Biomarkers of systemic lupus erythematosus identified using mass spectrometry‐based proteomics: a systematic review

Advances in mass spectrometry technologies have created new opportunities for discovering novel protein biomarkers in systemic lupus erythematosus (SLE). We performed a systematic review of published reports on proteomic biomarkers identified in SLE patients using mass spectrometry‐based proteomics and highlight their potential disease association and clinical utility. Two electronic databases, MEDLINE and EMBASE, were systematically searched up to July 2015. The methodological quality of studies included in the review was performed according to Preferred Reporting Items for Systematic Reviews and Meta‐analyses guidelines. Twenty‐five studies were included in the review, identifying 241 SLE candidate proteomic biomarkers related to various aspects of the disease including disease diagnosis and activity or pinpointing specific organ involvement. Furthermore, 13 of the 25 studies validated their results for a selected number of biomarkers in an independent cohort, resulting in the validation of 28 candidate biomarkers. It is noteworthy that 11 candidate biomarkers were identified in more than one study. A significant number of potential proteomic biomarkers that are related to a number of aspects of SLE have been identified using mass spectrometry proteomic approaches. However, further studies are required to assess the utility of these biomarkers in routine clinical practice.     

Quest for novel cardiovascular biomarkers by proteomic analysis

Atherosclerosis, and the resulting coronary heart disease and stroke, is the most common cause of death in developed countries. Atherosclerosis is an inflammatory process that results in the development of complex lesions or plaques that protrude into the arterial lumen. Plaque rupture and thrombosis result in the acute clinical complications of myocardial infarction (MI) and stroke. Although certain risk factors (dyslipidemias, diabetes, hypertension) and humoral markers of plaque vulnerability (C-reactive protein, interleukin-6, 10 and 18, CD40L) have been identified, a highly sensitive and specific biomarker or protein profile, which could provide information on the stability/vulnerability of atherosclerotic lesions, remains to be identified. In this review, we report several proteomic approaches which have been applied to circulating or resident cells, atherosclerotic plaques or plasma, in the search for new proteins that could be used as cardiovascular biomarkers. First, an example using a differential proteomic approach (2-DE and MS) comparing the secretome from control mammary arteries and atherosclerotic plaques is displayed. Among the different proteins identified, we showed that low levels of HSP-27 could be a potential marker of atherosclerosis. Second, we have revised several studies performed in cells involved in the pathogenesis of atherosclerosis (foam cells and smooth muscle cells). Another approach consists of performing proteomic analysis on circulating cells or plasma, which will provide a global view of the whole body response to atherosclerotic aggression. Circulating cells can bear information reflecting directly an inflammatory or pro-coagulant state related to the pathology. As an illustration, we report that circulating monocytes and plasma in patients with acute coronary syndromes has disclosed that mature Cathepsin D is increased both in the plasma and monocytes of these patients. Finally, the problems of applying proteomic approach directly to plasma will be discussed. The purpose of this review is to provide the reader with an overview of different proteomic approaches that can be used to identify new biomarkers in vascular diseases.     

An extracellular vesicle epitope profile is associated with acute myocardial infarction

The current standard biomarker for myocardial infarction (MI) is high‐sensitive troponin. Although powerful in clinical setting, search for new markers is warranted as early diagnosis of MI is associated with improved outcomes. Extracellular vesicles (EVs) attracted considerable interest as new blood biomarkers. A training cohort used for diagnostic modelling included 30 patients with STEMI, 38 with stable angina (SA) and 30 matched‐controls. Extracellular vesicle concentration was assessed by nanoparticle tracking analysis. Extracellular vesicle surface‐epitopes were measured by flow cytometry. Diagnostic models were developed using machine learning algorithms and validated on an independent cohort of 80 patients. Serum EV concentration from STEMI patients was increased as compared to controls and SA. EV levels of CD62P, CD42a, CD41b, CD31 and CD40 increased in STEMI, and to a lesser extent in SA patients. An aggregate marker including EV concentration and CD62P/CD42a levels achieved non‐inferiority to troponin, discriminating STEMI from controls (AUC = 0.969). A random forest model based on EV biomarkers discriminated the two groups with 100% accuracy. EV markers and RF model confirmed high diagnostic performance at validation. In conclusion, patients with acute MI or SA exhibit characteristic EV biomarker profiles. EV biomarkers hold great potential as early markers for the management of patients with MI.     

