[Establishing a noninvasive prediction model for type 2 diabetes mellitus based on a rural Chinese population].
[Establishing a noninvasive prediction model for type 2 diabetes mellitus based on a rural Chinese population].
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DOI：
10.3760/cma.j.issn.0253-9624.2016.05.003
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发表时间：
2016-05-01
影响因子：
--
通讯作者：
Hu, D S
中科院分区：
文献类型：
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作者：
Zhang, H Y;Shi, W H;Hu, D S
目的：为中国农村人群提供无创2型糖尿病（T2DM）预测模型。方法：2007年7月至8月和2008年7月至8月，采用整群抽样方法从河南省两个乡镇的农村人口中抽取20 194名年龄≥18岁的参与者。通过问卷调查、人体测量、空腹血糖和血脂检查收集数据。 2013年7月至8月和2014年7月至10月对17 265名参与者进行随访，最终筛选出12 285名参与者进行分析。将这些参与者的数据按1∶1分别随机分为推导组（推导数据集，n=6 143）和验证组（验证数据集，n=6 142）。通过使用计算机生成的随机数进行随机化。使用 Cox 回归模型分析推导数据集中 T2DM 的危险因素。通过将每个显着变量的 beta 乘以 10 建立 T2DM 预测模型。模型计算出总分后，进行受试者工作特征（ROC）曲线分析。 ROC 曲线下面积 (AUC) 用于评估模型的可预测性。此外，该模型的可预测性在验证数据集中得到了验证，并与芬兰糖尿病风险评分 (FINDRISC) 模型进行了比较。 结果：在 6 年研究期间，12 285 名参与者中共有 779 人患上了 T2DM。推导数据集中的发病率为 6.12% (n=376)，验证数据集中的发病率为 6.56% (n=403)。差异无统计学意义（χ(2)=1.00，P=0.316）。利用Cox回归模型总共建立了四种无创T2DM预测模型。预测模型计算出的风险评分的 ROC 表明，这些模型的 AUC 相似（0.67-0.70）。模型4的AUC和Youden指数最高。最佳截断值、敏感性、特异性和约登指数分别为 25、65.96%、66.47% 和 0.32。选择年龄、睡眠时间、BMI、腰围和高血压作为预测变量。使用年龄
OBJECTIVE: To provide a noninvasive type 2 diabetes mellitus (T2DM) prediction model for a rural Chinese population.METHODS: From July to August, 2007 and July to August, 2008, a total of 20 194 participants aged ≥18 years were selected by cluster sampling technique from a rural population in two townships of Henan province, China. Data were collected by questionnaire interview, anthropometric measurement, and fasting plasma glucose and lipid profile examination. A total 17 265 participants were followed up from July to August, 2013 and July to October, 2014. Finally, 12 285 participants were selected for analysis. Data for these participants were randomly divided into a derivation group (derivation dataset, n= 6 143) and validation group (validation dataset, n=6 142) by 1∶1, respectively. Randomization was carried out by the use of computer-generated random numbers. A Cox regression model was used to analyze risk factors of T2DM in the derivation dataset. A T2DM prediction model was established by multiplying beta by 10 for each significant variable. After the total score was calculated by the model, analysis of the receiver operating characteristic (ROC) curve was performed. The area under the ROC curve (AUC) was used for evaluating model predictability. Furthermore, the model's predictability was validated in the validation dataset and compared with the Finnish Diabetes Risk Score (FINDRISC) model.RESULTS: A total 779 of 12 285 participants developed T2DM during the 6-year study period. The incidence rate was 6.12% in the derivation dataset (n=376) and 6.56% in the validation dataset (n=403). The difference was not statistically significant (chi(2)=1.00, P=0.316). A total of four noninvasive T2DM prediction models were established using the Cox regression model. The ROCs of the risk score calculated by the prediction models indicated that the AUCs of these models were similar (0.67-0.70). The AUC and Youden index of model 4 was the highest. The optimal cut-off value, sensitivity, specificity, and Youden index were scores of 25, 65.96%, 66.47%, and 0.32, respectively. Age, sleep time, BMI, waist circumference, and hypertension were selected as predictive variables. Using age