Department of Cardiology, Texas Heart Institute, Houston, TX, USA.
Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Eur J Heart Fail. 2024 Oct;26(10):2183-2192. doi: 10.1002/ejhf.3443. Epub 2024 Sep 6.
Abnormalities in specific echocardiographic parameters and cardiac biomarkers have been reported among individuals with diabetes. However, a comprehensive characterization of diabetic cardiomyopathy (DbCM), a subclinical stage of myocardial abnormalities that precede the development of clinical heart failure (HF), is lacking. In this study, we developed and validated a machine learning-based clustering approach to identify the high-risk DbCM phenotype based on echocardiographic and cardiac biomarker parameters.
Among individuals with diabetes from the Atherosclerosis Risk in Communities (ARIC) cohort who were free of cardiovascular disease and other potential aetiologies of cardiomyopathy (training, n = 1199), unsupervised hierarchical clustering was performed using echocardiographic parameters and cardiac biomarkers of neurohormonal stress and chronic myocardial injury (total 25 variables). The high-risk DbCM phenotype was identified based on the incidence of HF on follow-up. A deep neural network (DeepNN) classifier was developed to predict DbCM in the ARIC training cohort and validated in an external community-based cohort (Cardiovascular Health Study [CHS]; n = 802) and an electronic health record (EHR) cohort (n = 5071). Clustering identified three phenogroups in the derivation cohort. Phenogroup-3 (n = 324, 27% of the cohort) had significantly higher 5-year HF incidence than other phenogroups (12.1% vs. 4.6% [phenogroup 2] vs. 3.1% [phenogroup 1]) and was identified as the high-risk DbCM phenotype. The key echocardiographic predictors of high-risk DbCM phenotype were higher NT-proBNP levels, increased left ventricular mass and left atrial size, and worse diastolic function. In the CHS and University of Texas (UT) Southwestern EHR validation cohorts, the DeepNN classifier identified 16% and 29% of participants with DbCM, respectively. Participants with (vs. without) high-risk DbCM phenotype in the external validation cohorts had a significantly higher incidence of HF (hazard ratio [95% confidence interval] 1.61 [1.18-2.19] in CHS and 1.34 [1.08-1.65] in the UT Southwestern EHR cohort).
Machine learning-based techniques may identify 16% to 29% of individuals with diabetes as having a high-risk DbCM phenotype who may benefit from more aggressive implementation of HF preventive strategies.
据报道，糖尿病患者存在特定超声心动图参数和心脏生物标志物异常。然而，对于糖尿病心肌病（DbCM）——一种心肌异常的亚临床阶段，它发生在临床心力衰竭（HF）发展之前，目前缺乏全面的描述。在这项研究中，我们开发并验证了一种基于机器学习的聚类方法，以基于超声心动图和心脏生物标志物参数识别高危 DbCM 表型。
在无心血管疾病和其他潜在心肌病病因（训练，n=1199）的社区动脉粥样硬化风险（ARIC）队列中的糖尿病患者中，使用超声心动图参数和神经激素应激和慢性心肌损伤的心脏生物标志物（共 25 个变量）进行无监督层次聚类。根据随访期间 HF 的发生情况确定高危 DbCM 表型。开发了一种深度神经网络（DeepNN）分类器来预测 ARIC 训练队列中的 DbCM，并在外部社区队列（心血管健康研究[CHS]；n=802）和电子健康记录（EHR）队列（n=5071）中进行验证。聚类在推导队列中确定了三个表型群。表型群 3（n=324，队列的 27%）的 5 年 HF 发生率明显高于其他表型群（12.1%比表型群 2的 4.6%[和表型群 1的 3.1%），被确定为高危 DbCM 表型。高危 DbCM 表型的关键超声心动图预测指标包括较高的 NT-proBNP 水平、左心室质量和左心房增大以及舒张功能障碍。在 CHS 和德克萨斯大学西南分校（UT）电子病历验证队列中，DeepNN 分类器分别识别出 16%和 29%的 DbCM 患者。外部验证队列中具有（与不具有）高危 DbCM 表型的参与者 HF 发生率显著更高（CHS 中的危险比[95%置信区间]为 1.61[1.18-2.19]，UT 西南电子病历队列中的 1.34[1.08-1.65]）。
基于机器学习的技术可能会识别出 16%至 29%的糖尿病患者存在高危 DbCM 表型，他们可能受益于更积极地实施 HF 预防策略。
