Published online before print October 16, 2006, doi: 10.1161/CIRCULATIONAHA.106.628396
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(Circulation. 2006;114:1811-1820.)
© 2006 American Heart Association, Inc.
Genetics |
Transcriptional Profiling in Coronary Artery Disease
Indications for Novel Markers of Coronary Collateralization
From the Angiogenesis Research Center (T.W.C., J.A.S., A.E.H., A.L., J.P., M.S.), Section of Cardiology, Departments of Medicine (T.W.C., J.A.S., A.E.H., A.A.L., J.D.P., M.S.), Pharmacology and Toxicology (M.S.), and Radiology (J.D.P.), and Norris Cotton Cancer Center, Department of Genetics (J.H.M.), Dartmouth Medical School, Hanover, NH; Bioinformatics Programme, Department of Statistics (T.W.C., J.A.S.), and Wellcome Trust Centre for Human Genetics (J.M.T.), University of Oxford, Oxford, UK; Department of Computer Science (F.X., J.D.P.), Dartmouth College, Hanover, NH; and Department of Pathology (H.D., S.M.S.), University of Washington, Seattle.
Correspondence to Michael Simons, MD, Section of Cardiology, Dartmouth Hitchcock Medical Center, 1 Medical Center Dr, Lebanon, NH 03755. E-mail michael.simons{at}dartmouth.edu
Received March 20, 2006; revision received August 1, 2006; accepted August 11, 2006.
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Abstract |
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Background— The development of collateral circulation plays an important role in protecting tissues from ischemic damage, and its stimulation has emerged as one of principal approaches to therapeutic angiogenesis. Clinical observations have documented substantial differences in the extent of collateralization among patients with coronary artery disease (CAD), with some individuals demonstrating marked abundance and others showing nearly complete absence of these vessels. Recent studies have suggested that circulating monocytes play a major role in collateral growth. The present study was undertaken to determine transcriptional profiles of circulating monocytes in CAD patients with different extents of collateral growth.
Methods and Results— Monocyte transcriptomes from CAD patients with and without collateral vessels were obtained by use of high-throughput expression profiling. Using a newly developed redundancy-based data mining method, we have identified a set of molecular markers characteristic of a "noncollateralgenic" phenotype. Moreover, we show that these transcriptional abnormalities are independent of the severity of CAD or any other known clinical parameter thought to affect collateral development and correlated with protein expression levels in monocytes and plasma.
Conclusions— Monocyte transcription profiling identifies sets of patients with extensive versus poorly developed collateral circulation. Thus, genetic factors may heavily influence coronary collateral vessel growth in CAD and affect prognosis and response to therapeutic interventions.
Key Words: angiogenesis • collateral circulation • coronary disease • genes • arteriogenesis
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Introduction |
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Coronary artery disease (CAD) is the most common cause of morbidity and mortality in industrialized societies. Typically, advancing atherosclerosis leads to narrowing or occlusion of major coronary arteries and their branches, resulting in angina, heart failure, or myocardial infarction. Clinical investigations suggest that a significant minority of CAD patients present with or develop in the course of their illness extra-arterial conduits, termed coronary collaterals, which link proximal and distal parts of the arterial tree bypassing areas of stenosis and/or occlusion.1 Thus, collateral arteries function as "natural bypasses," effectively restoring blood flow to compromised tissues. Moreover, the development of collateral circulation has been shown to play an important physiological role in promoting survival and protecting tissues from ischemic damage,2,3 and its stimulation has emerged as one of the principal approaches to therapeutic angiogenesis.4
Clinical Perspective p 1820
Factors responsible for the presence or absence of collateral circulation are poorly understood. Certain predictors of collateral presence have been proposed, such as a history of angina,5 hypercholesterolemia,6 plasma levels of homocysteine,7 reduced pericardial levels of endostatin,8 certain patterns of interindividual heterogeneity in hypoxic response,9 a haptoglobin phenotype,10 and C582-T582 HIF-1
gene polymorphism.11 Little is known about the biology of collateral growth, and considerable disagreement exists as to whether collateral development represents a mere remodeling of the preexisting (but not hitherto detectable) vasculature or is the result of de novo arterial growth or vasculogenesis.12–14
Recent studies have suggested that circulating monocytes may play a major role in the collateral formation.15–17 Abnormalities in monocyte function then may be one of the factors responsible for abnormal collateral growth in certain patient subsets.9 We therefore reasoned that monocyte transcriptome profiling from patients with CAD may shed some light on the differences in extent of collateral circulation in these patients and lead to an improved understanding of biology of arteriogenesis. To this end, we carried out comprehensive characterizations of the molecular determinants of the human CAD monocyte transcriptome using a combination of established supervised machine learning and knowledge-based algorithms, as well as a recently developed, gene redundancy reduction microarray bioinformatics data mining technique.18 We find that the absence or presence of coronary collaterals is associated with a distinct monocyte gene expression profile.
