Cardiovascular risk event prediction and uses thereof

Abstract

To provide biomarkers, methods, devices, reagents, systems, and kits that enable prediction of cardiovascular events within a 5 year period, in view of the fact that an improved test would optimally require only a single blood, urine, or other sample and a single assay.SOLUTION: Provided herein are biomarkers, methods, devices, reagents, systems, and kits used to assess an individual for prediction of the risk of developing a primary or secondary cardiovascular (CV) event over a 4 year period.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority to U.S. Provisional Application No. 62 / 895,383, filed September 3, 2019, which is incorporated herein by reference in its entirety for all purposes.

[0002] This application relates generally to methods for detecting biomarkers and assessing the risk of future cardiovascular events in individuals, and more specifically to one or more biomarkers, methods, devices, reagents, systems, and kits used to assess individuals for a four-year prediction of their risk of developing a primary or secondary cardiovascular (CV) event, including, but not limited to, myocardial infarction, stroke, transient ischemic attack, hospitalization due to heart failure, and death. [Background technology]

[0003] Cardiovascular disease is the leading cause of death in the United States. 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 There are many existing and important predictors of risk for cardiovascular disease ("How to Predict Recurrent Cardiovascular Disease: The Heart & Soul Study" Am. J. Med. 121:50-57 (2008)), which are widely used in clinical practice and treatment trials. Unfortunately, receiver operating characteristic curves, hazard ratios, and concordances indicate that the performance of existing risk factors and biomarkers is modest (an AUC of approximately 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 are short-term and personal, and that respond to beneficial (and harmful) interventions and lifestyle changes on their own. The widely used Framingham equation has three major problems. First, it is too long-term. The Framingham equation provides a 10-year risk calculation, but people tend to underestimate future risk and are reluctant to change their behavior and lifestyle based on that risk. Second, they are poorly responsive to interventions. The Framingham equation is highly dependent on chronological age, which cannot be reduced, and gender, which cannot be changed. Third, in the high-risk population assumed here, the Framingham factor does not effectively distinguish 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 subjects into finer strata (e.g., deciles), the observed event rates are similar across many deciles.

[0004] Cardiovascular disease risk factors are widely used to improve the intensity and nature of treatment, and their use has undoubtedly contributed to the reductions in cardiovascular morbidity and mortality observed over the past two decades. Although these factors have been routinely incorporated into algorithms, unfortunately, they do not capture all risk (the most common initial manifestation of cardiovascular disease remains death). In fact, they probably only capture half of the risk. The area under the ROC curve for such risk factors in primary prevention is typically around 0.76, with much worse performance in secondary prevention (typically 0.62), only about one-quarter to one-half of the performance between a coin toss of 0.5 and perfect performance of 1.0.

[0005] Furthermore, the Framingham study of 3209 people (Wang et al., “Multiple Biomarkers for the Prediction of First-Order Pharmacology”) In the study "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 significantly improve the AUC when added to existing risk factors. The AUC for events from 0 to 5 years was 0.76 when using age, sex, and traditional risk factors, and 0.77 when adding the best combination of biomarkers to this combination; for secondary prevention, the situation is even worse.

[0006] Early identification of patients at higher risk for cardiovascular events within a 1- to 5-year time frame is important because more aggressive treatment of high-risk individuals may improve outcomes. Thus, optimal management to reduce the risk of cardiovascular events in patients considered at higher risk requires aggressive intervention, while patients at lower risk for cardiovascular events may be spared expensive and potentially invasive procedures that may not provide any beneficial effect to the patient.

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

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

[0009] The utility of two-dimensional electrophoresis is limited by low detection sensitivity; issues related to protein solubility, charge, and hydrophobicity; gel reproducibility; and the possibility that a single spot may represent multiple proteins. The main limitations of mass spectrometry are sample processing and separation, sensitivity to low-abundance proteins, signal-to-noise considerations, and the inability to readily identify detected proteins, depending on the format used. A limitation of immunoassay approaches to biomarker discovery is the need for multiple assays to measure multiple analytes. The central problem is the inability to perform antibody-based multiplex assays. One might simply create an array of high-quality antibodies and measure the analytes bound by those antibodies without sandwiching. (This would formally be equivalent to using the nucleic acid sequences of an entire genome to measure all DNA or RNA sequences in an organism or cell by hybridization. Hybridization experiments work because hybridization can be a stringent test for identity.) However, even very good antibodies typically do not have sufficient stringency to function in the selection of their binding partners in the context of blood or even cell extracts because the protein populations in these matrices vary widely in abundance, which can result in poor signal-to-noise ratios. Therefore, for immunoassay-based approaches to biomarker discovery, a different approach must be used: a multiplexed ELISA assay (i.e., sandwich) would be needed to simultaneously measure many analytes and obtain sufficient stringency to determine which analytes are true biomarkers. Sandwich immunoassays cannot be scaled to high content, and therefore biomarker discovery using stringent sandwich immunoassays is not possible using standard array formats. Finally, antibody reagents suffer from large lot-to-lot variations and reagent instability. The present platform for protein biomarker discovery overcomes this problem.

[0010] Many of these methods rely on or require some type of sample fractionation prior to analysis. Thus, the sample preparation required to conduct well-powered studies designed to identify and discover statistically relevant biomarkers in a set 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, candidate markers may be unstable to processing, marker concentrations may change, inappropriate aggregation or dissociation may occur, and inadvertent sample contamination may occur, obscuring subtle changes that predict early disease events.

[0011] It is widely recognized that biomarker discovery and detection methods using these technologies have serious limitations for identifying 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, irreproducibility in sample processing and fractionation, and the overall irreproducibility and lack of robustness of the methods. Furthermore, the data from these studies introduce bias and do not adequately address the complexity of the sample populations, including appropriate controls, in terms of distribution and randomization required to identify and validate biomarkers within target disease populations.

[0012] Although attempts aimed at discovering novel and effective biomarkers have been ongoing for decades, most of these attempts have been unsuccessful. Biomarkers for various diseases have generally always been identified in university laboratories through serendipitous discoveries during basic research on some disease process. Based on these discoveries, papers suggesting the identification of novel biomarkers using small amounts of clinical data have been published. However, most of these proposed biomarkers have not been confirmed as true or useful biomarkers. This is primarily because testing with small numbers of clinical samples provides only weak statistical evidence that effective biomarkers have actually been discovered. That is, early identifications were not rigorous with respect to statistical fundamentals.

[0013] Based on the history of unsuccessful biomarker discovery attempts, theories have been proposed that further the general understanding that biomarkers for diagnosis, prognosis, or prediction of risk for developing diseases and conditions are rare and difficult to discover. Biomarker studies based on two-dimensional gels or mass spectrometry support these ideas. Very few useful biomarkers have been identified by these approaches. However, it is commonly overlooked that two-dimensional gels and mass spectrometry 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. Other than the biomarker discovery platform of the present invention, no other proteomic biomarker discovery platform exists that can accurately measure protein expression levels at very low concentrations.

[0014] Much is known about the biochemical pathways of complex human biology. Many biochemical pathways result in or are initiated by the secretion of proteins that function locally in pathology. For example, growth factors are secreted to stimulate the replication of other cells in pathology, while other factors are secreted to evade the immune system. 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 understanding of biochemical pathways will understand that many pathology-specific proteins will be present in the blood at concentrations below (or even below) the detection limits of two-dimensional gels and mass spectrometry. What is needed prior to identifying this relatively abundant number of disease biomarkers is a proteomics platform capable of analyzing proteins at concentrations below those detectable by two-dimensional gels or mass spectrometry.

[0015] As previously mentioned, cardiovascular events can be prevented through proactive treatment if the propensity for such events can be accurately determined. Targeting such interventions to those who need them most and / or away from those who need them least can improve the efficiency of healthcare resource allocation and simultaneously reduce costs. Furthermore, if patients have accurate, short-term information about their individualized likelihood of cardiovascular events, this information is less likely to be negated than long-term, population-based information, and it may lead to improved lifestyle choices and improved medication adherence, which may translate into benefits. Existing multi-marker tests require the collection of multiple samples from individuals or require samples to be divided among multiple assays. Optimal would be improved tests requiring only a single blood, urine, or other sample and a single assay. Thus, there is a need for biomarkers, methods, devices, reagents, systems, and kits that enable the prediction of cardiovascular events within a five-year time frame. Summary of the Invention

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

[0017] Cardiovascular disease involves multiple biological processes and tissues. Examples of biological systems and processes associated with cardiovascular disease are inflammation, thrombosis, disease-associated 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, menopausal status, and age, as well as according to the state of coagulation and vascular function. Because these systems communicate in part through protein-based signaling systems and multiple proteins can be measured in a single blood sample, the present invention provides a single-sample, single-assay, multiprotein-based test that focuses on proteins from specific biological systems and processes involved in cardiovascular disease.

[0018] In some embodiments, methods are provided for detecting the levels of a set of biomarkers. In some embodiments, such methods include: Embodiment 1. Method for detecting the level of a set of biomarker proteins in a sample from a subject It is a law, a. contacting the sample from the subject with a set of capture reagents, each capture reagent specifically binding to a different biomarker protein, and one capture reagent specifically binding to sTREM1; 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 of embodiment 1, wherein one capture reagent specifically binds to MMP-12.

[0020] Embodiment 3. The method of embodiment 1 or 2, wherein one capture reagent specifically binds to N-terminal proBNP.

[0021] Embodiment 4. The method of any one of embodiments 1 to 3, wherein one capture reagent specifically binds to antithrombin III.

[0022] Embodiment 5. The method of any one of embodiments 1 to 4, wherein one capture reagent specifically binds to GPR56.

[0023] Embodiment 6. The method of any one of embodiments 1-5, wherein one capture reagent specifically binds to gelsolin.

[0024] Embodiment 7. The method of any one of embodiments 1 to 6, wherein one capture reagent specifically binds to ST4S6.

[0025] Embodiment 8. The method of any one of embodiments 1-7, wherein one capture reagent specifically binds to CHSTC.

[0026] Embodiment 9. The method of any one of embodiments 1 to 8, wherein one capture reagent specifically binds to FSH.

[0027] Embodiment 10. The method of any one of embodiments 1-9, wherein one capture reagent specifically binds to IL-1 sRII.

[0028] Embodiment 11. The method of any one of embodiments 1 to 10, wherein one capture reagent specifically binds to PLXB2.

[0029] Embodiment 12. The method of any one of embodiments 1 to 11, wherein one capture reagent specifically binds to SAP.

[0030] Embodiment 13. The method of any one of embodiments 1 to 12, wherein one capture reagent specifically binds to TFPI.

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

[0032] Embodiment 15. The method of any one of embodiments 1 to 13, wherein the set of biomarkers comprises 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 at least 13 biomarkers.

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

[0034] Embodiment 17. The method of any one of embodiments 1 to 13, wherein the set of biomarkers comprises at least 13 biomarkers.

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

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

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

[0038] Embodiment 21. The method of any one of embodiments 1 to 20, comprising determining the subject's risk of a primary cardiovascular event within four years from the date the sample was collected from the subject.

[0039] Embodiment 22. The method of embodiment 21, wherein the subject is at risk of having a primary cardiovascular event within 1, 2, 3, or 4 years from the date the sample is taken from the subject.

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

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

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

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

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

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

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

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

[0048] Embodiment 31. The method of embodiment 1, 2, or 3, wherein one capture reagent specifically binds to GOLM1. 31. The method according to any one of claims 27 to 30.

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

[0050] Embodiment 33. The method of any one of embodiments 1, 2, or 27-32, wherein one capture reagent specifically binds to ERBB3.

[0051] Embodiment 34. The method of any one of embodiments 1, 2, or 27-33, wherein one capture reagent specifically binds to suPAR.

[0052] Embodiment 35. The method of any one of embodiments 1, 2, or 27-34, wherein one capture reagent specifically binds to GDF-11 / 8.

[0053] Embodiment 36. The method of any one of embodiments 1, 2, or 27-35, wherein one capture reagent specifically binds to JAM-B.

[0054] Embodiment 37. The method of any one of embodiments 1, 2, or 27-36, wherein one capture reagent specifically binds to ATS13.

[0055] Embodiment 38. The method of any one of embodiments 1, 2, or 27-37, wherein one capture reagent specifically binds to spondin-1.

[0056] Embodiment 39. The method of any one of embodiments 1, 2, or 27-38, wherein one capture reagent specifically binds to NCAM-120.

[0057] Embodiment 40. The method of any one of embodiments 1, 2, or 27-39, wherein one capture reagent specifically binds to TFF3.

[0058] Embodiment 41. The method of any one of embodiments 1, 2, or 27-40, wherein one capture reagent specifically binds to SIRT2.

[0059] Embodiment 42. The method of any one of embodiments 1, 2, or 27-41, wherein one capture reagent specifically binds to ANP.

