Zhao Jian-Yong, Dou Jia-Qing, Chen Ming-Wei
Department of Endocrinology, First Affiliated Hospital of Anhui Medical University, Hefei 230022, Anhui Province, China.
Department of Endocrinology, Chaohu Hospital of Anhui Medical University, Chaohu 238000, Anhui Province, China.
World J Diabetes. 2025 May 15;16(5):102141. doi: 10.4239/wjd.v16.i5.102141.
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.
To develop and validate a nomogram prediction model for hypertension in T2DM patients.
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.
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 ( = 7.066, = 0.562 for training; = 6.122, = 0.709 for validation), suggesting good model fit.
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.
2型糖尿病（T2DM）是一种常见的代谢紊乱疾病，与高血压的关联日益增加，带来了重大的健康风险。由于当前工具可能无法完全捕捉该人群的独特风险，因此显然需要为T2DM患者量身定制一个预测模型。本研究假设，纳入特定风险因素的列线图将改善T2DM患者的高血压风险预测。
开发并验证T2DM患者高血压的列线图预测模型。
使用来自安徽省基层医疗卫生信息管理系统（2022年至2024年）的26850例T2DM患者的数据进行回顾性观察研究。该研究纳入了年龄在18岁及以上且有关键变量可用数据的患者。排除标准为1型糖尿病、妊娠期糖尿病、数据不足、继发性高血压以及肝肾功能异常。使用最小绝对收缩和选择算子回归以及多变量逻辑回归构建列线图，并在单独的数据集中进行验证。
为T2DM患者开发的列线图纳入年龄、低密度脂蛋白、体重指数、糖尿病病程和尿蛋白水平作为关键预测因素。在训练数据集中，该模型显示出较高的判别力，受试者操作特征曲线（AUC）下面积为0.823，表明预测准确性较高。验证数据集以0.812的AUC证实了这些结果。校准曲线分析显示预测结果与观察结果之间具有良好的一致性，训练集的绝对误差为0.017，验证集的绝对误差为0.031。两组的Hosmer-Lemeshow检验结果均无统计学意义（训练组χ² = 7.066，P = 0.562；验证组χ² = 6.122，P = 0.709），表明模型拟合良好。
列线图可有效预测T2DM患者的高血压风险，为个性化风险评估和指导针对性干预提供了有价值的工具。该模型在T2DM和高血压合并症的管理方面取得了重大进展。
