CARDIOVASCULAR GENOMIC MEDICINE
Redefining Heart Failure
The Utility of Genomics
Mark P. Donahue, MD, MHS*, ,*,
Douglas A. Marchuk, PhD and
Howard A. Rockman, MD*,
* Division of Cardiovascular Medicine
Duke Clinical Research Institute
Department of Molecular Genetics and Microbiology, Durham, North Carolina
Manuscript received February 9, 2006;
accepted May 29, 2006.
* Reprint requests and correspondence: Dr. Mark Donahue, Duke University Medical Center, Box 3298 DUMC, Durham, North Carolina 27710 (Email: mark.donahue{at}duke.edu).
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Abstract
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In this era of genomics, new technologies and the information that they generate have a wide range of potential applications to heart failure. Though there has not been widespread practical use of genomic information in everyday practice, there are many examples of how this information is beginning to transform the way we look at disease states in terms of diagnosis, prognosis, and treatment. The experience of oncology and other fields helps inform the heart failure field of not only the use of this information in investigating diagnosis, prognosis, and treatment response, but the reciprocal nature of this information. This information can be clinically useful (for instance, predicting treatment response) as well as further drive laboratory investigation (teasing out the biological pathways in non-responders to treatment can be a focus of new drug discovery); this is the essence of translational medicine. We believe that this is a good time to review where new technologies and information they generate can be placed into our classic understanding of heart failure: that is how we might redefine cardiomyopathy given our new information. Here we will review genomic evidence to date and how it can and may be considered in the evaluation and management of cardiomyopathies.
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Abbreviations and Acronyms
| | ACC/AHA = American College of Cardiology/American Heart Association | | ACE = angiotensin-converting enzyme | | BNP = brain natriuretic peptide | | DCM = dilated cardiomyopathy | | HCM = hypertrophic cardiomyopathy | | ICM = ischemic cardiomyopathy | | LVAD = left ventricular assist device |
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Redefining heart failure: The utility of genomics
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In this era of genomics, new technologies and the information that they generate have a wide range of potential applications to heart failure. Genomics is a term broadly used that was "born from a marriage of molecular and cell biology with classical genetics and is fostered by computational science" (1). Here we use the term genomics to include the entire complement of genes and their resultant messenger RNA. The arenas of proteomics and biological markers, such as brain natriuretic peptide (BNP), have demonstrated clinical utility; however, a discussion of proteomics is beyond the scope of this review. The use of genomic information has long held the promise of transforming clinical practice. A perception of a slow or no transformation has largely been fueled by a disconnection between media and scientific timelines. Though there has not been widespread practical use of genomic information in everyday practice, there are many examples of how this information is beginning to transform the way we look at disease states in terms of diagnosis, prognosis, and treatment. Oncology has been at the forefront of applied genomics. This has been driven in large part by the wide availability of biological samples and the use of microarray technology to assess gene expression. The number of studies using microarray technology for subclassification of tumors (diagnosis and prognosis) and to evaluate the utility of therapy (directing treatment) has increased greatly in the last 5 years. In addition, genetic testing in familial cancers such as breast cancer and ovarian cancer (the BRCA 1 and 2 genes) and colon cancer are used routinely in practice. In addition to the basic and clinical science, there are great complexities surrounding such genetic information and any specific patient; these include disease variability, genetic counseling, and the potential for social and economic discrimination (i.e., insurance). The experience of oncology and other fields helps inform the heart failure field of not only the use of this information in investigating diagnosis, prognosis, and treatment response, but the reciprocal nature of this information. This information can be clinically useful (for instance disease prediction) as well as further drive laboratory investigation (teasing out the biological pathways in non-responders to treatment can be a focus of new drug discovery); this is the essence of translational medicine. We believe that this is a good time to review where new technologies and information they generate can be placed into our classic understanding of heart failure: that is, how we might redefine cardiomyopathy given our new information. Here we will review genomic evidence to date and how it can and may be considered in the evaluation and management of cardiomyopathies.
