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J Am Coll Cardiol, 2007; 49:1589-1599, doi:10.1016/j.jacc.2006.12.045
(Published online 30 March 2007). © 2007 by the American College of Cardiology Foundation |
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* Division of Hematology, Brigham and Women’s Hospital, Boston, Massachusetts
Division of Preventive Medicine and Center for Cardiovascular Disease Prevention, Brigham and Women’s Hospital, Boston, Massachusetts
Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
Donald W. Reynolds Cardiovascular Clinical Research Center on Atherosclerosis at Brigham and Women’s Hospital and the Harvard Medical School, Boston, Massachusetts.
Manuscript received June 6, 2006; revised manuscript received October 30, 2006, accepted December 4, 2006.
* Reprint requests and correspondence: Dr. Peter Libby, Donald W. Reynolds Cardiovascular Clinical Research Center, Cardiovascular Medicine, Brigham and Women’s Hospital, Mallinckrodt Professor of Medicine, Harvard Medical School, 77 Avenue Louis Pasteur, NRB 7, Boston, Massachusetts 02115. (Email: plibby{at}rics.bwh.harvard.edu).
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Abstract |
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 Top Abstract Genetic Studies of...Gene Expression Profiling in...Proteomic and Metabolomic...Scientific Community Genomic...Genomic Data IntegrationClinical Applications of...Genomics for Identifying...Summary for CliniciansReferences |
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Despite steady progress, atherosclerotic cardiovascular disease remains a growing public health burden in developed countries, and advances in cardiovascular research will not realize their full impact unless translated to care of individual patients (1). Hope for determining new therapeutic targets in atherosclerosis increasingly rests on research progress in genetic studies, expression profiling, and proteomics (2–7). Combining new markers of cardiovascular disease with currently available screening tools promises to promote this translational process.
Before a personalized medicine approach to atherosclerosis based on genomics, proteomics, and metabolomics can become reality, researchers must validate novel markers across different cohorts and in relation to various environmental modifiers. The operation of intricate networks of genes, environmental factors, and gene-by-environment interactions further complicates our understanding of the genetic components of atherosclerosis (8,9). Combined genomic approaches, often called genomic convergence, are necessary in atherosclerosis research (7).
Technological revolutions in genetics and genomics have facilitated 2 major approaches to understanding disease pathogenesis and risk. First, the Human Genome Project and International HapMap Project now allow us to obtain with relative speed vast amounts of deoxyribonucleic acid (DNA)-based information applicable to research subjects with atherosclerotic cardiovascular disease. Second, current technology enables collection of information on expression for thousands of genes in vascular cells and tissues under different conditions. The human DNA sequence laid the groundwork for studies of genetic susceptibility to disease, and expression databases assist definition of disease subtypes and variance related to environmental interactions. Although currently less mature technologies, proteomic and metabolomic studies promise to complement genomic approaches. Table 1 compares advantages and disadvantages of methods for marker identification.
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Genetic Studies of Atherosclerotic Cardiovascular Disease |
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TopAbstractGenetic Studies of...Gene Expression Profiling in...Proteomic and Metabolomic...Scientific Community Genomic...Genomic Data IntegrationClinical Applications of...Genomics for Identifying...Summary for CliniciansReferences |
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Notably, linkage analysis has successfully identified specific genes that may contribute to cardiovascular event risk, for example, myocyte enhancer factor-2 (MEF2A) with myocardial infarction (MI) risk (3) and arachidonate 5-lipoxygenase activating protein gene (ALOX5AP) with MI and stroke risk (17). Helgadottir et al. (17) showed linkage between the ALOX5AP gene region and MI in 296 Icelandic families, including 713 subjects. The ALOX5AP gene encodes the enzyme 5-lipoxygenase activating protein (FLAP), which participates in leukotriene synthesis. Such leukotrienes, especially leukotriene B4 (LTB4), mediate inflammation within the vasculature and participate in murine atherosclerosis (18). Helgadottir et al. (17) followed up their suspicions about ALOX5AP with a genetic association study approach (see the following text).
Human families with CAD are not the only resource for linkage analysis. Almost 2 decades ago, linkage analysis among inbred mouse strains identified an atherosclerosis susceptibility region on mouse chromosome 1 (Ath1). This region contains the tumor necrosis factor (ligand) superfamily member 4 (Tnfsf4) gene, also termed Ox40l (19). Atherogenic in mice, OX40L stimulates T cells. Other human studies recently linked the TNFSF4 region and the region containing the gene for OX40 (i.e., the OX40L receptor) to CAD (16,20), but did not identify the underlying gene in either case. Comparison of mouse and human genetics ultimately led to TNFSF4, and further studies showed an association between a TNFSF4 single nucleotide polymorphism (SNP) and MI risk in women from 2 human cohorts (19). Most recently, another study associated an OX40 SNP with MI risk (21), suggesting that this ligand-receptor interaction may provide a therapeutic target.
