Skip main navigation

Systems Biology and Noninvasive Imaging of Atherosclerosis

Originally published, Thrombosis, and Vascular Biology. 2016;36:e1–e8

    Atherosclerosis is a systemic disease of the arterial vessel wall. Although the mortality due to cardiovascular events is decreasing, the prevalence of atherosclerosis and its comorbidities, and the consequent heath care costs are expected to rise sharply in the near future.1

    Because the precise cause and pathogenesis of this complex, multifactorial disease are still not fully understood, the clinical assessment of cardiovascular risk has been traditionally based on population risk factors (RFs).2 However, this approach still largely fails to capture the individual’s cardiovascular risk: most cardiovascular events occur in patients with 1 or few traditional RFs, whereas individuals classified as high risk may never experience clinical events.3

    The past 10 years have seen a significant paradigm shift in our understanding of the mechanisms of atherogenesis. From being considered the mere result of passive lipid accumulation in the vessel wall, atherosclerosis is now classified as an active inflammatory condition.4,5 The presence of abundant, active inflammatory cells is a known hallmark of high risk, vulnerable atherosclerotic plaques.4,5 Many studies have identified several systemic proinflammatory conditions (such as lupus,6 rheumatoid arthritis,79 and primary cardiovascular events themselves10) as emerging, independent RFs for atherosclerosis. New evidence suggests that atherosclerosis arises from the complex influence of genetic, environmental, and behavioral variables on systemic and local inflammation through a complex network of molecules, cells, and organs.

    Thanks to the recent technological advancements of high-throughput ‘-omics’, a plethora of the genes, proteins, and cells involved in the atherosclerotic cascade have already been identified. However, many steps still need to be taken to fully exploit this information, and improve patients’ risk stratification and antiatherosclerotic therapies. The mutual relationship between genetic and molecular key drivers, and their interplay in peripheral blood, atherosclerotic plaques and other organs still need to be established. Furthermore, quantitative methods to noninvasively measure these markers in the vessel wall and other tissues need to be developed and validated before they can be routinely used in the clinical practice.

    In this review, we highlight novel work in high-throughput ‘-omics’, systems biology and noninvasive quantitative imaging of atherosclerosis, with specific emphasis on articles recently featured in ATVB. New advancements in these disciplines are discussed separately, as well as in their complementary applications, to showcase how these fields may be successfully integrated to improve cardiovascular risk prediction and patients’ stratification in the future clinical practice (Figure).


    Figure. Schematic representation of the integration between data from high-throughput ‘-omics’ and imaging phenotypes in a systems biology approach, focused on building biological networks from genes and molecules, up to tissues and organs to study disease mechanisms.

    Systems Biology and High-Throughput ‘-omics’ of Atherosclerosis

    Systems biology can be broadly defined as the combination of experimental and computational research used to understand complex biological systems.11 It involves the integration of data derived from high-throughput ‘-omics’ with computational/statistical tools to build comprehensive networks and predictive physiological models12 (Figure).

    In recent years, high-throughput ‘-omics’ have been intensely applied to the study of atherosclerosis,13 with the aim to deepen our knowledge of this disease and refine our tools for cardiovascular risk assessment. For example, recent data14 from the Erasmus Rucphen Family and Rotterdam Study have shown that common genetic variants for total cholesterol and low-density lipoprotein cholesterol are, in combination, significantly associated with subclinical and clinical outcomes of atherosclerosis. Other investigations have suggested the association between soluble interleukin-2 receptor subunit α (regulating lymphocytes activation) and cardiovascular disease (CVD), and also uncovered the genetic determinants of its levels.15 These results substantiate our existing knowledge on the impact of long-life, cumulative exposure to modifiable and nonmodifiable RFs on cardiovascular risk.16,17 Although previous genome-wide association studies (GWAS)18 have reported only marginal improvements19,20 in risk stratification compared with the Framingham Risk Score,3,21,22 more recent investigations23,24 found that genetic risk scores from validated GWAS significantly improved prediction of cardiovascular events over traditional RFs, from 4 to 5%23 up to 12%.24 Extensive bioinformatics analyses25 of loci known to be associated with coronary artery disease (CAD) from GWAS, suggest that sequence variations mainly occur in noncoding regions of the genome and promote CAD risk by either affecting gene expression or by leading to amino acid changes. By extending the list of candidate genes likely linked to CAD, these studies suggest that bioinformatics analysis of GWAS may be beneficial in the context of atherosclerosis as well as other diseases.

    By quantifying the expression levels of protein-coding genes, transcriptomic studies2629 identified several promising biomarkers for CVD26 and vulnerable plaques.30 The Systems Approach to Biomarker Research (SABRe) study (launched as part of the Framingham Heart Study) recently found that 35 genes and 3 gene clusters (metagenes) were differentially expressed in cases with coronary heart disease (CHD) versus controls, whereas GUK1 38 and other genes were differentially spliced in the 2 populations.31 More recently, transcriptomics has been extended to study noncoding, regulatory gene transcripts, such as microRNAs. Several types of circulating microRNAs have recently been implicated in CVD.3,3245 Using transcriptomics, the prospective Bruneck study found that 3 miRNAs (miR-126, miR-223, and miR-197) originating from platelets improved patients’ risk classification for CHD compared with the Framingham Risk Score.46 Transcriptomics analyses have also directly implicated several miRNAs in the development of vascular inflammation as mediated by wall sheer stress.47 Numerous flow-sensitive miRNAs (miR-10a, miR-633, miR-21, and miR-92a) have already been identified.48

    Proteomic studies4956 collectively identified >150 potential single biomarkers of CVD.49,57,58 More recent studies have seen a shift from the analysis of single protein markers to simultaneously quantifying the combination of the levels of several proteins in multimarker panel analyses. A recent study in 135 myocardial infarction cases and matched controls59 found that both single and multimarker analysis of plasma proteins were associated with incidence of myocardial infarction, with multimarkers analysis providing higher discrimination. Similar results were found in a prospective study, where multimarkers analysis in 336 patients was found to be predictive of CVD (P<0.0001), with moderate improvement over traditional RFs (C-statistic of 0.69 versus 0.73).59

    Although these and previous studies offer invaluable insights on the genetic, molecular, and metabolic basis of atherosclerosis, they still do not provide a cohesive, integrated approach to the study of this disease. Recent approaches have tried to overcome this obstacle by applying a combination of ‘-omics’. For example, a combination of GWAS and transcriptomics is being used to investigate the recently suggested association between the haplogroup 1 of the Y male chromosome and an increased risk of CAD.60 New studies at the interface between metabolomics (lipidomics6163) and proteomics have shed light on the complexity of the human lipoproteome,64,65 and helped characterizing >90 forms of high-density liproprotein particles associated with different lipoproteins, with a diverse range of antiatherosclerotic properties,66 beyond the known effects on cholesterol efflux. A combined approach67 using adipose tissue transcriptomics, high-density liproprotein lipidomics, and genotyping found a shift toward inflammatory high-density liproprotein particle types in individuals with low high-density liproprotein cholesterol, mirrored by an increase in inflammatory markers in adipose tissue and in the peripheral blood.

    By taking these approaches a step forward, Shang et al68,69 used gene subnetworks profiling of the Stockholm Atherosclerosis Gene Expression (STAGE) study to find a candidate gene strongly correlated to leukocyte migration, and assess its association with clinical manifestation of disease (coronary angiography and carotid intima-media thickness [CIMT] by ultrasound). This work offers an example of how a more integrated systems biology approach may be used to better understand the process of atherogenesis, from the genetic determinants to the phenotypic manifestations of systemic and vascular inflammation. As part of the SABRe study mentioned above, Huan et al70 also use an integrated systems biology approach to find differential gene coexpression modules in the blood of subjects with CHD and matched controls. By integrating these results with previous GWAS and single-nucleotide polymorphisms, the authors are able to draw a causal relationship between the differential gene coexpression modules and CHD in this cohort. With the further integration of Bayesian networks and protein–protein interaction, networks they also identify key drivers, regulatory genes important for the differential gene coexpression modules stability and therefore potential targets for novel drugs. This network-driven, integrated approach not only identifies genes related to CHD but also strives to build a network structure that informs on the molecular interactions of genes associated with CHD risk.

