Construction and verification of a nomogram model for predicting the risk of early diabetic kidney disease based on blood pressure and blood glucose variability
10.3969/j.issn.1006-6187.2025.04.003
- VernacularTitle:基于血压及血糖变异性参数构建与验证糖尿病肾脏疾病早期风险列线图预测模型的研究
- Author:
Yinping MENG
1
;
Shuyue SHI
1
Author Information
1. 072750 保定市第二中心医院内分泌科
- Publication Type:Journal Article
- Keywords:
Early diabetic kidney disease;
Blood pressure variability;
Blood glucose variability;
Nomogram model
- From:
Chinese Journal of Diabetes
2025;33(4):252-258
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish the construction and validation of a nomogram model for predicting the risk of early diabetic kidney disease(DKD)based on blood pressure and blood glucose variability.Methods Patients with early DKD were selected as the DKD group(n=247)and patients with T2DM alone as the T2DM group(n=150)from the Department of Endocrinology of our hospital during April 2020 to December 2023.All subjects were randomly divided into a training set(n=278)and a validation set(n=119)according to a ratio of 7∶3.The general data and biochemical indicators were compared between the two groups,and Cox proportional risk regression analysis was performed on the influencing factors for DKD in the training set.The risk nebulogram prediction model was constructed.The receiver operating characteristic(ROC)curve and consistency index(C-index)were used to analyze the effectiveness of the model in the training set and verification set,and Bootstrap internal verification was carried out.The calibration curve was drawn to evaluate the prediction calibration degree and discrimination validity of the model.Clinical decision curve(DCA)was applied to evaluate the clinical utility of the nomogram model.Results SBP,DBP,BUN,blood uric acid,24 h systolic blood pressure variation coefficient(24 hSBPCV),24 h diastolic blood pressure variation coefficient(24 h hDBPCV),coefficient of variation in daytime systolic blood pressure(dSBPCV),coefficient of variation in daytime diastolic blood pressure(dDBPCV),coefficient of variation in nighttime systolic blood pressure(nSBPCV),coefficient of Variation in nighttime diastolic blood pressure(nDBPCV),maximum daytime blood glucose fluctuation range(LAGE),average blood glucose throughout the day(MBG),and standard deviation of blood glucose throughout the day(SDBG),fasting blood glucose coefficient of variation(FBG-CV)and mean blood glucose fluctuation range(MAGE)were higher in DKD group than in T2DM group(P＜0.05).eGFR was lower in DKD group than in T2DM group(P＜0.05).Cox proportional risk regression analysis showed that 24 hSBPCV,24 hDBPCV,dSBPCV,dDBPCV,nSBPCV,nDBPCV,LAGE,MBG,SDBG,FBG-CV and MAGE were the influencing factors for the early occurrence of DKD.Accordingly,the prediction model of early DKD was established.ROC curve analysis showed that the area under the curve of the early DKD nomogram model were 0.834 and 0.805 in the training set and verification set,with the sensitivity 85.20%and 80.60%,and the specificity 71.40%and 72.40%,respectively.Calibration curve analysis showed that the C-index of the early DKD neomorph model were 0.839 and 0.801 in the training set and the verification set,respectively.The goodness of fit test results showed that the actual observed values are in good agreement with the predicted results.The results of DCA curve showed that when the threshold probability of early DKD occurrence was between 0 and 1,the net benefit value of this model was better.Conclusions In this study,the nomogram model constructed by BP and BG variability parameters can effectively predict the occurrence of early DKD,and provide a basis for clinical screening and evaluation.