Genetic association studies. Many genetic and environmental factors likely influence complex diseases such as atherosclerosis. Viewed separately, each factor exerts a relatively small effect. Combined, they might explain increased disease risk. Compared with linkage studies, genetic association studies provide greater statistical power and more detailed knowledge of a genetic region, enhancing our attempt to understand the genetics of complex diseases such as atherosclerosis. In general, these studies seek to identify differences in the inheritance of particular SNP alleles among subjects with a differing clinical phenotype, such as MI (cases vs. control subjects) or differing levels of a biomarker such as C-reactive protein (CRP) or cholesterol (high vs. low levels).
Candidate gene association studies. Association studies may take a candidate gene or genome-wide approach. Such studies often base selection of candidate genes on assumptions about biologically relevant genes. Thus, candidate gene studies are biased against identification of novel genes. For example, several groups including our own published candidate gene association studies on the relationship between SNPs in the CRP gene and CRP levels based on numerous prospective studies showing the influence of baseline CRP levels on cardiovascular risk (22,23). Three studies in particular provide comprehensive coverage of all common CRP SNPs (24–26). Figure 1 shows 7 common CRP SNPs (Fig. 1A), illustrating the sequence difference at an SNP within exon 2 (Fig. 1B) and the generation of 6 common haplotypes that result when SNP alleles combine (Fig. 1C).
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2%. These SNPs thus have only a modest influence on plasma CRP levels. Family studies indicate that there must be additional genetic factors contributing to CRP levels. To date, candidate gene SNP selection has achieved limited success in finding genetic variants of CRP level, an example of the aforementioned bias against novel gene identification. Case-control studies to date have not been adequately powered to evaluate whether or not these SNPs impact on incident disease (25,26,28). Testing greater numbers of SNPs can increase the chances of finding a disease-causing variant. Technological advances that reduce genotyping costs now allow researchers to "supersize" their candidate gene studies. A recent study focused on 11,053 SNPs located within genes, reasoning that those SNPs more likely would affect gene function or expression (29). Using a case-control design and 3 rounds of replication, Shiffman et al. (29) identified 4 SNPs associated with risk of MI (all odds ratios [ORs] indicate carriers of 2 vs. 0 risk alleles): the cytoskeletal protein paladin (KIAA0992 [OR 1.40]), a tyrosine kinase (ROS1 [OR 1.75]), and 2 G-protein coupled receptors (TAS2R50 [OR 1.58] and OR13G1 [OR 1.40]). Although these SNPs represent risks on par with accepted risk markers such as hypertension and CRP (23), other attempts at replication for this panel of SNPs yielded mixed results with as few as 1 in 5 SNPs having a similar magnitude and direction of effect (30). The determination of the generalizability of genetic associations observed in a given study population requires replication in independent samples, as discussed in detail elsewhere (31).
Combining different genetic methodologies facilitates discovery. For example, genetic association studies help refine regions identified by linkage studies. After identifying linkage between MI and the region containing ALOX5AP, Helgadottir et al. (17) focused more closely on ALOX5AP, as a candidate gene, sequencing the entire gene in 93 affected individuals and 93 control subjects to identify a panel of 48 common SNPs in ALOX5AP. A case-control association study genotyped these 48 ALOX5AP SNPs to determine differences in the inheritance of a particular pattern of SNPs, or haplotype, between subjects with MI (n = 779) and control subjects (n = 624). An inherited 4-SNP haplotype, HapA, conferred an increased risk (nearly 2-fold) for MI and stroke (relative risk 1.8, adjusted p = 0.005). The LTB4 production in stimulated neutrophils increased substantially in individuals with MI compared with control subjects, especially in male carriers of ALOX5AP HapA, who showed significantly greater LTB4 production compared with control subjects (p = 0.0042). Male carriers of the at-risk haplotype had the strongest association with disease and also had significantly greater production of leukotriene-B4 (LTB4). The HapA also increased risk of stroke (relative risk 1.67, p = 0.000095). To date, an association between ALOX5AP and stroke was observed in more than 1 cohort (32,33), but replication studies for ALOX5AP have provided mixed results (34).
