title: AUSDRISK: an Australian Type 2 Diabetes Risk Assessment Tool based on demographic, lifestyle and simple anthropometric measures.
abstract: OBJECTIVE
  To develop and validate a diabetes risk assessment tool for Australia based on demographic, lifestyle and simple anthropometric measures.
  
  
  METHODS
  5-year follow-up (2004-2005) of the Australian Diabetes, Obesity and Lifestyle study (AusDiab, 1999-2000).
  
  
  METHODS
  6060 AusDiab participants aged 25 years or older who did not have diagnosed diabetes at baseline.
  
  
  METHODS
  Incident diabetes at follow-up was defined by treatment with insulin or oral hypoglycaemic agents or by fasting plasma glucose level > or = 7.0 mmol/L or 2-hour plasma glucose level in an oral glucose tolerance test > or = 11.1 mmol/L. The risk prediction model was developed using logistic regression and converted to a simple score, which was then validated in two independent Australian cohorts (the Blue Mountains Eye Study and the North West Adelaide Health Study) using the area under the receiver operating characteristic curve (AROC) and the Hosmer-Lemeshow (HL) chi(2) statistic.
  
  
  RESULTS
  362 people developed diabetes. Age, sex, ethnicity, parental history of diabetes, history of high blood glucose level, use of antihypertensive medications, smoking, physical inactivity and waist circumference were included in the final prediction model. The AROC of the diabetes risk tool was 0.78 (95% CI, 0.76-0.81) and HL chi(2) statistic was 4.1 (P = 0.85). Using a score > or = 12 (maximum, 35), the sensitivity, specificity and positive predictive value for identifying incident diabetes were 74.0%, 67.7% and 12.7%, respectively. The AROC and HL chi(2) statistic in the two independent validation cohorts were 0.66 (95% CI, 0.60-0.71) and 9.2 (P = 0.32), and 0.79 (95% CI, 0.72-0.86) and 29.4 (P < 0.001), respectively.
  
  
  CONCLUSIONS
  This diabetes risk assessment tool provides a simple, non-invasive method to identify Australian adults at high risk of type 2 diabetes who might benefit from interventions to prevent or delay its onset.
authors.affiliation: Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
authors.forename: Lei
authors.initials: L
authors.lastname: Chen
authors.forename: Dianna J
authors.initials: DJ
authors.lastname: Magliano
authors.forename: Beverley
authors.initials: B
authors.lastname: Balkau
authors.forename: Stephen
authors.initials: S
authors.lastname: Colagiuri
authors.forename: Paul Z
authors.initials: PZ
authors.lastname: Zimmet
authors.forename: Andrew M
authors.initials: AM
authors.lastname: Tonkin
authors.forename: Paul
authors.initials: P
authors.lastname: Mitchell
authors.forename: Patrick J
authors.initials: PJ
authors.lastname: Phillips
authors.forename: Jonathan E
authors.initials: JE
authors.lastname: Shaw
mesh_terms: D000328:Adult; D000368:Aged; D000886:Anthropometry / Q000379:methods*; D001315:Australia / Q000453:epidemiology; D003710:Demography; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D015994:Incidence; D008019:Life Style; D008297:Male; D008875:Middle Aged; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D013997:Time Factors