Prediction of Cardiovascular Risk / Events and Use Thereof

Abstract

Provided are biomarkers, methods, devices, reagents, systems, and kits that can be used to assess an individual to predict their risk of developing a primary or secondary cardiovascular (CV) event over a four-year period.

Description

Technical Field

[0001] Cross - reference to Related Applications This application claims the benefit of priority of U.S. Provisional Application No. 62 / 895,383, filed on September 3, 2019, which is hereby incorporated by reference in its entirety for all purposes.

[0002] This application generally relates to methods for detecting biomarkers and assessing the risk of future cardiovascular events in an individual, and more specifically, to one or more biomarkers, methods, devices, reagents, systems, and kits used to evaluate an individual for the prediction of the risk of developing primary or secondary cardiovascular (CV) events over a four - year period. Such events include, but are not limited to, myocardial infarction, stroke, transient ischemic attack, hospitalization due to heart failure, and death.

Background Art

[0003] Cardiovascular disease is the leading cause of death in the United States. There are numerous existing and important predictors of risk for primary events (D'Agostino, R et al., "General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study" Circulation 117:743-53(2008); and Ridker, P. et al., "Development and Validation of Improved Algorithms for the Assessment of Global Cardiovascular Risk in Women" JAMA 297(6):611-619(2007)) and secondary events (Shlipak, M. et al. "Biomarkers to Predict Recurrent Cardiovascular Disease: The Heart & Soul Study" Am.J.Med.121:50-57(2008)), which are widely used in clinical practice and therapeutic trials. Unfortunately, receiver operating characteristic curves, hazard ratios, and concordances show that the performance of existing risk factors and biomarkers is not very high (AUC of about 0.75 means that these factors are only halfway between a coin toss and perfect). In addition to the need for improved diagnostic performance, there is a need for risk products that respond to short-term and individual beneficial (and harmful) interventions and lifestyle changes on their own. The widely used Framingham equation has three main problems. First, it is too long-term. Although the Framingham equation provides a 10-year risk calculation, people tend to underestimate future risk and are reluctant to change their behavior and lifestyle based on it. Second, it responds poorly to interventions. The Framingham equation is very dependent on chronological age, which cannot be reduced, and on gender, which cannot be changed. Third, in the high-risk population assumed here, the Framingham factor does not discriminate well between high and low risk.The hazard ratio between the high and low quartiles is only 2, and when attempting to use the Framingham score to individualize risk by stratifying the population into finer strata (e.g., deciles), the observed event rates are similar across many of the deciles.

[0004] Risk factors for cardiovascular disease are widely used to improve the intensity and nature of treatment, and there is no doubt that their use has contributed to the reduction in the incidence and mortality of cardiovascular disease observed over the past 20 years. These factors have typically been incorporated into algorithms, but unfortunately, they do not capture all risks (the most common first manifestation of heart disease is still death). In fact, they probably capture only about half of the risks. The area under the ROC curve for the primary prevention of such risk factors is usually about 0.76, and in secondary prevention, the performance is much worse (usually 0.62), only about 1 / 4 to 1 / 2 of the performance between a coin toss of 0.5 and a perfect 1.0.

[0005] Furthermore, in the Framingham study of 3,209 individuals (Wang et al., “Multiple Biomarkers for the Prediction of First Major Cardiovascular Events and Death” N.Eng.J.Med. 355:2631 - 2637 (2006)), the addition of 10 biomarkers (CRP, BNP, NT - proBNP, aldosterone, renin, fibrinogen, D - dimer, plasminogen activator inhibitor - 1, homocysteine, and urinary albumin - to - creatinine ratio) did not improve the AUC much when added to existing risk factors. The AUC for events up to 0 - 5 years was 0.76 when using age, sex, and conventional risk factors, and 0.77 when adding the best - combination biomarker to this combination. For secondary prevention, the situation is even worse.

[0006] Because the results can be improved by actively treating individuals at high risk, it is important to identify patients at higher risk of cardiovascular events earlier within a period of 1 to 5 years. Therefore, optimal management to reduce the risk of cardiovascular events in patients considered to be at higher risk requires aggressive intervention, while patients at lower risk of cardiovascular events can avoid expensive and potentially invasive treatments that may not provide beneficial effects to the patients.

[0007] The selection of biomarkers for predicting the risk of developing a specific pathological or disease state within a given period first involves identifying markers that have a measurable and statistically significant relationship with the probability and / or timing of an event for a specific medical use. Biomarkers can include molecules secreted or excreted either in the causal pathway leading to the state of interest, or downstream or in parallel to the onset or progression of a disease or condition, or both. They can be released into the bloodstream from cardiovascular tissue or other organs, as well as from surrounding tissues and circulating cells in response to biological processes underlying cardiovascular events, or they can reflect downstream effects of pathophysiology such as reduced renal function. Biomarkers can include small molecules, peptides, proteins, and nucleic acids. Some of the important issues affecting biomarker identification include overfitting of available data and data bias.

[0008] In attempts to identify biomarkers and diagnose or predict the risk of developing a disease or condition, various methods have been used. For protein-based markers, these methods include two-dimensional electrophoresis, mass spectrometry, and immunoassay methods. For nucleic acid markers, these methods include mRNA expression profiles, microRNA profiles, FISH, serial analysis of gene expression (SAGE), large-scale gene expression arrays, gene sequencing, and genotyping (SNP or small variant analysis).

[0009] The utility of two-dimensional electrophoresis is limited by low detection sensitivity; problems related to protein solubility, charge, and hydrophobicity; gel reproducibility; and the potential for a single spot to represent multiple proteins. The central limitations for mass spectrometry are sample processing and separation, sensitivity for low-abundance proteins, consideration of signal-to-noise ratio, and the inability to immediately identify detected proteins, depending on the format used. The central limitation in the antibody-based approach to biomarker discovery is the inability to perform antibody-based multiplex assays for measuring multiple analytes. Simply, there may be those who create an array of high-quality antibodies and measure the analytes bound to those antibodies without sandwiching them (this would formally be equivalent to using the entire genomic nucleic acid sequence to measure all DNA or RNA sequences in an organism or cell by hybridization. Hybridization experiments are effective because hybridization can be a stringent test for identity). However, even very good antibodies are usually not stringent enough in their binding partner selection in the context of blood or even cell extracts. This is because the protein populations in those matrices vary widely in abundance, which can lead to a poor signal-to-noise ratio. Therefore, for the antibody-based approach to biomarker discovery, different approaches must be used, i.e., it would be necessary to use multiplexed ELISA assays (i.e., sandwich) to obtain sufficient stringency to simultaneously measure multiple analytes and determine which analyte is the true biomarker. Sandwich immunoassays cannot be scaled to high throughput, and thus, biomarker discovery using stringent sandwich immunoassays in a standard array format is not possible. Finally, antibody reagents are subject to large lot-to-lot variability and reagent instability. The platform of the present invention for protein biomarker discovery overcomes this problem.

[0010] Many of these methods rely on or require some kind of sample fractionation prior to analysis. Thus, the sample preparation required to conduct a sufficiently powered study designed to identify and discover statistically relevant biomarkers in a series of defined sample populations is extremely difficult, costly, and time-consuming. During fractionation, a wide range of variations can be introduced into the various samples. For example, biomarker candidates can be unstable to processing, biomarker concentrations can change, inappropriate aggregation or dissociation can occur, inadvertent sample contamination can occur, obscuring the subtle changes predicted early in the disease.

[0011] Methods for biomarker discovery and detection using these techniques are widely recognized to have severe limitations for the identification of diagnostic or predictive biomarkers. These limitations include the inability to detect low-abundance biomarkers, the inability to consistently cover the entire dynamic range of the proteome, non-reproducibility in sample handling and fractionation, and the overall lack of reproducibility and robustness of this method. Furthermore, bias has been introduced into the data from these studies, and they do not adequately address the complexity of sample populations that include appropriate controls from the perspective of the distribution and randomization required to identify and validate biomarkers within the target disease population.

[0012] Attempts to discover novel and effective biomarkers have continued for decades, but most of these attempts have not been successful. Biomarkers for various diseases have generally been identified in university laboratories by serendipitous discoveries during basic research on several disease processes. Based on these discoveries, papers suggesting the identification of novel biomarkers have been published using a small amount of clinical data. However, most of these proposed biomarkers have not been confirmed to be true or useful biomarkers. The main reason is that testing with a small number of clinical samples only provides weak statistical evidence that a valid biomarker has actually been discovered. That is, the initial identification was not rigorous with respect to the basic elements of statistics.

[0013] Based on the history of unsuccessful attempts to discover biomarkers, a theory has been proposed that further promotes the general understanding that it is rare and difficult to discover biomarkers for diagnosing, predicting the prognosis, or predicting the risk of developing diseases and conditions. Research on biomarkers based on two-dimensional gels or mass spectrometry supports these ideas. Very few useful biomarkers have been identified by these approaches. However, it is usually overlooked that two-dimensional gel and mass spectrometry methods measure proteins present in blood at concentrations of about 1 nM or higher, and that this protein population is unlikely to change with the onset of disease or a particular condition. Except for the biomarker discovery platform of the present invention, there is no proteomics biomarker discovery platform that can accurately measure protein expression levels at very low concentrations.

[0014] Much is known about the complex biochemical pathways of human biology. A number of biochemical pathways result in the secretion of proteins that function locally in pathology or initiate biochemical pathways thereby. For example, growth factors are secreted to stimulate the replication of other cells in pathology, and other factors are secreted to evade the immune system, etc. Many of these secreted proteins function in a paracrine manner, while some act distally in the body. Those skilled in the art with a basic knowledge of biochemical pathways will understand that a number of disease-specific proteins should be present in the blood at concentrations below the detection limits of two-dimensional gels and mass spectrometry (and even lower than that). What is needed prior to the identification of this relatively large number of disease biomarkers is a proteomics platform capable of analyzing proteins at concentrations below the detectable concentrations by two-dimensional gels or mass spectrometry.

[0015] As described above, cardiovascular events can be prevented by aggressive treatment if the tendency for such events can be accurately determined, and the allocation efficiency of medical resources can be improved and costs can be reduced by targeting such interventions to those who need them most and / or keeping them away from those who do not need them most. Furthermore, if patients have accurate and short-term information regarding their individualized likelihood of cardiovascular events, it will be difficult to deny compared to long-term population-based information, and it will lead to an improvement in medication compliance that will lead to an improvement in lifestyle choices and benefits. Existing multiple marker tests require the collection of multiple samples from an individual or the sample needs to be divided among multiple assays. An improved test that requires only a single blood, urine, or other sample and one assay is optimal. Therefore, there is a need for biomarkers, methods, devices, reagents, systems, and kits that enable the prediction of cardiovascular events within a 5-year period. SUMMARY OF THE INVENTION

[0016] This application includes, for example, biomarkers, methods, reagents, devices, systems, and kits for predicting the risk of developing a cardiovascular (CV) event within, for example, four years or other defined periods. In some embodiments, the CV event is a primary CV event. In some embodiments, the CV event is a secondary CV event.

[0017] Multiple biological processes and tissues are involved in cardiovascular disease. Examples of biological systems and processes associated with cardiovascular disease are inflammation, thrombosis, disease-related angiogenesis, platelet activation, macrophage activation, the acute phase response of the liver, extracellular matrix remodeling, and renal function. These processes can be observed according to gender, menopause status, and age, as well as according to the status of coagulation and vascular function. Since these systems exchange information through protein-based signaling systems in part and multiple proteins can be measured in a single blood sample, the present invention provides a single-sample, single-assay, multiple-protein-based test focused on proteins derived from specific biological systems and processes involved in cardiovascular disease.

[0018] In some embodiments, a method for detecting the levels of a set of biomarkers is provided. In some embodiments, such method is as follows. Embodiment 1. A method for detecting the levels of a set of biomarker proteins in a sample from a subject, comprising: a. contacting the sample from the subject with a set of capture reagents, each capture reagent specifically binding to a different biomarker protein, wherein one capture reagent specifically binds to sTREM1; and b. detecting the amount of each capture reagent bound to the biomarker protein to which each capture reagent specifically binds.

[0019] Embodiment 2. The method according to Embodiment 1, wherein one capture reagent specifically binds to MMP-12.

[0020] The method according to embodiment 1 or 2, wherein one kind of capture reagent specifically binds to N-terminal proBNP.

[0021] The method according to any one of embodiments 1 to 3, wherein one kind of capture reagent specifically binds to antithrombin III.

[0022] The method according to any one of embodiments 1 to 4, wherein one kind of capture reagent specifically binds to GPR56.

[0023] The method according to any one of embodiments 1 to 5, wherein one kind of capture reagent specifically binds to gelsolin.

[0024] The method according to any one of embodiments 1 to 6, wherein one kind of capture reagent specifically binds to ST4S6.

[0025] The method according to any one of embodiments 1 to 7, wherein one kind of capture reagent specifically binds to CHSTC.

[0026] The method according to any one of embodiments 1 to 8, wherein one kind of capture reagent specifically binds to FSH.

[0027] The method according to any one of embodiments 1 to 9, wherein one kind of capture reagent specifically binds to IL-1 sRII.

[0028] The method according to any one of embodiments 1 to 10, wherein one kind of capture reagent specifically binds to PLXB2.

[0029] The method according to any one of embodiments 1 to 11, wherein one kind of capture reagent specifically binds to SAP.

[0030] The method according to any one of embodiments 1 to 12, wherein one kind of capture reagent specifically binds to TFPI.

[0031] Embodiment 14. The method according to any one of Embodiments 1 to 13, wherein the set of biomarkers includes at least three biomarkers.

[0032] Embodiment 15. The method according to any one of Embodiments 1 to 13, wherein the set of biomarkers includes at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen biomarkers.

[0033] Embodiment 16. The method according to any one of Embodiments 1 to 13, wherein the set of biomarkers consists of 2 to 13 biomarkers.

[0034] Embodiment 17. The method according to any one of Embodiments 1 to 13, wherein the set of biomarkers includes at least thirteen biomarkers.

[0035] Embodiment 18. The method according to any one of Embodiments 1 to 13, wherein the set of biomarkers consists of 13 biomarkers.

[0036] Embodiment 19. The method according to any one of Embodiments 1 to 18, wherein the subject is at least 40 years old.

[0037] Embodiment 20. The method according to any one of Embodiments 1 to 19, wherein the subject has no history of cardiovascular disease.

[0038] Embodiment 21. The method according to any one of Embodiments 1 to 20, including determining the risk that the subject will have a primary cardiovascular event within four years from the date the sample was taken from the subject.

[0039] Embodiment 22. The method according to Embodiment 21, wherein the risk that the subject will have a primary cardiovascular event is within 1, 2, 3, or 4 years from the date the sample was taken from the subject.

[0040] Embodiment 23. The method according to embodiment 21 or 22, wherein the primary cardiovascular event is myocardial infarction, stroke, transient ischemic attack, hospitalization due to heart failure, or death due to cardiovascular disease.

[0041] Embodiment 24. The method according to any one of embodiments 21 to 23, wherein the risk is determined as a quantitative probability.

[0042] Embodiment 25. The method according to any one of embodiments 21 to 23, wherein the risk is determined as a qualitative level of risk.

[0043] Embodiment 26. The method according to embodiment 25, wherein the qualitative level of the risk is low, intermediate, or high.

[0044] Embodiment 27. The method according to embodiment 1 or 2, wherein one capture reagent specifically binds to SVEP1.

[0045] Embodiment 28. The method according to any one of embodiments 1, 2, or 27, wherein one capture reagent specifically binds to ARL11.

[0046] Embodiment 29. The method according to any one of embodiments 1, 2, 27, or 28, wherein one capture reagent specifically binds to ANTR2.

[0047] Embodiment 30. The method according to any one of embodiments 1, 2, or 27 to 29, wherein one capture reagent specifically binds to CA125.

[0048] Embodiment 31. The method according to any one of embodiments 1, 2, or 27 to 30, wherein one capture reagent specifically binds to GOLM1.

[0049] Embodiment 32. The method according to any one of embodiments 1, 2, or 27 to 31, wherein one capture reagent specifically binds to PPR1A.

[0050] The method according to any one of Embodiments 1, 2, or 27 to 32, wherein one kind of capture reagent specifically binds to ERBB3.

[0051] The method according to any one of Embodiments 1, 2, or 27 to 33, wherein one kind of capture reagent specifically binds to suPAR.

[0052] The method according to any one of Embodiments 1, 2, or 27 to 34, wherein one kind of capture reagent specifically binds to GDF-11 / 8.