    Despite the enormous potential of a panomic/systems biology approach to atherosclerosis, several obstacles have to be overcome so that ‘-omics’ can be successfully used in future clinical practice. First, a causal relationship between biomarkers and disease mechanisms has to be solidly established. Dissecting causal effects from confounders can prove challenging in cross-sectional studies, whereas prospective, causal studies with cardiovascular events as end points are costly and lengthy to perform. Furthermore, performing ‘-omics’ analyses on direct tissue samples may not be always feasible, whereas peripheral blood analyses may only reflect transient changes in metabolites that do not necessarily inform on the overall disease activity.


    In recent years, medical imaging has made great strides in the evaluation of virtually every organ in the body, including atherosclerotic plaques. Modality-specific imaging traits (imaging phenotypes) emerge from the combination of tissues structure, physiology and function, and inform on organs physiology and pathology.71,72

    Several imaging modalities have already found widespread use in the clinical practice to evaluate atherosclerotic burden. CIMT by surface ultrasound73,74 is one such technique, although its clinical usefulness to significantly improve risk prediction over traditional RFs has recently been questioned.75,76 Recently, CIMT was found to decrease in subjects consuming a Mediterranean diet supplemented with 30 g/d of mixed nuts, compared with a control, low-fat diet, thereby corroborating the results form the Primary Prevention of Cardiovascular Disease with a Mediterranean Diet (PREDIMED) trial.77 Other than CIMT, surface ultrasound can be used to measure other parameters related to plaque vulnerability, such as vascular strain or the extent of plaque microvasculature using nontargeted micro bubbles.78 Ultrasound with micro bubbles targeted to vascular cell adhesion molecule 1 and platelet glycoprotein Ibα was recently validated in genetically modified mice as being able to assess the anti-inflammatory properties of apocynin, before detectable changes in macrophages burden.79

    More invasive procedures involving intravascular ultrasound or transesophageal ultrasound78 can also be performed. For example, transesophageal ultrasound was recently used in mongrel dogs to quantify changes in aortic area and elastic properties from velocity-vector imaging with aging.80 Another study used a combination of multivessel intravascular ultrasound and novel near-infrared spectroscopy81,82 to evaluate features of vulnerability in fibroatheromas of diabetic/hypercholesterolemic pigs.83 Longitudinally, intravascular ultrasound demonstrated a progressive increase in plaque and media areas, with the appearance of necrotic cores and regions of positive vascular remodeling. Compared with histological samples, near-infrared spectroscopy–positive lesions exhibited features of high-risk fibroatheromas, such as large plaque size, necrotic cores, thin fibrous cap, and abundant presence of inflammatory cells. A more recent study using intravascular ultrasound demonstrated a greater progression in patients with CAD classified as statin hyporesponders, compared with individuals who exhibited low-density lipoprotein cholesterol reductions of >15% from baseline.84

    Coronary calcium score evaluated by noncontrast enhanced computed tomography (CT),85,86 is another noninvasive measure of overall atherosclerotic burden, which has been described to predict the risk of future clinical events.87,88 A recent follow-up study of the Multi-Ethnic Study of Atherosclerosis (MESA) trial found that abdominal aortic calcium and coronary calcium score were predictors of CHD and cardiovascular events independent of one another, with only abdominal aortic calcium being independently associated with cardiovascular mortality, and showing a stronger association than coronary calcium score with overall mortality.89 Recent studies90 suggest that this measure could be complemented by cardiac computed tomographic angiography91 with the use of a iodinated contrast agent to provide additional information on the degree and distribution of coronary plaque stenosis, vessel wall positive remodeling, and plaque composition (such as presence of low-attenuation plaques and spotty calcification), which have been identified as markers of vulnerability.92

    Other modalities, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), are actively being investigated in both the preclinical and the clinical research arenas for their potential translation into clinical practice. With its superior soft tissue contrast compared with CT, and the possibility to image large segments of the vasculature with high-spatial resolution, noncontrast–enhanced MRI has been extensively investigated as a method to characterize atherosclerotic plaques components, such as lipid core, fibrous cap, intraplaque hemorrhage, and presence of thrombi.9395 A recent study96 performed on 1016 individuals from the Framingham Heart Study Offspring cohort investigated the prevalence and RF correlates for aortic plaque detected by MRI and CT. The study found that while aortic plaque by both imaging modalities is associated with smoking and increasing age, the association with other RFs differs between calcified plaques detected by CT and noncalcified lesions detected by MRI. The study postulates that the relative predictive value of aortic plaque detected by MRI and CT still needs to be investigated. Combined with the use of nonspecific gadolinium-based contrast agents, MRI has been also used to interrogate plaque physiology and quantify the extent of microvascular permeability,9799 another important hallmark of plaque vulnerability. Other physiological parameters, such as carotid arterial strain and distensibility calculated from MRI, have been shown to predict the future incidence of cerebral microbleeds in 2512 patients recruited as part of the prospective, population-based Age, Gene/Environment Susceptibility (AGES)-Reykjavik study.100 PET, combined with the anatomic information from CT101 or, more recently, MRI,88 has been extensively validated to quantify vascular inflammation itself and its changes on therapeutic intervention, both in humans and in animals using the tracer 18F fluorodeoxyglucose (FDG).102,103 Recently, 18F-FDG PET was used to demonstrate a decrease in vascular inflammation in Ldlr−/− atherosclerotic mice after treatment with melanocortin peptides.104 Other PET tracers are now being investigated, such as sodium fluoride105 targeting microcalcification, or tracers targeted to specific molecules, such as αvβ3, vascular cell adhesion molecule 1,102 68Ga-Fucoidan for P-selectin106 (abundantly expressed in vulnerable, but not stable plaques),64Cu-FBP8 for thrombus detection and fibrin quantification,107 and64 Cu-DOTATATE to selectively quantify plaque macrophages via the somatostatin receptor subtype-2.108

    Among emerging imaging modalities, optical coherence tomography is gaining increasing interest because of its ability to provide high-resolution images of tissues microstructure. Recently, optical coherence tomography was first validated in atherosclerotic rabbits and then used in a prospective study in patients to evaluate vascular healing after the implantation of drug-eluting stents, where it was found to be able to discriminate between immature and mature (healed) neointimal tissue.109 Another study used optical coherence tomography in 40 patients with mild coronary atherosclerosis to study the composition of coronary segments after stimulus with acetylcholine. The study found that the segments showing the presence of macrophages and microchannels (microvasculature), exhibited a more prominent change in the diameter of coronary arteries, indicating higher endothelial dysfunction.110 Recently, novel, in vivo multiphoton laser scanning microscopy was used to study plaque microvasculature and confirmed that plaque-associated vasa vasorum exhibit increased permeability, and increased leukocyte adhesion and extravasation.111

    Imaging as a Tool for Systems Biology

    From the account above, it emerges that through the noninvasive characterization of tissues anatomy and physiology, medical imaging may be an ideal complement to ‘-omics’ technologies for a comprehensive systems biology approach to CVD. Several approaches are currently being explored to successfully integrate imaging in this framework.

    Although confined to preclinical investigations, molecular imaging already reports on specific biological processes (optical imaging and fluorescence imaging), and can even directly quantify gene expression (bioluminescence)1 or cells (macrophages) development, migration,112,113 and presence in tissues throughout the body.114 Among translatable modalities, molecular imaging with MRI and PET can also similarly be used for this purpose. Aside from the increasing number of MRI contrast agents being developed to target specific biomarkers,93 MRI with ultrasmall superparamagnetic iron oxide particles has been widely validated as a tool to detect plaque macrophages content in atherosclerotic plaques in both animals115 and patients.116 Recent studies in mice have shown the successful integration of proteomics, metabolomics, and quantitative and anatomic MRI to phenotype transgenic mice in regard to creatinine and phosphocreatinine cardiac metabolism.117 In addition to the quantification of plaque local inflammation, the use of 18F-FDG PET was recently extended to study the interplay between local and systemic inflammation and to substantiate the existence of a cardiosplenic axis in humans (implicated from animal models10 in the high incidence of secondary cardiovascular events in patients with previous myocardial infarction).118 Similarly, another study has recently demonstrated increased vascular inflammation by 18F-FDG PET in patients with psoriasis, independent of cardiovascular RFs.119 These studies show an example of the integration of 18F-FDG PET in a systems physiology approach. Similarly, several clinical imaging modalities are currently being investigated to quantify noninvasively the extent (CT and MRI) and metabolic activity (18F-FDG PET) of visceral and subcutaneous body fat, regarded as a potential marker and risk predictor of CVD.120

    Some studies have already focused on investigating the genetic and molecular correlates of imaging traits. A recent study identified the genetic variations influencing the effect of smoking on CIMT, thereby exemplifying how the study of gene–environment interactions may explain the interindividual variation in both cardiovascular events and surrogate measures of cardiovascular risk.121 The Genetic Loci and the Burden of Atherosclerotic Lesions (GLOBAL) study122 (NCT01738828) brings this concept to a different level by aiming to comprehensively integrate plaque phenotype by cardiovascular imaging, with a panomic approach including genomic, transcriptomic, proteomic, metabolomics, and lipidomic in a systems biology framework. The study plans to examine single-omic and multi-omic associations with each imaging phenotype evaluated (coronary calcium score and CT angiography) in training and validation data sets.

