Identification of Individuals With Insulin Resistance Using Routine Clinical Measurements
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Stern, Steven; Williams, Ken; Ferrannini, Eleuterio; DeFronzo, Ralph; Bogardus, Clifton; Stern, Michael
Description
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...[Show more]
dc.contributor.author | Stern, Steven | |
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dc.contributor.author | Williams, Ken | |
dc.contributor.author | Ferrannini, Eleuterio | |
dc.contributor.author | DeFronzo, Ralph | |
dc.contributor.author | Bogardus, Clifton | |
dc.contributor.author | Stern, Michael | |
dc.date.accessioned | 2015-12-13T23:04:16Z | |
dc.identifier.issn | 0012-1797 | |
dc.identifier.uri | http://hdl.handle.net/1885/85297 | |
dc.description.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. | |
dc.publisher | American Diabetes Association | |
dc.source | Diabetes | |
dc.subject | Keywords: glucose; insulin; lipid; blood pressure; cardiovascular disease; clinical trial; comparative study; controlled clinical trial; controlled study; diabetes mellitus; diagnostic accuracy; diagnostic approach route; disease classification; family history; glu | |
dc.title | Identification of Individuals With Insulin Resistance Using Routine Clinical Measurements | |
dc.type | Journal article | |
local.description.notes | Imported from ARIES | |
local.description.refereed | Yes | |
local.identifier.citationvolume | 54 | |
dc.date.issued | 2005 | |
local.identifier.absfor | 030499 - Medicinal and Biomolecular Chemistry not elsewhere classified | |
local.identifier.ariespublication | MigratedxPub13632 | |
local.type.status | Published Version | |
local.contributor.affiliation | Stern, Steven, College of Business and Economics, ANU | |
local.contributor.affiliation | Williams, Ken, University of Texas | |
local.contributor.affiliation | Ferrannini, Eleuterio, National Research Council (CNR) Institute of Clinical Physiology | |
local.contributor.affiliation | DeFronzo, Ralph, University of Texas | |
local.contributor.affiliation | Bogardus, Clifton, National Institutes of Health | |
local.contributor.affiliation | Stern, Michael, University of Texas | |
local.description.embargo | 2037-12-31 | |
local.bibliographicCitation.issue | 2 | |
local.bibliographicCitation.startpage | 333 | |
local.bibliographicCitation.lastpage | 339 | |
local.identifier.doi | 10.2337/diabetes.54.2.333 | |
dc.date.updated | 2015-12-12T07:54:59Z | |
local.identifier.scopusID | 2-s2.0-12744262843 | |
Collections | ANU Research Publications |
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