Will Global Transcriptome Analysis Allow the Detection of Novel Prognostic Markers in Coronary Artery Disease and Heart Failure?

Coronary artery disease (CAD) is one of the leading causes of death in the developed countries. Myocardial infarction (MI) is an acute episode of CAD that results in myocardial injury and subsequent heart failure (HF). In the acute phase of MI several risk factors for future cardiovascular events have been found. The molecular mechanisms of these disorders are still unknown, but altered gene expression may play an important role in the development and progression of cardiovascular diseases. High-throughput techniques should greatly facilitate the elucidation of the mechanisms and provide novel insights into the pathophysiology of cardiovascular diseases. In this review we focus on the perspectives of gene-expression profiling conducted on cardiac tissues and blood for the determination of novel diagnostic and prognostic markers and therapeutic targets.     

Diagnosis of Coronary Heart Diseases Using Gene Expression Profiling; Stable Coronary Artery Disease,Cardiac Ischemia with and without Myocardial Necrosis

Cardiovascular disease (including coronary artery disease and myocardial infarction) is one of the leading causes of death in Europe, and is influenced by both environmental and genetic factors. With the recent advances in genomic tools and technologies there is potential to predict and diagnose heart disease using molecular data from analysis of blood cells. We analyzed gene expression data from blood samples taken from normal people (n = 21), non-significant coronary artery disease (n = 93), patients with unstable angina (n = 16), stable coronary artery disease (n = 14) and myocardial infarction (MI; n = 207). We used a feature selection approach to identify a set of gene expression variables which successfully differentiate different cardiovascular diseases. The initial features were discovered by fitting a linear model for each probe set across all arrays of normal individuals and patients with myocardial infarction. Three different feature optimisation algorithms were devised which identified two discriminating sets of genes, one using MI and normal controls (total genes = 6) and another one using MI and unstable angina patients (total genes = 7). In all our classification approaches we used a non-parametric k-nearest neighbour (KNN) classification method (k = 3). The results proved the diagnostic robustness of the final feature sets in discriminating patients with myocardial infarction from healthy controls. Interestingly it also showed efficacy in discriminating myocardial infarction patients from patients with clinical symptoms of cardiac ischemia but no myocardial necrosis or stable coronary artery disease, despite the influence of batch effects and different microarray gene chips and platforms.     

Proteomics in pancreatic cancer research

In this review, we give an overview of the actual role of proteomic technologies in the study of pancreatic cancers (PCs). We describe PC proteomics on the basis of sample origins, i.e. tissues, body fluids, and PC cell lines. As regards PC tissues, we report the identification of a number of candidate biomarkers of precursor lesions that may allow early diagnosis of this neoplasia. Moreover, we describe cytoskeletal and hypoxia-regulated proteins that confirm the involvement of cytoskeleton modifications and metabolism adaptations in carcinogenesis. We also discuss the most important biomarkers identified by proteomic analysis involved in local invasion and distant metastasis, and in the cross-talk between pancreatic tumor and the surrounding stroma. Furthermore, we report novel candidate biomarkers identified in serum, plasma, and pancreatic juice of cancer patients compared with cancer-free controls. Proteomic alterations in PC cell line models as compared to normal controls and studies on cell lines treated with drugs or new agents to understand their mechanism of pharmacological action or the onset of drug resistance are also presented. Finally, we discuss the recent improvements obtained in classical 2-DE and high-throughput proteomic strategies able to allow the overcoming of relevant proteomic drawbacks.     

Profiling signatures of ovarian cancer tumour suppression using 2D-DIGE and 2D-LC-MS/MS with tandem mass tagging

Epithelial ovarian cancer (EOC) is the most common form of gynaecological malignancy in the developed world and has a poor prognosis due to its late detection. Identifying molecular markers of the disease may provide novel approaches to screening and could enable targeted treatment and the design of novel therapies. Although blood is recognized as a highly important source of disease-related biomarkers, the complexity and dynamic range of protein abundance in body fluids has hampered proteomic biomarker discovery and alternative approaches using cell models may be more successful. Herein, we have utilized two cellular models of EOC, where transfer of normal chromosome 18 material into the EOC cell lines TOV-112D and TOV-21G induced in vitro and in vivo suppression of their tumourigenic phenotype. A combination of quantitative two-dimensional difference gel electrophoresis (2D-DIGE) and two-dimensional-liquid chromatography tandem mass spectrometry (2D-LC-MS/MS) with tandem mass tagging (TMT) was employed to examine the whole cell, secreted and crude membrane proteomes of the parental and hybrid cell models to identify differentially expressed proteins as potential markers of tumour suppression. Protein changes of interest were confirmed by immunoblotting in additional hybrid and revertant cell lines where incorporated chromosome 18 material was lost. One candidate marker was also tested in sera from a set of ovarian cancer cases and controls. We have identified a list of promising candidate biomarkers for further testing and functional characterization.     

