Zhao JY, Dou JQ, Chen MW. Construction of a risk prediction model for hypertension in type 2 diabetes: Independent risk factors and nomogram. World J Diabetes 2025; 16(5): 102141 [DOI: 10.4239/wjd.v16.i5.102141]
Corresponding Author of This Article
Ming-Wei Chen, MD, Professor, Department of Endocrinology, First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Bezirk Shushan, Hefei 230022, Anhui Province, China. mingweichen0703@126.com
Research Domain of This Article
Endocrinology & Metabolism
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Diabetes. May 15, 2025; 16(5): 102141 Published online May 15, 2025. doi: 10.4239/wjd.v16.i5.102141
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
Jian-Yong Zhao, Ming-Wei Chen, Department of Endocrinology, First Affiliated Hospital of Anhui Medical University, Hefei 230022, Anhui Province, China
Jian-Yong Zhao, Jia-Qing Dou, Department of Endocrinology, Chaohu Hospital of Anhui Medical University, Chaohu 238000, Anhui Province, China
Author contributions: Zhao JY, Dou JQ, and Chen MW contributed to the design of the study; Zhao JY and Dou JQ wrote the first edition of the manuscript and performed the experiments; Zhao JY, Dou JQ, and Chen MW made the figures; Zhao JY and Chen MW revised the final version of the manuscript; all authors have reviewed and approved this manuscript and consented to publish it.
Institutional review board statement: The study protocol was approved by the Ethics Committee of Chaohu Hospital Affiliated to Anhui Medical University (No. KYXM-202312-007).
Informed consent statement: No informed consent was required for this study.
Conflict-of-interest statement: The authors declared that they have no conflicts of interest regarding this work.
Data sharing statement: The data used to support the findings of this study are available from the corresponding author upon request.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ming-Wei Chen, MD, Professor, Department of Endocrinology, First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Bezirk Shushan, Hefei 230022, Anhui Province, China. mingweichen0703@126.com
Received: October 10, 2024 Revised: January 4, 2025 Accepted: February 26, 2025 Published online: May 15, 2025 Processing time: 197 Days and 17.1 Hours
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 (χ2 = 7.066, P = 0.562 for training; χ2 = 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.
Core Tip: This study focused on type 2 diabetes mellitus complicated by hypertension. It used a large dataset and statistical methods to identify key risk factors like age, low-density lipoprotein, body mass index, diabetes duration, and urine protein. A validated nomogram was built for personalized risk assessment.
