CLINICAL RESEARCH: HEART FAILURE
Lack of Association Between Adrenergic Receptor Genotypes and Survival in Heart Failure Patients Treated With Carvedilol or Metoprolol
Amy J. Sehnert, MD*,*,
Susan E. Daniels, PhD*,
Michael Elashoff, PhD*,
James A. Wingrove, PhD*,
Christopher R. Burrow, MD*,
Benjamin Horne, PhD ,
Joseph B. Muhlestein, MD, FACC,
Mark Donahue, MD ,
Stephen B. Liggett, MD ,
Jeffrey L. Anderson, MD, FACC and
William E. Kraus, MD, FACC
* CardioDx, Inc., Palo Alto, California
Departments of Cardiology and Cardiovascular Research, Intermountain Medical Center, University of Utah, Salt Lake City, Utah
Department of Medicine and Cardiology, Duke University Medical Center, Durham, North Carolina
Departments of Medicine and Physiology, University of Maryland School of Medicine, Baltimore, Maryland.
Manuscript received February 7, 2008;
revised manuscript received April 29, 2008,
accepted May 5, 2008.
* Reprint requests and correspondence: Dr. Amy J. Sehnert, CardioDx, Inc., 2500 Faber Place, Palo Alto, California 94303. (Email: asehnert{at}cardiodx.com).
 |
Abstract
|
|---|
Objectives: This study investigated the role of adrenergic receptor genetics on transplant-free survival in heart failure (HF).
Background: Discordant results exist for genetic associations between adrenergic receptor alleles and end points of β-blocker response in HF patients.
Methods: We identified 637 patients enrolled in 2 U.S. cardiovascular genetic registries with HF and left ventricular systolic dysfunction who were discharged on β-blocker, angiotensin-converting enzyme inhibitor (ACEI) or angiotensin II receptor blocker (ARB), and diuretic medications. End points were determined through the national Social Security Death Master File and transplant records. We genotyped 5 polymorphisms in 3 genes: ADRB1 (S49G, R389G), ADRB2 (G16R, Q27E), and ADRA2C (Del322-325) using 5' nuclease assays and performed a multivariable clinical-genetic analysis.
Results: A total of 190 events (29.8%) occurred over a median follow-up of 1,070 days. Multivariable analysis showed a significant effect of 4 clinical factors on survival: age (p = 0.006), gender (p = 0.005), ejection fraction (p = 0.0002), and hemoglobin (p = 0.00010). There was no significant effect of the polymorphisms or haplotypes analyzed on survival.
Conclusions: Genotypes and haplotypes of ADRB1, ADRB2, and ADRA2C did not significantly affect survival in metoprolol-treated or carvedilol-treated HF patients in this study. These results complement the findings of 2 similarly designed previous studies, but do not replicate an association of ADRB2 haplotypes and survival. All 3 studies differ from a survival benefit reported for bucindolol-treated homozygous ADRB1 R389 individuals. This may be attributable to a drug-specific interaction between genotype and outcome with bucindolol that does not seem to occur with metoprolol or carvedilol.
Key Words: heart failure • genetics • adrenergic receptors • β-blockers • haplotypes
|
Abbreviations and Acronyms
| | ACEI = angiotensin-converting enzyme inhibitor | | AR = adrenergic receptor | | ARB = angiotensin II receptor blocker | | CI = confidence interval | | EF = ejection fraction | | HF = heart failure | | HR = hazard ratio | | ICD = implantable cardioverter-defibrillator | | LV = left ventricle/ventricular | | LVEF = left ventricular ejection fraction | | NYHA = New York Heart Association | | SNP = single-nucleotide polymorphism |
|
Heart failure (HF) affects 5 million Americans and represents a major health care challenge, with 500,000 new cases and 250,000 deaths each year (1). Heart failure may arise from ischemic or nonischemic injury, and the natural history can differ widely among individuals, even those with similar cardiomyopathic states (2,3). Identifying responders and nonresponders to HF therapies could lead to improved quality of care and better allocation of medical resources (4,5).