Exploring the Human Plasma Proteome for Humoral Mediators of Remote Ischemic Preconditioning - A Word of Caution

Despite major advances in early revascularization techniques, cardiovascular diseases are still the leading cause of death worldwide, and myocardial infarctions contribute heavily to this. Over the past decades, it has become apparent that reperfusion of blood to a previously ischemic area of the heart causes damage in and of itself, and that this ischemia reperfusion induced injury can be reduced by up to 50% by mechanical manipulation of the blood flow to the heart. The recent discovery of remote ischemic preconditioning (RIPC) provides a non-invasive approach of inducing this cardioprotection at a distance. Finding its endogenous mediators and their operative mode is an important step toward increasing the ischemic tolerance. The release of humoral factor(s) upon RIPC was recently demonstrated and several candidate proteins were published as possible mediators of the cardioprotection. Before clinical applicability, these potential biomarkers and their efficiency must be validated, a task made challenging by the large heterogeneity in reported data and results. Here, in an attempt to reproduce and provide more experimental data on these mediators, we conducted an unbiased in-depth analysis of the human plasma proteome before and after RIPC. From the 68 protein markers reported in the literature, only 28 could be mapped to manually reviewed (Swiss-Prot) protein sequences. 23 of them were monitored in our untargeted experiment. However, their significant regulation could not be reproducibly estimated. In fact, among the 394 plasma proteins we accurately quantified, no significant regulation could be confidently and reproducibly assessed. This indicates that it is difficult to both monitor and reproduce published data from experiments exploring for RIPC induced plasma proteomic regulations, and suggests that further work should be directed towards small humoral factors. To simplify this task, we made our proteomic dataset available via ProteomeXchange, where scientists can mine for novel potential targets.     

In-silico selection of cancer blood plasma proteins by integrating genomic and proteomic databases

Blood protein markers have been studied for the clinical management of cancer. Due to the large number of the proteins existing in blood, it is often necessary to pre-select potential protein markers before experimental studies. However, to date there is a lack of automated method for in-silico selection of cancer blood proteins that integrates the information from both genetic and proteomic studies in a cancer-specific manner. In this work, we synthesized both genomic and proteomic information from several open access databases and established a bioinformatic pipeline for in-silico selection of blood plasma proteins overexpressed in specific type of cancer. We demonstrated the workflow of this pipeline with an example of breast cancer, while the methodology was applicable for other cancer types. With this pipeline we obtained 10 candidate biomarkers for breast cancer. The proposed pipeline provides a useful and convenient tool for in-silico selection of candidate blood protein biomarkers for a variety of cancer research.     

Proteomic Analysis of the Aqueous Humor in Age-related Macular Degeneration (AMD) Patients

Age-related macular degeneration (AMD) can lead to irreversible central vision loss in the elderly. Although large number of growth factor pathways, including the vascular endothelial growth factor (VEGF), has been implicated in the pathogenesis of AMD, no study has directly assessed the whole proteomic composition in the aqueous humor (AH) among AMD patients. The AH contains proteins secreted from the anterior segment tissue, and these proteins may play an important role in the pathogenesis of AMD. Thus, comparisons between the AH proteomic profiles of AMD patients and non-AMD controls may lead to the verification of novel pathogenic proteins useful as potential clinical biomarkers. In this study, we used discovery-based proteomics and Multiple Reaction Monitoring Mass Spectrometry (MRM-MS) to analyze AH from AMD patients and AH from controls who underwent cataract surgery. A total of 154 proteins with at least two unique peptides were identified in the AH. Of these 154 proteins identified by discovery-based proteomics, 10 AH proteins were novel identifications. The protein composition in the AH was different between AMD patients and non-AMD controls. Subsequently, a systematic MRM-MS assay was performed in seven highly abundant differentially expressed proteins from these groups. Differential expression of three proteins was observed in the AH of AMD patients compared with that of cataract controls (p < 0.0312). Elucidation of the aqueous proteome will establish a foundation for protein function analysis and identify differentially expressed markers associated with AMD. This study demonstrates that integrated proteomic technologies can yield novel biomarkers to detect exudative AMD.     

