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18 November 2003 | Volume 139 Issue 10 | Pages 802-809
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 specifity 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.
Editors' Notes
Context
Contribution
Implications
–The Editors
Author and Article Information
From Stanford University School of Medicine, Stanford, California.
Grant Support: By National Institutes of Health grants RR000070-40 and RR16071-01.
Potential Financial Conflicts of Interest: None disclosed.
Requests for Single Reprints: Gerald Reaven, MD, Division of Cardiovascular Medicine, Falk Cardiovascular Research Center, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305.
Current Author Addresses: Drs. McLaughlin and Chu: Division of Endocrinology, Stanford University School of Medicine, Room S005, Stanford, CA 94305-5103.
Drs. Reaven, Abbasi, and Ms. Lamendola: Division of Cardiovascular Medicine, Falk Cardiovascular Research Center, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305.
Ms. Cheal: Department of Psychiatry, Brigham and Women's Hospital, 350 Longwood Avenue, Suite 201, Boston, MA 02115.
Author Contributions: Conception and design: T. McLaughlin, K. Cheal, J. Chu, G. Reaven.
Analysis and interpretation of the data: T. McLaughlin, G. Reaven.
Drafting of the article: T. McLaughlin, G. Reaven.
Critical revision of the article for important intellectual content: T. McLaughlin, G. Reaven.
Final approval of the article: T. McLaughlin, G. Reaven.
Provision of study materials or patients: T. McLaughlin, F. Abbasi, C. Lamendola, G. Reaven.
Statistical expertise: T. McLaughlin, K. Cheal, G. Reaven.
Obtaining of funding: T. McLaughlin, G. Reaven.
Administrative, technical, or logistic support: T. McLaughlin, F. Abbasi, C. Lamendola.
Collection and assembly of data: T. McLaughlin, F. Abbasi. ARTICLE
Use of Metabolic Markers To Identify Overweight Individuals Who Are Insulin Resistant
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