Most recently, Helgadottir et al. (35) used additional candidate gene association studies to expand the ALOX5AP story (35). Using a pathway approach, they studied the leukotriene A4 hydrolase (LTA4H) gene, the enzyme responsible for the production of LTB4. They performed DNA sequencing of the LTA4H gene region (42 kb) in 93 subjects with MI to identify 8 novel SNPs. Considered as single markers, none of those 8 SNPs was significantly associated with MI in further studies. Considering groups of SNPs as haplotype blocks revealed a particular haplotype, called HapK, significantly associated with MI. Each inherited copy of HapK conferred a relative risk of 1.45 (p = 0.035 after adjusting for the number of haplotypes tested).
Replication of genetic associations among subjects of various ethnicities addresses variability in SNP allele inheritance patterns among ethnic groups that could influence the way a particular variant affects disease risk. As one example, Helgadottir et al. (35) studied LTA4H in 3 independent MI cohorts of European-Americans and African Americans from the U.S. The HapK was significantly associated with MI among the European-American subset of this cohort (relative risk 1.19; p = 0.006), and also conferred higher relative risk among the African-American subset (relative risk 3.57; p = 0.000022). Presumably, underlying genetic and environmental differences between the European-American and African-American subsets explain the difference in LTA4H-associated MI risk, underscoring the need to include different ethnic populations in genetic association studies (36). Rare variants in one population may be relatively common in another, for example, proprotein convertase subtilisin/kexin type 9 serine protease (PCSK9), where 2 nonsense mutations not present in Caucasians confer lower levels of LDL cholesterol and protection from CAD to African Americans (37).
Other efforts to determine genetic associations with atherosclerosis have achieved limited success in explaining a significant proportion of the population-attributable risk (38,39), including several associations summarized elsewhere (3,40). This inherent problem, not limited to the study of cardiovascular disease, occurs in all genetic association studies of complex disease in which many genetic markers individually explain only a fraction of the genetic contribution to disease. Moreover, results from a genetic association study require replication through independent studies, as discussed in detail elsewhere (41).
Genome-wide association studies. As the per-SNP costs of high-throughput genotyping decline, cardiovascular researchers are turning to genetic association studies on an even larger scale. In large prospective cohorts, so-called genome-wide association studies examine hundreds of thousands of SNPs throughout the genome. Such studies, not hypothesis-driven, are suited ideally to discovering previously unimagined pathways for particular diseases.
The most significant disadvantage of genome-wide association studies involves the statistical conundrum of multiple comparisons inherent in simultaneously performing association tests on thousands of markers. A study of 500,000 SNPs with a false-positive rate of 0.1% would generate 500 false-positive results, a very large number that necessitates multiple rounds of replication to confirm an association. Thus, thoroughly replicated results from genome-wide association studies applied to cardiovascular disease and related phenotypes have just now begun to appear (42).
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Gene Expression Profiling in Cardiovascular Disease |
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TopAbstractGenetic Studies of...Gene Expression Profiling in...Proteomic and Metabolomic...Scientific Community Genomic...Genomic Data IntegrationClinical Applications of...Genomics for Identifying...Summary for CliniciansReferences |
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Extending those findings, Karra et al. (46) recently reported studies of gene expression during atherosclerotic lesion progression. These studies compared a histologically determined disease state with patterns of gene expression in mouse aortic tissue from wild-type and apolipoprotein E–/– C57BL6/J mice exposed to different diets and at different ages. Gene expression patterns varied according to disease stage: no disease to early disease (197 genes, including many involved in lipid metabolism), early to intermediate disease (146 genes, including many involved in inflammation), intermediate to moderate disease (110 genes), and moderate to severe disease (650 genes).
Importantly, Karra et al. (46) and Tabibiazar et al. (47) provide additional validation to these gene expression patterns by showing consistent cross-species pattern comparisons. Referring the results of their mouse experiments to earlier results in humans (45), Karra et al. (46) identified 40 genes among 650 (p < 0.0001) that significantly correlated with disease progression in both mice and humans. Analyses based on the Gene Ontology database, a bioinformatics tool designed to facilitate clustering of genes based on functional pathways (48), bolstered the concordance between mice and humans in both studies.