Multiplexed mass spectrometric immunoassay in biomarker research: a novel approach to the determination of a myocardial infarct

Reported here is the development of a multiplexed mass spectrometric immunoassay (MSIA) for the detection of myocardial infarction (MI). The assay is the product of a study that systematically progresses from biomarker discovery--to identification and verification--to assay design, data analysis, and statistical challenge. During targeted population proteomics investigations, two novel biomarkers, serum amyloid A1alpha and S-sulfated transthyretin, were found to be responsive to MI. These putative markers were subsequently screened in larger cohorts of individuals to verify their responsiveness toward MI. Upon verification, a multiplexed assay was designed that was capable of simultaneously monitoring the new markers plus a previously established MI-marker (myoglobin). The multiplexed MSIA was applied to two 96-sample sets comprised of 48-MI/48-healthy and 19-MI/77-healthy, which served as training and case cohorts, respectively. Data evaluation using either preset reference levels or multivariate analysis exhibited sensitivities and specificities of >97%. These findings illustrate the importance of using systematic approaches in clinical proteomics to discover biomarkers and produce high-performance assays relevant to disease.     

Proteomics in atherothrombosis: a future perspective

Atherothrombosis is the primary cause of death in Western countries. The cellular and molecular mechanisms underlying atherosclerosis remain widely unknown. The complex nature of atherosclerotic cardiovascular diseases demands the development of novel technologies that enable discovery of new biomarkers for early disease detection and risk stratification, which may predict clinical outcome. In this review, we outline potential sources and recent proteomic approaches that could be applied in the search of novel biomarkers of cardiovascular risk. In addition, we describe some issues raised in relation to the application of proteomics to blood samples, as well as two novel emerging concepts, such as peptidomics and population proteomics. In the future, the use of high-throughput techniques (proteomic, genomics and metabolomics) will potentially identify novel patterns of biomarkers, which, along with traditional risk factors and imaging techniques, could help to target vulnerable patients and monitor the beneficial effects of pharmacological agents.     

Proteomics in atherothrombosis: a future perspective

Atherothrombosis is the primary cause of death in Western countries. The cellular and molecular mechanisms underlying atherosclerosis remain widely unknown. The complex nature of atherosclerotic cardiovascular diseases demands the development of novel technologies that enable discovery of new biomarkers for early disease detection and risk stratification, which may predict clinical outcome. In this review, we outline potential sources and recent proteomic approaches that could be applied in the search of novel biomarkers of cardiovascular risk. In addition, we describe some issues raised in relation to the application of proteomics to blood samples, as well as two novel emerging concepts, such as peptidomics and population proteomics. In the future, the use of high-throughput techniques (proteomic, genomics and metabolomics) will potentially identify novel patterns of biomarkers, which, along with traditional risk factors and imaging techniques, could help to target vulnerable patients and monitor the beneficial effects of pharmacological agents.     

Depression Increases Sympathetic Activity and Exacerbates Myocardial Remodeling after Myocardial Infarction: Evidence from an Animal Experiment

Depression is an independent risk factor for cardiovascular events and mortality in patients with myocardial infarction (MI). Excessive sympathetic activation and serious myocardial remodeling may contribute to this association. The aim of this study was to discuss the effect of depression on sympathetic activity and myocardial remodeling after MI. Wild-type (WT) rats were divided into a sham group (Sham), a myocardial infarction group (MI), a depression group (D), and a myocardial infarction plus depression group (MI+D). Compared with controls, the MI+D animals displayed depression-like behaviors and attenuated body weight gain. The evaluation of sympathetic activity showed an increased level in plasma concentrations of epinephrine and norepinephrine and higher expression of myocardial tyrosine hydroxylase in the MI+D group than the control groups (p<0.05 for all). Cardiac function and morphologic analyses revealed a decreased fractional shortening accompanied by increased left ventricular dimensions, thinning myocardium wall, and reduced collagen repair in the MI+D group compared with the MI group (p<0.05 for all). Frequent premature ventricular contractions, prolonged QT duration and ventricular repolarization duration, shorted effective refractory period, and increased susceptibility to ventricular arrhythmia were displayed in MI+D rats. These results indicate that sympathetic hyperactivation and exacerbated myocardial remodeling may be a plausible mechanism linking depression to an adverse prognosis after MI.     

