Harnessing Big Data to Advance Treatment and Understanding of Pulmonary Hypertension.
Harnessing Big Data to Advance Treatment and Understanding of Pulmonary Hypertension.
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DOI：
10.1161/circresaha.121.319969
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发表时间：
2022-04-29
影响因子：
20.1
通讯作者：
Maron, Bradley A.
中科院分区：
文献类型：
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作者：
Rhodes, Christopher J.;Sweatt, Andrew J.;Maron, Bradley A.
肺动脉高压（PH）是一种具有多种病因的复杂疾病，对应于表型异质性和可变的治疗反应。推进对PH发病机制的理解可能取决于综合方法，这些方法利用来自健康记录、成像、新型分子组学分析和其他模式的数据。在这篇综述中，我们总结了迄今为止在该领域产生的关键数据集，并描述了有望破译支持肺血管重塑的分子机制的分析方法，包括机器学习，网络医学和功能遗传学。我们还详细介绍了遗传和亚表型分析方法如何实现早期诊断，精确诊断和优化治疗预测。我们提出了识别功能重要的分子通路的策略，这些策略得到了多组学平台研究结果的支持，这些平台能够很好地实现个性化药物治疗选择，并在这种高度病态的疾病中推进精准医学。
Pulmonary hypertension (PH) is a complex disease with multiple aetiologies, corresponding to phenotypic heterogeneity and variable therapeutic responses. Advancing understanding of PH pathogenesis is likely to hinge on integrated methods that leverage data from health records, imaging, novel molecular ‘-omics’ profiling, and other modalities. In this review, we summarize key datasets generated thus far in the field and describe analytical methods that hold promise for deciphering the molecular mechanisms that underpin pulmonary vascular remodeling, including machine learning, network medicine, and functional genetics. We also detail how genetic and sub-phenotyping approaches enable earlier diagnosis, refined prognostication, and optimized treatment prediction. We propose strategies that identify functionally important molecular pathways, bolstered by findings across multi-omics platforms, which are well-positioned to individualize drug therapy selection and advance precision medicine in this highly morbid disease.