Diagnostic and prognostic methods for cardiovascular diseases and events

Abstract

To provide diagnostic and prognostic methods for cardiovascular diseases and events.SOLUTION: Compositions and methods are provided for diagnosis and / or prognosis of cardiovascular diseases or events in a subject. In some embodiments, the method includes measuring and comparing the levels of particular proteins to other proteins. In other embodiments, the method includes comparison with clinical variable information. Diagnosis or prognosis provided by the methods of the present disclosure are particularly important in defining and determining a therapeutic path forward for a patient receiving a positive diagnosis and / or positive prognosis of coronary artery disease, including cardiovascular death, myocardial infarct (MI), stroke, all cause death, or a composite thereof.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to and the benefit of U.S. Provisional Patent Application No. 62 / 289,513, filed February 1, 2016, and Provisional Patent Application No. 62 / 378,535, filed August 23, 2016, the contents of which are incorporated herein by reference in their entireties.

[0002] The present disclosure relates to biomarker panels, assays, and kits, and methods for diagnosing and / or prognosing cardiovascular disease or outcomes in a patient. [Background technology]

[0003] For example, atherosclerotic cardiovascular disease (ASCVD) and its associated cardiovascular events (CVE), including obstructive coronary artery disease (CAD), myocardial infarction (MI), stroke, and cardiovascular mortality (CVD), are primarily caused by an underlying endothelial process that results in the deposition of lipid materials and other proteins, which leads to atherosclerotic plaque formation in multiple vascular beds in the human body. Although this process is associated with identifiable and modifiable risk factors, the disease process and its associated events described above remain the leading cause of death and severe disability worldwide (Yusuf et al., Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries(the INTERHEART study): case-control study, Lancet, 364:937-52(2004)).

[0004] In Western countries, cardiovascular disease is generally associated with underlying atherosclerosis and is the leading cause of death (Martin-Ventura et al., 2009, Rev. Esp. Cardiol 62(6):677-688, citing Murray and Lopez, 1997, Lancet 349:1269-1276). Risk factors for heart disease are well known and include hypertension, diabetes, smoking, elevated cholesterol levels, obesity, and family history. However, despite proper assessment of the prevalence of obstructive coronary artery disease (CAD) and its risk factors, the association between the onset of symptoms and cardiac events requiring intervention remains elusive. Symptoms may be nonspecific, such as chest heaviness, which may reflect CAD, but may be explained by stomach discomfort, and pain in the left arm, which may be of cardiac origin or caused by arthritis. Even when pain is likely to be of cardiac origin, there may be questions regarding the type and intensity of treatment needed; in some situations, pharmacotherapy may be sufficient, whereas in others, interventional strategies are necessary to avoid CVE.

[0005] Many techniques have been developed to identify patients at high risk for adverse cardiac events. These include exercise and pharmacological stress testing using ECG response, cardiac wall motion response using ultrasound, and assessment of changes in myocardial perfusion using nuclear imaging techniques. Coronary angiography, the most invasive approach, is considered the "gold standard" diagnostic tool for evaluating coronary anatomy, cardiac structure, and function, but it is invasive, expensive, has defined complications, and is subject to operator-dependent variability (Sharma et al., 2010, Vasc. Health Risk Manag. 6:307-316). The use of coronary computed tomography angiography (Sharma et al., supra; Cury et al., 2008. J. Nucl. Cardiol. 15(4):564-575), biomarkers (e.g., Martin-Ventura et al., 2009, Rev. Esp. Cardiol 62(6'):677-688), adenosine stress magnetic resonance perfusion imaging (Ingkanisorn et al., 2006, J. Am. Coll. Cardiol. 47(7):1427-1432), clinical predictors (Tadros et al., 2003. South Med. J. 96(11):1113-1120; Schillinger et al., 2004, Wien Klin. Wochenschr. 116(3):83-89), as well as indicators of platelet activity (Marcucci et al., 2006, J. Am. Coll. Cardiol. 47(7):1427-1432). Other newer, less invasive options are being explored, including (Selvaraj et al., 2004, J. Throm. Thrombolysis 18(2):109-115; Selvaraj et al., 2009, Circulation 119:237-242 (originally published online December 31, 2008). Therefore, simple and reliable methods are needed to improve the diagnosis of cardiovascular pathologies and the prediction of CVEs. [Prior art documents] [Non-patent literature]

[0006] [Non-Patent Document 1] Yusuf et al., Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study, Lancet, 364:937-52 (2004) Summary of the Invention [Means for solving the problem]

[0007] The present disclosure provides a method for determining a diagnosis and / or prognosis of a cardiovascular disease or outcome in a subject, the method comprising: (i) determining the levels of at least one, at least two, at least three, at least four or more than four biomarkers in a biological sample obtained from the subject, wherein the biomarkers are selected from the group consisting of those listed in Tables 1A, 1B, 2A, and 2B; (ii) optionally determining the status of at least one clinical variable of the subject, wherein the clinical variable is selected from the group consisting of those listed in Tables 3A, 3B, 4A, and 4B; (iii) calculating a diagnostic or prognostic score for the subject based on the determined level of the at least one biomarker and, optionally, the status of the clinical variable(s) determined in step (ii); (iv) classifying the diagnostic or prognostic score as a positive or negative result; and (v) determining a therapeutic or diagnostic intervention regimen based on the positive or negative result.

[0008] The diagnosis or prognosis provided by the methods of the present disclosure is particularly important in defining and determining a treatment path forward for a patient who receives a positive coronary artery disease diagnosis and / or a positive prognosis of cardiovascular mortality, myocardial infarction (MI), stroke, all-cause mortality, or a combination thereof. In this manner, the determined diagnosis and / or prognosis of a cardiovascular disease or outcome in a subject facilitates a physician's determination of the need for therapeutic or diagnostic intervention in the subject.

[0009] In certain more specific embodiments, the biomarkers used in the methods are selected from the biomarkers listed in Tables 1A, 1B, 2A, and 2B, particularly those having a p-value of less than 0.1, less than 0.05, less than 0.01, or less than 0.001.

[0010] In one particular embodiment, the method comprises determining the level of at least one, at least two, three, at least four, or more than four biomarkers selected from the group consisting of adiponectin, apolipoprotein A-II, apolipoprotein CI, decorin, interleukin-8, kidney injury molecule-1, matrix metalloproteinase 9 (MMP-9), midkine, myoglobin, N-terminal prohormone brain natriuretic protein (NT-proBNP), osteopontin, pulmonary surfactant-associated protein D, stem cell factor, tissue inhibitor of metalloproteinase-1 (TIMP-1), troponin, and vascular cell adhesion molecule (VCAM).

[0011] In yet other embodiments, in addition to determining the biomarker level in the biological sample, the method further comprises determining the status of at least one clinical variable, such as those listed in Tables 3A, 3B, 4A, and 4B, particularly those having a p-value of less than 0.1, less than 0.05, less than 0.01, or less than 0.001.

[0012] In more particular embodiments, the method comprises determining the status of at least one clinical variable selected from the group consisting of age, history of coronary artery bypass graft (CABG), history of type 2 diabetes, history of hemodialysis, history of myocardial infarction (MI), history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stent placement), and gender.

[0013] In yet another embodiment of the present disclosure, step (i) of the method comprises determining the levels of at least one, at least two, at least three, at least four, or more than four biomarkers selected from the group consisting of adiponectin, apolipoprotein A-II, apolipoprotein CI, decorin, interleukin-8, kidney injury molecule-1, matrix metalloproteinase 9 (MMP-9), midkine, myoglobin, N-terminal prohormone brain natriuretic protein (NT-proBNP), osteopontin, pulmonary surfactant-associated protein D, stem cell factor, tissue inhibitor of metalloproteinase-1 (TIMP-1), troponin, and vascular cell adhesion molecule (VCAM); and step (ii) comprises determining the status of at least one clinical variable selected from age, history of coronary artery bypass graft (CABG), history of type 2 diabetes, history of hemodialysis, history of myocardial infarction (MI), history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stent placement), and gender.

[0014] The method advantageously provides for a diagnosis of obstructive coronary artery disease in a subject. In certain more particular embodiments, the diagnosis of obstructive coronary artery disease in a subject comprises a diagnosis of 70% or greater obstruction in a major epicardial vessel.

[0015] In yet further embodiments of the disclosure, step (i) of the method comprises determining the level of at least one, at least two, at least three, at least four, or more than four biomarkers selected from the group consisting of adiponectin, apolipoprotein CI, decorin, interleukin-8, kidney injury molecule-1, matrix metalloproteinase 9, midkine, myoglobin, pulmonary surfactant-associated protein D, stem cell factor, and troponin.

[0016] In more particular embodiments, the methods provide a prognosis of the likelihood for a cardiac outcome, e.g., an outcome selected from cardiovascular mortality, myocardial infarction (MI), stroke, mortality from any cause, or a combination thereof.

