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

被引:278
作者
Chen, Lei [2 ]
Magliano, Dianna J. [1 ]
Balkau, Beverley [1 ,3 ]
Colagiuri, Stephen [4 ]
Zimmet, Paul Z. [1 ]
Tonkin, Andrew M. [2 ]
Mitchell, Paul [5 ]
Phillips, Patrick J. [6 ]
Shaw, Jonathan E. [1 ]
机构
[1] Baker IDI Heart & Diabet Inst, Melbourne, Vic, Australia
[2] Monash Univ, Dept Epidemiol & Prevent Med, Melbourne, Vic 3004, Australia
[3] Univ Paris 11, INSERM, Ctr Res Epidemiol & Publ Hlth, Villejuif, France
[4] Univ Sydney, Inst Obes Nutr & Exercise, Sydney, NSW 2006, Australia
[5] Univ Sydney, Westmead Millennium Inst, Ctr Vis Res, Sydney, NSW 2006, Australia
[6] Queen Elizabeth Hosp, Dept Endocrinol, Adelaide, SA, Australia
关键词
IMPAIRED GLUCOSE-TOLERANCE; SCORE; OBESITY; POPULATION; PREVALENCE; PREDICT;
D O I
10.5694/j.1326-5377.2010.tb03478.x
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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) chi(2) statistic. Results: 362 people developed diabetes. Age, sex, ethnicity, parental history of diabetes, history of high blood glucose level, use of anti hypertensive 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% Cl, 0.76-0.81) and HL chi(2) 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 chi(2) statistic in the two independent validation cohorts were 0.66 (95% Cl, 0.60-0.71) and 9.2 (P = 0.32), and 0.79 (95% Cl, 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. MJA 2010; 192: 197-202
引用
收藏
页码:197 / 202
页数:6
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