Independent cohort cross-validation of the real-time DISTq estimation of insulin sensitivity

Abstract

Insulin sensitivity (SI) is useful in the diagnosis, screening and treatment of diabetes. However, most current tests cannot provide an accurate, immediate or real-time estimate. The DISTq method does not require insulin or C-peptide assays like most SI tests, thus enabling real-time, low-cost SI estimation. The method uses a posteriori parameter estimations in the absence of insulin or C-peptide assays to simulate accurate, patient-specific, insulin concentrations that enable SI identification.

Mathematical functions for the a posteriori parameter estimates were generated using data from 46 fully sampled DIST tests (glucose, insulin and C-peptide). SI values found using the DISTq from the 46 test pilot cohort and a second independent 218 test cohort correlated R = 0.890 and R = 0.825, respectively, to the fully sampled (including insulin and C-peptide assays) DIST SI metrics. When the a posteriori insulin estimation functions were derived using the second cohort, correlations for the pilot and second cohorts reduced to 0.765 and 0.818, respectively.

These results show accurate SI estimation is possible in the absence of insulin or C-peptide assays using the proposed method. Such estimates may only need to be generated once and then used repeatedly in the future for isolated cohorts. The reduced correlation using the second cohort was due to this cohort's bias towards low SI insulin resistant subjects, limiting the data set's ability to generalise over a wider range. All the correlations remain high enough for the DISTq to be a useful test for a number of clinical applications. The unique real-time results can be generated within minutes of testing as no insulin and C-peptide assays are required and may enable new clinical applications.

Introduction

Insulin resistance (IR) has been widely accepted as a strong indicator of an individual's risk of type 2 diabetes (T2DM) [1], [2]. A longitudinal study of the pathogenesis of T2DM has shown that those subjects who were diagnosed with T2DM had a 60% higher IR than average when assessed 10 years earlier [3]. IR is thus a strong predictor of T2DM risk and cardiovascular disease [4]. Therefore, low-cost, accurate estimation of IR could be used to screen patients, monitor interventional lifestyle changes, and to guide other therapies that could drastically reduce the incidence and cost associated with T2DM [5].

The various tests used to estimate insulin sensitivity (SI, SI = IR⁻¹) use various methods to provoke and measure the subject's glycaemic responses [6], [7]. The euglycemic hyperinsulinaemic clamp (EIC) aims to suppress endogenous glucose production (EGP) and significantly suppress endogenous insulin production (Uen) to assess tissue sensitivity to exogenous insulin [8]. In contrast, the intravenous glucose tolerance test (IVGTT) stimulates Uen to measure insulin sensitivity [9]. Hence, while the metrics obtained by these tests are similar, they are not equivalent. An ideal metric for clinical or diagnostic use would measure the efficiency of insulin to dispose of glucose to the periphery at physiologically relevant glucose and insulin concentrations.

The gold standard for SI testing is the EIC. It measures the rate of glucose disposal at basal glucose, driven by hyper-physiological insulin concentrations designed to suppress Uen. The EIC is accurate and repeatable [8], but takes 4–5 h and approximately 6 clinician hours to perform. The time, intensity and cost of the EIC prohibit its use in many clinical situations. A reliable result is not necessarily guaranteed with an inexperienced clinician.

The IVGTT measures the subject's response to a 20–25 g intravenous (IV) glucose bolus with very frequent 1–3 min sampling. Some protocols modify the response with a 2–3 U IV bolus of insulin following the glucose bolus (IM-IVGTT) [7]. SI is then typically obtained by fitting the minimal model [10] to the sampled data. The boluses in this test tend to be supra-physiological and the trial generally runs for 2–3 h requiring significant clinical effort due to the frequent sampling. Model parameters are often unidentifiable, in particular in subjects with low SI values [11], [12].

Lower cost and lower intensity surrogate tests include fasting glucose, 2-h oral glucose tolerance (2hr OGTT) and the homeostasis model assessment (HOMA). Fasting glucose allows a diagnosis of T2DM [13], but does not offer an estimate of SI. Elevated fasting glucose is a resulting symptom of significant IR and the inability to maintain glycaemic homeostasis. Once elevated fasting glucose is detected, significant, and often irreversible beta-cell damage has already occurred [14]. Hence, fasting glucose is not an effective screening tool for early risk diagnosis to prevent further disease development.

The 2hr OGTT measures the subject's ability to dispose a 75 g oral glucose load. Two hours after ingestion of the glucose load the blood glucose concentration is measured for a T2DM diagnostic. The accuracy of the 2hr OGTT is questionable, with studies finding intra-subject reclassification of diagnosis rates of 50–60% [15], [16], [17]. Similarly to a fasting glucose level, early diagnosis of risk factors prior to the development of T2DM is difficult with the OGTT.

HOMA multiplies the fasting insulin and glucose assay values from a single blood test to produce a surrogate estimation of IR. The underlying assumption is that subjects with low sensitivity will require more insulin to maintain glycaemic homeostasis, elevating fasting levels of glucose and/or insulin. This test has an inconsistent correlation with the clamp (R = −0.19 → R = −0.82) [18], [19], does not track changes from intervention well [20], and does not fully represent insulin–glucose dynamics at physiologically relevant concentrations.

The dynamic insulin sensitivity test (DIST) is a short, infrequently sampled, low-dose intravenous glucose tolerance test. The test takes 30–45 min to administer. Glucose, insulin and C-peptide data is used with a clinically validated physiological model [20], [21] to provide accurate estimates for Uen, insulin clearance rate, and SI. The DIST has shown good correlation to the EIC in virtual trials (R = 0.93) [20], and high repeatability in a clinical pilot study (Δ = 6%) [21], with a validation study ongoing.

This study presents the DISTq (quick DIST) which is an alternative method for solving DIST data using only glucose samples and the subjects’ physical attributes (height, weight, sex, and age). Glucose samples can be assayed at the test station during sampling, enabling low-cost test analysis. As no insulin or C-peptide assays are required, the DISTq can effectively provide SI immediately in “real-time”. To remove the need for insulin and C-peptide assays, the insulin concentrations in plasma and interstitium must be estimated using knowledge available at testing. Parameter relationships derived from the fully sampled clinical DIST pilot study data [21] can be used to generate the required estimates [22]. These parameter estimates are used with the physiological model shown in Fig. 1 to simulate an interstitial insulin profile with sufficient accuracy to identify SI (R = 0.86 to the fully sampled DIST) [22].

In this research study, the validity of the DISTq assumptions is tested on two separate cohorts. One cohort is used to generate the insulin estimation functions, which are then tested on both cohorts. The goal is to assess how applicable and valid these estimations are across cohorts, and thus estimate or identify any additional errors in using this.