[0017] According to another aspect of the present invention, there is provided a method for diagnosing the presence of obstructive coronary artery disease in a subject, comprising: (i) determining the levels of at least one, at least two, at least three, at least four, or more than four biomarkers in a biological sample obtained from the subject, wherein the biomarkers are selected from the group consisting of the biomarkers listed in Tables 1A and 1B; (ii) optionally determining the status of at least one clinical variable for the subject, wherein the clinical variable is selected from the group consisting of the clinical variables listed in Tables 3A and 3B; (iii) calculating a diagnostic score for the subject based on the determined level of the at least one biomarker and optionally the status of the at least one clinical variable; (iv) classifying the score as a positive or negative diagnosis of obstructive coronary artery disease; and (iv) determining a therapeutic or diagnostic intervention regimen based on the positive or negative diagnosis.

[0018] In certain more specific embodiments, the biomarkers are selected from those listed in Tables 1A and 1B having a p-value of less than 0.1, less than 0.05, less than 0.01, or less than 0.001.

[0019] In other more specific embodiments, step (i) of the method comprises determining the level of at least one, at least two, at least three, at least four, or more than four biomarkers selected from the group consisting of adiponectin, apolipoprotein CI, decorin, interleukin-8, kidney injury molecule-1, matrix metalloproteinase 9, midkine, myoglobin, pulmonary surfactant-associated protein D, stem cell factor, and troponin.

[0020] In other embodiments, the clinical variable(s) evaluated according to the present methods are selected from those listed in Tables 3A and 3B having a p-value of less than 0.1, less than 0.05, less than 0.01, or less than 0.001.

[0021] In yet other embodiments, step (ii) of the method comprises determining the status of at least one clinical variable selected from the group consisting of age, history of coronary artery bypass graft (CABG), history of type 2 diabetes, history of hemodialysis, history of myocardial infarction (MI), history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stent placement), and gender.

[0022] In a further embodiment, step (i) of the method comprises determining the levels of at least one, at least two, at least three, at least four, or more than four biomarkers selected from the group consisting of adiponectin, apolipoprotein CI, decorin, interleukin-8, kidney injury molecule-1, matrix metalloproteinase 9, midkine, myoglobin, pulmonary surfactant-associated protein D, stem cell factor, and troponin, and step (ii) comprises determining the status of at least one clinical variable selected from age, history of coronary artery bypass graft (CABG), history of type 2 diabetes, history of hemodialysis, history of myocardial infarction (MI), history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stent placement), and gender.

[0023] In other embodiments, positive diagnosis of obstructive coronary artery disease in a subject facilitates the physician's decision on the need for intervention or further testing.Intervention or further testing can include, but is not limited to, one or more of diagnostic cardiac catheterization (also referred to as "cath"), percutaneous coronary intervention (balloon angioplasty with or without stent placement), coronary artery bypass graft (CABG), and administration of pharmacological agents such as one or more selected from nitrates, beta blockers, ACE inhibitors, and lipid-lowering agents.

[0024] In other embodiments, a negative diagnosis of obstructive coronary artery disease in a subject facilitates a physician's determination of the need for intervention or further testing.Intervention or further testing may include, but is not limited to, ongoing monitoring and management of coronary risk factors, including hypertension, diabetes, and smoking, and one or more lifestyle modifications selected from dietary modification, exercise, and smoking cessation.

[0025] Another aspect of the present disclosure provides a method for prognosis of cardiac outcome in a subject, comprising: (i) determining a level of at least one biomarker in a biological sample obtained from the subject, wherein the biomarker is selected from the group consisting of the biomarkers listed in Tables 2A and 2B; (ii) optionally determining a status of at least one clinical variable for the subject, wherein the clinical variable is selected from the group consisting of the clinical variables listed in Tables 4A and 4B; (iii) calculating a prognostic score for the subject based on the at least one predetermined level of the at least one biomarker and the status of the clinical variable(s) determined in step (ii); (iv) classifying the prognostic score as a positive or negative prognosis; and (v) determining a therapeutic or diagnostic intervention regimen based on the positive or negative prognosis.

[0026] In more specific embodiments, the biomarkers evaluated in the present methods are selected from those listed in Tables 2A and 2B having a p-value of less than 0.1, less than 0.05, less than 0.01, or less than 0.001.

[0027] In other embodiments, the clinical variable(s) evaluated according to the present methods are selected from those listed in Tables 4A and 4B having a p-value of less than 0.1, less than 0.05, less than 0.01, or less than 0.001.

[0028] In other specific embodiments, step (i) of the method comprises determining the levels of at least one, at least two, at least three, at least four, or more than four biomarkers selected from the group consisting of apolipoprotein A-II, kidney injury molecule-1, midkine, N-terminal prohormone brain natriuretic protein (NT-proBNP), osteopontin, tissue inhibitor of metalloproteinase-1 (TIMP-1), and vascular cell adhesion molecule (VCAM).

[0029] In yet other embodiments, the cardiac outcome prognosis is a prognosis of cardiovascular mortality, myocardial infarction (MI), stroke, all-cause mortality, or a combination thereof.

[0030] In further embodiments, the positive prognosis of cardiac outcome facilitates physician's decision on the need for intervention or further testing.Intervention or further testing can include, but is not limited to, one or more of the following: stress test with ECG response or myocardial perfusion imaging, coronary computed tomography angiography, diagnostic cardiac catheterization, percutaneous coronary intervention (e.g., balloon angioplasty with or without stent placement), coronary artery bypass graft (CABG), clinical trial registration, and administration or monitoring of the effect of drugs selected from, but not limited to, nitrates, beta blockers, ACE inhibitors, antiplatelet agents, and lipid-lowering agents.

[0031] In other embodiments, a negative prognosis of cardiac outcome facilitates a physician's determination of the need for intervention or further testing, which may include, but is not limited to, ongoing monitoring and management of coronary risk factors, including hypertension, diabetes, dyslipidemia, and smoking, as well as one or more lifestyle modifications selected from dietary modification, exercise, and smoking cessation.