[0053] The method according to any one of Embodiments 1, 2, or 27 to 35, wherein one kind of capture reagent specifically binds to JAM-B.

[0054] The method according to any one of Embodiments 1, 2, or 27 to 36, wherein one kind of capture reagent specifically binds to ATS13.

[0055] The method according to any one of Embodiments 1, 2, or 27 to 37, wherein one kind of capture reagent specifically binds to SPON-1.

[0056] The method according to any one of Embodiments 1, 2, or 27 to 38, wherein one kind of capture reagent specifically binds to NCAM-120.

[0057] The method according to any one of Embodiments 1, 2, or 27 to 39, wherein one kind of capture reagent specifically binds to TFF3.

[0058] The method according to any one of Embodiments 1, 2, or 27 to 40, wherein one kind of capture reagent specifically binds to SIRT2.

[0059] The method according to any one of Embodiments 1, 2, or 27 to 41, wherein one kind of capture reagent specifically binds to ANP.

[0060] The method according to any one of Embodiments 1, 2, or 27 to 42, wherein one kind of capture reagent specifically binds to NELL1.

[0061] The method according to any one of Embodiments 1, 2, or 27 to 43, wherein one kind of capture reagent specifically binds to LRP11.

[0062] The method according to any one of Embodiments 1, 2, or 27 to 44, wherein one kind of capture reagent specifically binds to NDST1.

[0063] The method according to any one of Embodiments 1, 2, or 27 to 45, wherein one kind of capture reagent specifically binds to PTPRJ.

[0064] The method according to any one of Embodiments 1, 2, or 27 to 46, wherein one kind of capture reagent specifically binds to CILP2.

[0065] The method according to any one of Embodiments 1, 2, or 27 to 47, wherein one kind of capture reagent specifically binds to CA2D3.

[0066] The method according to any one of Embodiments 1, 2, or 27 to 48, wherein one kind of capture reagent specifically binds to ITI heavy chain H2.

[0067] The method according to any one of Embodiments 1, 2, or 27 to 49, wherein one kind of capture reagent specifically binds to IGDC4.

[0068] The method according to any one of Embodiments 1, 2, or 27 to 50, wherein one kind of capture reagent specifically binds to BNP.

[0069] The method according to any one of Embodiments 27 to 51, wherein the set of biomarkers includes at least three kinds of biomarkers.

[0070] Embodiment 53. The method according to any one of Embodiments 1, 2, or 27 to 51, wherein the set of biomarkers includes at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, or at least 27 biomarkers.

[0071] Embodiment 54. The method according to any one of Embodiments 1, 2, or 27 to 51, wherein the set of biomarkers consists of 2 to 27 biomarkers.

[0072] Embodiment 55. The method according to any one of Embodiments 1, 2, or 27 to 51, wherein the set of biomarkers includes at least 27 biomarkers.

[0073] Embodiment 56. The method according to any one of Embodiments 1, 2, or 27 to 51, wherein the set of biomarkers consists of 27 biomarkers.

[0074] Embodiment 57. The method according to any one of Embodiments 27 to 56, wherein the subject is at least 40 years old.

[0075] Embodiment 58. The method according to any one of Embodiments 27 to 57, wherein the subject has an overtly stable cardiovascular disease.

[0076] Embodiment 59. The method according to Embodiment 58, wherein the overtly stable cardiovascular disease includes a history of myocardial infarction, a history of stroke, a history of heart failure, a history of revascularization, an abnormal stress test, imaging suggesting coronary artery disease, or an abnormal coronary calcium score.

[0077] Embodiment 60. The method according to embodiment 59, wherein the myocardial infarction or the stroke occurred at least 6 months before the date on which the sample was taken from the subject.

[0078] Embodiment 61. The method according to embodiment 59, wherein the stress test abnormality is a treadmill test or a nuclear medicine-based test.

[0079] Embodiment 62. The method according to embodiment 59, wherein the imaging suggesting the coronary heart disease is an angiogram showing a coronary artery stenosis of 50% or more.

[0080] Embodiment 63. The method according to any one of embodiments 27 to 62, comprising determining the risk that the subject will experience a secondary cardiovascular event within 4 years from the date on which the sample was taken from the subject.

[0081] Embodiment 64. The method according to embodiment 63, wherein the risk that the subject will experience a secondary cardiovascular event is within 1, 2, 3, or 4 years from the date on which the sample was taken from the subject.

[0082] Embodiment 65. The method according to embodiment 63 or 64, wherein the secondary cardiovascular event is a myocardial infarction, a stroke, a transient ischemic attack, hospitalization due to heart failure, or death.

[0083] Embodiment 66. The method according to any one of embodiments 63 to 65, wherein the risk is determined as a quantitative probability.

[0084] Embodiment 67. The method according to any one of embodiments 63 to 65, wherein the risk is determined as a qualitative level of risk.

[0085] Embodiment 68. The method according to embodiment 67, wherein the qualitative level of risk is low, intermediate, or high.

[0086] In some embodiments, a method is provided for screening a subject for the risk of a cardiovascular (CV) event. In some such embodiments, the method comprises (a) forming a biomarker panel comprising N biomarkers selected from N-terminal proBNP, sTREM-1, MMP-12, antithrombin III, GPR56, gelsolin, ST4S6, CHSTC, FSH, IL-1 sRII, PLXB2, SAP, and TFPI, wherein N is an integer from 3 to 13; and (b) detecting the levels of each of the N biomarkers of the panel in a sample from the subject.

[0087] In some embodiments, the method comprises (a) forming a biomarker panel comprising N protein biomarkers selected from BNP, sTREM-1, MMP-12, SVEP1, ARL11, ANTR2, CA125, GOLM1, PPR1A, ERBB3, suPAR, GDF-11 / 8, JAM-B, ATS13, SPON1, NCAM-120, TFF3, SIRT2, ANP, NELL1, LRP11, NDST1, PTPRJ, CILP2, CA2D3, ITI heavy chain H2, and IGDC4, wherein N is an integer from 8 to 27; and (b) detecting the levels of each of the N biomarkers of the panel in a sample from the subject.

[0088] In some embodiments, a method is provided for predicting the likelihood that a subject will experience a CV event. In some such embodiments, the method comprises (a) forming a biomarker panel comprising N biomarkers selected from N-terminal proBNP, sTREM-1, MMP-12, antithrombin III, GPR56, gelsolin, ST4S6, CHSTC, FSH, IL-1 sRII, PLXB2, SAP, and TFPI, wherein N is an integer from 3 to 13; and (b) detecting the level of each of the N biomarker proteins in a sample from the subject in the panel.

[0089] In some embodiments, the method comprises (a) forming a biomarker panel comprising N protein biomarkers selected from BNP, sTREM-1, MMP-12, SVEP1, ARL11, ANTR2, CA125, GOLM1, PPR1A, ERBB3, suPAR, GDF-11 / 8, JAM-B, ATS13, SPON1, NCAM-120, TFF3, SIRT2, ANP, NELL1, LRP11, NDST1, PTPRJ, CILP2, CA2D3, ITI heavy chain H2, and IGDC4, wherein N is an integer from 8 to 27, the forming; (b) detecting the level of each of the N biomarker proteins in a sample from the subject in the panel.

[0090] In some embodiments, provided is a method for screening a subject for the risk or likelihood of a cardiovascular event (CV) event, comprising detecting the level of at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, or 27 of the biomarker proteins in a panel comprising N biomarker proteins.

[0091] In some embodiments, if the level of each of at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, or at least 13 biomarkers in a set of biomarkers is abnormal compared to the control level of each respective biomarker, the subject is at high risk or likelihood of experiencing a CV event within 4 years.

[0092] In some embodiments, the method includes detecting the level of one or more biomarkers in Table 1. In some embodiments, the method includes detecting the level of one or more biomarkers in Table 2.

[0093] In some embodiments, the subject has coronary artery disease. In some embodiments, the subject does not have a history of CV events. In some embodiments, the subject is classified as high risk by the American College of Cardiology (ACC) pooled cohort equation (PCE). See Goff DC, Jr. et al., “ACC / AHA Guideline on the Assessment of Cardiovascular Risk: A Report of the American College of Cardiology / American Heart Association Task Force on Practice Guidelines.” Circulation. 2013. In some embodiments, the subject is classified as medium risk by the PCE. In some embodiments, the subject is classified as low risk by the PCE. In some embodiments, the subject has experienced at least one CV event. In some embodiments, the CV event is selected from myocardial infarction, stroke, hospitalization due to heart failure, transient ischemic attack, and death.

[0094] In some embodiments, the sample is selected from a blood sample, a serum sample, a plasma sample, and a urine sample. In some embodiments, the sample is a plasma sample. In some embodiments, the method is performed in vitro.

[0095] In some embodiments, each biomarker is a protein biomarker. In some embodiments, the method includes contacting a biomarker of a sample from a subject with a set of biomarker capture reagents, wherein each biomarker capture reagent of the set of biomarker capture reagents specifically binds to a different biomarker to be detected. In some embodiments, each biomarker capture reagent is an antibody or an aptamer. In some embodiments, each biomarker capture reagent is an aptamer. In some embodiments, at least one aptamer is an aptamer with a slow dissociation rate. In some embodiments, at least one aptamer with a slow dissociation rate includes nucleotides having at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten modifications. In some embodiments, each aptamer with a slow dissociation rate binds to its target protein with a dissociation rate (t 1 / 2 ) of ≥ 30 minutes, ≥ 60 minutes, ≥ 90 minutes, ≥ 120 minutes, ≥ 150 minutes, ≥ 180 minutes, ≥ 210 minutes, or ≥ 240 minutes.

[0096] In some embodiments, the risk or likelihood of a CV event is related to the biomarker level and a) information corresponding to the presence of a cardiovascular risk factor selected from the group consisting of a history of myocardial infarction, angiographic evidence of > 50% stenosis in one or more coronary vessels, exercise-induced ischemia by treadmill or nuclear medicine test, or a history of coronary revascularization, b) information corresponding to the physical descriptors of the subject, c) information corresponding to the weight change of the subject, d) information corresponding to the ethnicity of the subject, e) information corresponding to the gender of the subject, f) information corresponding to the smoking history of the subject, g) Information corresponding to the target's drinking history, h) Information corresponding to the target's occupational history, i) Information corresponding to the family history of the target's cardiovascular disease or other circulatory system conditions, j) Information corresponding to the presence or absence in the target of at least one genetic marker associated with a higher risk of cardiovascular disease in the target or the target's family, k) Information corresponding to the clinical symptoms of the target, l) Information corresponding to other clinical tests, m) Information corresponding to the gene expression values of the target, as well as n) Information corresponding to the known cardiovascular risk factors of the target, such as the intake of a high-saturated-fat diet, a high-salt diet, and a high-cholesterol diet, o) Information corresponding to the imaging results of the target obtained by a technique selected from the group consisting of electrocardiogram, echocardiogram, carotid ultrasound diagnosis of intima-media complex thickness, flow-dependent vasodilation response test, pulse wave velocity, ankle-brachial blood pressure ratio, stress echocardiogram, myocardial blood flow imaging, coronary calcium examination by CT, high-resolution CT angiography, MRI imaging, and other imaging techniques, p) Information regarding the drug treatment of the target, q) Information corresponding to the age of the target, as well as r) At least one item of additional biomedical information selected from information regarding the renal function of the target.

[0097] In some embodiments, the risk or likelihood of a CV event is based on biomarker levels and at least the age of the target.

[0098] In some embodiments, the method includes determining the risk or likelihood of a CV event to determine a medical insurance premium or a life insurance premium. In some embodiments, the method further includes determining a coverage or premium for medical insurance or life insurance. In some embodiments, the method further includes using the information obtained by the method to predict and / or manage the utilization of medical resources. In some embodiments, the method further includes using the information obtained by the method to enable a decision to acquire or purchase a medical business, a hospital, or a company.

[0099] In some embodiments, a computer-implemented method for assessing the risk or likelihood of a cardiovascular (CV) event is provided. In some embodiments, the method comprises searching for biomarker information of a subject on a computer, wherein the biomarker information comprises (a) the levels of 3 to 13 biomarkers selected from Table 1 in a sample derived from the subject, or (b) the levels of 8 to 27 biomarkers selected from Table 2; performing, by the computer, a classification of each of the values of the biomarkers; and based on the plurality of classifications, presenting an assessment result of the risk of a CV event for the individual. In some embodiments, presenting an assessment result of the risk or likelihood of a CV event for a subject comprises displaying the result on a computer display. BRIEF DESCRIPTION OF THE DRAWINGS

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DETAILED DESCRIPTION OF THE INVENTION

[0101] The present invention is described with certain representative embodiments, but it is to be understood that the invention is defined by the claims and is not limited to those embodiments.

[0102] One of ordinary skill in the art will recognize many methods and materials similar or equivalent to those described herein that can be used in the practice of the present invention. The present invention is in no way limited to the methods and materials described.

[0103] Unless otherwise defined, technical and scientific terms used herein have the meanings commonly understood by one of ordinary skill in the art to which this invention belongs. Any methods, devices, and materials similar or equivalent to those described herein can be used in the practice of the present invention, but specific methods, devices, and materials are described herein.

[0104] All publications, published patent documents, and patent applications cited herein are hereby incorporated by reference to the extent that each individual publication, published patent document, or patent application is specifically and individually indicated as being incorporated by reference herein.

[0105] As used herein, the terms "comprises," "comprising," "includes," "including," "contains," "containing," and any variations thereof are intended to cover non-exclusive inclusion, such that a process, method, product-by-process, or composition of matter that comprises, includes, or contains an element or a series of elements may include other elements not expressly listed.

[0106] This application includes biomarkers, methods, devices, reagents, systems, and kits for predicting the risk of short-term CV events within a defined period, e.g., within 1 year, within 2 years, within 3 years, or within 4 years.

[0107] "Cardiovascular event" or "CV event" means a disorder or dysfunction of any part of the cardiovascular system. In one embodiment, "cardiovascular event" means stroke, transient ischemic attack (TIA), myocardial infarction (MI), sudden death due to cardiovascular system dysfunction, and / or hospitalization due to heart failure, or sudden death of unknown cause in a population where the most likely cause is cardiovascular. A primary CV event is the first CV event experienced by a subject. A secondary CV event is the second or subsequent CV event experienced by a subject.

[0108] Cardiovascular events can include thrombotic events such as MI, transient ischemic attack (TIA), stroke, acute coronary syndrome, and the need for coronary revascularization.

[0109] In some embodiments, biomarkers are provided for use either alone or in any of various combinations to assess the risk or likelihood of sudden death or future CV events within a four-year period, where CV events are defined as myocardial infarction, stroke, transient ischemic attack, death, and hospitalization due to heart failure. Representative embodiments include the biomarkers listed in Table 1 or Table 2, as described below.

[0110] Some of the described CV event biomarkers may be useful alone in assessing the risk or likelihood of a CV event, but methods for grouping multiple subsets of CV event biomarkers are also described herein, where each grouping or subset selection is useful as a panel of three or more biomarkers, and is referred to interchangeably herein as a "biomarker panel" and a panel. Thus, various embodiments provide combinations comprising at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, or all 13 of the biomarkers of Table 1. Various other embodiments provide combinations comprising at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, or all 27 of the biomarkers of Table 2.

[0111] "Biological sample", "sample", and "test sample" are used interchangeably herein to mean any substance, biological fluid, tissue, or cell obtained from or otherwise derived from an individual. Examples include blood (including, e.g., whole blood, white blood cells, peripheral blood mononuclear cells, buffy coat, plasma, and serum), sputum, tears, mucus, nasal washings, nasal aspirate, urine, saliva, peritoneal washings, ascites, cyst fluid, glandular fluid, lymph, bronchial aspirate, synovial fluid, joint aspirate, organ secretions, cells, cell extracts, and cerebrospinal fluid. Examples also include all experimentally isolated fractions of the foregoing. For example, a blood sample can be fractionated into serum, plasma, or a fraction containing a particular type of blood cell, e.g., red blood cells or white blood cells (leukocytes). In some embodiments, the blood sample is a dried blood spot. In some embodiments, the plasma sample is a dried plasma spot. In some embodiments, the sample can be a combination of samples from an individual, e.g., a combination of tissue and liquid samples. The term "biological sample" also includes substances containing a homogenized solid material, such as, e.g., a fecal sample, a tissue sample, or a tissue biopsy. The term "biological sample" also includes substances derived from tissue cultures or cell cultures. Any suitable method for obtaining a biological sample can be used, exemplary methods including, e.g., venipuncture, swabs (e.g., oral swabs), and fine needle aspiration biopsy. Exemplary tissues from which fine needle aspiration is possible include lymph nodes, lung, thyroid, breast, pancreas, and liver. Samples can also be collected, e.g., by microdissection (e.g., laser capture microdissection (LCM) or laser microdissection (LMD)), bladder washings, smear specimens (e.g., PAP smear specimens), or ductal lavage. A "biological sample" obtained from or derived from a subject also includes any such sample that has been processed by any suitable method after being obtained from the subject. In some embodiments, the biological sample is a plasma sample.