    Final Remarks

    In this report, we have reviewed the most recent advances in ‘-omics’/systems biology and noninvasive medical imaging applied to atherosclerosis and CVD, with specific focus on articles recently published in ATVB.

    The combination of ‘-omics’ and systems biology is nowadays used more and more frequently to elucidate mechanisms of disease, and it is also being investigated as a complement to clinical data to improve patients risk stratification. Examples of these approaches are GWAS,23,24 the study of coding and noncoding RNAs using transcriptomics,3145 as well as tissue and blood proteomics.4959 More recently, sophisticated analyses6870 aim to integrate this information in a comprehensive, systems biology approach to build a network structure of the molecular interactions in CVD.

    Medical imaging is also making tremendous advancements in the diagnosis and characterization of CVD. Modalities such as ultrasound, CT, and MRI already allow for the accurate quantification of plaque burden and lesion characterization. Other approaches, such as PET and MRI with contrast report on relevant physiological parameters, that is, plaque permeability and inflammation, whereas molecular imaging techniques can shed light on specific molecular/cellular processes.

    Although taken separately both ‘-omics’ and medical imaging can already tremendously contribute to our understanding of CVD and to our ability to stratify patients’ risk, their successful integration may bring additional, significant benefits.

    Similarly to what is being recently proposed in oncology,123129 the first step in integrating these 2 disciplines will be establishing the association130 between imaging phenotypes and specific genetic, molecular and cellular signatures in atherosclerotic plaques and other organs involved in atherogenesis. Once these correlations will be robustly established, the use of imaging phenotypes may be extended to function as predictors124,125 of plaques genetic and molecular makeup123129 in both the preclinical and the clinical arenas. In this scenario, the integration of imaging and ‘-omics’ in a systems biology framework may be better positioned to improve risk stratification and assessment of therapeutic response of atherosclerotic patients in the future clinical practice (Figure).


    Correspondence to Claudia Calcagno, MD, PhD, Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029. E-mail