Central to the development, progression, and mode of treatment in HF is the adrenergic system, which is modulated through a family of pre-synaptic and post-synaptic  - and β-adrenergic receptors (ARs) (6,7). In particular, pre-synaptic 2C-ARs (responsible for norepinephrine release) and cardiomyocyte β1- and β2-AR (the targets for norepinephrine and β-blockers) form a "circuit" that may initially promote maintenance of cardiac output, but ultimately act to accelerate progression of failure (8). These phenomena have been extensively studied and are shown to play an important role in the pathophysiology and progression of HF. As a result, treatment of HF patients with β-blockers has become a standard of care, which in the U.S. consists primarily of the use of 2 drugs, metoprolol succinate and carvedilol (9,10). Both metoprolol and carvedilol block β1-AR as well as display other unique and drug-specific properties. Given the existence of genetic polymorphisms in the adrenergic receptor genes encoding 2C-AR, β1-AR, and β2-AR (ADRA2C, ADRB1, ADRB2, respectively), the critical question has been raised as to whether genetic variations explain differences in individual β-blocker therapeutic response (11,12).
Over the past 10 years, several studies have shown that single-nucleotide polymorphisms (SNPs) in the ADRB1, ADRB2, and ADRA2C genes influence receptor function in vitro and in vivo (6,7). For example, the arginine residue at position 389 of the β1-AR gene (ADRB1 R389) alters the receptor-Gs interaction and acts as a gain-of-function polymorphism (13). This interesting result subsequently triggered a series of investigations into ADRB1 and other SNPs in HF patients to assess the potential relationship between patient genotype and response to therapy. Several of these studies report significantly different responses based on genotype for end points such as exercise capacity, initial tolerability during β-blocker titration, changes in left ventricular ejection fraction (LVEF), and changes in left ventricular (LV) remodeling in response to β-blocker therapy (14–19). To date, not all of the reported associations have been independently replicated, making it difficult to draw definitive conclusions for clinical use (11,12,20). For the more clinically relevant outcome of survival in HF patients, the results are also mixed, although 2 recent reports are notable. One study shows unfavorable survival outcomes for clinically treated HF patients with a homozygous haplotype in the ADRB2 gene, 16/27 RQ (21). Another study from the BEST (β-blocker Evaluation of Survival Trial) shows significantly reduced mortality in bucindolol- versus placebo-treated HF patients who are ADRB1 R389 homozygotes (16). To test the hypothesis that adrenergic receptor genotypes affect the critical end point of survival in β-blocker–treated HF patients, we performed a large, dual-center study with comprehensive genotyping and analysis of SNPs in the ADRB1, ADRB2, and ADRA2C genes and haplotypes in ADRB1 and ADRB2.
|
Methods
|
|---|
Study population.
The starting population for this study was identified within cardiac catheterization laboratory-based genetic registries at the Duke University Medical Center, CATHGEN (22), and the Utah-based Registry of the Intermountain Heart Collaborative Study (23). Registry sizes at the time of database search were 5,172 for CATHGEN and 14,085 for Registry of the Intermountain Heart Collaborative Study. Blood samples for genetic storage were obtained at the time of cardiac catheterization. Clinical data were obtained through a combination of registry database queries as well as manual chart abstraction of medication records and defined clinical data fields.
These studies were conducted in compliance with human studies committees at Duke University and Intermountain Healthcare. Written informed consent was obtained from the study subjects at each institution for participation in their cardiovascular genetics registries. The institutional committee on human research at Duke University and Intermountain Healthcare also approved the current study protocol as performed.