The role of proteomics in clinical cardiovascular biomarker discovery

Cardiovascular disease remains the most common cause of death in the developed world and is predicted by the World Health Organization to kill approximately 20 million people worldwide each year until at least 2015. In light of these figures, work on producing superior tools for clinical use in the cardiovascular field is intensive. As proteins are the primary effectors of cellular function, a significant majority of this work focuses on the role of proteins in the cardiovascular system in physiological and pathological states in order to outline both mechanisms and markers of disease. One of the most effective ways to investigate these on a global basis is through proteomic analysis, which allows for broad spectrum screening of cellular protein or peptide complements during cardiovascular pathogenesis. Furthermore, specific technologies are now available to screen animal model or human blood samples for novel, improved markers of chronic disease states, such as atherosclerosis or for earlier indicators of acute myocardial stress, including ischemia/reperfusion injury and heart failure. This review summarizes current literature on the key aspects of proteomics and peptidomics related to clinical cardiovascular science.     

Plasma biomarkers for detecting Hodgkin's lymphoma in HIV patients

The lifespan of people with human immunodeficiency virus (HIV) infection has increased as a result of effective antiretroviral therapy, and the incidences of the AIDS-defining cancers, non-Hodgkin''s lymphoma and Kaposi sarcoma, have declined. Even so, HIV-infected individuals are now at greater risk of other cancers, including Hodgkin''s lymphoma (HL). To identify candidate biomarkers for the early detection of HL, we undertook an accurate mass and elution time tag proteomics analysis of individual plasma samples from either HIV-infected patients without HL (controls; n = 14) and from HIV-infected patient samples with HL (n = 22). This analysis identified 60 proteins that were statistically (p<0.05) altered and at least 1.5-fold different between the two groups. At least three of these proteins have previously been reported to be altered in the blood of HL patients that were not known to be HIV positive, suggesting that these markers may be broadly useful for detecting HL. Ingenuity Pathway Analysis software identified “inflammatory response” and “cancer” as the top two biological functions associated with these proteins. Overall, this study validated three plasma proteins as candidate biomarkers for detecting HL, and identified 57 novel candidate biomarkers that remain to be validated. The relationship of these novel candidate biomarkers with cancer and inflammation suggests that they are truly associated with HL and therefore may be useful for the early detection of this cancer in susceptible populations.     

Altered Gene Expression Pattern in Peripheral Blood Mononuclear Cells in Patients with Acute Myocardial Infarction

Background

Despite a substantial progress in diagnosis and therapy, acute myocardial infarction (MI) is a major cause of mortality in the general population. A novel insight into the pathophysiology of myocardial infarction obtained by studying gene expression should help to discover novel biomarkers of MI and to suggest novel strategies of therapy. The aim of our study was to establish gene expression patterns in leukocytes from acute myocardial infarction patients.

Methods and Results

Twenty-eight patients with ST-segment elevation myocardial infarction (STEMI) were included. The blood was collected on the 1^st day of myocardial infarction, after 4–6 days, and after 6 months. Control group comprised 14 patients with stable coronary artery disease, without history of myocardial infarction. Gene expression analysis was performed with Affymetrix Human Gene 1.0 ST microarrays and GCS3000 TG system. Lists of genes showing altered expression levels (fold change >1.5, p<0.05) were submitted to Ingenuity Pathway Analysis. Gene lists from each group were examined for canonical pathways and molecular and cellular functions. Comparing acute phase of MI with the same patients after 6 months (stable phase) and with control group we found 24 genes with changed expression. In canonical analysis three pathways were highlighted: signaling of PPAR (peroxisome proliferator-activated receptor), IL-10 and IL-6 (interleukin 10 and 6).

Conclusions

In the acute phase of STEMI, dozens of genes from several pathways linked with lipid/glucose metabolism, platelet function and atherosclerotic plaque stability show altered expression. Up-regulation of SOCS3 and FAM20 genes in the first days of myocardial infarction is observed in the vast majority of patients.     