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Methods |
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Patient Selection
This study was approved by the Committee for Protection of Human Subjects at Dartmouth-Hitchcock Medical Center, and informed consent was obtained from all participants. Patients >18 years of age who were undergoing diagnostic coronary angiography were eligible for entry into the study. Patients were excluded if they had other conditions thought to influence potential neovascularization such as symptomatic peripheral arterial disease, recent ST-segment elevation myocardial infarction (<72 hours before enrollment), increased white blood counts, or a known malignancy in the past 5 years. Cardiac history and risk factors were documented, together with any data known to influence collateral growth, such as the use of medication,19–21 age,22 hypercholesterolemia,23 diabetes,24 and smoking.25 Only patients who had angiographically evident CAD and absent (score 0) or well-developed (score 2) collateral circulations were included in this analysis.
Coronary Angiography and Collateral Scoring
Severity of CAD on x-ray angiography was estimated through the use of a vessel score, defined as the number of vessels with at least one 50% stenosis, and the Gensini scoring system.26 Coronary collateral extent was assessed on the basis of a modified Rentrop scoring system.9 Angiograms were reviewed by an experienced angiographer and then by a different angiographer blinded to the initial reading. In cases of disagreement, the angiograms were reviewed by a third angiographer blinded to the initial 2 readings. Clinical and angiographic data were not revealed to those involved in gene expression or monocyte analysis. Left ventricular function was estimated by left ventriculography at the time of cardiac catheterization or by echocardiography performed during the same hospitalization. A total of 100 mL blood was collected from the side arm of the introducer sheath in the femoral artery before angiography and immediately processed for monocyte isolation as described below.
Human Monocyte Cell Separation
Human monocytes were separated from whole blood by standard procedures.27 Briefly, peripheral blood mononuclear cells were isolated by Ficoll density gradient centrifugation and then used immediately for monocyte isolation by positive selection with CD14 antibody–coated microbeads. Cells were then separated by use of autoMACS with the positive selection protocol, and cell collections were made from both positive and negative ports. Stained aliquots of the positive and negative cell fractions were collected and analyzed by flow cytometry to assess purity.
Human Monocyte RNA Extraction, Target Processing, and Labeling
Labeled cRNA was generated with the low-RNA input fluorescent linear amplification kit (Agilent, Santa Clara, Calif). All samples were labeled with cyanine (Cy) 5, and a reference cRNA was generated and labeled with Cy3. To generate a reference cRNA, 500 ng total RNA from each control sample was mixed, and 500 ng mixed total RNA was amplified and labeled with Cy3 (4 reactions were carried out to generate enough Cy3 NC for all 16 hybridizations). The hybridizations for each sample were performed with an Agilent in situ hybridization kit. For each hybridization, 0.75 µg Cy5-labeled, linearly amplified cRNA from each sample was mixed with equal amounts of Cy3-labeled, linearly amplified reference cRNA. The mixed cRNA was fragmented by incubation with the fragmentation buffer at 60°C for 30 minutes. An equal amount of 2x hybridization buffer was added to the fragmented cRNA mixture and hybridized to Agilent human whole-genome oligo array (G4112A) at 60°C for 17 hours. Fluorescent images of hybridized microarrays were obtained with an Agilent DNA microarray scanner and analyzed with Agilent Feature Extraction software, and the data were stored in a database.
