Zhou Hui, Yang Chen, Dong Chen, Guo Zhirong, Hu Xiaoshu, Xu Yong, Zhou Zhengyuan
1. School of Public Health, Medical College of Soochow University , SuZhou, China ; 2. The Center for Disease Control and Prevention of Suzhou Industry Park , SuZhou, China.
3. Center for Disease Control of Xiangcheng District in Suzhou City, Suzhou , Jiangsu, China.
Iran J Public Health. 2014 Apr;43(4):432-40.
The aim was to compare the predictive power for Type 2 Diabetes mellitus (T2DM) using dynamic change (Difference) of metabolic syndrome (MS), Difference of fasting plasma glucose (FPG), baseline MS and FPG in cohort study.
Overall, 3461 subjects were recruited from Prevention of Multiple Metabolic disorders and MS in Jiangsu of China Study with 3.8 years follow-up. Cox proportional-hazards regression and receiver operating characteristic were used to evaluate the predictive power for T2DM using Difference MS, Difference of FPG, baseline MS and FPG.
Adjusted relative risk (aRR 5.24, 95% CI 4.28-6.42) of Difference of FPG to T2DM was highest than other. Difference of FPG owns the largest AUC (0.89, P<0.05), the highest sensitivity (96.25%) and specificity (80.49%) demonstrating that Difference of FPG can provide strongest predictive information to T2DM, Difference of MS comes second. Between FPG related tools, sensitivity of Difference of FPG almost was twice than baseline FPG(96.25% vs. 54.38%) suggesting that using baseline FPG would missed found 46% T2DM patients. Among MS related indicators, sensitivity of Difference of MS almost was twice than baseline MS (sensitivity 66.25% vs. 39.38%) suggesting that using baseline would missed found 61% T2DM patients.
Dynamic change of FPG had the highest predictive power for T2DM in Chinese than Dynamic Change of MS, baseline MS and FPG.
在队列研究中，旨在比较使用代谢综合征（MS）的动态变化（差值）、空腹血糖（FPG）差值、基线MS和FPG对2型糖尿病（T2DM）的预测能力。
总体而言，从中国江苏的多重代谢紊乱和MS预防研究中招募了3461名受试者，随访3.8年。使用Cox比例风险回归和受试者工作特征曲线来评估使用MS差值、FPG差值、基线MS和FPG对T2DM的预测能力。
FPG差值对T2DM的调整后相对风险（aRR 5.24，95%CI 4.28 - 6.42）高于其他因素。FPG差值的曲线下面积最大（0.89，P<0.05），敏感性最高（96.25%）和特异性最高（80.49%），表明FPG差值可为T2DM提供最强的预测信息，MS差值次之。在与FPG相关的工具中，FPG差值的敏感性几乎是基线FPG的两倍（96.25%对54.38%），这表明使用基线FPG会漏诊46%的T2DM患者。在与MS相关的指标中，MS差值的敏感性几乎是基线MS的两倍（敏感性66.25%对39.38%），这表明使用基线会漏诊61%的T2DM患者。
在中国，FPG的动态变化对T2DM的预测能力高于MS的动态变化、基线MS和FPG。
