Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.
The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, People's Republic of China.
Diabetes Metab Res Rev. 2017 Oct;33(7). doi: 10.1002/dmrr.2911. Epub 2017 Jul 13.
Several prediction tools have been developed to identify people with type 2 diabetes mellitus (T2DM) and to quantify the probability of developing T2DM. However, most of the risk models were constructed based on cross-sectional studies and tea-drinking was not included.
A total of 15 768 participants without known T2DM were followed up from 2007-2008 to 2013-2014; 12 654 were randomly assigned to the derivation dataset and 3114 to the validation dataset. We constructed a risk-score model for T2DM by using a Cox proportional-hazards model. Risk scores were calculated by multiplying β by 10 in the derivation cohort and were verified in the validation dataset. The model's accuracy was assessed by the area under the receiver operating characteristic curve (AUC).
Predictors for T2DM risk in the derivation dataset were drinking tea frequently, body mass index ≥28.0 kg/m , waist to height ratio ≥ 0.5, triglycerides level 1.70 to 2.25 and ≥2.26 mmol/L, and fasting plasma glucose 5.6 to 6.0 and ≥6.1 mmol/L. The corresponding scores were -2, 7, 7, 4, 6, 11, and 25, respectively. The sensitivity, specificity, and AUC (95% confidence interval) for this full model were 69.63%, 75.56%, and 0.791 (0.783-0.799), respectively. The ability of the non-invasive models to predict T2DM was not superior to that of the full model. With the validation dataset, the predictive performance was better for our full model than the Framingham risk-score model (AUC 0.731 vs 0.525, P < .001).
Our risk-score model has fair efficacy for predicting 6-year risk of T2DM in a rural adult Chinese population.
已经开发出几种预测工具来识别 2 型糖尿病（T2DM）患者，并量化发生 T2DM 的概率。然而，大多数风险模型是基于横断面研究构建的，并且不包括饮茶因素。
共有 15768 名无已知 T2DM 的参与者从 2007-2008 年随访至 2013-2014 年；其中 12654 名被随机分配到推导数据集，3114 名分配到验证数据集。我们使用 Cox 比例风险模型构建了 T2DM 风险评分模型。在推导队列中，通过将β乘以 10 计算风险评分，并在验证数据集中进行验证。通过接受者操作特征曲线下面积（AUC）评估模型的准确性。
推导数据集中 T2DM 风险的预测因素包括经常饮茶、BMI≥28.0kg/m、腰高比≥0.5、甘油三酯水平 1.70-2.25 和≥2.26mmol/L、空腹血糖 5.6-6.0 和≥6.1mmol/L。相应的评分分别为-2、7、7、4、6、11 和 25。该全模型的灵敏度、特异度和 AUC（95%置信区间）分别为 69.63%、75.56%和 0.791（0.783-0.799）。非侵入性模型预测 T2DM 的能力并不优于全模型。使用验证数据集，我们的全模型比 Framingham 风险评分模型的预测性能更好（AUC 0.731 比 0.525，P<.001）。
我们的风险评分模型对预测中国农村成年人 6 年 T2DM 风险具有良好的效果。
