BACKGROUND: Numerous tests have been developed to estimate insulin sensitivity (SI). However, most of the established tests are either too expensive for widespread application or do not yield reliable results. The dynamic insulin sensitivity and secretion test (DISST) uses assays of glucose, insulin, and C-peptide from nine samples to quantify SI and endogenous insulin secretion (UN) at a comparatively low cost. The quick dynamic insulin sensitivity test has shown that the DISST SI values are robust to significant assay omissions. METHODS: Eight DISST-based variations of the nine-sample assay regimen are proposed to investigate the effects of assay omission within the DISST-based framework. SI and UN were identified using the fully-sampled DISST and data from 218 nine-sample tests undertaken in 74 female individuals with elevated diabetes risk. This same data was then used with appropriate assay omissions to identify SI and UN with the eight DISST-based assay variations. RESULTS: Median intraprocedure proportional difference between SI values from fully-sampled DISST and the DISST-based variants was in the range of -17.9 to 7.8%. Correlations were in the range of r = 0.71 to 0.92 with the highest correlations between variants with the greatest commonality with the nine-sample DISST. Metrics of UN correlated relatively well between tests when C-peptide was assayed (r = 0.72 to 1) but were sometimes not well estimated when samples were not assayed for C-peptide (r = -0.14 to 0.75). CONCLUSIONS: The DISST-based spectrum offers a series of tests with very distinct compromises of information yield, accuracy, assay cost, and clinical intensity. Thus, the spectrum of tests has the potential to enable researchers to better allocate funds by selecting an optimal test configuration for their particular application.
BACKGROUND: Numerous tests have been developed to estimate insulin sensitivity (SI). However, most of the established tests are either too expensive for widespread application or do not yield reliable results. The dynamic insulin sensitivity and secretion test (DISST) uses assays of glucose, insulin, and C-peptide from nine samples to quantify SI and endogenous insulin secretion (UN) at a comparatively low cost. The quick dynamic insulin sensitivity test has shown that the DISST SI values are robust to significant assay omissions. METHODS: Eight DISST-based variations of the nine-sample assay regimen are proposed to investigate the effects of assay omission within the DISST-based framework. SI and UN were identified using the fully-sampled DISST and data from 218 nine-sample tests undertaken in 74 female individuals with elevated diabetes risk. This same data was then used with appropriate assay omissions to identify SI and UN with the eight DISST-based assay variations. RESULTS: Median intraprocedure proportional difference between SI values from fully-sampled DISST and the DISST-based variants was in the range of -17.9 to 7.8%. Correlations were in the range of r = 0.71 to 0.92 with the highest correlations between variants with the greatest commonality with the nine-sample DISST. Metrics of UN correlated relatively well between tests when C-peptide was assayed (r = 0.72 to 1) but were sometimes not well estimated when samples were not assayed for C-peptide (r = -0.14 to 0.75). CONCLUSIONS: The DISST-based spectrum offers a series of tests with very distinct compromises of information yield, accuracy, assay cost, and clinical intensity. Thus, the spectrum of tests has the potential to enable researchers to better allocate funds by selecting an optimal test configuration for their particular application.
Authors: Christopher E Hann; J Geoffrey Chase; Jessica Lin; Thomas Lotz; Carmen V Doran; Geoffrey M Shaw Journal: Comput Methods Programs Biomed Date: 2005-03 Impact factor: 5.428
Authors: Russell Harris; Katrina Donahue; Saif S Rathore; Paul Frame; Steven H Woolf; Kathleen N Lohr Journal: Ann Intern Med Date: 2003-02-04 Impact factor: 25.391
Authors: J Geoffrey Chase; Jean-Charles Preiser; Jennifer L Dickson; Antoine Pironet; Yeong Shiong Chiew; Christopher G Pretty; Geoffrey M Shaw; Balazs Benyo; Knut Moeller; Soroush Safaei; Merryn Tawhai; Peter Hunter; Thomas Desaive Journal: Biomed Eng Online Date: 2018-02-20 Impact factor: 2.819