Overlapping genes identified in the 2 studies may provide a reliable molecular signature for atherosclerotic disease states (Table 2). Many overlapping genes—including chemokines, chemokine receptors, and cytokine-related genes, which consistently increased in both studies and at various stages of atherosclerotic lesion progression, and major histocompatibility complex molecules such as H2-Eb1 and H2-Ab1—associate with inflammation. Both studies identified increased levels of oncostatin M receptor (Osmr) in atherosclerosis. Oncostatin M (Osm) belongs to a cytokine family that regulates endothelial cell production of other cytokines, including interleukin-6, granulocyte colony-stimulating factor, and granulocyte macrophage colony-stimulating factor. Also Osm induces Abca1 in HepG2 cells (49) and Mmp3 and Timp3 gene expression via Janus kinase/signal transducers and activators of transcription signaling (50). Both studies observed that increased levels of Osmr, Jak3, and Abca1 associate with atherosclerosis progression. Both studies also detected increased levels of osteopontin (Spp1), a component of cell-mediated immunity (51), and a gene intensively studied in relation to atherosclerosis.
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Transcriptional profiling is especially advantageous in genomic studies designed to detect an environmental stimulus. This extension of the concept to cell culture has permitted investigation of the influence of vascular dynamics on transcripts related to atherosclerosis. Studying cultured human vascular endothelial cells exposed to varying shear stress, oxygen gradient, and oxidized LDL, Warabi et al. (52) showed differential expression of several Nrf2-related genes in response to laminar flow, underscoring the advantage of precise environmental regulation in cell culture for transcriptional profiling.
Detection of transcripts in circulating cells offers convenient clinical application of transcriptional profiling, but may not reflect gene expression in atherosclerotic lesions. Yet one such strategy may nonetheless offer novel insight into the mechanisms of coronary thrombosis. Transcriptional profiling of platelets from patients with acute coronary syndromes offers the ability to examine gene transcription that occurred in megakaryocytes some weeks before the onset of symptoms. This approach has identified myeloid-related protein-14 as a novel marker of coronary risk (53).
How do transcriptional profiling experiments impact cardiovascular therapeutics? Validated markers that emerge from gene expression profiling might improve risk stratification and personalization of treatment decisions. Gene expression profiling may aid identification of drug targets and validation of candidate drugs for the management of atherosclerosis. As proof of principle, Tuomisto et al. (54) showed that HMG-CoA reductase (the target of statin drugs) increased in atherosclerotic plaque. Using laser capture microdissection to isolate macrophage-rich tissue from human atherosclerotic lesions, they compared expression profiles to normal intimal tissue and THP-1 macrophage-like cells and identified augmented expression of 72 genes including HMG-CoA reductase. Studying the transcriptional effect of statin exposure on peripheral monocytes, Waehre et al. (55) provided evidence showing that statins inhibit expression of inflammatory cytokine interleukin-1ß, normally present at high levels among subjects with CAD (55). Such findings show the potential of genomic techniques to identify additional drug targets and provide greater understanding of their effects.
In another study based on expression profiling, CRP augmented the expression of matrix metalloproteinase (MMP)-1 and MMP-10 in human endothelial cell transcriptional profiling experiments (56). The MMPs are participants in plaque disruption and thrombosis. In another study, purified CRP caused increased expression of genes related to programmed cell death such as GADD153 in cultured vascular smooth muscle cells (57). Apoptosis of vascular smooth muscle cells occurs in atheromata (58). Thus, expression profiling described a molecular link between inflammation and atherosclerotic lesions and provided a means for testing the effect of statins or other compounds on apoptosis-associated genes, pointing to novel therapeutic approaches to atherosclerosis.
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Proteomic and Metabolomic Profiling in Atherosclerosis |
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TopAbstractGenetic Studies of...Gene Expression Profiling in...Proteomic and Metabolomic...Scientific Community Genomic...Genomic Data IntegrationClinical Applications of...Genomics for Identifying...Summary for CliniciansReferences |
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Challenges in the application of proteomics to cardiovascular disease begin with the selection of tissue samples. For example, one study that sampled endarterectomy sections containing atherosclerotic plaque determined decreased expression of heat shock protein-27 (HSP27) in plaque compared with healthy tissue, and confirmed these results by showing a similar trend for the amount of soluble HSP27 in plasma of subjects with atherosclerotic cardiovascular disease (60). Additionally, recent evidence suggests a possible connection between HSP27 and atherosclerosis at the level of estrogen signaling (61). Others have pointed to confounding in studies of atherosclerotic plaque because of the heterogeneity of cell types (62). Yet breaking down the components of a plaque without inducing experimental artifact also presents challenges. Although circulating plasma may not reflect all aspects of an atherosclerotic lesion, it offers accessibility and reproducibility of sample collection. Given the overwhelming abundance of certain proteins such as albumin in blood, ferreting out changes in levels of much less abundant proteins such as cytokines and growth factors presents a formidable technical challenge.
