood glucose level. A directly related mathematical model in [46] is validated in clinical glycemic control [47][48][49][50], insulin sensitivity testing [51][52][53][54], and virtual patients [55][56][57]. For this analysis, time-varying and steady state levels of insulin (plasma I(t) leading to interstitial Q(t) à Q SS in steady state), total nutrition (P(t) à P SS ) and glucose level (G(t) à G SS ) are used.…”

A Spectrum of Dynamic Insulin Sensitivity Test Protocols

“…Five of the sampling protocols were based on DISTq identification methods. 17 Thus, these protocols could not provide estimates of patient-specific first and subsequent pass hepatic extraction (x L and n L , respectively) and U N values.…”

“…[14][15][16] Subsequent investigation has shown that the cost of the original DISST could be significantly reduced by eliminating the insulin and C-peptide assays with only a moderate associated reduction in accuracy. 17,18 Using only glucose measurements, the quick dynamic insulin sensitivity test (DISTq) is able to identify SI in real time. 17,18 Although the DISTq can generate participant-specific SI values at a substantially lower cost, the standard DISST provides metrics of both SI and U N to provide a comprehensive observation of the participant's metabolic health.…”

“…17,18 Using only glucose measurements, the quick dynamic insulin sensitivity test (DISTq) is able to identify SI in real time. 17,18 Although the DISTq can generate participant-specific SI values at a substantially lower cost, the standard DISST provides metrics of both SI and U N to provide a comprehensive observation of the participant's metabolic health.…”

Determining the relative efficacy of a number of PID and PD models that relate insulin secretion to bolus induced glucose excursions

“…Experimental data was fit to the DISST model using the iterative integral method (IIM) (Docherty et al, 2012, Docherty et al, 2009. Initially, C-peptide data was deconvoluted using Equations 1-2 to define U N .…”