Statistical Analyses
Clinical results are reported as mean±SD. Analysis between groups for statistically significant differences in categorical data were performed with the 
2 test and for continuous variables with Student’s t test (STATA, StataCorp, College Station, Tex).
The primary human monocyte data set was made up of gene expression data from 16 patients: 8 patients with well-developed collateral vessels (score 2) and 8 patients with no collateral vessels (score 0). To assess the possible confounding effects of disease severity, these same subjects were regrouped according to the percentage or degree of occlusion of 
1 coronary vessels to form a secondary data set. In this secondary data set, patients with 2- to 3-vessel disease (n=7) were compared with patients with 1-vessel disease (n=9). The 2 data sets, the first based on collateral vessel scores and the second based on disease severity, were further filtered on the basis of critical probability values (P
0.05 and 0.01) as assessed between subjects through Welch approximation of unequal group variances. Significance analysis of microarrays28 was used to determine an approximate false discovery rate of 32% for the primary patient feature selection set.
To improve the accuracy of the 4 statistical classifiers described in the online data supplement, small subsets of top-ranked class membership predictors were generated with the HykGene classification18 method. GO::TermFinder software (available at http://go.princeton.edu/cgi-bin/GOTermFinder) was used to classify data according to biological process, molecular function, and cellular component. Chilibot text mining software (available at http://www.chilibot.net/) was used to assess known relationships between angiogenesis and statistically significant differentially expressed genes within the enriched biological process terms of the gene ontology (GO) analysis.29
Quantitative Polymerase Chain Reaction Validation
Total monocyte RNA was isolated and cDNA was synthesized as previously described. Polymerase chain reaction (PCR) amplification was carried out with gene-specific TaqMan-based assays (Applied Biosystems, Foster City, Calif) on a GeneAmp 5700 sequence detection system (Applied Biosystems)—LEPROTL1 (NM_015344; Hs00209745_m1), MAPKAPK-2 (NM_004759; Hs00358962_m1), GRB2-related protein (NM_004810; Hs00191325_m1), inositol polyphosphate-4-phosphatase (NM_003866;Hs00182580_m1), and ARID4B (NM_016374; Hs00249610_m1)—and normalized to ACTB (NM_001615.3; Hs00242273_m1). Relative gene expression was assessed by the 2–
CT method. Statistical significance was assessed in R with 1-sample Wilcoxon signed-rank or 1-sample t tests.
Protein Expression Assessment
ELISA Analysis
Human soluble intracellular adhesion molecule (sICAM-1) was analyzed in heparinized plasma with a sICAM-1 ELISA kit (R&D Systems, Inc, Minneapolis, Minn) in 100 µL diluted plasma (1:20) that was incubated on the ELISA plate for 1.5 hours, then washed and incubated with secondary reagents. Plates were read using a Multiskan microplate spectrophotometer (Thermo Electron, Waltham, Mass).
Western Blotting
Peripheral blood mononuclear cells from collateral score 0 and 2 patients were placed in pellets at 4°C and resuspended in 200 µL RIPA buffer. Protein from patients (10 µg) was resolved on an SDS-PAGE gel, transferred to a polyvinylidene fluoride membrane, and probed with a polycolonal anti-Cdc42 antibody (Cell Signaling Technology, Danvers, Mass) overnight at 4°C. The membrane was incubated with an anti-rabbit antibody conjugated to horseradish peroxidase and detected with the Phototope HRP Western Blot Detection System (Cell Signaling). Human serum albumin was used as a lane-loading marker. Membranes containing plasma proteins were incubated overnight at 4°C with a monoclonal antibody to human serum albumin (Sigma Chemical Corp, St Louis, Mo). The secondary antibody and detection conditions were the same as described for Cdc42.
The authors had full access to the data and take full responsibility for their integrity. All authors have read and agree to the manuscript as written.
