Department of Translational Medicine, Federico II University of Naples, Naples, Italy.
URT Genomics of Diabetes, Institute of Experimental Endocrinology and Oncology, National Research Council, Naples, Italy.
Clin Epigenetics. 2024 Aug 22;16(1):115. doi: 10.1186/s13148-024-01722-x.
Cardiovascular diseases (CVD) affect over half a billion people worldwide and are the leading cause of global deaths. In particular, due to population aging and worldwide spreading of risk factors, the prevalence of heart failure (HF) is also increasing. HF accounts for approximately 36% of all CVD-related deaths and stands as the foremost cause of hospitalization. Patients affected by CVD or HF experience a substantial decrease in health-related quality of life compared to healthy subjects or affected by other diffused chronic diseases.
For both CVD and HF, prediction models have been developed, which utilize patient data, routine laboratory and further diagnostic tests. While some of these scores are currently used in clinical practice, there still is a need for innovative approaches to optimize CVD and HF prediction and to reduce the impact of these conditions on the global population. Epigenetic biomarkers, particularly DNA methylation (DNAm) changes, offer valuable insight for predicting risk, disease diagnosis and prognosis, and for monitoring treatment. The present work reviews current information relating DNAm, CVD and HF and discusses the use of DNAm in improving clinical risk prediction of CVD and HF as well as that of DNAm age as a proxy for cardiac aging.
DNAm biomarkers offer a valuable contribution to improving the accuracy of CV risk models. Many CpG sites have been adopted to develop specific prediction scores for CVD and HF with similar or enhanced performance on the top of existing risk measures. In the near future, integrating data from DNA methylome and other sources and advancements in new machine learning algorithms will help develop more precise and personalized risk prediction methods for CVD and HF.
心血管疾病（CVD）影响全球超过 5 亿人，是全球死亡的主要原因。特别是，由于人口老龄化和全球危险因素的传播，心力衰竭（HF）的患病率也在增加。HF 约占所有 CVD 相关死亡的 36%，是住院的首要原因。与健康受试者或患有其他弥漫性慢性疾病的患者相比，患有 CVD 或 HF 的患者的健康相关生活质量会大幅下降。
对于 CVD 和 HF，已经开发了利用患者数据、常规实验室和进一步诊断测试的预测模型。虽然目前在临床实践中使用了其中一些评分，但仍需要创新方法来优化 CVD 和 HF 的预测，并降低这些疾病对全球人口的影响。表观遗传生物标志物，特别是 DNA 甲基化（DNAm）变化，为预测风险、疾病诊断和预后以及监测治疗提供了有价值的见解。本研究综述了与 DNAm、CVD 和 HF 相关的当前信息，并讨论了 DNAm 在改善 CVD 和 HF 的临床风险预测中的应用，以及 DNAm 年龄作为心脏衰老的替代指标的应用。
DNAm 生物标志物为提高 CV 风险模型的准确性提供了有价值的贡献。许多 CpG 位点已被采用，用于开发针对 CVD 和 HF 的特定预测评分，其性能与现有风险措施相似或有所提高。在不久的将来，整合来自 DNA 甲基组和其他来源的数据以及新机器学习算法的进步将有助于开发更精确和个性化的 CVD 和 HF 风险预测方法。
