Department of Endocrinology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
J Clin Epidemiol. 2010 Sep;63(9):1030-5. doi: 10.1016/j.jclinepi.2009.11.012. Epub 2010 Mar 1.
To develop and evaluate a simple tool, using data collected in a rural Chinese general practice, to identify those at high risk of Type 2 diabetes (T2DM) and prediabetes (PDM).
A total of 2,261 rural Chinese participants without known diabetes were used to derive and validate the models of T2DM and T2DM plus PDM. Logistic regression and classification tree analysis were used to build models.
The significant risk factors included in the logistic regression method were age, body mass index, waist/hip ratio (WHR), duration of hypertension, family history of diabetes, and history of hypertension for T2DM and T2DM plus PDM. In the classification tree analysis, WHR and duration of hypertension were the most important determining factors in the T2DM and T2DM plus PDM model. The sensitivity, specificity, positive predictive value, negative predictive value, and receiver operating characteristic area for detecting T2DM were 74.6%, 71.6%, 23.6%, 96.0%, and 0.731, respectively. For PDM plus T2DM, the results were 65.3%, 72.5%, 33.2%, 90.7%, and 0.689, respectively.
The classification tree model is a simple and accurate tool to identify those at high risk of T2DM and PDM. Central obesity strongly associates with T2DM in rural Chinese.
利用中国农村基层医疗实践中收集的数据，开发并评估一种简单的工具，以识别出患 2 型糖尿病（T2DM）和糖尿病前期（PDM）风险较高的人群。
共纳入 2261 名无已知糖尿病的农村中国参与者，用于推导和验证 T2DM 和 T2DM 加 PDM 的模型。使用逻辑回归和分类树分析来建立模型。
逻辑回归方法纳入的显著危险因素包括年龄、体重指数、腰臀比（WHR）、高血压持续时间、糖尿病家族史和高血压病史。在分类树分析中，WHR 和高血压持续时间是 T2DM 和 T2DM 加 PDM 模型中最重要的决定因素。检测 T2DM 的敏感性、特异性、阳性预测值、阴性预测值和受试者工作特征曲线面积分别为 74.6%、71.6%、23.6%、96.0%和 0.731。对于 PDM 加 T2DM，结果分别为 65.3%、72.5%、33.2%、90.7%和 0.689。
分类树模型是一种简单而准确的工具，可用于识别 T2DM 和 PDM 风险较高的人群。在中国农村，中心性肥胖与 T2DM 密切相关。
