Moving Towards Individualized Care for Cardiovascular Disease
by Jane A. Leopold, M.D., Brigham and Women’s Hospital and Harvard Medical School
Precision medicine takes an integrative approach to guide the prevention, diagnosis, and treatment of cardiovascular disease. This is accomplished by considering a person’s genetic makeup, their lifestyle habits, and disease or environmental exposures as key determinants of what constitutes cardiovascular health and disease. This approach moves away from presuming and treating all individuals with a similar cardiovascular disease phenotype the same and recognizes that there is inherent heterogeneity among patients with cardiovascular diseases. Precision medicine includes pan-omic (genomics, transcriptomics, proteomics, metabolomics, and microbiomics) profiling and incorporates this with clinical data from electronic or other health records to define a cardiovascular phenotype. The resultant big datasets are particularly amenable to network analysis, a systems biology analytical methodology that allows for unbiased discovery of previously unrecognized interactions that can inform molecular etiologies of disease or drug efficacy. Reticulotyping, which incorporates deep clinical, exposure, and panomics phenotyping to create person-specific networks, can be used to both predict and treat cardiovascular disease. This individualized precision medicine approach has the potential to allow people to achieve ideal cardiovascular health, personalize treatment of cardiovascular disease, and is likely to be recognized on a broad scale in the future.
Although there has been substantial progress in the diagnosis and treatment of cardiovascular diseases, they remain the leading cause of death worldwide, accounting for 31% of all global deaths. Of the 17.9 million deaths attributable to cardiovascular diseases annually, 85% are due to myocardial infarction and stroke.1 It has also been estimated that by 2030, up to 44% of adults in the United States will have some form of cardiovascular disease.2 In the United States in 2015-2016, only 2.5% of adults meet criteria for ideal cardiovascular health despite increased awareness of cardiovascular disease risk factors as well as lifestyle modifications to improve diet and nutrition, physical activity, smoking cessation, a non-overweight or obese weight.3 A recent meta-analysis that included 210,443 adults from 12 cohort studies, demonstrated the important contributions of these factors to cardiovascular disease risk. Individuals with more favorable cardiovascular risk factor profiles who were categorized as having ideal cardiovascular health were at lower risk for incident cardiovascular disease than individuals that did not meet metrics for ideal cardiovascular health (HR=0.28: 95% CI: 0.23-0.33) and lower cardiovascular mortality (RR: 0.25; 95% CI: 0.10-0.63).4,5 Nonetheless, within populations there is significant heterogeneity in cardiovascular disease burden that may be explained, in part, by differences in age, sex, race and ethnicity, socioeconomic status, and region as well as family history and prior exposures.6 Given the vast heterogeneity in cardiovascular disease risk factor profiles and its attendant impact on clinical outcomes, a precision approach to diagnosing, phenotyping, and treating individuals is warranted.
Our current approach to prevention and treatment of cardiovascular diseases in populations at large is applied through the lens of reductionism, which in medicine translates into focusing on a single symptom or disease process, inexact modification of risk factors, emphasis on maintaining homeostasis, and subscribing to add-on medical therapies.7,8 Current practice, which follows a reductionist approach to health care, relies on symptomatic patients recognizing and reporting symptoms to a medical professional and entering into the medical system. At this point testing begins, which can range from a history and physical to advanced imaging or provocative invasive studies. This personalized assessment leads to a treatment plan that aims to decrease symptoms, limit development or progression of disease, and, in some instances, decrease mortality.9 While these goals are ideal, there are uncertainties associated with achieving them, especially when dealing with complex diseases such as atherosclerosis and cardiovascular disease that may represent the final common endpoint of several disparate disease etiologies.