[0060] Embodiment 43. The method of any one of embodiments 1, 2, or 27-42, wherein one capture reagent specifically binds to NELL1.

[0061] Embodiment 44. The method of any one of embodiments 1, 2, or 27-43, wherein one capture reagent specifically binds to LRP11.

[0062] Embodiment 45. The method of any one of embodiments 1, 2, or 27-44, wherein one capture reagent specifically binds to NDST1.

[0063] Embodiment 46. The method of any one of embodiments 1, 2, or 27-45, wherein one capture reagent specifically binds to PTPRJ.

[0064] Embodiment 47. The method of any one of embodiments 1, 2, or 27-46, wherein one capture reagent specifically binds to CILP2.

[0065] Embodiment 48. The method of any one of embodiments 1, 2, or 27-47, wherein one capture reagent specifically binds to CA2D3.

[0066] Embodiment 49. The method of any one of embodiments 1, 2, or 27-48, wherein one capture reagent specifically binds to ITI heavy chain H2.

[0067] Embodiment 50. The method of any one of embodiments 1, 2, or 27-49, wherein one capture reagent specifically binds to IGDC4.

[0068] Embodiment 51. The method of any one of embodiments 1, 2, or 27-50, wherein one capture reagent specifically binds to BNP.

[0069] Embodiment 52. The method of any one of embodiments 27 to 51, wherein the set of biomarkers comprises at least three biomarkers.

[0070] Embodiment 53. The method of any one of embodiments 1, 2, or 27-51, wherein the set of biomarkers comprises 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 of any one of embodiments 1, 2, or 27-51, wherein the set of biomarkers consists of 2 to 27 biomarkers.

[0072] Embodiment 55. The method of any one of embodiments 1, 2, or 27-51, wherein the set of biomarkers comprises at least 27 biomarkers.

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

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

[0075] Embodiment 58. The method of any one of embodiments 27 to 57, wherein the subject has apparently stable cardiovascular disease.

[0076] Embodiment 59. The method of embodiment 58, wherein the apparently stable cardiovascular disease comprises a history of myocardial infarction, a history of stroke, a history of heart failure, a history of revascularization, an abnormal stress test, imaging suggestive of coronary heart disease, or an abnormal coronary calcium score.

[0077] Embodiment 60. The method of embodiment 59, wherein the myocardial infarction or stroke occurred at least 6 months prior to the date the sample was obtained from the subject.

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

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

[0080] Embodiment 63. The method of any one of embodiments 27 to 62, comprising determining the subject's risk of a secondary cardiovascular event within four years from the date the sample was collected from the subject.

[0081] Embodiment 64. The method of embodiment 63, wherein the subject is at risk of having a secondary cardiovascular event within 1, 2, 3, or 4 years from the date the sample was obtained from the subject.

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

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

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

[0085] Embodiment 68. The method of 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 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, where N is an integer between 3 and 13; (b) detecting the level in a sample from the subject of each of the N biomarkers in the panel.

[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, spondin-1, NCAM-120, TFF3, SIRT2, ANP, NELL1, LRP11, NDST1, PTPRJ, CILP2, CA2D3, ITI heavy chain H2, and IGDC4, where N is an integer between 8 and 27; (b) detecting the level in a sample from the subject of each of the N biomarkers in the panel.

[0088] In some embodiments, a method is provided for predicting the likelihood of a subject experiencing 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, where N is an integer between 3 and 13; (b) detecting the level in a sample from the subject of each of the N biomarkers 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, spondin-1, NCAM-120, TFF3, SIRT2, ANP, NELL1, LRP11, NDST1, PTPRJ, CILP2, CA2D3, ITI heavy chain H2, and IGDC4, where N is an integer between 8 and 27; (b) detecting the level in a sample from the subject of each of the N biomarkers in the panel.

[0090] In some embodiments, methods are provided for screening a subject for risk or likelihood of a cardiovascular (CV) event comprising detecting levels 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 biomarkers in a panel comprising N biomarkers.

[0091] In some embodiments, a subject has a high risk or likelihood of experiencing a CV event within four years 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 biomarkers in a set are each abnormal compared to a control level of the respective biomarker.

[0092] In some embodiments, the method comprises detecting the level of one or more biomarkers in Table 1. In some embodiments, the method comprises 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 has no history of a CV event. In some embodiments, the subject is classified as high risk by the American College of Cardiology (ACC) pooled cohort equation (PCE). Goff DC, Jr. et al., "ACC / AHA Guideline on the See, "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 intermediate risk by PCE. In some embodiments, the subject is classified as low risk by 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 for 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 comprises contacting biomarkers in a sample from a subject with a set of biomarker capture reagents, each biomarker capture reagent in the set specifically binding 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 a slow off-rate aptamer. In some embodiments, the at least one slow off-rate aptamer comprises nucleotides with 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 slow off-rate aptamer has a dissociation rate (t) of ≥ 30 minutes, ≥ 60 minutes, ≥ 90 minutes, ≥ 120 minutes, ≥ 150 minutes, ≥ 180 minutes, ≥ 210 minutes, or ≥ 240 minutes. 1 / 2 ) and binds to its target protein.

[0096] In some embodiments, the risk or likelihood of a CV event is determined based on a combination of biomarker levels and: a) Information corresponding to the presence of cardiovascular risk factors selected from the group consisting of a history of myocardial infarction, angiographic evidence of greater than 50% stenosis in one or more coronary vessels, exercise-induced ischemia by treadmill or nuclear medicine testing, or a history of coronary revascularization; b) information corresponding to the subject's physical descriptors; c) Information corresponding to the subject's weight change; d) Information corresponding to the ethnicity of the subject; e) Information corresponding to the subject's gender; f) Information corresponding to the subject's smoking history; g) Information corresponding to the subject's drinking history; h) Information corresponding to the subject's professional history; i) information corresponding to the subject's family history of cardiovascular disease or other cardiovascular conditions; 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 in the subject's family members; k) Information corresponding to the subject's clinical symptoms; l) Other information equivalent to clinical tests; m) information corresponding to the gene expression values of the subject; and n) information corresponding to the subject's known cardiovascular risk factors, for example, intake of a high saturated fat diet, a high salt diet, a high cholesterol diet; o) Information corresponding to the subject's imaging results obtained by techniques selected from the group consisting of electrocardiogram, echocardiography, carotid ultrasound of intima-media thickness, flow-mediated dilation test, pulse wave velocity, ankle-brachial index, stress echocardiography, myocardial perfusion imaging, coronary artery calcium testing by CT, high-resolution CT angiography, MRI imaging, and other imaging techniques; p) information about the subject's medication; q) Information appropriate to the age of the subject; and r) at least one item of additional biomedical information selected from information regarding the subject's renal function.

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

[0098] In some embodiments, the method includes determining the risk or likelihood of a CV event to determine medical or life insurance premiums. In some embodiments, the method further includes determining medical or life insurance coverage or premiums. In some embodiments, the method includes using the method to predict and / or manage 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, hospital, or 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 includes: searching, on a computer, for a subject's biomarker information, the biomarker information including (a) levels of 3 to 13 biomarkers selected from Table 1, or (b) levels of 8 to 27 biomarkers selected from Table 2, in a sample from the subject; performing, on the computer, a classification of each of the biomarker values; and presenting an assessment of the individual's risk of a CV event based on the plurality of classifications. In some embodiments, presenting the assessment of the subject's risk or likelihood of a CV event includes displaying the results on a computer display. [Brief explanation of the drawings]

[0100] [Figure 1] Kaplan-Meier survival curves for the HUNT3 training set stratified by four risk bins for the primary CVD model, with shaded regions representing the 95% confidence intervals of the Kaplan-Meier estimates. Lines are, from top to bottom, 1x (<0.0215), n=469; 2x-3x (<0.0505), n=944; 4x-5x (<0.077), n=285; and 6x or greater (>0.077), n=315. [Figure 2] Kaplan-Meier survival curves for the HUNT3 training set stratified by four risk bins for the quadratic CVD model are shown, with shaded areas representing the 95% confidence intervals of the Kaplan-Meier estimates. Lines are, from top to bottom: <0.075, n=117; <0.25, n=285; >0.5, n=121; and >0.5, n=82. [Figure 3] Kaplan-Meier survival curves for the ARIC study validation set at visit 5, stratified by four risk bins for the quadratic CVD model, with shaded regions representing the 95% confidence intervals of the Kaplan-Meier estimates. Lines, from top to bottom, are <0.075, n=35; <0.25, n=103; <0.5, n=43; and >0.5, n=27. [Figure 4]Survival curves for the HUNT3 validation set stratified by cutoff value are shown. From top to bottom, the lines are: <0.075, n=24; <0.25, n=61; <0.5, n=25; >0.5, n=29. [Figure 5] Survival curves for the ARIC study efficacy validation set at Visit 5 stratified by cutoff values are shown below: <0.075, n=13; <0.25, n=202; <0.5, n=271; >0.5, n=345. [Figure 6] 1 illustrates a non-limiting exemplary computer system for use with various computer-implemented methods described herein. [Figure 7] 1 shows non-limiting exemplary aptamer assays that can be used to detect one or more biomarkers in a biological sample. [Figure 8A] 1 shows certain exemplary modified pyrimidines that can be incorporated into aptamers, such as slow off-rate aptamers. [Figure 8B] 1 shows certain exemplary modified pyrimidines that can be incorporated into aptamers, such as slow off-rate aptamers. DETAILED DESCRIPTION OF THE INVENTION

[0101] While the invention will be described in conjunction with certain exemplary embodiments, it will be understood that the invention, as defined by the claims, is not limited to those embodiments.

[0102] One skilled in the art will recognize many methods and materials similar or equivalent to those described herein, which could be used in the practice of the present invention. It is never limited.

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

[0104] All publications, published patent documents, and patent applications cited in this specification are herein incorporated by reference to the same extent as if each individual publication, published patent document, or patent application was specifically and individually indicated to be incorporated by reference herein.

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

[0106] The present 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" refers to a disorder or malfunction of any part of the circulatory system. In one embodiment, "cardiovascular event" refers to stroke, transient ischemic attack (TIA), myocardial infarction (MI), sudden death due to malfunction of the circulatory system, 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 a second or subsequent CV event experienced by a subject.

[0108] Cardiovascular events may 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 that are used, either alone or in 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. As described below, representative embodiments include the biomarkers listed in Table 1 or Table 2.

[0110] While some of the described CV event biomarkers may be useful alone in assessing the risk or likelihood of a CV event, methods are also described herein for grouping multiple subsets of CV event biomarkers, where each grouping or subset selection is useful as a panel of three or more biomarkers, and are referred to interchangeably herein as "biomarker panels" and panels. Accordingly, various embodiments provide combinations that include 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 all thirteen of the biomarkers in Table 1. Other various embodiments provide combinations that include at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, or at least sixteen of the biomarkers in Table 2. Combinations including 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 species are provided.

[0111] The terms "biological sample," "sample," and "test sample" are used interchangeably herein to refer to any substance, biological fluid, tissue, or cell obtained from or otherwise derived from an individual. Examples include blood (including, for example, whole blood, white blood cells, peripheral blood mononuclear cells, buffy coat, plasma, and serum), sputum, tears, mucus, nasal washings, nasal aspirates, urine, saliva, peritoneal washings, ascites, cyst fluid, glandular fluid, lymph, bronchial aspirates, synovial fluid, joint aspirates, organ secretions, cells, cell extracts, and cerebrospinal fluid. Examples also include experimentally separated fractions of all of the above. For example, a blood sample can be fractionated into serum, plasma, or fractions containing specific types of blood cells, such as red blood cells or leukocytes (white blood cells). 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 may be a combination of samples from an individual, such as a combination of tissue and liquid samples. The term "biological sample" also includes materials containing homogenized solid material, such as from a stool sample, tissue sample, or tissue biopsy. The term "biological sample" also includes materials from tissue culture or cell culture. Any suitable method for obtaining a biological sample may be used, and exemplary methods include, for example, phlebotomy, swab (e.g., oral swab), and fine needle aspiration biopsy. Exemplary tissues amenable to fine needle aspiration include lymph node, lung, thyroid, breast, pancreas, and liver. Samples may also be collected by, for example, microdissection (e.g., laser capture microdissection (LCM) or laser microdissection (LMD)), bladder washing, smear (e.g., PAP smear), or breast ductal lavage. A "biological sample" obtained from or derived from a subject also includes any such sample 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 may be obtained by collecting biological samples from multiple subjects and pooling them, or by pooling aliquots of each subject's biological sample. The pooled sample may be processed as described herein as a sample from a single subject; for example, if a poor prognosis is confirmed in the pooled sample, each subject's biological sample may be retested to determine which subject(s) have a high or low risk of a CV event.

[0113] For 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 biological sample. The data may have been reformatted, modified, or have their values altered to some extent after generation, such as by conversion from units in one measurement system to units in another measurement system, but the data is understood to have been obtained from or generated using the biological sample.