As in the case of genomic markers, proteomic studies require rigorous validation of technology platforms and experimental results. Providing a foundation for study interpretation, the Human Proteome Organization initiated a Plasma Proteome Project. The pilot phase identified 345 cardiovascular disease-related proteins in human plasma (63,64). These catalogs determine additional novel proteins that might associate with cardiovascular disease in future proteomic discovery experiments. Such databases accelerate the identification of unknown markers present in atherosclerotic cardiovascular disease.
Proteomic studies on isolated plasma lipid fractions have yielded new insight into the composition of LDL and high-density lipoprotein particles, identifying 3 proteins previously not associated with LDL and 2 proteins not previously associated with high-density lipoprotein (65,66). Davidsson et al. (67) described unique patterns of LDL-associated apolipoproteins in subjects with type 2 diabetes and subclinical peripheral atherosclerosis compared with healthy control subjects, suggesting that particular distributions of LDL-associated apolipoproteins in subjects with type 2 diabetes could contribute to the increased incidence of atherosclerotic cardiovascular disease in that group.
Metabolomics seeks to quantify small molecules that serve as physiological indicators within circulating plasma or particular cells and tissues. The potential list of small molecules runs into the thousands and includes carbohydrates, peptides, lipids, and metabolic intermediates such as amino acids, organic acids, and drug metabolites. Quantification is generally based on various methods of spectroscopy or chromatography, techniques that could provide rapid high-throughput results at relatively low cost in clinical practice. Noninvasive sampling of plasma to identify real-time markers of CAD has been reported (68), but clinical applications of this technology must first overcome technological and statistical limitations in simultaneously detecting myriad compounds across a broad concentration range.
As proof of principle, Sabatine et al. (69) used mass spectrometry-based technology to identify differences in plasma metabolites among 18 subjects with ischemia induced by exercise stress testing compared with nonischemic individuals who also exercised; specifically, changes in 6 metabolites, including citric acid, accurately differentiated cases from control subjects (p < 0.0001). Although requiring further study, combining small molecule profiling with the arsenal of genomic and proteomic technologies should provide additional diagnostic information and improve our ability to identify new therapeutic targets. Identifying relevant proteomic and metabolomic markers will benefit from comparison with genetic and genomic data. Future experiments should compare fluctuations of cardiovascular biomarkers with other epidemiologic factors such as age, gender, ethnicity, and a variety of environmental exposures already recognized to influence disease risk and outcome.
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Scientific Community Genomic Resources |
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TopAbstractGenetic Studies of...Gene Expression Profiling in...Proteomic and Metabolomic...Scientific Community Genomic...Genomic Data IntegrationClinical Applications of...Genomics for Identifying...Summary for CliniciansReferences |
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Genomic Data Integration |
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TopAbstractGenetic Studies of...Gene Expression Profiling in...Proteomic and Metabolomic...Scientific Community Genomic...Genomic Data IntegrationClinical Applications of...Genomics for Identifying...Summary for CliniciansReferences |
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Comparisons of such large data sets are biased toward false-positive results. Statistical methods for meeting this challenge are manifold and reviewed elsewhere (72,73). In general terms, approaches to large genomic data sets include data normalization, filtering, and correction algorithms for the numerous significant p values generated by thousands of comparisons. At best, these measures will help refine the list of prime candidates for further replication. Identifying clinically applicable genomic, proteomic, and metabolomic markers requires consistent replication across populations, a challenge best addressed through collaborative research efforts.
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Clinical Applications of Genomics to Cardiovascular Medicine |
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TopAbstractGenetic Studies of...Gene Expression Profiling in...Proteomic and Metabolomic...Scientific Community Genomic...Genomic Data IntegrationClinical Applications of...Genomics for Identifying...Summary for CliniciansReferences |
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Data networks that combine various forms of information will greatly assist this process by identifying, for example, metabolomic markers that vary in response to a patient’s genetic makeup and environmental exposures. Decision algorithms will harness information generated by medical and family histories, clinical criteria such as the Framingham Score, and multiple genetic, genomic, and biomarker tests (Fig. 3). Clinicians might wonder why we are not yet testing patients for genetic markers such as ALOX5AP or TNFSF4 SNPs. Quite simply, our current knowledge of the risk attributable to these variants is applicable only to a population, not to individuals. Providing patient-specific risk information based on genomic markers awaits a more comprehensive understanding of how these variants interact with each patient’s genetic background and other risk factors.