In recent years, there has been a call to move away from reductionism and towards precision medicine. This has occurred because reductionism causes the medical system to make assumptions that bias the approach to patient care. In particular, it is presumed that all patients with the same symptoms and clinical presentation have the same disease phenotype and they will respond similarly to disease interventions. This concept is illustrated by the implementation of templates to screen, triage, and treat patients with suspected cardiovascular disease.10
While this has facilitated the application of guideline-directed medical therapies to patients in need, it also considers patients as a group rather than individuals and doesn’t account for heterogeneity in cardiovascular diseases.11-13 This practice also focuses on symptomatic or established cardiovascular disease and doesn’t address prevention or risk factor modification or treatment response. Since ~50% of cardiovascular medications that physicians prescribe aren’t taken or taken appropriately, this underscores the unmet need to move past a reductionist approach where standard clinical recommendations are broadly applied to large groups of patients.14 Based on these observations, cardiovascular diseases are the ideal candidate to illustrate the advantages of a precision medicine model.
Precision medicine overcomes the limitations associated with reductionism in medicine and has the added advantage of defining precise cardiovascular phenotypes by incorporating results from newer omics methodologies, such as genomics, transcriptomics, proteomics, metabolomics, and the microbiome. The aim of panomics profiling is to illustrate the complexities of cardiovascular disease phenotypes at a molecular scale level. When considered together with data from deep clinical phenotyping and personal exposures (personal, natural, or environmental), panomics profiling has the capacity to provide a more informed pathophenotypic assessment of patients. The promise of precision medicine is to collate and analyze these data to create an individual’s phenotypic profile. The potential to implement a precision medicine approach into active cardiovascular care also requires the application of novel analytical strategies to big datasets, such as systems biology and network analyses to integrate data. Ideally, the ultimate system should be facile; applicable to prevention, diagnosis, and therapeutics; and, able to improve health care in the cardiovascular field.9 While advances in this direction have been made in oncology, the cardiovascular field has been slower to adapt.
Population-based definitions of health and disease
The success of precision medicine is dependent upon understanding what constitutes cardiovascular health and a transition to disease. While this may seem intuitive, it is a strikingly complex concept as the definition of health shifts with age, ethnicity, and in some instances regionality. Furthermore, individuals may move continually along the continuum between health, at-risk, and disease states either as a result of lifestyle modifications or medical interventions.15 This is illustrated by considering hypercholesterolemia or hypertension in populations: at any given time, individuals may be considered as healthy, have high levels but not manifest overt disease, or have established disease. We define these categories based on population-level data that is utilized to identify high-risk patients. This practice, however, has the potential to lump all individuals with disease together based on a particular trait, such as hypertension, and not consider their complex and heterogeneous underlying pathophenotypes.15 For example, when clinical data from patients with the same heart failure phenotype were subjected to cluster analysis, several unique clusters that were associated with different clinical outcomes emerged.16
A second point to consider is that definitions of health and disease based on population level data are subject to change. For example, the range of systolic and diastolic blood pressures that are considered hypertensive have declined over time, thereby shifting the threshold for treatment. The same has occurred for hypercholesterolemia. When the American College of Cardiology/American Heart Association cholesterol guidelines were updated in 2013, an additional 13 million individuals met criteria to start treatment with statins.17 Similarly, when criteria for treatment established by the American College of Cardiology/American Heart Association guidelines was compared to those from the US Preventive Services Task Force, it was found that if the US Preventive Services Task Force guidelines were followed, approximately 9 million individuals who were recommended to take statins would no longer meet criteria for treatment.18 Thus, shifting population-based definitions of cardiovascular disease and thresholds for treatment illustrate the issues associated with relying on this approach to define treatment guidelines. Precision medicine can overcome this limitation by focusing on the individual as opposed to the population and potentially transitioning preventative and therapeutic interventions into ‘N=1’ trials.19