    • 1. Nahrendorf M, Frantz S, Swirski FK, Mulder WJ, Randolph G, Ertl G, Ntziachristos V, Piek JJ, Stroes ES, Schwaiger M, Mann DL, Fayad ZA. Imaging systemic inflammatory networks in ischemic heart disease.J Am Coll Cardiol. 2015; 65:1583–1591. doi: 10.1016/j.jacc.2015.02.034.CrossrefMedlineGoogle Scholar
    • 2. Https://www.Framinghamheartstudy.Org.Google Scholar
    • 3. Mayr M, Zampetaki A, Willeit P, Willeit J, Kiechl S. MicroRNAs within the continuum of postgenomics biomarker discovery.Arterioscler Thromb Vasc Biol. 2013; 33:206–214. doi: 10.1161/ATVBAHA.112.300141.LinkGoogle Scholar
    • 4. Virmani R, Ladich ER, Burke AP, Kolodgie FD. Histopathology of carotid atherosclerotic disease.Neurosurgery. 2006; 59(5 Suppl 3):S219–S227. doi: 10.1227/01.NEU.0000239895.00373.E4.MedlineGoogle Scholar
    • 5. Virmani R, Burke AP, Farb A, Kolodgie FD. Pathology of the vulnerable plaque.J Am Coll Cardiol. 2006; 47(Suppl 8):C13–C18. doi: 10.1016/j.jacc.2005.10.065.CrossrefMedlineGoogle Scholar
    • 6. van Leuven SI, Mendez-Fernandez YV, Wilhelm AJ, Wade NS, Gabriel CL, Kastelein JJ, Stroes ES, Tak PP, Major AS. Mycophenolate mofetil but not atorvastatin attenuates atherosclerosis in lupus-prone LDLr(-/-) mice.Ann Rheum Dis. 2012; 71:408–414. doi: 10.1136/annrheumdis-2011-200071.CrossrefMedlineGoogle Scholar
    • 7. Solomon DH, Peters MJ, Nurmohamed MT, Dixon W. Unresolved questions in rheumatology: motion for debate: the data support evidence-based management recommendations for cardiovascular disease in rheumatoid arthritis.Arthritis Rheum. 2013; 65:1675–1683. doi: 10.1002/art.37975.CrossrefMedlineGoogle Scholar
    • 8. He M, Liang X, He L, Wen W, Zhao S, Wen L, Liu Y, Shyy JY, Yuan Z. Endothelial dysfunction in rheumatoid arthritis: the role of monocyte chemotactic protein-1-induced protein.Arterioscler Thromb Vasc Biol. 2013; 33:1384–1391. doi: 10.1161/ATVBAHA.113.301490.LinkGoogle Scholar
    • 9. Furer V, Fayad ZA, Mani V, Calcagno C, Farkouh ME, Greenberg JD. Noninvasive cardiovascular imaging in rheumatoid arthritis: current modalities and the emerging role of magnetic resonance and positron emission tomography imaging.Semin Arthritis Rheum. 2012; 41:676–688. doi: 10.1016/j.semarthrit.2011.08.007.CrossrefMedlineGoogle Scholar
    • 10. Dutta P, Courties G, Wei Y, et al.. Myocardial infarction accelerates atherosclerosis.Nature. 2012; 487:325–329. doi: 10.1038/nature11260.CrossrefMedlineGoogle Scholar
    • 11. Kitano H. Computational systems biology.Nature. 2002; 420:206–210. doi: 10.1038/nature01254.CrossrefMedlineGoogle Scholar
    • 12. Ramsey SA, Gold ES, Aderem A. A systems biology approach to understanding atherosclerosis.EMBO Mol Med. 2010; 2:79–89. doi: 10.1002/emmm.201000063.CrossrefMedlineGoogle Scholar
    • 13. Björkegren JL, Kovacic JC, Dudley JT, Schadt EE. Genome-wide significant loci: how important are they? Systems genetics to understand heritability of coronary artery disease and other common complex disorders.J Am Coll Cardiol. 2015; 65:830–845. doi: 10.1016/j.jacc.2014.12.033.MedlineGoogle Scholar
    • 14. Thanassoulis G, Peloso GM, O’Donnell CJ. Genomic medicine for improved prediction and primordial prevention of cardiovascular disease.Arterioscler Thromb Vasc Biol. 2013; 33:2049–2050. doi: 10.1161/ATVBAHA.113.301814.LinkGoogle Scholar
    • 15. Durda P, Sabourin J, Lange EM, et al.. Plasma levels of soluble interleukin-2 receptor α: associations with clinical cardiovascular events and genome-wide association scan.Arterioscler Thromb Vasc Biol. 2015; 35:2246–2253. doi: 10.1161/ATVBAHA.115.305289.LinkGoogle Scholar
    • 16. Isaacs A, Willems SM, Bos D, Dehghan A, Hofman A, Ikram MA, Uitterlinden AG, Oostra BA, Franco OH, Witteman JC, van Duijn CM. Risk scores of common genetic variants for lipid levels influence atherosclerosis and incident coronary heart disease.Arterioscler Thromb Vasc Biol. 2013; 33:2233–2239. doi: 10.1161/ATVBAHA.113.301236.LinkGoogle Scholar
    • 17. Gebreab SY, Riestra P, Khan RJ, Xu R, Musani SK, Tekola-Ayele F, Correa A, Wilson JG, Rotimi CN, Davis SK. Genetic ancestry is associated with measures of subclinical atherosclerosis in African Americans: the Jackson Heart Study.Arterioscler Thromb Vasc Biol. 2015; 35:1271–1278. doi: 10.1161/ATVBAHA.114.304855.LinkGoogle Scholar
    • 18. Huang J, Huffman JE, Yamakuchi M, et al.; Cohorts for Heart and Aging Research in Genome Epidemiology (CHARGE) Consortium Neurology Working Group; CARDIoGRAM Consortium; CHARGE Consortium Hemostatic Factor Working Group. Genome-wide association study for circulating tissue plasminogen activator levels and functional follow-up implicates endothelial STXBP5 and STX2.Arterioscler Thromb Vasc Biol. 2014; 34:1093–1101. doi: 10.1161/ATVBAHA.113.302088.LinkGoogle Scholar
    • 19. Ripatti S, Tikkanen E, Orho-Melander M, et al.. A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses.Lancet. 2010; 376:1393–1400. doi: 10.1016/S0140-6736(10)61267-6.CrossrefMedlineGoogle Scholar
    • 20. Thanassoulis G, Peloso GM, Pencina MJ, Hoffmann U, Fox CS, Cupples LA, Levy D, D’Agostino RB, Hwang SJ, O’Donnell CJ. A genetic risk score is associated with incident cardiovascular disease and coronary artery calcium: the Framingham Heart Study.Circ Cardiovasc Genet. 2012; 5:113–121. doi: 10.1161/CIRCGENETICS.111.961342.LinkGoogle Scholar
    • 21. Casas JP, Cooper J, Miller GJ, Hingorani AD, Humphries SE. Investigating the genetic determinants of cardiovascular disease using candidate genes and meta-analysis of association studies.Ann Hum Genet. 2006; 70(Pt 2):145–169. doi: 10.1111/j.1469-1809.2005.00241.x.CrossrefMedlineGoogle Scholar
    • 22. Greenland P, Alpert JS, Beller GA, et al.; American College of Cardiology Foundation; American Heart Association. 2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines.J Am Coll Cardiol. 2010; 56:e50–103. doi: 10.1016/j.jacc.2010.09.001.CrossrefMedlineGoogle Scholar
    • 23. Ganna A, Magnusson PK, Pedersen NL, de Faire U, Reilly M, Arnlöv J, Sundström J, Hamsten A, Ingelsson E. Multilocus genetic risk scores for coronary heart disease prediction.Arterioscler Thromb Vasc Biol. 2013; 33:2267–2272. doi: 10.1161/ATVBAHA.113.301218.LinkGoogle Scholar
    • 24. Tikkanen E, Havulinna AS, Palotie A, Salomaa V, Ripatti S. Genetic risk prediction and a 2-stage risk screening strategy for coronary heart disease.Arterioscler Thromb Vasc Biol. 2013; 33:2261–2266. doi: 10.1161/ATVBAHA.112.301120.LinkGoogle Scholar
    • 25. Brænne I, Civelek M, Vilne B, et al.; Leducq Consortium CAD Genomics. Prediction of causal candidate genes in coronary artery disease loci.Arterioscler Thromb Vasc Biol. 2015; 35:2207–2217. doi: 10.1161/ATVBAHA.115.306108.LinkGoogle Scholar
    • 26. Siemelink MA, Zeller T. Biomarkers of coronary artery disease: the promise of the transcriptome.Curr Cardiol Rep. 2014; 16:513. doi: 10.1007/s11886-014-0513-4.CrossrefMedlineGoogle Scholar
    • 27. Pedrotty DM, Morley MP, Cappola TP. Transcriptomic biomarkers of cardiovascular disease.Prog Cardiovasc Dis. 2012; 55:64–69. doi: 10.1016/j.pcad.2012.06.003.CrossrefMedlineGoogle Scholar
    • 28. Bijnens AP, Lutgens E, Ayoubi T, Kuiper J, Horrevoets AJ, Daemen MJ. Genome-wide expression studies of atherosclerosis: critical issues in methodology, analysis, interpretation of transcriptomics data.Arterioscler Thromb Vasc Biol. 2006; 26:1226–1235. doi: 10.1161/01.ATV.0000219289.06529.f1.LinkGoogle Scholar