[0114] "Target," "target molecule," and "analyte" are used interchangeably herein to refer to any molecule of interest that may be present in a biological sample. A "molecule of interest" includes any minor changes in a particular molecule, such as, in the case of a protein, minor changes in amino acid sequence, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification, such as conjugation with a labeling moiety, that do not substantially change the identity of the molecule. A "target molecule," "target," or "analyte" refers to one or a set of copies of a molecule or multimolecular structure. Exemplary target molecules include proteins, polypeptides, nucleic acids, carbohydrates, lipids, polysaccharides, glycoproteins, and the like. , hormones, receptors, antigens, antibodies, affibodies, antibody mimetics, viruses, pathogens, toxic substances, 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 capable of specifically binding to a biomarker. "Target protein capture reagent" refers to a molecule capable of specifically binding 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 above capture reagents. In some embodiments, the capture reagent is selected from an aptamer and an antibody.

[0116] The term "antibody" refers to full-length antibodies of any species, 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 synthetically derived antibodies, such as antibodies and fragments derived by phage display, affibodies, nanobodies, etc.

[0117] As used herein, "marker" and "biomarker" are used interchangeably to refer to a target molecule that is indicative of or symptomatic of a normal or abnormal process in a subject, or that is indicative of or symptomatic of a disease or other condition in a subject. More specifically, a "marker" or "biomarker" is an anatomical, physiological, biochemical, or molecular parameter that is associated with the presence of a particular physiological condition or process, whether normal or abnormal, and, if abnormal, whether chronic or acute. Biomarkers can be detected and measured by a variety of methods, including 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 measurement obtained using any analytical method to detect a biomarker in a biological sample and indicating the presence, absence, absolute amount or concentration, relative amount or concentration, titer, level, expression level, ratio of measured levels, etc. of a biomarker in a biological sample, relating to, or corresponding to, a biomarker in a 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 symptomatic of an abnormal process or disease or other condition in a subject, the biomarker is generally described as being either overexpressed or underexpressed compared to an expression level or value of the biomarker that indicates or is symptomatic of the absence of a normal process or disease or other condition in the subject. "Upregulated," "upregulated," "overexpression," "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 typically detected in a similar biological sample from a healthy or normal subject. The 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] "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 that is typically detected in a similar biological sample from a healthy or normal subject. The terms 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 also be referred to as being "differentially expressed" or having a "differential level" or "differential value" compared to a "normal" expression level or value of that biomarker that is indicative of or symptomatic of a normal process or the absence of a disease or other condition in a subject. Thus, the "differential expression" of a biomarker may also be referred to as a variation from the "normal" expression level of that 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 who does not have a disease or condition, or from a subject who is not suspected of having or is not at risk of having a disease or condition, or from a subject who has experienced a first or initial cardiovascular event but not a secondary cardiovascular event, or from a subject who has stable cardiovascular disease. The control level can refer to the average level of the target molecule in samples from a population of subjects who do not have a disease or condition, or from subjects who are not suspected of having or are not at risk of having a disease or condition, or from subjects who have experienced a first or initial cardiovascular event but not a secondary cardiovascular event, or from subjects who have stable cardiovascular disease, or a combination thereof.

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

[0124] "Diagnosing," "diagnosing," "diagnosis," and variations thereof refer to detecting, determining, or distinguishing a subject's health state or condition based on one or more signs, symptoms, data, or other information about the subject. A subject's health state may be diagnosed as healthy / normal (i.e., a diagnosis of the absence of a 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 "diagnosing," "diagnosing," "diagnosis," and the like, with respect to a particular disease or condition, encompass the early detection of the disease; characterization or classification of the disease; detection of progression, remission, or recurrence of the disease; and detection of disease response after administering treatment or therapy to a subject. Predicting CV event risk includes distinguishing between subjects with and without an elevated CV event risk.

[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 encompass assessing the response of the disease or condition after administering a treatment or therapy to the subject.

[0126] "Assess," "assessing," "evaluation," and variations thereof are used interchangeably with "diagnosis" and " "Assessing" a CV event risk encompasses both "predicting" and "prognosing," and includes determining or predicting the future course of a disease or condition in a subject who is disease-free, and determining or predicting the risk of recurrence of the disease or condition in a subject who has apparently been cured of the disease or condition. The term "assessing" also encompasses assessing a subject's response to a treatment, e.g., predicting whether a subject is likely to respond well to a therapeutic agent or unlikely to respond to a therapeutic agent (or, e.g., whether a subject will experience toxic or other undesirable side effects), selecting a therapeutic agent to administer to a subject, or monitoring or determining a subject's response to a treatment administered to the subject. Thus, "assessing" a risk of a CV event can include, for example, any of the following: predicting a future CV event risk in a subject; predicting a CV event risk in a subject who does not have an apparent CV problem; predicting a particular type of CV event; predicting when a CV event will occur; or determining or predicting a subject's response to a CV treatment or selecting a CV treatment to administer to a subject based on determining biomarker values from a biological sample from the subject. Assessing the risk of a CV event can include embodiments such as assessing the risk of a CV event on a continuous scale or categorizing the risk of a CV event in ascending categories. Categorizing the risk can include, for example, categorizing into two or more categories, 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 assessment of the risk of a CV event is for a predetermined time period. Non-limiting exemplary such predetermined time 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 assessments of a subject using biomarkers other than any of those described herein that are related to CV risk, or more specifically, CV event risk. "Additional biomedical information" may include any of the following: subject's physical descriptors, including the subject's height and / or weight; subject's age; subject's sex; weight change; subject's ethnicity; occupational history; family history of cardiovascular disease (or other cardiovascular disorder); presence of genetic marker(s) correlated with a higher risk of cardiovascular disease (or other cardiovascular disorder) in the subject or family; changes in carotid intimal thickness; clinical symptoms such as chest pain, weight gain, or decreased gene expression values; subject's physical descriptors, including physical descriptors observed by radiological imaging; smoking status; alcohol drinking history; occupational history; dietary habits, i.e., salt, saturated fat, and cholesterol intake; caffeine intake; and imaging information, such as electrocardiogram, echocardiography, carotid ultrasound of intima-media thickness, flow-mediated dilation testing, pulse wave velocity, ankle-brachial index, stress echocardiography, myocardial perfusion imaging, coronary artery calcium testing by CT, high-resolution CT angiography, MRI imaging, and other imaging modalities; and subject's medications. Combining a test of biomarker levels with an assessment of any additional biomedical information, including other clinical tests (e.g., HDL, LDL tests, CRP levels, Nt-proBNP tests, BNP tests, high-sensitivity troponin tests, galectin-3 tests, serum albumin tests, creatine tests), may improve the sensitivity, specificity, and / or AUC of CV event prediction, compared to, for example, a biomarker test alone or assessment of any specific item of additional biomedical information alone (e.g., carotid intima thickness imaging alone). The additional biomedical information may be obtained from the subject using routine techniques known in the art, such as from the subject using a routine patient questionnaire or health history questionnaire, or from a healthcare professional, etc.Combining testing of biomarker levels with evaluation of any additional biomedical information may improve the sensitivity, specificity, and / or threshold for predicting CV events (or other cardiovascular-related applications), for example, compared to biomarker testing alone or evaluation of any particular item of additional biomedical information alone (e.g., CT imaging alone).

[0128] As used herein, "detecting" or "determining" with respect to a biomarker value includes the use of both the instrumentation used to observe and record a signal corresponding to the biomarker level as well as the substance or substances 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, etc.

[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 November 12, 2013 (Print ISSN: 0009-7322, Online ISSN: 1524-4539). As used herein, a "high" PCE risk category is a predicted 10-year risk of 20.0% or greater for an atherosclerotic cardiovascular disease (ASCVD) hard event (defined as a non-fatal myocardial infarction or coronary heart disease (CHD) death, or a first fatal or non-fatal stroke), an "intermediate" PCE risk category is a predicted 10-year risk of 10.0-19.9% for an ASCVD hard event, and a "low" PCE risk category is a predicted 10-year risk of less than 10.0% for an ASCVD hard event. See Goff, page 16, Table 5.

[0130] As used herein, "solid support" refers to any substrate having a surface to which molecules can be directly or indirectly attached, either covalently or non-covalently. A "solid support" can have a variety of physical forms, including, for example, membranes; chips (e.g., protein chips); slides (e.g., glass slides or cover slips); columns; hollow, solid, semi-solid particles with pores or cavities, such as beads; gels; fibers, including fiber optic materials; matrices; and sample containers. Exemplary sample containers include sample wells, tubes, capillaries, vials, and any other container, groove, or depression capable of holding a sample. Sample containers can be included in multi-sample platforms, such as microtiter plates, glass slides, microfluidic devices, and the like. Supports can be composed of natural or synthetic materials, organic or inorganic materials. The composition of the solid support to which the capture reagent is attached generally depends on the method of attachment (e.g., covalent attachment). Other exemplary containers include microdroplets, microfluidically controlled, or bulk oil-in-water 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 comprising the solid support may contain reactive groups, such as carboxy, amino, or hydroxyl groups, which are used to attach capture reagents. Polymeric solid supports include, for example, polystyrene, polyethylene glycol tetraphthalate, polyvinyl acetate, polyvinyl chloride, polyvinylpyrrolidone, 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, e.g., Luminex®-type coded particles, magnetic particles, and glass particles.

[0131] Exemplary Uses of Biomarkers In various exemplary embodiments, a method is provided for assessing the risk or likelihood of a CV event in a subject by detecting one or more biomarker values corresponding to one or more biomarkers present in the subject's circulation, for example, in blood, serum, or plasma, using a number of analytical methods, including, for example, any of the analytical methods described herein. These biomarkers are, for example, differentially expressed in subjects with a high risk of a CV event compared to subjects who do not have a high risk of a CV event. The detection of the differential expression of biomarkers in a subject can be used to predict the risk of a CV event, for example, within a 1-year, 2-year, 3-year, 4-year, or 5-year period.

[0132] In addition to testing biomarker levels as a stand-alone diagnostic test, biomarker levels may be combined with the determination of single nucleotide polymorphisms (SNPs) or other genetic lesions or genetic variability that indicate an increased risk of susceptibility to a disease or condition (see, e.g., Amos et al. (See, e.g., Wang et al., Nature Genetics 40, 616-622 (2009)). Biomarker levels may be used in conjunction with radiological screening. Biomarker levels may also be used in conjunction with associated symptom 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 assessed, including, for example, increasing the level of treatment for high-risk subjects after the risk of a CV event has been determined. In addition to testing biomarker levels in conjunction with associated symptoms or risk factors, information about the biomarker may also be evaluated in conjunction with other types of data, particularly data indicative of a subject's risk of a cardiovascular event (e.g., the patient's medical history, symptoms, family history of cardiovascular disease, smoking or drinking history, the presence of risk factors, such as genetic marker(s), and / or the status of other biomarkers). These various data may be evaluated by automated methods, such as computer programs / software that may be embodied in a computer or other apparatus / device.

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

[0134] Biomarker testing may be combined with guidelines and cardiovascular risk algorithms currently used in clinical practice. For example, the Framingham risk score uses risk factors to derive a risk score, including LDL-cholesterol and HDL-cholesterol levels, abnormal glucose concentrations, smoking, systolic blood pressure, and diabetes. The frequency of high-risk patients increases with age, and a higher proportion of men than women are at high risk.

[0135] Any of the described biomarkers can also be used in imaging studies, for example, an imaging agent can be attached to any of the described biomarkers, which can be used to aid in predicting cardiovascular event risk, to monitor response to therapeutic interventions, and to select target populations in clinical trials, among other uses.

[0136] Detection and Determination of Biomarkers and Biomarker Levels Biomarker levels of the biomarkers described herein can be detected using any of a variety of known analytical methods. In one embodiment, biomarker values are measured using a capture reagent The capture reagent is detected using a capture reagent. In various embodiments, the capture reagent can be exposed to the biomarker in solution or 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 in conjunction 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 to be 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 versions and fragments thereof.

[0137] In some embodiments, biomarker levels are detected using a biomarker / capture reagent complex.

[0138] In some embodiments, the biomarker level is obtained from a biomarker / capture reagent complex and is detected indirectly, e.g., as a result of a reaction following biomarker / capture reagent interaction, but dependent on the formation of a 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, biomarkers are detected using a multiplexed format that allows for simultaneous detection of two or more biomarkers in a biological sample. In some embodiments of the multiplexed format, capture reagents are immobilized, directly or indirectly, by covalent or non-covalent attachment to distinct locations on a solid support. In some embodiments, the multiplexed format uses distinct solid supports, where each solid support has a unique capture reagent bound to that solid support, e.g., quantum dots. In some embodiments, a separate device is used for detecting each of the multiple biomarkers to be detected in a biological sample. The separate device can be configured to allow each biomarker in a biological sample to be processed simultaneously. For example, a microtiter plate can be used, where each well in the plate is used to uniquely analyze one or more biomarkers to be detected in a biological sample.