We initially searched the 2 registry databases for patients with clinical diagnosis of HF and/or LV systolic dysfunction with ejection fraction (EF)  40% at the time of catheterization. Of the 1,405 patients identified, we subsequently excluded 768 subjects for 1 or more of the following reasons based on pre-defined study criteria: patient not discharged on standard HF medications including β-blocker (metoprolol succinate or carvedilol), angiotensin-converting enzyme inhibitor (ACEI) or angiotensin II receptor blocker (ARB), and/or loop diuretic; undocumented EF or EF >40% (diastolic HF); fewer than 30 days elapsed between most recent myocardial infarction and cardiac catheterization; serum creatinine >2.0 mg/dl; valve or congenital heart disease as primary cause of LV dysfunction; severe, life-threatening noncardiac comorbidities; or inadequate deoxyribonucleic acid (DNA) sample for genotyping. For patients with undocumented New York Heart Association (NYHA) functional class, we required treatment with a loop diuretic as evidence of clinically symptomatic HF (shortness of breath, fluid accumulation). Baseline LVEF 40% was required for inclusion, which for the majority of patients was determined by ventriculogram on the date of catheterization or by echocardiogram within 6 months of catheterization. For some patients, the method for determining EF was not documented in the chart. Ischemic or nonischemic HF etiology was assigned using results of coronary angiography and information in the medical record. Patients were classified as ischemic HF if they showed  70% diameter stenosis in at least 1 major coronary artery, or had a prior history of myocardial infarction or coronary revascularization. Within the nonischemic population, we also identified a "clean coronary" subgroup using the categorical cutoff points at each institution for minimal coronary artery stenosis (25% stenosis, Duke; and <10% stenosis, Registry of the Intermountain Heart Collaborative Study).
Definition of study period and end point determination.
The period of study for all patients was indexed from the date of cardiac catheterization (T0; range January 17, 1998, to June 26, 2006) through the date of end point determination (March 1, 2007) or until date of death or heart transplantation, whichever came first. End point determination was made through search of the Social Security Death Master File (24) and search of cardiac transplant records at each institution. Outcome data were kept at each institution and separate from genotyping and clinical data until the time of statistical analysis.
Genetic analysis (genotyping).
Genomic DNA was isolated from whole blood using a commercially available kit (PureGene, Gentra Systems, Minneapolis, Minnesota) and diluted to a concentration of 4 ng/µl; 12 ng was used in each assay. The genotypes were determined by polymerase chain reaction using 5' nuclease genotyping assays (Applied Biosystems, Foster City, California). For ADRB1 S49 and R389, Assays on Demand were used (Assay numbers C_8898508_10 and C_8898494_10). For ADRB2 G16 and Q27, Assays-by-Design were created using the primer and probe sequences listed in the Online Appendix. The amino acid position 322 insertion/deletion variant in the ADRA2C gene was less amenable to the genotyping platform, so we utilized a surrogate marker, which is an SNP that is in high linkage disequilibrium (r2 = 0.924) to the deletion as described by Small et al. (25) (SNP "H"). Primer and probe sequences for this assay are also shown in the Online Appendix. For purposes of presentation, these results are shown as the insertion/deletion polymorphism at position 322 because this variation has been shown to affect receptor function.
Statistical methods.
Clinical Analysis
To assess the association of clinical characteristics with study end points, a series of univariate proportional hazards regression models were fit, 1 model for each of 12 pre-defined clinical factors. We prospectively defined a significance level of p < 0.10 for inclusion of clinical factors in the multivariable analysis.
Clinical-Genetic Analysis
Hardy-Weinberg equilibrium was assessed by the chi-square test. When possible, missing SNP calls were imputed via the expectation maximization algorithm (26). To assess the association of SNPs with study end points, each SNP was analyzed in a proportional hazards regression model that included the significant factors from the clinical multivariable analysis. In addition, haplotypes were determined for ADRB1 and ADRB2 using 2 coding SNPs in each gene. In our population, there were 3 ADRB1 haplotypes (S49/R389, S49/G389, G49/R389) and 3 ADRB2 haplotypes (R16Q27, G16E27, G16Q27) present. These 6 haplotypes were analyzed individually in the same manner as the individual SNPs (clinical-adjusted proportional hazards regression model).
In the primary analysis, SNPs or haplotypes were analyzed in the models with a 0/1/2 code reflecting the number of copies of the minor allele for a particular SNP. The expectation maximization algorithm was used to estimate the number of copies from 0 to 2 of each haplotype for each individual. Secondary analyses were also performed where the heterozygotes were combined either with the 0 copy (01/2; wild type or 1 copy of minor allele vs. 2 copies of minor allele) or with the 2 copy group (0/12; wild type vs. at least 1 copy of minor allele). In addition, the 5 SNPs were assessed in a combined model to evaluate whether there was an aggregate genetic effect. All analysis was performed using the R statistical package (27).
