Prediction of cardiovascular risk events and its uses

Abstract

Provided are biomarkers, methods, devices, reagents, systems, and kits for assessing an individual to predict the risk of experiencing a primary or secondary cardiovascular (CV) event over a 4-year period.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] 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 any purpose. Field of the Invention

[0003] The present application generally relates to the detection of biomarkers and a method for assessing the risk of future cardiovascular events in an individual, and more specifically to one or more biomarkers, methods, devices, reagents, systems, and kits for assessing an individual to predict the risk of a primary or secondary cardiovascular (CV) event within a 4-year period. Such events include, but are not limited to, myocardial infarction, stroke, transient ischemic attack, hospitalization for heart failure, and death. Background Art

[0004] Cardiovascular disease is the leading cause of death in the U.S. There are many existing and important predictors of risk for both 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" J AMA 297(6):611-619 (2007)) and secondary events (Shlipak, M. et al., "Bio markers 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 show that the performance of existing risk factors and biomarkers is modest (an area under the curve (AUC) of approximately 0.75 indicates that these factors perform only about as well as a coin toss and perfectly accurate). Beyond the need for improved diagnostic performance, there is a need for risk products that reflect the immediate and personal response to beneficial (and damaging) interventions and lifestyle changes within individuals. The commonly used Framingham equation has three major problems. First, it has a long time horizon: it provides a 10-year risk calculation, but humans do not prioritize future risks and are reluctant to make behavioral and lifestyle changes based on these future risks. Second, it is not very responsive to interventions: it depends primarily on chronological age, which cannot be reduced, and sex, which cannot be changed. Third, within the high-risk group envisioned here, the Framingham factors do not discriminate well between high and low risk: the hazard ratio between the high and low quartiles is only 2, and when one attempts to use the Framingham score to personalize risk by stratifying subjects into more refined strata (e.g., deciles), the observed event rates are similar for many deciles.

[0005] Risk factors for cardiovascular disease are widely used to inform the intensity and nature of medical treatment, and their use has undoubtedly contributed to the reductions in cardiovascular morbidity and mortality that have been observed over the past two decades. These factors have routinely been incorporated into algorithms, but unfortunately, they do not capture all the risk (the most common first symptom of heart disease remains death). In fact, they may capture only half the risk. In primary prevention, an area under the receiver operating characteristic (ROC) curve of about 0.76 is typical for such risk factors, while in secondary prevention the performance is even worse (typically 0.62), a value that is only about one-quarter to one-half the performance between a coin-flipped 0.5 and an incredibly precise 1.0.

[0006] Furthermore, in the Framingham study of 3209 individuals (Wang et al., “Multiple Biomarkers for the Prediction of First Major Cardiovascular Events and Death” N. Eng. J. Med. 355:2631-2637 (2006)), the addition of 10 biomarkers (CRP, BNP, NT-BNP pro--, aldosterone, renin, fibrinogen, D-dimer, plasminogen activator inhibitor type 1, homocysteine, and urine albumin to creatinine ratio) did not significantly improve the AUC when added to existing risk factors: the AUC for 0- to 5-year events was 0.76 with age, sex, and conventional risk factors and 0.77 with the best combination of biomarkers added to the mix, and the situation was even worse in secondary prevention.

[0007] Early identification of patients at higher risk for cardiovascular events within a 1 to 5 year window is important because more aggressive treatment of individuals at elevated risk can improve outcomes. Optimal management therefore requires aggressive intervention to reduce the risk of cardiovascular events in those patients deemed to be at higher risk, while patients at lower risk for cardiovascular events may receive alternative, expensive, and potentially invasive treatments that may not have a beneficial effect on the patient.

[0008] The selection of biomarkers for predicting the risk of having a specific disease state or condition within a defined time period involves first identifying markers for a specific medical application that have a measurable and statistically significant relationship with the probability and / or timing of the event. Biomarkers may include secreted or shed molecules that are in a causal relationship with the condition of interest, or are in a downstream stage of the development or progression of the disease or condition or are in parallel with the development or progression of the disease or condition, or both. The molecules are released into the bloodstream from cardiovascular tissue or from other organs and peripheral tissues and circulating cells in response to biological processes that predispose to cardiovascular events, or they may reflect downstream effects of pathophysiology, such as a decline in renal function. Biomarkers may include small molecules, peptides, proteins, and nucleic acids. Some key issues affecting the identification of biomarkers include overfitting of the available data and bias in the data.

[0009] Various methods have been used to identify biomarkers and diagnose or predict the risk of a disease or illness. For protein-based markers, these methods include two-dimensional electrophoresis, mass spectrometry, and immunoassays. For nucleic acid markers, these methods include mRNA expression profiles, microRNA profiles, FISH, gene expression serial analysis (SAGE), large-scale gene expression arrays, gene sequencing, and genotyping (SNP or small variant analysis).

[0010] The utility of two-dimensional electrophoresis is limited by low detection sensitivity; issues regarding protein solubility, charge, and hydrophobicity; gel reproducibility; and the possibility that a single spot represents multiple proteins. For mass spectrometry, depending on the format used, limitations revolve around sample handling and separation, sensitivity to low-abundance proteins, signal-to-noise considerations, and the inability to immediately identify the detected proteins. Limitations of immunoassay methods for biomarker discovery focus on the inability of antibody-based multiplexed assays to measure a large number of analytes. One could simply print arrays of high-quality antibodies and measure analytes bound to these antibodies without a sandwich. (This would be the equivalent of using a whole genome of nucleic acid sequences 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 are often not stringent enough in the selection of their binding partners to function in the context of blood or even cell extracts, because the protein collections in these matrices have very different abundances, which can result in poor signal-to-noise ratios. Therefore, one must use a different approach than immunoassay-based methods for biomarker discovery—one would need to use multiplexed ELISA assays (i.e., sandwich) to obtain sufficient stringency to measure many analytes simultaneously to decide which analytes are indeed biomarkers. Sandwich immunoassays cannot be scaled up to high levels, and therefore biomarker discovery using stringent sandwich immunoassays is not feasible using standard array formats. Finally, antibody reagents have the disadvantages of substantial batch variability and reagent instability. Instant platforms for protein biomarker discovery overcome this problem.

[0011] Many of these methods rely on or require some type of sample classification before analysis. Therefore, it is very difficult, expensive and time-consuming to prepare the samples required for sufficiently powerful studies designed to identify and find statistically relevant biomarkers in a series of well-defined sample populations. During the classification period, various variability can be introduced into various samples. For example, potential markers may be unstable for the process, the concentration of the marker may change, improper aggregation or disintegration may occur, and unintentional sample contamination may occur, and therefore the subtle changes expected in early stage disease are blurred.

[0012] It is widely recognized that biomarker discovery and detection methods using these technologies have serious limitations with respect to differential 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 of sample handling and fractionation, and the overall irreproducibility and lack of robustness of the methods. In addition, these studies have introduced bias into the data and have been unable to adequately address the complexity of the sample population in terms of the distribution and randomization required to identify and validate biomarkers within the target disease population, including appropriate controls.

[0013] Although efforts to discover new and effective biomarkers have been ongoing for decades, these efforts have been largely unsuccessful. Biomarkers for various diseases have typically been identified in research laboratories, often through unexpected discoveries during basic research on some disease processes. Based on the discovery and use of a small amount of clinical data, papers proposing new biomarker identifications have been published. However, most of these proposed biomarkers have not yet been confirmed as true or useful biomarkers, primarily because the small amount of clinical samples tested only provides weak statistical evidence that effective biomarkers have in fact been found. In other words, the initial identification was not rigorous relative to the basic elements of statistics.

[0014] Based on the history of failed biomarker discovery efforts, a theory has been proposed to further promote general understanding: biomarkers for diagnosis, prognosis or prediction of the risk of developing diseases and disorders are rare and difficult to find. Biomarker studies based on 2D gels or mass spectrometry support these concepts. Very few useful biomarkers have been identified by these methods. However, it is often overlooked that 2D gels and mass spectrometry measure proteins present in the blood at concentrations of about 1 nM and higher, and this protein set is likely to be the set least likely to change with the development of a disease or a particular disorder. Except for real-time biomarker discovery platforms, there is no proteomic biomarker discovery platform that can accurately measure protein expression levels at much lower concentrations.

[0015] A lot of knowledge is known about the biochemical pathways of complex human biology. Many biochemical pathways end or begin with secreted proteins that act locally within the lesion; for example, growth factors are secreted to stimulate the replication of other cells in the lesion, and other factors are secreted to avoid the immune system, etc. Although many of these secreted proteins act in a paracrine form, some secreted proteins act remotely within the body. Those skilled in the art who have a basic understanding of biochemical pathways will understand that many lesion-specific proteins will inevitably be present in the blood at concentrations below (or even far below) the detection limits of 2D gels and mass spectrometry. What will inevitably be ahead of this relatively large number of disease biomarker identifications is a proteomic platform that can analyze proteins at concentrations lower than those detectable by 2D gels or mass spectrometry.

[0016] As discussed above, if the tendency of such events can be accurately determined, cardiovascular events can be prevented by active treatment, and by locking the target group of such intervention on the people who need such intervention the most and / or unlocking the people who do not need such intervention the most, the efficiency of medical resource allocation can be improved and costs can be reduced at the same time. Additionally, when patients learn the accurate recent information relevant to their personal likelihood of cardiovascular events, this is less likely to be denied than long-term information based on a group, and will bring about improved lifestyle choices and improved medication compliance, which will increase benefits. Existing multi-marker tests need to collect multiple samples from individuals or need to split samples between multiple assays. Optimally, improved tests will only require single blood, urine or other samples and single assays. Therefore, there is a need for biomarkers, methods, devices, reagents, systems and test kits that can predict cardiovascular events within a 5-year time period. Summary of the Invention

[0017] The present application includes biomarkers, methods, reagents, devices, systems and kits for predicting the risk of cardiovascular (CV) events, for example, over a 4-year period or other time period. In some embodiments, the CV events are primary CV events. In some embodiments, the CV events are secondary CV events.

[0018] Cardiovascular disease involves multiple biological processes and tissues. Examples of biological systems and processes associated with cardiovascular disease are inflammation, thrombosis, angiogenesis associated with the disease, platelet activation, macrophage activation, acute liver reaction, extracellular matrix remodeling, and renal function. These processes can be observed according to sex, postmenopausal status, and age, and according to coagulation status and vascular function. Because these systems communicate in part through protein-based signal transduction systems, and multiple proteins can be measured in a single blood sample, the present invention provides single samples, single assays, and multiple protein-based tests focusing on proteins from the specific biological systems and processes involved in cardiovascular disease.

[0019] In some embodiments, methods for detecting the levels of a panel of biomarkers are provided. In some embodiments, such methods are as follows:

[0020] Embodiment 1. A method of detecting the levels of a panel of biomarker proteins in a sample from a subject, the method comprising:

[0021] a. contacting the sample from the subject with a set of capture reagents, wherein each capture reagent specifically binds to a different biomarker protein, wherein one capture reagent specifically binds to sTREM1; and

[0022] b. Detecting the amount of each capture reagent bound to its specifically bound biomarker protein.

[0023] Embodiment 2. The method of embodiment 1, wherein one capture reagent specifically binds to MMP-12.

[0024] Embodiment 3. The method of embodiment 1 or 2, wherein one capture reagent specifically binds to the N-terminal BNP precursor.

[0025] Embodiment 4. The method of any one of embodiments 1 to 3, wherein one capture reagent specifically binds to antithrombin III.

[0026] Embodiment 5. The method of any one of embodiments 1 to 4, wherein one capture reagent specifically binds to GPR56.

[0027] Embodiment 6. The method of any one of embodiments 1 to 5, wherein one capture reagent specifically binds to gelsolin.

[0028] Embodiment 7. The method of any one of embodiments 1 to 6, wherein one capture reagent specifically binds to ST4S6.

[0029] Embodiment 8. The method of any one of embodiments 1 to 7, wherein one capture reagent specifically binds to CHSTC.

[0030] Embodiment 9. The method of any one of embodiments 1 to 8, wherein one capture reagent specifically binds to FSH.

[0031] Embodiment 10. The method of any one of embodiments 1 to 9, wherein one capture agent specifically binds to IL-1sRII.

[0032] Embodiment 11. The method of any one of embodiments 1 to 10, wherein one capture reagent specifically binds to PLXB2.

[0033] Embodiment 12. The method of any one of embodiments 1 to 11, wherein one capture reagent specifically binds to SAP.

[0034] Embodiment 13. The method of any one of embodiments 1 to 12, wherein one capture reagent specifically binds to TFPI.

[0035] Embodiment 14. The method of any one of embodiments 1 to 13, wherein the panel of biomarkers comprises at least three biomarkers.

[0036] Embodiment 15. The method of any one of embodiments 1 to 13, wherein the panels of biomarkers comprise 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.

[0037] Embodiment 16. The method of any one of embodiments 1 to 13, wherein the panel of biomarkers consists of 2 to 13 biomarkers.

[0038] Embodiment 17. The method of any one of embodiments 1 to 13, wherein the panel of biomarkers comprises at least 13 biomarkers.

[0039] Embodiment 18. The method of any one of embodiments 1 to 13, wherein the panel of biomarkers consists of 13 biomarkers.

[0040] Embodiment 19. The method of any one of embodiments 1 to 18, wherein the subject is at least 40 years old.

[0041] Embodiment 20. The method of any one of embodiments 1 to 19, wherein the subject has no known history of cardiovascular disease.

[0042] Embodiment 21. The method of any one of embodiments 1 to 20, comprising determining the subject's risk of having a primary cardiovascular event within four years from the day the sample is obtained from the subject.

[0043] Embodiment 22. The method of embodiment 21, wherein the risk that the subject has a primary cardiovascular event is within one, two, three, or four years from the day the sample is obtained from the subject.

[0044] 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.

[0045] Embodiment 24. The method of any one of Embodiments 21 to 23, wherein the risk is determined as a quantitative probability.

[0046] Embodiment 25. The method of any one of Embodiments 21 to 23, wherein the risk is determined as a qualitative risk level.

[0047] Embodiment 26. The method of embodiment 25, wherein the qualitative risk level is a low, medium, or high risk level.

[0048] Embodiment 27. The method of embodiment 1 or 2, wherein one capture reagent specifically binds to SVEP1.

[0049] Embodiment 28. The method of any one of embodiments 1, 2, or 27, wherein one capture reagent specifically binds to ARL11.

[0050] Embodiment 29. The method of any one of embodiments 1, 2, 27, or 28, wherein one capture reagent specifically binds to ANTR2.

[0051] Embodiment 30. The method of any one of embodiments 1, 2, or 27 to 29, wherein one capture reagent specifically binds to CA125.

[0052] Embodiment 31. The method of any one of embodiments 1, 2, or 27 to 30, wherein one capture reagent specifically binds to GOLM1.

[0053] Embodiment 32. The method of any one of embodiments 1, 2, or 27 to 31, wherein one capture reagent specifically binds to PPR1A.

[0054] Embodiment 33. The method of any one of embodiments 1, 2, or 27 to 32, wherein one capture reagent specifically binds to ERBB3.

[0055] Embodiment 34. The method of any one of embodiments 1, 2, or 27 to 33, wherein one capture reagent specifically binds to suPAR.

[0056] Embodiment 35. The method of any one of embodiments 1, 2, or 27 to 34, wherein one capture reagent specifically binds to GDF-11 / 8.

[0057] Embodiment 36. The method of any one of embodiments 1, 2, or 27 to 35, wherein one capture reagent specifically binds to JAM-B.

[0058] Embodiment 37. The method of any one of embodiments 1, 2, or 27 to 36, wherein one capture reagent specifically binds to ATS13.

[0059] Embodiment 38. The method of any one of embodiments 1, 2, or 27 to 37, wherein one capture reagent specifically binds to Spinalin-1.

[0060] Embodiment 39. The method of any one of embodiments 1, 2, or 27 to 38, wherein one capture agent specifically binds to NCAM-120.