[0032] In other aspects, the disclosure provides diagnostic or prognostic kits comprising a panel of biomarkers and, optionally, clinical variables as described. Examples of embodiments of the present invention include the following: (Item 1) 1. A method for determining a diagnosis and / or prognosis of a cardiovascular disease or outcome in a subject, comprising: (i) determining the levels of at least two biomarkers in a biological sample obtained from the subject, wherein the biomarkers are selected from the group consisting of those listed in Tables 1A, 1B, 2A, and 2B; (ii) optionally determining the status of at least one clinical variable of the subject, wherein the clinical variable is selected from the group consisting of those set forth in Tables 3A, 3B, 4A, and 4B; (iii) calculating a diagnostic or prognostic score based on the levels of said biomarkers determined in step (i) and, optionally, the status of said clinical variable(s) determined in step (ii); (iv) classifying the diagnostic or prognostic score as a positive or negative result; and (v) determining a therapeutic or diagnostic intervention regimen based on the positive or negative result. (Item 2) 2. The method of claim 1, wherein the biomarker has a p-value of less than 0.1, less than 0.05, less than 0.01, or less than 0.001. (Item 3) 2. The method of claim 1, wherein the at least one clinical variable has a p-value of less than 0.1, less than 0.05, less than 0.01, or less than 0.001. (Item 4) 2. The method of claim 1, wherein the method provides a diagnosis of obstructive coronary artery disease in the subject. (Item 5) 2. The method of item 1, wherein the method provides a prognosis of risk of myocardial infarction (MI), stroke, cardiovascular death, death from any cause, or a combination thereof. (Item 6) 2. The method of claim 1, wherein step (i) comprises determining the levels of at least two biomarkers selected from the group consisting of adiponectin, apolipoprotein A-II, apolipoprotein CI, decorin, interleukin-8, kidney injury molecule-1, matrix metalloproteinase 9, midkine, myoglobin, N-terminal prohormone brain natriuretic protein (NT-proBNP), osteopontin, pulmonary surfactant-related protein D, stem cell factor, tissue inhibitor of metalloproteinase-1 (TIMP-1), troponin, and vascular cell adhesion molecule. (Item 7) 2. The method according to item 1, wherein the optional step (ii) of determining the status of the at least one clinical variable is selected from age, previous coronary artery bypass graft (CABG), previous type 2 diabetes, previous hemodialysis, previous myocardial infarction (MI), previous percutaneous intervention, such as coronary stent and / or coronary angioplasty, and gender. (Item 8) 1. A method for diagnosing the presence of obstructive coronary artery disease in a subject, comprising: (i) determining the levels of at least two biomarkers selected from the group consisting of adiponectin, apolipoprotein CI, decorin, interleukin-8, kidney injury molecule-1, matrix metalloproteinase 9, midkine, myoglobin, pulmonary surfactant-associated protein D, stem cell factor, and troponin; (ii) optionally determining the status of at least one clinical variable selected from the group consisting of age, previous coronary artery bypass graft (CABG), previous type 2 diabetes, previous hemodialysis, previous myocardial infarction (MI), previous percutaneous intervention, e.g., coronary stent and / or coronary angioplasty, and gender; (iii) calculating a diagnostic score based on the levels of said biomarkers determined in step (i) and, optionally, the status of said at least one clinical variable determined in step (ii); (iv) classifying said diagnostic or prognostic score as a positive or negative diagnosis of obstructive coronary artery disease; (v) determining a therapeutic or diagnostic intervention regimen based on said positive or negative diagnosis. (Item 9) 9. The method of item 8, wherein step (i) comprises determining the levels of at least two biomarkers selected from the group consisting of adiponectin, apolipoprotein CI, kidney injury molecule-1, and midkine, and optional step (ii) comprises determining the status of at least one clinical variable selected from the group consisting of age, history of type 2 diabetes mellitus, history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stent placement), and gender. (Item 10) 9. The method of item 8, wherein step (i) comprises determining the levels of at least two biomarkers selected from the group consisting of adiponectin, decorin, and midkine, and optional step (ii) comprises determining the status of at least one clinical variable selected from the group consisting of history of myocardial infarction (MI), history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stent placement), and gender. (Item 11) 9. The method of claim 8, wherein step (i) comprises determining the levels of at least four biomarkers selected from the group consisting of adiponectin, apolipoprotein C-1, interleukin-8, kidney injury molecule-1, and stem cell factor, and optional step (ii) comprises determining the status of at least one clinical variable selected from the group consisting of age, history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stent placement), and gender. (Item 12) 9. The method of claim 8, wherein step (i) comprises determining the levels of adiponectin, apolipoprotein C-1, matrix metalloproteinase 9, midkine, myoglobin, pulmonary surfactant-associated protein D, and step (ii) comprises determining at least one clinical variable selected from the group consisting of coronary artery bypass graft (CABG) history, percutaneous coronary intervention (e.g., balloon angioplasty with or without stent placement), and gender. (Item 13) 9. The method according to item 8, wherein step (i) comprises determining levels of adiponectin, midkine, pulmonary surfactant-associated protein D, and troponin, and step (ii) comprises determining the status of at least one clinical variable selected from the group consisting of coronary artery bypass graft (CABG) history, hemodialysis history, myocardial infarction history, and gender. (Item 14) 14. The method according to any of items 8 to 13, wherein the diagnosis of obstructive coronary artery disease in the subject comprises a diagnosis of 70% or more obstruction in the major epicardial vessels. (Item 15) 15. The method according to any of items 8 to 14, wherein a positive diagnosis in the subject facilitates a physician's determination of the need for an intervention selected from one or more of the following: diagnostic cardiac catheterization, percutaneous coronary intervention (balloon angioplasty with or without stent placement), coronary artery bypass grafting (CABG), and administration of a pharmacological agent selected from nitrates, beta blockers, ACE inhibitors, and lipid-lowering agents. (Item 16) 15. The method according to any of items 8 to 14, wherein a negative diagnosis in the subject facilitates a physician's assessment of the need for an intervention selected from one or more of the following lifestyle modifications: ongoing monitoring and management of coronary risk factors including hypertension, diabetes, and smoking, and lifestyle modifications selected from dietary modification, exercise, and smoking cessation. (Item 17) 1. A method for prognosis of cardiac outcome in a subject within a time endpoint, comprising: (i) determining the levels of at least two biomarkers in a biological sample obtained from the subject, wherein the biomarkers are selected from the group consisting of apolipoprotein A-II, kidney injury molecule-1, midkine, N-terminal prohormone brain natriuretic protein (NT-proBNP), osteopontin, tissue inhibitor of metalloproteinase-1 (TIMP-1), and vascular cell adhesion molecule; (ii) optionally determining the status of at least one clinical variable selected from the group consisting of age, previous coronary artery bypass graft (CABG), previous type 2 diabetes, previous hemodialysis, previous myocardial infarction (MI), previous percutaneous intervention, e.g., coronary stent and / or coronary angioplasty, and gender; (iii) calculating a prognostic score based on the levels of said biomarkers determined in step (i) and, optionally, the status of said clinical variable(s) determined in step (ii); (iv) classifying the prognostic score as a positive or negative prognosis; (v) determining a therapeutic or diagnostic intervention regimen based on said positive or negative prognosis. (Item 18) 18. The method of claim 17, wherein the cardiac outcome is a prognosis of myocardial infarction (MI), stroke, cardiovascular death, death from any cause, or a combination thereof, and further wherein the time endpoint is selected from the group consisting of 0 to 365 days and 3 to 365 days. (Item 19) 18. The method of claim 17, wherein step (i) comprises determining the level of two or more biomarkers selected from the group consisting of apolipoprotein A-II, kidney injury molecule-1, midkine, N-terminal prohormone brain natriuretic protein (NT-proBNP), osteopontin, tissue inhibitor of metalloproteinase-1 (TIMP-1), and vascular cell adhesion molecule, and (ii) optionally, determining type 2 diabetes history status. (Item 20) 18. The method according to item 17, wherein step (i) comprises determining the levels of kidney injury molecule-1, midkine, N-terminal prohormone brain natriuretic protein (NT-proBNP), osteopontin, tissue inhibitor of metalloproteinases-1 (TIMP-1), and the prognosis is a 3-365 day prognosis of a composite of cardiovascular mortality (CVD), myocardial infarction (MI), or stroke. (Item 21) 18. The method according to item 17, wherein step (i) comprises determining the levels of kidney injury molecule-1, midkine, N-terminal prohormone brain natriuretic protein (NT-proBNP), osteopontin, tissue inhibitor of metalloproteinases-1 (TIMP-1), and the prognosis is a 0-365 day prognosis of a composite of cardiovascular mortality (CVD), myocardial infarction (MI), or stroke. (Item 22) 18. The method according to item 17, wherein step (i) comprises determining the levels of apolipoprotein A-II, N-terminal prohormone brain natriuretic protein (NT-proBNP), and osteopontin, and the prognosis is a 3-365 day prognosis of a composite of cardiovascular mortality (CVD) or myocardial infarction (MI). (Item 23) 18. The method according to item 17, wherein step (i) comprises determining the levels of N-terminal prohormone brain natriuretic protein (NT-proBNP) and osteopontin, and the prognosis is a 3-365 day prognosis of myocardial infarction (MI). (Item 24) 