Clear clinical problem.
Heart failure is a major public health burden in the U.S., causing approximately 200,000 deaths per year (2). Approximately 3 million people have chronic heart failure, with more than 400,000 new heart failure diagnoses annually (2). Heart failure is the most common Medicare diagnosis-related group (3), and the number of hospitalizations for heart failure as a primary or secondary diagnosis increased by approximately 50% between 1985 and 1995 (4). In the outpatient setting, heart failure is the second most common cardiovascular diagnosis, surpassed only by hypertension (5). This enormous clinical burden has a staggering economic impact costing the U.S. economy $38.1 billion dollars, or 5.4% of total health care expenditures for 1991 (5). Approximately 80% of patients with heart failure are older than 65 years (5), and 92% of the mortality attributed to heart failure occurs in this age group (3). This disease burden will only increase, potentially doubling the number of heart failure patients (6) because the number of people over 65 years is expected to be 70 million by 2030 (7). Thus, there is considerable morbidity and mortality associated with heart failure, despite innumerable pathophysiologic discoveries and therapeutic advances over the past 2 decades. The 2 ways to approach this problem are to further advance the field with new therapeutics (both for primary and secondary prevention), which will require new insights into pathophysiology, and to stratify those individuals currently receiving therapy (into responders and non-responders). Both of these areas can be advanced by genomics.
Phenotyping.
The disease heart failure is, in reality, a clinical syndrome that results from many etiologies and culminates in an inability for the heart to provide adequate flow to meet the metabolic needs of the body. Heart failure can occur in the setting of either normal or abnormal systolic function. Diastolic heart failure (heart failure with normal systolic function) and hypertrophic obstructive cardiomyopathy are also causes of the syndrome of heart failure. For the purposes of this review, we focus on heart failure in the setting of abnormal cardiac function or cardiomyopathy. Although the causes of cardiomyopathy funnel into common late stage pathways (and thus have similar/identical clinical presentations), the origins of the cardiac dysfunction and the timeline to the onset of the syndrome are varied. Whereas the origins of some cardiomyopathies are relatively clear, for example Chaga's disease or anthracycline exposure, others that fall under general designations such as dilated cardiomyopathy (DCM) are likely to represent many diseases (with only our poor ability to subclassify them preventing this from being obvious). Currently staging systems for heart failure rely largely on clinical characteristics and imaging for diagnosis. Historically, groups such as the World Health Organization have attempted to formalize a classification for cardiomyopathies (8). Although these early attempts at classification began to tease out the many different causes of cardiomyopathy, they focus on the dominant pathophysiology and some etiologic/pathogenetic factors. Recently, newer attempts have been made to both classify and stage cardiomyopathy. These attempts build upon prior work with an emphasis on recognition of the early stages of cardiomyopathy and an emphasis on precise determination of etiology (9). Similarly the newest American College of Cardiology/American Heart Association (ACC/AHA) guidelines for chronic heart failure (10) take a "new approach" to the classification of heart failure, one that emphasizes both the development and progression of disease. Table 1 summarizes staging of cardiomyopathy by current guidelines. Though recognition of early stages of disease may assist in prevention and therapy, the etiologic classification, in a majority of cases, at this time does not lead to specific upstream interventions. True prevention will come when specific etiologies are understood. In addition, cardiomyopathies that appear to have a clear origin, such as ischemic cardiomyopathy (ICM), can have highly varied disease timelines, a process mediated by a variety of factors.
Traditional management of heart failure has focused on the management of individuals with symptomatic disease (stages C and D of the ACC/AHA guidelines). The heart failure community has not traditionally focused on the identification of asymptomatic disease (or pre-structural/pre-symptomatic disease). Newer technologies may assist in identifying those in stage A who will progress to have structural disease and those in stage B who will develop symptoms. Such earlier identification will allow more focused preventative measures and treatments, individualized so that the number of patients needed to treat (to prevent a clinical event) can be reduced.