[0112] Furthermore, in some embodiments, the biological sample can be obtained by collecting biological samples from a number of subjects and pooling them, or pooling aliquots of the biological samples of each subject. The pooled sample can be processed as described herein as a sample from a single subject. For example, if a poor prognosis is confirmed in the pooled sample, the biological sample of each subject can be retested to determine which subject(s) has a high or low risk of CV events.

[0113] For the purposes of this specification, the phrase "data attributable to a biological sample from a subject" is intended to mean that the data in some form was obtained from or generated using the subject's subject biological sample. The data may be reformatted, modified, or numerically altered to some extent, such as by conversion from units in one measurement system to units in another measurement system, after it is generated, but the data is understood to have been obtained from or generated using the biological sample.

[0114] As used herein, "target", "target molecule", and "analyte" are used interchangeably to refer to any molecule of interest that may be present in a biological sample. A "molecule of interest" includes any minor change in a particular molecule, e.g., in the case of a protein, minor changes in, for example, amino acid sequence, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other optional manipulation or modification, e.g., conjugation with a labeling component, which do not substantially change the identity of the molecule. "Target molecule", "target", or "analyte" refers to one or a set of replicas of one type of molecule or multimolecular structure. Exemplary target molecules include proteins, polypeptides, nucleic acids, carbohydrates, lipids, polysaccharides, glycoproteins, hormones, receptors, antigens, antibodies, affibodies, antibody mimetics, viruses, pathogens, toxins, substrates, metabolites, transition state analogs, cofactors, inhibitors, drugs, dyes, nutrients, growth factors, cells, tissues, and any fragment or portion of any of the foregoing. In some embodiments, the target molecule is a protein, in which case the target molecule may be referred to as a "target protein".

[0115] As used herein, "capture agent" or "capture reagent" refers to a molecule that can specifically bind to a biomarker. A "target protein capture reagent" refers to a molecule that can specifically bind to a target protein. Non-limiting exemplary capture reagents include aptamers, antibodies,adnectins,ankyrins, other antibody mimetics and other protein scaffolds, autoantibodies, chimeras, small molecules, nucleic acids, lectins, ligand-binding receptors, imprinted polymers, avimers, peptidomimetics, hormone receptors, cytokine receptors, synthetic receptors, and modified forms or fragments of any of the foregoing capture reagents. In some embodiments, the capture reagent is selected from aptamers and antibodies.

[0116] The term "antibody" refers to antibodies of any species in their full length, as well as fragments and derivatives of such antibodies, including Fab fragments, F(ab’)2 fragments, single-chain antibodies, Fv fragments, and single-chain Fv fragments. The term "antibody" also refers to antibodies obtained by synthesis, such as antibodies and fragments obtained by phage display, affibodies, nanobodies, etc.

[0117] As used herein, "marker" and "biomarker" are used interchangeably to refer to a target molecule that indicates or is a sign of a normal or abnormal process in a subject, or that indicates or is a sign of a disease or other condition in a subject. More specifically, a "marker" or "biomarker" is an anatomical parameter, physiological parameter, biochemical parameter, or molecular parameter that is associated with the presence of a particular physiological state or process, regardless of whether it is normal or abnormal, and if abnormal, regardless of whether it is chronic or acute. Biomarkers can be detected and measured by various methods such as laboratory assays and medical imaging. In some embodiments, the biomarker is a target protein.

[0118] As used herein, "biomarker level" and "level" refer to a measured value obtained using any analytical method for detecting a biomarker in a biological sample, indicating the presence, absence, absolute amount or concentration, relative amount or concentration, titer, level, expression level, ratio of measurement levels, etc. of the biomarker in the biological sample, with respect to or corresponding to the biomarker in the biological sample. The exact nature of the "level" depends on the specific design and components of the particular analytical method used to detect the biomarker.

[0119] When a biomarker indicates, or is a sign of, an abnormal process, disease, or other condition in a subject, the biomarker is generally described as being either overexpressed or underexpressed compared to the expression level or value of a biomarker that indicates, or is a sign of, the absence of a normal process, disease, or other condition in the subject. The terms “upregulated,” “upregulated,” “overexpressed,” “overexpressed,” and any variations thereof are used interchangeably to refer to a value or level of a biomarker in a biological sample that exceeds the value or level (or range of values or levels) of that biomarker normally detected in a similar biological sample from a healthy or normal subject. This term can also refer to a value or level of a biomarker in a biological sample that exceeds the value or level (or range of values or levels) of that biomarker that can be detected at different stages of a particular disease.

[0120] The terms “downregulated,” “downregulated,” “underexpressed,” “underexpressed,” and any variations thereof are used interchangeably to refer to a value or level of a biomarker in a biological sample that is less than the value or level (or range of values or levels) of that biomarker normally detected in a similar biological sample from a healthy or normal subject. This term can also refer to a value or level of a biomarker in a biological sample that is less than the value or level (or range of values or levels) of that biomarker that can be detected at different stages of a particular disease.

[0121] Furthermore, a biomarker that is either overexpressed or underexpressed may indicate the absence of a normal process or a disease or other condition in a subject, or may be said to have an "altered expression" or "altered level" or "altered value" as compared to the "normal" expression level or value of the biomarker that is a sign thereof. Thus, "altered expression" of a biomarker can also be referred to as variation from the "normal" expression level of the biomarker.

[0122] The "control level" of a target molecule refers to the level of the target molecule in the same type of sample from a subject without a disease or condition, or from a subject not suspected of having a disease or condition, or at risk thereof, or from a subject who has had a primary or first cardiovascular event but not a secondary cardiovascular event, or from a subject with a stable cardiovascular disease. The control level can refer to the average level of the target molecule in samples from a population of subjects without a disease or condition, or not suspected of having a disease or condition, or at risk thereof, or who have had a primary or first cardiovascular event but not a secondary cardiovascular event, or who have a stable cardiovascular disease, or a combination thereof.

[0123] As used herein, "individual", "subject", and "patient" are used interchangeably to refer to a mammal. The mammalian subject can be human or non-human. In various embodiments, the subject is human. A healthy or normal subject is a subject in whom a disease or condition of interest (including cardiovascular events such as hospitalization due to myocardial infarction, stroke, and heart failure) is not detected by conventional diagnostic methods.

[0124] "Diagnose," "diagnosing," "diagnosis," and variations thereof refer to detecting, determining, or identifying a subject's health state or condition based on one or more signs, symptoms, data, or other information about the subject. The subject's health state may be diagnosed as healthy / normal (i.e., a diagnosis of no disease or condition) or as diseased / abnormal (i.e., a diagnosis of the presence of a disease or condition, or an assessment of the characteristics of a disease or condition). The terms "diagnose," "diagnosing," "diagnosis," etc. include, with respect to a particular disease or condition, initial detection of the disease; characterization or classification of the disease; detection of the progression, remission, or recurrence of the disease; and detection of the disease response after treatment or therapy has been administered to the subject. Prediction of CV event risk includes distinguishing subjects with high CV event risk from those without.

[0125] "Prognose," "prognosing," "prognosis," and variations thereof refer to predicting the future course of a disease or condition in a subject having the disease or condition (e.g., predicting patient survival), and such terms include assessing the response of the disease or condition after treatment or therapy has been administered to the subject.

[0126] "To evaluate", "evaluating", "evaluation", and variations thereof include both "diagnosis" and "prognosis prediction", and also include a determination or prediction regarding the future course of the disease or condition in a subject without the disease, and a determination or prediction regarding the risk of recurrence of the disease or condition in a subject in whom the disease has clearly been cured or the condition has regressed. The term "to evaluate" includes evaluating the response of a subject to treatment, for example, predicting whether a subject is likely to respond well to a therapeutic agent or is unlikely to respond to a therapeutic agent (or, for example, whether the subject will experience toxic effects or other unfavorable side effects), selecting a therapeutic agent to administer to the subject, or monitoring or determining the response of the subject to the treatment administered to the subject. Thus, "evaluating" the risk of a CV event can include, for example, any of the following: predicting the future CV event risk in a subject; predicting the CV event risk in a subject who clearly has no CV problems; predicting a specific type of CV event; predicting when a CV event will occur; or determining or predicting the response of a subject to a CV treatment, or selecting a CV treatment to be administered to the subject based on a determination of biomarker values derived from a biological sample of the subject. The evaluation of the risk of a CV event can include embodiments such as, for example, evaluation of the risk of a CV event on a continuous scale, or classification of the risk of a CV event in a stepwise increasing classification. The classification of the risk can include, for example, classification into two or more classifications, such as "intermediate risk of a CV event", "high risk of a CV event", and / or "low risk of a CV event". In some embodiments, the evaluation of the CV event risk is for a predefined period. Non-limiting exemplary such predefined periods include 1 year, 2 years, 3 years, 4 years, 5 years, and more than 5 years.

[0127] As used herein, "additional biomedical information" refers to one or more evaluations of a subject using anything other than the biomarkers described herein, related to CV risk, or more specifically, CV event risk. "Additional biomedical information" may include any of the following: physical descriptors of the subject including the subject's height and / or weight; the subject's age; the subject's gender; weight change; the subject's ethnicity; occupational history; family history of cardiovascular disease (or other circulatory disorders); the presence of genetic marker(s) (plural) correlated with a higher risk of cardiovascular disease (or other circulatory disorders) in the subject or family; changes in carotid intima-media thickness; clinical symptoms such as chest pain, weight gain, or decrease in gene expression values; physical descriptors of the subject including physical descriptors observed by radiation imaging; smoking status; alcohol consumption history; occupational history; eating habits, i.e., intake of salt, saturated fat, and cholesterol; caffeine intake; and imaging information, e.g., electrocardiogram, echocardiogram, carotid ultrasound of intima-media complex thickness, flow-dependent vasodilation response test, pulse wave velocity, ankle-brachial blood pressure ratio, stress echocardiogram, myocardial blood flow imaging, coronary artery calcium examination by CT, high-resolution CT angiography, MRI imaging, and other imaging techniques; and the subject's drug therapy. The combination of biomarker-level testing and the evaluation of any additional biomedical information including other clinical tests (e.g., HDL, LDL tests, CRP level, Nt-proBNP test, BNP test, high-sensitivity troponin test, galectin-3 test, serum albumin test, creatinine test) may improve the sensitivity, specificity, and / or AUC of CV event prediction compared to, for example, biomarker testing alone or the evaluation of any specific item of additional biomedical information alone (e.g., imaging of carotid intima-media thickness only). Additional biomedical information can be obtained from the subject using standard techniques known in the art, e.g., using a standard patient questionnaire or health history questionnaire from the subject himself or herself, or can be obtained from healthcare providers, etc.The combination of biomarker level testing and the evaluation of any additional biomedical information can improve sensitivity, specificity, and / or thresholds for the prediction of CV events (or other cardiovascular-related applications), for example, compared to biomarker testing alone or the evaluation of any particular item of additional biomedical information alone (e.g., CT imaging only).

[0128] As used herein, "detecting" or "determining" with respect to a biomarker value includes the use of both the instrument used to observe and record the signal corresponding to the biomarker level, and the substance(s) / substance required to generate that signal. In various embodiments, the biomarker level is detected using any suitable method including fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic waves, mass spectrometry, infrared spectroscopy, Raman spectroscopy, atomic force microscopy, scanning tunneling microscopy, electrochemical detection methods, nuclear magnetic resonance, quantum dots, and the like.

[0129] As used herein, “PCE risk classification” is determined according to Goff et al., “2013 ACC / AHA Guideline on the Assessment of Cardiovascular Risk: A Report of the American College of Cardiology / American Heart Association Task Force on Practice Guidelines,” published online in Circulation on Nov. 12, 2013 (Print ISSN: 0009-7322, Online ISSN: 1524-4539). As used herein, the “high” PCE risk classification is a 10-year risk predicted to be 20.0% or greater for atherosclerotic cardiovascular disease (ASCVD) hard events (defined as the first occurrence of nonfatal myocardial infarction or death due to coronary heart disease (CHD), or fatal or nonfatal stroke), the “intermediate” PCE risk classification is a 10-year risk predicted to be 10.0-19.9% for ASCVD hard events, and the “low” PCE risk classification is a 10-year risk predicted to be less than 10.0% for ASCVD hard events. See Goff, page 16, Table 5.

[0130] As used herein, "solid support" refers to any substrate having a surface to which a molecule can be directly or indirectly attached, either by covalent or non-covalent bonds. The "solid support" can have various physical forms, including, for example, membranes; chips (e.g., protein chips); slides (e.g., glass slides or cover glasses); columns; particles having hollow, solid, semi-solid, pores or cavities, such as beads; gels; fibers, including optical fiber materials; matrices; and sample containers. Exemplary sample containers include sample wells, tubes, capillaries, vials, and any other container, grooves or depressions capable of holding a sample. The sample container can be included in a multi-sample platform, such as a microtiter plate, glass slide, microfluidic device, etc. The support can be composed of natural or synthetic materials, organic or inorganic materials. The composition of the solid support to which the capture reagent is bound generally depends on the method of attachment (e.g., covalent bonding). Other exemplary containers include microdroplets, microfluidically controlled, or bulk water-in-oil emulsions in which assays and related operations can be performed. Suitable solid supports include, for example, plastics, resins, polysaccharides, silica or silica-based materials, functionalized glass, modified silicon, carbon, metals, inorganic glass, membranes, nylon, natural fibers (e.g., silk, wool, and cotton), polymers, etc. The material constituting the solid support can contain reactive groups, such as carboxy, amino, or hydroxyl groups, which are used for the attachment of the capture reagent. Exemplary polymeric solid supports include, for example, polystyrene, polyethylene glycol tetraphthalate, polyvinyl acetate, polyvinyl chloride, polyvinyl pyrrolidone, polyacrylonitrile, polymethyl methacrylate, polytetrafluoroethylene, butyl rubber, styrene-butadiene rubber, natural rubber, polyethylene, polypropylene, (poly)tetrafluoroethylene, (poly)vinylidene fluoride, polycarbonate, and polymethylpentene. Suitable solid support particles that can be used include, for example, coded particles, such as Luminex® type coded particles, magnetic particles, and glass particles.

[0131] Exemplary Use of Biomarkers In various exemplary embodiments, for example, by a number of analytical methods including any of the analytical methods described herein, one or more biomarker values corresponding to one or more biomarkers present in the circulation of a subject, such as in blood, serum, or plasma, are detected to evaluate the risk or likelihood of a CV event in the subject. These biomarkers are differentially expressed, for example, in subjects having a high risk of a CV event compared to subjects not having a high risk of a CV event. Detection of differential expression of a biomarker in a subject is used, for example, to enable prediction of the risk of a CV event within a period of 1 year, 2 years, 3 years, 4 years, or 5 years.

[0132] In addition to biomarker level testing as an independent diagnostic test, biomarker levels may be done in conjunction with the determination of a single nucleotide polymorphism (SNP) or other genetic lesion or genetic variability that indicates an increased risk of susceptibility to a disease or condition (see, e.g., Amos et al., Nature Genetics 40, 616 - 622 (2009)). Biomarker levels may be used in conjunction with radiologic screening. Biomarker levels may be used in conjunction with related symptoms or genetic testing. Detection of any of the biomarkers described herein may be useful in appropriately managing a subject's clinical care after the risk of a CV event has been evaluated, e.g., after the CV event risk has been determined, including increasing the level of treatment of high - risk subjects to a more aggressive level. In addition to testing biomarker levels in conjunction with related symptoms or risk factors, information about the biomarker may be evaluated in conjunction with other types of data, particularly data indicating the risk of a cardiovascular event for the subject (e.g., the patient's medical history, symptoms, family history of cardiovascular disease, smoking or alcohol consumption history, risk factors such as the presence of genetic marker(s), and / or the status of other biomarkers, etc.). These various data may be evaluated by automated methods such as computer programs / software that may be embodied on a computer or other device.

