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A spectrum of dynamic insulin sensitivity test protocols.

Paul D Docherty¹, J Geoffrey Chase, Lisa Te Morenga

¹Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand. paul.docherty@canterbury.ac.nz

Journal of Diabetes Science and Technology

|January 10, 2012

Summary

The dynamic insulin sensitivity and secretion test (DISST) offers cost-effective insulin sensitivity (SI) and secretion (UN) estimation. Variations of the DISST allow researchers to balance accuracy and cost for optimal study design.

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Area of Science:

Endocrinology
Metabolic Research
Clinical Diagnostics

Background:

Established insulin sensitivity (SI) tests are often expensive or unreliable.
The dynamic insulin sensitivity and secretion test (DISST) provides a cost-effective method for assessing SI and endogenous insulin secretion (UN).
Previous findings indicate DISST SI values are robust to assay omissions.

Purpose of the Study:

To evaluate eight DISST-based variations with omitted assays.
To determine the impact of assay omissions on the accuracy of SI and UN quantification.
To explore a spectrum of DISST configurations balancing cost, accuracy, and clinical intensity.

Main Methods:

Eight DISST variations were created by omitting assays from the standard nine-sample regimen.
SI and UN were calculated using both the full DISST and its variations.
Data from 218 nine-sample tests in 74 women at elevated diabetes risk were analyzed.

Main Results:

Median differences in SI between the full DISST and variants ranged from -17.9% to 7.8%.
Correlations for SI ranged from r = 0.71 to 0.92, with higher values for variants closer to the nine-sample DISST.
UN correlations were strong (r = 0.72 to 1) when C-peptide was assayed, but variable otherwise (r = -0.14 to 0.75).

Conclusions:

The DISST-based spectrum provides flexible test configurations.
Researchers can select optimal DISST variations to balance information yield, accuracy, cost, and clinical effort.
This approach facilitates better resource allocation in metabolic research.