[0061] Embodiment 40. The method of any one of embodiments 1, 2, or 27 to 39, wherein one capture reagent specifically binds to TFF3.

[0062] Embodiment 41. The method of any one of embodiments 1, 2, or 27 to 40, wherein one capture agent specifically binds to SIRT2.

[0063] Embodiment 42. The method of any one of embodiments 1, 2, or 27 to 41, wherein one capture agent specifically binds to ANP.

[0064] Embodiment 43. The method of any one of embodiments 1, 2, or 27 to 42, wherein one capture reagent specifically binds to NELL1.

[0065] Embodiment 44. The method of any one of embodiments 1, 2, or 27 to 43, wherein one capture reagent specifically binds to LRP11.

[0066] Embodiment 45. The method of any one of embodiments 1, 2, or 27 to 44, wherein one capture reagent specifically binds to NDST1.

[0067] Embodiment 46. The method of any one of embodiments 1, 2, or 27 to 45, wherein one capture reagent specifically binds to PTPRJ.

[0068] Embodiment 47. The method of any one of embodiments 1, 2, or 27 to 46, wherein one capture reagent specifically binds to CILP2.

[0069] Embodiment 48. The method of any one of embodiments 1, 2, or 27 to 47, wherein one capture reagent specifically binds to CA2D3.

[0070] Embodiment 49. The method of any one of embodiments 1, 2, or 27 to 48, wherein one capture reagent specifically binds to ITI heavy chain H2.

[0071] Embodiment 50. The method of any one of embodiments 1, 2, or 27 to 49, wherein one capture reagent specifically binds to IGDC4.

[0072] Embodiment 51. The method of any one of embodiments 1, 2, or 27 to 50, wherein one capture reagent specifically binds to BNP.

[0073] Embodiment 52. The method of any one of Embodiments 27 to 51, wherein the panel of biomarkers comprises at least three biomarkers.

[0074] Embodiment 53. The method of any one of embodiments 1, 2, or 27 to 51, wherein the panel 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.

[0075] Embodiment 54. The method of any one of embodiments 1, 2, or 27 to 51, wherein the panel of biomarkers consists of 2 to 27 biomarkers.

[0076] Embodiment 55. The method of any one of embodiments 1, 2, or 27 to 51, wherein the panel of biomarkers comprises at least 27 biomarkers.

[0077] Embodiment 56. The method of any one of embodiments 1, 2, or 27 to 51, wherein the panel of biomarkers consists of 27 biomarkers.

[0078] Embodiment 57. The method of any one of embodiments 27 to 56, wherein the subject is at least 40 years old.

[0079] Embodiment 58. The method of any one of embodiments 27 to 57, wherein the subject has significantly stable cardiovascular disease.

[0080] Embodiment 59. The method of embodiment 58, wherein the apparently stable cardiovascular disease comprises a history of myocardial infarction, stroke, heart failure, revascularization, abnormal stress test, imaging suggestive of coronary artery disease, or abnormal coronary artery calcium score.

[0081] Embodiment 60. The method of embodiment 59, wherein the myocardial infarction or stroke occurred at least six months prior to the day on which the sample was obtained from the subject.

[0082] Embodiment 61. The method of embodiment 59, wherein the abnormal stress test is a treadmill exercise test or a nuclear medicine-based test.

[0083] Embodiment 62. The method of embodiment 59, wherein the imaging suggestive of coronary artery disease is an angiogram showing 50% or greater coronary artery stenosis.

[0084] Embodiment 63. The method of any one of Embodiments 27 to 62, comprising determining the subject's risk of having a secondary cardiovascular event within four years from the day the sample is obtained from the subject.

[0085] Embodiment 64. The method of embodiment 63, wherein the risk that the subject has a secondary cardiovascular event is within one, two, three, or four years from the day the sample is obtained from the subject.

[0086] 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.

[0087] Embodiment 66. The method of any one of Embodiments 63 to 65, wherein the risk is determined as a quantitative probability.

[0088] Embodiment 67. The method of any one of Embodiments 63 to 65, wherein the risk is determined as a qualitative risk level.

[0089] Embodiment 68. The method of embodiment 67, wherein the qualitative risk level is a low, medium, or high risk level.

[0090] In some embodiments, methods are provided for screening a subject for risk of a cardiovascular event (CV) event. In some such embodiments, a method comprises

[0091] (a) forming a biomarker panel comprising N biomarkers selected from the group consisting of: N-terminal pro-BNP, sTREM-1, MMP-12, antithrombin III, GPR56, gelsolin, ST4S6, CHSTC, FSH, IL-1sRII, PLXB2, SAP, and TFPI, wherein N is an integer from 3 to 13; and

[0092] (b) detecting the level of each of the N biomarkers in the panel in a sample from the subject.

[0093] In some embodiments, a method comprises

[0094] (a) forming a biomarker panel comprising N protein biomarkers selected from the group consisting of BNP, sTREM-1, MMP-12, SVEP1, ARL11, ANTR2, CA125, GOLM1, PPR1A, ERBB3, suPAR, GDF-11 / 8, JAM-B, ATS13, Spinalin-1, NCAM-120, TFF3, SIRT2, ANP, NELL1, LRP11, NDST1, PTPRJ, CILP2, CA2D3, ITI heavy chain H2, and IGDC4, wherein N is an integer from 8 to 27; and

[0095] (b) detecting the level of each of the N biomarkers in the panel in a sample from the subject.

[0096] In some embodiments, methods are provided for predicting the likelihood that a subject will have a CV event. In some such embodiments, a method comprises

[0097] (a) forming a biomarker panel comprising N biomarkers selected from the group consisting of: N-terminal pro-BNP, sTREM-1, MMP-12, antithrombin III, GPR56, gelsolin, ST4S6, CHSTC, FSH, IL-1sRII, PLXB2, SAP, and TFPI, wherein N is an integer from 3 to 13; and

[0098] (b) detecting the level of each of the N biomarkers in the panel in a sample from the subject.

[0099] In some embodiments, a method comprises

[0100] (a) forming a biomarker panel comprising N protein biomarkers selected from the group consisting of BNP, sTREM-1, MMP-12, SVEP1, ARL11, ANTR2, CA125, GOLM1, PPR1A, ERBB3, suPAR, GDF-11 / 8, JAM-B, ATS13, Spinalin-1, NCAM-120, TFF3, SIRT2, ANP, NELL1, LRP11, NDST1, PTPRJ, CILP2, CA2D3, ITI heavy chain H2, and IGDC4, wherein N is an integer from 8 to 27; and

[0101] (b) detecting the level of each of the N biomarkers in the panel in a sample from the subject.

[0102] In some embodiments, methods are provided for screening a subject for risk or likelihood of a cardiovascular (CV) event, the method comprising detecting the 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 biomarkers from the panel of N biomarkers.

[0103] In some embodiments, the risk or likelihood of the subject having a CV event within 4 years is high if the levels of at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, or at least 13 of the biomarkers in the panel of biomarkers are each abnormal relative to the control levels of the corresponding biomarkers.

[0104] In some embodiments, the methods include detecting the level of one or more biomarkers listed in Table 1. In some embodiments, the methods include detecting the level of one or more biomarkers listed in Table 2.

[0105] In some embodiments, the subject suffers from coronary artery disease. In some embodiments, the subject does not have a history of CV events. In some embodiments, the subject has a high-risk classification in the American College of Cardiology (ACC) Summary Cohort Equation (PCE). See Goff DC, Jr. et al., "ACC / AHA Guideline on theAssessment of Cardiovascular Risk:A Report of the American College ofCardiology / American Heart Association Task Force on Practice Guidelines." Circulation.2013. In some embodiments, the subject has a moderate-risk classification in the PCE. In some embodiments, the subject has a low-risk classification in the PCE. In some embodiments, the subject has had 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.

[0106] 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.

[0107] In some embodiments, each biomarker is a protein biomarker. In some embodiments, the method comprises contacting the biomarkers of the sample from the subject with a set of biomarker capture reagents, wherein each biomarker capture reagent in the set of biomarker capture reagents specifically binds to a different biomarker being 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, at least one slow off-rate aptamer comprises 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 nucleotides with modifications. In some embodiments, each slow off-rate aptamer has an off-rate (t) of ≥30 minutes, ≥60 minutes, ≥90 minutes, ≥120 minutes, ≥150 minutes, ≥180 minutes, ≥210 minutes, or ≥240 minutes. 1 / 2 ) binds to its target protein.

[0108] In some embodiments, the risk or likelihood of a CV event is based on the biomarker level and at least one additional biomedical information selected from the group consisting of

[0109] a) information corresponding to the presence of a cardiovascular risk factor selected from the group consisting of: previous myocardial infarction, angiographic evidence of greater than 50% stenosis in one or more coronary vessels, exercise-induced ischemia as measured by treadmill exercise testing or nuclear testing, or previous coronary revascularization,

[0110] b) information corresponding to the physical descriptors of the subject,

[0111] c) information corresponding to changes in weight of the subject,

[0112] d) information corresponding to the ethnicity of the subject,

[0113] e) information corresponding to the sex of the subject,

[0114] f) information corresponding to the subject's smoking history,

[0115] g) information corresponding to the subject's drinking history,

[0116] h) information corresponding to the occupational history of the subject,

[0117] i) information corresponding to the subject's family history of cardiovascular disease or other circulatory system disorders,

[0118] j) information corresponding to the presence or absence of at least one genetic marker in the subject, said at least one genetic marker being associated with a higher risk of cardiovascular disease in the subject or in a family member of the subject,

[0119] k) information corresponding to the clinical symptoms of the subject,

[0120] l) information corresponding to other laboratory tests,

[0121] m) information corresponding to gene expression values ​​of the subject, and

[0122] n) information corresponding to the subject's possession of known cardiovascular risk factors, such as a diet high in saturated fat, high in salt, high in cholesterol,

[0123] o) information corresponding to imaging results of the subject obtained by a technique selected from the group consisting of electrocardiogram, echocardiogram, carotid ultrasound for intima-media thickness, flow-mediated dilation, pulse wave velocity, ankle-brachial index, stress echocardiography, myocardial perfusion imaging, CT coronary artery calcification, high-resolution CT angiography, MRI imaging, and other imaging modalities,

[0124] p) Information about the subject's medication

[0125] q) information corresponding to the age of the subject, and

[0126] r) information about the subject's renal function.

[0127] In some embodiments, the risk or likelihood of a CV event is based on the biomarker level and at least the age of the subject.

[0128] In some embodiments, the method includes determining the risk or likelihood of a CV event for the purpose of determining health insurance premiums or life insurance premiums. In some embodiments, the method also includes determining health insurance or life insurance coverage or premiums. In some embodiments, the method also includes using information derived from the method to predict and / or manage the utilization of medical resources. In some embodiments, the method also includes using information derived from the method to make decisions about acquiring or purchasing a medical practice, hospital, or company.

[0129] In some embodiments, a computer-implemented method for assessing the risk or likelihood of a cardiovascular (CV) event is provided. In some embodiments, the method comprises: retrieving biomarker information for a subject on a computer, wherein the biomarker information comprises (a) levels of 3 to 13 biomarkers selected from Table 1 in a sample from the subject; or (b) levels of 8 to 27 biomarkers selected from Table 2; classifying each of the biomarker values ​​using the computer; and indicating a result of the assessment of the risk of a CV event for the subject based on a plurality of classifications. In some embodiments, indicating the result of the assessment of the risk or likelihood of a CV event for the subject comprises displaying the result on a computer display. BRIEF DESCRIPTION OF THE DRAWINGS

[0130] Figure 1Kaplan-Meier survival curves for the HUNT3 training set are shown, stratified by the four risk bins of the primary CVD model, with the shaded area representing the 95% confidence intervals of the Kaplan-Meier estimates. The lines are, from top to bottom, 1x (<0.0215), n=469; 2x-3x (<0.0505), n=944; 4x-5x (<0.077), n=285; 6x and above (>0.077), n=315.

[0131] Figure 2 Kaplan-Meier survival curves for the HUNT3 training set are shown, stratified by the four risk classes of the secondary CVD model, with the shaded area representing the 95% confidence intervals for the Kaplan-Meier estimates. The lines are, from top to bottom, <0.075, n=117; <0.25, n=285; >0.5, n=121; >0.5, n=82.

[0132] Figure 3 Kaplan-Meier survival curves for the ARIC visit 5 validation set, stratified by the four risk classes of the secondary CVD model, are shown, with the shaded area representing the 95% confidence intervals of the Kaplan-Meier estimates. The lines are, from top to bottom, <0.075, n=35; <0.25, n=103; <0.5, n=43; >0.5, n=27.

[0133] Figure 4 Survival curves for the HUNT3 validation set are shown, stratified by cutoff values. The lines from top to bottom are <0.075, n=24; <0.25, n=61; <0.5, n=25; >0.5, n=29.

[0134] Figure 5 Survival curves for the ARIC visit 5 validation set are shown, stratified by cutoff values. The lines are from top to bottom <0.075, n=13; <0.25, n=202; <0.5, n=271; >0.5, n=345.

[0135] Figure 6 A non-limiting, exemplary computer system is shown for use with the various computer-implemented methods described herein.

[0136] Figure 7 Non-limiting exemplary aptamer assays that can be used to detect one or more biomarkers in a biological sample are shown.

[0137] Figure 8A and Figure 8BShown are certain exemplary modified pyrimidines that can be incorporated into aptamers, such as slow off-rate aptamers. DETAILED DESCRIPTION

[0138] While the present invention will be described in conjunction with certain representative embodiments, it will be understood that the invention is defined by the claims and is not limited to these embodiments.

[0139] One skilled in the art will recognize that many methods and materials similar or equivalent to those described herein can be used in the practice of the present invention.The present invention is in no way limited to the methods and materials described.

[0140] Unless defined otherwise, technical and scientific terms used herein have the meaning commonly understood by one of ordinary skill in the art to which the 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, certain methods, devices, and materials are described herein.

[0141] All publications, published patent documents, and patent applications cited herein are hereby 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.

[0142] As used herein, the terms "comprises," "comprising," "includes," "including," "contains," "containing," and any variations thereof are intended to cover a non-exclusive inclusion such that a process, method, process-defined product, or composition of matter that comprises, includes, or contains an element or list of elements may include additional elements not expressly listed.

[0143] The present application includes biomarkers, methods, devices, reagents, systems and kits for predicting the risk of a recent CV event within a defined time period, such as within 1 year, within 2 years, within 3 years, or within 4 years.

[0144] "Cardiovascular event" or "CV event" refers to a malfunction or dysfunction of any part of the circulatory system. In some embodiments, "cardiovascular event" refers to a stroke, transient ischemic attack (TIA), myocardial infarction (MI), sudden death attributable to dysfunction of the circulatory system and / or hospitalization for heart failure, or sudden death of unknown etiology in a population where the most likely cause is cardiovascular disease. A primary CV event is the first CV event experienced by a subject. A secondary CV event is the second or additional CV event experienced by a subject.

[0145] Cardiovascular events can include thrombotic events such as MI, transient ischemic attack (TIA), stroke, acute coronary syndrome, and events requiring coronary revascularization.

[0146] In some embodiments, biomarkers are provided for use alone or in various combinations to assess the risk or likelihood of sudden death or future CV events within a 4-year period, wherein a CV event is defined as myocardial infarction, stroke, transient ischemic attack, death, and hospitalization for heart failure. As described in detail below, exemplary embodiments include the biomarkers provided in Table 1 or Table 2.

[0147] Although some of the described CV event biomarkers may be able to be used alone to assess the risk or likelihood of a CV event, methods for grouping multiple subsets of CV event biomarkers are also described herein, wherein each grouping or subset selection can be used as a group of three or more biomarkers, referred to interchangeably herein as a "biomarker panel" and a group. Thus, various embodiments provide a combination comprising 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 biomarkers in Table 1. Other different embodiments provide a combination comprising at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty, at least twenty-one, at least twenty-two, at least twenty-three, at least twenty-four, at least twenty-five, at least twenty-six, or all twenty-seven biomarkers in Table 2.