Study Power
Given the cohort size, event rate, and range of gene allele frequencies analyzed, this study was 80% powered to detect between a 9% difference in survival (for most prevalent SNP) and 13% difference in survival (for least prevalent SNP).
|
Results
|
|---|
Clinical analysis.
The study cohort consists of 637 subjects (CATHGEN n = 344, Registry of the Intermountain Heart Collaborative Study n = 293), whose complete baseline clinical characteristics are shown in Table 1. All 637 patients had LV systolic dysfunction and were discharged on a combination of β-blocker (metoprolol or carvedilol), ACEI or ARB, and loop diuretic; 69% had ischemic heart disease and 22% had implanted defibrillator devices.
There were 190 events (29.8%; 150 deaths, 40 transplants) over a median follow-up of 1,070 days (2.9 years; range 5 to 3,188 days). Minimum follow-up for a subject without an event was 248 days. Clinical factors selected for univariable end point analysis included those previously shown to be clinical predictors of survival in the Seattle HF model (age, gender, LVEF, NYHA functional class, HF etiology, systolic blood pressure, hemoglobin, and statin use) (28). In addition to these factors, we also studied the effect of race, history of diabetes, β-blocker medication, and presence of an implantable cardioverter-defibrillator (ICD) device on survival in our cohort. Eight clinical factors met the pre-defined univariable significance of p < 0.1 for inclusion in the multivariable model (Table 2). Of these, 4 retained significance (p < 0.05) in the multivariable analysis: age (p = 0.006), male gender (p = 0.005), low EF (p = 0.0002), and low hemoglobin (p = 0.0001). Systolic blood pressure showed a trend toward significance (Table 2).
Clinical-genetic analysis—single SNPs.
Genotypes were generated for 637 patients with a call rate for all SNPs across the entire cohort of 98.3%. All alleles were in Hardy-Weinberg equilibrium, and minor allele frequencies for our cohort were in agreement with other major studies: ADRB1 G49 15%, G389 29.5%, and ADRB2 R16 40%, E27 36.5% (12,21,29,30). Allele frequencies for the deletion polymorphism in ADRA2C differed by race, but were in Hardy-Weinberg equilibrium for each race when considered separately.
We found no significant effect on transplant-free survival for the primary analysis of 5 SNPs in the ADRB1, ADRB2, or ADRA2C genes (Fig. 1, Table 3). A death-only analysis also did not yield significant results. Considering the potential influence of ICDs on outcome, we performed secondary SNP analyses in the device (n = 141) and no-device (n = 496) subgroups, but did not identify a significant effect. The number of patients with biventricular ICDs was too small (n = 39) to provide any further meaningful analysis. Other independent secondary subgroup analyses of the "clean coronary" nonischemic patients and NYHA functional class III/IV patients were also noninformative.
View larger version (20K):
[in this window]
[in a new window]
[Download PPT slide]
|
Figure 1 Kaplan-Meier Survival Curves
Kaplan-Meier curves showing the percent transplant-free survival for subjects over time (in days) for single-nucleotide polymorphisms tested. (A)
ADRB1-49. (B)
ADRB1-389. (C)
ADRB2-16. (D)
ADRB2-27. (E)
ADRA2C-del322-325 (Caucasian). (F)
ADRA2C-del322-325 (African American). Black line = homozygous major allele (0 copies minor allele); red line = heterozygous (1 copy minor allele); green line = homozygous minor allele (2 copies minor allele). Vertical tick marks on the curves represent censored subjects.
|
|
A weak univariable trend toward better survival in black patients was observed, as an additive function of the number of alleles in the ADRA2C deletion polymorphism (hazard ratio [HR]: 0.55, 95% confidence interval [CI]: 0.28 to 1.11, p = 0.094 for black patients only; compared with HR: 0.95, 95% CI: 0.57 to 1.59, p = 0.85 for Caucasians only) (Figs. 1E and 1F). There were no other trends for Caucasian or black patients when performing secondary analyses within each race.
Clinical-genetic analysis—haplotypes.
