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 > 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.
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 > 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: Miles C Benton; Rod A Lea; Donia Macartney-Coxson; Melanie A Carless; Harald H Göring; Claire Bellis; Michelle Hanna; David Eccles; Geoffrey K Chambers; Joanne E Curran; Jacquie L Harper; John Blangero; Lyn R Griffiths Journal: Am J Hum Genet Date: 2013-12-05 Impact factor: 11.025
Authors: Ying Jiang; Susan Rogers Van Katwyk; Yang Mao; Heather Orpana; Gina Argwal; Margaret de Groh; Monique Skinner; Robyn Clarke; Howard Morrison Journal: Health Promot Chronic Dis Prev Can Date: 2017-04 Impact factor: 3.240
Authors: Stephen Colagiuri; Philip Vita; Magnolia Cardona-Morrell; Maria Fiatarone Singh; Louise Farrell; Andrew Milat; Marion Haas; Adrian Bauman Journal: BMC Public Health Date: 2010-06-10 Impact factor: 3.295