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Gene expression
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The technology to quantitate and monitor gene expression has exploded in that past 10 years since the microarray was popularized by the group at Stanford headed by Pat Brown and David Botstein (11). DNA microarrays allow the simultaneous measurement of the level of transcription of thousands of genes. There are several types of arrays; some are available commercially, and others can be custom made depending on the particular research interest. Commercially available microarrays are populated by a standard set of genes derived from the human genome project. Custom microarrays can contain a large number of genes depending on the research interest. This technology has been powerful in stimulating thinking about disease classification and the reciprocal flow of information.
The use of gene expression data has already resulted in a palpable effect in the field of oncology. Initially, microarrays assisted in tumor classification as an adjunct to histology (12,13). The technology then showed utility in chemotherapy response in diffuse large B-cell lymphoma (14) and survival in breast cancer (15). In a similar fashion, we know that histologic classification of non-ICM has a poor yield for an etiologic diagnosis, so it is possible that newer genomic technologies will allow for a similar subclassification of disease states. Such staging would allow for a better clinical understanding and application of this technology to diagnosis, prognosis, and therapeutics. In addition, gene expression signatures have identified patterns of pathway deregulation across human cancers that not only elucidate the biology of the process but also allow for the development of more targeted therapy (16). Two practical examples come from the transplant literature. The CARGO (Cardiac Allograft Rejection Gene Expression Observational) Investigators used microarray technology to develop an 11-gene expression profile with a high negative predictive value for the presence of rejection in patients after cardiac transplant (17). An example of the reciprocal flow of information comes from the renal transplant literature (18) where biopsies of individuals with acute rejection were investigated via gene expression analysis. Though there were no differences on conventional histology, there were considerable differences in gene expression patterns, which suggested that additional histologic staining would allow previously indistinguishable samples to be accurately subtyped. Thus, the gene expression information helped redefine the approach to rejection with conventional histology. We believe that genomics will help redefine aspects of etiology, initiation, and progression of heart failure.
Discovery science.
Investigators have used both the commercially available Affymetrix (Santa Clara, California) oligonucleotide arrays and as well as custom cDNA arrays to study cardiomyopathy. A custom chip with a cardiovascular focus, called the Cardiochip, was developed by Barrans et al. (19). It contains 10,368 different cardiovascular-based genes and was utilized is several studies to be highlighted. Much of the current literature regarding the use of gene expression in the investigation of cardiomyopathy falls in the category of discovery science. Discovery science involves using methods, such as gene expression, to find underlying mechanisms of disease that were not previously known. Studies to date look broadly in small cohorts at failing versus non-failing hearts and the myocardium pre- and post-left ventricular assist device (LVAD). Given the requirement for myocardial tissue specimens, these studies almost uniformly use cardiac explants from individuals undergoing transplant and the control (or non-failing) hearts from rejected donor hearts. The cases, therefore, represent end-stage cardiomyopathy (stage D or 3). Table 2 summarizes these discovery projects (20–30). These studies largely center on finding new pathways in the hope that better disease understanding will ultimately lead to improvements in diagnostics, prognostics, and drug discovery. These studies find hundreds of genes that are differentially expressed and implicating varied pathways including calcium signaling, energy metabolism, apoptotic pathways, stress response, signal transduction, and the maintenance of the cytoskeletal and extracellular matrix.
Disease classification and prognosis.