[0148] "Biological sample," "sample," and "test sample" are used interchangeably herein to refer to any substance, biological fluid, tissue, or cell obtained or otherwise derived from an individual. This includes blood (including whole blood, white blood cells, peripheral blood mononuclear cells, buffy coat, plasma, and serum), sputum, tears, mucus, nasal washes, nasal aspirates, urine, saliva, peritoneal washings, ascites, cystic fluid, glandular fluid, lymphatic fluid, bronchial aspirates, synovial fluid, joint aspirates, organ secretions, cells, cell extracts, and cerebrospinal fluid. This also includes experimentally separated portions of all of the aforementioned substances. For example, a blood sample can be fractionated into serum, plasma, or a portion containing a specific type of blood cell, such as red blood cells or white blood cells (leukocytes). In some embodiments, a blood sample is a dried blood spot. In some embodiments, a plasma sample is a dried plasma spot. In some embodiments, a sample can be a combination of samples from an individual, such as a combination of tissue and fluid samples. The term "biological sample" also includes, for example, a substance containing homogenized solid matter (such as from a stool sample, a tissue sample, or a tissue biopsy). The term "biological sample" also includes a substance derived from a tissue culture or cell culture. Any suitable method for obtaining a biological sample can be used; exemplary methods include, for example, phlebotomy, swabs (e.g., oral swabs), and fine needle aspiration biopsy procedures. Exemplary tissues that are susceptible to fine needle aspiration include lymph nodes, lungs, thyroid, breasts, pancreas, and liver. Samples can also be collected, for example, by microdissection (e.g., laser capture microdissection (LCM) or laser microdissection (LMD)), bladder washing, smears (e.g., PAP smears), or ductal lavage. A "biological sample" obtained or obtained from a subject includes any such sample that has been processed in any suitable manner after being obtained from the subject. In some embodiments, the biological sample is a plasma sample.

[0149] Additionally, in some embodiments, a biological sample can be obtained by taking biological samples from multiple subjects and pooling them, or by pooling aliquots of each subject's biological sample. For samples from a single subject, the pooled sample can be processed as described herein, and, for example, if a poor prognosis is determined in the pooled sample, each subject's biological sample can be retested to determine which subject or subjects have an increased or decreased risk of a CV event.

[0150] For the purposes of this specification, the phrase "data attributable to a biological sample from a subject" is intended to mean that the data, in some form, is derived from the subject's biological sample or generated using the biological sample. The data may be reformatted, modified, or mathematically altered to some extent after generation, such as by converting units from one measurement system to units from another measurement system; however, the data should be understood as originally derived from or generated using the biological sample.

[0151] "Target", "target molecule" and "analyte" are used interchangeably herein to refer to any molecule of interest that may be present in a biological sample."Molecule of interest" includes any subtle changes in a particular molecule, such as in the case of a protein, for example, subtle changes in the amino acid sequence, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification (such as conjugation with a labeling component that does not substantially change the identity of the molecule). "Target molecule", "target" or "analyte" refers to a set of copies of a type or type of molecule or multimolecular structure. Exemplary target molecules include proteins, polypeptides, nucleic acids, carbohydrates, lipids, polysaccharides, glycoproteins, hormones, receptors, antigens, antibodies, affibodies, antibody mimics, 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 substances. In some embodiments, the target molecule is a protein, in which case the target molecule can be referred to as a "target protein".

[0152] As used herein, "capture agent" or "capture reagent" refers to a molecule that can specifically bind to a biomarker. "Target protein capture reagent" refers to a molecule that can specifically bind to a target protein. Non-limiting exemplary capture reagents include aptamers, antibodies, adnectins, ankyrins, other antibody mimics and other protein scaffolds, autoantibodies, chimeras, small molecules, nucleic acids, lectins, ligand-binding receptors, imprinted polymers, high-affinity multimers (avimers), peptide mimetics, hormone receptors, cytokine receptors, synthetic receptors, and modifications and fragments of any of the above capture reagents. In some embodiments, the capture reagent is selected from aptamers and antibodies.

[0153] The term "antibody" refers to full-length antibodies of any species and fragments and derivatives of such antibodies, including Fab fragments, F(ab')2 fragments, single-chain antibodies, Fv fragments, and single-chain Fv fragments. The term "antibody" also refers to antibodies of synthetic origin, such as antibodies and fragments derived from phage display technology, affibodies, nanobodies, etc.

[0154] As used herein, "marker" and "biomarker" are used interchangeably to refer to a target molecule that indicates or suggests a normal or abnormal process in a subject or a disease or other condition in a subject. More specifically, a "marker" or "biomarker" is an anatomical, physiological, biochemical, or molecular parameter associated with the presence of a specific physiological state or process, whether normal or abnormal, and if abnormal, whether chronic or acute. Biomarkers can be detected and measured by various methods, including laboratory assays and medical imaging. In some embodiments, a biomarker is a target protein.

[0155] As used herein, "biomarker level" and "level" refer to a measurement achieved using any analytical method for detecting a biomarker in a biological sample and indicates the presence, absence, absolute amount or concentration, relative amount or concentration, titer, level, expression level, ratio of measured levels, etc. of, for, or corresponding to a biomarker in a biological sample. The precise nature of the "level" depends on the specific design and composition of the particular analytical method used to detect the biomarker.

[0156] When a biomarker indicates or suggests an abnormal process or disease or other condition in a subject, the biomarker is generally described as being overexpressed or underexpressed compared to the expression level or value of the biomarker that indicates or suggests a normal process or the absence of a disease or other condition in the subject. "Upregulated," "upregulated," "overexpressed," "overexpressed," and any variations thereof are used interchangeably to refer to a value or level of a biomarker in a biological sample that is greater than the value or level (or range of values ​​or levels) of the biomarker typically detected in a similar biological sample from a healthy or normal subject. These terms may also refer to a value or level of a biomarker in a biological sample that is greater than the value or level (or range of values ​​or levels) of the biomarker that may be detected at different stages of a particular disease.

[0157] "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 the biomarker typically detected in a similar biological sample from a healthy or normal subject. These terms may 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 the biomarker that may be detected at different stages of a particular disease.

[0158] Additionally, overexpressed or underexpressed biomarkers 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 the biomarker that indicates or suggests a normal process in the subject or the absence of a disease or other condition. Thus, "differential expression" of a biomarker may also be referred to as a deviation from a "normal" expression level of the biomarker.

[0159] A "control level" of a target molecule refers to the level of the target molecule in the same sample type from a subject who does not suffer from a disease or disorder, or from a subject who is not suspected of suffering from a disease or disorder or is not at risk of suffering from a disease or disorder, or from a subject who has had a primary or first cardiovascular event but has not had a secondary cardiovascular event, or from a subject with stable cardiovascular disease. A control level can refer to the average level of the target molecule in a sample from a subject population that does not suffer from a disease or disorder, or is not suspected of suffering from a disease or disorder or is not at risk of suffering from a disease or disorder, or has had a primary or first cardiovascular event but has not had a secondary cardiovascular event, or has stable cardiovascular disease, or a combination thereof.

[0160] As used herein, "individual," "subject," and "patient" are used interchangeably to refer to a mammal. A mammalian subject can be a human or non-human subject. In various embodiments, the subject is a human. A healthy or normal subject is one in whom a disease or condition of interest (including, for example, cardiovascular events such as myocardial infarction, stroke, and hospitalization for heart failure) cannot be detected by conventional diagnostic methods.

[0161] "Diagnose," "diagnosing," "diagnosis," and variations thereof refer to detecting, determining, or discerning the health state or condition of a subject based on one or more signs, symptoms, data, or other information related to the subject. A subject's health state may be diagnosed as healthy / normal (i.e., the absence of a disease or condition is diagnosed) or as sick / abnormal (i.e., the presence of a disease or condition is diagnosed, or the characteristics of a disease or condition are assessed). The terms "diagnose," "diagnosing," "diagnosis," and the like, with respect to a specific disease or condition, encompass initial detection of the disease; characterization or classification of the disease; detection of disease progression, remission, or recurrence; and detection of disease response following administration of treatment or therapy to a subject. Risk prediction for a CV event includes distinguishing subjects who have an increased risk of a CV event from subjects who do not have an increased risk of a CV event.

[0162] "Prognose," "prognosing," "prognosis," and variations thereof refer to a prediction of the future course of a disease or disorder in a subject suffering from the disease or disorder (e.g., prediction of patient survival), and such terms encompass the assessment of a subject's response to a disease or disorder following administration of a treatment or therapy.

[0163] "Evaluate," "evaluating," and "evaluation" and variations thereof encompass both "diagnosis" and "prognosis," and also encompass determinations or predictions regarding the future course of a disease or disorder in a subject who does not have the disease or condition, as well as determinations or predictions regarding the risk that a disease or disorder will recur in a subject who has apparently been cured of the disease or has experienced remission of the disease. The term "evaluate" also encompasses assessing a subject's response to therapy, such as, for example, predicting whether a subject is likely to respond well to a therapeutic agent or is unlikely to respond to a therapeutic agent (or, for example, will experience toxicity or other undesirable side effects), selecting a therapeutic agent for administration to a subject, or monitoring or determining a subject's response to a therapy that has been administered to the subject. Thus, "evaluating" the risk of a CV event can include, for example, any of the following: predicting the future risk of a CV event occurring in a subject; predicting the risk of a CV event occurring in a subject who does not apparently have a CV problem; predicting a particular type of CV event; predicting the time at which 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 a determination of a biomarker value from a biological sample from the subject. The risk assessment of a CV event can include embodiments such as continuously assessing the risk of a CV event or categorizing the risk of a CV event in an ascending classification. Risk categorization includes, for example, classification into two or more categories, such as "moderate risk of a CV event"; "high risk of a CV event"; and / or "low risk of a CV event." In some embodiments, the risk assessment of a CV event is for a defined time period. Non-limiting exemplary defined time periods include 1 year, 2 years, 3 years, 4 years, 5 years, and more than 5 years.

[0164] As used herein, "additional biomedical information" refers to one or more assessments of a subject, other than an assessment performed using any of the biomarkers described herein, that correlate with CV risk, or more specifically, risk of a CV event. "Additional biomedical information" includes any of the following: physical descriptors of the subject, including the subject's height and / or weight; the subject's age; the subject's sex; weight change; the subject's race; occupational history; family history of cardiovascular disease (or other circulatory system disorders); the presence of one or more genetic markers associated with a higher risk of cardiovascular disease (or other circulatory system disorders) in the subject or changes in carotid intima-media thickness in a family member; clinical symptoms, such as chest pain, weight gain or weight loss gene expression values; physical descriptors of the subject, including physical descriptors observed by radiographic imaging; smoking status; alcohol consumption history; occupational history; dietary habits - salt, saturated fat and cholesterol intake; caffeine intake; and imaging information, such as electrocardiogram, echocardiogram, carotid ultrasound for intima-media thickness, flow-mediated dilation, pulse wave velocity, ankle-brachial index, stress echocardiography, myocardial perfusion imaging, CT coronary artery calcification, high-resolution CT angiography, MRI imaging and other imaging modalities; and the subject's medication. Testing the biomarker levels in conjunction with the evaluation of any additional biomedical information, including other laboratory tests (e.g., HDL, LDL testing, CRP levels, Nt-BNP precursor testing, BNP testing, high-sensitivity troponin testing, galectin 3 testing, serum albumin testing, creatine testing) can, for example, improve the sensitivity, specificity, and / or AUC for CV event prediction compared to a single biomarker test or additional biomedical information evaluating any particular item alone (e.g., carotid intima-media thickness imaging alone). The additional biomedical information can be obtained from the subject using conventional techniques known in the art, such as from the subject themselves using a routine patient questionnaire or health history questionnaire, or from a licensed physician. Testing the biomarker levels in conjunction with the evaluation of any additional biomedical information can, for example, improve the sensitivity, specificity, and / or threshold for CV event prediction (or other cardiovascular-related uses) compared to a single biomarker test or additional biomedical information evaluating any particular item alone (e.g., CT imaging alone).

[0165] As used herein, "detection" or "determination" of a biomarker value includes both the use of instruments for observing and recording a signal corresponding to the biomarker level and one or more materials required to generate the 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.

[0166] As used herein, PCE risk categories are determined according to "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" by Goff et al., 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 10-year predicted risk of 20.0% or greater for a hard atherosclerotic / cardiovascular disease (ASCVD) event (defined as a first occurrence of nonfatal myocardial infarction or coronary heart disease (CHD) death, or fatal or nonfatal stroke); a "moderate" PCE risk category is a 10-year predicted risk of 10.0% to 19.9% ​​for a hard ASCVD event; and a "low" PCE risk category is a 10-year predicted risk of <10.0% for a hard ASCVD event. See Table 5 on page 16 of Goff.

[0167] "Solid support" refers herein to any substrate having a surface to which molecules can be attached directly or indirectly by covalent or non-covalent bonds. A "solid support" can have various physical forms, which may include, for example, membranes; chips (e.g., protein chips); slides (e.g., slides or coverslips); columns; hollow, solid, semi-solid, particles containing holes 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 vessels, grooves, or recesses capable of holding samples. Sample containers can be housed on multi-sample platforms, such as microtiter plates, slides, microfluidic devices, and the like. The carrier 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 attachment method (e.g., covalent attachment). Other exemplary containers include droplets and microfluidic controls or bulk oily / aqueous emulsions in which assays and related operations can be performed. Suitable solid carriers include, for example, plastics, resins, polysaccharides, silicon dioxide or silicon dioxide-based materials, functionalized glass, modified silicon, carbon, metal, inorganic glass, film, nylon, natural fibers (such as, silk, wool and cotton), polymers, etc. The material constituting the solid carrier may include reactive groups, such as carboxyl, amino or hydroxyl groups, which are used to attach capture reagents. Polymeric solid carriers may include, for example, polystyrene, polyethylene glycol tetraphthalate (polyethyleneglycol tetraphthalate), polyvinyl acetate, polyvinyl chloride, polyvinyl pyrrolidone, polyacrylonitrile, polymethyl methacrylate, polytetrafluoroethylene (PTFE), butyl rubber, styrene-butadiene rubber (SBR), natural rubber, polyethylene, polypropylene, (poly) tetrafluoroethylene, (poly) vinylidene fluoride, polycarbonate and polymethylpentene. Operable suitable solid carrier particles include, for example, coded particles (such as type-coded particles), magnetic particles, and glass particles.

[0168] Exemplary Uses of Biomarkers

[0169] In various exemplary embodiments, by detecting one or more biomarker values ​​corresponding to one or more biomarkers present in the circulation of the subject (such as in blood, serum or plasma) via any number of analytical methods (including any analytical methods described herein), a method for assessing the risk or likelihood of a CV event occurring in a subject is provided. These biomarkers are, for example, differentially expressed in subjects with an increased risk of CV events compared to subjects without an increased risk of CV events. For example, detection of differential expression of biomarkers in a subject can be used to allow prediction of the risk of CV events within a 1-year, 2-year, 3-year, 4-year or 5-year timeframe.

[0170] In addition to testing biomarker levels as independent diagnostic tests, biomarker levels can also be completed in conjunction with the determination of single nucleotide polymorphisms (SNPs) or other genetic lesions or variability, and the risk of these genetic lesions or variability indication diseases or disease susceptibility increases. (See, for example, Amos et al., Nature Genetics 40, 616-622 (2009)). Biomarker levels can also be used in conjunction with radiological screening. Biomarker levels can also be used in conjunction with relevant symptoms or genetic testing. After assessing the risk of CV events, it may be useful to detect any biomarker described herein to guide the appropriate clinical care of the subject, including increasing to a more active level of care for high-risk subjects after having determined the risk of CV events. In addition to testing biomarker levels in conjunction with relevant symptoms or risk factors, other types of data, particularly data indicating the risk of cardiovascular events of the subject (e.g., patient clinical history, symptoms, family history of cardiovascular disease, smoking history or drinking history, risk factors (such as the presence of one or more genetic markers and / or the state of other biomarkers, etc.)) can be used to assess information about biomarkers. These various data may be assessed by automated methods (such as computer programs / software) that may be embodied in a computer or other device / apparatus.

