Identification of individuals with insulin resistance using routine clinical measurements

Steven E. Stern, Ken Williams, Eleuterio Ferrannini, Ralph A. DeFronzo, Clifton Bogardus, Michael P. Stern

Research output: Contribution to journalReview articleResearchpeer-review

245 Citations (Scopus)

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/m 2, 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.

Original languageEnglish
Pages (from-to)333-339
Number of pages7
JournalDiabetes
Volume54
Issue number2
DOIs
Publication statusPublished - Feb 2005
Externally publishedYes

Fingerprint

Insulin Resistance
Insulin
Homeostasis
Sensitivity and Specificity
Fasting
Lipids
Potassium Iodide
Glucose
Glucose Clamp Technique
Cardiovascular Diseases
Obesity
Blood Pressure
Serum

Cite this

Stern, S. E., Williams, K., Ferrannini, E., DeFronzo, R. A., Bogardus, C., & Stern, M. P. (2005). Identification of individuals with insulin resistance using routine clinical measurements. Diabetes, 54(2), 333-339. https://doi.org/10.2337/diabetes.54.2.333
Stern, Steven E. ; Williams, Ken ; Ferrannini, Eleuterio ; DeFronzo, Ralph A. ; Bogardus, Clifton ; Stern, Michael P. / Identification of individuals with insulin resistance using routine clinical measurements. In: Diabetes. 2005 ; Vol. 54, No. 2. pp. 333-339.
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Stern, SE, Williams, K, Ferrannini, E, DeFronzo, RA, Bogardus, C & Stern, MP 2005, 'Identification of individuals with insulin resistance using routine clinical measurements' Diabetes, vol. 54, no. 2, pp. 333-339. https://doi.org/10.2337/diabetes.54.2.333

Identification of individuals with insulin resistance using routine clinical measurements. / Stern, Steven E.; Williams, Ken; Ferrannini, Eleuterio; DeFronzo, Ralph A.; Bogardus, Clifton; Stern, Michael P.

In: Diabetes, Vol. 54, No. 2, 02.2005, p. 333-339.

Research output: Contribution to journalReview articleResearchpeer-review

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AU - Williams, Ken

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AB - 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/m 2, 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.

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