Details

Autor(en) / Beteiligte

• Chen, Lei
• Magliano, Dianna J
• Balkau, Beverley
• Colagiuri, Stephen
• Zimmet, Paul Z
• Tonkin, Andrew M
• Mitchell, Paul
• Phillips, Patrick J
• Shaw, Jonathan E

Titel

AUSDRISK: an Australian Type 2 Diabetes Risk Assessment Tool based on demographic, lifestyle and simple anthropometric measures

Ist Teil von

• Medical journal of Australia, 2010-02, Vol.192 (4), p.197-202

Ort / Verlag

Sydney: Australasian Medical Publishing Company

Erscheinungsjahr

2010

Quelle

Wiley Online Library - AutoHoldings Journals

Beschreibungen/Notizen

• 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 ≥ 7.0 mmol/L or 2‐hour plasma glucose level in an oral glucose tolerance test ≥ 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) χ2 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 χ2 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 χ2 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.