[0171] In addition to testing biomarker levels in conjunction with radiological screening tests for high-risk subjects (e.g., assessing biomarker levels in conjunction with blockages detected during coronary angiography), information about biomarkers can also be assessed in conjunction with other types of data, particularly data indicating a subject's risk for a CV event (e.g., patient clinical history, symptoms, family history of cardiovascular disease, risk factors (such as whether the subject is a smoker, a heavy drinker, and / or other biomarker status), etc.). These various data can be assessed by automated methods (such as computer programs / software) that can be embodied in a computer or other device / apparatus.

[0172] Biomarker testing can also be correlated with guidelines and cardiovascular risk algorithms currently used in clinical practice. For example, the Framingham risk score uses risk factors to provide a risk score, such risk factors include LDL-cholesterol and HDL-cholesterol levels, decreased glucose levels, smoking, systolic blood pressure, and diabetes. The incidence of high-risk patients increases with age, and men make up a larger proportion of high-risk patients than women.

[0173] Any of the described biomarkers can also be used in imaging tests. For example, imaging agents can be coupled to any of the described biomarkers, which can be used to assist in predicting the risk of cardiovascular events, monitoring response to therapeutic interventions, selecting target populations in clinical trials, and other uses.

[0174] Detection and determination of biomarkers and biomarker levels

[0175] Any of a variety of known analytical methods can be used to detect the biomarker levels of the biomarkers described herein. In one embodiment, a capture reagent is used to detect the biomarker value. In various embodiments, the capture reagent can be exposed to the biomarker in solution or can be exposed to the biomarker when the capture reagent is fixed on a solid support. In other embodiments, the capture reagent contains features that react with secondary features on the solid support. In these embodiments, the capture reagent can be exposed to the biomarker in solution, and then the features on the capture reagent can be used in conjunction with the secondary features on the solid support to fix the biomarker on the solid support. The capture reagent is selected based on the type of analysis to be performed. Capture agents 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, peptide mimetics, hormone receptors, cytokine receptors, and synthetic receptors, as well as modifications and fragments of these capture agents.

[0176] In some embodiments, biomarker levels are detected using a biomarker / capture reagent complex.

[0177] In some embodiments, the biomarker level is derived from the biomarker / capture reagent complex and is detected indirectly, such as, for example, as a result of a reaction subsequent to the biomarker / capture reagent interaction, but dependent upon the formation of the biomarker / capture reagent complex.

[0178] In some embodiments, biomarker levels are detected directly from the biomarker in a biological sample.

[0179] In some embodiments, a multiplex format that allows simultaneous detection of two or more biomarkers in a biological sample is used to detect biomarkers. In some embodiments of the multiplex format, capture reagents are directly or indirectly, covalently or non-covalently fixed to discrete positions on a solid support. In some embodiments, the multiplex format uses discrete solid supports, wherein each solid support has a unique capture reagent associated with the solid support, such as quantum dots. In some embodiments, a separate device is used to detect each biomarker in a variety of biomarkers to be detected in a biological sample. A 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 so that each well in the plate is used to uniquely analyze one or more biomarkers to be detected in a biological sample.

[0180] In one or more of the foregoing embodiments, a fluorescent tag can be used to label components of the biomarker / capture reagent complex to enable detection of biomarker levels. 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 levels 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 such compounds.

[0181] In some embodiments, fluorescent label is a fluorescent dye molecule. In some embodiments, fluorescent dye molecule includes at least one substituted indole ring system, wherein the substituent on the 3-carbon of indole ring contains chemically reactive groups or conjugated substances. In some embodiments, dye molecule includes AlexFluor molecule, such as AlexaFluor 488, AlexaFluor532, AlexaFluor 647, AlexaFluor 680 or AlexaFluor 700. In other embodiments, dye molecule includes the dye molecule of the first type and the second type, such as two different AlexaFluor molecules. In some embodiments, dye molecule includes the dye molecule of the first type and the second type, and these two dye molecules have different emission spectra.

[0182] Fluorescence can be measured using a variety of instruments compatible with a wide variety of assay formats. For example, spectrofluorometers have been designed to analyze microtiter plates, microscope slides, printed arrays, cuvettes, and the like. See JR Lakowicz, Principles of Fluorescence Spectroscopy, Springer Science+Business Media, 2004. See Bioluminescence & Chemiluminescence: Progress & Current Applications; Philip E. Stanley and Larry J. Kricka, eds., World Scientific Publishing Company, January 2002.

[0183] In one or more embodiments, chemiluminescent tags may optionally be used to label components of the biomarker / capture complex to enable detection of biomarker levels. Suitable chemiluminescent materials include any of the following: oxalyl chloride, rhodamine 6G, Ru(bipy)32+ , TMAE (tetrakis(dimethylamino)ethylene), pyrogallol (1,2,3-trihydroxybenzene), lucigenin, peroxy oxalate, aryl oxalate, acridinium ester, dioxetane, etc.

[0184] In some embodiments, detection methods include enzyme / substrate combinations that generate detectable signals corresponding to biomarker levels. Typically, enzymes catalyze chemical changes in chromogenic substrates, which can be measured using various techniques including spectrophotometry, fluorescence, and chemiluminescence. Suitable enzymes include, for example, luciferase, luciferin, malate dehydrogenase, urease, horseradish peroxidase (HRPO), alkaline phosphatase, beta-galactosidase, glucoamylase, lysozyme, glucose oxidase, galactose oxidase, and glucose-6-phosphate dehydrogenase, uricase, xanthine oxidase, lactoperoxidase, microperoxidase, etc.

[0185] In some embodiments, the detection method may be a combination of fluorescence, chemiluminescence, radionuclide, or enzyme / substrate combinations that generate a measurable signal. In some embodiments, multimodal signaling may have unique and advantageous features in biomarker assay formats.

[0186] In some embodiments, as discussed below, biomarker levels of the biomarkers described herein can 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, and the like.

[0187] Determining biomarker levels using aptamer-based assays

[0188] Numerous assays for the detection and quantification of physiologically important molecules in biological and other samples are important tools in scientific research and health care. One type of assay involves the use of a microarray comprising one or more aptamers immobilized on a solid support. Each of the aptamers is capable of binding to a target molecule with high specificity and very high affinity. See, for example, U.S. Patent No. 5,475,096, entitled "Nucleic Acid Ligands"; see also, for example, U.S. Patent No. 6,242,246, U.S. Patent No. 6,458,543, and U.S. Patent No. 6,503,715, each of which is entitled "Nucleic Acid Ligand Diagnostic Biochip." Once the microarray is in contact with the sample, the aptamers bind to their respective target molecules present in the sample and are thus able to determine the levels of biomarkers corresponding to the biomarkers.

[0189] As used herein, an "aptamer" refers to a nucleic acid that has a specific binding affinity for a target molecule. It should be recognized that affinity interactions are a matter of degree; however, in this context, the "specific binding affinity" of an aptamer for its target means that the aptamer typically binds to its target with a much higher degree of affinity than it binds to other components in the test sample. An "aptamer" is a set of copies of a type or species of nucleic acid molecule containing a specific nucleotide sequence. An aptamer can include any suitable number of nucleotides, including any number of chemically modified nucleotides. A "multiple aptamers" refers to more than one set of such molecules. Different aptamers can have 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 can include higher-order structures. Aptamers can also be photoaptamers, in which photoreactive or chemically reactive functional groups are included in the aptamer to allow it to covalently bind 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 further described below, an aptamer may include a tag. If the aptamer includes a tag, not all copies of the aptamer need have the same tag. In addition, if different aptamers each include a tag, these different aptamers can have the same tag or different tags.

[0190] 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 synthesis and enzymatic synthesis.

[0191] The terms "SELEX" and "SELEX process" are used interchangeably herein and generally refer to the combination of (1) selecting aptamers that interact with a target molecule in a desired manner, such as binding to a protein with high affinity, and (2) amplification of these selected nucleic acids. The SELEX process can be used to identify aptamers with high affinity for a specific target or biomarker.

[0192] SELEX generally involves: preparing a candidate mixture of 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 acid from the affinity complex; purifying the nucleic acid; and identifying a specific aptamer sequence. The process may include multiple cycles to further enhance the affinity of the selected aptamer. The process may 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 covalently bind to their target as well as aptamers that bind to their target non-covalently. See, for example, U.S. Patent No. 5,705,337, entitled "Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Chemi-SELEX."

[0193] The SELEX process can be used to identify high-affinity aptamers containing modified nucleotides that impart improved characteristics to the aptamers, such as improved in vivo stability or improved delivery characteristics. Examples of such modifications include chemical substitutions at ribose and / or phosphate and / or base positions. The aptamers containing modified nucleotides identified by the SELEX process are described in U.S. Patent No. 5,660,985, entitled "High Affinity Nucleic Acid Ligands Containing Modified Nucleotides," which describes oligonucleotides containing chemically modified nucleotide derivatives at the 5'- and 2'-positions of pyrimidines. Referring above, U.S. Patent No. 5,580,737 describes high-specificity aptamers containing one or more nucleotides modified by 2'-amino (2'-NH2), 2'-fluoro (2'-F) and / or 2'-O-methyl (2'-OMe). See also US Patent Application Publication No. 20090098549, entitled "SELEX and PHOTOSELEX," which describes nucleic acid libraries with expanded physical and chemical properties and their use in SELEX and photoSELEX.

[0194] SELEX can also be used to identify aptamers with desired off-rate characteristics. Referring to U.S. Publication No. 20090004667, entitled "Method for Generating Aptamers with Improved Off-Rates," the publication describes an improved SELEX method for generating aptamers that can be bound to a target molecule. A method for generating aptamers and photoaptamers is described, wherein the rate at which the aptamers and photoaptamers dissociate from their corresponding target molecules is relatively slow. The method involves contacting a candidate mixture with a target molecule, allowing the formation of a nucleic acid-target complex, and performing a slow off-rate enrichment process, wherein nucleic acid-target complexes with a rapid off-rate will dissociate and will not form again, while complexes with a slow off-rate will remain intact. Additionally, the method includes using modified nucleotides when generating a candidate nucleic acid mixture to generate aptamers with improved off-rate performance. Non-limiting exemplary modified nucleotides include, for example, modified pyrimidines shown in Figure 8. In some embodiments, the aptamer comprises at least one nucleotide with a modification (such as a base modification). In some embodiments, the aptamer comprises at least one nucleotide with a hydrophobic modification (such as a hydrophobic base modification) to allow hydrophobic contact with the target protein. In some embodiments, such hydrophobic contact contributes to the greater affinity and / or slower off-rate binding of the aptamer. Non-limiting exemplary nucleotides with hydrophobic modifications are shown in Figure 8. In some embodiments, the 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 10 nucleotides with a hydrophobic modification, wherein each hydrophobic modification can be the same or different from other hydrophobic modifications. 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 10 hydrophobic modifications in the aptamer can be independently selected from the hydrophobic modifications shown in Figure 8.

[0195] In some embodiments, slow off-rate aptamers (including aptamers comprising at least one nucleotide with a hydrophobic modification) have an off-rate (t1 / 2) of ≥30 minutes, ≥60 minutes, ≥90 minutes, ≥120 minutes, ≥150 minutes, ≥180 minutes, ≥210 minutes, or ≥240 minutes.

[0196] In some embodiments, the assay employs an aptamer comprising a photoreactive functional group that enables the aptamer to covalently bind or "photocrosslink" its target molecule. See, e.g., U.S. Patent No. 6,544,776, entitled "Nucleic Acid Ligand Diagnostic Biochip." These photoreactive aptamers are also referred to as photoaptamers. See, e.g., U.S. Patent No. 5,763,177, U.S. Patent No. 6,001,577, and U.S. Patent No. 6,291,184, each of which is entitled "Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Photoselection of Nucleic Acid Ligands and Solution SELEX"; see also, e.g., U.S. Patent No. 6,458,539, entitled "Photoselection of Nucleic Acid Ligands." After the microarray is contacted with the sample and the photoaptamer has had an opportunity to bind to its target molecule, the photoaptamer is photoactivated and the solid support is washed to remove any non-specifically bound molecules. Harsh washing conditions can be used because the target molecule bound to the photoaptamer is generally not removed due to the covalent bond created by one or more photoactivated functional groups on the photoaptamer. In this way, the assay achieves the detection of the biomarker level corresponding to the biomarker in the test sample.

[0197] In some assay formats, the aptamer is immobilized on a solid support before contact with the sample. However, in some cases, immobilizing the aptamer before contact with the sample may not provide the optimal assay. For example, pre-immobilization of the aptamer may result in inefficient mixing of the aptamer and the target molecule on the surface of the solid support, which may result in excessively long reaction times and, therefore, extended incubation periods to allow the aptamer to effectively bind to its target molecule. In addition, when photoaptamers are used in the assay and depending on the material used as the solid support, the solid support may tend to scatter or absorb light used to influence the formation of a covalent bond between the photoaptamer and its target molecule. Furthermore, depending on the method used, detecting the binding of the target molecule to its aptamer may be imprecise because the surface of the solid support may also be exposed to and affected by any labeling agent used. Finally, immobilization of the aptamer on a solid support typically involves an aptamer preparation step (i.e., immobilization) before exposing the aptamer to the sample, and this preparation step may affect the activity or function of the aptamer.

[0198] Aptamer assays have also been described that allow an aptamer to capture its target in solution, followed by a separation step designed to remove specific components of the aptamer-target mixture prior to detection (see U.S. Publication No. 20090042206, entitled "Multiplexed Analyses of Test Samples"). The described aptamer assay methods enable 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 generate nucleic acid surrogates (i.e., aptamers) for the detection and quantification of non-nucleic acid targets, thereby allowing the application of a variety of nucleic acid technologies, including amplification, to a wider range of desired targets, including protein targets.

[0199] Aptamers can be constructed to facilitate separation of assay components from aptamer-biomarker complexes (or photoaptamer-biomarker covalent complexes) and permit separation of aptamers for detection and / or quantification. In one embodiment, these constructs can include cleavable or releasable elements within the aptamer sequence. In other embodiments, additional functionality can be introduced into the aptamer, such as a labeled or detectable component, a spacer component, or a specific binding tag or fixed element. For example, the aptamer can include a tag, a label, a spacer component, and a cleavable portion that is attached to the aptamer via a cleavable portion. In one embodiment, the cleavable element is a photocleavable linker. The photocleavable linker can be attached to the biotin portion and the spacer segment, can include an NHS group for amine derivatization, and can be used to introduce a biotin group into the aptamer, thereby allowing the aptamer to be released later in the assay method.

[0200] Homogeneous assays, using 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 their 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 target of each of the one or more aptamers or antibodies, etc. can be a biomarker listed in Table 1 or Table 2.

[0201] In some embodiments, the method for signal generation utilizes the anisotropic signal change caused by the interaction of a fluorophore-labeled capture agent with its specific biomarker target. When the labeled capture reacts with its target, the increased molecular weight causes the rotational motion of the fluorophore attached to the complex to become much slower, thereby changing the anisotropy value. 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 methods, time-resolved fluorescence quenching, chemiluminescence, fluorescence resonance energy transfer, etc.

[0202] An exemplary solution-based aptamer assay that can be used to detect the level of a biomarker in a biological sample comprises the following: (a) preparing a mixture by contacting the biological sample with an aptamer comprising a first tag and having a specific affinity for the biomarker, wherein an aptamer affinity complex is formed when the biomarker is present in the sample; (b) exposing the mixture to a first solid support comprising a first capture element and allowing the first tag to associate with the first capture element; (c) removing any components of the mixture that are not associated with the first solid support; (d) attaching 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 and allowing the second tag to associate with the second capture element; (g) removing any uncomplexed aptamer from the mixture by partitioning the uncomplexed aptamer from the aptamer affinity complex; (h) eluting the aptamer from the solid support; and (i) detecting the biomarker by detecting the aptamer component of the aptamer affinity complex.