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Genomics for Identifying Therapeutic Targets |
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TopAbstractGenetic Studies of...Gene Expression Profiling in...Proteomic and Metabolomic...Scientific Community Genomic...Genomic Data IntegrationClinical Applications of...Genomics for Identifying...Summary for CliniciansReferences |
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500 (74). Genomic approaches can identify novel drug targets. Unfortunately, the path from potential target discovery to development of useful therapeutics presents considerable challenges. Microarray experiments might identify multiple potential targets, but prioritization of that list presents a challenge. Given a list of 100 or 1,000 candidate genes, researchers likely will focus on the ones they recognize; however, this bias could overlook viable targets. However, progress in CAD gene identification drives development of potential therapeutics. For example, treatment of 191 subjects who carried at-risk variants in the ALOX5AP or LTA4H genes with a FLAP inhibitor reduced levels of LTB4, a biomarker associated with MI risk (75), PCSK9 inhibitors seem a ripe target for adjunct therapy in patients in whom LDL targets are not achieved on statins either as monotherapy or combined with cholesterol absorption inhibitors, or who cannot tolerate high-dose statins. Moreover, studies of LTA4H and PCSK9 among African Americans may increase our understanding of ethnicity’s contribution to CAD.
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Summary for Clinicians |
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TopAbstractGenetic Studies of...Gene Expression Profiling in...Proteomic and Metabolomic...Scientific Community Genomic...Genomic Data IntegrationClinical Applications of...Genomics for Identifying...Summary for CliniciansReferences |
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Clinical translation will require validation in prospective studies that encompass large, ethnically diverse cohorts. Combining classical epidemiology with modern genomics will yield unprecedented insight into mechanisms of disease, and also will generate ever more risk markers. Performing tests on patients is relatively easy. Despite test sophistication, high-throughput automation will allow clinical laboratories to offer testing at costs similar to most diagnostic imaging studies. Interpreting such test results will truly provide a challenge. Indeed, prospective validation of new risk markers likely will limit the rate of progress more than any technical or experimental factors.
Genetic, genomic, proteomic, and metabolomic tests will add to rather than replace the clinical utility of traditional cardiovascular risk markers. Elements of the Framingham Score will remain relevant. However, newer information will temper and refine the relative importance of traditional markers. Framingham Scores do not include family history data, leaving room to incorporate genetic and genomic information into the screening process. Because DNA-based genetic markers remain static throughout life, predictive genetic tests could be performed at very young ages to facilitate early intervention. On the other hand, gene expression profiling and serum biomarkers provide real-time information that integrates current environmental influences, and likely aid diagnostic categorization and the early detection of disease. Again, a coordinated screening approach that maximizes the benefits of each screening modality will provide the most clinical utility.
Finally, genomic approaches have already identified potential drug targets. Inhibitors of FLAP, OX40L-OX40 binding, MMP, and PCSK9 are all attractive candidates for CAD risk modifiers. Although inherent challenges of bringing any new drug to the clinic will influence the pace of introduction of therapeutic changes, genomic approaches to cardiology promise to bring lasting improvements to patient care. Clinical translation of genomic, proteomic, and metabolomic information will require collaborative efforts within the cardiovascular disease community at both bench and bedside.
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Footnotes |
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1 Dr. Miller received fellowship support from the National Heart, Lung, and Blood Institute (NHLBI 1 F32 HL78274-01). 
2 Dr. Ridker is also supported by grants HL43851, HL63293, and HL58755 from the NHLBI, with additional support from the Leducq Foundation (Paris, France) and a Distinguished Clinical Scientist Award from the Doris Duke Charitable Foundation (New York, New York). Dr. Ridker is listed as a co-inventor on patents held by the Brigham and Women’s Hospital that relate to the use of inflammatory biomarkers in cardiovascular disease and diabetes. Dr. Ridker also has received investigator-initiated research support from AstraZeneca, Roche, Dade-Behring, Novartis, and Sanofi-Aventis, and has been a consultant to AstraZeneca, Novartis, and ISIS. Cardiovascular Genomic Medicine series edited by Geoffrey S. Ginsburg, MD, PhD.
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TopAbstractGenetic Studies of...Gene Expression Profiling in...Proteomic and Metabolomic...Scientific Community Genomic...Genomic Data IntegrationClinical Applications of...Genomics for Identifying...Summary for CliniciansReferences |
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