18. The method according to item 17, wherein step (i) comprises determining the levels of apolipoprotein A-II and osteopontin, and the prognosis is a 3-365 day prognosis of cardiovascular mortality (CVD). (Item 25) 25. The method according to any one of items 17 to 24, wherein a positive prognosis of cardiac outcome facilitates a physician's determination of the need for an intervention selected from one or more of the following: stress test with ECG response or myocardial perfusion imaging, coronary computed tomography angiography, diagnostic cardiac catheterization, percutaneous coronary intervention, coronary artery bypass grafting (CABG), enrollment in a clinical trial, and administration of or monitoring the effect of a drug selected from, but not limited to, nitrates, beta blockers, ACE inhibitors, antiplatelet agents, and lipid-lowering agents. (Item 26) 25. The method according to any one of items 17 to 24, wherein a negative prognosis of cardiac outcome facilitates a physician's assessment of the need for an intervention selected from one or more of the following lifestyle modifications: ongoing monitoring and management of coronary risk factors including hypertension, diabetes, dyslipidemia, and smoking, and lifestyle modifications selected from dietary modification, exercise, and smoking cessation. (Item 27) A panel for the diagnosis of obstructive coronary artery disease, comprising at least two biomarkers selected from those listed in Tables 1A and 1B, and optionally at least one clinical variable selected from the group consisting of those listed in Tables 3A and 3B. (Item 28) 28. The panel of item 27, wherein the at least two biomarkers have a p-value of less than 0.1, less than 0.05, less than 0.01, or less than 0.001. (Item 29) 28. The panel of item 27, wherein the at least one clinical variable has a p-value of less than 0.1, less than 0.05, less than 0.01, or less than 0.001. (Item 30) 28. The panel of item 27, wherein the at least two biomarkers are selected from the group consisting of adiponectin, apolipoprotein CI, decorin, interleukin-8, kidney injury molecule-1, matrix metalloproteinase 9, midkine, myoglobin, pulmonary surfactant-associated protein D, stem cell factor, and troponin. (Item 31) 28. The panel of item 27, wherein the at least one clinical variable is selected from the group consisting of age, history of coronary artery bypass graft (CABG), history of type 2 diabetes, history of hemodialysis, history of myocardial infarction (MI), history of percutaneous intervention, such as coronary stent and / or coronary angioplasty, and gender. (Item 32) A panel for the diagnosis of 70% or greater occlusion in any major epicardial vessel, including biomarkers for adiponectin, apolipoprotein C-1, kidney injury molecule-1, and midkine, as well as the clinical variables of history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stent placement), and gender. (Item 33) A panel for the diagnosis of 70% or greater occlusion in any major epicardial vessel, comprising biomarkers for adiponectin, midkine, pulmonary surfactant-related protein D, and troponin, and the clinical variables of coronary artery bypass graft (CABG) history, hemodialysis history, myocardial infarction history, and gender. (Item 34) A panel for prognosis of cardiac outcome, comprising at least two biomarkers selected from the biomarkers listed in Tables 2A and 2B, and optionally at least one clinical variable selected from the group consisting of those described in Tables 4A and 4B. (Item 35) 35. The panel of item 34, wherein the at least two biomarkers have a p-value of less than 0.1, less than 0.05, less than 0.01, or less than 0.001. (Item 36) 35. The panel of item 34, wherein the at least one clinical variable has a p-value of less than 0.1, less than 0.05, less than 0.01, or less than 0.001. (Item 37) 35. The panel of claim 34, wherein the at least two biomarkers are selected from the group consisting of apolipoprotein A-II, kidney injury molecule-1, midkine, N-terminal prohormone brain natriuretic protein (NT-proBNP), osteopontin, tissue inhibitor of metalloproteinase-1 (TIMP-1), and vascular cell adhesion molecule. (Item 38) 38. The panel of item 37, wherein the at least one clinical variable is diabetes history. (Item 39) A panel for composite prognosis of cardiovascular mortality (CVD), myocardial infarction (MI), or stroke, comprising biomarkers for kidney injury molecule-1, N-terminal prohormone brain natriuretic protein (NT-proBNP), osteopontin, and tissue inhibitor of metalloproteinases-1 (TIMP-1). (Item 40) Item 41. A panel for the composite prognosis of cardiovascular mortality (CVD) or myocardial infarction (MI), comprising biomarkers for apolipoprotein A-II, N-terminal prohormone brain natriuretic protein (NT-proBNP), and osteopontin. A panel for the prognosis of myocardial infarction (MI), comprising biomarkers for N-terminal prohormone brain natriuretic protein (NT-proBNP) and osteopontin. (Item 42) A panel for the prognosis of cardiovascular mortality (CVD), comprising biomarkers for apolipoprotein A-II and osteopontin. (Item 43) A diagnostic or prognostic kit comprising a panel according to any one of items 27 to 42. (Item 44) 1. A diagnostic kit useful for diagnosing obstructive coronary artery disease, comprising at least two antibodies or binding fragments thereof, the antibodies or binding fragments thereof being specific for a biomarker selected from the group consisting of adiponectin, apolipoprotein CI, decorin, interleukin-8, kidney injury molecule-1, matrix metalloproteinase 9, midkine, myoglobin, pulmonary surfactant-associated protein D, stem cell factor, and troponin. (Item 45) 1. A prognostic kit useful for the prognosis of myocardial infarction (MI), stroke, cardiovascular mortality, all-cause mortality, or a combination thereof, comprising at least two antibodies or binding fragments thereof specific for a biomarker selected from the group consisting of apolipoprotein A-II, kidney injury molecule-1, midkine, N-terminal prohormone brain natriuretic protein (NT-proBNP), osteopontin, tissue inhibitor of metalloproteinase-1, and vascular cell adhesion molecule. (Item 46) 1. A method for assessing cardiovascular status in a subject, comprising: (i) obtaining a sample from a subject selected for evaluation; (ii) performing one or more assays configured to detect said biomarkers selected from the group consisting of those set forth in Tables 1A, 1B, 2A, and 2B by (a) contacting the sample with one or more antibodies that specifically bind to detect the biomarker(s) being assayed, and (b) introducing the sample obtained from the subject into an assay device, which generates one or more assay results indicative of binding of each biomarker being assayed to a respective antibody to provide one or more assay results; (iii) optionally, determining the status of at least one clinical variable for said subject, said clinical variable being selected from the group consisting of those set forth in Tables 3A, 3B, 4A, and 4B; (iv) correlating the assay result(s) produced by the assay device, and optionally the status of the clinical variables, with the cardiovascular condition of the subject, the correlating step comprising correlating the assay result(s) with one or more of risk stratification, prognosis, diagnosis, classification, and monitoring of the cardiovascular condition of the subject, the correlating step comprising assigning a probability of a positive or negative diagnosis, or one or more future changes in a cardiovascular condition for the subject based on the assay result(s); (v) treating the patient based on the predetermined subpopulation of individuals to which the patient has been assigned, wherein the treatment comprises a therapeutic or diagnostic intervention regimen. (Item 47) 1. A method for diagnosing obstructive coronary artery disease in a subject, comprising: (i) obtaining a sample from a subject selected for evaluation; (ii) performing one or more assays configured to detect said biomarkers selected from the group consisting of those listed in Tables 1A and 1B by (a) contacting the sample with one or more antibodies that specifically bind to detect the biomarker(s) being assayed, and (b) introducing the sample obtained from the subject into an assay device, which generates one or more assay results indicative of binding of each biomarker being assayed to a respective antibody to provide one or more assay results; (iii) optionally, determining the status of at least one clinical variable for said subject, said clinical variable being selected from the group consisting of those listed in Tables 3A and 3B; (iv) correlating the assay result(s) produced by the assay device, and optionally the status of the clinical variable, with obstructive coronary artery disease, the correlating step comprising correlating the assay result(s) and optionally the clinical variable(s) with a diagnostic score, the correlating step comprising assigning the score to a positive or negative result; (v) treating the patient based on the positive or negative result, wherein the treating comprises a therapeutic or diagnostic intervention regimen. (Item 48) 1. A method for prognosis of cardiac outcome in a subject within a time endpoint, comprising: (i) obtaining a sample from a subject selected for evaluation; (ii) performing one or more assays configured to detect said biomarkers selected from the group consisting of those listed in Tables 2A and 2B by (a) contacting the sample with one or more antibodies that specifically bind to detect the biomarker(s) being assayed, and (b) introducing the sample obtained from the subject into an assay device, which generates one or more assay results indicative of binding of each biomarker being assayed to a respective antibody to provide one or more assay results; (iii) optionally, determining the status of at least one clinical variable for said subject, said clinical variable being selected from the group consisting of those set forth in Tables 4A and 4B; (iv) correlating the assay result(s) produced by the assay device and optionally the status of the clinical variable(s) with a likelihood of a cardiac outcome in the subject, the correlating step comprising correlating the assay result(s) and optionally the clinical variable(s) with a prognostic score, the correlating step comprising assigning the score to a positive or negative result; (v) treating the patient based on the positive or negative result, wherein the treating comprises a therapeutic or diagnostic intervention regimen. [Brief description of the drawings]

