Enabling Precision Cardiology Through Multiscale Biology and Systems Medicine.
Enabling Precision Cardiology Through Multiscale Biology and Systems Medicine.
复制标题
DOI：
10.1016/j.jacbts.2016.11.010
复制
发表时间：
2017-06
期刊：
影响因子：
--
通讯作者：
Dudley JT
中科院分区：
文献类型：
--
作者：
Johnson KW;Shameer K;Glicksberg BS;Readhead B;Sengupta PP;Björkegren JLM;Kovacic JC;Dudley JT
心血管疾病研究的传统范式来自于对特征明确的病理学的大规模、广泛包容的临床研究。然后根据标准化的临床指南将这些见解付诸实践。然而，新的心血管疗法的发展停滞和治疗反应的变化意味着这种模式不足以减少心血管疾病的负担。在这篇最先进的综述中，我们研究了我们提出的3个相互关联的想法，作为实现向精确心脏病学过渡的关键概念：1）使用机器学习方法精确表征心血管疾病; 2）疾病网络模型的应用，以拥抱疾病的复杂性;以及3）使用来自前两个想法的见解来使药理学和多药理学系统能够实现更精确的药物-患者匹配和患者-疾病分层。最后，我们探讨了将精确方法应用于心脏病学的挑战，这些挑战来自所需资源和基础设施的不足，以及这种新生方法的临床有效性的新证据。
The traditional paradigm of cardiovascular disease research derives insight from large-scale, broadly inclusive clinical studies of well-characterized pathologies. These insights are then put into practice according to standardized clinical guidelines. However, stagnation in the development of new cardiovascular therapies and variability in therapeutic response implies that this paradigm is insufficient for reducing the cardiovascular disease burden. In this state-of-the-art review, we examine 3 interconnected ideas we put forth as key concepts for enabling a transition to precision cardiology: 1) precision characterization of cardiovascular disease with machine learning methods; 2) the application of network models of disease to embrace disease complexity; and 3) using insights from the previous 2 ideas to enable pharmacology and polypharmacology systems for more precise drug-to-patient matching and patient-disease stratification. We conclude by exploring the challenges of applying a precision approach to cardiology, which arise from a deficit of the required resources and infrastructure, and emerging evidence for the clinical effectiveness of this nascent approach.