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Results |
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To explore the role of monocytes in collateral development in patients with CAD, 16 patients with angiographically evident disease, grouped on the basis of their degree of angiographically detectable collateral circulation, were evaluated in this study. The score 2 group comprised 8 patients with well-developed coronary collaterals; the score 0 group was made up of 8 patients with no angiographically evident collateral circulations. The groups were not statistically different in terms of age, CAD risk factors (such as weight and diabetic status), clinical presentation, indications for coronary angiography, total cholesterol and low-density lipoprotein levels, or past coronary revascularization procedures (Table 1). The only observed difference between groups was the extent of CAD measured by a vessel score and Gensini score (score 2 versus score 0 collaterals groups: 2.4±0.9 versus 1.1±0.12 diseased vessels [P=0.003], and 50±23 versus 23±20 Gensini score [P=0.02]).
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Agilent human whole genome oligonucleotide arrays (G4112A) comprising 44 000 features representing 33 000 unique genes were profiled using total RNA extracted from peripheral blood monocytes. Two subsets of transcripts demonstrating statistically robust differences (P0.05 and 0.01) in abundance between patient groups were identified. An inclusive subset of 1327 transcripts (P0.05) (S1) was used for GO analysis, whereas a more statistically restricted (P<0.01) subset comprising 256 transcripts (S2) was used as a feature set to predict patient class membership via the newly developed redundancy-based HykGene classification18 method.
The GO analysis (Table 2) shows that after correction for multiple hypothesis testing there are statistically significant enrichments of transcripts within the S1 subset displaying transcriptional activator and transcription cofactor activities. These differences in molecular function also are observed as significant enrichments of transcripts involved in the biological processes of transcription, cell organization and biogenesis, cellular localization, and intracellular transport response to stress, apoptosis, and cell proliferation. Score 0 group patients have significantly elevated expression levels of retinoblastoma 1 (RB1) and cyclin-dependent kinase inhibitor 2D with concomitant decreased expression levels of Cdc42, 3 Kruppel-like transcription factors, and cyclin-dependent kinase inhibitor 2B (Table 3), a profile consistent with transcriptional abnormalities related to apoptosis and cell proliferation. Moreover, the apparently coordinated dysregulation of specific gene families, including the syntaxins, tubulins, Rabs, and adaptor-related protein complex genes, suggests that abnormalities in transcriptional regulation of cellular organization and intracellular transport did not arise by chance (Table 3).
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Significant cellular component terms, comprising the nucleus, cytoplasm, endosome, and Golgi stack, support appropriate compartmentalization of the observed biological processes and molecular functions within the S1 transcript list. A partial tabulation of differential S1 transcripts that comprise these GO terms is shown in Table 3. The complete GO analysis is included in the results section in the Data Supplement (available online).
To confirm that these GO terms did not arise by chance, 10 randomly permuted data sets containing the same 16 CAD patients were generated and analyzed in the same manner. The random data permutations resulted in loss of significant associative GO terms (not shown), further indicating that enriched GO terms in the S1 list represent relevant biological differences between patient groups.
Agglomerative hierarchical clustering30 and principal component analysis (PCA)31 were used to visualize the 256 (P0.01) transcripts of the S2 subset (Figure 1A and 1B). Subjects 1 through 8 are from the score 2 coronary collateral vessel group (group 1); subjects 9 through 16 are from the score 0 coronary collateral vessel group (group 2). The heat map in Figure 1A shows strong statistical segregation in expression values between patient groups and demonstrates that the majority of the transcripts in the S2 probe set are significantly upregulated in score 0 group patients.
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To confirm these findings, we performed PCA on the S2 transcript list. PCA indicates that the first and second principal components cumulatively explain 77.96% of the variability within the 256 transcripts used for analysis (Figure 1B). Score 2 group subjects form the blue encircled patient cluster; score 0 subjects shape the second red patient cluster. These visualizations support the power of the S2 data set to capture variation in expression relevant to discrimination of patient classes.
