Li Liying, Wang Ziqiong, Zhang Muxin, Ruan Haiyan, Zhou Linxia, Wei Xin, Zhu Ye, Wei Jiafu, He Sen
Department of Cardiology, West China Hospital of Sichuan University, Chengdu, China.
Department of Cardiology, First People's Hospital, Longquanyi District, Chengdu, China.
Prev Med Rep. 2021 Oct 24;24:101618. doi: 10.1016/j.pmedr.2021.101618. eCollection 2021 Dec.
The prevalence of diabetes is increasing rapidly and becoming a major public health issue worldwide. We aimed to develop a novel nomogram model for long-term diabetic risk prediction in a Chinese population. A prospective cohort study was performed on 687 nondiabetic individuals who underwent routine physical examination in 1992 and 2007. Using the least absolute shrinkage and selection operator model to optimize feature selection. Multiple Cox regression analysis was performed, and a simple nomogram was constructed. The area under receiver operating characteristic curve (AUC) and calibration plot were conducted to assess the predictive accuracy of the model. The model was subjected to bootstrap internal validation. Of the 687 participants without diabetes at baseline, 74 developed diabetes during the follow-up time. This simple nomogram model was constructed by family history of diabetes, height, waist circumference, triglycerides, fasting plasma glucose and white blood cell count. The AUCs were 0.812 (95% CI: 0.729-0.895) and 0.794 (95% CI: 0.734-0.854) for 10-year and 15-year diabetic risk. The bootstrap corrected c-index was 0.771 (95% CI: 0.721-0.821). The calibration plot also achieved good agreement between observational and actual diabetic incidence. The stratification into different risk groups by optimal cut-off value of 12.8 allowed significant distinction between cumulative diabetic incidence curves in the whole cohort and several subgroups. We established and internally validated a novel nomogram which can provide individual diabetic risk prediction for Chinese population and this practical screening model may help clinicians to identify individuals at high risk of diabetes.
糖尿病的患病率正在迅速上升，并成为全球主要的公共卫生问题。我们旨在开发一种用于预测中国人群长期糖尿病风险的新型列线图模型。对1992年和2007年接受常规体检的687名非糖尿病个体进行了一项前瞻性队列研究。使用最小绝对收缩和选择算子模型优化特征选择。进行了多重Cox回归分析，并构建了一个简单的列线图。采用受试者工作特征曲线下面积（AUC）和校准图来评估模型的预测准确性。对该模型进行了自抽样内部验证。在687名基线时无糖尿病的参与者中，有74人在随访期间患了糖尿病。这个简单的列线图模型是由糖尿病家族史、身高、腰围、甘油三酯、空腹血糖和白细胞计数构建的。10年和15年糖尿病风险的AUC分别为0.812（95%CI：0.729 - 0.895）和0.794（95%CI：0.734 - 0.854）。自抽样校正后的c指数为0.771（95%CI：0.721 - 0.821）。校准图在观察到的和实际的糖尿病发病率之间也取得了良好的一致性。通过最佳临界值12.8将其分为不同风险组，使得整个队列和几个亚组的累积糖尿病发病率曲线之间有显著差异。我们建立并内部验证了一种新型列线图，它可以为中国人群提供个体糖尿病风险预测，这种实用的筛查模型可能有助于临床医生识别糖尿病高危个体。