We analyzed 3 haplotypes for both ADRB1 (S49/R389, S49/G389, G49/R389) and ADRB2 (R16Q27, G16E27, G16Q27) that were inferred using the expectation maximization algorithm and a 0/1/2 coding to reflect the number of copies of each particular haplotype. We found no genetic association in any of the primary haplotype analyses or in any of the secondary subgroup analyses described above.
|
Discussion
|
|---|
Based on the potential of adrenergic receptor genetics to influence survival in HF patients, we chose to examine a cohort of clinically treated HF patients who had been prescribed metoprolol or carvedilol. To extend published studies in this area by White et al. (30), de Groote et al. (29), and Shin et al. (21), our cohort included a larger number of patients from 2 centers and a greater proportion of nonwhite patients, and we comprehensively examined SNPs and haplotypes in 3 major adrenergic receptor genes (Tables 4 and 5). The cohort was enrolled from patients who underwent cardiac catheterization between January 1, 1998, and June 30, 2006, which represents an interval of standardized HF pharmacotherapy. Subjects were included who met the criteria of LV systolic dysfunction (EF 40%), had a clinical diagnosis of HF, were more than 30 days from previous myocardial infarction at the time of catheterization, and were discharged on standard HF medications including β-blocker, ACEI or ARB, and/or loop diuretic. Patients were followed up to the end point of death or transplant.
In this study, older age, male gender, low EF, and low hemoglobin at baseline negatively influenced survival. This is consistent with what would be expected from other predictive models such as the Seattle HF score, and also reiterates the importance of recognizing anemia in this condition (28,31). At the same time, we found no significant effect of the polymorphisms or haplotypes we analyzed in ADRB1, ADRB2, or ADRA2C on transplant-free survival. Despite showing almost identical haplotype frequencies, we did not replicate either an adverse effect on transplant-free survival of 2 copies of the ADRB2 RQ haplotypes previously reported by Shin et al. (21), or an adverse effect of 2 copies of the ADRB2 GQ haplotype as previously reported by de Groote et al. (30) (Table 4). This may be in part attributable to smaller numbers of patients in those studies or to a population effect. In terms of the other SNPs analyzed, the published datasets show no effect of the ADRB1 R389G SNP in HF survival in now over 1,500 β-blocker–treated subjects (637 from this study) (Table 6). Furthermore, no effect of SNPs in ADRB1 (S49G), ADRB2 (G16R and Q27E), or haplotypes in ADRB1 have been found in over 1,200 β-blocker–treated subjects. Taken together, these results lead us to conclude that there is no significant association between adrenergic receptor genotype and survival in the clinical setting of HF treatment with metoprolol and carvedilol.
For the ADRB1 R389G polymorphism, a recent BEST DNA substudy of 1,040 patients (515 treated) has shown a significant pharmacogenetic association in HF survival between bucindolol-treated R389 homozygotes and placebo (HR: 0.62, 95% CI: 0.40 to 0.96, p = 0.03) (16). Bucindolol has a specific pharmacologic profile (reviewed by Liggett et al. [16]), which may be the basis for the unique association of ADRB1 R389R with treatment response in heart failure. Unlike metoprolol (but like carvedilol), bucindolol has affinity for both the β1- and β2-AR subtypes. Although initial studies in nonhuman tissue suggested a partial agonist effect in the heart, subsequent studies in humans showed no such effect. In ex vivo human trabeculae studies from failing explanted hearts, bucindolol showed inverse agonist properties (i.e., it decreased contractility), but only in ADRB1 R389 homozygotes (16). In contrast, carvedilol had no such effect (16), nor does metoprolol (S.B. Liggett, unpublished data, June 2007). In addition, bucindolol evokes a distinct sympatholysis effect (by an unclear mechanism), lowering plasma norepinephrine levels to a much greater extent than metoprolol or carvedilol. Taken together, these characteristics of bucindolol may account for the pharmacogenetic effect of ADRB1-389 when this drug is used for heart failure treatment. Another possibility is that a BEST-specific population effect exists; however, this can only be addressed through independent replication.
Study limitations.