[0033] [Figure 1]Figure 1 shows the receiver operating characteristic curve for the Prevencio CAD panel FM139 / 685 (described in Example 1) in the internal validation set (N=278) for diagnosing the presence of severe CAD (greater than or equal to 70% stenosis in any vessel). The panel had a robust area under the curve (AUC) of 0.87. [Diagram 2] 1 shows the distribution of CAD panel FM139 / 685 (described in Example 1) in an internal validation set (N=278) for diagnosing the presence of severe CAD (70% or more stenosis in any vessel). A bimodal distribution is evident, with the vast majority of those with significant CAD distributed at the higher scores. Positive = subjects with at least one coronary artery stenosis of 70% or more, negative = subjects without coronary artery stenosis of 70% or more. [Diagram 3] Figure 1 shows the results of dividing the CAD panel FM139 / 685 (described in Example 1) into 5 categories of predictability of CAD (70% or more stenosis in any vessel) in an internal validation set (N=278). In doing so, 42% of subjects can be "included" or "excluded", with a PPV of 93% and an NPV of 91%, respectively, for severe CAD. [Figure 4] 1 shows Kaplan-Meier survival curves showing time to incident acute MI as a function of CAD score in panel FM139 / 685 (described in Example 1). Although developed as a diagnostic tool for CAD, the score also predicted incident acute MI during follow-up. [Diagram 5] Figure 1 shows the receiver operating characteristic curve for the Prevencio CAD panel FM144 / 696 in the internal validation set (N=278) for diagnosing the presence of severe CAD (greater than or equal to 70% stenosis in any vessel). The panel had a robust area under the curve (AUC) of 0.87. [Figure 6] Figure 1 shows the receiver operating characteristic curve for the Prevencio CAD panel FM145 / 701 in the internal validation set (N=278) for diagnosing the presence of severe CAD (greater than or equal to 70% stenosis in any vessel). The panel had an area under the curve (AUC) of 0.72. [Figure 7] Figure 1 shows the receiver operating characteristic curve for the Prevencio CAD panel FM146 / 690 in the internal validation set (N=278) for diagnosing the presence of severe CAD (greater than or equal to 70% stenosis in any vessel). The panel had an area under the curve (AUC) of 0.69. [Figure 8] Figure 1 shows the receiver operating characteristic curve for the Prevencio CAD panel FM152 / 757 in the internal validation set (N=278) for diagnosing the presence of severe CAD (greater than or equal to 70% stenosis in any vessel). The panel had an area under the curve (AUC) of 0.73. [Figure 9] Figure 1 shows the receiver operating characteristic curve for the Prevencio CAD panel FM117a / 657 in the internal validation set (N=278) for diagnosing the presence of severe CAD (greater than or equal to 70% stenosis in any vessel). The panel had a robust area under the curve (AUC) of 0.84. [Figure 10] Figure 1 shows the receiver operating characteristic curve for the Prevencio CAD panel FM139CLa / 658 in the internal validation set (N=278) for diagnosing the presence of severe CAD (70% or greater stenosis in any vessel). The panel had a robust area under the curve (AUC) of 0.80. [Figure 11] Figure 1 shows the receiver operating characteristic curve for the Prevencio CAD panel FM139CLb / 750 in the internal validation set (N=278) for diagnosing the presence of severe CAD (greater than or equal to 70% stenosis in any vessel). The panel had a robust area under the curve (AUC) of 0.84. [Figure 12] Figure 1 shows the receiver operating characteristic curve for the Prevencio CAD panel FM139CLc / 751 in the internal validation set (N=278) for diagnosing the presence of severe CAD (greater than or equal to 70% stenosis in any vessel). The panel had a robust area under the curve (AUC) of 0.83. [Figure 13]Figure 1 shows the receiver operating characteristic curve for the Prevencio CAD panel FM117b / 663 in the internal validation set (N=278) for diagnosing the presence of severe CAD (greater than or equal to 70% stenosis in any vessel). The panel had a robust area under the curve (AUC) of 0.85. [Figure 14] Figure 1 shows the receiver operating characteristic curve for the Prevencio CAD panel FM139CLd / 752 in the internal validation set (N=278) for diagnosing the presence of severe CAD (greater than or equal to 70% stenosis in any vessel). The panel had a robust area under the curve (AUC) of 0.86. [Figure 15] Figure 1 shows the receiver operating characteristic curve for the Prevencio CAD panel FM139CLe / 753 in the internal validation set (N=278) for diagnosing the presence of severe CAD (greater than or equal to 70% stenosis in any vessel). The panel had a robust area under the curve (AUC) of 0.85. [Figure 16] Figure 1 shows the receiver operating characteristic curve for the Prevencio CAD panel FM139CLf / 754 in the internal validation set (N=278) for diagnosing the presence of severe CAD (greater than or equal to 70% stenosis in any vessel). The panel had a robust area under the curve (AUC) of 0.86. [Figure 17] Figure 1 shows the receiver operating characteristic curve for the Prevencio CAD panel FM139CLg / 755 in the internal validation set (N=278) for diagnosing the presence of severe CAD (greater than or equal to 70% stenosis in any vessel). The panel had a robust area under the curve (AUC) of 0.86. [Figure 18] Figure 1 shows the receiver operating characteristic curve for the Prevencio CAD panel FM46 / 572 (described in Example 2) in the internal validation set (N=278) for diagnosing the presence of severe CAD (70% or greater stenosis in any vessel). The panel had a robust area under the curve (AUC) of 0.84. [Figure 19]1 shows the distribution of CAD panel FM46 / 572 (described in Example 2) in an internal validation set (N=278) for diagnosing the presence of severe CAD (70% or more stenosis in any vessel). A bimodal distribution is evident, with the vast majority of those with significant CAD distributed at the higher scores. Positive = subjects with at least one coronary artery stenosis of 70% or more, Negative = subjects with no coronary artery stenosis of 70% or more. [Figure 20] 1 shows Kaplan-Meier survival curves showing time to incident acute MI as a function of the CAD score panel FM 46 / 572 (described in Example 2). Although developed as a diagnostic tool for CAD, the score also predicted incident acute MI during follow-up. [Figure 21] Figure 1 shows the receiver operating characteristic curve for the Prevencio CAD panel FM46Fd / 586 in the internal validation set (N=278) for diagnosing the presence of severe CAD (greater than or equal to 70% stenosis in any vessel). The panel had a robust area under the curve (AUC) of 0.84. [Figure 22] Figure 1 shows the receiver operating characteristic curve for the Prevencio CAD panel FM46Fe / 587 in the internal validation set (N=278) for diagnosing the presence of severe CAD (greater than or equal to 70% stenosis in any vessel). The panel had a robust area under the curve (AUC) of 0.83. [Figure 23] Figure 1 shows the receiver operating characteristic curve for the Prevencio CAD panel FM46Ff / 588 in the internal validation set (N=278) for diagnosing the presence of severe CAD (greater than or equal to 70% stenosis in any vessel). The panel had a robust area under the curve (AUC) of 0.80. [Figure 24] Figure 1 shows the receiver operating characteristic curve for the Prevencio CAD panel FM186 / 796 in the internal validation set (N=278) for diagnosing the presence of severe CAD (greater than or equal to 70% stenosis in any vessel). The panel had a robust area under the curve (AUC) of 0.84. [Diagram 25]Figure 1 shows the receiver operating characteristic curve for the Prevencio CAD panel FM189 / 798 in the internal validation set (N=278) for diagnosing the presence of severe CAD (greater than or equal to 70% stenosis in any vessel). The panel had a robust area under the curve (AUC) of 0.83. [Figure 26] Figure 1 shows the receiver operating characteristic curve for the Prevencio CAD panel FM187 / 792 in the internal validation set (N=278) for diagnosing the presence of severe CAD (greater than or equal to 70% stenosis in any vessel). The panel had a robust area under the curve (AUC) of 0.85. [Figure 27] Figure 1 shows the receiver operating characteristic curve for the Prevencio CAD panel FM188 / 794 in the internal validation set (N=278) for diagnosing the presence of severe CAD (greater than or equal to 70% stenosis in any vessel). The panel had a robust area under the curve (AUC) of 0.85. [Figure 28] Figure 1 shows the receiver operating characteristic curve for the Prevencio CAD panel FM02 / 410 (as described in Example 3) in the internal validation set (N=243) for diagnosing the presence of severe CAD (greater than or equal to 70% stenosis in any vessel). The panel had a robust area under the curve (AUC) of 0.89. [Figure 29] Figure 1 shows the receiver operating characteristic curve for the Prevencio CAD panel FM01 / 390 in the internal validation set (N=243) for diagnosing the presence of severe CAD (greater than or equal to 70% stenosis in any vessel). The panel had a robust area under the curve (AUC) of 0.87. [Diagram 30] Figure 1 shows the receiver operating characteristic curve for Prevencio prognostic panel FM160 / 02 (described in Example 4) in the internal validation set (N=278) for 1-year (3-365 days) prognosis of a composite of cardiovascular death, myocardial infarction, or stroke. The panel had an area under the curve (AUC) of 0.79. [Diagram 31]Figure 1 shows the receiver operating characteristic curve for Prevencio prognostic panel FM96 / 04 (described in Example 5) in the internal validation set (N=278) for 1-year (0-365 days) prognosis of a composite of cardiovascular death, myocardial infarction, or stroke. The panel had an area under the curve (AUC) of 0.77. [Diagram 32] Receiver operating characteristic curve for Prevencio prognostic panel FM190 / 33 in the internal validation set (N=278) for 1-year (3-365 days) prognosis of a composite of cardiovascular death, myocardial infarction, or stroke. The panel had an area under the curve (AUC) of 0.78. [Diagram 33] Receiver operating characteristic curve for Prevencio prognostic panel FM98 / 03 in the internal validation set (N=278) for 1-year (0-365 days) prognosis of a composite of cardiovascular death, myocardial infarction, or stroke. The panel had an area under the curve (AUC) of 0.75. [Diagram 34] Receiver operating characteristic curve for Prevencio prognostic panel FM209 / 02 in the internal validation set (N=278) for 1-year (3-365 days) prognosis of a composite of all-cause death, myocardial infarction, or stroke. The panel had an area under the curve (AUC) of 0.79. [Diagram 35] Receiver operating characteristic curve for Prevencio prognostic panel FM111 / 05 in the internal validation set (N=278) for 1-year (0-365 days) prognosis of a composite of all-cause death, myocardial infarction, or stroke. The panel had an area under the curve (AUC) of 0.77. [Diagram 36] Receiver operating characteristic curve for Prevencio prognostic panel FM210 / 03 in the internal validation set (N=278) for 1-year (3-365 days) prognosis of a composite of all-cause death, myocardial infarction, or stroke. The panel had an area under the curve (AUC) of 0.78. [Figure 37]Receiver operating characteristic curve for Prevencio prognostic panel FM110 / 04 in the internal validation set (N=278) for 1-year (0-365 days) prognosis of a composite of all-cause death, myocardial infarction, or stroke. The panel had an area under the curve (AUC) of 0.75. [Figure 38] Receiver operating characteristic curve for Prevencio prognostic panel FM211 / 03 in the internal validation set (N=278) for 1-year (3-365 days) prognosis of the composite of cardiovascular death or myocardial infarction. The panel had an area under the curve (AUC) of 0.79. [Figure 39] Receiver operating characteristic curve for Prevencio prognostic panel FM77 / 26 in the internal validation set (N=278) for 1-year (0-365 days) prognosis of the composite of cardiovascular death or myocardial infarction. The panel had an area under the curve (AUC) of 0.77. [Diagram 40] Receiver operating characteristic curve for Prevencio prognostic panel FM212 / 02 in the internal validation set (N=278) of 1-year (3-365 days) prognosis of the composite of cardiovascular death or myocardial infarction. The panel had an area under the curve (AUC) of 0.79. [Diagram 41] Receiver operating characteristic curve for Prevencio prognostic panel FM201 / MI002 in the internal validation set (N=278) of 1-year (3-365 days) prognosis of myocardial infarction. The panel had an area under the curve (AUC) of 0.76. [Diagram 42] Receiver operating characteristic curve for Prevencio prognostic panel FM204 / MI003 in the internal validation set (N=278) of 1-year (3-365 days) prognosis of myocardial infarction. The panel had an area under the curve (AUC) of 0.76. [Diagram 43] Receiver operating characteristic curve for Prevencio prognostic panel FM202 / MI005 in the internal validation set (N=278) of 1-year (3-365 days) prognosis of myocardial infarction. The panel had an area under the curve (AUC) of 0.75. [Diagram 44]Receiver operating characteristic curve for Prevencio prognostic panel FM205 / MI007 in the internal validation set (N=278) of 1-year (3-365 days) prognosis of myocardial infarction. The panel had an area under the curve (AUC) of 0.75. [Diagram 45] Receiver operating characteristic curve for Prevencio prognostic panel FM63 / 64 in the internal validation set (N=278) of 1-year (0-365 days) prognosis of myocardial infarction. The panel had an area under the curve (AUC) of 0.73. [Diagram 46] Receiver operating characteristic curve for Prevencio prognostic panel FM52 / 244 in the internal validation set (N=278) of 1-year (0-365 days) prognosis of cardiovascular mortality. The panel had a robust area under the curve (AUC) of 0.80. [Figure 47] Receiver operating characteristic curve for Prevencio prognostic panel FM194 / CVD001 in the internal validation set (N=278) of 1-year (3-365 days) prognosis of cardiovascular mortality. The panel had a robust area under the curve (AUC) of 0.80. [Figure 48] Receiver operating characteristic curve for Prevencio prognostic panel FM193 / R08 in the internal validation set (N=278) of 1-year (3-365 days) prognosis of cardiovascular mortality. The panel had a robust area under the curve (AUC) of 0.80. [Figure 49] Receiver operating characteristic curve for Prevencio prognostic panel FM53 / 237 in the internal validation set (N=278) of 1-year (0-365 days) prognosis of cardiovascular mortality. The panel had a robust area under the curve (AUC) of 0.81. [Figure 50] Receiver operating characteristic curve for Prevencio prognostic panel FM195 / CVD002 in the internal validation set (N=278) of 1-year (3-365 days) prognosis of cardiovascular mortality. The panel had a robust area under the curve (AUC) of 0.81. [Figure 51]Receiver operating characteristic curve for Prevencio prognostic panel FM207 / 04 in the internal validation set (N=278) of 1-year (3-365 days) prognosis of cardiovascular mortality. The panel had a robust area under the curve (AUC) of 0.83. [Figure 52] Receiver operating characteristic curve for Prevencio prognostic panel FM208 / R05 in the internal validation set (N=278) of 1-year (3-365 days) prognosis of cardiovascular mortality. The panel had a robust area under the curve (AUC) of 0.82. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0034] The practice of the present invention employs, unless expressly indicated to the contrary, conventional methods of chemistry, biochemistry, organic chemistry, molecular biology, microbiology, recombinant DNA techniques, genetics, immunology, and cell biology that are within the skill of those in the art, many of which are described below for illustrative purposes. Such techniques are fully explained in the literature. See, for example, Sambrook, et al., Molecular Cloning: A Laboratory Manual (3rd Edition, 2001); Sambrook, et al., Molecular Cloning: A Laboratory Manual (2nd Edition, 2002); Edition, 1989), Maniatis et al., Molecular Cloning: A Laboratory Manual (1982), Ausubel et al., Current Protocols in Molecular Biology (John Wiley and Sons, updated July 2008), Short Protocols in Molecular Biology: A Compendium of Methods from Current Protocols in Molecular Biology, Greene Pub. Associates and Wiley-Interscience, Glover, DNA Cloning: A Practical Approach, vol. I & II (IRL Press, Oxford, 1985), Anand, Techniques for the Analysis of Complex Genomes, (Academic Press, New York, 1992), Transcription and Translation (B. Hames & S. Higgins, Eds., 1984), Perbal, A Practical Guide to Molecular Cloning (1984), and Harlow and Lane, Antibodies, (Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, 1998).

