Use of the DISST Model to Estimate the HOMA and Matsuda Indexes Using Only a Basal Insulin Assay

Abstract

Type 2 diabetes mellitus (T2DM) has been declared an epidemic by the World Health Organization. 1 Treatment of T2DM individuals places considerable economic strain on health care systems worldwide. 2-4 However, it is hypothesized that early detection of T2DM can prompt intervention to delay the onset of symptoms, 5,6 and thus potentially reduce costs and improve quality of life. Insulin sensitivity (SI) is widely regarded as one of the better measures of the progression of an individual from normal glucose tolerance (NGT) to T2DM. 7-9 However, accurate determination of SI usually requires tests that are relatively clinically intense and expensive. 10 The "gold standard" test used to determine SI is the hyper-insulinemic euglycemic clamp test (EIC), which typically takes place over 2.5 to 4 hours, and requires a large number of blood samples (at least 20) to be drawn. 11 Hence other diagnostic tests are recommended in clinical practice. 12 One such test is the 2-hour Oral Glucose Tolerance Test (OGTT), which typically assesses glucose levels 2 hours after ingestion of a glucose drink. If the 2-hour glucose is in excess of 200 mg/dl a preliminary diagnoses of Type 2 Diabetes can be made. 12 However, due to the lack of an SI measurement, this process can only diagnose someone who already has T2DM, or, at lower glycemic thresholds, someone who is already displaying impaired glucose tolerance. Thus, it is not able to estimate increased risk and could not effectively prompt early preventative measures. Means do exist to approximate SI based on OGTT data. The Matsuda Index requires 4 blood samples at t = 0, 30, 60, and 120 to be taken and assayed for both glucose and insulin. 13 As such, it is far more expensive and clinically intensive than the HOMA or 2-hour OGTT 532490D STXXX10.

The Impact of Exogenous Insulin Input on Calculating Hepatic Clearance Parameters

“…The model has several fixed parameters and limited samples, both of which may limit the robustness of identified values. However, the model has been proven to accurately describe dynamics to the limit the model defines them in both clinical use for glycemic control 25,41,47,48 and has demonstrated excellent accuracy in identifying insulin sensitivity and other metabolic characteristics. Regarding sampling rates, extra assays with a dense sampling grid are shown to not always improve identification, 5 where this work by Docherty et al showed subjecting a subject to extra samples may be unnecessary as sparser sampling is sufficient for robust identification.…”