Nomogram prediction for the 3-year risk of type 2 diabetes in healthy mainland China residents
Nomogram prediction for the 3-year risk of type 2 diabetes in healthy mainland China residents
复制标题
中国大陆健康居民3年2型糖尿病风险列线图预测
DOI：
10.1007/s13167-019-00181-2
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发表时间：
2019-09-01
期刊：
影响因子：
6.5
通讯作者：
Liu, Chengyun
中科院分区：
文献类型：
--
作者：
Wang, Kun;Gong, Meihua;Liu, Chengyun
AimsTo开发一个精确的个性化的2型糖尿病（T2 DM）预测模型的成本效益和现成的参数在中国中部population.MethodsA 3年的队列研究进行了5557非糖尿病的个人谁接受了每年的体检作为训练队列，和随后的验证队列的1870人进行了使用相同的程序。进行多元Logistic回归分析，并通过逐步方法构建简单的诺模图。通过500次bootstrap重采样进行受试者工作特征（ROC）曲线和决策曲线分析，分别评估诺模图的确定性和临床价值。我们还估计了每个危险因素预测T2 DM的最佳截止值。结果T2 DM的3年累积发病率为10.71%。我们使用年龄、BMI、空腹血糖（FBG）、低密度脂蛋白胆固醇（LDLc）、高密度脂蛋白胆固醇（HDLc）和甘油三酯（TG）等参数开发了预测女性和男性T2 DM风险的简单列线图。在训练队列中，ROC曲线下面积（AUC）显示出统计准确性（女性AUC = 0.863，男性AUC = 0.751），在随后的验证队列中显示出类似的结果（女性AUC = 0.847，男性AUC = 0.755）。决策曲线分析证明了该诺模图的临床价值。为了最佳预测T2 DM的风险，年龄、BMI、FBG、收缩压、舒张压、总胆固醇、LDLc、HDLc和TG的临界值分别为47.5和46.5岁、22.9和23.7 kg/m2、5.1和5.4 mmol/L、118和123 mmHg、71和85 mmHg、5.06和4.94 mmol/L，结论该列线图可作为一种简单、合理、经济、可广泛应用的预测华中地区居民T2 DM个体化危险度的工具。在早期阶段成功识别风险个体和干预可以从预测，预防和个性化医疗的角度提供先进的策略。
AimsTo develop a precise personalized type 2 diabetes mellitus (T2DM) prediction model by cost-effective and readily available parameters in a Central China population.MethodsA 3-year cohort study was performed on 5557 nondiabetic individuals who underwent annual physical examination as the training cohort, and a subsequent validation cohort of 1870 individuals was conducted using the same procedures. Multiple logistic regression analysis was performed, and a simple nomogram was constructed via the stepwise method. Receiver operating characteristic (ROC) curve and decision curve analyses were performed by 500 bootstrap resamplings to assess the determination and clinical value of the nomogram, respectively. We also estimated the optimal cutoff values of each risk factor for T2DM prediction.ResultsThe 3-year cumulative incidence of T2DM was 10.71%. We developed simple nomograms that predict the risk of T2DM for females and males by using the parameters of age, BMI, fasting blood glucose (FBG), low-density lipoprotein cholesterol (LDLc), high-density lipoprotein cholesterol (HDLc), and triglycerides (TG). In the training cohort, the area under the ROC curve (AUC) showed statistical accuracy (AUC = 0.863 for female, AUC = 0.751 for male), and similar results were shown in the subsequent validation cohort (AUC = 0.847 for female, AUC = 0.755 for male). Decision curve analysis demonstrated the clinical value of this nomogram. To optimally predict the risk of T2DM, the cutoff values of age, BMI, FBG, systolic blood pressure, diastolic blood pressure, total cholesterol, LDLc, HDLc, and TG were 47.5 and 46.5 years, 22.9 and 23.7 kg/m2, 5.1 and 5.4 mmol/L, 118 and 123 mmHg, 71 and 85 mmHg, 5.06 and 4.94 mmol/L, 2.63 and 2.54 mmol/L, 1.53 and 1.34 mmol/L, and 1.07 and 1.65 mmol/L for females and males, respectively.ConclusionOur nomogram can be used as a simple, plausible, affordable, and widely implementable tool to predict a personalized risk of T2DM for Central Chinese residents. The successful identification of at-risk individuals and intervention at an early stage can provide advanced strategies from a predictive, preventive, and personalized medicine perspective.