Based on expression patterns, K-Nearest Neighbors,32 Support Vector Machines,33 C4.5,34 and Naïve Bayes/Diagonal Linear Discriminant Analysis35 classification algorithms were used to assess the predictive power of this cluster of transcripts to assign patients to either the score 2 or score 0 group. Leave-one-out cross-validation36 was used to evaluate the efficiency of each classifier (supplemental Table I). Reduction of observation expression redundancy improved K-Nearest Neighbors, Support Vector Machines, and Naïve Bayes/Diagonal Linear Discriminant Analysis but not C4.5 classification accuracy (supplemental Table II).
After correction for expression redundancy, patient classification accuracy was improved in part by identifying the following transcripts: retinoblastoma binding protein 1–like 1 (ARID4B), mitogen-activated protein kinase–activated protein kinase 2 (MAPKAK-2), leptin receptor overlapping transcript-like 1 (LEPROTL1), inositol polyphosphate-4-phosphatase, type II (INPP4B), and growth factor receptor–binding protein (GRB2-related 2). These genes represent a partial consensus of top-ranked HykGene transcripts (Table 4).
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To confirm the HykGene findings and to determine the possible effect of a cell-processing bias found in the primary collateral group data set, we carried out quantitative PCR (qPCR) analysis of differential gene expression both between these groups of CAD patients using the original patient population and in 12 additional clinically matched CAD patients with 3-vessel disease (6 patients with collateral score 2, 6 with collateral score 0). In both cases, qPCR confirmed differential abundance of the top HykGene-determined expression markers (Table 4) between patient classes. Furthermore, qPCR analysis of these 12 additional 3-vessel–disease patients suggests that relative collateral marker expression is not dependent on disease severity.
To demonstrate that transcriptional changes in monocyte gene expression correlated with changes in corresponding protein expression, 2 additional studies were carried out using patient samples that were different from the original 16-patient data set. Chilibot text mining was used to determine whether known relationships existed between angiogenesis, the hypothesized queried term, and statistically significant, differentially expressed genes within the enriched biological process terms of the GO analysis to further narrow the list of candidates for assessment of differential protein expression (Figure 2). On the basis of this analysis, we identified 4 prospective proteins—ICAM-1, Cdc42, osteopontin (SSP1), and RB1—as suitable markers of differences between the score 2 and score 0 collateral groups. In agreement with the microarray analyses, plasma ELISA measurements of circulating sICAM-1 levels in an independent group of 29 patients demonstrated significantly higher levels (287.29 12.69 versus 235.80 12.73 ng/mL; P<0.01) in the plasma of patients with score 2 collaterals (n=14) compared with patients with score 0 collaterals (n=15) (Figure 3).
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Western blot analysis of Cdc42 expression in circulating monocytes demonstrated, in agreement with the microarray findings, more frequent expression in patients with score 2 collaterals (15 of 17 patients) versus score 0 collaterals (6 of 17 patients, 2=10.1; P<0.01).
To further assess the possible confounding effects of disease severity on collateral group classification membership, subjects of the initial microarray analysis were then regrouped according to the angiographic extent of coronary disease to form a secondary disease-severity data set. This data set, which represents a reordering of patient class membership, was statistically evaluated in the same manner as the primary collateralization data set. Patients with 2- to 3-vessel disease (n=7) were compared with patients with 1-vessel disease (n=9). As with the primary collateral data set, GO was used to characterize the significance of the statistically assessed disease-severity data set. Unlike the collateral data set, evaluation of the disease-severity groups indicates that the only significant molecular difference resides in protein binding.
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Discussion |
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TopAbstractIntroductionMethodsResultsDiscussionConclusionsReferences |
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The differences in the extent of collateral circulation among patients with seemingly similar clinical characteristics raise the possibility that genetic and molecular differences may play a role in collateral development. Because circulating monocytes are considered the key element in the development of collateral circulation, we set out to uncover novel transcriptional determinants of human coronary collateralization using a combination of established and newly developed microarray bioinformatics analysis techniques. The principal finding of this study is that the monocyte transcriptome of closely matched patients with CAD who possess abundant collateral circulation is significantly different from the transcriptome of collateral-poor CAD subjects. Interestingly, many of the observed changes in gene expression in this study parallel prior observations in a mouse model of hind-limb ischemia.37
The key differences include significant alterations in transcriptional regulation of specific determinants of monocyte biology that can be directly related to abnormalities in intracellular transport, apoptosis, and cell proliferation. These differences were confirmed in independent patient populations using qPCR analysis, Cdc42 protein expression, and plasma sICAM-1 levels.
