基于中国农村人群的非侵袭性2型糖尿病风险预测模型的建立  

Establishing a noninvasive prediction model for type 2 diabetes mellitus based on a rural Chinese population

作　　者：张红艳[1] 石文惠[2] 张明[3] 尹磊 庞超 冯天平 张璐[1] 任永成[1] 王炳源[1] 杨香玉[1] 周俊梅[3] 韩成义 赵阳[1] 赵景志 胡东生[1] 

机构地区：[1]郑州大学公共卫生学院,450001 [2]中国疾病预防控制中心慢性病防治与社区卫生处 [3]深圳大学医学院 [4]河南省军区医院预防保健科

出　　处：《中华预防医学杂志》2016年第5期397-403,共7页Chinese Journal of Preventive Medicine

基　　金：国家自然科学基金（81373074、81402752）

摘　　要：目的建立基于中国农村人群的非侵袭性2型糖尿病（T2DM）风险预测模型。方法于2007年7—8月和2008年7—8月选择河南省某县的两个乡（镇）为研究现场，以自然村为单位，采用整群抽样的方法，选取i〉18岁农村居民为调查对象，共20194名。对调查对象进行问卷调查、体格检查、FPG及脂质谱检测。于2013年7—8月和2014年7—10月对上述调查对象进行随访，共随访到17265名，最终纳入12285名调查对象。通过计算机产生随机数字，以1：1的比例将其随机分为建立预测模型组（建模组，6143名）和验证预测效能组（验证组，6142名）。在建模组，应用多因素Cox比例风险回归模型分析T2DM的危险因素，并以10倍的B值为每个有统计学意义的预测变量赋值，由此建立T2DM风险预测模型。应用预测模型计算出个体的风险分值后进行受试者工作特征（ROC）曲线分析，以ROC曲线下面积（AUC）反映模型的预测效能。在验证组检验模型的预测效能，并与芬兰FINDRISC模型相比较。结果调查对象随访6年后新发T2DM779例，其中建模组376例，发病率为6．12％；验证组403例，发病率为6．56％，两组人群T2DM发病率差异无统计学意义（x。=1．00，尸=0．316）。在建模组，应用多因素Cox比例风险回归模型方法共建立4个非侵袭性T2DM风险预测模型；4个模型的AUC相近（0．67—0．70）。模型4的AUC及约登指数最高；模型4以25分为最佳切点，此处的灵敏度为65．96％，特异度为66．47％，约登指数为0．32。该模型纳入的预测变量有年龄、睡眠时间、BMI、腰围和高血压。以〈30岁为参照组，30～44、45～59和／〉60岁的B值分别为1．07、1．58和1．67，分别赋予11、16和17分；以睡眠时间〈8．0h／d为参照组，／〉10．0h／d的B值为0．27，赋予3分；以BMI18．5～23．9kg／m。为参照组，BMI24．0～27．9和〉／28．0kg／m。�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 13 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 （x2=l.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, se

关 键 词：糖尿病 2型 预测 农村人口 队列研究 

分 类 号：R587.1[医药卫生—内分泌]