Luo Zilang, Pi Damao, Xi Tianlan, Jiang Wenli, Qiu Feng, Yang Jiadan
Department of Pharmacy, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
College of Pharmacy, Chongqing Medical University, Chongqing, China.
Front Endocrinol (Lausanne). 2025 Aug 8;16:1641114. doi: 10.3389/fendo.2025.1641114. eCollection 2025.
Diabetic cardiomyopathy (DCM) is a myocardial dysfunction disorder driven by diabetes-associated metabolic disorders, significantly elevating the risk of heart failure in patients with type 2 diabetes mellitus (T2DM). We aimed to develop and validate a nomogram for individualized DCM risk prediction in T2DM populations.
This retrospective study enrolled 525 consecutive T2DM patients admitted to our hospital (June 2022-June 2024). Participants were randomly allocated to training (70%) or validation (30%) cohorts. Baseline clinical characteristics, laboratory profiles, and echocardiographic parameters were collected. Predictors were identified via univariate then multivariate logistic regression, followed by nomogram construction. Model validation included: (1) internal validation via 1000 bootstrap resamples; (2) discrimination assessed by the area under the receiver operating characteristic curve (AUC-ROC); (3) calibration evaluated using calibration plots and the Hosmer-Lemeshow goodness-of-fit test; (4) clinical utility determined by decision curve analysis (DCA) and clinical impact curves (CIC).
Six independent predictors-age, duration of type 2 diabetes mellitus (T2DM Duration), systolic blood pressure (SBP), urinary albumin-to-creatinine ratio (UACR), left atrial diameter (LAD), and left ventricular posterior wall thickness at end-diastole (LVPWd)-were incorporated. The model showed excellent discrimination: AUC 0.947 (95% CI: 0.916-0.967) in training and 0.922 (95% CI: 0.870-0.956) in validation cohorts. Calibration indicated strong agreement (Hosmer-Lemeshow χ² = 9.2119, = 0.3247). DCA and CIC confirmed clinical utility.
This nomogram integrates routine clinical/echocardiographic parameters to predict DCM risk in T2DM patients, facilitating individualized risk stratification and guiding early cardioprotective interventions in high-risk populations.
https://www.chictr.org.cn/index.html, identifier ChiCTR2400093755.
糖尿病性心肌病（DCM）是一种由糖尿病相关代谢紊乱驱动的心肌功能障碍疾病，显著增加了2型糖尿病（T2DM）患者发生心力衰竭的风险。我们旨在开发并验证一种用于T2DM人群DCM个体化风险预测的列线图。
这项回顾性研究纳入了我院连续收治的525例T2DM患者（2022年6月至2024年6月）。参与者被随机分配到训练队列（70%）或验证队列（30%）。收集基线临床特征、实验室检查结果和超声心动图参数。通过单因素然后多因素逻辑回归确定预测因素，随后构建列线图。模型验证包括：（1）通过1000次自抽样重采样进行内部验证；（2）通过受试者操作特征曲线下面积（AUC-ROC）评估辨别力；（3）使用校准图和Hosmer-Lemeshow拟合优度检验评估校准；（4）通过决策曲线分析（DCA）和临床影响曲线（CIC）确定临床实用性。
纳入了六个独立预测因素——年龄、2型糖尿病病程（T2DM病程）、收缩压（SBP）、尿白蛋白与肌酐比值（UACR）、左心房内径（LAD）和舒张末期左心室后壁厚度（LVPWd）。该模型显示出优异的辨别力：训练队列中的AUC为0.947（95%CI：0.916-0.967），验证队列中的AUC为0.922（95%CI：0.870-0.956）。校准表明一致性良好（Hosmer-Lemeshow χ² = 9.2119，P = 0.3247）。DCA和CIC证实了临床实用性。
该列线图整合了常规临床/超声心动图参数以预测T2DM患者的DCM风险，有助于进行个体化风险分层并指导高危人群的早期心脏保护干预。