[0035] All patents, patent applications, articles and publications mentioned herein, referenced supra and infra, are hereby expressly incorporated by reference in their entireties.

[0036] Unless otherwise defined herein, all technical and scientific terms have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. Various scientific dictionaries, including the terms contained herein, are known and available to those skilled in the art. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of this disclosure, some preferred methods and materials are described. Thus, the terms defined immediately below are generally more fully understood by referring to this specification. It should be understood that this disclosure is not limited to the specific methodology, protocols, and reagents described, as these may vary depending on the context in which they are used by those skilled in the art.

[0037] As used herein, the singular terms "a," "an," and "the" include their plural referents unless clearly indicated otherwise.

[0038] As used herein, the term "invention" or "the present invention" is an open-ended term and is not intended to refer to any single embodiment of a particular invention, but encompasses all possible embodiments described in the specification and claims.

[0039] Throughout this specification, for example, reference to "an embodiment," "one embodiment," "another embodiment," "particular embodiment," "related embodiment," "certain particular embodiment," "additional embodiment," or "further embodiment," or combinations thereof, means that the particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Thus, the appearances of the foregoing phrases in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0040] As used herein, the term "about" or "approximately" refers to an amount, level, value, number, frequency, percentage, dimension, size, amount, weight, or length that varies by as much as 30, 25, 20, 25, 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1% relative to a reference amount, level, value, number, frequency, percentage, dimension, size, amount, weight, or length. In certain embodiments, the term "about" or "approximately" preceding a numerical value indicates a value in the range of plus or minus 15%, 10%, 5%, or 1%.

[0041] Throughout this specification, unless the context requires otherwise, the words "comprise, comprise" and "comprising" will be understood to imply the inclusion of a stated step or element or group of steps or elements, and not the exclusion of any other step or element or group of steps or elements. "Consisting of" means including, but not limited to, whatever follows the phrase "consisting of". Thus, the phrase "consisting of" indicates that the recited elements are necessary or mandatory, and that no other elements may be present. "Consisting essentially of" means including any elements listed after the phrase, limited to other elements that do not interfere with or contribute to the activity or action specified in the disclosure for the recited elements. Thus, the phrase "consisting essentially of" indicates that the recited elements are necessary or mandatory, but that the other elements are not optional and may or may not be present depending on whether they affect the activity or action of the recited elements.

[0042] Cardiovascular Diseases and Events The present disclosure generally relates to the diagnosis and / or prognosis of cardiovascular disease and / or adverse cardiovascular events. As used herein, the term "diagnosis" refers to the identification or likelihood of the presence of a cardiovascular disease or event in a subject. Also, as used herein, the term "prognosis" refers to the likelihood or risk of a subject developing a particular outcome or event.

[0043] The term "cardiovascular disease" refers to a class of diseases involving the heart or blood vessels, including coronary artery disease (CAD), myocardial infarction (commonly known as heart attack), stroke, hypertensive heart disease, rheumatic heart disease, cardiomyopathies, cardiac arrhythmias (i.e., atrial fibrillation, ventricular tachycardia, etc.), cerebrovascular disease, peripheral arterial disease, and arterial thrombosis.

[0044] As used herein, the term "cardiovascular events" refers to a variety of adverse outcomes related to the cardiovascular system, including, but not limited to, myocardial infarction, cardiovascular death, and stroke.

[0045] The term "coronary artery disease" or "CAD" refers to a specific type of cardiovascular disease. "Obstructive coronary artery disease" is characterized by atherosclerotic blockages in the coronary arteries. Such blockages may be clinically relevant at levels of 50% or more, 60% or more, 70% or more, 80% or more, 90% or more, or 100%. Atherosclerotic plaques are the hallmark of atherosclerosis and progressively narrow the coronary artery lumen, reducing antegrade myocardial blood flow. The reduced coronary flow may be symptomatic or asymptomatic. Symptoms of coronary artery occlusion typically occur during exertion but can occur at rest and, depending on the severity and rapidity of the blockage, may lead to myocardial infarction, stroke, and / or cardiovascular death.

[0046] As used herein, the term "cardiovascular mortality" or "CV mortality" or "CVD" refers to mortality resulting from acute myocardial infarction (MI), sudden cardiac death, mortality from heart failure (HF), mortality from stroke, mortality from cardiovascular (CV) surgery, mortality from CV thrombosis or hemorrhage, mortality from cardiac infection, and other recognized CV mortality occurring within a specified period after the test sample is obtained.

[0047] The terms "myocardial infarction" or "MI" and "acute myocardial infarction" or "AMI", commonly known as a heart attack, occur when blood flow becomes restricted to a portion of the heart, causing damage to the heart muscle. The most common symptom is chest pain or discomfort that may migrate to the shoulder, arm, back, neck, or jaw. Often, the symptoms are in the center or left side of the chest and last for more than a few minutes. The discomfort may often feel like heartburn. Other symptoms may include shortness of breath, nausea, fainting, cold sweats, or fatigue. Most MIs are caused by obstructive coronary artery disease. Identified risk factors for obstructive coronary artery disease include, among others, high blood pressure, smoking, diabetes, lack of exercise, obesity, high blood cholesterol, poor diet, family history of premature cardiovascular death or myocardial infarction, and excessive alcohol intake. The mechanism of MI often involves the progression or rupture of atherosclerotic plaques that cause a complete blockage of a coronary artery. MI is caused by spasm of the coronary arteries, which, although uncommon, can be due to drugs such as cocaine, significant emotional stress, and extreme cold, among others.