[0141] In one or more of the foregoing embodiments, a component of the biomarker / capture reagent complex can be labeled with a fluorescent tag to enable detection of the biomarker level. In various embodiments, a fluorescent label can be conjugated to a capture reagent specific for any of the biomarkers described herein using known techniques, and the corresponding biomarker level can then 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 carbon 3 position of the indolium ring contains a chemically reactive group or a conjugated substance. In some embodiments, the dye molecule is an AlexFluor molecule, e.g., Alexafluor 488, Alexafluor 532, Alexafluor 647, Alexa Alexafluor 680, or Alexafluor 700. In other embodiments, the dye molecules comprise a first type of dye molecule and a second type of dye molecule, e.g., two different Alexafluor molecules. In some embodiments, the dye molecules comprise a first type of dye molecule and a second type of dye molecule, wherein the two types of dye molecules have different emission spectra.

[0143] Fluorescence can be measured by a variety of instrumentation adaptable to a wide range of assay formats. For example, spectrofluorometers are designed to analyze microtiter plates, microscope slides, printed arrays, cuvettes, etc. See J.R. Lakowicz, Principles of Fluorescence Spectroscopy, Springer Science + Business Media, Inc., 2004. See Bioluminescence and Chemiluminescence: Progress Current Applications; Philip E. Stanley and 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 components of the biomarker / capture complex to allow for detection of biomarker levels. Suitable chemiluminescent materials include oxalyl chloride, rhodamine 6G, Ru(bipy)3, and the like. 2+ , TMAE (tetrakis(dimethylamino)ethylene), pyrogallol (1,2,3-trihydroxybenzene), lucigenin, peroxyoxalate, aryloxalate, acridinium ester, dioxetane, and the like.

[0145] In some embodiments, the detection method involves an enzyme / substrate combination that generates a detectable signal corresponding to the level of the biomarker. Generally, the enzyme catalyzes a chemical change of a chromogenic substrate, which can be measured using a variety of 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, and the like.

[0146] In some embodiments, the detection method may be a combination of fluorescent, chemiluminescent, radionuclide, or enzyme / substrate combinations that generate a measurable signal. In some embodiments, multimodal signal generation may have unique and advantageous features in biomarker assay formats.

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

[0148] Determining biomarker levels using aptamer-based assays Assays aimed at the detection and quantification of physiologically important molecules in biological and other samples are important tools in scientific research and healthcare. One class of such assays involves the use of microarrays containing one or more aptamers immobilized on a solid support. Each aptamer binds to a target molecule in a highly specific manner and with extremely high affinity. aptamers can bind to the respective target molecules present in the sample, thereby allowing for the measurement of the biomarker levels corresponding to the biomarkers. See, e.g., U.S. Patent No. 5,475,096, entitled "Nucleic Acid Ligands." See also, e.g., U.S. Patent Nos. 6,242,246, 6,458,543, and 6,503,715, each entitled "Nucleic Acid Ligand Diagnostic Biochip." When the microarray is contacted with a sample, the aptamers bind to the respective target molecules present in the sample, thereby allowing for the measurement of the biomarker levels corresponding to the biomarkers.

[0149] As used herein, "aptamer" refers to a nucleic acid that has specific binding affinity for a target molecule. It is recognized that affinity interaction is a matter of degree; however, in this context, the "specific binding affinity" of an aptamer to 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 containing 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 or different numbers of nucleotides. Aptamers can be DNA or RNA or chemically modified nucleic acids, and can be single-stranded, double-stranded, or contain double-stranded regions and higher-order structures. The aptamer may be a photoaptamer, which contains a photoreactive or chemically reactive functional group to allow the aptamer to be covalently linked to its corresponding target. Any of the aptamer methods disclosed herein may include the use of two or more aptamers that specifically bind to the same target molecule. As will be further described below, the aptamer may contain a tag. When an aptamer contains a tag, all copies of the aptamer do not need to have the same tag. Furthermore, when different aptamers each contain a tag, these different aptamers can have either the same tag or different tags.

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

[0151] The terms "SELEX" and "SELEX process" are generally used interchangeably herein to refer to the 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 with 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 a specific aptamer sequence. This process can be performed multiple times to further improve the affinity of the selected aptamer. This process can include an amplification step at one or more points in the process. See, for example, U.S. Patent No. 5,475,096, entitled "Nucleic Acid Ligands." The SELEX process can be used to generate aptamers that bind covalently to a target, as well as aptamers that bind non-covalently to a target. See, for example, 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 to identify high-affinity aptamers containing modified nucleotides that confer improved properties to 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. Pat. 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 the pyrimidine. U.S. Pat. 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 off-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 capable of binding to target molecules. A method for generating aptamers and photoaptamers with slower off-rates from their respective target molecules is described. The method includes contacting a candidate mixture with the target molecule, forming a nucleic acid-target complex, and performing a process to enrich for aptamers with slow off-rates, wherein nucleic acid-target complexes with fast off-rates dissociate and do not reform, while complexes with slow off-rates remain intact. Additionally, the method includes using modified nucleotides in the generation of the candidate nucleic acid mixture to generate aptamers with improved off-rate performance. Non-limiting exemplary modified nucleotides include, for example, the modified pyrimidines shown in FIG. 8. In some embodiments, an aptamer comprises at least one nucleotide with a modification, such as a base modification. In some embodiments, an aptamer comprises at least one nucleotide with a hydrophobic modification, such as a hydrophobic base modification, that allows hydrophobic contact with a target protein. In some embodiments, such hydrophobic contacts contribute to more hydrophilic and / or slower dissociation rates of binding by the aptamer. Non-limiting exemplary nucleotides with hydrophobic modifications are shown in FIG. 8. In some embodiments, an aptamer comprises 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 nucleotides with hydrophobic modifications, where each hydrophobic modification may be the same or different from one another. In some embodiments, 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 hydrophobic modifications in an aptamer may be independently selected from the hydrophobic modifications shown in FIG. 8.

[0155] In some embodiments, slow dissociation rate aptamers (including aptamers comprising at least one nucleotide with a hydrophobic modification) have 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 assays employ aptamers that contain photoreactive functional groups that allow the aptamer to covalently bind or be "photocrosslinked" to its target molecule. See, e.g., "Nucleic Acid Ligand Diagnostic Bioc See U.S. Patent No. 6,544,776, entitled "HIP." 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, entitled "Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Photoselection of Nucleic Acid Ligands and Solution SELEX," respectively. See, e.g., U.S. Patent No. 6,544,776, entitled "HIP." 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, 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 "A Microarray Assay for Immunoassays of Nucleic Acid Ligands." After the microarray is contacted with a sample and the photoaptamers have an opportunity to bind to their target molecules, the photoaptamers are photoactivated and the solid support is washed to remove any non-specifically bound molecules. Because the covalent bond created by the photoactivated functional group(s) on the photoaptamer typically is not removed, stringent washing conditions can be used. In this manner, the assay allows for the detection of biomarker levels corresponding to the biomarker in the test sample.

[0157] In some assay formats, aptamers are immobilized on solid supports before contacting with samples.However, under certain circumstances, immobilizing aptamers before contacting with samples may not provide optimal assays.For example, pre-immobilization of aptamers may result in inefficient mixing of aptamers with target molecules on the surface of solid supports, which may prolong the reaction time, and thus extending the incubation time allows aptamers to efficiently bind with their target molecules.In addition, when photoaptamers are used in assays, depending on the material used as solid supports, the solid supports may tend to scatter or absorb the light used to affect the formation of covalent bonds between photoaptamers and their target molecules.In addition, depending on the method used, the surface of the solid support may be exposed to any labeling agent used and may be affected by it, which may make the detection of the target molecule bound to aptamers prone to inaccuracy. Finally, immobilization of aptamers onto a solid support generally involves an aptamer preparation step (i.e., immobilization) prior to exposing the aptamer to a sample, which preparation step may affect the activity or functionality of the aptamer.

[0158] Aptamer assays have also been described that allow aptamers to capture their targets in solution, followed by a separation step designed to remove certain components of the aptamer-target mixture prior to detection (see U.S. Patent Application Publication No. 20090042206, entitled "Multiplexed Analyses of Test Samples"). The described aptamer assays allow for the detection and quantification of non-nucleic acid targets (e.g., protein targets) in a test sample by detecting and quantifying nucleic acids (i.e., aptamers). The described methods create nucleic acid surrogates (i.e., aptamers) for detecting and quantifying non-nucleic acid targets, thereby allowing a wide variety of nucleic acid technologies, including amplification, to be applied to a wider range of desired targets, including protein targets.

[0159] Aptamers can be constructed to facilitate separation of assay components from the aptamer-biomarker complex (or covalent complex of photoaptamer-biomarker), allowing for isolation of the aptamer for detection and / or quantification. In one embodiment, these constructs can include a cleavable or releasable element within the aptamer sequence. In other embodiments, additional functionality can be introduced into the aptamer, such as a label or detectable moiety, a spacer moiety, or a specific binding tag or immobilization element. For example, an aptamer can include a tag linked to the aptamer via a cleavable moiety, a label, a spacer component separating the labels, and a cleavable moiety. In one embodiment In the example, the cleavable element is a photocleavable linker, which may be attached to a biotin moiety and a spacer moiety and may contain an NHS group for amine derivatization, which may be used to introduce a biotin group into the aptamer, thereby allowing for subsequent release of the aptamer in an assay.

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

[0161] In some embodiments, the signal generation method utilizes the anisotropic signal change resulting from the interaction of a fluorophore-labeled capture reagent with its specific biomarker target. When the labeled capture reagent reacts with its target, the increased molecular weight significantly slows the rotational motion of the fluorophore bound to the complex, resulting in a change in anisotropy. By monitoring the anisotropy change, the binding event can be used to quantitatively measure the biomarker in solution. Other methods include fluorescence polarization assays, molecular beacon techniques, time-resolved fluorescence quenching, chemiluminescence, and fluorescence resonance energy transfer.

[0162] An exemplary solution-based aptamer assay that can be used to detect biomarker levels in a biological sample includes: (a) preparing a mixture by contacting the biological sample with an aptamer that includes a first tag and has specific affinity for the biomarker, such that if the biomarker is present in the sample, an aptamer affinity complex is formed; (b) exposing the mixture to a first solid support that includes a first capture element, causing the first tag to associate with the first capture element; (c) removing any component of the mixture that is 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 comprising a second capture element, causing the second tag to associate with the second capture element; (g) removing any uncomplexed aptamer from the mixture by separating it 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] Any means known in the art can be used to detect the aptamer component of an aptamer affinity complex and thereby detect biomarker values. Numerous different detection methods can be used to detect the aptamer component of an affinity complex, such as hybridization assays, mass spectrometry, or QPCR. In some embodiments, nucleic acid sequencing can be used to detect the aptamer component of an aptamer affinity complex and thereby detect biomarker values. In summary, a test sample can be subjected to any type of nucleic acid sequencing method to identify and quantify one or more aptamer sequences or sequences present in the test sample. In some embodiments, the sequence includes the entire aptamer molecule or any portion of the molecule that can be used to uniquely identify the molecule. In other embodiments, the identification sequence is a specific sequence added to the aptamer; 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, including RNA and DNA containing chemical modifications at any position, into any other type of nucleic acid suitable for sequencing.

[0164] In some embodiments, the sequencing method comprises one or more cloning steps, while in other embodiments, the sequencing method comprises direct sequencing without cloning.

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

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

[0167] A non-limiting exemplary method for detecting biomarkers in biological samples using aptamers is described in Example 1. See also Kraemer et al., 2011, PLoS One 6(10):e26332.

[0168] Determining biomarker levels using immunoassays Immunoassay methods are based on the reaction of antibodies with their corresponding targets or analytes, and can detect analytes in samples depending on the specific assay format. To improve the specificity and sensitivity of immunoreactivity-based assay methods, monoclonal antibodies and their fragments are frequently used due to their specific epitope recognition. Polyclonal antibodies have also been successfully used in various immunoassays due to their higher affinity for targets compared to monoclonal antibodies. Immunoassays are designed for use 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 prepared using known concentrations of the specific analyte to be detected. The response or signal from an unknown sample is plotted against 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 an analyte. The method is based on the binding of a label to either the analyte or the antibody, where the label component comprises an enzyme, either directly or indirectly. ELISA tests can be in formats for direct, indirect, competitive, or sandwich detection of the analyte. Other methods include, for example, the use of radioisotopes (I 125 ) or based on labels such as fluorescence. Additional techniques include, for example, agglutination, nephelometry, turbidimetry, Western blot, immunoprecipitation, immunocytostaining, immunohistochemistry, flow cytometry, Luminex assay, etc. (ImmunoAssay: AP) (See Practical Guide, edited by Brian Law, published by Taylor & Francis, Ltd., 2005).