    • 29. Erbilgin A, Siemers N, Kayne P, Yang WP, Berliner J, Lusis AJ. Gene expression analyses of mouse aortic endothelium in response to atherogenic stimuli.Arterioscler Thromb Vasc Biol. 2013; 33:2509–2517. doi: 10.1161/ATVBAHA.113.301989.LinkGoogle Scholar
    • 30. Perisic L, Hedin E, Razuvaev A, Lengquist M, Osterholm C, Folkersen L, Gillgren P, Paulsson-Berne G, Ponten F, Odeberg J, Hedin U. Profiling of atherosclerotic lesions by gene and tissue microarrays reveals PCSK6 as a novel protease in unstable carotid atherosclerosis.Arterioscler Thromb Vasc Biol. 2013; 33:2432–2443. doi: 10.1161/ATVBAHA.113.301743.LinkGoogle Scholar
    • 31. Joehanes R, Ying S, Huan T, Johnson AD, Raghavachari N, Wang R, Liu P, Woodhouse KA, Sen SK, Tanriverdi K, Courchesne P, Freedman JE, O’Donnell CJ, Levy D, Munson PJ. Gene expression signatures of coronary heart disease.Arterioscler Thromb Vasc Biol. 2013; 33:1418–1426. doi: 10.1161/ATVBAHA.112.301169.LinkGoogle Scholar
    • 32. Tijsen AJ, Pinto YM, Creemers EE. Circulating microRNAs as diagnostic biomarkers for cardiovascular diseases.Am J Physiol Heart Circ Physiol. 2012; 303:H1085–H1095. doi: 10.1152/ajpheart.00191.2012.CrossrefMedlineGoogle Scholar
    • 33. Deddens JC, Colijn JM, Oerlemans MI, Pasterkamp G, Chamuleau SA, Doevendans PA, Sluijter JP. Circulating microRNAs as novel biomarkers for the early diagnosis of acute coronary syndrome.J Cardiovasc Transl Res. 2013; 6:884–898. doi: 10.1007/s12265-013-9493-9.CrossrefMedlineGoogle Scholar
    • 34. Kuwabara Y, Ono K, Horie T, Nishi H, Nagao K, Kinoshita M, Watanabe S, Baba O, Kojima Y, Shizuta S, Imai M, Tamura T, Kita T, Kimura T. Increased microRNA-1 and microRNA-133a levels in serum of patients with cardiovascular disease indicate myocardial damage.Circ Cardiovasc Genet. 2011; 4:446–454. doi: 10.1161/CIRCGENETICS.110.958975.LinkGoogle Scholar
    • 35. Oerlemans MI, Mosterd A, Dekker MS, de Vrey EA, van Mil A, Pasterkamp G, Doevendans PA, Hoes AW, Sluijter JP. Early assessment of acute coronary syndromes in the emergency department: the potential diagnostic value of circulating microRNAs.EMBO Mol Med. 2012; 4:1176–1185. doi: 10.1002/emmm.201201749.CrossrefMedlineGoogle Scholar
    • 36. Tijsen AJ, Creemers EE, Moerland PD, de Windt LJ, van der Wal AC, Kok WE, Pinto YM. MiR423-5p as a circulating biomarker for heart failure.Circ Res. 2010; 106:1035–1039. doi: 10.1161/CIRCRESAHA.110.218297.LinkGoogle Scholar
    • 37. De Guire V, Robitaille R, Tétreault N, Guérin R, Ménard C, Bambace N, Sapieha P. Circulating miRNAs as sensitive and specific biomarkers for the diagnosis and monitoring of human diseases: promises and challenges.Clin Biochem. 2013; 46:846–860. doi: 10.1016/j.clinbiochem.2013.03.015.CrossrefMedlineGoogle Scholar
    • 38. Liu G, Abraham E. MicroRNAs in immune response and macrophage polarization.Arterioscler Thromb Vasc Biol. 2013; 33:170–177. doi: 10.1161/ATVBAHA.112.300068.LinkGoogle Scholar
    • 39. Fernández-Hernando C, Ramírez CM, Goedeke L, Suárez Y. MicroRNAs in metabolic disease.Arterioscler Thromb Vasc Biol. 2013; 33:178–185. doi: 10.1161/ATVBAHA.112.300144.LinkGoogle Scholar
    • 40. Boon RA, Vickers KC. Intercellular transport of microRNAs.Arterioscler Thromb Vasc Biol. 2013; 33:186–192. doi: 10.1161/ATVBAHA.112.300139.LinkGoogle Scholar
    • 41. Dang LT, Lawson ND, Fish JE. MicroRNA control of vascular endothelial growth factor signaling output during vascular development.Arterioscler Thromb Vasc Biol. 2013; 33:193–200. doi: 10.1161/ATVBAHA.112.300142.LinkGoogle Scholar
    • 42. Fiedler J, Thum T. MicroRNAs in myocardial infarction.Arterioscler Thromb Vasc Biol. 2013; 33:201–205. doi: 10.1161/ATVBAHA.112.300137.LinkGoogle Scholar
    • 43. Wagner J, Riwanto M, Besler C, Knau A, Fichtlscherer S, Röxe T, Zeiher AM, Landmesser U, Dimmeler S. Characterization of levels and cellular transfer of circulating lipoprotein-bound microRNAs.Arterioscler Thromb Vasc Biol. 2013; 33:1392–1400. doi: 10.1161/ATVBAHA.112.300741.LinkGoogle Scholar
    • 44. Wei Y, Nazari-Jahantigh M, Neth P, Weber C, Schober A. MicroRNA-126, -145, and -155: a therapeutic triad in atherosclerosis?Arterioscler Thromb Vasc Biol. 2013; 33:449–454. doi: 10.1161/ATVBAHA.112.300279.LinkGoogle Scholar
    • 45. Huan T, Rong J, Tanriverdi K, et al.. Dissecting the roles of microRNAs in coronary heart disease via integrative genomic analyses.Arterioscler Thromb Vasc Biol. 2015; 35:1011–1021. doi: 10.1161/ATVBAHA.114.305176.LinkGoogle Scholar
    • 46. Zampetaki A, Willeit P, Tilling L, Drozdov I, Prokopi M, Renard JM, Mayr A, Weger S, Schett G, Shah A, Boulanger CM, Willeit J, Chowienczyk PJ, Kiechl S, Mayr M. Prospective study on circulating MicroRNAs and risk of myocardial infarction.J Am Coll Cardiol. 2012; 60:290–299. doi: 10.1016/j.jacc.2012.03.056.CrossrefMedlineGoogle Scholar
    • 47. Bryan MT, Duckles H, Feng S, Hsiao ST, Kim HR, Serbanovic-Canic J, Evans PC. Mechanoresponsive networks controlling vascular inflammation.Arterioscler Thromb Vasc Biol. 2014; 34:2199–2205. doi: 10.1161/ATVBAHA.114.303424.LinkGoogle Scholar
    • 48. Kumar S, Kim CW, Simmons RD, Jo H. Role of flow-sensitive microRNAs in endothelial dysfunction and atherosclerosis: mechanosensitive athero-miRs.Arterioscler Thromb Vasc Biol. 2014; 34:2206–2216. doi: 10.1161/ATVBAHA.114.303425.LinkGoogle Scholar
    • 49. Lindsey ML, Mayr M, Gomes AV, Delles C, Arrell DK, Murphy AM, Lange RA, Costello CE, Jin YF, Laskowitz DT, Sam F, Terzic A, Van Eyk J, Srinivas PR; American Heart Association Council on Functional Genomics and Translational Biology, Council on Cardiovascular Disease in the Young, Council on Clinical Cardiology, Council on Cardiovascular and Stroke Nursing, Council on Hypertension, and Stroke Council. Transformative impact of proteomics on cardiovascular health and disease: a scientific statement from the American Heart Association.Circulation. 2015; 132:852–872. doi: 10.1161/CIR.0000000000000226.LinkGoogle Scholar
    • 50. Didangelos A, Yin X, Mandal K, Saje A, Smith A, Xu Q, Jahangiri M, Mayr M. Extracellular matrix composition and remodeling in human abdominal aortic aneurysms: a proteomics approach.Mol Cell Proteomics. 2011; 10:M111.008128. doi: 10.1074/mcp.M111.008128.CrossrefMedlineGoogle Scholar
    • 51. Zimmerli LU, Schiffer E, Zürbig P, Good DM, Kellmann M, Mouls L, Pitt AR, Coon JJ, Schmieder RE, Peter KH, Mischak H, Kolch W, Delles C, Dominiczak AF. Urinary proteomic biomarkers in coronary artery disease.Mol Cell Proteomics. 2008; 7:290–298. doi: 10.1074/mcp.M700394-MCP200.CrossrefMedlineGoogle Scholar
    • 52. Brown CE, McCarthy NS, Hughes AD, Sever P, Stalmach A, Mullen W, Dominiczak AF, Sattar N, Mischak H, Thom S, Mayet J, Stanton AV, Delles C. Urinary proteomic biomarkers to predict cardiovascular events.Proteomics Clin Appl. 2015; 9:610–617. doi: 10.1002/prca.201400195.CrossrefMedlineGoogle Scholar
    • 53. Zhang ZY, Thijs L, Petit T, Gu YM, Jacobs L, Yang WY, Liu YP, Koeck T, Zürbig P, Jin Y, Verhamme P, Voigt JU, Kuznetsova T, Mischak H, Staessen JA. Urinary Proteome and Systolic Blood Pressure as Predictors of 5-Year Cardiovascular and Cardiac Outcomes in a General Population.Hypertension. 2015; 66:52–60. doi: 10.1161/HYPERTENSIONAHA.115.05296.LinkGoogle Scholar
    • 54. Gerszten RE, Asnani A, Carr SA. Status and prospects for discovery and verification of new biomarkers of cardiovascular disease by proteomics.Circ Res. 2011; 109:463–474. doi: 10.1161/CIRCRESAHA.110.225003.LinkGoogle Scholar