Limitations of the single pathogenic gene theory of cardiovascular disease
Early on, family history was identified as a hallmark risk factor for cardiovascular disease leading to the implication that a pathogenic gene(s) or gene product was responsible for disease. While there are several important cardiovascular diseases, such as Marfan syndrome, familial hypercholesterolemia, and hypertrophic cardiomyopathy, are attributable to monogenic disorders, no single gene has been identified that underlies the development of atherosclerosis or myocardial infarction.20-25 One limitation associated with attempting to ascribe these cardiovascular diseases to a single pathogenic gene include gene penetrance, where there are examples of family pedigrees with differences in disease expression. Similarly, there are limitations when considering the relationship between genotype and phenotype as there are known instances where a pathogenic gene was ultimately found to associate with several different cardiovascular phenotypes, as was shown for the alpha subunit of the type voltage-gated sodium channel (SCN5A).26
Genome-wide association studies (GWAS) have also been performed in the quest to identify genetic variants that explain cardiovascular disease; however, these studies also have limitations owing to incomplete genetic mapping, the associative nature of the studies, and a high degree of genetic heterogeneity in the variants. In fact, a GWAS meta-analysis that included almost 200,000 participants compared variants in healthy controls and patients with myocardial infarction. This study revealed that both common and rare genetic variants were present in both groups and likely explained the genetic heterogeneity observed in myocardial infarction.27
Genes that promote resilience to cardiovascular disease have also been examined as there have been studies that identified disease causing mutations considered to be highly penetrant in individuals that had no overt signs of disease.28,29 This alone suggests that additional factors, such as lifestyle or environment are drivers of the cardiovascular disease phenotype. One study that defined a healthy cardiovascular lifestyle as maintaining a non-obese weight, absence of tobacco use, and a healthy diet found that individuals with a high cardiovascular risk on the basis of a polygenic risk score who maintained a healthy cardiovascular lifestyle had a 46% decrease in the risk of cardiovascular events.30 Taken together, this body of evidence supports the concept that a monogenic or multigenic factors alone are unlikely to explain complex cardiovascular diseases.
Limitations to the ‘magic bullet’ theory of cardiovascular disease therapeutics
Paul Ehrlich, a Nobel laureate who discovered Salvarsan, an arsenical compound that cured syphilis, first advanced the hypothesis of a ‘magic bullet’.31 His pioneering work with chemotherapeutics for infectious microbes led him to suggest that there exists a ‘magic bullet’ that targets and ‘hits’ disease-causing entities using an analogy to the action of a bullet fired from a gun hitting its target. The idea
of a magic bullet persisted in medicine and was ultimately expanded to refer to a drug that cured a disease with high sensitivity, specificity, and an acceptable side effect profile.9 Among cardiovascular disease therapeutics, several have achieved ‘magic bullet’ status when they were first introduced to the market, notably statins, but haven’t cured disease. In retrospect, this is not surprising as cardiovascular diseases are heterogeneous and diverse and a single therapeutic is unlikely to target the diverse panomic factors and environmental contributors that mediate disease. Investigators have attempted to overcome this and improve efficacy by constructing a polypill that contained a statin, beta-blocker, diuretic, and angiotensin converting enzyme inhibitor. When combined with aspirin, the polypill was associated with a lower rate of cardiovascular events (HR=0.69; 95% CI: 0.50-0.97), but an increased incidence of side effects, such as hypotension and dizziness.32 Widely used biologicals also tend towards improved specificity, but don’t fulfill the requirements to be considered a ‘magic bullet’ as they are also subject to potential off-target effects or even on-target toxicities.