[0133] In addition to testing biomarker levels in high - risk subjects in conjunction with radiologic screening (e.g., evaluating biomarker levels in conjunction with occlusions detected on a coronary angiogram), information about the biomarker may be evaluated in conjunction with other types of data, particularly data indicating the risk that the subject will experience a CV event (e.g., the patient's medical history, symptoms, family history of cardiovascular disease, risk factors such as whether the subject is a smoker, heavy alcohol drinker, and / or the status of other biomarkers, etc.). These various data may be evaluated by automated methods such as computer programs / software that may be embodied on a computer or other device.

[0134] Biomarker tests may be combined with currently used guidelines and cardiovascular risk algorithms in clinical practice. For example, in the Framingham Risk Score, risk factors are used to obtain a risk score, and such risk factors include LDL-cholesterol and HDL-cholesterol levels, abnormal glucose concentration, smoking, systolic blood pressure, and diabetes. The frequency of high-risk patients increases with age, and among high-risk patients, men account for a higher proportion than women.

[0135] Any of the described biomarkers may also be used in imaging tests. For example, a contrast agent may be bound to any of the described biomarkers, which may be used, among other uses, particularly to assist in predicting the risk of cardiovascular events, to monitor the response to therapeutic interventions, and to select a target population in clinical trials.

[0136] Detection and determination of biomarkers and biomarker levels The biomarker levels described herein can be detected using any of a variety of known analytical methods. In one embodiment, biomarker values are detected using a capture reagent. In various embodiments, the capture reagent can be exposed to the biomarker in solution or the capture reagent can be exposed to the biomarker while immobilized on a solid support. In other embodiments, the capture reagent has a feature that reacts with a second feature on the solid support. In these embodiments, the capture reagent can be exposed to the biomarker in solution and then the feature on the capture reagent can be used with the second feature on the solid support to immobilize the biomarker on the solid support. The capture reagent is selected based on the type of analysis being performed. Capture reagents include, but are not limited to, aptamers, antibodies, adnectins, ankyrins, other antibody mimetics, and other protein scaffolds, autoantibodies, chimeras, small molecules, F(ab’)2 fragments, single-chain antibody fragments, Fv fragments, single-chain Fv fragments, nucleic acids, lectins, ligand-binding receptors, affibodies, nanobodies, imprinted polymers, avimers, peptidomimetics, hormone receptors, cytokine receptors, and synthetic receptors, as well as modified forms and fragments thereof.

[0137] In some embodiments, the biomarker level is detected using a biomarker / capture reagent complex.

[0138] In some embodiments, the biomarker level is obtained from the biomarker / capture reagent complex and is detected indirectly, for example, as a result of a reaction following the interaction of the biomarker / capture reagent, but is dependent on the formation of the biomarker / capture reagent complex.

[0139] In some embodiments, the biomarker level is detected directly from the biomarker in the biological sample.

[0140] In some embodiments, the biomarker is detected using a multiplex format that allows for the simultaneous detection of two or more biomarkers in a biological sample. In some embodiments of the multiplex format, the capture reagent is immobilized directly or indirectly, by covalent or non-covalent attachment, at distinct locations on a solid support. In some embodiments, the multiplex format uses distinct solid supports, where each solid support has a unique capture reagent associated therewith, e.g., conjugated to a quantum dot. In some embodiments, a distinct device is used for the detection of each of the plurality of biomarkers to be detected in the biological sample. The distinct device can be configured to allow for the simultaneous processing of each biomarker in the biological sample. For example, a microtiter plate can be used, such that each well in the plate is used to uniquely analyze one or more biomarkers to be detected in the biological sample.

[0141] In one or more of the foregoing embodiments, to enable detection of biomarker levels, fluorescent tags can be used to label the components of the biomarker / capture reagent complex. In various embodiments, the fluorescent label can be conjugated, using known techniques, to a capture reagent specific for any of the biomarkers described herein, and then the corresponding biomarker level can be detected using the fluorescent label. Suitable fluorescent labels include rare earth chelates, fluorescein and its derivatives, rhodamine and its derivatives, dansyl, allophycocyanin, PBXL-3, Qdot 605, Lissamine, phycoerythrin, Texas Red, and other similar compounds.

[0142] In some embodiments, the fluorescent label is a fluorescent dye molecule. In some embodiments, the fluorescent dye molecule comprises at least one substituted indolium ring system in which the substituent at the 3-position carbon of the indolium ring contains a chemically reactive group or a conjugated substance. In some embodiments, dye molecules include AlexFluor molecules, such as Alexafluor 488, Alexafluor 532, Alexafluor 647, Alexafluor 680, or Alexafluor 700. In other embodiments, the dye molecule comprises a first type of dye molecule and a second type of dye molecule, such as two different Alexafluor molecules. In some embodiments, the dye molecule comprises a first type of dye molecule and a second type of dye molecule, and the two types of dye molecules have different emission spectra.

[0143] Fluorescence can be measured by various measurement means adaptable to a wide range of assay formats. For example, spectrofluorometers are designed to analyze microtiter plates, microscope slides, printed arrays, cuvettes, and the like. See Principles of Fluorescence Spectroscopy by J.R. Lakowicz, Springer Science + Business Media, Inc., 2004. See Bioluminescence Chemiluminescence: Progress Current Applications; Philip E. Stanley, Larry J. Kricka editors, World Scientific Publishing Company, January 2002.

[0144] In one or more embodiments, a chemiluminescent tag may optionally be used to label the components of the biomarker / capture complex to enable detection of biomarker levels. Suitable chemiluminescent substances include oxalyl chloride, rhodamine 6G, Ru(bipy)3 2+, any of TMAE (tetrakis(dimethylamino)ethylene), pyrogallol (1,2,3-trihydroxybenzene), lucigenin, peroxalate, aryloxalate, acridinium ester, dioxetane, etc. may be mentioned.

[0145] In some embodiments, the detection method includes an enzyme / substrate combination that generates a detectable signal corresponding to the biomarker level. Generally, the enzyme catalyzes a chemical change in the chromogenic substrate, and this chemical change can be measured using various techniques including spectrophotometry, fluorescence, and chemiluminescence. Suitable enzymes include, for example, luciferase, luciferin, malate dehydrogenase, urease, horseradish peroxidase (HRPO), alkaline phosphatase, β-galactosidase, glucoamylase, lysozyme, glucose oxidase, galactose oxidase, and glucose-6-phosphate dehydrogenase, uricase, xanthine oxidase, lactoperoxidase, microperoxidase, etc.

[0146] In some embodiments, the detection method can be a combination of fluorescence, chemiluminescence, radionuclide, or an enzyme / substrate combination that generates a measurable signal. In some embodiments, the generation of diverse signals can have unique and advantageous features in the biomarker assay format.

[0147] In some embodiments, the biomarker level of the biomarker described herein can be detected using any analytical method including, as described below, singleplex aptamer assay, multiplex aptamer assay, singleplex or multiplex immunoassay, mRNA expression profiling, miRNA expression profiling, mass spectrometry, histological / cytological methods, etc.

[0148] Determination of Biomarker Level Using Aptamer-Based Assay Assays for the detection and quantification of physiologically important molecules in biological and other samples are important tools in scientific research and the healthcare field. One class of such assays involves the use of a microarray that includes one or more aptamers immobilized on a solid support. Each aptamer can bind to a target molecule in a highly specific manner and with extremely high affinity. See, for example, U.S. Patent No. 5,475,096, entitled “Nucleic Acid Ligands”. See also, for example, U.S. Patent No. 6,242,246, entitled “Nucleic Acid Ligand Diagnostic Biochip”, U.S. Patent No. 6,458,543, and U.S. Patent No. 6,503,715. When the microarray is contacted with a sample, the aptamers bind to each target molecule present in the sample, thereby enabling the measurement of biomarker levels corresponding to the biomarker.

[0149] As used herein, "aptamer" refers to a nucleic acid having specific binding affinity for a target molecule. While it is recognized that affinity interactions are a matter of degree, in this context, the "specific binding affinity" of an aptamer for its target generally means that the aptamer binds to its target with a much higher degree of binding affinity than it binds to other components in a test sample. An "aptamer" is a set of copies of one type or species of nucleic acid molecule that contains a specific nucleotide sequence. An aptamer can contain any suitable number of nucleotides, including any number of chemically modified nucleotides. An "aptamer" refers to two or more sets of such molecules. Different aptamers can have either the same number or different numbers of nucleotides. An aptamer can be DNA, RNA, or a chemically modified nucleic acid, can be single-stranded, double-stranded, or can contain double-stranded regions and can include higher-order structures. An aptamer can be a photoaptamer in which a photoreactive or chemically reactive functional group is included in the aptamer to enable the aptamer to be covalently linked to its corresponding target. Any of the aptamer methods disclosed herein can include the use of two or more aptamers that specifically bind to the same target molecule. As further described below, an aptamer can include a tag. If an aptamer includes a tag, not all copies of the aptamer need to have the same tag. Further, if different aptamers each include a tag, these different aptamers can have either the same tag or different tags.

[0150] An aptamer can be identified using any known method, including the SELEX process. Once identified, an aptamer can be prepared or synthesized according to any known method, including chemical synthesis methods and enzymatic synthesis methods.

[0151] The terms "SELEX" and "SELEX process" are generally used interchangeably herein to refer to a combination of (1) the selection of aptamers that interact with a target molecule in a desired manner, e.g., bind to a protein with high affinity, and (2) the amplification of those selected nucleic acids. The SELEX process can be used to identify aptamers that have high affinity for a specific target or biomarker.

[0152] SELEX generally involves preparing a mixture of candidate nucleic acids, binding the candidate mixture to a desired target molecule to form an affinity complex, separating the affinity complex from unbound candidate nucleic acids, separating and isolating the nucleic acids from the affinity complex, purifying the nucleic acids, and identifying specific aptamer sequences. To further improve the affinity of the selected aptamers, this process can be carried out in multiple rounds. This process can include an amplification step at one or more points in the process. See, e.g., U.S. Patent No. 5,475,096 entitled "Nucleic Acid Ligands". The SELEX process can also be used to generate aptamers that non-covalently bind to a target as well as aptamers that covalently bind to a target. See, e.g., U.S. Patent No. 5,705,337 entitled "Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Chemi-SELEX".

[0153] The SELEX process can be used, for example, to identify high affinity aptamers containing modified nucleotides that confer improved properties on the aptamer, such as improved in vivo stability or improved delivery characteristics. Examples of such modifications include chemical substitutions at the ribose and / or phosphate and / or base positions. Aptamers containing modified nucleotides identified by the SELEX process are described in U.S. Patent No. 5,660,985, entitled "High Affinity Nucleic Acid Ligands Containing Modified Nucleotides", which describes oligonucleotides containing nucleotide derivatives chemically modified at the 5' and 2' positions of pyrimidines. U.S. Patent No. 5,580,737 (see above) describes highly specific aptamers containing one or more nucleotides modified with 2'-amino (2'-NH2), 2'-fluoro (2'-F), and / or 2'-O-methyl (2'-OMe). See also U.S. Patent Application Publication No. 20090098549, entitled "SELEX and PHOTOSELEX", which describes nucleic acid libraries with enhanced physical and chemical properties and their use in SELEX and photoSELEX.

[0154] SELEX can also be used to identify aptamers with desired dissociation rate characteristics. See U.S. Patent Application Publication No. 20090004667, entitled "Method for Generating Aptamers with Improved Off-Rates," which describes an improved SELEX process for generating aptamers that can bind to a target molecule. Methods for generating aptamers and photoaptamers with slower dissociation rates from each target molecule are described. This method involves contacting a candidate mixture with a target molecule, forming a nucleic acid-target complex, and performing a process of enriching aptamers with slow dissociation rates, wherein nucleic acid-target complexes with fast dissociation rates dissociate and do not reform, while complexes with slow dissociation rates remain intact. In addition, this method includes using modified nucleotides in the generation of a candidate nucleic acid mixture to generate aptamers with improved dissociation rate performance. Non-limiting exemplary modified nucleotides include, for example, the modified pyrimidines shown in FIG. 8. In some embodiments, the aptamer includes at least one nucleotide having a modification such as a base modification. In some embodiments, the aptamer includes at least one nucleotide having a hydrophobic modification such as a hydrophobic base modification that allows for hydrophobic contact with a target protein. In some embodiments, such hydrophobic contact contributes to higher hydrophilicity and / or slower dissociation rate binding by the aptamer. Nucleotides having non-limiting exemplary hydrophobic modifications are shown in FIG. 8. In some embodiments, the aptamer includes at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 nucleotides having hydrophobic modifications, where each hydrophobic modification may be the same as or different from one another. In some embodiments, at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 hydrophobic modifications in the aptamer can be independently selected from the hydrophobic modifications shown in FIG. 8.

[0155] In some embodiments, the slow dissociation rate aptamer (including an aptamer comprising at least one nucleotide with a hydrophobic modification) has a dissociation rate (t1 / 2) of ≧30 minutes, ≧60 minutes, ≧90 minutes, ≧120 minutes, ≧150 minutes, ≧180 minutes, ≧210 minutes, or ≧240 minutes.

[0156] In some embodiments, the assay uses an aptamer that includes a photoreactive functional group that allows the aptamer to covalently bind or be “photocrosslinked” to its target molecule. See, e.g., U.S. Patent No. 6,544,776, entitled “Nucleic Acid Ligand Diagnostic Biochip”. These photoreactive aptamers are also referred to as photoaptamers. See, e.g., U.S. Patent Nos. 5,763,177, 6,001,577, and 6,291,184, each entitled “Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Photoselection of Nucleic Acid Ligands and Solution SELEX”. See also U.S. Patent No. 6,458,539, entitled “Photoselection of Nucleic Acid Ligands”. After the microarray is contacted with the sample and the photoaptamer is given the opportunity to bind to its target molecule, the photoaptamer is photoactivated, the solid support is washed, and any non-specifically bound molecules are removed. Due to the covalent bond(s) generated by the photoactivated functional group(s) on the photoaptamer, the target molecule bound to the photoaptamer is not typically removed, so stringent wash conditions can be used. In this way, the assay enables detection of biomarker levels corresponding to biomarkers in the test sample.

[0157] In some assay formats, the aptamer is immobilized on a solid support before contacting with the sample. However, under certain circumstances, immobilizing the aptamer before contacting with the sample may not provide an optimal assay. For example, pre-immobilization of the aptamer can result in inefficient mixing of the aptamer with its target molecule on the surface of the solid support, which may prolong the reaction time. Thus, by extending the incubation time, efficient binding of the aptamer to its target molecule becomes possible. Further, when a photoaptamer is used in an assay, depending on the material used as the solid support, the solid support may tend to scatter or absorb the light used to affect the formation of covalent bonds between the photoaptamer and its target molecule. Depending on the method further used, the surface of the solid support may also be exposed to any labeling agent used, and thus may also be affected, making the detection of the target molecule bound to the aptamer prone to inaccuracy. Finally, immobilization of the aptamer on the solid support generally includes an aptamer preparation step (i.e., immobilization) before exposure of the aptamer to the sample, and this preparation step may affect the activity or functionality of the aptamer.

[0158] Aptamer assays are also described that utilize a separation step designed to allow the aptamer to capture its target in solution and then remove specific components of the aptamer-target mixture prior to detection (see U.S. Patent Application Publication No. 20090042206, entitled “Multiplexed Analyses of Test Samples”). The aptamer assays described enable the detection and quantification of non-nucleic acid targets (e.g., protein targets) in a test sample by detecting and quantifying the nucleic acid (i.e., the aptamer). The methods described create nucleic acid surrogates (i.e., aptamers) for detecting and quantifying non-nucleic acid targets, thereby enabling a wide variety of nucleic acid technologies that include amplification to be applied to a broader range of desired targets that include protein targets.

[0159] An aptamer can be constructed to facilitate the separation of assay components from an aptamer biomarker complex (or covalently bound complex of a photoaptamer and a biomarker) and enable the isolation of the aptamer for detection and / or quantification. In one embodiment, these constructs may include a cleavable or releasable element within the aptamer sequence. In other embodiments, additional functionality can be introduced into the aptamer, for example, a label or detectable component, a spacer component, or a specific binding tag or immobilization element can be introduced. For example, an aptamer can include a tag, a label, a spacer component separating the labels, and a cleavable portion linked to the aptamer via a cleavable moiety. In one embodiment, the cleavable element is a photocleavable linker. The photocleavable linker can be attached to a biotin moiety and a spacer moiety, can include an NHS group for derivatization of amines, and can be used to introduce a biotin group into the aptamer, thereby enabling the release of the aptamer at a later stage in an assay method.