[0203] Any means known in the art can be used to detect biomarker values ​​by detecting the aptamer component of the aptamer affinity complex. Many different detection methods can be used to detect the aptamer component of the affinity complex, such as hybridization assays, mass spectrometry, or QPCR. In some embodiments, nucleic acid sequencing methods can be used to detect the aptamer component of the aptamer affinity complex and thereby detect biomarker values. In short, any type of nucleic acid sequencing method can be performed on the test sample to identify and quantify one or more sequences of one or more aptamers 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, identification sequencing is the addition of a specific sequence 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 convert any type of nucleic acid (including RNA and DNA containing chemical modifications to any position) into any other type of nucleic acid suitable for sequencing.

[0204] In some embodiments, the sequencing method comprises one or more cloning steps. In other embodiments, the sequencing method comprises a direct sequencing method without cloning.

[0205] In some embodiments, the sequencing method comprises a directed approach utilizing specific primers that target one or more aptamers in the test sample. In other embodiments, the sequencing method comprises a shotgun approach that targets all aptamers in the test sample.

[0206] In some embodiments, the sequencing method includes an enzymatic step to amplify the molecule whose target is set to be sequenced. In other embodiments, the sequencing method directly sequences a single molecule. Exemplary nucleic acid sequencing-based methods that can be used to detect biomarker values ​​corresponding to biomarkers in biological samples include the following: (a) a mixture of aptamers containing chemically modified nucleotides is converted into unmodified nucleic acids using an enzymatic step; (b) the unmodified nucleic acids obtained are subjected to shotgun sequencing using a large number of parallel sequencing platforms (e.g., 454 sequencing systems (454 Life Sciences / Roche), Illumina sequencing systems (Illumina), ABISOLiD sequencing systems (Applied Biosystems), HeliScope single molecule sequencers (Helicos Biosciences), or Pacific Biosciences real-time single molecule sequencing systems (Pacific BioSciences), or Polonator G sequencing systems (Dover Systems)); and (c) the aptamers present in the mixture are identified and quantified by specific sequence and sequence counting.

[0207] 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.

[0208] Determine biomarker levels using immunoassays

[0209] Immunoassays are based on the reaction of antibodies with their corresponding targets or analytes, and can detect analytes in samples according to specific assay formats. In order to improve the specificity and sensitivity of assays based on immunoreactivity, monoclonal antibodies and their fragments are generally used due to their specific epitope recognition. Polyclonal antibodies have also been successfully used in various immunoassays because they have increased affinity for targets compared to monoclonal antibodies. Immunoassays have been designed for a variety of biological sample matrices. Immunoassay formats have been designed to provide qualitative, semi-quantitative and quantitative results.

[0210] Quantitative results are generated by using a standard curve generated at known concentrations of the specific analyte to be detected. The response or signal from the unknown sample is plotted onto the standard curve and the amount or level corresponding to the target in the unknown sample is established.

[0211] A variety of immunoassay formats have been designed. ELISA or EIA can quantitatively detect an analyte. This method relies on attaching a label to the analyte or antibody, and the label component can include an enzyme directly or indirectly. ELISA test formats can be designed for direct, indirect, competitive, or sandwich detection of the analyte. Other methods rely on labels, such as radioisotopes (I 125 ) or fluorescence. Additional techniques include, for example, agglutination, turbidimetry, turbidimetry, Western blotting, immunoprecipitation, immunocytochemistry, immunohistochemistry, flow cytometry, Luminex assays, and the like (see ImmunoAssay: A Practical Guide, edited by Brian Law, published by Taylor & Francis, Ltd., 2005 edition).

[0212] Exemplary assay formats include enzyme-linked immunosorbent assay (ELISA), radioimmunoassay, fluorescence, chemiluminescence, and fluorescence resonance energy transfer (FRET) or time-resolved FRET (TR-FRET) immunoassays. Examples of procedures 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, and the like.

[0213] The method for detecting and / or quantifying the detectable label or signal generating material depends on the nature of the label. The reaction products catalyzed by an appropriate enzyme (wherein 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, photometers, and densitometers.

[0214] Any detection method can be carried out in any form that allows any suitable reaction preparation, processing and analysis. This can be, for example, in a multi-well assay plate (e.g., 96 or 386 wells) or using any suitable array or microarray. Stock solutions of various agents can be prepared manually or automatically, and all subsequent pipetting, dilution, mixing, distribution, washing, incubation, sample reading, data collection and analysis can be automatically completed using commercially available analytical software, robots and detection instruments that can detect detectable labels.

[0215] Determining biomarker levels using gene expression profiling

[0216] In some embodiments, measuring mRNA in a biological sample can be used as an alternative form of detecting 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.

[0217] 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 produce cDNA from mRNA. cDNA can be used in qPCR assays to produce fluorescence as the DNA amplification process proceeds. By comparing with a standard curve, qPCR can produce absolute measurements, such as the mRNA copy number of each cell. Northern blotting, microarrays, Invader assays, and RT-PCR combined with capillary electrophoresis have all been used to measure the expression level of mRNA in a sample. Referring to Gene Expression Profiling: Methods and Protocols, edited by Richard A. Shimkets, Humana Press, 2004.

[0218] Detecting biomarkers using in vivo molecular imaging

[0219] In some embodiments, the biomarkers described herein can be used in molecular imaging tests. For example, an imaging agent can be coupled to a capture agent, which can be used to detect the biomarker in vivo.

[0220] In vivo imaging techniques provide non-invasive methods for determining specific disease states in a subject. For example, all parts of the body, or even the entire body, can be viewed as a three-dimensional image, providing valuable information about the morphology and structure within the body. Such techniques can be combined with the detection of biomarkers described herein to provide information about in vivo biomarkers.

[0221] The use of in vivo molecular imaging techniques is expanding due to various advances in technology. These advances include the development of new contrast agents or labels, such as radiolabels and / or fluorescent labels, which can provide strong signals in vivo; and the development of powerful new imaging technologies that can detect and analyze these signals from outside the body, said new imaging technologies having sufficient sensitivity and accuracy to provide useful information. The contrast agents can be visualized in an appropriate imaging system, thereby providing an image of one or more parts of the body in which the contrast agent is located. The contrast agents can be combined or associated with the following items: capture agents, such as, for example, aptamers or antibodies, and / or peptides or proteins, or oligonucleotides (for example, for detection of gene expression) or complexes containing any of these items with one or more macromolecules and / or other particulate forms.

[0222] Contrast agents can also have the characteristics of radioactive atoms that can be used for imaging. Suitable radioactive atoms include technetium-99m or iodine-123 for scintigraphic studies. Other easily detectable parts include, for example, 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 of ordinary skill in the art.

[0223] Standard imaging techniques include, but are not limited to, magnetic resonance imaging, computed tomography, positron emission tomography (PET), single photon emission computed tomography (SPECT), and the like. For diagnostic in vivo imaging, the type of detection instrument available is a major factor in selecting a given contrast agent, such as a given radionuclide and the specific biomarker (protein, mRNA, etc.) to be targeted. The radionuclide selected typically has a decay type that can be detected by a given type of instrument. In addition, when selecting a radionuclide for in vivo diagnosis, its half-life should be long enough to enable detection at the time of maximum uptake by the target tissue, but short enough to minimize harmful radiation to the host.

[0224] Exemplary imaging techniques include, but are not limited to, PET and SPECT, which are imaging techniques in which radionuclides are administered systemically (synthetically) or locally to a subject. The subsequent uptake of the radiotracer is measured over time and used to obtain information about targeted tissues and biomarkers. Due to the high-energy (gamma-ray) emissions of the specific isotopes employed and the sensitivity and precision of the instruments used to detect these emissions, two-dimensional radioactivity distribution can be inferred from outside the body.

[0225] Commonly used positron-emitting nuclides in PET include, for example, carbon-11, nitrogen-13, oxygen-15, and fluorine-18. Isotopes that decay by electron capture and / or gamma emission are used in SPECT and include, for example, iodine-123 and technetium-99m. An exemplary method for labeling amino acids with technetium-99m involves reducing the pertechnetate ion in the presence of a chelating precursor to form an unstable technetium-99m-precursor complex, which in turn reacts with the metal binding group of a bifunctionally modified chemotactic peptide to form a technetium-99m-chemotactic peptide conjugate.

[0226] Antibodies are often used in such in vivo imaging diagnostic methods. The preparation and use of antibodies for in vivo diagnosis are well known in the art. Similarly, aptamers can be used in such in vivo imaging diagnostic methods. For example, aptamers for identifying specific biomarkers described herein can be appropriately labeled and injected into a subject to detect the biomarkers in vivo. As previously mentioned, the label used is selected based on the imaging modality to be used. Compared to other imaging agents, aptamer-directed imaging agents can have unique and advantageous characteristics related to tissue penetration, tissue distribution, kinetics, elimination, efficacy, and selectivity.

[0227] Such techniques can also optionally be performed using labeled oligonucleotides, for example, for detecting gene expression by imaging using antisense oligonucleotides. These methods are used for in situ hybridization, for example, using fluorescent molecules or radionuclides as labels. Other methods for detecting gene expression include, for example, detecting the activity of reporter genes.

[0228] Another general type of imaging technique is optical imaging, in which fluorescent signals within a subject are detected by an optical device external to the subject. These signals may be attributed to actual fluorescence and / or bioluminescence. Increased sensitivity of optical detection devices has increased the usefulness of optical imaging in in vivo diagnostic assays.

[0229] For a review of other techniques, see N. Blow, Nature Methods, 6, 465-469, 2009.

[0230] Determining biomarker levels using mass spectrometry

[0231] Mass spectrometers of various configurations can be used to detect biomarker levels. Several types of mass spectrometers are available, or they can be produced in different configurations. Typically, a mass spectrometer has the following main components: a sample inlet, an ion source, a mass analyzer, a detector, a vacuum system, an instrument control system, and a data system. The differences between the sample inlet, the ion source, and the mass analyzer typically define the instrument type and its capabilities. For example, the inlet can be a capillary column liquid chromatography source, or can be a direct probe or stage such as that used in matrix-assisted laser desorption. Common ion sources are, for example, 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)).

[0232] Protein biomarkers and biomarker levels can be detected and measured by any of the following: electrospray ionization mass spectrometry (ESI-MS), ESI-MS / MS, ESI-MS / (MS)n, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption / ionization time-of-flight mass spectrometry (SELDI-TOF-MS), desorption / ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), tandem time-of-flight (TOF / TOF) technology (known as 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.

[0233] Sample preparation strategies are used to label and enrich samples prior to mass spectrometric characterization of protein biomarkers and determination of biomarker levels. Labeling methods include, but are not limited to, isobaric tags for relative and absolute quantification (iTRAQ) and stable isotope labeling with amino acids in cell culture (SILAC). Capture reagents for selectively enriching candidate 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, ankyrin, domain antibodies, alternative antibody scaffolds (e.g., diabodies, etc.), imprinted polymers, high-affinity multimers, peptide mimetics, peptidomimetics, peptide nucleic acids, threose nucleic acids, hormone receptors, cytokine receptors, and synthetic receptors, as well as modifications and fragments thereof.

[0234] Determining biomarker levels using proximity ligation assays

[0235] Proximity ligation assay can be used to determine biomarker values. In short, the test sample is contacted with a pair of affinity probes that can be a pair of antibodies or a pair of aptamers, wherein each member of the pair is extended with oligonucleotides. The target of the affinity probe can be two different determinants (determinates) on a protein or a determinant on each of two different proteins, which can exist as homologous or heterologous multimer complexes. When the probe is bound to the target determinant, the free end of the oligonucleotide extension becomes close enough to hybridize together. The hybridization of the oligonucleotide extension is facilitated by a common connecting oligonucleotide, and when the oligonucleotide extension is positioned in a sufficiently close place, the connecting oligonucleotide is used to extend the oligonucleotides and bridge them together. Once the oligonucleotide extensions of the probe are hybridized, the ends of the extension are linked together by enzymatic DNA connection.

[0236] Each oligonucleotide extension contains a primer site for PCR amplification. Once the oligonucleotide extensions are linked together, the oligonucleotides form a continuous DNA sequence that reveals information about the identity and amount of the target protein by PCR amplification, as well as information about protein-protein interactions, where the target determinants are on two different proteins. Proximity ligation can provide highly sensitive and specific determinations of real-time protein concentration and interaction information by using real-time PCR. Probes that do not bind to the determinant of interest do not have a corresponding oligonucleotide extension that is close enough and will not be linked or PCR amplified, resulting in no signal being generated.

[0237] The aforementioned assays enable the detection of biomarker values ​​that can be used in methods for predicting risk of a CV event, wherein the method comprises detecting in a biological sample from a subject 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 thirteen biomarkers selected from the biomarkers in Table 1; or at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty, at least twenty-one, at least twenty-two, at least twenty-three, at least twenty-four, at least twenty-five, at least twenty-six, or all twenty-seven biomarkers in Table 2; wherein, as described below, classification using the biomarker values ​​indicates whether the subject has a high risk of experiencing a CV event within a 1-year, 2-year, 3-year, or 4-year time period. According to any of the methods described herein, the biomarker values ​​can be detected and classified individually, or the biomarker values ​​can be detected and classified collectively, for example, in a multiplex assay format.

[0238] Classification of biomarkers and calculation of disease scores

[0239] In some embodiments, the biomarker "signature" for a given diagnostic test contains a set of biomarkers, each of which has a characteristic level in the population of interest. In some embodiments, the characteristic level can refer to the mean or average of the biomarker levels for subjects in a particular group. In some embodiments, the diagnostic methods described herein can be used to assign an unknown sample from a subject to one of two groups (whether or not having an increased risk of a CV event).

[0240] Assigning a sample to one of two or more groups is referred to as classification, and the program for completing this assignment is referred to as a classifier or classification method. Classification method may also be referred to as a scoring method. There are many classification methods that can be used to construct a diagnostic classifier from a group of biomarker levels. In some cases, classification method is performed using supervised learning techniques, wherein data sets are collected using samples obtained from two (or more, for multiple classification states) different groups of subjects that people wish to distinguish. Since the category (group or colony) to which each sample belongs is known in advance for each sample, classification method can be trained to give the classification response of expectation. Unsupervised learning techniques may also be used to produce a diagnostic classifier.

[0241] Common methods for developing diagnostic classifiers include: decision trees; bagging + boosting + forests; rule-based reasoning learning; Parzen windows; linear models; logic; neural network methods; unsupervised clustering; K-means; hierarchical ascending / descending; semi-supervised learning; prototype methods; nearest neighbors; kernel density estimation; support vector machines; hidden Markov models; Boltzmann learning; and classifiers can be combined simply or in a way that minimizes a specific objective function. For a review, see, for example, Pattern Classification, edited by R. O. Duda et al., John Wiley & Sons, 2nd edition, 2001; and also see The Elements of Statistical Learning - Data Mining, Inference, and Prediction, edited by T. Hastie et al., Springer Science + Business Media, LLC, 2nd edition, 2009.

[0242] In order to produce a classifier using supervised learning techniques, a group of samples referred to as training data is obtained. In the context of diagnostic tests, training data includes samples from different groups (categories) to which unknown samples will be assigned later. For example, samples collected from subjects in control groups and subjects in specific disease groups can constitute training data, so that unknown samples (or more particularly, subjects from whom samples are obtained) can be classified as having the disease or not having the disease. Developing a classifier from training data is referred to as training a classifier. The specific details about classifier training depend on the nature of supervised learning techniques. Training a naive Bayesian classifier is an example of this supervised learning technique (see, for example, Pattern Classification, edited by R. O. Duda et al., John Wiley & Sons, 2nd edition, 2001; In addition, see The Elements of Statistical Learning-Data Mining, Inference, and Prediction, edited by T. Hastie et al., Springer Science + Business Media, LLC, 2nd edition, 2009). For example, training of naive Bayes classifiers is described in US Publication Nos. 2012 / 0101002 and 2012 / 0077695.