[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. Exemplary techniques for detecting biomarkers include biomarker immunoprecipitation followed by quantitative methods that allow size and peptide level differentiation, such as gel electrophoresis, capillary electrophoresis, planar electrochromatography, etc.

[0172] Methods for detecting and / or quantifying a detectable label or signal-producing substance depend on the nature of the label. The products of the reaction catalyzed by a suitable enzyme (where the detectable label is an enzyme; see above) can be, but are not limited to, fluorescent, luminescent, or radioactive, 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 format that allows for any suitable preparation, processing, and analysis of the reaction. 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 robotically, and all subsequent pipetting, dilution, mixing, dispensing, washing, incubation, sample reading, data collection, and analysis can be performed robotically using commercially available analysis software, robots, and detection equipment that can detect the detectable label.

[0174] Determining biomarker levels using gene expression profiling In some embodiments, measuring mRNA 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, a biomarker or biomarker panel described herein can be detected by detecting the appropriate RNA.

[0175] In some embodiments, mRNA expression levels are measured by reverse transcription quantitative polymerase chain reaction (RT-PCR followed by qPCR). 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 progresses. qPCR can provide absolute measurements, such as the number of mRNA copies per cell, by comparison with a standard curve. Northern blots, microarrays, Invader assays, and RT-PCR combined with capillary electrophoresis have all been used to measure mRNA expression levels in samples. See Gene Expression Profiling: Methods and Protocols, Richard A. Shimkets, editor, Humana Press, 2004.

[0176] Biomarker detection using in vivo molecular imaging techniques In some embodiments, the biomarkers described herein can be used in molecular imaging studies, for example, an imaging agent can be attached to a capture reagent that can be used to detect the 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, an entire body part or the entire body can be visualized as a three-dimensional image. These techniques can be combined with the detection of biomarkers described herein to provide information about biomarkers in vivo.

[0178] Various technological advances have led to the development of in vivo molecular imaging techniques. These advances 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 agents can be visualized with an appropriate imaging system, thereby providing an image of the body part or parts in which the contrast agents are present. The contrast agents can be bound to or associated with, for example, capture agents such as aptamers or antibodies, and / or peptides or proteins or oligonucleotides (e.g., for detecting gene expression), or complexes containing any of these together with one or more macromolecules and / or other particulate forms.

[0179] Contrast agents may be characterized by radioactive atoms useful in imaging. Suitable radioactive atoms include technetium-99m or iodine-123 for scintigraphy. Other easily detectable moieties include spin labels for magnetic resonance imaging (MRI), such as 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 easily 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 instrument available is an important factor in selecting a given contrast agent, for example, a given radionuclide and the specific biomarker (protein, mRNA, etc.) to be targeted using it. The radionuclide selected usually exhibits a certain type of decay that is detectable by a given type of instrument. In addition, 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 to a subject either systemically or locally. 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 nuclides commonly used in PET include, for example, carbon-11, nitrogen-13, oxygen-15, and fluorine-18. SPECT uses isotopes that decay by electron capture and / or gamma emission, including, for example, iodine-123 and technetium-99m. An exemplary method for labeling amino acids with technetium-99m is the reduction of pertechnetate ions in the presence of a chelating 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] Antibodies are frequently used in such in vivo imaging diagnostic methods. The preparation and use of antibodies for diagnostic purposes in vivo are well known in the art. Similarly, aptamers may be used in such in vivo imaging diagnostic methods. For example, the aptamers used to identify specific biomarkers described herein can 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 imaging agents may have unique and advantageous properties compared to other imaging agents in terms of tissue penetration, biodistribution, kinetics, elimination, efficacy, and selectivity.

[0184] Such techniques may optionally be carried out using labeled oligonucleotides to detect gene expression, for example, by imaging with antisense oligonucleotides. These methods may also be carried out using, for example, fluorescent molecules or radionuclides as labels. It is used in situ hybridization. Other methods for detecting gene expression include, for example, detecting the activity of a reporter gene.

[0185] Another common type of imaging technique is optical imaging, in which fluorescent signals within a subject are detected by optical devices external to the subject. These signals can result from actual fluorescence and / or bioluminescence. Improvements in the sensitivity of optical detection devices have 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] Determining biomarker levels using mass spectrometry Mass spectrometers of various configurations can be used to detect biomarker levels. Several types of mass spectrometers are available or can be manufactured in various configurations. Generally, a mass spectrometer has the following major 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 are electrospray, including nanospray and microspray, or matrix-assisted laser desorption. Common mass analyzers include quadrupole mass filters, ion trap mass analyzers, and time-of-flight mass analyzers. Additional 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), silicon-assisted desorption / ionization (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), a 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, sample preparation strategies are used to label and enrich samples. Labeling methods include, but are not limited to, iso-mass tagging for relative or absolute quantitation (iTRAQ) and stable isotope labeling with amino acids in cell culture (SILAC). Capture reagents used to selectively enrich potential biomarker proteins in samples prior to mass spectrometry analysis 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), imprinted polymers, avimers, peptidomimetics, peptoids, peptide nucleic acids, threose nucleic acids, hormone receptors, cytokine receptors, and synthetic receptors, as well as modified versions and fragments thereof.

[0190] Determining biomarker levels using proximity ligation assays Proximity ligation assays can be used to determine biomarker values. Briefly, a test sample is contacted with a pair of affinity probes, each of which can be a pair of antibodies or a pair of aptamers, with each member of the pair extended with an oligonucleotide. The targets of a pair of affinity probes can be two different determinants on a single protein, or one determinant each on two different proteins that can exist as a homo- or hetero-multimeric complex. When the probes bind to the target determinants, the free ends of the oligonucleotide extensions are brought into sufficient proximity to hybridize together. Hybridization of the oligonucleotide extensions is facilitated by a common connector oligonucleotide, which serves to bridge the oligonucleotide extensions together when they are positioned sufficiently close. Once the oligonucleotide extensions of the probes are hybridized, the ends of the extensions are linked 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, through PCR amplification, reveals information about the identity and amount of the target protein, as well as information about protein-protein interactions if the target determinant exists on two different proteins. Proximity ligation can provide a highly 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 extension into proximity, and ligation or PCR amplification cannot proceed, resulting in no signal generation.

[0192] The foregoing assays allow for the detection of biomarker values useful in a method for predicting 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 of the biomarkers of Table 2 in a biological sample from a subject, wherein classification using the biomarker values, as described below, indicates whether the subject has an elevated risk of developing a CV event within a 1-year, 2-year, 3-year, or 4-year period. According to any of the methods described herein, biomarker values can be detected and classified individually, or can be detected and classified collectively, such as in a multiplex assay format.

[0193] Biomarker classification and disease score calculation In some embodiments, a biomarker "signature" for a given diagnostic test comprises a set of biomarkers, each with a characteristic level in a population of interest. In some embodiments, the characteristic level may refer to the mean or average value of the biomarker for subjects within a particular group. In some embodiments, the diagnostic methods described herein may be used to assign unknown samples from subjects to one of two groups: either those with a high risk of a CV event or those without.

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

[0195] Common techniques for developing diagnostic classifiers include decision trees; bagging + boosting + forests; inference rule-based learning; 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; and Boltzmann learning. Classifiers can be combined simply or in a way that minimizes a specific objective function. For a general discussion, see, for example, Pattern Classification, R.O.Duda, et al. 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 diagnostic testing, training data includes samples from different groups (classes) to which unknown samples will be assigned later.For example, samples collected from subjects in a control population and samples collected from subjects in a specific disease population may constitute training data for developing a classifier that can classify unknown samples (or more specifically, the subjects from which the samples are obtained) into either diseased or disease-free.Developing a classifier from training data is known as classifier training.The specific details of classifier training depend on the nature of the supervised learning technique. Training a naive Bayes classifier is one example of such a supervised learning technique (see, e.g., 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 a naive Bayes classifier is described, for example, in U.S. Patent Publication Nos. 2012 / 0101002 and 2012 / 0077695.

[0197] Usually, because there may be many higher biomarker levels compared to the samples in training set, care must be taken to avoid overfitting.Overfitting occurs when statistical model represents random error 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 biomarkers are independent of each other, limiting the complexity of the underlying statistical model used, and ensuring that the underlying statistical model fits data.

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

[0199] The performance of a naive Bayes classifier depends on the number and quality of biomarkers used to construct and train the classifier. A single biomarker will perform according to the K-S (Kolmoborov-Smirnov) distance. Subsequent addition of biomarkers with good K-S distances (e.g., >0.3) will generally improve classification performance, provided that the subsequently added biomarkers are independent of the first biomarker. By using specificity in addition to sensitivity as the classifier score, a large number of highly scoring classifiers can be generated using a type of greedy method. (A greedy method is any algorithm that follows a metaheuristic for problem solving, making locally optimal choices at each stage with the goal of finding a globally optimal solution.)

[0200] Another way to depict classifier performance is through receiver operating characteristics (ROC), or simply ROC curves or ROC plots. ROC is a graphical plot of a binary classifier's sensitivity or true positive rate versus false positive rate (1 minus specificity or 1 minus true negative rate) as the system's discrimination threshold changes. This ROC can equivalently be represented by plotting the ratio of true positives among positives (TPR = true positive rate) against the ratio of false positives among negatives (FPR = false positive rate). This is also known as a relative operating characteristic curve because it is a comparison of two operating characteristics (TPR and FPR) as the criteria change. The area under the ROC curve (AUC) is commonly used as a summary measure of diagnostic accuracy. It can range from 0.0 to 1.0. AUC has important statistical properties. That is, the AUC of a classifier is equal to the probability that the classifier will rank a randomly selected positive case higher than a randomly selected negative case (Fawcett T, 2006. An introduction to ROC analysis. Pattern Recognition Letters. 27:861-874). It is equivalent to the Wilcoxon rank test (Hanley, JA, McNeil, BJ, 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 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. RO While the AUC under the C-curve is optimal for assessing the performance of a two-class classifier, stratification and personalized medicine rely on the inference that the population contains more than two classes. For such comparisons, the hazard ratio of the upper versus lower quartile (or other stratification such as deciles) may be more appropriately used.

[0201] The risk and likelihood predictions enabled by the present invention may be applied to subjects in primary care or specialized cardiovascular centers, or even targeted to consumers. In some embodiments, the classifiers used to predict events may require some calibration to the population to which they are applied, since there may be variations due to, for example, ethnicity or region. In some embodiments, such calibrations may be pre-established through testing on large populations and thus incorporated before making risk predictions when applied to individual patients. A venous blood sample is drawn, appropriately processed, and analyzed as described herein. Once analysis is complete, a risk prediction may be made mathematically, with or without incorporating other metadata from the medical record, such as genetics or demographics, as described herein. Various formats of information output are possible, depending on the consumer's level of expertise. 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 1 to 4)? Yes / No," or alternatively, something similar to a "traffic light" red / orange / green, or verbal or written equivalents such as high / medium / low risk. For consumers seeking more detail, in some embodiments, risk may be output as a number or graphic showing the probability of an event per unit time as a continuous score, or as more numerous strata (e.g., deciles), and / or as the mean time to an event and / or the most likely type of event. In some embodiments, the output may include treatment recommendations. Longitudinal monitoring of the same patient over time would allow for graphs of response to interventions or lifestyle changes. In some embodiments, two or more types of output may be provided simultaneously to meet the needs of the patient and the needs of individual members of the healthcare management team with different levels of expertise.

[0202] In some embodiments, the biomarkers shown in Table 1 or Table 2 are detected in a blood sample (e.g., a plasma or serum sample) from a subject using an aptamer, such as a slow dissociation rate aptamer. The logarithmic RFU value is used to calculate the risk or likelihood of the subject experiencing a CV event, or a prognostic index (PI).

[0203] Given a PI, the probability that a subject will suffer a cardiovascular event (CV event) in the next "t" years is given by:

number

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

[0205] In some embodiments, the kit includes: (a) one or more biomarkers in a biological sample, the biomarkers being 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 in 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, or at least 20 biomarkers selected from the biomarkers in Table 2; 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 an elevated CV event risk, or for determining the likelihood that the subject has an elevated CV event risk, as further described herein. Alternatively, rather than one or more computer program products, one or more instructions for manually performing the above steps by a person may be provided.

[0206] In some embodiments, the kit includes 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. Additionally, the kit may be used with a computer system or software for analyzing biological samples and reporting the results of the analysis.

[0207] The kits may also include one or more reagents for processing the biological sample (e.g., solubilization buffer, detergent, wash solution, or buffer). Any of the kits described herein may also include, for example, buffers, blocking agents, mass spectrometry matrix materials, antibody capture agents, positive control samples, negative control samples, software, and information, such as protocols, guidelines, and reference data.

