title: Random glucose is useful for individual prediction of type 2 diabetes: results of the Study of Health in Pomerania (SHIP).
abstract: OBJECTIVE
  Random glucose is widely used in routine clinical practice. We investigated whether this non-standardized glycemic measure is useful for individual diabetes prediction.
  
  
  METHODS
  The Study of Health in Pomerania (SHIP), a population-based cohort study in north-east Germany, included 3107 diabetes-free persons aged 31-81 years at baseline in 1997-2001. 2475 persons participated at 5-year follow-up and gave self-reports of incident diabetes. For the total sample and for subjects aged ≥50 years, statistical properties of prediction models with and without random glucose were compared.
  
  
  RESULTS
  A basic model (including age, sex, diabetes of parents, hypertension and waist circumference) and a comprehensive model (additionally including various lifestyle variables and blood parameters, but not HbA1c) performed statistically significantly better after adding random glucose (e.g., the area under the receiver-operating curve (AROC) increased from 0.824 to 0.856 after adding random glucose to the comprehensive model in the total sample). Likewise, adding random glucose to prediction models which included HbA1c led to significant improvements of predictive ability (e.g., for subjects ≥50 years, AROC increased from 0.824 to 0.849 after adding random glucose to the comprehensive model+HbA1c).
  
  
  CONCLUSIONS
  Random glucose is useful for individual diabetes prediction, and improves prediction models including HbA1c.
authors.affiliation: Institute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany. bernd.kowall@ddz.uni-duesseldorf.de
authors.forename: Bernd
authors.initials: B
authors.lastname: Kowall
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Rathmann
authors.forename: Guido
authors.initials: G
authors.lastname: Giani
authors.forename: Sabine
authors.initials: S
authors.lastname: Schipf
authors.forename: Sebastian
authors.initials: S
authors.lastname: Baumeister
authors.forename: Henri
authors.initials: H
authors.lastname: Wallaschofski
authors.forename: Matthias
authors.initials: M
authors.lastname: Nauck
authors.forename: Henry
authors.initials: H
authors.lastname: Völzke
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015415:Biomarkers / Q000097:blood; D001786:Blood Glucose / Q000378:metabolism*; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000175:diagnosis* / Q000453:epidemiology; D005260:Female; D005858:Germany / Q000453:epidemiology; D006442:Glycated Hemoglobin / Q000378:metabolism; D006801:Humans; D015994:Incidence; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011379:Prognosis; D018570:Risk Assessment; D012307:Risk Factors; D057566:Self Report; D013997:Time Factors
chemical_list: D015415:Biomarkers; D001786:Blood Glucose; D006442:Glycated Hemoglobin; C517652:hemoglobin A1c protein, human