    • 55. Melander O, Modrego J, Zamorano-León JJ, Santos-Sancho JM, Lahera V, López-Farré AJ. New circulating biomarkers for predicting cardiovascular death in healthy population.J Cell Mol Med. 2015; 19:2489–2499. doi: 10.1111/jcmm.12652.CrossrefMedlineGoogle Scholar
    • 56. Bagnato C, Thumar J, Mayya V, Hwang SI, Zebroski H, Claffey KP, Haudenschild C, Eng JK, Lundgren DH, Han DK. Proteomics analysis of human coronary atherosclerotic plaque: a feasibility study of direct tissue proteomics by liquid chromatography and tandem mass spectrometry.Mol Cell Proteomics. 2007; 6:1088–1102. doi: 10.1074/mcp.M600259-MCP200.CrossrefMedlineGoogle Scholar
    • 57. Basak T, Varshney S, Akhtar S, Sengupta S. Understanding different facets of cardiovascular diseases based on model systems to human studies: a proteomic and metabolomic perspective.J Proteomics. 2015; 127(Pt A):50–60. doi: 10.1016/j.jprot.2015.04.027.CrossrefMedlineGoogle Scholar
    • 58. Martinez-Pinna R, Madrigal-Matute J, Tarin C, Burillo E, Esteban-Salan M, Pastor-Vargas C, Lindholt JS, Lopez JA, Calvo E, de Ceniga MV, Meilhac O, Egido J, Blanco-Colio LM, Michel JB, Martin-Ventura JL. Proteomic analysis of intraluminal thrombus highlights complement activation in human abdominal aortic aneurysms.Arterioscler Thromb Vasc Biol. 2013; 33:2013–2020. doi: 10.1161/ATVBAHA.112.301191.LinkGoogle Scholar
    • 59. Yin X, Subramanian S, Hwang SJ, O’Donnell CJ, Fox CS, Courchesne P, Muntendam P, Gordon N, Adourian A, Juhasz P, Larson MG, Levy D. Protein biomarkers of new-onset cardiovascular disease: prospective study from the systems approach to biomarker research in cardiovascular disease initiative.Arterioscler Thromb Vasc Biol. 2014; 34:939–945. doi: 10.1161/ATVBAHA.113.302918.LinkGoogle Scholar
    • 60. Bloomer LD, Nelson CP, Eales J, Denniff M, Christofidou P, Debiec R, Moore J, Consortium C, Zukowska-Szczechowska E, Goodall AH, Thompson J, Samani NJ, Charchar FJ, Tomaszewski M. Male-specific region of the Y chromosome and cardiovascular risk: phylogenetic analysis and gene expression studies.Arterioscler Thromb Vasc Biol. 2013; 33:1722–1727. doi: 10.1161/ATVBAHA.113.301608.LinkGoogle Scholar
    • 61. Meikle PJ, Wong G, Barlow CK, Kingwell BA. Lipidomics: potential role in risk prediction and therapeutic monitoring for diabetes and cardiovascular disease.Pharmacol Ther. 2014; 143:12–23. doi: 10.1016/j.pharmthera.2014.02.001.CrossrefMedlineGoogle Scholar
    • 62. Hinterwirth H, Stegemann C, Mayr M. Lipidomics: quest for molecular lipid biomarkers in cardiovascular disease.Circ Cardiovasc Genet. 2014; 7:941–954. doi: 10.1161/CIRCGENETICS.114.000550.LinkGoogle Scholar
    • 63. Stegemann C, Pechlaner R, Willeit P, Langley SR, Mangino M, Mayr U, Menni C, Moayyeri A, Santer P, Rungger G, Spector TD, Willeit J, Kiechl S, Mayr M. Lipidomics profiling and risk of cardiovascular disease in the prospective population-based Bruneck study.Circulation. 2014; 129:1821–1831. doi: 10.1161/CIRCULATIONAHA.113.002500.LinkGoogle Scholar
    • 64. Gordon SM, Li H, Zhu X, Shah AS, Lu LJ, Davidson WS. A comparison of the mouse and human lipoproteome: suitability of the mouse model for studies of human lipoproteins.J Proteome Res. 2015; 14:2686–2695. doi: 10.1021/acs.jproteome.5b00213.CrossrefMedlineGoogle Scholar
    • 65. Li H, Gordon SM, Zhu X, Deng J, Swertfeger DK, Davidson WS, Lu LJ. Network-based analysis on orthogonal separation of human plasma uncovers distinct high density lipoprotein complexes.J Proteome Res. 2015; 14:3082–3094. doi: 10.1021/acs.jproteome.5b00419.CrossrefMedlineGoogle Scholar
    • 66. Vaisar T, Pennathur S, Green PS, et al.. Shotgun proteomics implicates protease inhibition and complement activation in the antiinflammatory properties of HDL.J Clin Invest. 2007; 117:746–756. doi: 10.1172/JCI26206.CrossrefMedlineGoogle Scholar
    • 67. Laurila PP, Surakka I, Sarin AP, et al.. Genomic, transcriptomic, and lipidomic profiling highlights the role of inflammation in individuals with low high-density lipoprotein cholesterol.Arterioscler Thromb Vasc Biol. 2013; 33:847–857. doi: 10.1161/ATVBAHA.112.300733.LinkGoogle Scholar
    • 68. Shang MM, Talukdar HA, Hofmann JJ, et al.. Lim domain binding 2: a key driver of transendothelial migration of leukocytes and atherosclerosis.Arterioscler Thromb Vasc Biol. 2014; 34:2068–2077. doi: 10.1161/ATVBAHA.113.302709.LinkGoogle Scholar
    • 69. Civelek M, Lusis AJ. From hairballs to an understanding of transendothelial migration of monocytes in atherosclerosis.Arterioscler Thromb Vasc Biol. 2014; 34:1809–1810. doi: 10.1161/ATVBAHA.114.304151.LinkGoogle Scholar
    • 70. Huan T, Zhang B, Wang Z, et al.; Coronary ARteryDIsease Genome wide Replication and Meta-analysis (CARDIoGRAM) Consortium, International Consortium for Blood Pressure GWAS (ICBP). A systems biology framework identifies molecular underpinnings of coronary heart disease.Arterioscler Thromb Vasc Biol. 2013; 33:1427–1434. doi: 10.1161/ATVBAHA.112.300112.LinkGoogle Scholar
    • 71. Fernández-Friera L, Ibáñez B, Fuster V. Imaging subclinical atherosclerosis: is it ready for prime time? A review.J Cardiovasc Transl Res. 2014; 7:623–634. doi: 10.1007/s12265-014-9582-4.CrossrefMedlineGoogle Scholar
    • 72. Fuster V, Lois F, Franco M. Early identification of atherosclerotic disease by noninvasive imaging.Nat Rev Cardiol. 2010; 7:327–333. doi: 10.1038/nrcardio.2010.54.CrossrefMedlineGoogle Scholar
    • 73. Katakami N, Kaneto H, Shimomura I. Carotid ultrasonography: a potent tool for better clinical practice in diagnosis of atherosclerosis in diabetic patients.J Diabetes Investig. 2014; 5:3–13. doi: 10.1111/jdi.12106.CrossrefMedlineGoogle Scholar
    • 74. Sillesen H, Fuster V. Predicting coronary heart disease: from Framingham Risk Score to ultrasound bioimaging.Mt Sinai J Med. 2012; 79:654–663. doi: 10.1002/msj.21343.CrossrefMedlineGoogle Scholar
    • 75. Zhang Y, Guallar E, Qiao Y, Wasserman BA. Is carotid intima-media thickness as predictive as other noninvasive techniques for the detection of coronary artery disease?Arterioscler Thromb Vasc Biol. 2014; 34:1341–1345. doi: 10.1161/ATVBAHA.113.302075.LinkGoogle Scholar
    • 76. Inaba Y, Chen JA, Bergmann SR. Carotid plaque, compared with carotid intima-media thickness, more accurately predicts coronary artery disease events: a meta-analysis.Atherosclerosis. 2012; 220:128–133. doi: 10.1016/j.atherosclerosis.2011.06.044.CrossrefMedlineGoogle Scholar
    • 77. Sala-Vila A, Romero-Mamani ES, Gilabert R, Núñez I, de la Torre R, Corella D, Ruiz-Gutiérrez V, López-Sabater MC, Pintó X, Rekondo J, Martínez-González MÁ, Estruch R, Ros E. Changes in ultrasound-assessed carotid intima-media thickness and plaque with a Mediterranean diet: a substudy of the PREDIMED trial.Arterioscler Thromb Vasc Biol. 2014; 34:439–445. doi: 10.1161/ATVBAHA.113.302327.LinkGoogle Scholar
    • 78. de Korte CL, Hansen HH, van der Steen AF. Vascular ultrasound for atherosclerosis imaging.Interface Focus. 2011; 1:565–575. doi: 10.1098/rsfs.2011.0024.CrossrefMedlineGoogle Scholar
    • 79. Khanicheh E, Qi Y, Xie A, Mitterhuber M, Xu L, Mochizuki M, Daali Y, Jaquet V, Krause KH, Ruggeri ZM, Kuster GM, Lindner JR, Kaufmann BA. Molecular imaging reveals rapid reduction of endothelial activation in early atherosclerosis with apocynin independent of antioxidative properties.Arterioscler Thromb Vasc Biol. 2013; 33:2187–2192. doi: 10.1161/ATVBAHA.113.301710.LinkGoogle Scholar