The importance of precision phenotyping and panomics profiling
Deep phenotyping is a core principle of precision medicine. At present, this is broadly considered to include data collected from electronic health records and panomics testing using high-throughput next generation platforms (Figure 1). There are, however, limitations to this process. First, precision phenotyping is dependent upon well described clinical data and disease phenotypes using a universal language, which is not always apparent on review of medical records. Movements to overcome this issue have been suggested, such as the using the Human Phenotype Ontology as the standard, but it has yet to gain traction.33 Second, endophenotypes, which are intermediate phenotypes, such as hypertension or hypercholesterolemia, that associate with cardiovascular diseases like myocardial infarction also require universal definitions and language as endophenotypes are also informative of the overt clinical phenotype.34,35 Examining endophenotypes with GWAS have illustrated the importance of applying panomics as part of precision phenotyping. In population-based studies where total cholesterol was considered as the endophenotype, the genetic variants rs693 and rs562338 associated strongly with total cholesterol but had only a limited association with the phenotype of interest, myocardial infarction.36 In population-based studies, such as the National Finnish FINRISK study that included 7,256 individuals, targeted metabolomic profiling identified 4 metabolites (phenylalanine, monounsaturated fatty acids, omega-6 fatty acids, and docosahexaenoic acid, that were associated with cardiovascular events after adjusting for relevant endophenotypes like hypertension, thereby showing the value of panomics profiling.37
Panomics testing, network analysis, and reticulotyping
As panomics profiling becomes more accessible, consideration of how to analyze these big datasets and integrate with electronic health records and other clinical data requires consideration. As cardiovascular diseases are heterogeneous and represent a composite of different endophenotypes, it is becoming more apparent that results from panomics testing will serve as mechanism to define unique cardiovascular disease phenotypes. For example, cluster analysis revealed that patients with heart failure and preserved ejection fraction are heterogeneous, but could be separated into 4 distinct groups with implications for all-cause hospitalization and mortality based on advanced profiling.16 Adding omics further enhanced the phenotypic description: proteomic analysis revealed that inflammatory proteins were associated with more severe disease and comorbidity burden.38 A panomics study design should also be considered when lifestyle or environmental exposures are believed to play an important role in modifying the endophenotype or phenotype.
Analytical methods for panomics data are varied but may start with a simplistic post-analysis data integration of the omics datasets. Here, the relevant data from each of the panomics datasets is selected after individual analysis of each of the datasets. A second method relies on computational tools to integrate the panomics data into a single big dataset and analyzing the data in toto. This latter approach is often preferred as it has the potential to minimize bias.39
Network analysis, which represents biological systems as interaction networks, is a newer analytical approach to panomics data that facilitates discovery of previously unrecognized relationships between genes, proteins, and metabolites in an unbiased manner within the integrated panomics dataset. Network analysis takes known interactions between genes, metabolites, and/or proteins and maps these components to a network where each component is a node and the links between the nodes are referred to as edges (Figure 2). Within networks, interactions between nodes suggest functional relationships.40,41 Networks contain modules, which are comprised of a group of nodes with similar function or potentially contain disease-related factors or pathways. Nodes can be shared by multiple modules and, as a result of this overlap, predict novel relationships between diseases or druggable targets.40,41
Network analysis has been used to unravel heterogeneity in cardiopulmonary diseases. For example, the Human Disease Symptom network was constructed to examine shared symptoms and gene networks. Analysis of the network revealed that diseases that have a wide variety of associated symptoms tended to have more complex molecular mechanisms.42 A second example is a network that was constructed from variables analyzed during an invasive cardiopulmonary exercise test. Here, 39 unique variables were found to cluster patients into 4 groups with differing risk for clinical outcomes.43 With a similar goal in mind, the NIH/ NHLBI-sponsored pulmonary vascular disease phenomics (PVDOMICS) study aims to integrate deep clinical phenotyping and panomics testing in order to aid pulmonary hypertension disease reclassification.44
Reticulotyping is a type of precision phenotyping based on individual’s integrative network.45 Reticulotypes are as unique as a fingerprint and no two persons have the same reticulome, or network representing an integrated biological, medical, and exposure profile. Therefore, reticulotyping and individualized reticulotype-based network analysis represents a goal of precision medicine. Reticulotyping is also a step beyond genotype-phenotype associations and can identify disease phenotypes for an individual based on their network as well as therapies that target nodes in the network.45
Precision therapeutics and systems pharmacology
There have been recent notable attempts to be selective about therapeutics, such as using pharmacogenetics, to identify nonresponders before they are given a drug that will have limited efficacy. The P2Y12 inhibitor clopidogrel, which prevents platelet aggregation, is given to patients following coronary artery stent implantation. However, there are individuals that have had adverse thrombotic events after taking clopidogrel suggesting that these individuals are “non-responders.” This phenotype was subsequently associated with CYP2C19 loss-of-function alleles.46-49
Early attempts to capitalize on point-of-care genetic testing for tailored therapy with clopidogrel in patients with coronary stents found that pharmacogenetic screening had no effect on cardiovascular death, myocardial infarction, or stent thrombosis in clinical trials.50 More recently, point-of-care testing was used to identify CYP2C19 loss-of-function alleles and direct antiplatelet therapy: individuals with loss-of-function alleles were treated with ticagrelor or prasugrel while those that didn’t have the loss-of-function alleles were treated with clopidogrel. In this study that included 2,488 patients, the genotype- guided strategy was non-inferior at one year and resulted in fewer bleeding events.51 While pharmacogenetic profiling illustrates a more precise method for tailoring therapeutics for patients than simply dispensing a drug to all-comers, network analyses and systems pharmacology advances the field further towards precision medicine.
