Ou Qianying, Jin Wei, Lin Leweihua, Lin Danhong, Chen Kaining, Quan Huibiao
Department of Endocrinology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China.
Aging Male. 2023 Dec;26(1):2205510. doi: 10.1080/13685538.2023.2205510.
Formal risk assessment is crucial for diabetes prevention. We aimed to establish a practical nomogram for predicting the risk incidence of prediabetes and prediabetes conversion to diabetes.
A cohort of 1428 subjects was collected to develop prediction models. The LASSO was used to screen for important risk factors in prediabetes and diabetes and was compared with other algorithms (LR, RF, SVM, LDA, NB, and Treebag). Multivariate logistic regression analysis was used to construct the prediction model of prediabetes and diabetes, and drawn the predictive nomogram. The performance of the nomograms was evaluated by receiver-operating characteristic curve and calibration.
These findings revealed that the other six algorithms were not as good as LASSO in terms of diabetes risk prediction. The nomogram for individualized prediction of prediabetes included "Age," "FH," "Insulin_F," "hypertension," "Tgab," "HDL-C," "Proinsulin_F," and "TG" and the nomogram of prediabetes to diabetes included "Age," "FH," "Proinsulin_E," and "HDL-C". The results showed that the two models had certain discrimination, with the AUC of 0.78 and 0.70, respectively. The calibration curve of the two models also indicated good consistency.
We established early warning models for prediabetes and diabetes, which can help identify prediabetes and diabetes high-risk populations in advance.
正式的风险评估对糖尿病预防至关重要。我们旨在建立一个实用的列线图，用于预测糖尿病前期的风险发生率以及糖尿病前期向糖尿病的转化。
收集了1428名受试者组成队列以建立预测模型。采用LASSO筛选糖尿病前期和糖尿病的重要危险因素，并与其他算法（LR、RF、SVM、LDA、NB和Treebag）进行比较。使用多变量逻辑回归分析构建糖尿病前期和糖尿病的预测模型，并绘制预测列线图。通过受试者工作特征曲线和校准来评估列线图的性能。
这些发现表明，在糖尿病风险预测方面，其他六种算法不如LASSO。用于个体化预测糖尿病前期的列线图包括“年龄”“家族史”“空腹胰岛素”“高血压”“甲状腺球蛋白”“高密度脂蛋白胆固醇”“胰岛素原（空腹）”和“甘油三酯”，而用于预测糖尿病前期向糖尿病转化的列线图包括“年龄”“家族史”“胰岛素原（空腹）”和“高密度脂蛋白胆固醇”。结果显示，这两个模型具有一定的区分度，曲线下面积分别为0.78和0.70。两个模型的校准曲线也显示出良好的一致性。
我们建立了糖尿病前期和糖尿病的预警模型，可有助于提前识别糖尿病前期和糖尿病高危人群。