    • 80. Kim SA, Lee KH, Won HY, Park S, Chung JH, Jang Y, Ha JW. Quantitative assessment of aortic elasticity with aging using velocity-vector imaging and its histologic correlation.Arterioscler Thromb Vasc Biol. 2013; 33:1306–1312. doi: 10.1161/ATVBAHA.113.301312.LinkGoogle Scholar
    • 81. Sanon S, Dao T, Sanon VP, Chilton R. Imaging of vulnerable plaques using near-infrared spectroscopy for risk stratification of atherosclerosis.Curr Atheroscler Rep. 2013; 15:304. doi: 10.1007/s11883-012-0304-6.CrossrefMedlineGoogle Scholar
    • 82. Jaguszewski M, Klingenberg R, Landmesser U. Intracoronary near-infrared spectroscopy (NIRS) imaging for detection of lipid content of coronary plaques: current experience and future perspectives.Curr Cardiovasc Imaging Rep. 2013; 6:426–430. doi: 10.1007/s12410-013-9224-2.CrossrefMedlineGoogle Scholar
    • 83. Patel D, Hamamdzic D, Llano R, Patel D, Cheng L, Fenning RS, Bannan K, Wilensky RL. Subsequent development of fibroatheromas with inflamed fibrous caps can be predicted by intracoronary near infrared spectroscopy.Arterioscler Thromb Vasc Biol. 2013; 33:347–353. doi: 10.1161/ATVBAHA.112.300710.LinkGoogle Scholar
    • 84. Kataoka Y, St John J, Wolski K, Uno K, Puri R, Tuzcu EM, Nissen SE, Nicholls SJ. Atheroma progression in hyporesponders to statin therapy.Arterioscler Thromb Vasc Biol. 2015; 35:990–995. doi: 10.1161/ATVBAHA.114.304477.LinkGoogle Scholar
    • 85. Youssef G, Kalia N, Darabian S, Budoff MJ. Coronary calcium: new insights, recent data, and clinical role.Curr Cardiol Rep. 2013; 15:325. doi: 10.1007/s11886-012-0325-3.CrossrefMedlineGoogle Scholar
    • 86. Danad I, Min JK. Computed tomography: the optimal imaging method for differentiation of ischemic vs non-ischemic cardiomyopathy.J Nucl Cardiol. 2015; 22:961–967. doi: 10.1007/s12350-015-0146-z.CrossrefMedlineGoogle Scholar
    • 87. Nasir K, Michos ED, Blumenthal RS, Raggi P. Detection of high-risk young adults and women by coronary calcium and National Cholesterol Education Program Panel III guidelines.J Am Coll Cardiol. 2005; 46:1931–1936. doi: 10.1016/j.jacc.2005.07.052.CrossrefMedlineGoogle Scholar
    • 88. Raggi P, Khan A, Arepali C, Stillman AE. Coronary artery calcium scoring in the age of ct angiography: what is its role?Curr Atheroscler Rep. 2008; 10:438–443.CrossrefMedlineGoogle Scholar
    • 89. Criqui MH, Denenberg JO, McClelland RL, Allison MA, Ix JH, Guerci A, Cohoon KP, Srikanthan P, Watson KE, Wong ND. Abdominal aortic calcium, coronary artery calcium, and cardiovascular morbidity and mortality in the Multi-Ethnic Study of Atherosclerosis.Arterioscler Thromb Vasc Biol. 2014; 34:1574–1579. doi: 10.1161/ATVBAHA.114.303268.LinkGoogle Scholar
    • 90. Kalra DK, Heo R, Valenti V, Nakazato R, Min JK. Role of computed tomography for diagnosis and risk stratification of patients with suspected or known coronary artery disease.Arterioscler Thromb Vasc Biol. 2014; 34:1144–1154. doi: 10.1161/ATVBAHA.113.302074.LinkGoogle Scholar
    • 91. Min JK, Hachamovitch R, Rozanski A, Shaw LJ, Berman DS, Gibbons R. Clinical benefits of noninvasive testing: coronary computed tomography angiography as a test case.JACC Cardiovasc Imaging. 2010; 3:305–315. doi: 10.1016/j.jcmg.2009.04.017.CrossrefMedlineGoogle Scholar
    • 92. Voros S, Rinehart S, Qian Z, Joshi P, Vazquez G, Fischer C, Belur P, Hulten E, Villines TC. Coronary atherosclerosis imaging by coronary CT angiography: current status, correlation with intravascular interrogation and meta-analysis.JACC Cardiovasc Imaging. 2011; 4:537–548. doi: 10.1016/j.jcmg.2011.03.006.CrossrefMedlineGoogle Scholar
    • 93. Bakermans AJ, Abdurrachim D, Moonen RP, Motaal AG, Prompers JJ, Strijkers GJ, Vandoorne K, Nicolay K. Small animal cardiovascular MR imaging and spectroscopy.Prog Nucl Magn Reson Spectrosc. 2015; 88–89:1–47. doi: 10.1016/j.pnmrs.2015.03.001.CrossrefMedlineGoogle Scholar
    • 94. Singh N, Moody AR, Roifman I, Bluemke DA, Zavodni AE. Advanced MRI for carotid plaque imaging [published online ahead of print August 21, 2015].Int J Cardiovasc Imag. doi: 10.1007/s10554-015-0743-6. Scholar
    • 95. Usman A, Sadat U, Graves MJ, Gillard JH. Magnetic resonance imaging of atherothrombotic plaques.J Clin Neurosci. 2015; 22:1722–1726. doi: 10.1016/j.jocn.2015.03.060.CrossrefMedlineGoogle Scholar
    • 96. Chuang ML, Gona P, Oyama-Manabe N, Manders ES, Salton CJ, Hoffmann U, Manning WJ, O’Donnell CJ. Risk factor differences in calcified and noncalcified aortic plaque: the Framingham Heart Study.Arterioscler Thromb Vasc Biol. 2014; 34:1580–1586. doi: 10.1161/ATVBAHA.114.303600.LinkGoogle Scholar
    • 97. Wasserman BA. Advanced contrast-enhanced MRI for looking beyond the lumen to predict stroke: building a risk profile for carotid plaque.Stroke. 2010; 41(Suppl 10):S12–S16. doi: 10.1161/STROKEAHA.110.596288.LinkGoogle Scholar
    • 98. Calcagno C, Cornily JC, Hyafil F, Rudd JH, Briley-Saebo KC, Mani V, Goldschlager G, Machac J, Fuster V, Fayad ZA. Detection of neovessels in atherosclerotic plaques of rabbits using dynamic contrast enhanced MRI and 18F-FDG PET.Arterioscler Thromb Vasc Biol. 2008; 28:1311–1317. doi: 10.1161/ATVBAHA.108.166173.LinkGoogle Scholar
    • 99. Kerwin W, Hooker A, Spilker M, Vicini P, Ferguson M, Hatsukami T, Yuan C. Quantitative magnetic resonance imaging analysis of neovasculature volume in carotid atherosclerotic plaque.Circulation. 2003; 107:851–856.LinkGoogle Scholar
    • 100. Ding J, Mitchell GF, Bots ML, Sigurdsson S, Harris TB, Garcia M, Eiriksdottir G, van Buchem MA, Gudnason V, Launer LJ. Carotid arterial stiffness and risk of incident cerebral microbleeds in older people: the Age, Gene/Environment Susceptibility (AGES)-Reykjavik study.Arterioscler Thromb Vasc Biol. 2015; 35:1889–1895. doi: 10.1161/ATVBAHA.115.305451.LinkGoogle Scholar
    • 101. Alie N, Eldib M, Fayad ZA, Mani V. Inflammation, atherosclerosis, and coronary artery disease: PET/CT for the evaluation of atherosclerosis and inflammation.Clin Med Insights Cardiol. 2014; 8(Suppl 3):13–21. doi: 10.4137/CMC.S17063.MedlineGoogle Scholar
    • 102. Orbay H, Hong H, Zhang Y, Cai W. Positron emission tomography imaging of atherosclerosis.Theranostics. 2013; 3:894–902. doi: 10.7150/thno.5506.CrossrefMedlineGoogle Scholar
    • 103. Tawakol A, Singh P, Mojena M, et al.. HIF-1α and PFKFB3 mediate a tight relationship between proinflammatory activation and anerobic metabolism in atherosclerotic macrophages.Arterioscler Thromb Vasc Biol. 2015; 35:1463–1471. doi: 10.1161/ATVBAHA.115.305551.LinkGoogle Scholar
    • 104. Rinne P, Silvola JM, Hellberg S, Ståhle M, Liljenbäck H, Salomäki H, Koskinen E, Nuutinen S, Saukko P, Knuuti J, Saraste A, Roivainen A, Savontaus E. Pharmacological activation of the melanocortin system limits plaque inflammation and ameliorates vascular dysfunction in atherosclerotic mice.Arterioscler Thromb Vasc Biol. 2014; 34:1346–1354. doi: 10.1161/ATVBAHA.113.302963.LinkGoogle Scholar
    • 105. Dweck MR, Chow MW, Joshi NV, Williams MC, Jones C, Fletcher AM, Richardson H, White A, McKillop G, van Beek EJ, Boon NA, Rudd JH, Newby DE. Coronary arterial 18F-sodium fluoride uptake: a novel marker of plaque biology.J Am Coll Cardiol. 2012; 59:1539–1548. doi: 10.1016/j.jacc.2011.12.037.CrossrefMedlineGoogle Scholar
    • 106. Li X, Bauer W, Israel I, Kreissl MC, Weirather J, Richter D, Bauer E, Herold V, Jakob P, Buck A, Frantz S, Samnick S. Targeting P-selectin by gallium-68-labeled fucoidan positron emission tomography for noninvasive characterization of vulnerable plaques: correlation with in vivo 17.6T MRI.Arterioscler Thromb Vasc Biol. 2014; 34:1661–1667. doi: 10.1161/ATVBAHA.114.303485.LinkGoogle Scholar