[0160] Homogeneous assays performed using all assay components in solution do not require separation of the sample and reagents prior to signal detection. These methods are rapid and easy to use. These methods generate a signal based on the capture of molecules or binding reagents that react with specific targets. In some embodiments of the methods described herein, the molecule capture reagent can include one or more aptamers or antibodies, etc., and the specific target of each of the one or more aptamers or antibodies, etc., can be a biomarker shown in Table 1 or Table 2.

[0161] In some embodiments, the signal generation method utilizes anisotropic signal changes resulting from the interaction between a fluorophore-labeled capture reagent and its specific biomarker target. When the labeled capture reagent reacts with its target, the rotational movement of the fluorophore bound to the complex becomes very slow due to the increase in molecular weight, and the anisotropy value changes. By monitoring the anisotropy change, the binding event can be used to quantitatively measure the biomarker in solution. Other methods include fluorescence polarization assay, molecular beacon method, time-resolved fluorescence quenching, chemiluminescence, fluorescence resonance energy transfer, and the like.

[0162] Exemplary solution-based aptamer assays that can be used to detect biomarker levels in a biological sample include: (a) preparing a mixture by contacting the biological sample with an aptamer that includes a first tag and has specific affinity for the biomarker, wherein an aptamer affinity complex is formed if the biomarker is present in the sample; (b) exposing the mixture to a first solid support that includes a first capture element and associating the first tag with the first capture element; (c) removing any components of the mixture that are not associated with the first solid support; (d) binding a second tag to the biomarker component of the aptamer affinity complex; (e) releasing the aptamer affinity complex from the first solid support; (f) exposing the released aptamer affinity complex to a second solid support that includes a second capture element and associating the second tag with the second capture element; (g) removing any uncomplexed aptamer from the mixture by separating the uncomplexed aptamer from the aptamer affinity complex; (h) eluting the aptamer from the solid support; and (i) detecting the biomarker by detecting the aptamer component of the aptamer affinity complex.

[0163] To detect biomarker values by detecting the aptamer component of the aptamer affinity complex, any means known in the art can be used. A number of different detection methods for detecting the aptamer component of the affinity complex can be used, such as hybridization assays, mass spectrometry, or QPCR. In some embodiments, nucleic acid sequencing can be used to detect the aptamer component of the aptamer affinity complex and thereby detect biomarker values. In summary, the test sample can be subjected to any type of nucleic acid sequencing method to identify and quantify the sequence or sequences of one or more aptamers present in the test sample. In some embodiments, the sequence includes the whole of the aptamer molecule or any part of the molecule that can be used to uniquely identify the molecule. In other embodiments, the identifying sequence is a specific sequence added to the aptamer, and such sequences are often referred to as "tags", "barcodes", or "zip codes". In some embodiments, the sequencing method includes an enzymatic step to amplify the aptamer sequence or to convert any type of nucleic acid containing RNA and DNA with chemical modifications at any position into any other type of nucleic acid suitable for sequencing.

[0164] In some embodiments, the sequencing method includes one or more cloning steps. In other embodiments, the sequencing method includes a direct sequencing method that does not use cloning.

[0165] In some embodiments, the sequencing method includes a directed approach using specific primers that target one or more aptamers in the test sample. In other embodiments, the sequencing method includes a shotgun approach that targets all aptamers in the test sample.

[0166] In some embodiments, the sequencing method includes an enzymatic step for amplifying the molecule targeted for sequencing. In other embodiments, the sequencing method directly sequences a single molecule. Exemplary nucleic acid sequencing-based methods that can be used to detect biomarker values corresponding to biomarkers in a biological sample include the following: (a) converting a mixture of aptamers containing chemically modified nucleotides to unmodified nucleic acids using an enzymatic step; (b) shotgun sequencing the unmodified nucleic acids obtained using a massively parallel sequencing platform, such as the 454 sequencing system (454 Life Sciences / Roche), the Illumina sequencing system (Illumina), the ABI SOLiD sequencing system (Applied Biosystems), the HeliScope single molecule sequencer (Helicos Biosciences), or the Pacific BioSciences real-time single molecule sequencing system (Pacific BioSciences), or the Polonator G sequencing systems (Dover Systems); and (c) identifying and quantifying the aptamers present in the mixture by specific sequences and sequence counts.

[0167] Non-limiting exemplary methods for detecting biomarkers in a biological sample using aptamers are described in Example 1. See also Kraemer et al., 2011, PLoS One 6(10):e26332.

[0168] Determination of Biomarker Levels Using Immunoassays Immunoassay methods are based on the reaction of an antibody to its corresponding target or analyte and can detect analytes in a sample depending on the specific assay format. Monoclonal antibodies and their fragments are frequently used for their specific epitope recognition to improve the specificity and sensitivity of assay methods based on immunoreactivity. Polyclonal antibodies are also successfully used in various immunoassays because of their higher affinity for the target compared to monoclonal antibodies. Immunoassays are designed to be used with a wide range of biological sample matrices. Immunoassay formats are designed to provide qualitative, semi - quantitative, and quantitative results.

[0169] Quantitative results are obtained by using a calibration curve created with a specific analyte of known concentration to be detected. The reaction or signal from an unknown sample is plotted on the calibration curve, and the amount or level corresponding to the target in the unknown sample is determined.

[0170] Numerous immunoassay formats have been designed. ELISA or EIA can be quantitative for the detection of analytes. This method is based on the binding of a label to either the analyte or the antibody, and the components of the label contain an enzyme either directly or indirectly. ELISA tests can be in formats for direct detection, indirect detection, competitive detection, or sandwich detection of analytes. Other methods are based on labels such as radioisotopes (I 125 ) or fluorescence. Additional techniques include, for example, agglutination, nephelometry, turbidimetry, Western blot, immunoprecipitation, immunocytostaining, immunohistostaining, flow cytometry, Luminex assay, etc. (see ImmunoAssay: A Practical Guide, edited by Brian Law, published by Taylor & Francis, Ltd., 2005 edition).

[0171] Exemplary assay formats include enzyme-linked immunosorbent assay (ELISA), radioimmunoassay, fluorescence, chemiluminescence, and fluorescence resonance energy transfer (FRET) or time-resolved FRET (TR-FRET) immunoassays. Examples of techniques for detecting biomarkers include biomarker immunoprecipitation followed by quantitative methods that allow for size and peptide level discrimination, such as gel electrophoresis, capillary electrophoresis, planar electrochromatography, and the like.

[0172] Methods for detecting and / or quantifying detectable labels or signal generating substances depend on the nature of the label. Products of reactions catalyzed by suitable enzymes (where the detectable label is the enzyme; see above) can be, but are not limited to, fluorescence, luminescence, or radioactivity, or they can absorb visible or ultraviolet light. Examples of detectors suitable for detecting such detectable labels include, but are not limited to, X-ray film, radioactivity counters, scintillation counters, spectrophotometers, colorimeters, fluorometers, luminometers, and densitometers.

[0173] Any of the detection methods can be performed in any suitable format that allows for any suitable preparation, processing, and analysis of reactions. The detection methods can be performed, for example, in multi-well assay plates (e.g., 96-well or 386-well) or using any suitable array or microarray. Stock solutions of various agents can be made manually or by a robot, and all subsequent pipetting, dilution, mixing, dispensing, washing, incubation, sample reading, data collection, and analysis can be performed by a robot using commercially available analysis software, robots, and detection equipment capable of detecting detectable labels.

[0174] Determination of Biomarker Levels Using Gene Expression Profiling In some embodiments, mRNA measurements in a biological sample can be used as a surrogate to detect the level of the corresponding protein in the biological sample. Thus, in some embodiments, the biomarkers or biomarker panels described herein can be detected by detecting the appropriate RNA.

[0175] In some embodiments, the mRNA expression level is measured by reverse transcription quantitative polymerase chain reaction (qPCR following RT-PCR). RT-PCR is used to generate cDNA from mRNA. The cDNA can be used in a qPCR assay to generate fluorescence as the DNA amplification process proceeds. In qPCR, absolute measurements such as the copy number of mRNA per cell can be obtained by comparison to a calibration curve. Northern blot, microarray, Invader assay, and combinations of RT-PCR with capillary electrophoresis have all been used to measure the mRNA expression level in a sample. See Gene Expression Profiling: Methods and Protocols, Richard A. Shimkets, editor, Humana Press, 2004.

[0176] Detection of Biomarkers Using In Vivo Molecular Imaging Techniques In some embodiments, the biomarkers described herein can be used in molecular imaging studies. For example, a contrast agent can be conjugated to a capture reagent that can be used to detect a biomarker in vivo.

[0177] In vivo imaging techniques provide a non-invasive method for determining the state of a particular disease in a subject's body. For example, all or part of the body can be displayed as a three-dimensional image, thereby providing useful information about the body's shape and structure. Such techniques can be combined with the detection of the biomarkers described herein to provide information about the biomarkers in vivo.

[0178] Due to various technological advancements, in vivo molecular imaging technology has been developing. These advancements include the development of new contrast agents or labels such as radiolabels and / or fluorescent labels that can generate strong signals within the body; and the development of powerful new imaging technologies that can detect and analyze these signals from outside the body with sufficient sensitivity and accuracy to provide useful information. The contrast agent can be visualized with an appropriate imaging system, thereby providing an image of a part or parts of the body where the contrast agent is present. The contrast agent can be bound to or associated with, for example, a capture reagent such as an aptamer or an antibody, and / or a peptide or protein or oligonucleotide (e.g., for detecting gene expression), or a complex containing any of these together with one or more macromolecules and / or other particulate forms.

[0179] The contrast agent may be characterized by a radioactive atom useful in imaging. Suitable radioactive atoms include technetium 99m or iodine 123 for scintigraphy examinations. Other readily detectable moieties include, for example, spin labels for magnetic resonance imaging (MRI), such as, for example, iodine 123, iodine 131, indium 111, fluorine 19, carbon 13, nitrogen 15, oxygen 17, gadolinium, manganese, or iron. Such labels are well known in the art and can be readily selected by those skilled in the art.

[0180] Standard imaging techniques include, but are not limited to, magnetic resonance imaging, computed tomography, positron emission tomography (PET), single photon emission computed tomography (SPECT), etc. In in vivo diagnostic imaging, the type of detection equipment available is an important factor in the selection of a given contrast agent, e.g., a given radionuclide and the specific biomarker (protein, mRNA, etc.) to which it is targeted. The radionuclide typically selected exhibits a certain attenuation detectable by a given type of equipment. Also, when selecting a radionuclide for in vivo diagnosis, its half-life should be long enough to allow detection when maximally taken up by the target tissue and short enough to minimize harmful radiation to the host.

[0181] Exemplary imaging techniques include, but are not limited to, PET and SPECT, which are imaging techniques in which a radionuclide is administered systemically or locally to a subject. The subsequent uptake of the radioactive tracer is measured over time and used to obtain information about the targeted tissue and biomarker. Due to the high-energy (gamma-ray) emission of the specific isotope used and the sensitivity and sophistication of the equipment used to detect it, the two-dimensional distribution of radioactivity can be estimated from outside the body.

[0182] Positron-emitting radionuclides commonly used in PET include, for example, carbon-11, nitrogen-13, oxygen-15, and fluorine-18. In SPECT, isotopes that decay by electron capture and / or gamma emission are used, such as iodine-123 and technetium-99m. An exemplary method for labeling an amino acid with technetium-99m is to reduce pertechnetate ions in the presence of a chelate precursor to form an unstable technetium-99m-precursor complex, which then reacts with the metal-binding group of a bifunctionally modified chemotactic peptide to form a technetium-99m-chemotactic peptide conjugate.

[0183] In such in vivo imaging diagnostic methods, antibodies are frequently used. The preparation and use of antibodies for in vivo diagnosis are well known in the art. Similarly, aptamers may be used in such in vivo imaging diagnostic methods. For example, aptamers used to identify specific biomarkers described herein may be appropriately labeled and injected into a subject to detect the biomarker in vivo. The label used is selected according to the imaging technique used, as described above. Aptamer-directed contrast agents may have unique and advantageous properties compared to other contrast agents with respect to tissue permeability, biodistribution, kinetics, clearance, efficacy, and selectivity.

[0184] Such techniques may optionally be performed using labeled oligonucleotides, for example, to detect gene expression by imaging using antisense oligonucleotides. These methods are used, for example, in in situ hybridization using a fluorescent molecule or a radionuclide as a label. Other methods for detecting gene expression include, for example, detection of the activity of a reporter gene.

[0185] Another general type of imaging technique is optical imaging in which a fluorescent signal within a subject is detected by an optical device external to the subject. These signals can result from actual fluorescence and / or bioluminescence. Improvement in the sensitivity of optical detection devices has increased the usefulness of optical imaging for in vivo diagnostic assays.

[0186] For a review of other techniques, see N. Blow, Nature Methods, 6, 465 - 469, 2009.

[0187] Determination of Biomarker Levels Using Mass Spectrometry To detect biomarker levels, mass spectrometers of various configurations can be used. Several types of mass spectrometers are available or can be manufactured in various configurations. Generally, a mass spectrometer has the following main components: a sample inlet, an ion source, a mass analyzer, a detector, a vacuum system, and an instrument control system, and a data system. Differences in the sample inlet, ion source, and mass analyzer generally define the type of instrument and its capabilities. For example, the inlet can be a capillary column liquid chromatography source or a direct probe or stage such as used in matrix-assisted laser desorption. Common ion sources include, for example, electrospray, which includes nanospray and microspray, or matrix-assisted laser desorption. Common mass analyzers include quadrupole mass filters, ion trap mass spectrometers, and time-of-flight mass spectrometers. Further mass spectrometry methods are well known in the art (see Burlingame et al., Anal. Chem. 70:647R-716R (1998); Kinter and Sherman, New York (2000)).

[0188] Protein biomarkers and biomarker levels can be detected and measured by any of the following: electrospray ionization mass spectrometry (ESI-MS), ESI-MS / MS, ESI-MS / (MS)n, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption / ionization time-of-flight mass spectrometry (SELDI-TOF-MS), desorption / ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), tandem time-of-flight (TOF / TOF) technology called UltraFlex III TOF / TOF, atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS / MS, APCI-(MS)N, atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS / MS, and APPI-(MS)N, quadrupole mass spectrometry, Fourier transform mass spectrometry (FTMS), quantitative mass spectrometry, and ion trap mass spectrometry.

[0189] Prior to characterizing protein biomarkers and determining biomarker levels by mass spectrometry, a sample preparation strategy is used to label and concentrate the sample. Labeling methods include, but are not limited to, isobaric tags for relative or absolute quantification (iTRAQ), and stable isotope labeling by amino acids in cell culture (SILAC). Capture reagents used to selectively concentrate biomarker protein candidates in the sample prior to mass spectrometry include, but are not limited to, aptamers, antibodies, nucleic acid probes, chimeras, small molecules, F(ab’)2 fragments, single-chain antibody fragments, Fv fragments, single-chain Fv fragments, nucleic acids, lectins, ligand-binding receptors, affibodies, nanobodies, ankyrins, domain antibodies, alternative antibody scaffolds (e.g., diabodies, etc.), imprinted polymers, avimers, peptidomimetics, peptoids, peptide nucleic acids, threose nucleic acids, hormone receptors, cytokine receptors, and synthetic receptors, as well as modified forms and fragments thereof.

[0190] Determination of Biomarker Levels Using Proximity Ligation Assay To determine biomarker values, a proximity ligation assay can be used. Briefly, a pair of affinity probes, which can be a pair of antibodies or a pair of aptamers, each member of which is extended with an oligonucleotide, is contacted with a test sample. The targets of the pair of affinity probes can be two different determinants on one protein, or one determinant on each of two different proteins that can exist as a homo- or hetero-multimeric complex. When the probes bind to the determinants of the target, the free ends of the oligonucleotide extensions are close enough to hybridize together. Hybridization of the oligonucleotide extensions is facilitated by a common connector oligonucleotide that serves to cross-link them when the oligonucleotide extensions are positioned close enough together. Once the oligonucleotide extensions of the probes have hybridized, the ends of the extensions are ligated together by enzymatic DNA ligation.