Systems pharmacology, the use of networks to understand drug-disease or drug-drug interactions, allows for in silico predictions of drug efficacy as well as drug interactions. This methodology maps drugs to the network via their known targets. As an example, more than 900 drugs approved by the Food and Drug Administration were mapped to the human interactome based on their known drug targets. By measuring proximity within the network between the drug and disease nodes, novel drug-disease interactions were identified.
The validity of these interactions was tested subsequently in large healthcare databases that contained information on drug use and clinical outcomes. This analysis found that hydroxychloroquine was associated with a lower risk of coronary artery disease. In vitro studies performed in endothelial cells confirmed that hydroxychloroquine targeted pro-inflammatory proteins in the network.52
Drug repurposing studies based on network analysis were undertaken for vascular calcification, a disorder that is highly prevalent in patients with diabetes mellitus, chronic kidney disease, and atherosclerosis. Using network proximity analysis, calcification was shown to overlap with modules related to inflammation, thrombosis, and fibrosis, all of which have been implicated in the pathogenesis of vascular calcification. Furthermore, the analysis predicted that the drugs everolimus and pomalidomide, on the basis of their respective drug targets, would ameliorate calcification. The drugs were both trialed in vitro and shown to decrease calcification of vascular smooth muscle cells.53
Precision medicine is poised to revolutionize medical practice as well as prevention and treatment of cardiovascular disease. Precision medicine will facilitate individual phenotype specificity and the selection of the best drugs with maximal efficacy and limited side effects.
By focusing on reticulotyping, precision medicine has the potential to demonstrate that individual phenotype-based interventions exceed the current standard of care. The ensuing challenge lies in expanding panomics profiling for precision phenotyping and advancing analytics and network analyses to make reticulotyping accessible to mainstream practitioners.
Jane A. Leopold, M.D. is an Associate Professor of Medicine at Harvard Medical School, a clinical interventional cardiologist, and Director of the Women’s Interventional Cardiology Health Initiative at Brigham and Women’s Hospital. She has developed clinical programs and led pre-clinical and clinical studies that are aimed at understanding the mechanisms of cardiopulmonary disease and finding novel therapies. She is also an internationally recognized vascular biology researcher with expertise in precision medicine and vascular phenotyping. The longstanding focus of her translational science research laboratory has been to investigate the role of vascular endothelial function in the pathobiology of cardiopulmonary vascular and ventricular disease with an emphasis on precision medicine phenotyping. Her work is funded by the NIH/NHLBI and the American Heart Association and she is the recipient of several research awards. She has served as the Chair of peer review committees, American Heart Association National leadership committees, and is a standing member of an NIH/NHLBI study section. She is a former Associate Editor of Circulation and Circulation:Cardiovascular Interventions where she now serves as a Guest Editor and is a member of several editorial boards. She actively mentors students, fellows, and junior faculty in cardiopulmonary research.
Jane A. Leopold, MD
Brigham and Women’s Hospital
77 Avenue Louis Pasteur, NRB 0630K Boston, MA 02115
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Sources of Funding:
This work was supported in part by the American Heart Association 19AIML34980000 and NIH grants U01 125215.