title: Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study.
abstract: OBJECTIVE
  To identify existing prediction models for the risk of development of type 2 diabetes and to externally validate them in a large independent cohort.
  
  
  METHODS
  Systematic search of English, German, and Dutch literature in PubMed until February 2011 to identify prediction models for diabetes.
  
  
  METHODS
  Performance of the models was assessed in terms of discrimination (C statistic) and calibration (calibration plots and Hosmer-Lemeshow test).The validation study was a prospective cohort study, with a case cohort study in a random subcohort.
  
  
  METHODS
  Models were applied to the Dutch cohort of the European Prospective Investigation into Cancer and Nutrition cohort study (EPIC-NL).
  
  
  METHODS
  38,379 people aged 20-70 with no diabetes at baseline, 2506 of whom made up the random subcohort.
  
  
  METHODS
  Incident type 2 diabetes.
  
  
  RESULTS
  The review identified 16 studies containing 25 prediction models. We considered 12 models as basic because they were based on variables that can be assessed non-invasively and 13 models as extended because they additionally included conventional biomarkers such as glucose concentration. During a median follow-up of 10.2 years there were 924 cases in the full EPIC-NL cohort and 79 in the random subcohort. The C statistic for the basic models ranged from 0.74 (95% confidence interval 0.73 to 0.75) to 0.84 (0.82 to 0.85) for risk at 7.5 years. For prediction models including biomarkers the C statistic ranged from 0.81 (0.80 to 0.83) to 0.93 (0.92 to 0.94). Most prediction models overestimated the observed risk of diabetes, particularly at higher observed risks. After adjustment for differences in incidence of diabetes, calibration improved considerably.
  
  
  CONCLUSIONS
  Most basic prediction models can identify people at high risk of developing diabetes in a time frame of five to 10 years. Models including biomarkers classified cases slightly better than basic ones. Most models overestimated the actual risk of diabetes. Existing prediction models therefore perform well to identify those at high risk, but cannot sufficiently quantify actual risk of future diabetes.
authors.affiliation: Department of Epidemiology, University of Groningen, University Medical Centre Groningen, Groningen, Netherlands. a.abbasi@umcg.nl
authors.forename: Ali
authors.initials: A
authors.lastname: Abbasi
authors.forename: Linda M
authors.initials: LM
authors.lastname: Peelen
authors.forename: Eva
authors.initials: E
authors.lastname: Corpeleijn
authors.forename: Yvonne T
authors.initials: YT
authors.lastname: van der Schouw
authors.forename: Ronald P
authors.initials: RP
authors.lastname: Stolk
authors.forename: Annemieke M W
authors.initials: AM
authors.lastname: Spijkerman
authors.forename: Daphne L
authors.initials: DL
authors.lastname: van der A
authors.forename: Karel G M
authors.initials: KG
authors.lastname: Moons
authors.forename: Gerjan
authors.initials: G
authors.lastname: Navis
authors.forename: Stephan J L
authors.initials: SJ
authors.lastname: Bakker
authors.forename: Joline W J
authors.initials: JW
authors.lastname: Beulens
mesh_terms: D000328:Adult; D000368:Aged; D015415:Biomarkers; D001786:Blood Glucose; D015331:Cohort Studies; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000453:epidemiology; D005060:Europe / Q000453:epidemiology; D005260:Female; D006801:Humans; D015994:Incidence; D016239:MEDLINE; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods / Q000706:statistics & numerical data; D012307:Risk Factors
chemical_list: D015415:Biomarkers; D001786:Blood Glucose