[0048] The term "stroke", also known as cerebrovascular accident (CVA), cerebral circulatory insufficiency (CVI), or brain attack, refers to a situation when insufficient blood flow to the brain leads to brain cell death. There are two main types of strokes: ischemic, due to lack of blood flow caused by thrombosis or embolism, and hemorrhagic, due to bleeding. They typically result in significant functional impairment in the affected part of the brain. Signs and symptoms of a stroke may include, among others, an inability to move or experience touch, hot and cold sensations, or pain on one side of the body, problems with understanding or speaking, a feeling that the world is spinning, or reduced vision on one side. Signs and symptoms often appear immediately after a stroke occurs.

[0049] The term "death from any cause" or "all-cause mortality" is defined as death from any non-trauma-related cause, including CV death, within a specified period of time after the test sample is obtained.

[0050] Biomarkers and clinical variables The biomarkers of the invention described herein may be advantageously used in the diagnosis and prognosis of cardiovascular diseases and events. The terms "marker" and "biomarker" are used interchangeably throughout this disclosure. As used herein, a biomarker generally refers to a protein or polypeptide whose level or concentration is associated with a particular biological state, particularly a state associated with a cardiovascular disease, event, or outcome. The panels, assays, kits, and methods of the invention may include antibodies, binding fragments thereof, or other types of binding agents that are specific for the biomarkers described herein.

[0051] The terms "polypeptide" and "protein" as used interchangeably herein refer to polymeric forms of amino acids of any length, which may include coded and non-coded amino acids, chemically or biochemically modified or derivatized amino acids, and polypeptides with modified peptide backbones. In various embodiments, detection of levels of naturally occurring biomarker proteins in a biological sample is contemplated for use in the diagnostic, prognostic, or monitoring methods disclosed herein. The terms also include fusion proteins, including but not limited to, naturally occurring fusion proteins with heterologous amino acid sequences, fusions with heterologous and homologous leader sequences, with or without an N-terminal methionine residue, immunologically tagged proteins, and the like.

[0052] "Substantially isolated" or "isolated" material is one that is substantially free of the surrounding materials with which it is naturally associated. Substantially free means at least 50%, preferably at least 70%, more preferably at least 80%, and even more preferably at least 90% free of the materials with which it is naturally associated. As used herein, "isolated" can refer to polynucleotides, polypeptides, antibodies, cells, samples, and the like.

[0053] Certain exemplary diagnostic biomarkers of the present invention can be found listed in Tables 1A and 1B, while certain exemplary prognostic biomarkers can be found listed in Tables 2A and 2B. Based on the information in Tables 1A, 1B, 2A, and 2B, one of skill in the art can readily identify, select, and implement a biomarker or combination of biomarkers in accordance with the present disclosure.

[0054] In certain embodiments, biomarkers for use in accordance with the present invention include those listed in Tables 1A, 1B, 2A, and 2B, particularly those associated with a p-value of less than 0.1, less than 0.05, less than 0.01, or less than 0.001.

[0055] In other specific embodiments, the biomarkers used in accordance with the present disclosure are selected from the group consisting of adiponectin, apolipoprotein A-II, apolipoprotein CI, decorin, interleukin-8, kidney injury molecule-1, matrix metalloproteinase 9, midkine, myoglobin, N-terminal prohormone brain natriuretic protein (NT-proBNP), osteopontin, pulmonary surfactant-associated protein D, stem cell factor, tissue inhibitor of metalloproteinase-1 (TIMP-1), troponin, and vascular cell adhesion molecule (VCAM).

[0056] As used herein, "adiponectin" refers to a protein involved in the regulation of glucose and the breakdown of fatty acids. It is also called GBP-28, apM1, AdipoQ, and Acrp30. Adiponectin is a 244 amino acid peptide secreted by adipose tissue, whose roles include regulating glucose and fatty acid metabolism.

[0057] As used herein, "apolipoprotein A-II" refers to an apolipoprotein found in high density lipoprotein (HDL) cholesterol in plasma.

[0058] As used herein, "apolipoprotein CI" is a protein component of lipoproteins normally found in plasma, which is involved in the activation of esterified lecithin cholesterol and the removal of cholesterol from tissues.

[0059] As used herein, "decorin," also known as PG40 and PGS2, is a protein that belongs to the small leucine-rich proteoglycan family. It regulates the assembly of the extracellular collagen matrix.

[0060] As used herein, "Interleukin-8," also known as IL8, neutrophil chemotactic factor, chemokine ligand 8, and CXCL8, is a chemokine produced by macrophages and other cell types such as epithelial cells, airway smooth muscle cells, and endothelial cells. It induces chemotaxis in target cells, primarily neutrophils but also other granulocytes, to migrate toward the site of infection. Upon arrival, IL-8 also induces phagocytosis. IL-8 is also known to be a potent promoter of angiogenesis. In target cells, IL-8 initiates a series of physiological responses required for migration and phagocytosis, such as intracellular Ca2+, Ca2+, and Ca2+. 2+ Induces increased ATP, exocytosis (e.g., histamine release), and the respiratory burst.

[0061] As used herein, "kidney injury molecule-1," also known as "KIM-1," is a type 1 cell membrane glycoprotein that serves as a receptor for oxidized lipoproteins and plays a functional role in the kidney. KIM-1 is a proximal tubule marker whose concentrations have been linked to acute kidney injury.

[0062] As used herein, "matrix metalloproteinase 9," also known as MMP-9, 92 kDa type IV collagenase, 92 kDa gelatinase, and gelatinase B or GELB, is a matrixin, a class of enzymes belonging to the zinc-metalloproteinase family involved in the degradation of extracellular matrix. Proteins of the matrix metalloproteinase (MMP) family are involved in the degradation of extracellular matrix in normal physiological processes such as embryonic development, regeneration, angiogenesis, bone development, wound healing, cell migration, learning and memory, as well as in pathological processes such as arthritis, intracerebral hemorrhage, and metastasis.

[0063] As used herein, "midkine," also known as "neurite outgrowth promoting factor 2" or "NEGF2," refers to a low molecular weight basic heparin-binding growth factor, which forms a family with pleiotrophin. Midkine is a heparin-binding cytokine / growth factor with a molecular weight of 13 kDa.

[0064] As used herein, "myoglobin" is an iron- and oxygen-binding protein generally found in muscle tissue of nearly all mammalian vertebrates. Myoglobin is released from damaged muscle tissue (rhabdomyolysis), which has very high concentrations of myoglobin. The released myoglobin is filtered by the kidney, but is toxic to the renal tubular epithelium, and thus can cause acute kidney injury. It is not myoglobin itself that is toxic (it is a protoxin), but the ferrihemate moiety that is degraded from myoglobin in an acidic environment (e.g., acidic urine, lysosomes). Myoglobin is a sensitive marker for muscle damage, making it a potential marker for heart attack in patients with chest pain.

[0065] As used herein, "N-terminal prohormone of brain natriuretic peptide" or "NT-PBNP," also known as "NT-proBNP" or "BNPT," refers to the N-terminal inactive protein that is cleaved from proBNP to release brain natriuretic peptide.

[0066] As used herein, "osteopontin," also known as "bone sialoprotein I," "BSP-1," "BNSP," "early T-lymphocyte activation," "ETA-1," "secreted phosphoprotein 1," "SPP1," "2ar," "Rickettsia resistant," or "Ric," first identified in osteoblasts, refers to a glycoprotein (small integrin-binding ligand N-linked glycoprotein). It includes all isoforms and post-translational modifications.

[0067] As used herein, "pulmonary surfactant-associated protein D," also referred to as surfactant, lung-associated protein D, or SP-D or SFTPD, is a protein that contributes to the lung's defense against inhaled microorganisms, organic antigens, and toxins.

[0068] As used herein, "Stem Cell Factor", also known as SCF, KIT-ligand, KL, and Steel Factor, is a cytokine that binds to the c-KIT receptor (CD117). SCF can exist as both a transmembrane protein and a soluble protein. This cytokine plays an important role in hematopoiesis (the formation of blood cells), spermatogenesis, and melanogenesis.

[0069] As used herein, "tissue inhibitor of metalloproteinases-1," also known as "TIMP-1" or "TIMP metallopeptidase inhibitor 1," refers to a glycoprotein expressed in several tissues. It is a natural inhibitor of matrix metalloproteinases, a group of peptidases involved in the degradation of the extracellular matrix. It can promote cell proliferation in a wide range of cell types.

[0070] As used herein, "troponin", also known as the troponin complex, is a complex of three regulatory proteins (troponin C, troponin I, and troponin T) that are essential for muscle contraction in skeletal and cardiac muscles, but not in smooth muscles. As used herein, troponin biomarkers may identify each of these proteins individually or in combination. Increased levels of circulating cardiac protein isoforms of troponin in blood have been shown to be biomarkers of cardiac damage, the most important of which is myocardial infarction. Elevated levels of troponin indicate cardiac cell death when the molecule is released into the blood upon injury to the heart.