Contained within these discovery projects are areas of potential clinical application, such as cardiomyopathy classification and predicting response to treatment. Two studies featured in Table 2 were suggestive that disease classification is possible. In 1 study of 8 individuals with DCM, patterns in individuals with alcohol-related cardiomyopathy and familial cardiomyopathy were significantly different (22). In another study of individuals receiving an LVAD, the expression data segregated into 2 distinct groupings corresponding to ICM and DCM (27). In addition to these discovery projects, there have been some early attempts at disease classification. One study demonstrated that individuals with advanced stage hypertrophic cardiomyopathy (HCM) and DCM had 621 and 399 genes, respectively, up-regulated compared with control subjects and 236 and 51 genes, respectively, down-regulated. Of these figures, only 48% of the up-regulated genes and 22% of the down-regulated genes were in common. Though these entities are readily discernable on the clinical level, these findings lend credence to the concept of the using of gene expression for cardiomyopathy classification. Another case in point is the study by Kittleson et al. (31) who sought to use the gene expression information as a diagnostic to predict (or classify) cardiomyopathy etiology. Gene expression profiles were obtained on 25 patients with end-stage cardiomyopathy (10 ICM and 15 DCM), 16 patients post-LVAD (3 ischemic and 13 DCM), and 7 patients with newly diagnosed cardiomyopathy (3 ICM and 4 DCM). An etiology prediction profile was formulated and tested; the authors report an 89% sensitivity and 89% specificity for predicting an ischemic versus non-ischemic classification of cardiomyopathy specimens. In addition to the potential in diagnostics, there are potential prognostic uses. One study of gene expression pre- and post-LVAD was supportive of the potential for gene expression to be used in conjunction with clinical parameters to predict recovery post-LVAD (27). Using clinical and tissue-based gene expression data appears promising in these early studies of disease. In contrast with discovery science, which investigates small numbers of patients, such studies targeting disease classification as well as prognosis will require larger cohorts to demonstrate their clinical utility.
The enthusiasm for gene expression technology must be tempered, however, as there are many variabilities and practical matters to consider. Some studies suggest that gene expression can vary by site of tissue acquisition right ventricle versus left ventricle within an individual (24,32). Another study demonstrated the critical need to account for clinical variables as investigators demonstrated that even elementary clinical variables such as age and gender can have a significant influence on gene expression (33). There will also be a need to account for other comorbidities and modifying elements, such as medications; thus, high-fidelity clinical information will be an essential partner in genomic research. This integration of clinical and genomic information into predictive models will require innovative statistical approaches. In addition, as a practical matter, these gene expression studies require myocardial tissue, which is not readily accessible. Most of the discovery studies to date used tissue from myocardial explants. Some studies have employed the use of tissue from myocardial biopsies, though that approach raises potential issues with the site of tissue acquisition and need for RNA amplification. Given that heart failure is a systemic process, sampling RNA from circulating blood cells may hold some promise, but, in a similar fashion to myocardial samples, prior studies have demonstrated variation in gene expression from human blood depending on age, gender, and diurnal patterns (34).
In summary, microarray technology and its ability to assay thousands of genes simultaneously is beginning to show its potential in discovery science. Though there are also some promising areas such as a better defined cardiomyopathy classification (diagnostics) and possibly the ability to predict recovery after placement of a LVAD (prognostics), there is no evidence that these types of assays are ready for general clinical use. As we are able to subclassify cardiomyopathies, we may be able to better understand their varied pathophysiology and as a result develop not only better diagnostics and prognostics but also novel therapeutic interventions. As the potential of this technology becomes evident, the clinical research enterprise needs to address where, when, and how to test this new, powerful (and at times unwieldy) information.
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Genetics
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Medical genetics is predicated on the concept of genetic variation (polymorphism) resulting in disease. Medical genetics has traditionally focused on diseases that can be tracked through families. These familial disorders, such as Huntington's disease, are the result of a single gene defect and are often termed single-gene or monogenetic disorders. Common diseases—such as hypertension and coronary artery disease—are not caused by a single gene. These are termed complex diseases, where there are contributions of a variety of genes as well as environmental factors. Although a genetic variant may have a large contribution and directly cause the disease (causative gene), there also may be a modest contribution producing the disease when coupled with other factors such as environmental exposure (a susceptibility gene), or there may be a minor contribution that depends on the presence of other genetic and environmental factors (a modifier gene). Sometimes the differences between these categories can be blurred. For instance, a familial cardiomyopathy may track through a pedigree manifesting variably (often referred to as variable penetrance). The gene (in this case for familial cardiomyopathy), however, may in reality be a susceptibility gene that only becomes apparent after an exposure to some environmental influence. The critical point is that genetics influence the variability in the presentation, course, and outcome of disease in a variety of ways; these influences can occur at the origin of the disease process as well as any time point along the disease process.