A bioinformatics approach for biomarker identification in radiation-induced lung inflammation from limited proteomics data

Many efforts have been made to discover novel bio-markers for early disease detection in oncology. However, the lack of efficient computational strategies impedes the discovery of disease-specific biomarkers for better understanding and management of treatment outcomes. In this study, we propose a novel graph-based scoring function to rank and identify the most robust biomarkers from limited proteomics data. The proposed method measures the proximity between candidate proteins identified by mass spectrometry (MS) analysis utilizing prior reported knowledge in the literature. Recent advances in mass spectrometry provide new opportunities to identify unique biomarkers from peripheral blood samples in complex treatment modalities such as radiation therapy (radiotherapy), which enables early disease detection, disease progression monitoring, and targeted intervention. Specifically, the dose-limiting role of radiation-induced lung injury known as radiation pneumonitis (RP) in lung cancer patients receiving radiotherapy motivates the search for robust predictive biomarkers. In this case study, plasma from 26 locally advanced non-small cell lung cancer (NSCLC) patients treated with radiotherapy in a longitudinal 3 × 3 matched-control cohort was fractionated using in-line, sequential multiaffinity chromatography. The complex peptide mixtures from endoprotease digestions were analyzed using comparative, high-resolution liquid chromatography (LC)-MS to identify and quantify differential peptide signals. Through analysis of survey mass spectra and annotations of peptides from the tandem spectra, we found candidate proteins that appear to be associated with RP. On the basis of the proposed methodology, α-2-macroglobulin (α2M) was unambiguously ranked as the top candidate protein. As independent validation of this candidate protein, enzyme-linked immunosorbent assay (ELISA) experiments were performed on independent cohort of 20 patients' samples resulting in early significant discrimination between RP and non-RP patients (p = 0.002). These results suggest that the proposed methodology based on longitudinal proteomics analysis and a novel bioinformatics ranking algorithm is a potentially promising approach for the challenging problem of identifying relevant biomarkers in sample-limited clinical applications.     

Myocardial infarction does not affect fatty-acid profiles in rats

Plasma and red blood cell fatty-acid (RBC FA) composition have both been proposed as biomarkers for cardiovascular (CV) risk. Since case/control studies using samples obtained after a CV constitute a source of supporting evidence, demonstrating that FA profiles are not affected by a myocardial infarction (MI) would improve our understanding of the usefulness of such studies. The primary goal of the present study was to determine the impact of an MI on RBC and whole plasma eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) levels and to do so with sufficient power to conclude that there was no effect. FA profiles were obtained from rats 24 h after an MI or a sham-MI and compared to control animals by tests for differences and equivalence. In RBCs, neither DHA nor EPA was changed and were statistically equivalent in control and MI rats, as were a majority of other FAs and FA composite indices; only shingolipid-associated fatty acids had abundances that were changed in either MI or sham-MI animals. In whole plasma 8 of 22 FAs were changed in MI or sham-MI rats, including EPA which was reduced from 2.53 (2.3, 2.8)% to 1.71 (1.4, 2)%; mean (95% CI). In conclusion, the levels of EPA, DHA, and most other FAs in RBCs are unaffected by an MI or by sham surgery, whereas the same cannot be said of plasma. This finding suggests that differences between cases and controls have prognostic implications.     

Interpretation of elevated plasma visfatin concentrations in patients with ST-elevation myocardial infarction

Visfatin is a cytokine that is expressed in many tissues, including the heart, and has been proposed to play a role in plaque destabilization leading to acute myocardial injury. The present study evaluates plasma levels of visfatin in acute ST-elevation myocardial infarction (STEMI) patients and examines the temporal changes in visfatin levels from the acute period to the subacute period to determine a correlation with the degree of myocardial ischemia. We evaluated 54 patients with STEMI. Circulating levels of visfatin and brain natriuretic peptide (BNP) were measured by ELISA. In addition, local expression of visfatin and BNP were detected by quantitative real-time polymerase chain reaction and immunohistochemical (IHC) analysis of left ventricular myocytes in a mouse model of myocardial infarction (MI). Plasma levels of visfatin were significantly increased in patients with STEMI on admission, relative to controls (effort angina patients and individuals without coronary artery disease). The visfatin levels reached a peak 24 h after percutaneous coronary intervention (PCI) and then decreased toward the control range during the first week after PCI. The basal plasma visfatin levels were found to correlate with peak troponin-I, peak creatine kinase-MB, total white blood cell count, and BNP levels. Trend analyses confirmed that visfatin levels correlated with the number of diseased coronary arteries. Further, in MI mice, mRNA levels of visfatin and BNP were found to be higher than in sham-treated mice. IHC analysis showed that visfatin and BNP immunoreactivity was diffusely observable in left ventricular myocytes of the MI mice. This study indicates that plasma visfatin levels are significantly higher in STEMI patients and that these higher visfatin levels correlate with elevated levels of cardiac enzymes, suggesting that increased plasma visfatin may be closely related to the degree of myocardial damage.