[0191] Each oligonucleotide extension contains a primer region for PCR amplification. When the oligonucleotide extensions are ligated together, the oligonucleotides form a continuous DNA sequence, which, by PCR amplification, reveals information regarding the identity and amount of the target protein, and, if the target determinants are present on two different proteins, information regarding protein-protein interactions. Proximity ligation can provide a sensitive and specific assay for real-time protein concentration and interaction information by using real-time PCR. Probes that do not bind to the determinant of interest will not bring the corresponding oligonucleotide extensions into proximity, and ligation or PCR amplification cannot proceed, resulting in no signal generation.

[0192] The aforementioned assay enables the detection of biomarker values useful for a method of predicting the risk of a CV event, wherein the method comprises detecting at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, or 13 biomarkers selected from the biomarkers of Table 1; or at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, or all 27 biomarkers of Table 2 in a biological sample derived from a subject, and as described below, classification using the biomarker values indicates whether the subject has a high risk of experiencing a CV event within a period of 1, 2, 3, or 4 years. According to any of the methods described herein, the biomarker values can be detected and classified individually or, for example, together as in a multiplex assay format.

[0193] Classification of Biomarkers and Calculation of Disease Scores In some embodiments, the biomarker “signature” of a given diagnostic test includes a set of biomarkers, each biomarker having a characteristic level in the population of interest. In some embodiments, the characteristic level can refer to the mean or average value of the biomarker for subjects within a particular group. In some embodiments, the diagnostic methods described herein can be used to assign an unknown sample from a subject to one of two groups, either the group having a high risk of a CV event or the other group.

[0194] Assigning a sample to one of two or more groups is known as classification, and the techniques used to achieve this assignment are known as classifiers or classification methods. Classification methods may also be referred to as scoring methods. There are a number of classification methods that can be used to construct a diagnostic classifier from a set of biomarker levels. In some cases, the classification method is performed using supervised learning techniques where a dataset is collected using samples obtained from individuals in two distinct groups (or more in the case of multi-class states) that are to be distinguished. Since the class (group or population) to which each sample belongs is known in advance for each sample, the classification method can be trained to obtain the desired classification response. It is also possible to generate a diagnostic classifier using unsupervised learning techniques.

[0195] Common techniques for developing diagnostic classifiers include decision trees; bagging + boosting + forests; learning based on inference rules; Parzen windows; linear models; logistic curves; neural network methods; unsupervised clustering; K-means; hierarchical ascending / descending classification; semi-supervised learning; prototype methods; nearest neighbor methods; kernel density estimation; support vector machines; hidden Markov models; Boltzmann learning, and the classifiers can be simply combined or combined in a way that minimizes a specific objective function. For an overview, see, for example, Pattern Classification, R. O. Duda, et al., editors, John Wiley & Sons, 2nd edition, 2001. See also The Elements of Statistical Learning - Data Mining, Inference, and Prediction, T. Hastie, et al., editors, Springer Science+Business Media, LLC, 2nd edition, 2009.

[0196] To generate a classifier using supervised learning techniques, a set of samples called training data is obtained. In the context of a diagnostic test, the training data includes samples from different groups (classes) to which unknown samples will later be assigned. For example, samples collected from subjects in a control population and samples collected from subjects in a specific disease population can constitute training data for developing a classifier that can classify an unknown sample (or more specifically, the subject from which the sample was obtained) as either having or not having the disease. The development of a classifier from training data is known as training the classifier. Specific details regarding the training of the classifier depend on the nature of the supervised learning technique. Training of a naive Bayes classifier is an example of such a supervised learning technique (see, for example, Pattern Classification, R. O. Duda, et al., editors, John Wiley & Sons, 2nd edition, 2001; see also The Elements of Statistical Learning - Data Mining, Inference, and Prediction, T. Hastie, et al., editors, Springer Science+Business Media, LLC, 2nd edition, 2009). Training of a naive Bayes classifier is described, for example, in U.S. Patent Publication Nos. 2012 / 0101002 and 2012 / 0077695.

[0197] Typically, since there may be a large number of higher biomarker levels compared to the samples in the training set, care must be taken to avoid overfitting. Overfitting occurs when a statistical model represents random errors or noise instead of the underlying relationship. Overfitting can be avoided in various ways, including, for example, limiting the number of biomarkers used in classifier development, assuming that the responses of the biomarkers are independent of each other, limiting the complexity of the underlying statistical model used, and ensuring that the underlying statistical model fits the data.

[0198] As a specific example of the development of a diagnostic test using a set of biomarkers, the application of a simple Bayesian classifier, which is a simple probabilistic classifier based on Bayes' theorem with strict independent processing of the biomarkers, can be mentioned. Each biomarker is described by a class-dependent probability density function (pdf) for the measured RFU values or log RFU (relative fluorescence unit) values in each class. The combined pdf for a set of biomarkers in one class is estimated to be the product of the individual class-dependent pdfs for each biomarker. Training the simple Bayesian classifier in this context is equivalent to assigning parameters ("parameterizing") to characterize the class-dependent pdf. Any underlying model can be used for the class-dependent pdf, but the model generally must fit the data observed in the training set.

[0199] The performance of the simple Bayesian classifier depends on the number and quality of the biomarkers used to construct and train the classifier. A single biomarker will act according to the KS (Kolmogorov–Smirnov) distance. The subsequent addition of biomarkers with a good KS distance (e.g., > 0.3) will generally improve the classification performance if the subsequently added biomarkers are independent of the first biomarker. By using specificity in addition to sensitivity as a classifier score, a number of highly scoring classifiers can be generated using a type of greedy method. (The greedy method is any algorithm that follows a metaheuristic for problem solving that makes locally optimal choices at each step with the aim of finding a global optimum solution.)

[0200] Another way to characterize classifier performance is by the Receiver Operating Characteristic (ROC), or simply the ROC curve or ROC plot. The ROC is a graphical plot of the sensitivity or true positive rate versus the false positive rate (1 - specificity or 1 - true negative rate) of a binary classifier system as a function of the change in the discrimination threshold of the binary classifier system. This ROC can equivalently be represented by plotting the ratio of true positives among the positives (TPR = true positive rate) against the ratio of false positives among the negatives (FPR = false positive rate). Since this is a comparison of two operating characteristics (TPR and FPR) as a function of the change in the criterion, it is also known as the relative operating characteristic curve. The area under the ROC curve (AUC) is commonly used as an aggregate measure of diagnostic accuracy. This can take values from 0.0 to 1.0. The AUC has important statistical properties. That is, the AUC of a classifier is equal to the probability that the classifier ranks a randomly selected positive instance higher than a randomly selected negative instance (Fawcett T, 2006. An introduction to ROC analysis. Pattern Recognition Letters. 27:861 - 874). This is equivalent to the Wilcoxon rank - sum test (Hanley, J.A., McNeil, B.J., 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29 - 36). Another way to express the performance of a diagnostic test relative to a known reference standard is the net reclassification improvement, which is the ability of a new test to accurately increase or decrease risk compared to the reference standard test. See, for example, Pencina et al., 2011, Stat. Med. 30:11 - 21. Although the AUC under the ROC curve is optimal for evaluating the performance of a two - class classifier, stratified and personalized medicine rely on the inference that the population contains more than two classes. For such comparisons, the hazard ratio of the upper quartile to the lower quartile (or other stratifications such as deciles) may be more appropriately used.

[0201] The risk and likelihood predictions enabled by the present invention may be applicable to subjects during initial treatment or to subjects in a specialized cardiovascular center, or even to consumers. In some embodiments, the classifiers used to predict an event may require some calibration for the population to which they are applied, because there may be variations due to, for example, ethnicity or region. In some embodiments, such calibration may be pre-established by testing in a large population and thus incorporated before they make risk predictions when applied to individual patients. Venous blood samples are taken, appropriately processed, and analyzed as described herein. Once the analysis is complete, the risk prediction can be made mathematically, with or without incorporating other metadata from medical records described herein, such as genetics or demographics. Depending on the level of consumer expertise, outputs of various forms of information are possible. For consumers seeking the simplest type of output, in some embodiments, the information may be "Is this person likely to have an event in the next x years (where x is between 1 and 4), Yes / No", or alternatively something similar to the red / orange / green of a "traffic light", or words or their written equivalents such as high / medium / low risk. For consumers seeking more detail, in some embodiments, the risk may be output as a number or diagram showing the probability of an event per unit time as a continuous score, or as a larger number of bins (e.g., deciles), and / or as the average time until an event and / or the most likely type of event occurs. In some embodiments, the output may include treatment recommendations. Longitudinal monitoring of the same patient over time will be able to graphically show the response to an intervention or a change in lifestyle. In some embodiments, two or more outputs may be provided simultaneously to meet the needs of the patient and the individual members of the medical management team with different levels of expertise.

[0202] In some embodiments, biomarkers shown in Table 1 or Table 2 in a blood sample (e.g., a plasma sample or a serum sample) from a subject are detected using aptamers such as aptamers with a slow dissociation rate. Using the logarithmic RFU value, the risk or likelihood that a subject will experience a CV event, or a prognostic index (PI), is calculated.

[0203] Given the PI, the probability that a subject will develop a cardiovascular event (CV event) in the next "t" years is obtained by the following formula.

Equation

[0204] Kit For example, any combination of biomarkers described herein can be detected using a suitable kit for use in performing the methods disclosed herein. Additionally, any kit can include one or more detectable labels as described herein, such as a fluorescent moiety.

[0205] In some embodiments, the kit comprises: (a) one or more capture reagents (e.g., at least one aptamer or antibody) for detecting one or more biomarkers in a biological sample, wherein the one or more biomarkers are selected from the biomarkers in Table 1 and include at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, or 13 biomarkers; or the one or more biomarkers are selected from the biomarkers in Table 2 and include all of at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, or 27 biomarkers; and optionally, (b) one or more software or computer program products for classifying the subject from whom the biological sample was obtained as having or not having a high CV event risk, as further described herein, or for determining the likelihood that the subject has a high CV event risk. Alternatively, instead of one or more computer program products, one or more instructions for manual execution of the above steps by a person may be provided.

[0206] In some embodiments, the kit comprises a solid support, a capture reagent, and a signal generating substance. The kit may also include instructions for use of the device and reagents, sample handling, and data analysis. Further, the kit may be used with a computer system or software for analyzing a biological sample and reporting the results of the analysis.

[0207] The kit may also include one or more reagents for processing a biological sample (e.g., solubilization buffer, surfactant, washing solution, or buffer). Any of the kits described herein may include, for example, a buffer, a blocking agent, a matrix substance for mass spectrometry, an antibody capture agent, a positive control sample, a negative control sample, software, and information, such as protocols, guidelines, and reference data.

[0208] In some embodiments, a kit is provided for analyzing the state of CV event risk, where the kit includes PCR primers for one or more aptamers specific to the biomarkers described herein. In some embodiments, the kit may further include instructions for use of the biomarker and instructions regarding its relationship to the prediction of the risk of CV events. In some embodiments, the kit may also include a DNA array containing a complement of one or more aptamers specific to the biomarkers described herein, reagents, and / or enzymes for amplifying or isolating sample DNA. In some embodiments, the kit may include reagents for real-time PCR, such as TaqMan probes and / or primers, and enzymes.

[0209] For example, the kit may include: (a) a reagent comprising at least one capture reagent for determining the level of one or more biomarkers in a test sample, and optionally, (b) one or more algorithms or computer programs for performing the step of comparing the amount of each quantified biomarker in the test sample with one or more predetermined cut-off values. In some embodiments, the algorithm or computer program assigns a score to each quantified biomarker based on the comparison, and in some embodiments, the assigned scores of each quantified biomarker are combined to obtain a total score. Further, in some embodiments, the algorithm or computer program compares the total score with a predetermined score and uses this comparison to determine whether the subject has a high risk of CV events. Alternatively, instead of one or more algorithms or computer programs, one or more instructions for a human to perform the above steps manually may be provided.

[0210] Biomarker panel In some embodiments, one or more of the biomarkers listed in Table 1 are detected. In some embodiments, all of the biomarkers listed in the following table are detected. In some embodiments, the level of each protein listed in Table 1 is detected. In some embodiments, the detection of one or more biomarkers or all biomarkers is performed to determine the risk or likelihood that a subject will experience a primary CV event within a predetermined period. In some such embodiments, the predetermined period is 1 year, 2 years, 3 years, 4 years, or 5 years. In some embodiments, the predetermined period is 4 years. [Table 1]

[0211] In some embodiments, one or more of the biomarkers listed in Table 2 are detected. In some embodiments, all of the biomarkers listed in the following table are detected. In some embodiments, the level of each protein listed in Table 2 is detected. In some embodiments, the detection of one or more biomarkers or all biomarkers is performed to determine the risk or likelihood that a subject will experience a secondary CV event within a predetermined period. In some such embodiments, the predetermined period is 1 year, 2 years, 3 years, 4 years, or 5 years. In some embodiments, the predetermined period is 4 years.

Table 2-1

Table 2-2

[0212] Computer Methods and Software A method for assessing the risk or likelihood of a CV event in a subject may include: 1) obtaining a biological sample; 2) performing an analytical method to detect and measure a panel of biomarkers or a set of biomarkers in the biological sample; 3) optionally performing any data normalization or standardization; 4) determining the level of each biomarker; and 5) reporting the results. In some embodiments, the results are calibrated against the ethnicity of the population / subject. In some embodiments, the biomarker levels are combined in some way and a single value for the combined biomarker levels is reported. In this approach, in some embodiments, the score may be a single numerical value determined by the integration of all biomarkers that is compared to a preset threshold that is an indicator of the presence or absence of the disease. Alternatively, the diagnostic or predictive score may be a series of bars each indicating a biomarker value, and the response pattern may be compared to a preset pattern for determining the presence or absence of a high (or low) risk of a disease, condition, or event.

[0213] At least some embodiments of the methods described herein can be implemented using a computer. An example of a computer system 100 is shown in FIG. 6. Referring to FIG. 6, system 100 is shown to be composed of hardware elements electrically connected via a bus 108, including a processor 101, an input device 102, an output device 103, a storage device 104, a computer-readable storage medium reader 105a, a communication system 106, an acceleration processing device (e.g., a DSP or a special-purpose processor) 107, and a memory 109. The computer-readable storage medium reader 105a is further connected to a computer-readable storage medium 105b, and this combination corresponds generally to a storage medium, such as a memory, including remote, local, fixed, and / or removable storage devices for temporarily and / or more persistently containing computer-readable information, and this combination includes the storage device 104, the memory 109, and / or any other such accessible system 100 resources. System 100 also includes a software element, such as an operating system 192 and other code 193, e.g., programs, data, etc. (shown here as existing within a working memory 191).

[0214] Referring to FIG. 6, system 100 has a wide range of flexibility and configurability. Thus, for example, a single architecture can be utilized to implement one or more servers, and such servers can be further configured according to generally desired protocols, protocol changes, extensions, etc. However, it will be apparent to those skilled in the art that embodiments will be utilized according to more specific application requirements. For example, one or more system elements can be implemented as lower-level elements within the components of system 100 (e.g., within communication system 106). Customized hardware may be utilized, and / or specific elements may be implemented in hardware, software, or both. Further, connections to other computing devices such as network input / output devices (not shown) can be used, but it should be understood that wired, wireless, modem, and / or other connections or multiple connections to other computing devices may be utilized.

[0215] In one aspect, the system can include a database containing the characteristics of biomarkers that exhibit the predictive characteristics of CV event risks. Biomarker data (or biomarker information) can be utilized as an input to a computer for use as part of a computer-implemented method. The biomarker data can include the data described herein.

[0216] In one aspect, the system further includes one or more devices for providing input data to one or more processors.

[0217] The system further includes a memory for storing a dataset of ranked data elements.

[0218] In another aspect, the device for providing input data includes a detector for detecting the characteristics of data elements such as, for example, a mass spectrometer or a gene chip reader.

[0219] The system may additionally include a database management system. User requests or inquiries may be formatted in an appropriate language understood by a database management system that processes the inquiries and extracts relevant information from the training set database.

[0220] The system may be connectable to a network to which a network server and one or more clients are connected. The network can be, as is well known in the art, a local area network (LAN) or a wide area network (WAN). Preferably, the server includes the hardware necessary to execute a computer program product (e.g., software) for accessing the data in the database to process user requests.

[0221] The system may include an operating system (e.g., UNIX or Linux) for executing instructions from the database management system. In one aspect, the operating system can operate on a global communication network such as the Internet and can utilize a global communication network server for connecting to such a network.