Familial cardiomyopathy: genes of clear causation.
Molecular genetics has provided the most insight with improved classification of idiopathic cardiomyopathies. As stated earlier, the idiopathic cardiomyopathies were originally named as such when there was no clear antecedent event such as ischemia. It has been estimated that 35% of individuals with an idiopathic DCM will have inherited disease or familial DCM (35). Among these individuals, there are some distinct phenotypes including groups with cardiomyopathy and conduction disease and cardiomyopathy with associated muscle disease (35). The most common mode of transmission in familial DCM is autosomal dominant (56%) (36). As molecular geneticists had success in mapping the disease genes in some of these diseased families, a number of things emerged. One concept was that multiple genes (or alleles) can cause cardiomyopathy (allelic heterogeneity). A second is that within a gene multiple different mutations can cause cardiomyopathy (locus heterogeneity). Third and adding greatly to the complexity is that distinct mutations in the same gene can cause different types of cardiomyopathy. For instance, mutations in cardiac beta-myosin heavy chain or cardiac troponin T can result in a phenotype of DCM or HCM (37,38) depending on the location of the mutation in relationship to the contractile apparatus. At the level of the myocyte, some mutations appear to effect the sarcomere and the ability to generate force while others the cytoskeleton and the ability to transmit force. Whereas many of the genetic variants causing DCM are from defects in the cytoskeleton, other pathways may be affected such as changes in calcium signaling (38–40). Given that we are at only the beginning of our understanding of the causes of familial DCM, then it should come as no great surprise that little is known about the presentation and course of the disease for which the genetic defect is known. The natural history of familial DCM is not known with the exception of some rare cases (lamin A/C [41]), and mutation-specific interventions beyond standard heart failure therapy and genetic counseling do not exist (42). Table 3 depicts many of the genes implicated in cardiomyopathy, and it is designed to illustrate the wide array of genes that have been discovered to date to be involved in the development of idiopathic DCM (37,40,43–54).
The discovery of causative mutations has important implications for diagnosis, prognosis, and intervention. Much previous discovery work has been accomplished through linkage analysis of human pedigrees. The use of model systems, such as the mouse, show promise for identifying novel genes that may cause human cardiomyopathy (55) and more recently even simpler model systems, such as the drosophila, show similar utility (56). As the mechanisms underlying these cardiomyopathies are explored and understood, the potential for novel and earlier interventions exist. Given the current complexity of the field, no clinically useful genetic testing is available. Technology, however, continues to rapidly improve; low-density DNA microarrays (57) have demonstrated some preliminary success in screening for mutations in HCM, and advances such as this could allow simultaneous diagnostic testing of thousands of genetic variants. Once this diagnosis is possible, then the mutation-specific disease course (prognosis) can be better understood and disease-specific interventions utilized (both novel therapeutics and existing medications used earlier).
Modifier genes.
In contrast with genes that cause cardiomyopathy, we are also interested in those genes that modify the presentation, course, and outcome of a cardiomyopathy; we refer to these genes in this regard as modifier genes. A modifier may exert its effect through a variety of mechanisms including gene–gene interactions and gene–environment interaction. We use environment as an all-encompassing term to mean any factor external to the disease process that may alter the disease process; the most influential of these items in medicine are medications. Modifier genes will most often be polymorphisms, a term that means a mutation (often a single-base change) occurring at a frequency higher than 1% of the population. We are only beginning to understand modifier genes. Often these genetic variants (polymorphisms) do not exert a large phenotypic effect, but taken as a composite they exert a significant effect. There are several examples in the cardiomyopathy literature of modifier genes: how they influence comorbidities (such as arrhythmia), drug effect (pharmacogenetics), each other (gene–gene interactions), and survival. To date, most of the genes in the literature that have been investigated in these association studies have been candidate genes chosen because of their biological plausibility. Less biased methods have also been employed using model systems such as the mouse to isolate novel modifier genes (58,59). Here we discuss some notable modifier genes from the literature.
