Advanced phenotyping of cardiovascular diseases has evolved with the application of high-resolution omics screening to populations enrolled in large-scale observational and clinical trials. This strategy has revealed that considerable heterogeneity exists at the genotype, endophenotype, and clinical phenotype levels in cardiovascular diseases, a feature of the most common diseases that has not been elucidated by conventional reductionism. In this discussion, we address genomic context and (endo)phenotypic heterogeneity, and examine commonly encountered cardiovascular diseases to illustrate the genotypic underpinnings of (endo)phenotypic diversity. We highlight the existing challenges in cardiovascular disease genotyping and phenotyping that can be addressed by the integration of big data and interpreted using novel analytical methodologies (network analysis). Precision cardiovascular medicine will only be broadly applied to cardiovascular patients once this comprehensive data set is subjected to unique, integrative analytical strategies that accommodate molecular and clinical heterogeneity rather than ignore or reduce it.

中文翻译：

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大数据在心血管疾病中的应用：精准医学的途径。

随着高分辨率组学筛查技术应用于大规模观察和临床试验的人群，心血管疾病的高级表型得到了发展。该策略已表明，心血管疾病的基因型，内表型和临床表型水平存在相当大的异质性，这是常规还原论尚未阐明的最常见疾病的特征。在本讨论中，我们讨论了基因组背景和（内切）表型异质性，并研究了常见的心血管疾病，以说明（内切）表型多样性的基因型基础。我们重点介绍了心血管疾病的基因分型和表型方面的现有挑战，这些挑战可以通过整合大数据来解决，并可以使用新颖的分析方法（网络分析）进行解释。仅当此综合数据集经过独特的，综合的分析策略以适应分子和临床异质性而不是忽略或减少它时，精密心血管医学才会广泛应用于心血管患者。