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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

The SI shift measured in-silico by the DIST and DISTq methods compared to the clinically measured euglycaemic clamp and HOMA derived SI shift. The SI values are sorted increasing from left to right by the SI shift observed between euglycaemic clamp tests. The red line shows the clamp derived SI shift overlaid on the results from the other tests.
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Figure 7: The SI shift measured in-silico by the DIST and DISTq methods compared to the clinically measured euglycaemic clamp and HOMA derived SI shift. The SI values are sorted increasing from left to right by the SI shift observed between euglycaemic clamp tests. The red line shows the clamp derived SI shift overlaid on the results from the other tests.

Mentions: The potentially most important component of an SI test is the ability to define changes in SI accurately over time, to monitor intervention or treatment. As expected, the DIST test showed a stronger ability to capture the insulin sensitivity shift seen in the euglycaemic clamp tests (R=0.97) than the DISTq (R=0.92). Both DIST methods performed significantly better than HOMA (R=-0.22). Fig. (7) shows the SI shift over four tests, sorted by increasing SI shift according to the clinically measured change in euglycaemic clamp results for the 73 subjects of the lifestyle intervention trial used in the Monte Carlo analysis [20]. It is visually clear that the DIST and DISTq both capture the clinically relevant changes, and that the HOMA metric is not clinically effective for tracking these changes due to intervention or any equivalently modest but clinically significant changes over time.


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)

The SI shift measured in-silico by the DIST and DISTq methods compared to the clinically measured euglycaemic clamp and HOMA derived SI shift. The SI values are sorted increasing from left to right by the SI shift observed between euglycaemic clamp tests. The red line shows the clamp derived SI shift overlaid on the results from the other tests.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: The SI shift measured in-silico by the DIST and DISTq methods compared to the clinically measured euglycaemic clamp and HOMA derived SI shift. The SI values are sorted increasing from left to right by the SI shift observed between euglycaemic clamp tests. The red line shows the clamp derived SI shift overlaid on the results from the other tests.
Mentions: The potentially most important component of an SI test is the ability to define changes in SI accurately over time, to monitor intervention or treatment. As expected, the DIST test showed a stronger ability to capture the insulin sensitivity shift seen in the euglycaemic clamp tests (R=0.97) than the DISTq (R=0.92). Both DIST methods performed significantly better than HOMA (R=-0.22). Fig. (7) shows the SI shift over four tests, sorted by increasing SI shift according to the clinically measured change in euglycaemic clamp results for the 73 subjects of the lifestyle intervention trial used in the Monte Carlo analysis [20]. It is visually clear that the DIST and DISTq both capture the clinically relevant changes, and that the HOMA metric is not clinically effective for tracking these changes due to intervention or any equivalently modest but clinically significant changes over time.

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