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

Docherty PD, Chase JG, Lotz T, Hann CE, Shaw GM, Berkeley JE, Mann JI, McAuley K - Open Med Inform J (2009)

Bottom Line: The gap between these tests presents an opportunity for new approaches.Correlations of the resulting SI values was R=0.91.This estimate has enough resolution for SI prediction and monitoring of response to therapy.

View Article: PubMed Central - PubMed

Affiliation: Department of Mechanical Engineering, University of Canterbury, New Zealand.

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.

No MeSH data available.


Related in: MedlinePlus

Relationships between SIDIST from the fully sampled DIST tests and SIDISTq for the basic DISTq method (left), and with the additional variation of liver clearance cycle (right).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2812840&req=5

Figure 5: Relationships between SIDIST from the fully sampled DIST tests and SIDISTq for the basic DISTq method (left), and with the additional variation of liver clearance cycle (right).

Mentions: The basic DISTq method showed a strong correlation to the fully sampled DIST test SI (R=0.86), which improved to R=0.91 using the nL parameter variation method. Fig. (5) shows the relationship between the SIDIST solved with the full data set and the iterative-integral method, and the SIDISTq using limited data and the DISTq method.


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

Docherty PD, Chase JG, Lotz T, Hann CE, Shaw GM, Berkeley JE, Mann JI, McAuley K - Open Med Inform J (2009)

Relationships between SIDIST from the fully sampled DIST tests and SIDISTq for the basic DISTq method (left), and with the additional variation of liver clearance cycle (right).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2812840&req=5

Figure 5: Relationships between SIDIST from the fully sampled DIST tests and SIDISTq for the basic DISTq method (left), and with the additional variation of liver clearance cycle (right).
Mentions: The basic DISTq method showed a strong correlation to the fully sampled DIST test SI (R=0.86), which improved to R=0.91 using the nL parameter variation method. Fig. (5) shows the relationship between the SIDIST solved with the full data set and the iterative-integral method, and the SIDISTq using limited data and the DISTq method.

Bottom Line: The gap between these tests presents an opportunity for new approaches.Correlations of the resulting SI values was R=0.91.This estimate has enough resolution for SI prediction and monitoring of response to therapy.

View Article: PubMed Central - PubMed

Affiliation: Department of Mechanical Engineering, University of Canterbury, New Zealand.

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.

No MeSH data available.


Related in: MedlinePlus