Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/65394
Type: Journal article
Title: AUSDRISK: an Australian Type 2 Diabetes Risk Assessment Tool based on demographic, lifestyle and simple anthropometric measures
Author: Chen, Lei
Magliano, Dianna J.
Balkau, Beverley
Colagiuri, Stephen
Zimmet, Paul
Tonkin, Andrew M.
Mitchell, Paul
Phillips, Patrick J.
Shaw, Jonathon
Citation: Medical Journal of Australia, 2010; 192(4):197-202
Publisher: Australasian Medical Publublications
Issue Date: 2010
ISSN: 0025-729X
School/Discipline: School of Medicine : Medicine
Statement of
Responsibility: 
Lei Chen, Dianna J Magliano, Beverley Balkau, Stephen Colagiuri, Paul Z Zimmet, Andrew M Tonkin, Paul Mitchell, Patrick J Phillips and Jonathan E Shaw
Abstract: Objective: To develop and validate a diabetes risk assessment tool for Australia based on demographic, lifestyle and simple anthropometric measures. Design and setting: 5-year follow-up (2004–2005) of the Australian Diabetes, Obesity and Lifestyle study (AusDiab, 1999–2000). Participants: 6060 AusDiab participants aged 25 years or older who did not have diagnosed diabetes at baseline. Main outcome measures: Incident diabetes at follow-up was defined by treatment with insulin or oral hypoglycaemic agents or by fasting plasma glucose level ≥7.0mmol/L or 2- hour plasma glucose level in an oral glucose tolerance test ≥11.1mmol/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) 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 x2 statistic was 4.1 (P = 0.85). Using a score ≥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 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.
Rights: Copyright status unknown
Published version: http://www.mja.com.au/public/issues/192_04_150210/che10062_fm.html
Appears in Collections:Medicine publications

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