Lacking a placebo-controlled, single β-blocker trial such as the genetic substudies performed in BEST and MERIT-HF (Metoprolol CR/XL Randomized Intervention Trial in Congestive Heart Failure), we used intent to treat with β-blockers at discharge as an inclusion criterion. To address this limitation, we evaluated medication records for 310 patients at a second time point at least 60 days from the index date, and found that 94% remained on β-blocker therapy. Sufficient data did not exist to perform a genotype–β-blocker dosage effect in this study. We identified HF patients in cardiac catheterization laboratory-based registries as opposed to HF clinics, and although the baseline clinical characteristics are remarkably similar to those of other studies (Table 5), we cannot exclude other potential biases of this approach. The inherent difficulty in defining the onset of HF in an individual patient remains an overall limitation for a time-to-event analysis. For consistency across patients and sites in this study, we defined the date of catheterization as the index time. Finally, although we examined the most common coding SNPs in ADRA2C, ADRB1, and ADRB2, we cannot exclude that other SNPs in noncoding regions or more extensive haplotypes not tested in this study may be associated with β-blocker response in heart failure (25,32,33).
|
Conclusions
|
|---|
We conclude that the findings of the current study and the weight of published evidence show no effect of the selected β1-, β2-, and 2C-AR polymorphisms or limited haplotypes on survival in HF patients treated with metoprolol or carvedilol. The complexities of HF and varied responses to HF polypharmacy (some of which have a genetic origin) warrant future prospective studies to dissect the impact of multiple genes and pathways on treatment response in HF.
|
Appendix
|
|---|
For primer and probe sequences, please see the online version of this article.
|
Acknowledgments
|
|---|
The authors thank the patients who enrolled in the cardiovascular genetic registries at Duke University and Intermountain medical centers, without whom this research could not have been performed. We thank John Carlquist, Bridget Moore, Elaine Dowdy, Melissa Hurdle, Stephanie Decker, Eric Johnson, and Lori Littleford for their work in the laboratory and with clinical data collection at each institution, and Nick Paoni, PhD, Steve Rosenberg, PhD, and Philip Sager, MD, for their review of the manuscript.
|
Footnotes
|
|---|
Drs. Sehnert, Daniels, Elashoff, and Wingrove are paid employees of CardioDx, Inc. This work was funded by CardioDx, Inc.
|
References
|
|---|
1. Rosamond W, Flegal K, Furie K, et al. Heart disease and stroke statistics—2008 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee Circulation 2008;117:e25-e146.[Free Full Text]2. McMurray JJ, Pfeffer MA. Heart failure Lancet 2005;365:1877-1889.[CrossRef][Web of Science][Medline] 3. Morita H, Seidman J, Seidman CE. Genetic causes of human heart failure J Clin Invest 2005;115:518-526.[CrossRef][Web of Science][Medline] 4. Donahue MP, Marchuk DA, Rockman HA. Redefining heart failure: the utility of genomics J Am Coll Cardiol 2006;48:1289-1298.[Abstract/Free Full Text] 5. Tang WH, Francis GS. The year in heart failure J Am Coll Cardiol 2007;50:2344-2351.[Free Full Text] 6. Liggett SB. Pharmacogenetics of beta-1- and beta-2-adrenergic receptors Pharmacology 2000;61:167-173.[CrossRef][Web of Science][Medline] 7. Small KM, McGraw DW, Liggett SB. Pharmacology and physiology of human adrenergic receptor polymorphisms Annu Rev Pharmacol Toxicol 2003;43:381-411.[CrossRef][Web of Science][Medline] 8. Feldman DS, Carnes CA, Abraham WT, Bristow MR. Mechanisms of disease: beta-adrenergic receptors—alterations in signal transduction and pharmacogenomics in heart failure Nat Clin Pract Cardiovasc Med 2005;2:475-483.