[0243] Because there are often many more potential biomarker levels than samples in the training set, care must be taken to avoid overfitting. Overfitting occurs when the statistical model describes random errors or noise rather than the underlying relationship. Overfitting can be avoided in a variety of ways, including, for example, limiting the number of biomarkers used in developing the classifier, assuming that biomarker responses are independent of each other, limiting the complexity of the underlying statistical model used, and ensuring that the underlying statistical model fits the data.

[0244] An illustrative example of developing a diagnostic test using a set of biomarkers includes applying a naive Bayesian classifier, a simple probabilistic classifier based on Bayes' theorem that treats biomarkers strictly independently. Each biomarker is described by a class-dependent probability density function (pdf) of the measured RFU values ​​or logarithmic RFU (relative fluorescence units) values ​​in each class. The joint pdf of the set of biomarkers in a class is assumed to be the product of the individual class-dependent pdfs of each biomarker. Training a naive Bayesian classifier in this context is equivalent to assigning parameters ("parameterization") to characterize the class-dependent pdfs. Any potential model of the class-dependent pdfs can be used, but the model should generally conform to the data observed in the training set.

[0245] The performance of a naive Bayes classifier depends on the quantity and quality of the biomarkers used to build and train the classifier. A single biomarker will be represented by its KS distance (Kolmogorov-Smirnov). If the subsequently added biomarkers are independent of the first biomarker, then adding subsequent biomarkers with good KS distances (e.g., >0.3) will generally improve classification performance. Using sensitivity plus specificity as the classifier score, a variation of the greedy algorithm can be used to generate many high-scoring classifiers. (A greedy algorithm is any algorithm that follows a problem-solving metaheuristic approach that makes a local optimal choice at each stage in the hope of finding the global optimum.)

[0246] Another way to describe classifier performance is to use the receiver operating characteristic (ROC), or simply the ROC curve or ROC plot. The ROC is a graph of the sensitivity, or true positive rate, of a binary classifier system against the false positive rate (1-specificity or 1-true negative rate), as its discrimination threshold is varied. The ROC can also be equivalently represented by plotting the fraction of true positives among positives (TPR = true positive rate) against the fraction of false positives among negatives (FPR = false positive rate). This is also known as a relative operating characteristic curve because it compares two operating characteristics (TPR and FPR) as the standard varies. The area under the ROC curve (AUC) is often used as a comprehensive measure of diagnostic accuracy. It can take values ​​between 0.0 and 1.0. The AUC has an important statistical property: the AUC of a classifier is equivalent to the probability that the classifier will rank a randomly selected positive instance higher than a randomly selected negative instance (Fawcett T, 2006. An introduction to ROC analysis. Pattern Recognition Letters. 27:861–874). This is equivalent to the Wilcoxon test of ranks (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 describe the performance of a diagnostic test relative to a known reference standard is the net reclassification index: the ability of a new test to correctly upgrade or downgrade risk when compared to the reference standard test. See, for example, Pencina et al., 2011, Stat. Med. 30: 11-21. Although the AUC under the ROC curve is optimal for assessing the performance of a 2-class classifier, stratification and personalized medicine rely on inference that the population contains more than 2 classes. For such comparisons, the hazard ratio of the upper quartile to the lower quartile (or other stratifications, such as deciles) may be more applicable.

[0247] The risk and likelihood predictions implemented herein can be applied to subjects in primary care or specialized cardiovascular centers, or even directly to consumers. In some embodiments, the classifiers used to predict events may involve some calibrations for the populations to which they are applied—for example, due to ethnicity or geographic environment, there may be variations. In some embodiments, such calibrations can be established in advance from a large number of population studies, so when applied to a single patient, these calibrations are incorporated before risk prediction is performed. As described herein, a venous blood sample is obtained, appropriately processed, and analyzed. Once the analysis is complete, risk prediction can be performed mathematically with or without incorporating other metadata from the medical records described herein, such as genetic or demographic metadata. Depending on the consumer's level of expertise, various forms of information output are possible. For consumers seeking the simplest type of output, in some embodiments, the information may be "Is this person likely to have an event within the next x years (where x is 1-4), yes / no?" or alternatively, a red light / yellow light / green light similar to a "traffic light" or its verbal or written equivalent, such as high / medium / low risk. For consumers seeking more detail, in some embodiments, risk can be output as a numerical or graphical output, showing the probability of an event per unit time as a continuous score, or more strata (such as deciles), and / or the average time to an event and / or the most likely type of event. In some embodiments, the output can include treatment recommendations. Longitudinal monitoring of the same patient over time will enable graphs showing response to interventions or lifestyle changes. In some embodiments, more than one type of output can be provided simultaneously to meet the needs of patients and various members of the care team with different levels of expertise.

[0248] In some embodiments, the biomarkers shown in Table 1 or Table 2 are detected in a blood sample (such as a plasma sample or a serum sample) from a subject, for example using an aptamer (such as a slow off-rate aptamer). The logarithmic RFU value is used to calculate the risk or likelihood of the subject having a CV event, or the prognostic index (PI).

[0249] Taking into account the PI, the probability that a subject will suffer a cardiovascular event (CV event) in the next "t" years is given by the following formula:

[0250]

[0251] Wherein PI is the prognostic index (or linear predictive value), and s is the associated scale parameter of the extreme value distribution.In various embodiments, "t" is 5 years or less, 4 years or less, 3 years or less, or 2 years or less.

[0252] Reagent test kit

[0253] Any combination of biomarkers described herein can be detected using a suitable kit (such as a kit for performing the methods disclosed herein). In addition, any kit may contain one or more detectable labels as described herein, such as fluorescent moieties, etc.

[0254] In some embodiments, the kit comprises (a) one or more capture reagents (such as, for example, at least one aptamer or antibody) for detecting one or more biomarkers in a biological sample, wherein the biomarkers include 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 thirteen biomarkers selected from the biomarkers in Table 1; or at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty, at least twenty-one, at least twenty-two, at least twenty-three, at least twenty-four, at least twenty-five, at least twenty-six, or all twenty-seven biomarkers in Table 2; and optionally (b) one or more software or computer program products, as further described herein, for classifying a subject from whom the biological sample was obtained as having or not having an increased risk for a CV event, or for determining the likelihood that a subject has an increased risk for a CV event. Alternatively, one or more instructions for a person to manually perform the above steps may be provided instead of one or more computer program products.

[0255] In some embodiments, the kit includes a solid support, a capture reagent, and a signal generating material. The kit may also include instructions for using the apparatus and reagents, processing samples, and analyzing data. Additionally, the kit may be used in conjunction with a computer system or software to analyze and report the results of the analysis of a biological sample.

[0256] The kit may also contain one or more reagents (e.g., solubilization buffers, detergents, washing solutions, or buffers) for processing biological samples. Any kit 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, instructions, and reference data).

[0257] 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 for the biomarkers described herein. In some embodiments, the kit may also include instructions for use of the biomarkers and the correlation of the biomarkers with risk prediction of CV events. In some embodiments, the kit may further include a DNA array containing the complement of one or more of the following items: aptamers specific for the biomarkers described herein, reagents and / or enzymes for amplifying or isolating sample DNA. In some embodiments, the kit may include reagents for real-time PCR, such as TaqMan probes and / or primers, and enzymes.

[0258] For example, the kit may include (a) reagents including at least one capture reagent for determining the level of one or more biomarkers in the test sample, and optionally (b) one or more algorithms or computer programs for performing the step of comparing the amount of each biomarker quantitatively measured in the test sample with one or more predetermined cutoff values. In some embodiments, the algorithm or computer program assigns a score to each biomarker quantitatively measured based on the comparison, and in some embodiments, the assigned scores of each biomarker quantitatively measured are combined to obtain a total score. In addition, in some embodiments, the algorithm or computer program compares the total score with a predetermined score, and uses the comparison to determine whether the subject has an increased risk of CV events. Alternatively, one or more instructions for manually performing the above steps by a person, rather than one or more algorithms or computer programs, may be provided.

[0259] Biomarker panel

[0260] 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 of the proteins listed in Table 1 is detected. In some embodiments, the detection of one or more or all of the biomarkers is performed to determine the risk or likelihood that a subject will have a primary CV event within a defined time period. In some such embodiments, the defined time period is one, two, three, four, or five years. In some embodiments, the defined time period is four years.

[0261] Table 1

[0262]

[0263] 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 of the proteins listed in Table 2 is detected. In some embodiments, the detection of one or more or all of the biomarkers is performed to determine the risk or likelihood that a subject will have a secondary CV event within a defined time period. In some such embodiments, the defined time period is one, two, three, four, or five years. In some embodiments, the defined time period is four years.

[0264] Table 2

[0265]

[0266] Computer methods and software

[0267] A method for assessing the risk or likelihood of a CV event occurring in a subject may comprise the following: 1) obtaining a biological sample; 2) performing an analytical method to detect and measure a biomarker 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 according to the subject's population / ethnicity. In some embodiments, the biomarker levels are combined in some manner and a single value for the combined biomarker levels is reported. In this method, in some embodiments, the score may be a single numerical value determined from the integral of all biomarkers, which is compared to a preset threshold indicating the presence or absence of a 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 to determine the presence or absence of a disease, disorder, or increased risk of an event (or the absence of an increased risk of an event).

[0268] At least some embodiments of the methods described herein can be implemented using a computer. Figure 6 An example of a computer system 100 is shown in FIG. Figure 6, system 100 is shown as including hardware elements electrically coupled via bus 108, including a processor 101, an input device 102, an output device 103, a storage device 104, a computer-readable storage medium reader 105a, a communication system 106, a processing acceleration device (e.g., a DSP or special-purpose processor) 107, and a memory 109. The computer-readable storage medium reader 105a is further coupled to a computer-readable storage medium 105b, the combination of which comprehensively represents remote, local, fixed, and / or removable storage devices plus storage media, memory, etc. for temporarily and / or more permanently containing computer-readable information, which may include storage device 104, memory 109, and / or any other such accessible system 100 resources. System 100 also includes software elements (shown as currently located within working memory 191), including an operating system 192 and other code 193, such as programs, data, etc.

[0269] Relative to Figure 6 , the system 100 has extensive flexibility and configurability. Thus, for example, a single architecture may be utilized to implement one or more servers that may be further configured according to a currently desired solution, solution variations, extensions, and the like. However, it will be apparent to those skilled in the art that implementation schemes may be better utilized according to more specific application requirements. For example, one or more system elements may be implemented as sub-elements within a component of the system 100 (e.g., within the communication system 106). For example, customized hardware may also be utilized and / or specific elements may be implemented in hardware, software, or both. Additionally, while connections to other computing devices, such as network input / output devices (not shown), may be employed, it will be understood that wired, wireless, modem, and / or other one or more connections to other computing devices may also be utilized.

[0270] In one aspect, the system may include a database containing biomarker signatures representing risk prediction characteristics for CV events. Biomarker data (or biomarker information) may be used as input to a computer for use as part of a computer-implemented method. Biomarker data may include data as described herein.

[0271] In one aspect, the system further comprises one or more means for providing input data to the one or more processors.

[0272] The system also includes a memory for storing a data set of hierarchical data elements.

[0273] In another aspect, the means for providing input data comprises a detector for detecting characteristics of data elements, such as, for example, a mass spectrometer or a gene chip reader.

[0274] The system may further comprise a database management system.User requests or queries may be formatted in a suitable language understood by the database management system, which processes the query to extract relevant information from the database of training sets.

[0275] The system may be capable of connecting to a network server and a network to which one or more clients are connected. As is known in the art, the network may be a local area network (LAN) or a wide area network (WAN). Preferably, the server includes the hardware required to run a computer program product (e.g., software) to access database data to process user requests.

[0276] The system may include an operating system (e.g., UNIX or Linux) for executing instructions from a database management system. In one aspect, the operating system may operate on a global communications network such as the Internet and utilize a global communications network server to connect to such a network.

[0277] The system may include one or more devices comprising a graphical display interface, the graphical display interface including interface elements such as buttons, pull-down menus, scroll bars, fields for entering text, and the like, as are conventionally found in graphical user interfaces known in the art. Requests entered on the user interface may be transmitted to an application in the system for formatting to search for relevant information in one or more of the system's databases. Requests or queries entered by a user may be constructed in any suitable database language.

[0278] The graphical user interface may be generated by graphical user interface code that is part of the operating system and may be used to input data and / or display the inputted data. The results of the processed data may be displayed in the interface, printed on a printer in communication with the system, stored in a memory device, and / or transmitted over a network, or may be provided in the form of a computer-readable medium.

[0279] The system can communicate with an input device to provide data about data elements (e.g., expression values) to the system. In one aspect, the input device can include a gene expression profiling system, including, for example, a mass spectrometer, a gene chip or array reader, and the like.

[0280] The method and apparatus for analyzing CV event risk prediction biomarker information according to various embodiments can be implemented in any suitable manner, for example, using a computer program operating on a computer system. Conventional computer systems including a processor and random access memory can be used, such as application servers, network servers, personal computers, or workstations that can be accessed remotely. Additional computer system components may include memory devices or information storage systems (such as mass storage systems) and user interfaces, such as conventional monitors, keyboards, and tracking devices. The computer system may be a standalone system or a part of a computer network including a server and one or more databases.

[0281] The CV event risk prediction biomarker analysis system can provide the functions and operations of completing data analysis, such as data collection, processing, analysis, reporting and / or diagnosis. For example, in one embodiment, a computer system can execute a computer program that can receive, store, search, analyze and report information related to CV event risk prediction 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 supplementary data, and an analysis module for analyzing raw data and supplementary data to generate CV event risk prediction status and / or diagnosis or risk calculation. Calculating the risk status of a CV event can optionally include generating or collecting any other information (including additional biomedical information) about the condition of the subject relative to a disease, illness or event, identifying whether further testing may be needed, or otherwise assessing the health status of the subject.

[0282] Some embodiments described herein may be implemented as a computer program product that may include a computer-readable medium having computer-readable program code embodied therein for causing an application to be executed on a computer having a database.

[0283] As used herein, a "computer program product" refers to an organized set of instructions in the form of natural or programming language statements, embodied on a physical medium of any nature (e.g., written, electronic, magnetic, optical, or other form) and usable with a computer or other automated data processing system. Such programming language statements, when executed by a computer or data processing system, cause the computer or data processing system to function in accordance with the specific content of the statements. Computer program products include, but are not limited to, source code and object code embedded in a computer-readable medium and / or programs in a test or data library. In addition, computer program products that enable a computer system or data processing device to function in a preselected manner may be provided in a variety of forms, including, but not limited to, original source code, assembly code, object code, machine language, encrypted or compressed versions of the foregoing, and any and all equivalent forms.

[0284] In one aspect, a computer program product for assessing the risk of a CV event is provided. The computer program product includes a computer-readable medium embodying program code, the program code executable by a processor of a computing device or system, the program code comprising: code for retrieving data pertaining to a biological sample from a subject, wherein the data comprises biomarker levels each corresponding to one of the biomarkers in Table 1 or Table 2; and code for executing a classification method that indicates a CV event risk status of the subject based on the biomarker values.

[0285] In another aspect, a computer program product for indicating the likelihood or risk of a CV event is provided. The computer program product includes a computer-readable medium embodying program code, the program code executable by a processor of a computing device or system, the program code comprising: code for retrieving data pertaining to a biological sample from a subject, wherein the data includes at least one biomarker value corresponding to at least one biomarker in the biological sample selected from the biomarkers provided in Table 1 or Table 2; and code for executing a classification method that indicates the CV event risk status of the subject based on the biomarker value.

