title: Is there a simple way to identify insulin-resistant individuals at increased risk of cardiovascular disease?
abstract: The goal of this study was to evaluate the ability of various routine measures of lipoprotein metabolism to identify patients who were insulin resistant and dyslipidemic, and therefore, at increased risk of cardiovascular disease. For this purpose, insulin resistance was quantified by determining the steady-state plasma glucose concentration during the insulin suppression test in 449 apparently healthy patients. The low-density lipoprotein (LDL) particle diameter and subclass phenotype were measured by gradient gel electrophoresis in 1,135 patients. Pearson's correlation coefficients and receiver-operating characteristic curves were used to evaluate measures of lipoprotein metabolism as potential markers of insulin resistance and LDL phenotype. The results indicated that the ratio of the plasma concentrations of triglyceride to high-density lipoprotein cholesterol was the best predictor of insulin resistance and LDL particle diameter. The optimal triglyceride/high-density lipoprotein cholesterol ratio for predicting insulin resistance and LDL phenotype was 3.5 mg/dl; a value that identified insulin-resistant patients with a sensitivity and specificity comparable to the criteria currently proposed to diagnose the metabolic syndrome. The sensitivity and specificity were even greater for identification of patients with small, dense, LDL particles. In conclusion, a plasma triglyceride/high-density lipoprotein cholesterol concentration ratio > or =3.5 provides a simple means of identifying insulin-resistant, dyslipidemic patients who are likely to be at increased risk of cardiovascular disease.
authors.affiliation: Stanford University School of Medicine, Stanford, California, USA. tmclaugh@stanford.edu
authors.forename: Tracey
authors.initials: T
authors.lastname: McLaughlin
authors.forename: Gerald
authors.initials: G
authors.lastname: Reaven
authors.forename: Fahim
authors.initials: F
authors.lastname: Abbasi
authors.forename: Cindy
authors.initials: C
authors.lastname: Lamendola
authors.forename: Mohammed
authors.initials: M
authors.lastname: Saad
authors.forename: David
authors.initials: D
authors.lastname: Waters
authors.forename: Joel
authors.initials: J
authors.lastname: Simon
authors.forename: Ronald M
authors.initials: RM
authors.lastname: Krauss
mesh_terms: D000328:Adult; D002318:Cardiovascular Diseases / Q000097:blood*; D008076:Cholesterol, HDL / Q000097:blood*; D008078:Cholesterol, LDL / Q000097:blood*; D005260:Female; D006801:Humans; D007333:Insulin Resistance; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D010641:Phenotype; D011237:Predictive Value of Tests; D012372:ROC Curve; D018570:Risk Assessment; D014280:Triglycerides / Q000097:blood*
chemical_list: D008076:Cholesterol, HDL; D008078:Cholesterol, LDL; D014280:Triglycerides