Influence on comorbidities.
Arrhythmia and sudden cardiac death are comorbidities significant in cardiomyopathy. Although the implantable cardioverter-defibrillator has become the standard of care in the management of cardiomyopathy, there has been little advance in the understanding of who is at most risk of a lethal arrhythmia. Much in the way that molecular biology has enhanced the understanding of the long QT syndromes, there exists potential for similar understanding (and subsequent risk stratification) in patients with cardiomyopathy. The syndromes caused by abnormalities in cardiac ion channels discovered through molecular biology have been termed channelopathies. A gene that has been studied extensively, SCN5A, encodes alpha subunits of the cardiac sodium channel (60), and mutations in the gene have been associated with several rare arrhythmias. Family-based studies have implicated mutations in SCN5A to syndromes of cardiomyopathy and atrial arrhythmias. In addition, a polymorphism in the SCN5A gene, which is common in individuals of West African and Caribbean descent, is associated with an increased risk of ventricular arrhythmia (61–63). These studies may assist in better subclassification of cardiomyopathy (here with regard to predisposition to arrhythmia). Indeed, future studies may implicate specific genes that will constitute modifier channelopathies (i.e., channelopathies resulting in either atrial or ventricular arrhythmias that modify the course and outcome of cardiomyopathy). These studies suggest such genes could determine the use of pharmacologic or device therapy for the treatment of arrhythmias in the setting of cardiomyopathy in the future.
Pharmacogenetics.
The most promising area for clinical application for modifier genes is in the arena of pharmacogenomics. As a simple example, let's take a hypothetical drug A that blocks a receptor B and is metabolized in the liver by cytochrome C. If an individual has a polymorphism in receptor A that does not allow the drug to block effectively at normal levels, then the drug will likely not be as effective in that individual. If the same individual has a polymorphism in cytochrome C that leads to slower metabolism then this may increase the drug level relative to receptor B, and subsequently the effect of drug A may relatively increase. In contrast, if a polymorphism exists that leads to faster metabolism, then the drug concentration will be even lower, and drug A will have even less of an ability to block receptor B. There are already several examples in the literature with respect to chemotherapeutic agents and warfarin metabolism. Differential response to pharmacotherapy in patients with heart failure was the origin of A-HeFT (African American Heart Failure Trial), which demonstrated a survival benefit of a fixed dose combination of isosorbide dinitrate and hydralazine (64). Although the biological specifics for the benefit of this medication strategy in African-American patients are not entirely clear, such findings suggest the role of underlying genetic variation. Such studies provide a small step toward the ultimate goal of individualizing therapy—finding the right drug for the right patient.
Angiotensin-converting enzyme (ACE) inhibitors and beta-blockers are standard treatments for patients with heart failure (65) and both have been investigated in terms of pharmacogenetics. Genetic variation in the beta1 adrenergic receptor has been well characterized (66). The Arg389Gly polymorphism in the beta1 receptor has been shown to cause differential stimulation with respect to agonists and thus a different response to blockade (66). Animal models confirm a differential physiologic response to beta-blocker based on genotype and suggest that clinical outcomes in humans may also be affected (66). Beta1 adrenergic receptor variation (the Arg389Gly) has been associated with exercise capacity in patients with heart failure (67) and differential receptor signaling. A retrospective study found an improvement in myocardial ejection fraction in those individuals treated with carvedilol who carried the Arg389 variant (66). Observations of differential physiological and pathophysiological responses based on a genotype can inform future clinical studies with the idea of isolating responders from non-responders.
