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The application of big data to cardiovascular disease: paths to precision medicine
Jane A. Leopold, … , Bradley A. Maron, Joseph Loscalzo
Jane A. Leopold, … , Bradley A. Maron, Joseph Loscalzo
Published January 2, 2020
Citation Information: J Clin Invest. 2020;130(1):29-38. https://doi.org/10.1172/JCI129203.
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The application of big data to cardiovascular disease: paths to precision medicine

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Abstract

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.

Authors

Jane A. Leopold, Bradley A. Maron, Joseph Loscalzo

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Figure 2

Heterogeneity in cardiovascular disease and convergence on a common end-pathophenotype.

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Heterogeneity in cardiovascular disease and convergence on a common end-...
(A) Cardiovascular diseases are complex clinical phenotypes that involve many different endophenotypes (e.g., inflammation, thrombosis inflammation, thrombosis, calcification, fibrosis) that cannot be explained solely by a single pathogenic variant. (B) Heterogeneity in cardiovascular diseases is evident as shown by the relationships among genetic variants (genotypes), the biochemical and cellular consequences of harboring these variants (endophenotypes), and clinically observed pathophenotypes. (C) In a model based on big data and network analyses, specific endophenotypes are determined by modules or a (sub)network of protein-protein interactions within a larger disease network. Crosstalk between pathways that regulate different endophenotypes via a critical gene may occur. In this way, post-transcriptional and epigenetic mechanisms that are important in the pathogenesis of disease endophenotypes are emphasized and, collectively, converge to produce a complex pathophenotype. DCM, dilated cardiomyopathy; HFpEF, heart failure with preserved ejection fraction; LDL, low-density lipoprotein; LV, left ventricle; MI, myocardial infarction; RV, right ventricle; VSMC, vascular smooth muscle cell; VT, ventricular tachycardia. Adapted with permission from the Journal of the American College of Cardiology (network image in Figure 2C of, and bottom right panel of central illustration of, ref. 31).

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