[CrossRef][Web of Science][Medline] 9. Effect of metoprolol CR/XL in chronic heart failure: Metoprolol CR/XL Randomised Intervention Trial in Congestive Heart Failure (MERIT-HF) Lancet 1999;353:2001-2007.[CrossRef][Web of Science][Medline] 10. Packer M, Fowler MB, Roecker EB, et al. Effect of carvedilol on the morbidity of patients with severe chronic heart failure: results of the carvedilol prospective randomized cumulative survival (COPERNICUS) study Circulation 2002;106:2194-2199.[Abstract/Free Full Text] 11. Muthumala A, Drenos F, Elliott PM, Humphries SE. Role of beta adrenergic receptor polymorphisms in heart failure: systematic review and meta-analysis Eur J Heart Fail 2008;10:3-13.[Abstract/Free Full Text] 12. Shin J, Johnson JA. Pharmacogenetics of β-blockers Pharmacotherapy 2007;27:874-887.[CrossRef][Web of Science][Medline] 13. Mason DA, Moore JD, Green SA, Liggett SB. A gain-of-function polymorphism in a G-protein coupling domain of the human beta1-adrenergic receptor J Biol Chem 1999;274:12670-12674.[Abstract/Free Full Text] 14. de Groote P, Helbecque N, Lamblin N, et al. Association between beta-1 and beta-2 adrenergic receptor gene polymorphisms and the response to beta-blockade in patients with stable congestive heart failure Pharmacogenet Genomics 2005;15:137-142.[Web of Science][Medline] 15. Kaye DM, Smirk B, Williams C, Jennings G, Esler M, Holst D. Beta-adrenoceptor genotype influences the response to carvedilol in patients with congestive heart failure Pharmacogenetics 2003;13:379-382.[CrossRef][Web of Science][Medline] 16. Liggett SB, Mialet-Perez J, Thaneemit-Chen S, et al. A polymorphism within a conserved beta(1)-adrenergic receptor motif alters cardiac function and β-blocker response in human heart failure Proc Natl Acad Sci U S A 2006;103:11288-11293.[Abstract/Free Full Text] 17. Mialet Perez J, Rathz DA, Petrashevskaya NN, et al. Beta 1-adrenergic receptor polymorphisms confer differential function and predisposition to heart failure Nat Med 2003;9:1300-1305.[CrossRef][Web of Science][Medline] 18. Terra SG, Hamilton KK, Pauly DF, et al. Beta1-adrenergic receptor polymorphisms and left ventricular remodeling changes in response to β-blocker therapy Pharmacogenet Genomics 2005;15:227-234.[Medline] 19. Terra SG, Pauly DF, Lee CR, et al. beta-Adrenergic receptor polymorphisms and responses during titration of metoprolol controlled release/extended release in heart failure Clin Pharmacol Ther 2005;77:127-137.[CrossRef][Web of Science][Medline] 20. Morgan TM, Krumholz HM, Lifton RP, Spertus JA. Nonvalidation of reported genetic risk factors for acute coronary syndrome in a large-scale replication study JAMA 2007;297:1551-1561.[Abstract/Free Full Text] 21. Shin J, Lobmeyer MT, Gong Y, et al. Relation of beta(2)-adrenoceptor haplotype to risk of death and heart transplantation in patients with heart failure Am J Cardiol 2007;99:250-255.[CrossRef][Web of Science][Medline] 22. Duke University School of Medicine, Division of Cardiovascular Medicine Cardiovascular Genetic and Genomic Sciences http://cardiology.duke.edu/modules/div_cv_rsch_cggs/ 2007Accessed December 2005 to July 2006. 23. ClinicalTrials.gov Database Registry of the Intermountain Heart Collaborative Study http://www.clinicaltrials.gov/ct2/show/NCT00406185?term=NCT00406185&rank=1 2007Accessed April 2006 to July 2006. 24. The Social Security Death Master File http://www.ssdmf.com 2007Accessed March 2007. 25. Small KM, Mialet-Perez J, Seman CA, Theiss CT, Brown KM, Liggett SB. Polymorphisms of cardiac presynaptic alpha2C adrenergic receptors: diverse intragenic variability with haplotype-specific functional effects Proc Natl Acad Sci U S A 2004;101:13020-13025.