Insulin Resistant Individuals - Using Clinical Measurements

Abstract and Introduction

Abstract

Insulin resistance is a treatable precursor of diabetes and potentially of cardiovascular disease as well. To identify insulin-resistant patients, we developed decision rules from measurements of obesity, fasting glucose, insulin, lipids, and blood pressure and family history in 2,321 (2,138 nondiabetic) individuals studied with the euglycemic insulin clamp technique at 17 European sites; San Antonio, Texas; and the Pima Indian reservation. The distribution of whole-body glucose disposal appeared to be bimodal, with an optimal insulin resistance cutoff of <28 µmol/min · kg lean body mass. Using recursive partitioning, we developed three types of classification tree models: the first, based on clinical measurements and all available laboratory determinations, had an area under the receiver operator characteristic curve (aROC) of 90.0% and generated a simple decision rule: diagnose insulin resistance if any of the following conditions are met: BMI >28.9 kg/m², homeostasis model assessment of insulin resistance (HOMA-IR) >4.65, or BMI >27.5 kg/m² and HOMA-IR >3.60. The fasting serum insulin concentrations corresponding to these HOMA-IR cut points were 20.7 and 16.3 µU/ml, respectively. This rule had a sensitivity and specificity of 84.9 and 78.7%, respectively. The second model, which included clinical measurements but no laboratory determinations, had an aROC of 85.0% and generated a decision rule that had a sensitivity and specificity of 78.7 and 79.6%, respectively. The third model, which included clinical measurements and lipid measurements but not insulin (and thus excluded HOMA-IR as well), had a similar aROC (85.1%), sensitivity (81.3%), and specificity (76.3%). Thus, insulin-resistant individuals can be identified using simple decision rules that can be tailored to specific needs.

Introduction

There is abundant evidence that insulin resistance is a precursor of type 2 diabetes^[1,2] and perhaps of cardiovascular disease as well.^[3,4,5] The latter association, which is independent of diabetes, may be partially a consequence of the relationship between insulin resistance and the "metabolic syndrome," which consists of obesity, particularly abdominal obesity; impaired glucose regulation; dyslipidemia of the high-triglyceride/low-HDL cholesterol type; and hypertension.^[4,6]

A number of techniques are available for making definitive measurements of insulin resistance, including the hyperinsulinemic-euglycemic clamp technique,^[7] the frequently sampled intravenous glucose tolerance test,^[8] and the insulin suppression test.^[9,10] These techniques, however, are complicated, cumbersome, and, in general, not suitable for large-scale population studies or routine clinical work. For that reason a wide variety of indexes based on simpler, clinical measurements have been proposed for assessing insulin resistance. We recently reviewed a number of these indexes.^[11] Most have been validated with either the euglycemic clamp or the frequently sampled intravenous glucose tolerance test, but the populations in which these validations have been carried out typically have been small to moderate, ranging from <50 to ~650. An exception is IRAS (the Insulin Resistance Atherosclerosis Study), which validated several indexes, including the homeostasis model assessment of insulin resistance (HOMA-IR), in 1,460 individuals.^[12] Although the HOMA-IR^[13] has been the most widely used of these indexes, neither it nor any of the others has become the standard for diagnosing insulin resistance.

Indexes of insulin resistance have acquired new salience with the development of various pharmaceutical agents, specifically metformin and the thiazolidinediones, that sensitize the body to the action of endogenous insulin. Although initially developed for the treatment of diabetes, these agents also have a potential role in reducing the risk of diabetes and perhaps also of cardiovascular disease in insulin-resistant nondiabetic individuals. Moreover, the potential public health impact of such treatment could be large because it has been estimated that in developed countries as many as 25% of the nondiabetic population are as insulin resistant as patients with type 2 diabetes.^[3] Clinical trials would of course be needed to document the benefits of treating insulin-resistant nondiabetic individuals with insulin-sensitizing agents. Efforts to document the benefits of such treatment, however, have been hampered by the lack of an accepted method for assessing insulin resistance based on routine clinical measurements. Although a clinical trial could conceivably be performed based on enrolling insulin-resistant patients as defined by one of the definitive tests, translation of the results of such a trial into ordinary clinical practice would be problematic, given the lack of a clinical test for identifying the target population for treatment.

In the current study we have assembled what we believe to be the largest collection of euglycemic clamp data in the world from numerous research centers, and we have used recursively partitioned classification trees to develop decision rules for identifying insulin-resistant individuals based on routinely available clinical measurements.