[0286] Although various embodiments have been described as methods or devices, it should be understood that the embodiments can be implemented by a code coupled to a computer, such as a code resident on a computer or accessible by a computer. For example, many of the methods discussed above can be implemented using software and databases. Therefore, in addition to the embodiments implemented by hardware, it should also be noted that these embodiments can be implemented using an article comprising a computer-usable medium having a computer-readable program code embedded therein, which brings about the implementation of the functions disclosed in this specification. Therefore, it is expected that the embodiments presented in their program code means are also considered to be protected by this patent. In addition, the embodiments can be embodied as a code stored in almost any type of computer-readable memory, including but not limited to RAM, ROM, magnetic media, optical media or magneto-optical media. Even more generally, the embodiments can be implemented in software or hardware or any combination thereof, including but not limited to software, microcode, programmable logic array (PLA) or application-specific integrated circuit (ASIC) running on a general-purpose processor.

[0287] It is also contemplated that embodiments may be implemented as computer signals embodied in carrier waves, as well as signals propagated through transmission media (e.g., electrical and optical signals). Thus, the various types of information discussed above may be formatted as structures, such as data structures, and transmitted as electrical signals through transmission media or stored on computer-readable media.

[0288] It should also be noted that many structures, materials, and actions described herein can be described as means for performing a function or as steps for performing a function. Therefore, it should be understood that this language is entitled to cover all such structures, materials, or actions disclosed in this specification and their equivalents, including those incorporated by reference.

[0289] The use of the biomarkers disclosed herein and various methods for determining biomarker values ​​are described in detail above with respect to risk assessment of CV events. However, the application of this process, the use of the identified biomarkers, and the methods for determining biomarker values ​​are fully applicable to other specific types of cardiovascular disease, any other disease or medical condition, or the identification of subjects who may or may not benefit from adjunctive medical treatment.

[0290] Other methods

[0291] In some embodiments, biomarkers and methods described herein are used to determine that medical insurance premiums or coverage are determined and / or life insurance premiums or coverage are determined. In some embodiments, the result of methods described herein is used to determine medical insurance premiums and / or life insurance premiums. In some such cases, a tissue request for medical insurance or life insurance is provided or otherwise obtains information on the risk or possibility of the CV event of the relevant experimenter, and uses this information to determine the appropriate medical insurance premiums or life insurance premiums of the experimenter. In some embodiments, a tissue request for medical insurance or life insurance is provided and payment is made for the test. In some embodiments, a test is used to predict future liabilities or costs in the case where the acquisition continues by a potential acquirer of a practice or health system or company.

[0292] In some embodiments, the biomarkers and methods described herein are used to predict and / or manage the utilization of medical resources. In some such embodiments, these methods are not implemented for the purpose of such prediction, but rather the information obtained from the method is used in such prediction and / or management of the utilization of medical resources. For example, a testing institution or hospital can aggregate information from the present method for many subjects in order to predict and / or manage the utilization of medical resources at a specific institution or in a specific geographic area.

[0293] Example

[0294] The following examples are provided for illustrative purposes only and are not intended to limit the scope of the present application as defined in the appended claims. The conventional molecular biology techniques described in the following examples can be implemented as described in standard laboratory manuals, such as Sambrook et al., Molecular Cloning: A Laboratory Manual, 3rd edition, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, (2001).

[0295] Example 1: Exemplary biomarker detection using aptamers

[0296] Exemplary methods for detecting one or more biomarker proteins in a sample are described, for example, in Kraemer et al., PLoS One 6(10):e26332, and are described below. Three different quantitative methods are described: microarray-based hybridization, Luminex bead-based methods, and qPCR.

[0297] Reagents

[0298] HEPES, NaCl, KCl, EDTA, EGTA, MgCl2 and Tween-20 can be purchased, for example, from Fisher Biosciences. Dextran sulfate sodium salt (DxSO4) with a nominal molecular weight of 8000 can be purchased, for example, from AIC and dialyzed with deionized water, changing once every 20 hours at least. 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. 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, transparent, 96 wells, 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 aliquots for single use. IL-8, MIP-4, lipocalin-2, RANTES, MMP-7, and MMP-9 can be purchased, for example, from R&D Systems. Resistin and MCP-1 can be purchased, for example, from PeproTech, and tPA can be purchased, for example, from VWR.

[0299] Nucleic Acids

[0300] Conventional (including amine and biotin substituted) oligodeoxynucleotides can be purchased, for example, from Integrated DNA Technologies (IDT). Z-blocks are single-stranded oligodeoxynucleotides of the sequence 5′-(AC-BnBn)7-AC-3′, where Bn indicates a benzyl substituted deoxyuridine residue. Z-blocks can be synthesized using conventional phosphoramidite chemistry. Aptamer capture reagents can also be synthesized using conventional phosphoramidite chemistry and can be purified, for example, on a 21.5×75 mm PRP-3 column operated at 80° C. on a Waters Autopurification 2767 system (or Waters 600 series semi-automated system) using, for example, a timberline TL-600 or TL-150 heater and a gradient of triethylammonium bicarbonate (TEAB) / ACN for elution of the product. Detection was performed at 260 nm and fractions were collected across the main peak before pooling the best fractions.

[0301] buffer

[0302] Buffer SB18 consists of 40 mM HEPES, 101 mM NaCl, 5 mM KCl, 5 mM MgCl2, 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 MgCl2, 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. Neutralization buffer contains 500 mM HEPES, 500 mM HCl, and 0.05% (v / v) Tween-20. Agilent hybridization buffer is a proprietary formulation supplied as part of the kit (Oligo aCGH / ChIP on-chip hybridization kit). Agilent wash buffer 1 is a proprietary formulation (Oligo aCGH / ChIP on-chip wash buffer 1, Agilent). Agilent wash buffer 2 is a proprietary formulation (Oligo aCGH / ChIP on-chip wash buffer 2, Agilent). TMAC hybridization solution consists of 4.5M tetramethylammonium chloride, 6mM trisodium EDTA, 75mM Tris-HCl (pH 8.0), and 0.15% (v / v) Sarkosyl. KOD buffer (10x concentrated) consists of 1200mM Tris-HCl, 15mM MgSO4, 100mM KCl, 60mM (NH4)2SO4, 1% v / v Triton-X 100, and 1mg / mL BSA.

[0303] Sample preparation

[0304] Serum (stored at -80°C in 100 μL aliquots) was thawed in a 25°C water bath for 10 minutes and then stored on ice prior to sample dilution. The sample was mixed by gently vortexing for 8 seconds. A 6% serum sample solution was prepared by diluting into 0.94×SB17 supplemented with 0.6 mM MgCl2, 1 mM trisodium EGTA, 0.8 mM AEBSF, and 2 μM Z-block. A portion of the 6% serum stock solution was diluted 10-fold in SB17 to produce 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.

[0305] Capture reagent (aptamer) and streptavidin plate preparation

[0306] Aptamers were divided into two mixtures based on the relative abundance of their cognate analytes (or biomarkers). The stock concentration for each aptamer was 4 nM, and the final concentration of each aptamer was 0.5 nM. The aptamer stock mixture was diluted four-fold in SB17 buffer, heated to 95°C for 5 minutes, and cooled to 37°C over a 15-minute period before use. This denaturation-renaturation cycle is intended to normalize the aptamer conformer distribution and thus ensure reproducible aptamer activity despite variable history. Prior to use, the streptavidin plate was washed twice with 150 μL of buffer PB1.

[0307] Incubation and plate capture

[0308] The heat-cooled 2× aptamer mixture (55 μL) was combined with an equal volume of 6% or 0.6% serum dilution to produce a mixture containing 3% and 0.3% serum. The plate was sealed with a silicone seal (Axymat silicone seal, 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 a further two hours on an Eppendorf Thermomixer set at 37° C. with shaking at 800 rpm.

[0309] Manual measurement

[0310] 4-well plate was incubated for 5 minutes. The plate was washed 3 times with 1% NHS-PEO4-biotin buffer PB1. The plate was then incubated for 5 minutes ...biotin buffer PB1. The plate was then incubated for 5 minutes with 1% NHS-biotin buffer PB1. The plate was then incubated for 5 minutes with 1% NHS-biotin buffer PB1. The plate was then incubated for 5 minutes with 1% NHS-biotin buffer PB1. The plate was then incubated for 5 minutes with 1% NHS-biotin buffer PB1. The plate was then incubated for 5 minutes with 1% NHS-biotin buffer PB1. The plate was then incubated for 5 minutes with 1% NHS-biotin buffer PB1. The plate was then incubated Then 85 μL buffer PB1 supplemented with 1mM DxSO4 is added to each well, and under BlackRay ultraviolet lamp (nominal wavelength 365nm), the plate is irradiated at a distance of 5cm under vibration for 20 minutes. The sample is transferred to the unused well of a new washed streptavidin coated plate or an existing washed streptavidin plate, so that a high sample dilution and a low sample dilution mixture are combined into a single well. At room temperature, the sample is incubated for 10 minutes under vibration. Unabsorbed material is removed, and the plate is washed 8 times, each time for 15 seconds, using buffer PB1 supplemented with 30% glycerol. The plate is then washed once using buffer PB1. At room temperature, the aptamer is eluted for 5 minutes using 100 μL CAPSO elution buffer. 90 μL eluates are transferred to a 96-well HybAid plate, and 10 μL neutralization buffer is added.

[0311] Semi-automated assay

[0312] The streptavidin plate carrying the adsorbed mixture is placed on the platform of a BioTek EL406 plate washer, which is programmed to perform the following steps: remove unadsorbed material by aspiration, and wash the wells 4 times with 300 μL buffer PB1 supplemented with 1 mM dextran sulfate and 500 μM biotin. Then wash the wells 3 times with 300 μL buffer PB1. Add a solution of 150 μL freshly prepared (prepared from 100 mM DMSO stock solution) of buffer PB1 containing 1 mM NHS-PEO4-biotin. Under shaking, the plate is incubated for 5 minutes. Aspirate the liquid, and wash the wells 8 times with 300 μL buffer PB1 supplemented with 10 mM glycine. Add 100 μL buffer PB1 supplemented with 1 mM dextran sulfate. After these automated steps, the plate was removed from the plate washer and placed on a thermal shaker mounted below a UV light source (BlackRay, nominal wavelength 365nm) at a distance of 5cm for 20 minutes. The thermal shaker was set to 800rpm and 25°C. After 20 minutes of irradiation, the sample was manually transferred to a new washed streptavidin plate (or an unused well of an existing washed plate). At this point, the high abundance (3% serum + 3% aptamer mixture) and low abundance reaction mixture (0.3% serum + 0.3% aptamer mixture) were combined into a single well. This "Catch-2" plate was placed on the platform of a BioTek EL406 plate washer, which was programmed to perform the following steps: the plate was incubated for 10 minutes under shaking. The liquid was aspirated and the wells were washed 21 times with 300μL buffer PB1 supplemented with 30% glycerol. The wells were washed 5 times with 300μL buffer PB1, and the final wash solution was aspirated. 100 μL CAPSO elution buffer was added and the aptamer was eluted for 5 minutes with shaking.Following these automated steps, the plate was then removed from the platform of the plate washer and 90 μL aliquots of the sample were manually transferred to wells of a HybAid 96-well plate containing 10 μL neutralization buffer.

[0313] Hybridization to custom Agilent 8×15k microarray

[0314] 24 μL of the neutralized eluate was transferred to a new 96-well plate, and 6 μL of 10× Agilent blocks (Oligo aCGH / ChIP on-chip hybridization kit, large volume, Agilent 5188–5380) containing a set of hybridization controls consisting of 10 Cy3 aptamers were 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 pad slide (hybridization pad slide, 8 microarrays per slide format, Agilent). Custom Agilent microarray slides, each carrying 10 probes complementary to a 40-nucleotide random region of each aptamer with 20×dT adapters, were placed onto the pad slide according to the manufacturer’s protocol. The assembly was clamped (Hybridization Chamber Kit - enabled by SureHyb, Agilent) and incubated at 60°C for 19 hours while rotating at 20 rpm.

[0315] Post-hybridization washes

[0316] Place approximately 400 mL of Agilent Wash Buffer 1 into each of two separate glass staining dishes. While immersed in Wash Buffer 1, disassemble and separate the slides (no more than two at a time) before transferring them to a slide rack in a second staining dish also containing Wash Buffer 1. Incubate the slides in Wash Buffer 1 for an additional 5 minutes under agitation. Transfer the slides to Wash Buffer 2 pre-equilibrated to 37°C and incubate for 5 minutes under agitation. Transfer the slides to a fourth staining dish containing acetonitrile and incubate for 5 minutes under agitation.

[0317] Microarray imaging

[0318] Microarray slides were imaged using the Cy3 channel at 5 μm resolution using an Agilent G2565CA microarray scanner system at 100% PMT setting and the XRD option enabled at 0.05. The resulting TIFF images were processed using Agilent Feature Extraction Software version 10.5.1.1 under the GE1_105_Dec08 protocol.

[0319] Luminex probe design

[0320] The probe immobilized on the beads contains 40 deoxynucleotides complementary to the 3' end of the 40-nucleotide random region of the target aptamer. The aptamer complementary region is coupled to the Luminex microspheres via a hexaethylene glycol (HEG) linker carrying a 5' amino terminus. The biotinylated detection oligonucleotide contains 17 to 21 deoxynucleotides complementary to the 5' primer region of the target aptamer. The biotin moiety is attached to the 3' end of the detection oligomer.

[0321] Coupling the probe to Luminex microspheres

[0322] The probes were coupled to Luminex Microplex microspheres essentially according to the manufacturer's instructions with the following modifications: the amount of amino-terminal oligonucleotide was 100 μl per 2.5 × 10 6 The amount of EDC added was 0.08 nMol per microsphere and the second EDC addition was 5 μL at 10 mg / mL. The coupling reaction was performed in an Eppendorf ThermoShaker set at 25° C. and 600 rpm.

[0323] Microsphere hybridization

[0324] 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 1.5×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 1×TE buffer containing 15 nM biotinylated detection oligonucleotide stock solution was added to each reaction and mixed. 10 μL of the neutralized assay sample was added and the plate was sealed with a silicon cover gasket seal. The plate was first incubated at 96°C for 5 minutes and incubated overnight in a conventional hybridization oven at 50°C without stirring. Utilize 75 μ L of 1 × TMAC hybridization solution supplemented with 0.5% (w / v) BSA to pre-wet the filter plate (Durapore, Millipore part number MSBVN1250, 1.2 μ m pore size). The entire sample volume from the hybridization reaction is transferred to the filter plate. Utilize 75 μ L of 1 × TMAC hybridization solution containing 0.5% BSA to rinse the hybridization plate, and any remaining material is transferred to the filter plate. Filter the sample under slow vacuum, wherein 150 μ L of buffer is evacuated over approximately 8 seconds. Utilize 75 μ L of 1 × TMAC hybridization solution containing 0.5% BSA to wash the filter plate once, and the microspheres in the filter plate are resuspended in 75 μ L of 1 × TMAC hybridization solution containing 0.5% BSA. The filter plate is protected from light and incubated at 1000 rpm for 5 minutes on an Eppendorf Thermalmixer R. The filter plate was then washed once with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. 75 μL of 1×TMAC hybridization solution containing 10 μg / mL streptavidin phycoerythrin (SAPE-100, MOSS) was added to each reaction and incubated at 1000 rpm at 25°C for 60 minutes on an Eppendorf Thermalmixer R. The filter plate was washed twice with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA, and the microspheres in the filter plate were resuspended in 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. The filter plate was then incubated at 1000 rpm on an Eppendorf Thermalmixer R for 5 minutes under protection from light. The filter plate was then washed once with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. Microspheres were resuspended in 75 μL of 1×TMAC hybridization solution supplemented with 0.5% BSA and analyzed on a Luminex 100 instrument running XPonent 3.0 software.At least 100 microspheres were counted for each bead type at high PMT calibration and dual discriminator settings of 7500 to 18000.