Polymorphisms in the ACE pathway may also be revealing. In a population of chronic heart failure patients, the ACE DD polymorphism was significantly associated with death or the need for transplant when compared with II or ID genotypes, but those with ACE DD treated with beta-blockers had significantly improved survival compared with those not on beta-blockers (68). This observation was not seen in either the II or ID group. In a similar study, patients with the DD polymorphism had worse outcomes on low-dose ACE inhibitor therapy compared with high dose (69), and the regiment of high-dose ACE inhibitors and beta-blockers had the greatest impact on transplant-free survival in those with the DD variant. These studies suggest a differential clinical response to standard heart failure therapy based on information on the ACE I/D variant. These preliminary studies of genetic variation in the beta receptor and ACE pathways suggest that such genetic information in the future may assist in the choice of type and dose of medication for patients with heart failure. There are many genetic variants that influence the efficacy of pharmacotherapy by impacting some aspect of absorption, metabolism, or physiologic effect of a drug; many of these have yet to be elucidated. Table 4 illustrates some examples of the potential of pharmacogenetics and its current level of evidence (66,68–72).
Gene–gene interactions.
Given the complexity of biological systems, it can certainly be appreciated that for any given disease there are likely numerous genetic variants exerting an influence. That being said, there are very few studies that investigate gene-gene interactions. One such study in the heart failure literature investigated the Arg389Gly variant of the beta1 adrenergic receptor in conjunction with an alpha2 adrenergic polymorphism (a 3-base pair deletion at position 322-325, termed Del322-325) that is common in African American patients (76). Although the Arg389Gly was not predictive of heart failure by itself, the study found that those individuals homozygous for the Del322-325 in conjunction with Arg389 variant had between a 3.87 (for heterozygotes) and 10.11 (for homozygotes) risk for heart failure. Given the prevalence of the Del322-325 allele in African Americans compared with whites, these findings reach statistical significance (for the given sample size) in the African American cohort.
Survival.
Prognostic markers are valuable clinical tools for patients with heart failure because they may identify individuals that require earlier and more aggressive intervention. Several polymorphisms have been implicated in retrospective studies to be associated with a poorer prognosis in patients with cardiomyopathy (Table 5) (77,78). The studies suggest that these markers could be used for risk stratification; however, despite a strong association, there has been no subsequent literature validating the findings. Once validated, the association could be tested in clinical care, for instance, should individuals with variants suggestive of poorer outcomes be treated earlier with more aggressive existing therapies or listed for transplant earlier.
Taken as a composite, it may be possible in the future to generate patient-specific data of heart-failure-related genes (perhaps through a panel of genes on a microarray) that will profile genes of diagnostic, prognostic, and therapeutic importance. The current landscape of genetics in this field does not support the routine use of any genetic tests in the management of cardiomyopathy due to systolic dysfunction.
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Conclusions
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New high-throughput technologies and the information they generate have promise for clinical use in cardiomyopathy and can be integrated into framework of heart failure management (Fig. 1). While these technologies generated an early exuberance of their immediate potential, the reality is that their impact will likely only be realized over time. There is no current evidence supporting the routine clinical use of genomic information in the care of patients with cardiomyopathy. Genomic information is similar to any other piece of clinical or laboratory information, whether that information is weight or a BNP level; testing this information to learn where it should be placed in the context of improving patient care is the critical step. An efficient system that takes newly discovered genetic information, places it into a clinical context, and then takes appropriate next steps to study this material on the larger scale, a scale that is generalizable to larger populations, will provide great advances to the field.
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Figure 1 Integrating genomic evidence into the management of cardiomyopathy. Genomics can potentially assist at many points of our current care model. This figure illustrates the potential points of integration of genomic information into current standard of care. EP = electrophysiology.
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Acknowledgments
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The authors thank John Michon, MD, for his critical reading of the manuscript and helpful comments.
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Footnotes
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Cardiovascular Genomic Medicine series edited by Geoffrey S. Ginsburg, MD, PhD.
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References
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Abstract
Redefining heart failure: The...