Construction of a risk prediction model for hypertension in type 2 diabetes: Independent risk factors and nomogram

Jian-Yong Zhao; Jia-Qing Dou; Ming-Wei Chen

doi:10.4239/wjd.v16.i5.102141

Construction of a risk prediction model for hypertension in type 2 diabetes: Independent risk factors and nomogram

World J Diabetes. 2025 May 15;16(5):102141. doi: 10.4239/wjd.v16.i5.102141.

Authors

Jian-Yong Zhao^{1

2}, Jia-Qing Dou², Ming-Wei Chen³

Affiliations

¹ Department of Endocrinology, First Affiliated Hospital of Anhui Medical University, Hefei 230022, Anhui Province, China.
² Department of Endocrinology, Chaohu Hospital of Anhui Medical University, Chaohu 238000, Anhui Province, China.
³ Department of Endocrinology, First Affiliated Hospital of Anhui Medical University, Hefei 230022, Anhui Province, China. mingweichen0703@126.com.

Abstract

Background: Type 2 diabetes mellitus (T2DM) is a prevalent metabolic disorder increasingly linked with hypertension, posing significant health risks. The need for a predictive model tailored for T2DM patients is evident, as current tools may not fully capture the unique risks in this population. This study hypothesizes that a nomogram incorporating specific risk factors will improve hypertension risk prediction in T2DM patients.

Aim: To develop and validate a nomogram prediction model for hypertension in T2DM patients.

Methods: A retrospective observational study was conducted using data from 26850 T2DM patients from the Anhui Provincial Primary Medical and Health Information Management System (2022 to 2024). The study included patients aged 18 and above with available data on key variables. Exclusion criteria were type 1 diabetes, gestational diabetes, insufficient data, secondary hypertension, and abnormal liver and kidney function. The Least Absolute Shrinkage and Selection Operator regression and multivariate logistic regression were used to construct the nomogram, which was validated on separate datasets.

Results: The developed nomogram for T2DM patients incorporated age, low-density lipoprotein, body mass index, diabetes duration, and urine protein levels as key predictive factors. In the training dataset, the model demonstrated a high discriminative power with an area under the receiver operating characteristic curve (AUC) of 0.823, indicating strong predictive accuracy. The validation dataset confirmed these findings with an AUC of 0.812. The calibration curve analysis showed excellent agreement between predicted and observed outcomes, with absolute errors of 0.017 for the training set and 0.031 for the validation set. The Hosmer-Lemeshow test yielded non-significant results for both sets (χ ² = 7.066, P = 0.562 for training; χ ² = 6.122, P = 0.709 for validation), suggesting good model fit.

Conclusion: The nomogram effectively predicts hypertension risk in T2DM patients, offering a valuable tool for personalized risk assessment and guiding targeted interventions. This model provides a significant advancement in the management of T2DM and hypertension comorbidity.

Keywords: Hypertension; Nomogram; Prediction model; Risk factors; Type 2 diabetes mellitus.