ABSTRACT
Introduction
Cardiovascular disease persists as the leading cause of death worldwide despite continued advances in diagnostics and therapeutics. Our current approach to patients with cardiovascular disease is rooted in reductionism, which presupposes that all patients share a similar phenotype and will respond the same to therapy; however, this is unlikely as cardiovascular diseases exhibit complex heterogeneous phenotypes.
Areas covered
With the advent of high-throughput platforms for omics testing, phenotyping cardiovascular diseases has advanced to incorporate large-scale molecular data with classical history, physical examination, and laboratory results. Findings from genomics, proteomics, and metabolomics profiling have been used to define more precise cardiovascular phenotypes and predict adverse outcomes in population-based and disease-specific patient cohorts. These molecular data have also been utilized to inform drug efficacy based on a patient’s unique phenotype.
Expert opinion
Multiscale phenotyping of cardiovascular disease has revealed diversity among patients that can be used to personalize pharmacotherapies and predict outcomes. Nonetheless, precision phenotyping for cardiovascular disease remains a nascent field that has not yet translated into widespread clinical practice despite its many potential advantages for patient care. Future endeavors that demonstrate improved pharmacotherapeutic responses and associated reduction in adverse events will facilitate mainstream adoption of precision cardiovascular phenotyping.
Article highlights
Cardiovascular diseases are associated with significant morbidity and are the leading cause of death around the world. Current medical practice based on clinical trial data and guidelines is an example of reductionism in medicine, which assumes that patients with the same outward phenotype will have the same response to interventions.
Studies utilizing high-throughput platforms for omics testing, including genomics, proteomics, and metabolomics, have identified molecular factors that characterize cardiovascular diseases and predict adverse outcomes. These studies have also revealed that patients with cardiovascular disease are heterogeneous and have complex pathophenotypes.
Clinical studies have confirmed that there are missed opportunities to utilize omics testing to phenotype cardiovascular disorders. Among patients being evaluated or treated for prevalent cardiovascular diseases, exome sequencing has diagnosed previously unrecognized monogenic cardiovascular disorders [1].
Cardiovascular phenotyping using omics methodology has been beneficial in defining the phenotype of aberrant lipid profiles; select arrhythmias, including Brugada syndrome and long QT syndrome; aortic diseases, such as Marfan’s syndrome and Ehlers Danlos syndrome; and hypertrophic cardiomyopathy among other cardiovascular diseases.
Precision phenotyping is capable of predicting drug efficacy and side effect profiles in patients and populations.
As more data become available from large studies, barriers to implementing precision cardiovascular phenotyping for personalized therapeutics will need to be addressed before it gains widespread acceptance in clinical practice. These barriers include standardizing testing platforms, addressing data privacy and security, and acceptance of the pipeline by patients and the medical community.