Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
Med J Aust. 2010 Feb 15;192(4):197-202. doi: 10.5694/j.1326-5377.2010.tb03507.x.
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.
目的:基于人口统计学、生活方式和简单人体测量学指标,为澳大利亚开发并验证一种糖尿病风险评估工具。
设计和设置:澳大利亚糖尿病、肥胖和生活方式研究(AusDiab,1999-2000 年)的 5 年随访(2004-2005 年)。
参与者:6060 名年龄在 25 岁及以上、基线时未被诊断患有糖尿病的 AusDiab 参与者。
主要观察指标:随访时新发糖尿病的定义为接受胰岛素或口服降糖药治疗,或空腹血糖水平≥7.0mmol/L 或口服葡萄糖耐量试验 2 小时血糖水平≥11.1mmol/L。使用逻辑回归建立风险预测模型,并将其转换为简单评分,然后使用接受者操作特征曲线(ROC)下面积(AUC)和 Hosmer-Lemeshow(HL)χ²检验在两个独立的澳大利亚队列(蓝山眼科研究和北阿德莱德健康研究)中进行验证。
结果:362 人发生糖尿病。最终预测模型纳入年龄、性别、种族、父母糖尿病史、高血糖史、降压药物使用、吸烟、身体活动不足和腰围。糖尿病风险工具的 AUC 为 0.78(95%CI,0.76-0.81),HL χ²检验为 4.1(P=0.85)。使用评分>或=12(最高 35),识别新发糖尿病的敏感性、特异性和阳性预测值分别为 74.0%、67.7%和 12.7%。在两个独立验证队列中,AUC 和 HL χ²检验分别为 0.66(95%CI,0.60-0.71)和 9.2(P=0.32),0.79(95%CI,0.72-0.86)和 29.4(P<0.001)。
结论:该糖尿病风险评估工具提供了一种简单、非侵入性的方法,可用于识别澳大利亚成年人患 2 型糖尿病的风险,这些人可能受益于预防或延迟其发病的干预措施。
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