[0208] In some embodiments, a kit for analyzing CV event risk status is provided, wherein 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 biomarkers and their relationship to predicting CV event risk. In some embodiments, the kit may also include a DNA array containing complements 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, e.g., TaqMan probes and / or primers, and enzymes.

[0209] For example, the kit may include (a) reagents, including 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 to one or more predetermined cutoff values. In some embodiments, the algorithm or computer program assigns a score to each quantified biomarker based on the comparison, and in some embodiments, combines the assigned scores for each quantified biomarker to obtain a total score. Further, in some embodiments, the algorithm or computer program compares the total score to a predetermined score and uses this comparison to determine whether the subject has an elevated risk of a CV event. Alternatively, rather than one or more algorithms or computer programs, one or more instructions for a person to manually perform the above steps 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 table below are detected. In some embodiments, the level of each protein listed in Table 1 is detected. In some embodiments, detection of one or more or all of the biomarkers is performed to determine the risk or likelihood that a subject will experience a primary CV event within a predetermined period of time. In some such embodiments, the predetermined period of time is 1 year, 2 years, 3 years, 4 years, or 5 years. In some embodiments, the predetermined period of time 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 table below are detected. In some embodiments, the level of each protein listed in Table 2 is detected. In some embodiments, detection of one or more or all of the biomarkers is performed to determine the risk or likelihood that a subject will experience a secondary CV event within a predetermined period of time. In some such embodiments, the predetermined period of time is 1 year, 2 years, 3 years, 4 years, or 5 years. In some embodiments, the predetermined period is four 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 for the ethnicity of the population / subject. In some embodiments, biomarker levels are combined in some way, and a single value for the combined biomarker level is reported. In this approach, in some embodiments, the score may be a single numerical value determined by integrating all biomarkers, which is compared to a preset threshold that is indicative of the presence or absence of disease. Alternatively, the diagnostic or predictive score may be a series of bars, each representing a biomarker value, and the response pattern may be compared to a preset pattern for determining the presence or absence of elevated (or non-elevated) risk of a disease, condition, or event.

[0213] At least some embodiments of the methods described herein may be implemented using a computer. An example of a computer system 100 is shown in FIG. 6. Referring to FIG. 6, the system 100 includes 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 accelerated processing unit (e.g., 1. System 100 is shown to be comprised of hardware elements electrically connected via bus 108, including a processor (e.g., a DSP or special-purpose processor) 107, and memory 109. Computer-readable storage medium reader 105a is further connected to computer-readable storage medium 105b, which collectively represents storage media, memory, etc., in addition to remote, local, fixed, and / or removable storage devices for containing computer-readable information on a temporary and / or more persistent basis, and which includes storage device 104, memory 109, and / or any other such accessible system 100 resource. System 100 also includes software elements (shown here as residing in working memory 191), including operating system 192 and other code 193, e.g., programs, data, etc.

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

[0215] In one embodiment, the system may include a database containing biomarker features that are indicative of predictive characteristics of risk for a CV event. Biomarker data (or biomarker information) may be utilized as input to a computer for use as part of a computer-implemented method. Biomarker data may include data described herein.

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

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

[0218] In another embodiment, the device for providing input data includes a detector for detecting characteristics of the 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 queries may be formatted in an appropriate language understood by the database management system, which processes the queries and extracts relevant information from a database of training sets.

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

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

[0222] The system may include one or more devices that include a graphical display interface, including interface elements such as buttons, pull-down menus, scroll bars, text entry fields, etc., commonly found in graphical user interfaces known in the art. Requests entered at the user interface may be transmitted to application programs in the system for formatting to search for relevant information in one or more system databases. Requests or queries entered by users may be formulated in any suitable database language.

[0223] A graphical user interface may be generated by graphical user interface code as part of the operating system and may be used to input data and / or display input data. The results of processed data may be displayed on the interface, printed on a printer in communication with the system, saved to a storage device, and / or transmitted over a network, or provided in the form of a computer-readable medium.

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

[0225] According to various embodiments, the methods and devices for analyzing biomarker information for CV event risk prediction can be implemented in any suitable manner, for example, using a computer program running on a computer system. Conventional computer systems including a processor and random access memory, such as a remotely accessible application server, network server, personal computer, or workstation, can be used. Additional computer system elements can include a storage or information storage system, for example, a mass storage system, and a user interface, for example, 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 predictive 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 may receive, store, retrieve, analyze, and report information related to CV event risk predictive biomarkers. The computer program may include multiple modules that perform various functions or operations, such as a processing module for processing raw data and generating supplemental data, and an analysis module for analyzing the raw data and supplemental data to generate a predicted status and / or diagnosis or risk calculation of CV event risk. Calculating a CV event risk status may optionally include generating or retrieving any other information, including additional biomedical information regarding the individual's status related to the disease, condition, or event, ascertaining whether further testing may be desirable, or otherwise assessing the individual's health status.

[0227] Some embodiments described herein may be implemented to include a computer program product. The computer program product may include an application program for executing a database. The present invention may include a computer-readable medium having computer-readable program code embodied in the medium for execution by a computer having the medium.

[0228] As used herein, a "computer program product" refers to a set of instructions, organized in the form of natural or programming language statements, contained on a physical medium of any nature (e.g., written, electronic, magnetic, optical, or otherwise) and usable by a computer or other automatic data processing system. Such programming language statements, when executed by a computer or data processing system, cause the computer or data processing system to operate in accordance with the specific content of the statements. 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. Furthermore, computer program products that enable a computer system or data processing device to operate in a preselected manner may be provided in numerous 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 for assessing risk of a CV event is provided. The computer program product includes a computer-readable medium having program code embodied thereon executable by a processor of a computing device or system, the program code including: code for retrieving data from a biological sample from a subject, the data including biomarker levels each corresponding to one of the biomarkers in Table 1 or Table 2; and code for implementing a classification method that indicates the subject's CV event risk status as a function of the biomarker values.

[0230] In yet another aspect, a computer program product for indicating likelihood or risk of a CV event is provided. The computer program product includes a computer-readable medium having program code embodied thereon executable by a processor of a computing device or system, the program code including: code for retrieving 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 for executing a classification method to indicate a CV event risk status of the subject as a function of the biomarker value.

[0231] While various embodiments have been described as methods or apparatus, it should be understood that the embodiments may be implemented via code in conjunction with a computer, e.g., code resident on or accessible by a computer. For example, software and databases may be utilized to implement many of the above-described methods. Accordingly, in addition to embodiments achieved through hardware, it should also be noted that these embodiments may be achieved through the use of an article of manufacture comprising a computer-usable medium having computer-readable program code embodied thereon that enables the performance of the functions disclosed herein. Accordingly, it is desirable that the embodiments be considered equally protected by this patent in their program code form. Furthermore, the embodiments may be embodied as code stored in virtually any type of computer-readable memory, including, but not limited to, RAM, ROM, magnetic, optical, or magneto-optical media. Even more generally, the embodiments may be implemented in software, or in hardware, or any combination thereof, including, but not limited to, software running on a general-purpose processor, microcode, programmable logic arrays (PLAs), or application-specific integrated circuits (ASICs).

[0232] It is further contemplated that embodiments may be achieved as computer signals embodied in carrier waves and signals propagated over transmission media (e.g., electrical and optical). Thus, the various types of information described above may be formatted in structures, such as data structures, and transmitted as electrical signals over transmission media or stored on computer-readable media.

[0233] It should also be noted that many of the structures, materials, and acts recited herein may be recited as a means for performing a function or a step for performing a function, and therefore, such language should be understood to encompass all such structures, materials, or acts disclosed within this specification, including material incorporated by reference, and their equivalents.

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

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

[0236] In some embodiments, the biomarkers and methods described herein are used to predict and / or manage healthcare resource utilization. In some such embodiments, the methods are not performed for the purpose of such prediction, but information obtained by the methods is used in predicting and / or managing such healthcare resource utilization. For example, a laboratory or hospital may use the methods to gather information about a large number of subjects in order to predict and / or manage healthcare resource utilization in a particular facility or in a particular geographic area. [Example]

[0237] The following examples are provided for illustrative purposes only and are not intended to limit the scope of this application, which is defined by the appended 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, NY, (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. Three different quantification methods are described: microarray-based hybridization, Luminex bead-based method, and qPCR.

[0239] reagent HEPES, NaCl, KCl, EDTA, EGTA, MgCl2, and Tween-20 can be purchased, for example, from Fisher Biosciences. Dextran sulfate sodium salt (DxSO4), nominally 8000 molecular weight, can be purchased, for example, from AIC and dialyzed against deionized water for at least 20 hours with one change. KOD EX DNA polymerase can be purchased, for example, from VWR. Tetramethylammonium chloride and CAPSO can be purchased, for example, from Sigma-Aldrich, and 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. Streptavidin-coated 96-well plates 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 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 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- and biotin-substituted oligonucleotides) can be purchased, for example, from Integrated DNA Technologies (IDT). Z-Blocks are single-stranded oligodeoxynucleotides with the sequence 5'-(AC-BnBn)7-AC-3', where Bn represents a benzyl-substituted deoxyuridine residue. Z-Blocks can be synthesized using conventional phosphoramidite chemistry. Aptamer capture reagents can be synthesized using conventional phosphoramidite chemistry and purified, for example, on a 21.5 x 75 mm PRP-3 column operated at 80°C on a Waters Autopurification 2767 system (or a Waters 600 series semi-automated system) using, for example, a Timberline TL-600 or TL-150 heater and a triethylammonium bicarbonate (TEAB) / ACN gradient to elute the product. Detection is performed at 260 nm and fractions are collected across the main peak before the best fractions are pooled.

[0241] buffer solution Buffer SB18 consists of 40 mM HEPES, 101 mM NaCl, 5 mM KCl, 5 mM MgCl, and 0.05% (v / v) Tween 20, adjusted to pH 7.5 with NaOH. Buffer SB17 is SB18 supplemented with 1 mM trisodium EDTA. Buffer PB1 consists of 10 mM HEPES, 101 mM NaCl, 5 mM KCl, 5 mM MgCl, 1 mM trisodium EDTA, and 0.05% (v / v) Tween-20, adjusted to pH 7.5 with NaOH. CAPSO elution buffer consists of 100 mM CAPSO (pH 10.0) and 1 M NaCl. The neutralization buffer contains 500 mM HEPES, 500 mM HCl, and 0.05% (v / v) Tween-20. The Agilent Hybridization Buffer is a proprietary formulation supplied as part of the kit (Oligo aCGH / ChIP-on-chip Hybridization Kit). 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) sarkosyl. KOD buffer (10x concentrated) consists of 1200 mM Tris-HCl, 15 mM MgSO, 100 mM KCl, 60 mM (NH)SO, 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 before sample dilution. Samples are mixed by gently vortexing for 8 seconds. A 6% serum sample solution is prepared by diluting into 0.94x SB17 supplemented with 0.6 mM MgCl, 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 into 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 reagents (aptamers) and streptavidin plates Aptamers are sorted into two mixtures according to the relative abundance of their associated analytes (or biomarkers). The stock concentration is 4 nM for each aptamer, and the final concentration of each aptamer is 0.5 nM. The aptamer stock mixture is diluted 4-fold in SB17 buffer and heated to 95°C for 5 minutes and cooled to 37°C over 15 minutes before use. This denaturation-renaturation cycle is intended to normalize the conformational isomer distribution of the aptamers, thereby ensuring reproducible aptamer activity despite historical variations. Streptavidin plates are washed twice with 150 μL of buffer PB1 before use.

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

[0245] Manual Assay Unless otherwise stated, the liquid is removed by discarding it and then tapping twice on a stack of 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, then four times for 15 seconds with Buffer PB1. A freshly made 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 was added to each well, and the plate was incubated under a BlackRay ultraviolet lamp (nominal wavelength 365 nm) at a distance of 5 cm with shaking for 20 minutes. Irradiate. Transfer the samples to a new, washed streptavidin-coated plate or to unused wells of an existing, washed streptavidin plate, combining the high and low dilution sample mixtures into a single well. Incubate the samples with shaking for 10 minutes at room temperature. Remove unadsorbed material and wash eight times for 15 seconds each with Buffer PB1 supplemented with 30% glycerol. Then wash the plate once with Buffer PB1. Elute the aptamers with 100 μL of CAPSO elution buffer for 5 minutes at room temperature. Transfer 90 μL of the eluate to a 96-well HybAid plate and add 10 μL of neutralization buffer.

