Use of metabolic markers to identify overweight individuals who are insulin resistant

Abstract

Background: Insulin resistance is more common in overweight individuals and is associated with increased risk for type 2 diabetes mellitus and cardiovascular disease. Given the current epidemic of obesity and the fact that lifestyle interventions, such as weight loss and exercise, decrease insulin resistance, a relatively simple means to identify overweight individuals who are insulin resistant would be clinically useful.

Objective: To evaluate the ability of metabolic markers associated with insulin resistance and increased risk for cardiovascular disease to identify the subset of overweight individuals who are insulin resistant.

Design: Cross-sectional study.

Setting: General clinical research center.

Patients: 258 nondiabetic, overweight volunteers.

Measurements: Body mass index; fasting glucose, insulin, lipid and lipoprotein concentrations; and insulin-mediated glucose disposal as quantified by the steady-state plasma glucose concentration during the insulin suppression test. Overweight was defined as body mass index of 25 kg/m2 or greater, and insulin resistance was defined as being in the top tertile of steady-state plasma glucose concentrations. Receiver-operating characteristic curve analysis was used to identify the best markers of insulin resistance; optimal cut-points were identified and analyzed for predictive power.

Results: Plasma triglyceride concentration, ratio of triglyceride to high-density lipoprotein cholesterol concentrations, and insulin concentration were the most useful metabolic markers in identifying insulin-resistant individuals. The optimal cut-points were 1.47 mmol/L (130 mg/dL) for triglyceride, 1.8 in SI units (3.0 in traditional units) for the triglyceride-high-density lipoprotein cholesterol ratio, and 109 pmol/L for insulin. Respective sensitivity and specificity for these cut-points were 67%, 64%, and 57% and 71%, 68%, and 85%. Their ability to identify insulin-resistant individuals was similar to the ability of the criteria proposed by the Adult Treatment Panel III to diagnose the metabolic syndrome (sensitivity, 52%, and specificity, 85%).

Conclusions: Three relatively simple metabolic markers can help identify overweight individuals who are sufficiently insulin resistant to be at increased risk for various adverse outcomes. In the absence of a standardized insulin assay, we suggest that the most practical approach to identify overweight individuals who are insulin resistant is to use the cut-points for either triglyceride concentration or the triglyceride-high-density lipoprotein cholesterol concentration ratio.