title: IRIS II study: the IRIS II score--assessment of a new clinical algorithm for the classification of insulin resistance in patients with Type 2 diabetes.
abstract: OBJECTIVE
  With the increasing availability of new drugs for the treatment of insulin resistance in patients with Type 2 diabetes, simple methods for their identification is an important challenge. The aim of our study was to compute a new algorithm for estimating insulin resistance in a routine clinical setting.
  
  
  METHODS
  Clinical data and blood samples were collected from 4265 Type 2 diabetic patients from 149 clinical sites. A clinical algorithm to estimate insulin resistance was developed by stepwise multiple regression analysis. The new generated score was compared with the HOMAIR-score, calculated from fasting insulin and glucose levels measured in a central laboratory. In a subgroup of 48 patients, the score was verified against a frequently sampled intravenous glucose tolerance test with subsequent modified minimal model analysis according to Bergman.
  
  
  RESULTS
  Multiple regression analysis revealed fasting blood glucose, BMI, triglycerides and HDL as the most powerful predictors of insulin resistance which were used for further computation of the IRIS II score. A significant overall correlation was found between the HOMAIR-score and the new clinical IRIS II score (r = 0.42; P < 0.0001). Compared with HOMAIR, the new score revealed a specificity of 0.95, a sensitivity of 0.34 and a positive predictive value of 0.95. This was in good agreement with the subset analysis of the intravenous glucose tolerance test, where a sensitivity of 0.37 and a specificity of 0.85 of the IRIS II score was calculated. Patients with insulin resistance according to the IRIS II score revealed an increased odds ratio for overall vascular complications (1.28; 1.11-1.46; P < 0.001).
  
  
  CONCLUSIONS
  The new IRIS II score can identify insulin resistance in Type 2 diabetic patients with high predictive value and high specificity.
authors.affiliation: Institute for Clinical Research and Development, Mainz, Germany. thomasf@ikfe.de
authors.forename: T
authors.initials: T
authors.lastname: Forst
authors.forename: E
authors.initials: E
authors.lastname: Standl
authors.forename: C
authors.initials: C
authors.lastname: Hohberg
authors.forename: T
authors.initials: T
authors.lastname: Konrad
authors.forename: J
authors.initials: J
authors.lastname: Schulze
authors.forename: H J
authors.initials: HJ
authors.lastname: Strotmann
authors.forename: G
authors.initials: G
authors.lastname: Lübben
authors.forename: S
authors.initials: S
authors.lastname: Pahler
authors.forename: A
authors.initials: A
authors.lastname: Bachinger
authors.forename: M
authors.initials: M
authors.lastname: Langenfeld
authors.forename: A
authors.initials: A
authors.lastname: Pfützner
mesh_terms: D000465:Algorithms; D001786:Blood Glucose / Q000378:metabolism; D015992:Body Mass Index; D008076:Cholesterol, HDL / Q000378:metabolism; D003924:Diabetes Mellitus, Type 2 / Q000209:etiology / Q000378:metabolism*; D003925:Diabetic Angiopathies / Q000209:etiology / Q000378:metabolism*; D005260:Female; D005951:Glucose Tolerance Test; D006801:Humans; D006973:Hypertension / Q000209:etiology / Q000378:metabolism; D007333:Insulin Resistance / Q000502:physiology*; D008297:Male; D008875:Middle Aged; D012044:Regression Analysis; D012307:Risk Factors; D012680:Sensitivity and Specificity; D014280:Triglycerides / Q000378:metabolism
chemical_list: D001786:Blood Glucose; D008076:Cholesterol, HDL; D014280:Triglycerides