[0222] The system may include one or more devices including a graphical display interface including interface elements such as buttons, pull-down menus, scroll bars, text input fields, etc., as commonly found in graphical user interfaces known in the art. Requests entered in the user interface may be sent to an application program in the system for formatting to search for relevant information in one or more system databases. Requests or inquiries entered by the user may be constructed in any suitable database language.

[0223] A graphical user interface can be generated by graphical user interface code as part of an operating system and can be used to input data and / or display the input data. The results of the processed data can be displayed on the interface, printed by a printing machine communicating with the system, stored in a storage device, and / or transmitted via a network, or provided in the form of a computer-readable medium.

[0224] The system can communicate with an input device for providing data (e.g., expression values) regarding data elements to the system. In one aspect, the input device can include a gene expression profiling system, such as, for example, a mass spectrometer, a gene chip, or an array reader.

[0225] According to various embodiments, a method and apparatus for analyzing biomarker information for CV event risk prediction can be implemented in any suitable manner, such as using a computer program operating on a computer system. A conventional computer system including a processor and random access memory, such as a remotely accessible application server, a network server, a personal computer, or a workstation, can be used. Additional computer system elements can include a storage device or information storage system, such as a mass storage system, and a user interface, such as a conventional monitor, keyboard, and tracking device. The computer system can be a stand-alone system or part of a network of computers including a server and one or more databases.

[0226] A CV event risk prediction biomarker analysis system may provide functions and operations for completing data analysis such as data collection, processing, analysis, reporting, and / or diagnosis. For example, in one embodiment, a computer system may execute a computer program that can receive, store, search, analyze, and report information regarding CV event risk prediction biomarkers. The computer program may include a plurality of modules that perform various functions or operations, such as a processing module for processing raw data and generating supplementary data, and an analysis module for analyzing the raw data and the supplementary data to generate a predicted state of CV event risk and / or a diagnosis or risk calculation. The calculation of the risk state of a CV event may optionally include generating or retrieving any other information that includes additional biomedical information regarding the state of an individual related to a disease, condition, or event, checking whether further tests may be desirable, or otherwise evaluating the health state of the individual.

[0227] Some embodiments described herein may be implemented to include a computer program product. The computer program product may include a computer-readable medium having computer-readable program code embodied therein for causing an application program to be executed on a computer having a database.

[0228] As used herein, a "computer program product" refers to a set of instructions organized in the form of statements of a natural or programming language, embodied in a physical medium of any nature (e.g., document, electronic, magnetic, optical, or otherwise), and capable of being used in a computer or other automated data processing system. Such programming language statements, when executed in a computer or data processing system, cause the computer or data processing system to operate in accordance with the statements of a particular content. Examples of computer program products include, but are not limited to, programs in source and object code, and / or test or data libraries stored on a computer-readable medium. Further, computer program products that enable a computer system or data processing device to operate in a preselected manner may be provided in many forms, including, but not limited to, original source code, assembly code, object code, machine language, encrypted or compressed versions of the foregoing, and any equivalents.

[0229] In one aspect, a computer program product is provided for assessing the risk of a CV event. The computer program product includes a computer-readable medium embodying program code executable by a processor of a computing device or system, the program code including code for retrieving data from a biological sample derived from a subject, the data including biomarker levels each corresponding to one of the biomarkers of Table 1 or Table 2; and code for executing a classification method indicative of the state of the subject's CV event risk as a function of the biomarker values.

[0230] In yet another aspect, a computer program product is provided for indicating the likelihood or risk of a CV event. The computer program product includes a computer-readable medium embodying program code executable by a processor of a computing device or system, the program code including code to extract data from a biological sample from a subject, the data including at least one biomarker value in the biological sample corresponding to at least one biomarker selected from the biomarkers provided in Table 1 or Table 2; and code to execute a classification method indicating the state of the subject's CV event risk as a function of the biomarker value.

[0231] Although various embodiments have been described as methods or apparatuses, it should be understood that the embodiments can be implemented via code used in conjunction with a computer, e.g., code inherent in a computer or accessible by a computer. For example, software and databases can be utilized to implement many of the above methods. Accordingly, in addition to embodiments achieved by hardware, it should also be noted that these embodiments can be achieved by using a manufactured article comprising a computer-usable medium embodying computer-readable program code that provides the ability to execute the functions disclosed herein. Thus, it is desirable that the embodiments be considered to be equally protected by this patent in their program code means as well. Further, the embodiments can be embodied as code stored in substantially any type of computer-readable memory, including but not limited to RAM, ROM, magnetic media, optical media, or magneto-optical media. Even more generally, the embodiments can be implemented in software, or in hardware, or in any combination thereof, including but not limited to software operating on a general-purpose processor, microcode, a programmable logic array (PLA), or an application specific integrated circuit (ASIC).

[0232] It is also contemplated that further embodiments may be achieved as computer signals embodied in a carrier wave and signals propagated through a transmission medium (e.g., electrical and optical). Thus, the various types of information described above may be formatted in a structure such as a data structure and transmitted as an electrical signal through a transmission medium or stored on a computer-readable medium.

[0233] It should also be noted that many of the structures, materials, and acts recited herein may be recited as means for performing a function or steps for performing a function. Thus, it should be understood that such terms can include all such structures, materials, or acts disclosed herein, including those incorporated by reference, and their equivalents.

[0234] The use of biomarkers and the various methods for determining biomarker values disclosed herein are detailed with respect to the assessment of the risk of CV events. However, the use of the process, the identified biomarkers, and the methods for determining biomarker values are well applicable to the identification of subjects who may or may not benefit from other specific types of cardiovascular conditions, any other disease or medical condition, or adjunctive medical treatments.

[0235] Other methods In some embodiments, the biomarkers and methods described herein are used to determine medical insurance premiums or coverage determinations and / or life insurance premiums or coverage determinations. In some embodiments, the results of the methods described herein are used to determine medical insurance premiums and / or life insurance premiums. In some such examples, an organization providing medical insurance or life insurance requests or otherwise obtains information regarding the risk or likelihood of a subject's CV event and uses that information to determine an appropriate medical insurance or life insurance premium for the subject. In some embodiments, the tests are requested by and payment is made by an organization providing medical insurance or life insurance. In some embodiments, the tests are used by a business or insurance system or an acquisition candidate of a company to predict future liabilities or costs and determine whether to proceed with an acquisition.

[0236] In some embodiments, the biomarkers and methods described herein are used to predict and / or manage the utilization of medical resources. In some such embodiments, the method is not performed for such prediction purposes, but the information obtained by the method is used in such prediction and / or management of the utilization of medical resources. For example, a testing facility or hospital may collect information regarding a number of subjects by the method to predict and / or manage the utilization of medical resources in a particular facility or a particular geographic region.

Examples

[0237] The following examples are provided for illustrative purposes only and are not intended to limit the scope of the present application as defined by the appended patent claims. The routine molecular biology techniques described in the following examples can be performed as described in standard laboratory manuals such as Sambrook et al., Molecular Cloning: A Laboratory Manual, 3rd ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y., (2001).

[0238] Example 1: Exemplary Biomarker Detection Using Aptamers Exemplary methods for detecting one or more biomarker proteins in a sample are described, for example, in Kraemer et al., PLoS One 6(10):e26332 and are described below. Three different quantification methods: microarray-based hybridization, Luminex bead-based methods, and qPCR are described.

[0239] Reagents HEPES, NaCl, KCl, EDTA, EGTA, MgCl2, and Tween-20 can be purchased, for example, from Fisher Biosciences. Dextran sulfate sodium salt (DxSO4) with a nominal molecular weight of 8000 can be purchased, for example, from AIC and is dialyzed against deionized water for at least 20 hours in one exchange. KOD EX DNA polymerase can be purchased, for example, from VWR. Tetramethylammonium chloride and CAPSO can be purchased, for example, from Sigma-Aldrich, streptavidin-phycoerythrin (SAPE) can be purchased, for example, from Moss Inc. 4-(2-Aminoethyl)-benzenesulfonyl fluoride hydrochloride (AEBSF) can be purchased, for example, from Gold Biotechnology. 96-well plates coated with streptavidin can be purchased, for example, from Thermo Scientific (Pierce Streptavidin Coated Plates HBC, clear, 96-well, product number 15500 or 15501). NHS-PEO4-biotin can be purchased, for example, from Thermo Scientific (EZ-Link NHS-PEO4-Biotin, product number 21329), dissolved in anhydrous DMSO, and can be stored frozen in single-use aliquots. IL-8, MIP-4, lipocalin-2, RANTES, MMP-7, and MMP-9 can be purchased, for example, from R&D Systems. Resistin and MCP-1 can be purchased, for example, from PeproTech, and tPA can be purchased, for example, from VWR.

[0240] Nucleic acid Conventional oligodeoxynucleotides (including amine-substituted and biotin-substituted ones) can be purchased, for example, from Integrated DNA Technologies (IDT). Z-Block is a single-stranded oligodeoxynucleotide with the sequence 5’-(AC-BnBn)7-AC-3’, where Bn represents a benzyl-substituted deoxyuridine residue. Z-Block can be synthesized using conventional phosphoramidite chemistry. The aptamer capture reagent can be synthesized by conventional phosphoramidite chemistry and purified, for example, on a 21.5×75 mm PRP-3 column operating at 80 °C on a Waters Autopurification 2767 system (or a Waters 600 series semi-automatic system) using a gradient of triethylammonium bicarbonate (TEAB) / ACN to elute the product. Detection is performed at 260 nm, and after fractions are collected over the main peak, the best fractions are pooled.

[0241] Buffer Buffer SB18 is composed of 40 mM HEPES, 101 mM NaCl, 5 mM KCl, 5 mM MgCl2, and 0.05% (v / v) Tween 20, and is adjusted to pH 7.5 with NaOH. Buffer SB17 is SB18 supplemented with 1 mM trisodium EDTA. Buffer PB1 is composed of 10 mM HEPES, 101 mM NaCl, 5 mM KCl, 5 mM MgCl2, 1 mM trisodium EDTA, and 0.05% (v / v) Tween-20, and is adjusted to pH 7.5 with NaOH. CAPSO elution buffer consists of 100 mM CAPSO (pH 10.0) and 1 M NaCl. Neutralization buffer contains 500 mM HEPES, 500 mM HCl, and 0.05% (v / v) Tween-20. Agilent Hybridization Buffer is a proprietary formulation supplied as part of a kit (Oligo aCGH / ChIP-on-chip hybridization kit). Agilent Wash Buffer 1 is a proprietary formulation (Oligo aCGH / ChIP-on-chip Wash Buffer 1, Agilent). Agilent Wash Buffer 2 is a proprietary formulation (Oligo aCGH / ChIP-on-chip Wash Buffer 2, Agilent). TMAC hybridization solution consists of 4.5 M tetramethylammonium chloride, 6 mM trisodium EDTA, 75 mM Tris-HCl (pH 8.0), and 0.15% (v / v) sarcosyl. KOD buffer (10x concentrate) consists of 1200 mM Tris-HCl, 15 mM MgSO4, 100 mM KCl, 60 mM (NH4)2SO4, 1% v / v Triton-X100, and 1 mg / mL BSA.

[0242] Sample Preparation Serum (stored at -80 °C in 100 μL aliquots) is thawed in a 25 °C water bath for 10 minutes and then stored on ice prior to sample dilution. The sample is mixed by gently vortexing for 8 seconds. A 6% serum sample solution is prepared by diluting it in 0.94×SB17 supplemented with 0.6 mM MgCl2, 1 mM trisodium EGTA, 0.8 mM AEBSF, and 2 μM Z-Block. A portion of the 6% serum stock solution is diluted 10-fold in SB17 to create a 0.6% serum stock solution. In some embodiments, the 6% and 0.6% stock solutions are used to detect high and low abundance analytes, respectively.

[0243] Preparation of capture reagent (aptamer) and streptavidin plate The aptamers are sorted into two mixtures according to the relative abundance of their associated analytes (or biomarkers). The stock solution concentration is 4 nM for each aptamer and the final concentration of each aptamer is 0.5 nM. The aptamer stock solution mixtures are diluted 4-fold in SB17 buffer, heated to 95 °C for 5 minutes before use, and cooled to 37 °C over 15 minutes. This denaturation-renaturation cycle aims to normalize the conformational isomer distribution of the aptamers, thereby ensuring reproducible aptamer activity regardless of historical variations. The streptavidin plate is washed twice with 150 μL of buffer PB1 before use.

[0244] Incubation and capture on the plate The heated-cooled 2× aptamer mixture (55 μL) is combined with an equal volume of 6% or 0.6% serum diluent to create mixtures containing 3% and 0.3% serum. The plate is sealed with a silicon sealing mat (Axymat silicon sealing mat, VWR) and incubated at 37 °C for 1.5 hours. The mixture is then transferred to the wells of a washed 96-well streptavidin plate and incubated for an additional 2 hours while shaking at 800 rpm on an Eppendorf Thermomixer set at 37 °C.

[0245] Manual assay Unless otherwise specified, the liquid is discarded and then removed by tapping twice on the stacked paper towels. The wash volume is 150 μL and all shaking incubations are performed on an Eppendorf Thermomixer set at 25 °C and 800 rpm. The mixture is removed by pipetting and the plate is washed twice for 1 minute with buffer PB1 supplemented with 1 mM dextran sulfate and 500 μM biotin, and then washed four times for 15 seconds with buffer PB1. A freshly prepared solution of 1 mM NHS-PEO4-biotin in buffer PB1 (150 μL / well) is added and the plate is incubated for 5 minutes with shaking. The NHS-biotin solution is removed and the plate is washed three times with buffer PB1 supplemented with 20 mM glycine and three times with buffer PB1. Then, 85 μL of buffer PB1 supplemented with 1 mM DxSO4 is added to each well and the plate is irradiated for 20 minutes with shaking at a distance of 5 cm under a BlackRay ultraviolet lamp (indicated wavelength 365 nm). The sample is transferred to a newly washed streptavidin-coated plate or an unused well of an existing washed streptavidin plate, and the high and low dilution sample mixtures are combined in a single well. The sample is incubated for 10 minutes at room temperature with shaking. The unadsorbed material is removed and the plate is washed eight times for 15 seconds each with buffer PB1 supplemented with 30% glycerol. Then, the plate is washed once with buffer PB1. The aptamer is eluted at room temperature for 5 minutes using 100 μL of CAPSO elution buffer. 90 μL of the eluate is transferred to a 96-well HybAid plate and 10 μL of neutralization buffer is added.

[0246] Semi-automated assay Place a streptavidin plate with the adsorbed mixture on the deck of a BioTek EL406 plate washer. The washer is programmed to perform the following steps. Remove unadsorbed substances by aspiration and wash the wells four times with 300 μL of buffer PB1 supplemented with 1 mM dextran sulfate and 500 μM biotin. Then wash the wells three times with 300 μL of buffer PB1. Add a solution of 1 mM NHS-PEO4-biotin in 150 μL of buffer PB1 freshly prepared (from a 100 mM stock solution in DMSO). Incubate the plate for 5 minutes with shaking. Aspirate the liquid and wash the wells eight times with 300 μL of buffer PB1 supplemented with 10 mM glycine. Add 100 μL of buffer PB1 supplemented with 1 mM dextran sulfate. After these automated steps, remove the plate from the plate washer and place it 5 cm apart on a thermoshaker mounted under a UV light source (BlackRay, indicated wavelength 365 nm) for 20 minutes. The thermoshaker is set to 800 rpm and 25 °C. After 20 minutes of irradiation, transfer the samples manually to a new washed streptavidin plate (or unused wells of an existing washed plate). Combine high-abundance (3% serum + 3% aptamer mixture) and low-abundance reaction mixtures (0.3% serum + 0.3% aptamer mixture) into a single well at this point. Place this "Catch-2" plate on the deck of a BioTek EL406 plate washer. The washer is programmed to perform the following steps. Incubate the plate for 10 minutes with shaking. Aspirate the liquid and wash the wells 21 times with 300 μL of buffer PB1 supplemented with 30% glycerol. Wash the wells five times with 300 μL of buffer PB1 and aspirate the final wash. Add 100 μL of CAPSO elution buffer and elute the aptamers for 5 minutes with shaking. After these automated steps, then remove the plate from the deck of the plate washer and manually transfer a 90 μL aliquot of the sample to the wells of a HybAid 96-well plate containing 10 μL of neutralization buffer.