[0246] Semi-automated assay The streptavidin plate with the adsorbed mixture is placed on the deck of a BioTek EL406 plate washer. The washer is programmed to perform the following steps: Unadsorbed material is removed by aspiration, and the wells are washed four times with 300 μL of Buffer PB1 supplemented with 1 mM dextran sulfate and 500 μM biotin. The wells are then washed three times with 300 μL of Buffer PB1. 150 μL of a freshly prepared solution of 1 mM NHS-PEO4-biotin in Buffer PB1 (from a 100 mM stock solution in DMSO) is added. The plate is incubated for 5 minutes with shaking. The liquid is aspirated, and the wells are washed eight times with 300 μL of Buffer PB1 supplemented with 10 mM glycine. 100 μL of Buffer PB1 supplemented with 1 mM dextran sulfate is added. After these automated steps, the plate was removed from the plate washer and placed 5 cm away on a thermoshaker mounted under a UV light source (BlackRay, nominal wavelength 365 nm) for 20 minutes. The thermoshaker was set at 800 rpm and 25°C. After 20 minutes of irradiation, samples were manually transferred to a new, washed streptavidin plate (or to unused wells of an existing, washed plate). The high-abundance (3% serum + 3% aptamer mixture) and low-abundance reaction mixtures (0.3% serum + 0.3% aptamer mixture) were now combined into a single well. This "Catch-2" plate was placed on the deck of a BioTek EL406 plate washer. The washer was programmed to perform the following steps: Incubate the plate with shaking for 10 minutes. 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 allow to elute the aptamer for 5 minutes with shaking. After these automated steps, remove the plate from the plate washer deck and manually transfer 90 μL aliquots of the sample to wells of a HybAid 96-well plate containing 10 μL of neutralization buffer.

[0247] Hybridization to custom Agilent 8x15k microarrays 24 μL of neutralized eluate was transferred to a new 96-well plate, and 6 μL of 10× Agilent Block (Oligo aCGH / ChIP-on-chip Hybridization Kit, Large Capacity, Agilent 5188-5380) containing a set of hybridization controls consisting of 10 Cy3 aptamers was added to each well. 30 μL of 2× Agilent hybridization buffer was added to each sample and mixed. 40 μL of the resulting hybridization solution was manually pipetted into each "well" of a hybridization gasket slide (Hybridization Gasket Slide, 8 microarrays per slide format, Agilent). Custom-made Agilent microarray slides with 10 probes per array complementary to random 40-nucleotide regions of each aptamer with 20× dT linkers were placed on the gasket slide according to the manufacturer's protocol. The assembly (Hybridization Chamber Kit, SureHyb compatible, Agilent) is clamped and incubated at 60° C. for 19 hours with rotation at 20 rpm.

[0248] Post-hybridization washes Approximately 400 mL of Agilent Wash Buffer 1 is placed into each of two separate glass staining dishes. Slides (no more than two at a time) are disassembled and separated while immersed in Wash Buffer 1, then transferred to the slide rack in the second staining dish, which also contains Wash Buffer 1. The slides are incubated in Wash Buffer 1 for an additional 5 minutes with agitation. The slides are transferred to Wash Buffer 2, pre-equilibrated to 37°C, and incubated for 5 minutes with agitation. The slides are transferred to a fourth staining dish containing acetonitrile and incubated for 5 minutes with agitation.

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

[0250] Luminex probe design The bead-immobilized probes contain 40 deoxynucleotides complementary to a random 40-nucleotide region at the 3' end of the target aptamer. The aptamer-complementary region is attached to a Luminex microsphere via a hexaethylene glycol (HEG) linker with a 5' amino terminus. The biotinylated detector deoxyoligonucleotides contain 17-21 deoxynucleotides complementary to the 5' primer region of the target aptamer. A biotin moiety is added to the 3' end of the detector oligo.

[0251] Binding of probes to Luminex microspheres The probes were coupled to Luminex microspheres largely according to the manufacturer's instructions, with the following modifications: the amount of amino-terminal oligonucleotide was 2.5 × 10 6 The coupling reaction is carried out in an Eppendorf ThermoShaker set at 25° C. and 600 rpm.

[0252] Microsphere hybridization The microsphere stock solution (approximately 40,000 microspheres / μL) was vortexed and sonicated for 60 seconds in a Health Sonics ultrasonic cleaner (model: T1.9C) to suspend the microspheres. The suspended microspheres were diluted to 2,000 microspheres per reaction in 1.5x TMAC hybridization solution and mixed by vortexing and sonication. 33 μL of the bead mixture per reaction was transferred to a 96-well HybAid plate. 7 μL of 15 nM biotinylated detection oligonucleotide stock solution in 1x TE buffer was added to each reaction and mixed. 10 μL of neutralized assay sample was added, and the plate was sealed with a silicone cap mat seal. The plate was first incubated at 96°C for 5 minutes and then at 50°C overnight without agitation in a conventional hybridization oven. A filter plate (Durapore, Millipore part number MSBVN1250, 1.2 μm pore size) is pre-wetted with 75 μL of 1×TMAC hybridization solution supplemented with 0.5% (w / v) BSA. The entire sample volume from the hybridization reaction is transferred to the filter plate. The hybridization plate is rinsed with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA to transfer any remaining material to the filter plate. The sample is filtered using 150 μL of buffer under a gentle vacuum and vented for approximately 8 seconds. The filter plate is pre-wetted with 75 μL of 1×TMAC hybridization solution supplemented with 0.5% BSA. The filter plate is washed once with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA, and the microspheres in the filter plate are resuspended in 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. The filter plate is protected from light and incubated on an Eppendorf Thermalmixer R at 1000 rpm for 5 minutes. The filter plate is then washed once with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. 75 μL of 10 μg / mL streptavidin phycoerythrin (SAPE-100, MOSS, Inc.) in 1×TMAC hybridization solution is added to each reaction, and the reaction is incubated on an Eppendorf Thermalmixer R at 25° C. and 1000 rpm for 60 minutes. The filter plate was washed twice with 75 μL of 1x TMAC hybridization solution containing 0.5% BSA, and the microspheres in the filter plate were resuspended in 75 μL of 1x TMAC hybridization solution containing 0.5% BSA. The filter plate was then protected from light and incubated for 5 minutes at 1000 rpm on an Eppendorf Thermalmixer®. The filter plate was then washed once with 75 μL of 1x TMAC hybridization solution containing 0.5% BSA. The microspheres were resuspended in 75 μL of 1x TMAC hybridization solution supplemented with 0.5% BSA and analyzed on a Luminex 100 instrument running XPonent 3.0 software. At least 100 microspheres per bead type were counted using high PMT calibration and doublet discriminator settings of 7500-18000.

[0253] QPCR readout qPCR standard curve samples are prepared in 10-fold dilutions with water, ranging from 10 to 10 copies, and a no-template control is prepared. Neutralized assay samples are diluted 40-fold in diH2O. qPCR master mix is prepared at 2x final concentration (2x KOD buffer, 400 μM dNTP mix, 400 nM forward and reverse primer mix, 2x SYBR Green I, and 0.5 U KOD EX). 10 μL of 2x qPCR master mix is added to 10 μL of diluted assay sample. qPCR is performed using a BioRad MyIQ iCycler at 96°C for 2 minutes, followed by 40 cycles of 96°C for 5 seconds and 72°C for 30 seconds.

[0254] Example 2. Cardiovascular event model for prediction of primary cardiovascular events A primary cardiovascular disease (CVD) model containing a panel of 13 biomarker proteins was developed to predict the risk or likelihood of a primary CV event within four years in subjects with no history of cardiovascular disease. A primary CV event was defined as myocardial infarction, stroke, transient ischemic attack, hospitalization for heart failure, or death due to CVD. Training / validation analyses were initiated using the HUNT3 dataset, a case-cohort study design enriched for primary CVD events. The study included 2,515 individuals, of whom 41.51% experienced a CVD event within five years (see Krokstad et al., Int J Epidemiol. 2013;42:968-977). The data were split into 80% for training and 20% for validation. Based on the Whitehall II study (Marmot et al., "Health inequalities among British Predictions on an independent replication set from the Civil Servants: the Whitehall II study. See Lancet 1991;337:1387-1393) were performed in the validation phase.

[0255] Model The primary cardiovascular disease (CVD) model is an accelerated mortality time (AFT) parametric survival model using a Weibull distribution. This model features 13 biomarkers, age, and their interactions with age. 4-year risk was reported. Predictions were made based on the four risk factors. The scores and corresponding actual event rates in the training set over 4 years are reported in Table 3. [Table 3]

[0256] result The concordance statistic (C-statistic), area under the ROC curve (AUC), and net reclassification improvement (NRI) compared to the refitted PCE model (refitted to the HUNT3 training data) and published PCE model for 5-year predictions are shown below in Table 4. The final model was also evaluated on the HUNT3 20% holdout validation dataset. [Table 4] In the HUNT3 training set, 468 patients had a PCE risk score of less than 7.5%. The closest calculated 4-year proteomic risk cutoff was 2.15%, and 469 patients were predicted to have a risk of less than 2.15%. Therefore, the healthy baseline stratum was defined as individuals predicted to have a proteomic risk of less than 2.15% at 4 years. The average proteomic risk in this population was 1.53%. Four risk bins were calculated as 1x, 2x, 3x, 4x, 5x, and 6x or greater (see Table 3). Figure 1 shows Kaplan-Meier survival curves stratified into the four risk bins for the HUNT3 training set. Figure 1 shows the experience of a primary CVD event over time. It provides an overview of how well the distributions separate between different predicted risk bins, with the shaded areas representing the 95% confidence intervals of the Kaplan-Meier estimates. Figure 1 clearly shows the separation between the risk bins, with no overlapping survival distributions at 4 years.

[0257] The final model was also evaluated across all individuals in the training and validation datasets (all PCE risk scores, including those below 0.05). The results are shown in Table 5. The final model also outperformed the competing refitted clinical PCE model across all individuals. [Table 5] The primary CVD model was further characterized by refinement of several parameters. No significant effects were found based on gender or in preliminary interferent assessment. The model was applied to 2005 replicate assay QC samples. Predictions were reproducible with a mean of 0.05 and a standard deviation of 0.008. No significant variation was found based on sample processing time.

[0258] Validation Model validation was performed on the Whitehall II dataset, which included 265 individuals, 101 of whom had CV events (38.11%). Analyte RFU values in the dataset were log10 transformed before analysis. Outlying log10 RFU values were imputed using aptamer-specific maximum and minimum values calculated by winsorization during model development using the HUNT3 training data. The final proteomic model was then evaluated on the 5-year dataset, and the net reclassification improvement (NRI) was calculated compared to the published PCE model. This is shown in Table 6. [Table 6] The NRI for prediction on the Whitehall II validation dataset using the proteomic model was positive, and the results were better than those of the refitted PCE model (developed on the HUNT3 training data) on the HUNT3 validation data.

[0259] Example 3. Cardiovascular Event Panel for Prediction of Secondary Cardiovascular Events A secondary cardiovascular disease (CVD) model containing a panel of 27 biomarker proteins was developed to predict the risk or likelihood of a secondary CV event within four years in subjects with known, apparently stable cardiovascular disease. Secondary CV events were defined as myocardial infarction, stroke, transient ischemic attack, hospitalization for heart failure, or death. Training / validation analyses were initiated using a subcohort of the HUNT3 dataset consisting of individuals with known, apparently stable CVD who met eligibility criteria. The HUNT3 study included 754 individuals whose samples passed QC metrics, with 208 (28%) CV events observed within four years. The data were split into 80% for training and 20% for validation. Analyses were supplemented with a 20% validation subset of the Atherosclerosis Risk in Communities (ARIC) study visit 5 dataset. (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% at visit 5 of the ARIC study and 20% of the HUNT3 data set were unlocked during validation.

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

[0261] result The C-statistic, AUC, and NRI compared to the refitted PCE model (refitted to the HUNT3 training data) for 4-year predictions are shown below in Table 8. The quadratic CVD model had higher C-statistic and AUC values and a positive NRI over 4 years in the training (HUNT3) and validation (ARIC Study Visit 5) datasets. [Table 8] The risk probabilities of the training set were then classified into four bins, such that the baseline group was relatively healthy (5% event rate at 4 years) and the high-risk group had a significantly enhanced event rate (65% event rate at 4 years). See Table 7 above. All four classifications differed at the 95% confidence level over 4 years. The Kaplan-Meier survival curves in Figures 2 and 3 show groups stratified by the four risk bins for the HUNT3 training set (Figure 2) and the ARIC Visit 5 validation set (Figure 3). Figures 2 and 3 provide an overview of how the empirical distribution of secondary CVD events over time separates between the different predicted risk bin groups, with the shaded areas representing the 95% confidence intervals of the Kaplan-Meier estimates.

