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Cited by 384Diabetes, Cardiovascular Risks, and LipoproteinsAdipokines, Inflammation, and Metabolic DiseasesPancreatic function and diabetes

The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.

Identification of Individuals With Insulin Resistance Using Routine Clinical Measurements

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Stern, Steven E., Williams, Ken, Ferrannini, Eleuterio, DeFronzo, Ralph A., Bogardus, Clifton, Stern, Michael P.·2005·Diabetes
DOI 10.2337/diabetes.54.2.333
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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.

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Metadata

Authors
Stern, S. E., Williams, K., Ferrannini, E., DeFronzo, R. A., Bogardus, C., & Stern, M. P.
Affiliations
  • Australian National University (AU)
  • The University of Texas at San Antonio Health Science Center (US)
  • Istituto di Fisiologia Clinica (IT)
  • National Institutes of Health (US)
  • Division of Clinical Epidemiology, Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas
ORCID
Venue
Diabetes
Publisher
American Diabetes Association
Published
2005-02-01
Volume
54
Issue
2
Pages
333-339
ISSN
  • 0012-1797 (ISSN-L)
  • 1939-327X
DOI
10.2337/diabetes.54.2.333
OpenAlex
W2151716502
Semantic Scholar
4e8c3e8e3b4db413e21a0acf7ac1a349884ba391
Cited by
384
References
30
Citations by year
2026: 2 · 2025: 9 · 2024: 19 · 2023: 7 · 2022: 19 · 2021: 10 · 2020: 18 · 2019: 14 · 2018: 27 · 2017: 19 · 2016: 21 · 2015: 29 · 2014: 21 · 2013: 19 · 2012: 22
FWCI
13.45
Type
journal article
Language
en
Open access
Locations
  • Diabetes (publishedVersion)
  • PubMed (publishedVersion)
  • CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
  • ANU Open Research (Australian National University) (submittedVersion)
Funders
American Diabetes Association
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Adipokines, Inflammation, and Metabolic Diseases
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Pancreatic function and diabetes
Keywords
insulin resistance, diabetes prevention, cardiovascular risk, HOMA-IR, decision rules, classification trees, obesity, body mass index, fasting insulin, glucose disposal, hyperinsulinemic-euglycemic clamp, sensitivity and specificity, Pima Indians
MeSH terms
Blood Glucose, Homeostasis, Humans, Hyperinsulinism, Insulin, Insulin Resistance, Glucose Clamp Technique

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