[0325] QPCR readout

[0326] A qPCR standard curve was prepared in water at a 10-fold dilution and a no-template control within the range of 108 to 102 copies. The neutralized assay sample was diluted 40-fold into diH2O. A qPCR master mix was prepared at 2× final concentration (2× KOD buffer, 400 μM dNTP mix, 400 nM forward and reverse primer mix, 2× SYBR Green I, and 0.5 U KOD EX). 10 μL of the 2× qPCR master mix was added to the 10 μL diluted assay sample. qPCR was run on a BioRad MyIQiCycler, initially at 96°C for 2 minutes, followed by 40 cycles of 96°C for 5 seconds and 72°C for 30 seconds.

[0327] Example 2. Cardiovascular event model for predicting primary cardiovascular events

[0328] To predict the risk or likelihood that subjects with no known history of cardiovascular disease will have a primary CV event within 4 years, a primary model for cardiovascular disease (CVD) containing a panel of 13 biomarker proteins was developed. Primary CV events were defined as myocardial infarction, stroke, transient ischemic attack, hospitalization for heart failure, or death attributed to CVD. Training / validation analysis was performed using the HUNT3 dataset, a case-cohort study design enriched for primary CVD events. The study included 2,515 individuals, 41.51% of whom had a CVD event within 5 years. See Krokstad et al., Int J Epidemiol. 2013; 42: 968-977. The data was split 80% for training and 20% for validation. Predictions were made on an independent replication set during the validation phase based on the Whitehall II study (see Marmot et al., "Health inequalities among British Civil Servants: the Whitehall II study." Lancet 1991; 337: 1387-1393).

[0329] Model

[0330] The primary cardiovascular disease (CVD) model was a Weibull-distributed accelerated failure time (AFT) parameterized survival model. This model was characterized by 13 biomarkers, age, and interactions with age. Four-year risk was reported. Predictions were categorized into four risk levels and three risk categories. Table 3 reports the scores and corresponding actual event rates over a four-year training set.

[0331] Table 3

[0332]

[0333] result

[0334] The concordance index (C-index), area under the ROC curve (AUC), and class-independent net reclassification index (NRI) for 5-year predictions relative to the trimmed PCE model (trimmed on the HUNT3 training data) and the published PCE model are given in Table 4 below. The final model will also be evaluated on the 20% held-out HUNT3 validation dataset.

[0335] Table 4

[0336]

[0337] In the HUNT3 training set, 468 patients had a PCE risk score below 7.5%. The closest 4-year proteomic risk cutoff was calculated to be 2.15%, of which 469 patients had a predicted risk below 2.15%. Therefore, the healthy baseline stratum was defined as individuals with a predicted 4-year proteomic risk <2.15%. The average proteomic risk in this population was 1.53%. Four risk levels were calculated: 1x, 2x-3x, 4x-5x, and 6x and above (see Table 3). Figure 1 Shown are Kaplan-Meier survival curves for the HUNT3 training set, stratified by four risk classes. Figure 1 A summary is provided of how the empirical distribution of primary CVD events over time separates between groups of different predicted risk levels, with shaded areas representing 95% confidence intervals for Kaplan–Meier estimates. Figure 1 The intervals between risk classes are clearly shown with non-overlapping 4-year survival distributions.

[0338] The final model was also evaluated for all individuals in the training and validation datasets (including all PCE risk scores < 0.05). The results are shown in Table 5. The final model also outperformed the competing trimmed clinical PCE model on all individuals.

[0339] Table 5

[0340]

[0341] The CVD primary model was further characterized during refinement of several parameters. No significant effects were found based on gender or in the preliminary interfering substance assessment. The model was applied to 2005 replicates of the QC samples, and the predictions were reproducible with a mean of 0.05 and a standard deviation of 0.008. No significant changes were found based on sample processing time.

[0342] check

[0343] Model validation was performed on a Whitehall II dataset of 265 individuals, 101 of whom had CV events (38.11%). Analyte RFU values ​​in the dataset were log10 transformed prior to analysis. Out-of-range log10 RFU values ​​were estimated using aptamer-specific maximum and minimum values, which were calculated via winsorization during model development using the HUNT3 training data. The final proteomics model was then evaluated for this dataset at a 5-year time point, and the net reclassification index (NRI) was calculated as a comparison with the published PCE model, which is shown in Table 6.

[0344] Table 6

[0345] measure value NRI (NRI+ / NRI-) 0.176(0.120 / 0.055) C-index 0.66

[0346] The NRIs for predictions made using the proteomics model on the Whitehall II validation dataset were positive, and these results were superior to those achieved by the trimmed PCE model (developed on the HUNT3 training data) on the HUNT3 validation data.

[0347] Example 3. Cardiovascular event panel for predicting secondary cardiovascular events

[0348] In order to predict the risk or probability that subjects with known, apparently stable cardiovascular disease will have a secondary CV event within 4 years, a secondary model of cardiovascular disease (CVD) containing a set of 27 biomarker proteins was developed. Secondary CV events were defined as myocardial infarction, stroke, transient ischemic attack, hospitalization for heart failure, or death. Training / validation analysis was carried out using a sub-cohort of the HUNT3 data set, which included individuals who met the eligibility criteria for known, apparently stable CVD. The HUNT3 study included 754 individuals whose samples passed the QC indicators, of which 208 (28%) CV events were observed within 4 years. The data was divided into 80% for training and 20% for verification. The analysis was strengthened by a 20% validation subset of the Atherosclerosis Risk in Communities (ARIC) visit 5 data set. (See The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators. Am. J. Epidemiol. 1989; 129(4): 687-702.) Predictions made on an independent replication set using the remaining 80% of the ARIC visit 5 and 20% of the HUNT3 dataset were not blinded during validation.

[0349] Model

[0350] The cardiovascular disease (CVD) secondary model is an AFT parameterized survival model with a Weibull distribution. The model has a set of 27 biomarkers (log-10 and scaled around the center) as features. 4-year risk was reported. The prediction was divided into four risk levels and three risk categories. The scores and corresponding actual event rates within 4 years in the training set were reported in Table 7.

[0351] Table 7

[0352]

[0353] result

[0354] The C-index, AUC, and NRI for 4-year prediction relative to the trimmed PCE model (trimmed on HUNT3 training data) are given below in Table 8. The CVD secondary model had higher C-index and AUC values ​​and a positive NRI at 4 years in both the training (HUNT3) and validation (ARIC visit 5) datasets.

[0355] Table 8

[0356]

[0357] The risk probability of the training set was then classified into 4 levels, such that the baseline group was relatively healthy (5% 4-year event rate) and the high-risk group had a significantly accelerated event rate (65% 4-year event rate). See Table 7 above. With 95% confidence at 4 years, all 4 categories were different. Figure 2 and Figure 3 For the HUNT3 training set ( Figure 2 ) and ARIC visit 5 validation set ( Figure 3 ) Kaplan-Meier survival curves are shown for each group, stratified into four risk categories. Figure 2 and Figure 3 A summary of how the empirical distribution of secondary CVD events over time separates between groups of different predicted risk levels is provided, with shaded areas representing 95% confidence intervals for Kaplan-Meier estimates.

[0358] check

[0359] Model validation was performed on a 20% holdout validation set for HUNT3 and an 80% holdout validation set for ARIC. Analyte RFU values ​​in the dataset were log10 transformed prior to analysis. Analyte RFU values ​​were then centered and scaled based solely on the distribution from the training set, and out-of-range log10 RFU values ​​were imputed using values ​​calculated using winsorization. The C-index and AUV of the final CVD secondary model were evaluated for this dataset at the 4-year time point and compared to the trimmed PCE model. The NRI was calculated as a comparison to the trimmed PCE model, which is shown in Table 9.

[0360] Table 9

[0361]

[0362] The CVD secondary model outperformed the modified PCE-trimmed clinical model (using clinical and demographic parameters from the ACC risk equation, including separate coefficients for sex and race) in classifying 4-year event versus event-free subjects and in predicting early events in the training, validation, and validation sets. The NRI was positive for both validation sets (HUNT3 and ARICvisit 5). Figure 4 and Figure 5 Survival curves for the HUNT3 and ARIC visit 5 validation sets, stratified by cutoff values, are shown. The categorical permutations and slopes are similar to the expected distributions from the training sets and the empirically observed event rates. The highest risk groups are identified.

Claims

1. Use of a biomarker protein capture reagent in the preparation of a composition or kit for screening a subject for risk of cardiovascular (CV) events, wherein the biomarker protein capture reagent binds to the biomarker protein, wherein the biomarker protein comprises N-terminal BNP precursor, sTREM-1, MMP-12, antithrombin III, GPR56, gelsolin, ST4S6, CHSTC, FSH, IL-1 sRII, PLXB2, SAP and TFPI.

2. Use of a biomarker protein capture reagent in the preparation of a composition or kit for screening a subject for risk of cardiovascular (CV) events, wherein the biomarker protein capture reagent binds to biomarker proteins, wherein the biomarker proteins comprise BNP, sTREM-1, MMP-12, SVEP1, ARL11, ANTR2, CA125, GOLM1, PPR1A, ERBB3, suPAR, GDF-11 / 8, JAM-B, ATS13, Spinalin-1, NCAM-120, TFF3, SIRT2, ANP, NELL1, LRP11, NDST1, PTPRJ, CILP2, CA2D3, ITI heavy chain H2, and IGDC4.

3. Use of a biomarker protein capture reagent in the preparation of a composition or kit for predicting the likelihood that a subject will have a CV event, wherein the biomarker protein capture reagent binds to the biomarker protein, wherein the biomarker protein comprises N-terminal BNP precursor, sTREM-1, MMP-12, antithrombin III, GPR56, gelsolin, ST4S6, CHSTC, FSH, IL-1 sRII, PLXB2, SAP and TFPI.

4. Use of a biomarker protein capture reagent in the preparation of a composition or kit for predicting the likelihood that a subject will have a CV event, wherein the biomarker protein capture reagent binds to biomarker proteins, wherein the biomarker proteins comprise BNP, sTREM-1, MMP-12, SVEP1, ARL11, ANTR2, CA125, GOLM1, PPR1A, ERBB3, suPAR, GDF-11 / 8, JAM-B, ATS13, Spinalin-1, NCAM-120, TFF3, SIRT2, ANP, NELL1, LRP11, NDST1, PTPRJ, CILP2, CA2D3, ITI heavy chain H2, and IGDC4.

5. The use of any one of claims 1 to 4, wherein the risk or likelihood of the subject having a CV event within 4 years is high if the levels of at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, or at least 13 of the biomarkers in the panel of biomarkers are each abnormal relative to the control levels of the corresponding biomarkers.

6. The use according to any one of claims 1 to 4, wherein the CV event is myocardial infarction, stroke, transient ischemic attack, hospitalization for heart failure or death.

7. The method of claim 2 or 4, wherein the subject suffers from coronary artery disease.

8. The method of claim 1 or 3, wherein the subject has no history of CV events.

9. The method of claim 2 or 4, wherein the subject has had at least one CV event.

10. The use according to any one of claims 1 to 4, wherein the sample is selected from the group consisting of a blood sample, a serum sample, a plasma sample and a urine sample.

11. The use according to claim 10, wherein the sample is a blood sample.

12. The use according to any one of claims 1 to 4, wherein the use is performed in vitro.

13. The use of claim 1 or 3, wherein the use comprises contacting the biomarkers from the sample from the subject with a panel of biomarker protein capture reagents, wherein each capture reagent in the panel specifically binds to a different biomarker being detected.

14. The use of claim 2 or 4, wherein the use comprises contacting the biomarkers from the sample from the subject with a panel of biomarker protein capture reagents, wherein each capture reagent in the panel specifically binds to a different biomarker being detected.

15. The use according to any one of claims 1 to 4, wherein each capture agent is an antibody or an aptamer.

16. The use of claim 15, wherein each biomarker capture agent is an aptamer.

17. The use according to claim 16, wherein at least one aptamer is a slow off-rate aptamer.

18. The use of claim 17, wherein at least one slow off-rate aptamer comprises 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 nucleotides with modifications.

19. The use of claim 17, wherein each slow off-rate aptamer is expressed with an off-rate (t ½ ) binds to its target protein.

20. The method of claim 2 or 4, wherein the subject has apparently stable cardiovascular disease.

21. The use of claim 20, wherein the apparently stable cardiovascular disease comprises a history of myocardial infarction, stroke, heart failure, revascularization, abnormal stress testing, imaging suggestive of coronary artery disease, or abnormal coronary artery calcium score.

22. The use of claim 21, wherein the myocardial infarction or stroke occurred at least six months before the day the sample was obtained from the subject.

23. The use of claim 21, wherein the abnormal stress test is a treadmill exercise test or a nuclear medicine-based test.

24. The use of claim 21, wherein the imaging suggestive of coronary artery disease is an angiogram showing 50% or greater coronary artery stenosis.

25. The use of any one of claims 1 to 4, wherein the subject is at least 40 years old.

26. The use of any one of claims 1 to 4, comprising determining the risk or likelihood of the subject having a cardiovascular event within four years from the day the sample is obtained from the subject.

27. The use of claim 26, wherein the risk or likelihood that the subject has a cardiovascular event is within one, two, three or four years of the day the sample is obtained from the subject.

28. The use of claim 26, wherein the cardiovascular event is myocardial infarction, stroke, transient ischemic attack, hospitalization for heart failure, or death from cardiovascular disease.

29. The use of claim 26, wherein the risk is determined as a quantitative probability.

30. The use of claim 26, wherein the risk is determined as a qualitative risk level.

31. The use of claim 30, wherein the qualitative risk level is a low, medium or high risk level.

32. The use of any one of claims 1 to 4, wherein the risk or likelihood of a CV event is based on the biomarker level and at least one additional biomedical information selected from the group consisting of a) information corresponding to the presence of a cardiovascular risk factor selected from the group consisting of: previous myocardial infarction, angiographic evidence of greater than 50% stenosis in one or more coronary vessels, exercise-induced ischemia as measured by treadmill exercise testing or nuclear testing, or previous coronary revascularization, b) information corresponding to the physical descriptors of the subject, c) information corresponding to the subject's weight change, d) information corresponding to the ethnicity of the subject, e) information corresponding to the sex of the subject, f) information corresponding to the subject's smoking history, g) information corresponding to the subject's drinking history, h) information corresponding to the professional history of the subject, i) information corresponding to the subject's family history of cardiovascular disease or other circulatory system disorders, j) information corresponding to the presence or absence of at least one genetic marker in the subject, said at least one genetic marker being associated with a higher risk of cardiovascular disease in the subject or in a family member of the subject, k) information corresponding to the clinical symptoms of the subject, l) information corresponding to other laboratory tests, m) information corresponding to gene expression values ​​of the subject, and n) information corresponding to the subject's possession of known cardiovascular risk factors, such as a diet high in saturated fat, high in salt, high in cholesterol, o) information corresponding to imaging results of the subject obtained by a technique selected from the group consisting of electrocardiogram, echocardiogram, carotid ultrasound for intima-media thickness, flow-mediated dilation, pulse wave velocity, ankle-brachial index, stress echocardiography, myocardial perfusion imaging, CT coronary artery calcification, high-resolution CT angiography, MRI imaging, and other imaging modalities, p) information on the subject's medications, q) information corresponding to the subject's age, and r) information about the subject's renal function.

33. The use according to claim 32, wherein the at least one additional item of biomedical information is information corresponding to the age of the subject.

34. The use of any one of claims 1 to 4, wherein the use comprises determining the risk or likelihood of a CV event for the purpose of determining medical insurance premiums or life insurance premiums.

35. The use of claim 34, wherein the use further comprises determining coverage or premiums for medical insurance or life insurance.

36. The use of any one of claims 1 to 4, wherein the use further comprises using information derived from the use to predict and / or manage utilization of medical resources.

37. The use of any one of claims 1 to 4, wherein the use further comprises using information derived from the use to make a decision to acquire or purchase a medical practice, hospital, or company.

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