The application of big data to cardiovascular disease: paths to precision medicine

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.

Figure 1. Big data enhances precision cardiovascular phenotyping.

Contemporary understanding of the heterogeneity in cardiovascular disease requires compilation of a diverse array of big data sources. Data from these domains are amenable to novel network medicine analytics to generate individual patient networks to define networks based on population-level data as well as a reticulotype (i.e., a patient’s unique molecular network that allows an exploration of how perturbations affect phenotype). While precision phenotyping may define clusters of patients, reticulotyping provides further resolution to clusters by identifying the molecular (network) drivers of unique patient-specific characteristics.

Figure 2. Heterogeneity in cardiovascular disease and convergence on a common end-pathophenotype.

(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).

Figure 3. Big data informs reticulotyping and cardiovascular disease phenotyping.

(A) The current viewpoint of cardiovascular disease phenotyping focuses on reductionism, which posits that a pathogenic variant is causal for a disease trait, or endophenotype, and, therefore, a key determinant of developing a cardiovascular disease. (B) Network medicine allows for precision endophenotyping and phenotyping for individuals with similar clinical signs and symptoms. Using big data, patient-specific integrated networks (e.g., protein-protein interaction networks) can be constructed, and the consequences of perturbations owing to an individual’s unique genomic and molecular makeup, known as the reticulotype, can be explored. The reticulotype, in turn, also governs endophenotype and defines a patient-specific phenotype that may not have been evident previously.