[Abstract/Free Full Text] 26. Dempster A, Laird N, Rubin D. Maximum likelihood from incomplete data via the EM algorithm J R Stat Soc B 1977;39:1-38. 27. Team RDC R: A Language and Environment for Statistical ComputingVienna, Austria: R Foundation for Statistical Computing; 2007. 28. Levy WC, Mozaffarian D, Linker DT, et al. The Seattle Heart Failure Model: prediction of survival in heart failure Circulation 2006;113:1424-1433.[Abstract/Free Full Text] 29. de Groote P, Lamblin N, Helbecque N, et al. The impact of beta-adrenoreceptor gene polymorphisms on survival in patients with congestive heart failure Eur J Heart Fail 2005;7:966-973.[Abstract/Free Full Text] 30. White HL, de Boer RA, Maqbool A, et al. An evaluation of the beta-1 adrenergic receptor Arg389Gly polymorphism in individuals with heart failure: a MERIT-HF sub-study Eur J Heart Fail 2003;5:463-468.[CrossRef][Web of Science][Medline] 31. Tang WH, Tong W, Jain A, Francis GS, Harris CM, Young JB. Evaluation and long-term prognosis of new-onset, transient, and persistent anemia in ambulatory patients with chronic heart failure J Am Coll Cardiol 2008;51:569-576.[Abstract/Free Full Text] 32. Hawkins GA, Tantisira K, Meyers DA, et al. Sequence, haplotype, and association analysis of ADRbeta2 in a multiethnic asthma case-control study Am J Respir Crit Care Med 2006;174:1101-1109.[Abstract/Free Full Text] 33. Small KM, Mialet-Perez J, Liggett SB. Genetic variation within the beta1-adrenergic receptor gene results in haplotype-specific expression phenotypes J Cardiovasc Pharmacol 2008;51:106-110.[Web of Science][Medline]
Related Articles
-
Inside This Issue of JACC
J. Am. Coll. Cardiol. 2008 52: A29-A30.
[Full Text]
[PDF]
-
Pharmacogenomics and the Failing Heart: Are We Waiting for Godot?
- Douglas L. Mann and Dennis M. McNamara
J. Am. Coll. Cardiol. 2008 52: 652-654.
[Full Text]
[PDF]
This article has been cited by other articles:
|
|
|
|
 
F. Triposkiadis, G. Karayannis, G. Giamouzis, J. Skoularigis, G. Louridas, and J. Butler
The sympathetic nervous system in heart failure physiology, pathophysiology, and clinical implications.
J. Am. Coll. Cardiol.,
November 3, 2009;
54(19):
1747 - 1762.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
M. M. Fung, Y. Chen, M. S. Lipkowitz, R. M. Salem, V. Bhatnagar, M. Mahata, C. M. Nievergelt, F. Rao, S. K. Mahata, N. J. Schork, et al.
Adrenergic beta-1 receptor genetic variation predicts longitudinal rate of GFR decline in hypertensive nephrosclerosis
Nephrol. Dial. Transplant.,
September 10, 2009;
(2009)
gfp471v1.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
G. W. Dorn II and S. B. Liggett
Mechanisms of Pharmacogenomic Effects of Genetic Variation within the Cardiac Adrenergic Network in Heart Failure
Mol. Pharmacol.,
September 1, 2009;
76(3):
466 - 480.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
S. Cresci, R. J. Kelly, T. P. Cappola, A. Diwan, D. Dries, S. L.R. Kardia, and G. W. Dorn II
Clinical and genetic modifiers of long-term survival in heart failure.
J. Am. Coll. Cardiol.,
July 28, 2009;
54(5):
432 - 444.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
D. Rosskopf and M. C. Michel
Pharmacogenomics of G Protein-Coupled Receptor Ligands in Cardiovascular Medicine
Pharmacol. Rev.,
December 1, 2008;
60(4):
513 - 535.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
W.H. W. Tang and G. S. Francis
The Year in Heart Failure
J. Am. Coll. Cardiol.,
November 11, 2008;
52(20):
1671 - 1678.
[Full Text]
[PDF]
|
|
|
|
|
|
|
D. L. Mann and D. M. McNamara
Pharmacogenomics and the Failing Heart: Are We Waiting for Godot?
J. Am. Coll. Cardiol.,
August 19, 2008;
52(8):
652 - 654.
[Full Text]
[PDF]
|
|
|
|
Top
Abstract
Methods