    • 107. Blasi F, Oliveira BL, Rietz TA, Rotile NJ, Naha PC, Cormode DP, Izquierdo-Garcia D, Catana C, Caravan P. Multisite thrombus imaging and fibrin content estimation with a single whole-body PET scan in rats.Arterioscler Thromb Vasc Biol. 2015; 35:2114–2121. doi: 10.1161/ATVBAHA.115.306055.LinkGoogle Scholar
    • 108. Pedersen SF, Sandholt BV, Keller SH, Hansen AE, Clemmensen AE, Sillesen H, Højgaard L, Ripa RS, Kjær A. 64Cu-DOTATATE PET/MRI for detection of activated macrophages in carotid atherosclerotic plaques: studies in patients undergoing endarterectomy.Arterioscler Thromb Vasc Biol. 2015; 35:1696–1703. doi: 10.1161/ATVBAHA.114.305067.LinkGoogle Scholar
    • 109. Malle C, Tada T, Steigerwald K, Ughi GJ, Schuster T, Nakano M, Massberg S, Jehle J, Guagliumi G, Kastrati A, Virmani R, Byrne RA, Joner M. Tissue characterization after drug-eluting stent implantation using optical coherence tomography.Arterioscler Thromb Vasc Biol. 2013; 33:1376–1383. doi: 10.1161/ATVBAHA.113.301227.LinkGoogle Scholar
    • 110. Choi BJ, Matsuo Y, Aoki T, Kwon TG, Prasad A, Gulati R, Lennon RJ, Lerman LO, Lerman A. Coronary endothelial dysfunction is associated with inflammation and vasa vasorum proliferation in patients with early atherosclerosis.Arterioscler Thromb Vasc Biol. 2014; 34:2473–2477. doi: 10.1161/ATVBAHA.114.304445.LinkGoogle Scholar
    • 111. Rademakers T, Douma K, Hackeng TM, Post MJ, Sluimer JC, Daemen MJ, Biessen EA, Heeneman S, van Zandvoort MA. Plaque-associated vasa vasorum in aged apolipoprotein E-deficient mice exhibit proatherogenic functional features in vivo.Arterioscler Thromb Vasc Biol. 2013; 33:249–256. doi: 10.1161/ATVBAHA.112.300087.LinkGoogle Scholar
    • 112. Taqueti VR, Nahrendorf M, Di Carli MF. Translational molecular imaging: repurposing an old technique to track cell migration into human atheroma.J Am Coll Cardiol. 2014; 64:1030–1032. doi: 10.1016/j.jacc.2014.07.004.CrossrefMedlineGoogle Scholar
    • 113. van der Valk FM, Kroon J, Potters WV, Thurlings RM, Bennink RJ, Verberne HJ, Nederveen AJ, Nieuwdorp M, Mulder WJ, Fayad ZA, van Buul JD, Stroes ES. In vivo imaging of enhanced leukocyte accumulation in atherosclerotic lesions in humans.J Am Coll Cardiol. 2014; 64:1019–1029. doi: 10.1016/j.jacc.2014.06.1171.CrossrefMedlineGoogle Scholar
    • 114. Swirski FK, Nahrendorf M. Imaging macrophage development and fate in atherosclerosis and myocardial infarction.Immunol Cell Biol. 2013; 91:297–303. doi: 10.1038/icb.2012.72.CrossrefMedlineGoogle Scholar
    • 115. Sosnovik DE, Nahrendorf M. Cells and iron oxide nanoparticles on the move: magnetic resonance imaging of monocyte homing and myocardial inflammation in patients with ST-elevation myocardial infarction.Circ Cardiovasc Imaging. 2012; 5:551–554. doi: 10.1161/CIRCIMAGING.112.978932.LinkGoogle Scholar
    • 116. Tawakol A, Migrino RQ, Bashian GG, Bedri S, Vermylen D, Cury RC, Yates D, LaMuraglia GM, Furie K, Houser S, Gewirtz H, Muller JE, Brady TJ, Fischman AJ. In vivo 18F-fluorodeoxyglucose positron emission tomography imaging provides a noninvasive measure of carotid plaque inflammation in patients.J Am Coll Cardiol. 2006; 48:1818–1824. doi: 10.1016/j.jacc.2006.05.076.CrossrefMedlineGoogle Scholar
    • 117. Phillips D, Ten Hove M, Schneider JE, Wu CO, Sebag-Montefiore L, Aponte AM, Lygate CA, Wallis J, Clarke K, Watkins H, Balaban RS, Neubauer S. Mice over-expressing the myocardial creatine transporter develop progressive heart failure and show decreased glycolytic capacity.J Mol Cell Cardiol. 2010; 48:582–590. doi: 10.1016/j.yjmcc.2009.10.033.CrossrefMedlineGoogle Scholar
    • 118. Emami H, Singh P, MacNabb M, et al.. Splenic metabolic activity predicts risk of future cardiovascular events: demonstration of a cardiosplenic axis in humans.JACC Cardiovasc Imaging. 2015; 8:121–130. doi: 10.1016/j.jcmg.2014.10.009.CrossrefMedlineGoogle Scholar
    • 119. Naik HB, Natarajan B, Stansky E, et al.. Severity of psoriasis associates with aortic vascular inflammation detected by FDG PET/CT and Neutrophil Activation in a Prospective Observational Study.Arterioscler Thromb Vasc Biol. 2015; 35:2667–2676. doi: 10.1161/ATVBAHA.115.306460.LinkGoogle Scholar
    • 120. Wang H, Chen YE, Eitzman DT. Imaging body fat: techniques and cardiometabolic implications.Arterioscler Thromb Vasc Biol. 2014; 34:2217–2223. doi: 10.1161/ATVBAHA.114.303036.LinkGoogle Scholar
    • 121. Wang L, Rundek T, Beecham A, Hudson B, Blanton SH, Zhao H, Sacco RL, Dong C. Genome-wide interaction study identifies RCBTB1 as a modifier for smoking effect on carotid intima-media thickness.Arterioscler Thromb Vasc Biol. 2014; 34:219–225. doi: 10.1161/ATVBAHA.113.302706.LinkGoogle Scholar
    • 122. Voros S, Maurovich-Horvat P, Marvasty IB, Bansal AT, Barnes MR, Vazquez G, Murray SS, Voros V, Merkely B, Brown BO, Warnick GR. Precision phenotyping, panomics, and system-level bioinformatics to delineate complex biologies of atherosclerosis: rationale and design of the “Genetic Loci and the Burden of Atherosclerotic Lesions” study.J Cardiovasc Comput Tomogr. 2014; 8:442–451. doi: 10.1016/j.jcct.2014.08.006.CrossrefMedlineGoogle Scholar
    • 123. Golugula A, Lee G, Master SR, Feldman MD, Tomaszewski JE, Madabhushi A. Supervised regularized canonical correlation analysis: integrating histologic and proteomic data for predicting biochemical failures.Conf Proc IEEE Eng Med Biol Soc. 2011; 2011:6434–6437. doi: 10.1109/IEMBS.2011.6091588.MedlineGoogle Scholar
    • 124. Gevaert O, Xu J, Hoang CD, Leung AN, Xu Y, Quon A, Rubin DL, Napel S, Plevritis SK. Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data–methods and preliminary results.Radiology. 2012; 264:387–396. doi: 10.1148/radiol.12111607.CrossrefMedlineGoogle Scholar
    • 125. Nair VS, Gevaert O, Davidzon G, Napel S, Graves EE, Hoang CD, Shrager JB, Quon A, Rubin DL, Plevritis SK. Prognostic PET 18F-FDG uptake imaging features are associated with major oncogenomic alterations in patients with resected non-small cell lung cancer.Cancer Res. 2012; 72:3725–3734. doi: 10.1158/0008-5472.CAN-11-3943.CrossrefMedlineGoogle Scholar
    • 126. Colen R, Foster I, Gatenby R, et al.. NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures.Transl Oncol. 2014; 7:556–569. doi: 10.1016/j.tranon.2014.07.007.CrossrefMedlineGoogle Scholar
    • 127. Zinn PO, Mahajan B, Majadan B, Sathyan P, Singh SK, Majumder S, Jolesz FA, Colen RR. Radiogenomic mapping of edema/cellular invasion MRI-phenotypes in glioblastoma multiforme.PLoS One. 2011; 6:e25451. doi: 10.1371/journal.pone.0025451.CrossrefMedlineGoogle Scholar
    • 128. Segal E, Sirlin CB, Ooi C, Adler AS, Gollub J, Chen X, Chan BK, Matcuk GR, Barry CT, Chang HY, Kuo MD. Decoding global gene expression programs in liver cancer by noninvasive imaging.Nat Biotechnol. 2007; 25:675–680. doi: 10.1038/nbt1306.CrossrefMedlineGoogle Scholar
    • 129. Diehn M, Nardini C, Wang DS, McGovern S, Jayaraman M, Liang Y, Aldape K, Cha S, Kuo MD. Identification of noninvasive imaging surrogates for brain tumor gene-expression modules.Proc Natl Acad Sci U S A. 2008; 105:5213–5218. doi: 10.1073/pnas.0801279105.CrossrefMedlineGoogle Scholar
    • 130. Arbab-Zadeh A, Fuster V. The myth of the “vulnerable plaque”: transitioning from a focus on individual lesions to atherosclerotic disease burden for coronary artery disease risk assessment.J Am Coll Cardiol. 2015; 65:846–855. doi: 10.1016/j.jacc.2014.11.041.CrossrefMedlineGoogle Scholar