DISTq: an iterative analysis of glucose data for low-cost, real-time and accurate estimation of insulin sensitivity

摘要

Insulin sensitivity (SI) estimation has numerous uses in medical and clinical situations. However, highresolutiontests 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. Thegap 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 populationestimates to eliminate the need for insulin or C-peptide assays to enable a high resolution, low-intensity, real-timeevaluation of SI. The method predicts patient specific insulin responses to the DIST test protocol with enough accuracy toyield 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 hyperinsulinaemicclamp (EIC) in an in-silico Monte-Carlo analysis and showed a good ability to re-evaluate SIEIC (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 enablesthe simulation of insulin profiles that are sufficiently accurate to estimate SI to a relatively high precision. Thus, costlyinsulin and C-peptide assays are not necessary to obtain an accurate, but inexpensive, real-time estimate of insulinsensitivity. This estimate has enough resolution for SI prediction and monitoring of response to therapy. In borderlinecases, re-evaluation of stored (frozen) blood samples for insulin and C-peptide would enable greater accuracy wherenecessary, enabling a hierarchy of tests in an economical fashion.