CS is associated with both high mortality and a high prevalence of co-morbidities, even when biochemical cure rates are between 80% and 90%.

These findings suggest that DISST may be an easily performed dynamic test to quantify IS with high resolution, especially among those with reduced IS.

The objective was to validate the methodology for the dynamic insulin sensitivity and secretion test (DISST) and to demonstrate its potential in clinical and research settings. One hundred twenty-three men and women had routine clinical and biochemical measurements, an oral glucose tolerance test, and a DISST. For the DISST, participants were cannulated for blood sampling and bolus administration. Blood samples were drawn at t = 0, 10, 15, 25, and 35 minutes for measurement of glucose, insulin, and C-peptide. A 10-g bolus of intravenous glucose at t = 5 minutes and 1 U of intravenous insulin immediately after the t = 15 minute sample were given. Fifty participants also had a hyperinsulinemic-euglycemic clamp. Relationships between DISST insulin sensitivity (SI) and the clamp, and both DISST SI and secretion and other metabolic variables were measured. A Bland-Altman plot showed little bias in the comparison of DISST with the clamp, with DISST underestimating the glucose clamp by 0.1·10(-2)·mg·L·kg(-1)·min(-1)·pmol(-1) (90% confidence interval, -0.2 to 0). The correlation between SI as measured by DISST and the clamp was 0.82; the c unit for the receiver operating characteristic curve analysis for the 2 tests was 0.96. Metabolic variables showed significant correlations with DISST SI and the second phase of insulin release. The DISST also appears able to distinguish different insulin secretion patterns in individuals with identical SI values. The DISST is a simple, dynamic test that compares favorably with the clamp in assessing SI and allows simultaneous assessment of insulin secretion. The DISST has the potential to provide even more information about the pathophysiology of diabetes than more complicated tests.

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 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 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.

Insulin sensitivity (SI) is useful in the diagnosis, screening and treatment of diabetes. However, most current tests cannot provide an accurate, immediate or real-time estimate. The DISTq method does not require insulin or C-peptide assays like most SI tests, thus enabling real-time, low-cost SI estimation. The method uses a-posteriori parameter estimations in the absence of insulin or C-peptide assays to simulate accurate, patient-specific, insulin concentrations that enable SI identification.Mathematical functions for the a-posteriori parameter estimates were generated using data from 46 fully sampled DIST tests (glucose, insulin and C-peptide). SI values found using the DISTq from the 46 test pilot cohort and a second independent 218 test cohort correlated R=0.890 and R=0.825, respectively, to the fully sampled (including insulin and C-peptide assays) DIST SI metrics. When the a-posteriori insulin estimation functions were derived using the second cohort, correlations for the pilot and second cohorts reduced to 0.765 and 0.818, respectively.These results show accurate SI estimation is possible in the absence of insulin or Cpeptide assays using the proposed method. Such estimates may only need to be generated once and then used repeatedly in the future for isolated cohorts. The reduced correlation using the second cohort was due to this cohort's bias towards low SI insulin resistant subjects, limiting the dataset's ability to generalise over a wider range. All the correlations remain high enough for the DISTq to be a useful test for a number of clinical applications. The unique real-time results can be generated within minutes of testing as no insulin and C-peptide assays are required and may enable new clinical applications.