[0247] Hybridization to a custom Agilent 8×15k microarray Transfer 24 μL of the neutralized eluate to a new 96-well plate and add 6 μL of 10× Agilent Block (Oligo aCGH / ChIP-on-chip Hybridization Kit, high capacity, Agilent 5188-5380), which contains a set of hybridization controls consisting of 10 Cy3 aptamers, to each well. Add 30 μL of 2× Agilent hybridization buffer to each sample and mix. Pipette 40 μL of the resulting hybridization solution manually into each "well" of a hybridization gasket slide (Hybridization Gasket Slide, 8 microarrays per slide format, Agilent). Place a custom Agilent microarray slide, which has 10 probes per array complementary to the random 40-nucleotide region of each aptamer and has a 20× dT linker, on the gasket slide according to the manufacturer's protocol. Clamp the assembly (Hybridization Chamber Kit, SureHyb compatible, Agilent) and incubate at 60 °C for 19 h while rotating at 20 rpm.

[0248] Washing after hybridization Pour approximately 400 mL of Agilent Wash Buffer 1 into each of two separate glass staining dishes. Disassemble and separate the slides (no more than two at a time) while immersing them in Wash Buffer 1, and then transfer them to a slide rack in a second staining dish that also contains Wash Buffer 1. Incubate the slides for an additional 5 min while stirring in Wash Buffer 1. Transfer the slides to Wash Buffer 2 that has been pre-equilibrated to 37 °C and incubate for 5 min while stirring. Transfer the slides to a fourth staining dish that contains acetonitrile and incubate for 5 min while stirring.

[0249] Imaging of the microarray The microarray slides are imaged using an Agilent G2565CA microarray scanner system at a resolution of 5 μm, in the Cy3-channel with 100% PMT setting, and using the XRD option enabled at 0.05. The resulting TIFF images are processed using Agilent's feature extraction software (version 10.5.1.1) according to the GE1_105_Dec08 protocol.

[0250] Design of Luminex probes The probes immobilized on the beads have 40 deoxynucleotides complementary to a random 40-nucleotide region at the 3'-end of the target aptamer. The aptamer complementary region is conjugated to the Luminex microspheres via a hexaethylene glycol (HEG) linker with a 5'-amino terminus. The biotinylated detection deoxynucleotide contains 17 - 21 deoxynucleotides complementary to the 5'-primer region of the target aptamer. The biotin moiety is added to the 3'-end of the detection oligo.

[0251] Binding of probes to Luminex microspheres The probes are conjugated to the Luminex microspheres mostly according to the manufacturer's instructions, with the following modifications: the amount of the amino-terminal oligonucleotide is 0.08 nmol per 2.5×10 6 microspheres, and the second EDC addition is 5 μL at 10 mg / mL. The coupling reaction is carried out on an Eppendorf ThermoShaker set at 25 °C and 600 rpm.

[0252] Hybridization of microspheres Vortex the microsphere storage solution (approx. 40,000 microspheres / μL) and sonicate it for 60 seconds using a Health Sonics ultrasonic cleaner (model: T1.9C) to suspend the microspheres. Dilute the suspended microspheres to 2,000 microspheres per reaction in 1.5×TMAC hybridization solution and mix by vortexing and sonication. Transfer 33 μL of the bead mixture per reaction to a 96-well HybAid plate. Add 7 μL of a 15 nM biotinylated detection oligonucleotide storage solution in 1×TE buffer to each reaction and mix. Add 10 μL of neutralized assay sample and seal the plate with a silicon cap mat seal. Incubate this plate first at 96 °C for 5 minutes and then overnight at 50 °C in a normal hybridization oven without stirring. Wet a filter plate (Dura pore, Millipore part number MSBVN1250, 1.2 μm pore size) with 75 μL of 1×TMAC hybridization solution supplemented with 0.5% (w / v) BSA. Transfer the entire sample volume from the hybridization reaction to the filter plate. Rinse the hybridization plate with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA and transfer any remaining material to the filter plate. Filter the sample under slow vacuum using 150 μL of buffer and vent for approximately 8 seconds. Wash the filter plate once with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA and resuspend the microspheres in the filter plate in 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. Protect the filter plate from light and incubate it on an Eppendorf Thermalmixer R at 1,000 rpm for 5 minutes. Then wash the filter plate once with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA.Add 75 μL of streptavidin phycoerythrin (SAPE-100, MOSS, Inc.) at 10 μg / mL in 1×TMAC hybridization solution to each reaction and incubate at 25 °C at 1000 rpm for 60 min on an Eppendorf Thermalmixer R. Wash the filter plate twice with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA and resuspend the microspheres in the filter plate in 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. Then, protect the filter plate from light and incubate at 1000 rpm for 5 min on an Eppendorf Thermalmixer R. Next, wash the filter plate once with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. Resuspend the microspheres in 75 μL of 1×TMAC hybridization solution supplemented with 0.5% BSA and analyze on a Luminex 100 instrument running Xponent 3.0 software. Count at least 100 microspheres per bead type with high PMT calibration and a doublet discriminator set at 7500 - 18000.

[0253] Reading of QPCR Dilute the qPCR calibration curve samples 10-fold with water to prepare a range of 10^8 - 10^2 copies and prepare a template-free control. Dilute the neutralized assay samples 40-fold in diH2O. Prepare the qPCR master mix at a 2× final concentration (2× KOD buffer, 400 μM dNTP mix, 400 nM forward and reverse primer mix, 2× SYBR Green I, and 0.5 U KOD EX). Add 10 μL of the 2× qPCR master mix to 10 μL of the diluted assay sample. Run qPCR using a BioRad MyIQ iCycler at 96 °C for 2 min, followed by 40 cycles of 96 °C for 5 s and 72 °C for 30 s.

[0254] Example 2. Cardiovascular Event Model for Prediction of Primary Cardiovascular Events To predict the risk or likelihood that a subject without a history of cardiovascular disease will experience a primary CV event within 4 years, a primary cardiovascular disease (CVD) model containing a panel of 13 biomarker proteins was developed. A primary CV event was defined as myocardial infarction, stroke, transient ischemic attack, hospitalization due to heart failure, or death due to CVD. The training / validation analysis was initiated using the HUNT3 dataset, a case cohort study design rich in primary CVD events. The study included 2,515 individuals, of which 41.51% experienced a CVD event within 5 years. See Krokstad et al., Int J Epidemiol. 2013;42:968-977. The data were split into 80% for training and 20% for validation. Prediction in an independent replication set from the Whitehall II study (see Marmot et al., “Health inequalities among British Civil Servants: the Whitehall II study.” Lancet 1991;337:1387-1393) was performed at the validation stage of effectiveness.

[0255] Model The cardiovascular disease (CVD) primary model is an accelerated failure time (AFT) parametric survival model using the Weibull distribution. This model features 13 biomarkers, age, and an interaction with age. A 4-year risk was reported. The predictions were binned into 4 risk bins and 3 risk classifications. The scoring in the training set over 4 years and the corresponding actual event incidence rates are reported in Table 3. [Table 3]

[0256] Results The concordance statistic (C statistic), the area under the ROC curve (AUC), the net reclassification improvement (NRI) for making no classification compared to the refitted PCE model (refitted to the HUNT3 training data), and the publicly available PCE model for 5-year prediction are shown in Table 4 below. The final model was also evaluated on the 20% holdout validation dataset of HUNT3. [Table 4] In the HUNT3 training set, 468 patients had a PCE risk score of less than 7.5%. The closest calculated 4-year proteomics risk cut-off value was 2.15%, and 469 patients were predicted to have a risk of less than 2.15%. Thus, the healthy baseline stratum was defined as individuals predicted to have a proteomics risk of less than 2.15% at 4 years. The mean proteomics risk in this population was 1.53%. Four risk bins were calculated as 1x, 2x, 3x, 4x, 5x, and 6x or more (see Table 3). Figure 1 shows the Kaplan-Meier survival curves stratified by the four risk bins for the HUNT3 training set. Figure 1 provides an overview of how well the empirical distribution of CVD primary events over time separates between the predicted risk bin groups, and the shaded area represents the 95% confidence interval of the Kaplan-Meier estimates. Figure 1 clearly shows separation between the risk bins, with no overlap in the survival distributions at 4 years.

[0257] The final model was also evaluated for all individuals in the training and validation datasets (all PCE risk scores including those less than 0.05). The results are shown in Table 5. The final model also had better performance compared to competing refitted clinical PCE models for all individuals. [Table 5] The primary CVD model was further characterized by improvements in several parameters. No significant effects based on gender or in the evaluation of potential confounders were seen. The model was applied to the assay QC samples of the 2005 iteration. The prediction was reproducible, with a mean of 0.05 and a standard deviation of 0.008. No large variations based on sample processing time were seen.

[0258] Validation of effectiveness Model effectiveness validation was performed on the Whitehall II dataset containing 265 individuals, 101 of whom had a CV event (38.11%). The RFU values of the analytes in the dataset were log10-transformed prior to analysis. Out-of-range log10 RFU values were complemented using the aptamer-specific maximum and minimum values calculated by winsorization during model development using the HUNT3 training data. The final proteomics model was then evaluated on the dataset at the 5-year time point, and the net reclassification improvement (NRI) was calculated as a comparison with the published PCE model. This is shown in Table 6.

Table 6

[0259] Example 3. Cardiovascular event panel for prediction of secondary cardiovascular events To predict the risk or likelihood that a subject with known established cardiovascular disease will have a secondary CV event within 4 years, a secondary cardiovascular disease (CVD) model containing a panel of 27 biomarker proteins was developed. Secondary CV events were defined as myocardial infarction, stroke, transient ischemic attack, hospitalization due to heart failure, or death. The training / validation analysis was initiated using a subcohort of the HUNT3 dataset composed of individuals meeting eligibility criteria for known established CVD. The HUNT3 study included 754 individuals whose samples passed QC metrics, and 208 (28%) CV events were observed within 4 years. The data were split into 80% for training and 20% for validation. The analysis was complemented by a 20% validation subset of the fifth visit dataset of the Atherosclerosis Risk in Communities (ARIC) study. (See The Atherosclerosis Risk in Communities(ARIC) Study:design and objectives.The ARIC investigators.Am.J.Epidemiol.1989;129(4):687-702). Predictions in an independent replication set using the remaining 80% of the fifth visit of the ARIC study and 20% of the HUNT3 dataset were keyed during validation.

[0260] Model The secondary cardiovascular disease (CVD) model is an AFT parametric survival model using the Weibull distribution. This model features a panel of 27 biomarkers (transformed to the log10 scale, centered, and scaled). The 4-year risk was reported. Predictions were binned into 4 risk bins and 3 risk classifications. Scoring in the training set over 4 years and the corresponding actual event incidence rates are reported in Table 7.

Table 7

[0261] Results The C-statistic, AUC, and NRI compared to the refitted PCE model for the 4-year prediction (refitted to the HUNT3 training data) are shown in Table 8 below. The secondary CVD model had higher C-statistic and AUC values and positive NRI over 4 years in the training (HUNT3) and validation (5th visit in the ARIC study) datasets.

Table 8

[0262] Validation of effectiveness Model effectiveness validation was performed on the 20% holdout of HUNT3 and the 80% holdout validation set of ARIC. The RFU values of the analytes in the dataset were log10-transformed before analysis. The analyte RFU values were then centered and scaled based only on the distribution of the training set, and out-of-range log10 RFU values were complemented using values calculated by winsorization. The C-statistic and AUV of the secondary CVD final model were evaluated in the dataset at the 4-year time point and compared to the refitted PCE model. The NRI compared to the refitted PCE model was calculated. This is shown in Table 9.

Table 9

Claims

**Claim 1** A method for screening a subject for the risk of a cardiovascular (CV) event or predicting the likelihood that the subject will experience a CV event, comprising: (a) forming a biomarker panel comprising 13 protein biomarkers including N-terminal proBNP, sTREM-1, MMP-12, antithrombin III, GPR56, gelsolin, ST4S6, CHSTC, FSH, IL-1 sRII, PLXB2, SAP, and TFPI; and (b) detecting the levels of each of the biomarkers in a sample from the subject of the panel. **Claim 2** The method according to claim 1, wherein if the levels of at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, or at least 13 of the biomarkers in a set of biomarkers are each abnormal compared to the control level of the respective biomarker, the subject has a high risk or likelihood of experiencing a CV event within 4 years. **Claim 3** The method according to claim 1 or 2, wherein the CV event is myocardial infarction, stroke, transient ischemic attack, hospitalization due to heart failure, or death. **Claim 4** The method according to any one of claims 1 to 3, wherein the subject has no history of CV events. **Claim 5** The method according to any one of claims 1 to 4, wherein the sample is selected from a blood sample, a serum sample, a plasma sample, and a urine sample. **Claim 6** The method according to claim 5, wherein the sample is a blood sample. **Claim 7** The method according to any one of claims 1 to 6, wherein the method is performed in vitro. **Claim 8** The method according to any one of claims 1 to 7, wherein the method comprises contacting the biomarkers of the sample from the subject with a set of capture reagents, each capture reagent of the set of capture reagents specifically binding to a different biomarker to be detected. **Claim 9** The method according to claim 8, wherein each capture reagent is an antibody or an aptamer. **Claim 10** The method according to claim 9, wherein each biomarker capture reagent is an aptamer. **Claim 11** At least one of the aptamers is an aptamer with a slow dissociation rate, and each of the aptamers with a slow dissociation rate binds to its target protein at a dissociation rate (t １／２ ), which is ≧ 30 minutes, ≧ 60 minutes, ≧ 90 minutes, ≧ 120 minutes, ≧ 150 minutes, ≧ 180 minutes, ≧ 210 minutes, or ≧ 240 minutes. The method according to claim 10 **Claim 12** The method according to claim 11, wherein at least one aptamer with a slow dissociation rate comprises nucleotides having at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 modifications.

13. The method according to any one of claims 1 to 12, wherein the subject is at least 40 years old.

14. The method according to any one of claims 1 to 13, wherein the risk or likelihood of a CV event is the risk or likelihood that the subject will have a cardiovascular event within 1 year, within 2 years, within 3 years, or within 4 years from the date the sample was taken from the subject.

15. The method according to claim 14, wherein the cardiovascular event is myocardial infarction, stroke, transient ischemic attack, hospitalization due to heart failure, or death due to cardiovascular disease.

16. The method according to claim 14 or 15, wherein the risk is determined as a quantitative probability.

17. The method according to any one of claims 14 to 16, wherein the risk is determined as a qualitative level of risk, and the qualitative level of risk is low, intermediate, or high.

18. The risk or likelihood of a CV event is related to a biomarker level and a) information corresponding to the presence of a cardiovascular risk factor selected from the group consisting of a history of myocardial infarction, angiographic evidence of stenosis greater than 50% in one or more coronary vessels, exercise-induced ischemia by treadmill or nuclear medicine test, or a history of coronary revascularization, b) information corresponding to the physical descriptors of the subject, c) information corresponding to the weight change of the subject, d) information corresponding to the ethnicity of the subject, e) information corresponding to the gender of the subject, f) information corresponding to the smoking history of the subject, g) information corresponding to the drinking history of the subject, h) information corresponding to the occupational history of the subject, i) information corresponding to the family history of cardiovascular disease or other circulatory system conditions of the subject, j) information corresponding to the presence or absence in the subject of at least one genetic marker associated with a higher risk of cardiovascular disease in the subject or the subject's family, k) information corresponding to the clinical symptoms of the subject, l) information corresponding to other clinical tests, m) information corresponding to the gene expression value of the subject, n) information corresponding to the known cardiovascular risk factors of the subject. Information corresponding to the imaging results of the subject obtained by a technique selected from the group consisting of electrocardiogram, echocardiogram, carotid ultrasound diagnosis of intima-media complex thickness, flow-mediated vasodilation response test, pulse wave velocity, ankle-brachial blood pressure ratio, stress echocardiogram, myocardial blood flow imaging, coronary artery calcium examination by CT, high-resolution CT angiography, MRI imaging, and other imaging techniques, p) Information regarding the drug treatment of the subject, q) Information corresponding to the age of the subject, and r) At least one item of additional biomedical information selected from information regarding the renal function of the subject, based on the method according to any one of claims 1 to 17.

19. The method according to any one of claims 1 to 18, wherein the risk or likelihood of a CV event is the risk or likelihood of a CV event for determining medical insurance premiums or life insurance premiums.

20. The method according to any one of claims 1 to 19, further comprising using the information obtained by the method for predicting and / or managing the utilization of medical resources, or for enabling a decision to acquire or purchase a medical business, hospital, or enterprise.

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