[0262] Validation Model validation was performed on the HUNT3 20% holdout and ARIC 80% holdout validation sets. Analyte RFU values in the datasets were log10 transformed before analysis. Analyte RFU values were then centered and scaled based solely on the distribution of the training set, and outlying log10 RFU values were imputed using values calculated by winsorization. The C-statistic and AUV of the quadratic CVD final model were evaluated on the 4-year dataset and compared with the refitted PCE model. The NRI compared with the refitted PCE model was calculated and is shown in Table 9. [Table 9] The quadratic CVD model outperformed the refitted PCE clinical model (using clinical and demographic parameters from the ACC risk equation, including separation factors by sex and ethnicity) in the training, validation, and validation sets for classification of subjects with and without events at 4 years and in concordance statistics for prediction of earlier events. The NRI was positive in both validation sets (HUNT3 and ARIC visit 5). Figures 4 and 5 show survival curves for the HUNT3 and ARIC validation sets at Visit 5, stratified by cutoff value. The order and slope of the categories were similar to the distributions expected from the training set and empirically observed event rates. The highest risk groups were distinguished.

Claims

1. 1. A method for screening a subject for risk of a cardiovascular (CV) event, comprising: (a) forming a biomarker panel comprising N protein biomarkers selected from N-terminal proBNP, sTREM-1, MMP-12, antithrombin III, GPR56, gelsolin, ST4S6, CHSTC, FSH, IL-1 sRII, PLXB2, SAP, and TFPI, where N is an integer between 3 and 13; (b) detecting the level in a sample from the subject of each of the N biomarkers of the panel.

2. 1. A method for screening a subject for risk of a cardiovascular (CV) event, comprising: (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, spondin-1, NCAM-120, TFF3, SIRT2, ANP, NELL1, LRP11, NDST1, PTPRJ, CILP2, CA2D3, ITI heavy chain H2, and IGDC4, wherein N is an integer between 8 and 27; (b) detecting the level in a sample from the subject of each of the N biomarkers of the panel.

3. 1. A method for predicting the likelihood of a subject experiencing a CV event, comprising: (a) forming a biomarker panel comprising N protein biomarkers selected from BNP, sTREM-1, MMP-12, antithrombin III, GPR56, gelsolin, ST4S6, CHSTC, FSH, IL-1 sRII, PLXB2, SAP, and TFPI, where N is an integer between 3 and 13; (b) detecting the level in a sample from the subject of each of the N biomarkers of the panel.

4. 1. A method for predicting the likelihood of a subject experiencing a CV event, comprising: (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, spondin-1, NCAM-120, TFF3, SIRT2, ANP, NELL1, LRP11, NDST1, PTPRJ, CILP2, CA2D3, ITI heavy chain H2, and IGDC4, wherein N is an integer between 8 and 27; (b) detecting the level in a sample from the subject of each of the N biomarkers of the panel.

5. 4. The method of claim 1 or 3, wherein N is 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 13.

6. N is 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.

7. 7. The method of any one of claims 1-6, wherein the subject is at risk or likely to experience a CV event within four years 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 biomarkers of a set of biomarkers are each abnormal compared to a control level of the respective biomarker.

8. The method of any one of claims 1 to 7, wherein the CV event is myocardial infarction, stroke, transient ischemic attack, hospitalization for heart failure, or death.

9. 9. The method of any one of claims 2, 4, or 6-8, wherein the subject has coronary artery disease.

10. 10. The method of any one of claims 1, 3, 5, 7, or 8, wherein the subject has no history of a CV event.

11. 10. The method of any one of claims 2, 4, or 6-9, wherein the subject has experienced at least one CV event.

12. 10. The method of any one of the preceding claims, wherein the sample is selected from a blood sample, a serum sample, a plasma sample and a urine sample.

13. The method of claim 12, wherein the sample is a blood sample.

14. 10. The method of any one of the preceding claims, wherein the method is carried out in vitro.

15. 15. The method of any one of claims 1, 3, 5, 7, 8, 10, or 12-14, wherein the method comprises contacting 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.

16. 15. The method of any one of claims 2, 4, 6-9, or 11-14, wherein the method comprises contacting 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.

17. The method of claim 15 or 16, wherein each capture reagent is an antibody or an aptamer.

18. 18. The method of claim 17, wherein each biomarker capture reagent is an aptamer.

19. 19. The method of claim 18, wherein at least one aptamer is a slow off-rate aptamer.

20. 20. The method of claim 19, wherein the at least one slow off-rate aptamer comprises 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 10 modifications.

21. Each slow off rate aptamer exhibits an off rate (t １／２ 21. The method of claim 19 or claim 20, wherein the target protein is bound by a

22. The method of any one of claims 15 to 21, wherein one capture reagent specifically binds to sTREM1.

23. The method of any one of claims 15 to 22, wherein one capture reagent specifically binds to MMP-12.

24. The method of any one of claims 15 or 17 to 23, wherein one capture reagent specifically binds to N-terminal proBNP.

25. The method of any one of claims 15 or 17 to 24, wherein one capture reagent specifically binds to antithrombin III.

26. The method of any one of claims 15 or 17 to 25, wherein one capture reagent specifically binds to GPR56.

27. The method of any one of claims 15 or 17-26, wherein one capture reagent specifically binds to gelsolin.

28. The method of any one of claims 15 or 17 to 27, wherein one capture reagent specifically binds to ST4S6.

29. The method of any one of claims 15 or 17 to 28, wherein one capture reagent specifically binds to CHSTC.

30. 30. The method of any one of claims 15 or 17 to 29, wherein one capture reagent specifically binds to FSH.

31. The method of any one of claims 15 or 17-30, wherein one capture reagent specifically binds to IL-1 sRII.

32. The method of any one of claims 15 or 17 to 31, wherein one capture reagent specifically binds to PLXB2.

33. The method of any one of claims 15 or 17 to 32, wherein one capture reagent specifically binds to SAP.

34. The method of any one of claims 15 or 17-33, wherein one capture reagent specifically binds to TFPI.

35. 35. The method of any one of claims 1 to 34, wherein the set of biomarkers comprises at least 13 biomarkers.

36. The method of any one of claims 1 to 34, wherein the set of biomarkers consists of 13 biomarkers.

37. 24. The method according to claim 16, wherein one capture reagent specifically binds to SVEP1. The method described.

38. The method of any one of claims 16 to 23 or 37, wherein one capture reagent specifically binds to ARL11.

39. The method of any one of claims 16 to 23, 37, or 38, wherein one capture reagent specifically binds to ANTR2.

40. The method of any one of claims 16 to 23 or 37 to 39, wherein one capture reagent specifically binds to CA125.

41. The method of any one of claims 16 to 23 or 37 to 40, wherein one capture reagent specifically binds to GOLM1.

42. The method of any one of claims 16 to 23 or 37 to 41, wherein one capture reagent specifically binds to PPR1A.

43. The method of any one of claims 16 to 23 or 37 to 42, wherein one capture reagent specifically binds to ERBB3.

44. The method of any one of claims 16 to 23 or 37 to 43, wherein one capture reagent specifically binds to suPAR.

45. The method of any one of claims 16 to 23 or 37 to 44, wherein one capture reagent specifically binds to GDF-11 / 8.

46. The method of any one of claims 16 to 23 or 37 to 45, wherein one capture reagent specifically binds to JAM-B.

47. The method of any one of claims 16 to 23 or 37 to 46, wherein one capture reagent specifically binds to ATS13.

48. The method of any one of claims 16 to 23 or 37 to 47, wherein one capture reagent specifically binds to spondin-1.

49. The method of any one of claims 16 to 23 or 37 to 48, wherein one capture reagent specifically binds to NCAM-120.

50. 50. The method of any one of claims 16 to 23 or 37 to 49, wherein one capture reagent specifically binds to TFF3.

51. The method of any one of claims 16-23 or 37-50, wherein one capture reagent specifically binds to SIRT2.

52. The method of any one of claims 16 to 23 or 37 to 51, wherein one capture reagent specifically binds to ANP.

53. The method of any one of claims 16-23 or 37-52, wherein one capture reagent specifically binds to NELL1.

54. The method of any one of claims 16 to 23 or 37 to 53, wherein one capture reagent specifically binds to LRP11.

55. The method of any one of claims 16 to 23 or 37 to 54, wherein one capture reagent specifically binds to NDST1.

56. The method of any one of claims 16 to 23 or 37 to 55, wherein one capture reagent specifically binds to PTPRJ.

57. The method of any one of claims 16 to 23 or 37 to 56, wherein one capture reagent specifically binds to CILP2.

58. The method of any one of claims 16 to 23 or 37 to 57, wherein one capture reagent specifically binds CA2D3.

59. The method of any one of claims 16 to 23 or 37 to 58, wherein one capture reagent specifically binds to ITI heavy chain H2.

60. The method of any one of claims 16 to 23 or 37 to 59, wherein one capture reagent specifically binds to IGDC4.

61. The method of any one of claims 16 to 23 or 37 to 60, wherein one capture reagent specifically binds to BNP.

62. 62. The method of any one of claims 2, 4, 6-9, 11-14, 16-23, or 37-61, wherein the set of biomarkers comprises at least 27 biomarkers.

63. 62. The method of any one of claims 2, 4, 6-9, 11-14, 16-23, or 37-61, wherein the set of biomarkers consists of 27 biomarkers.

64. 64. The method of any one of claims 2, 4, 6-9, 11-14, 16-23, or 37-63, wherein the subject has apparently stable cardiovascular disease.

65. 65. The method of claim 64, wherein the apparently stable cardiovascular disease comprises a history of myocardial infarction, a history of stroke, a history of heart failure, a history of revascularization, an abnormal stress test, imaging suggestive of coronary heart disease, or an abnormal coronary calcium score.

66. 66. The method of claim 65, wherein the myocardial infarction or stroke occurred at least six months prior to the date the sample was taken from the subject.

67. 66. The method of claim 65, wherein the stress test abnormality is a treadmill test or a nuclear medicine-based test.

68. 66. The method of claim 65, wherein the imaging suggestive of coronary heart disease is an angiogram showing coronary artery stenosis of 50% or greater.

69. 69. The method of any one of claims 1 to 68, wherein the subject is at least 40 years old.

70. 70. The method of any one of claims 1 to 69, comprising determining the risk or likelihood of said subject experiencing a cardiovascular event within four years from the date said sample was taken from said subject. method.

71. 71. The method of claim 70, wherein the risk or likelihood of the subject experiencing the cardiovascular event is within 1, 2, 3, or 4 years from the date the sample was taken from the subject.

72. 72. The method of claim 70 or 71, wherein the cardiovascular event is myocardial infarction, stroke, transient ischemic attack, hospitalization due to heart failure, or death due to cardiovascular disease.

73. 73. The method of any one of claims 70 to 72, wherein the risk is determined as a quantitative probability.

74. 73. The method of any one of claims 70 to 72, wherein the risk is determined as a qualitative level of risk.

75. 75. The method of claim 74, wherein the qualitative level of risk is low, intermediate, or high.

76. The risk or likelihood of a CV event is correlated with the biomarker level. a) information corresponding to the presence of cardiovascular risk factors selected from the group consisting of a history of myocardial infarction, angiographic evidence of greater than 50% stenosis in one or more coronary vessels, exercise-induced ischemia by treadmill or nuclear medicine study, or a history of coronary revascularization; b) information corresponding to physical descriptors of said subject; c) information corresponding to the subject's weight change; d) information corresponding to the ethnicity of said subject; e) information corresponding to the subject's gender; f) information corresponding to the subject's smoking history; g) information corresponding to the subject's drinking history; h) information corresponding to the subject's occupational history; i) information corresponding to the subject's family history of cardiovascular disease or other cardiovascular conditions; j) information corresponding to the presence or absence in said subject of at least one genetic marker associated with a higher risk of cardiovascular disease in said subject or in said subject's family members; k) information corresponding to the subject's clinical symptoms; l) Other information equivalent to clinical tests; m) information corresponding to the subject's gene expression values; and n) information corresponding to the subject's known cardiovascular risk factors, for example, intake of a high saturated fat diet, a high salt diet, a high cholesterol diet; o) information corresponding to imaging results of said subject obtained by techniques selected from the group consisting of electrocardiogram, echocardiography, carotid ultrasound of intima-media thickness, flow-mediated dilation test, pulse wave velocity, ankle-brachial index, stress echocardiography, myocardial perfusion imaging, coronary artery calcium testing by CT, high-resolution CT angiography, MRI imaging, and other imaging techniques; p) information regarding the subject's medication; q) information corresponding to the subject's age; and r) at least one item of additional biomedical information selected from information regarding the subject's renal function.

77. 77. The method of claim 76, wherein at least one item of said additional biomedical information is information corresponding to the age of said subject.

78. 10. A method according to any one of the preceding claims, wherein the method comprises determining the risk or likelihood of the CV event for purposes of determining medical or life insurance premiums.

79. 80. The method of claim 78, wherein the method further comprises determining medical or life insurance coverage or premiums.

80. 80. The method of any one of claims 1 to 79, wherein the method further comprises using information obtained by the method to predict and / or manage healthcare resource utilization.

81. 81. The method of any one of claims 1 to 80, wherein the method further comprises using information obtained by the method to enable a decision to acquire or purchase a medical business, hospital, or company.

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