ICAM-1 is a cell adhesion receptor expressed on a number of cell types, including monocytes and endothelial cells, that is thought to play an important role in the mediation of inflammatory and immune responses. Because arteriogenesis often is viewed as a process dependent on certain immune and inflammatory processes such as activation of specific T-cell38 and mononuclear cell15 subsets, it is a logical choice for a marker of a collateralgenic phenotype. Therefore, the increased amounts of circulating sICAM-1 in patients with more extensive collateral development may reflect activation of the "inflammatory" state necessary for collateral growth.
Cdc42 is a member of the Rho family of GTPases involved in regulation of cell motility that is required for sensing colony-stimulating factor-1 concentration gradient and directed migration of monocytes/macrophages.39 Increased Cdc42 levels would increase the ability of monocytes to sense and respond to the presence of CSF-1, a step thought to be important in collateral development.40 These data point to the underlying differences in monocyte biology that may account for the variable extent of collateral formation in CAD patients and to the utility of comparative GO analysis for the evaluation and discovery of specific determinants of various pathological states.
The collateral-rich and collateral-poor patient populations in the study were carefully matched for all known CAD and collateral development risk factors, with the only significant difference being the angiographic extent of CAD as documented by the Gensini and vessel scoring systems. The severity of CAD has long been considered a predictor of collateral development.1,13,41 Another important determinant of the collateral presence is thought to be the gradual development of coronary stenosis rather than a sudden coronary occlusion.5,13 To evaluate the potential effect of differences in the severity of CAD on our results, we carried out additional analyses to evaluate the contribution of those differences on the GO class memberships describing differences in monocyte transcriptome. Reordering the data set into 2 classes based on the angiographic extent of CAD resulted in the loss of significant GO feature terms associated with collateral class, indicating the presence of 2 distinct monocyte transcriptional processes in these patients. This finding also strongly suggests that angiographic CAD extent does not substantially influence transcriptional regulation of coronary collateralization. As a result, human collateral vessel growth and maturation may be influenced to a much greater degree by still-unknown genetic factors, not by the presence and severity of CAD.
Although recent studies have shown the efficacy of high-throughput DNA microarray platforms for phenotypic classification of various pathobiological states, major statistical concerns arise in classification analysis of gene expression data as a result, in part, of the large number of features and the relatively small number of samples. The hybrid HykGene classification system directly addresses this issue and was therefore used to assess the transcriptional correlates of the monocyte phenotype in CAD patients with and without collateral vessels. Applying this new classification method derived small sets of highly statistically significant discriminant marker genes, which were then confirmed in an independent patient data set. Interestingly, these novel collateral vessel markers are involved in the same molecular functions and biological processes proposed by GO analysis. Thus, HykGene classification provided additional statistical confirmation of the GO analysis.
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Conclusions |
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The major findings of this study include the demonstration of collateralgenic monocyte transcription profiles in patients with CAD, delineation of novel transcriptional regulatory determinants of coronary collateralization, and the suggestion that the presence of collateral circulation may be independent of the angiographic extent of CAD. These findings support the contention that genetic factors heavily influence coronary collateral vessel growth and maturation in humans. The identification of such factors may aid in the delineation of patient prognosis and response to therapy.
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Acknowledgments |
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We thank Susan Kennedy (Dartmouth-Hitchcock Medical Center) for assistance with processing patient material.
Sources of Funding
This work was supported in part by National Institutes of Health (NIH) and American Heart Association (AHA) grants HL–53793 (M.S.) and 0525982T (T.C.).
Disclosures
None.
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The online-only Data Supplement is available with this article at http://circ.ahajournals.org/cgi/content/full/CIRCULATIONAHA.106.628396/DC1.
Related Article:
Circulation 2006 114: 1793.
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