[0071] As used herein, "vascular cell adhesion molecule," also known as VCAM-1, VCAM, differentiation cluster 106, and CD106, is a cell adhesion molecule. VCAM-1 protein mediates adhesion of lymphocytes, monocytes, eosinophils, and basophils to vascular endothelium. It also functions in leukocyte-endothelial cell signaling and may play a role in the development of atherosclerosis and rheumatoid arthritis.

[0072] Those of skill in the art will understand that these and other biomarkers disclosed herein (e.g., those set forth in Tables 1A, 1B, 2A, and 2B) can be readily identified, made, and used in the context of the present disclosure in light of the information provided herein.

[0073] As used herein, the term "score" refers to a bivariate, multilevel, or continuous outcome when it comes to determining a diagnosis or prognosis.

[0074] As used herein, the term "panel" refers to a particular combination of biomarkers and clinical markers used to determine the diagnosis or prognosis of a cardiovascular disease or outcome in a subject. The term "panel" can also refer to an assay that includes a set of biomarkers used to determine the diagnosis or prognosis of a cardiovascular disease or outcome in a subject.

[0075] As further described herein, a "training set" is a set of patients or patient samples used in the process of training (i.e., generating, evaluating, and constructing) a final diagnostic or prognostic model. A "validation set" is a set of patients or patient samples that are subtracted from the training process and are used solely to validate the performance of the final diagnostic or prognostic model.

[0076] As further described herein, the biomarkers of the present invention can optionally be used in combination with certain clinical variables to provide improved diagnosis and / or prognosis of a cardiovascular disease or event in a subject. For example, exemplary clinical variables useful in the context of the present disclosure can be found and listed in Tables 3A, 3B, 4A, and 4B.

[0077] Table 1A below shows biomarker concentrations and their diagnostic relevance that differ between those who had at least one coronary artery stenosis of 70% or more (N=428) and those who did not in a training set (N=649) in a cohort of subjects who received a coronary artery catheter with or without any peripheral catheter.

[0078] [Table 1A-1] [Table 1A-2] [Table 1A-3] [Table 1A-4] [Table 1A-5] [Table 1A-6]

[0079] Table 1B below shows biomarker concentrations and their diagnostic relevance that differed between those who had at least one coronary artery stenosis of 70% or more (N=361) and those who did not in a training set (N=566) of subjects who received only coronary artery catheters.

[0080] [Table 1B-1] [Table 1B-2] [Table 1B-3] [Table 1B-4] [Table 1B-5] [Table 1B-6]

[0081] Table 2A shows biomarker concentrations and their prognostic associations in patients with major adverse cardiac events (MACE) within 365 days of blood draw (for the training set, N=649). The figures in this table were calculated using a composite endpoint of 1-year MACE due to CV death, MI, or major stroke, and these proteins produce similar results using a composite endpoint of 1-year MACE due to any cause death, MI, and / or major stroke.

[0082] [Table 2A-1] [Table 2A-2] [Table 2A-3] [Table 2A-4] [Table 2A-5]

[0083] Table 2B below shows the biomarker concentrations and their prognostic associations that differed between those with and those without a major adverse cardiac event (MACE) 3-365 days from blood draw in the training set (N=648). The numbers in this table were calculated using a composite endpoint of 1-year MACE due to CV death, MI, or major stroke, and these proteins produce similar results using a composite endpoint of 1-year MACE due to any cause death, MI, and / or major stroke.

[0084] [Table 2B-1] [Table 2B-2] [Table 2B-3] [Table 2B-4] [Table 2B-5]

[0085] Table 3A below shows baseline clinical variables and their diagnostic relevance that differed between those with at least one coronary artery stenosis of 70% or greater (N=428) and those who did not in a training set (N=649) in a cohort of subjects who received a coronary artery catheter with or without any peripheral catheter.

[0086] [Table 3A-1] [Table 3A-2] [Table 3A-3] [Table 3A-4] All continuous variables are expressed as mean ± standard deviation unless otherwise specified. BP = blood pressure, MI = myocardial infarction, COPD = chronic obstructive pulmonary disease, CVA / TIA = cerebrovascular accident / transient ischemic attack, CKD = chronic kidney disease, CABG = coronary artery bypass grafting, ACE-I / ARB = angiotensin-converting enzyme inhibitor / angiotensin receptor blocker, CCB = calcium channel blocker, LVEF = left ventricular ejection fraction, RVSP = right ventricular systolic pressure, eGFR = estimated glomerular filtration rate, LDL = low density lipoprotein, HGB = hemoglobin.

[0087] Table 3B below shows baseline clinical variables and their diagnostic relevance that differed between those who had at least one coronary artery stenosis of 70% or greater (N=361) and those who did not in the training set (N=566) in a cohort of subjects who received only a coronary artery catheter.

[0088] [Table 3B-1] [Table 3B-2] [Table 3B-3] All continuous variables are expressed as mean ± standard deviation unless otherwise specified. BP = blood pressure, MI = myocardial infarction, COPD = chronic obstructive pulmonary disease, CVA / TIA = cerebrovascular accident / transient ischemic attack, CKD = chronic kidney disease, CABG = coronary artery bypass grafting, ACE-I / ARB = angiotensin-converting enzyme inhibitor / angiotensin receptor blocker, CCB = calcium channel blocker, LVEF = left ventricular ejection fraction, RVSP = right ventricular systolic pressure, eGFR = estimated glomerular filtration rate, LDL = low density lipoprotein, HGB = hemoglobin.

[0089] Table 4A below shows baseline clinical variables and their prognostic associations that differed between those in the training set (N=649) and those in patients with major adverse cardiac events (MACE) within 365 days of blood draw. The figures in this table were calculated using a composite endpoint of 1-year MACE due to CV death, MI, or major stroke, and these proteins produce similar results using a composite endpoint of 1-year MACE due to any cause death, MI, and / or major stroke.

[0090] [Table 4A-1] [Table 4A-2] [Table 4A-3] All continuous variables are expressed as mean ± standard deviation unless otherwise specified. BP = blood pressure, MI = myocardial infarction, COPD = chronic obstructive pulmonary disease, CVA / TIA = cerebrovascular accident / transient ischemic attack, CKD = chronic kidney disease, CABG = coronary artery bypass grafting, ACE-I / ARB = angiotensin-converting enzyme inhibitor / angiotensin receptor blocker, CCB = calcium channel blocker, LVEF = left ventricular ejection fraction, RVSP = right ventricular systolic pressure, eGFR = estimated glomerular filtration rate, LDL = low density lipoprotein, HGB = hemoglobin.

[0091] Table 4B below shows baseline clinical variables and their prognostic associations that differed between those with and those without a major adverse cardiac event (MACE) 3-365 days from blood draw in the training set (N=648). The numbers in this table were calculated using a composite endpoint of 1-year MACE due to CV death, MI, or major stroke, and these proteins produce similar results using a composite endpoint of 1-year MACE due to any cause death, MI, and / or major stroke.

[0092] [Table 4B-1] [Table 4B-2] [Table 4B-3] All continuous variables are expressed as mean ± standard deviation unless otherwise specified. BP = blood pressure, MI = myocardial infarction, COPD = chronic obstructive pulmonary disease, CVA / TIA = cerebrovascular accident / transient ischemic attack, CKD = chronic kidney disease, CABG = coronary artery bypass grafting, ACE-I / ARB = angiotensin-converting enzyme inhibitor / angiotensin receptor blocker, CCB = calcium channel blocker, LVEF = left ventricular ejection fraction, RVSP = right ventricular systolic pressure, eGFR = estimated glomerular filtration rate, LDL = low density lipoprotein, HGB = hemoglobin.

[0093] Diagnostic and Prognostic Methods The methods of the present invention generally relate to providing a diagnosis and / or prognosis of a cardiovascular disease or outcome in a subject, the methods comprising the steps of: (i) determining the level of at least one biomarker in a biological sample obtained from the subject, in particular the biomarker is selected from the group consisting of those listed in Tables 1A, 1B, 2A, and 2B; (ii) optionally determining the status of at least one clinical variable of the subject, the clinical variable being selected from the group consisting of those listed in Tables 3A, 3B, 4A, and 4B; (iii) calculating a diagnostic or prognostic score based on the level of the biomarker determined in step (i) and, optionally, the status of the clinical variable(s) determined in step (ii); (iv) classifying the diagnostic or prognostic score as a positive or negative result; and (v) determining a therapeutic or diagnostic intervention regimen based on the positive or negative result.

[0094] An embodiment of the invention provides a method for assessing a cardiovascular condition in a subject, the method comprising: (i) obtaining a sample from a subject selected for assessment; (ii) performing one or more assays configured to detect a biomarker selected from the group consisting of those set forth in Tables 1A, 1B, 2A, and 2B by (a) contacting the sample with one or more antibodies that specifically bind to detect the biomarker(s) being assayed, and (b) introducing the sample obtained from the subject into an assay device that generates one or more assay results indicative of binding of each biomarker being assayed to a respective antibody to provide one or more assay results; and (iii) optionally determining the status of at least one clinical variable of the subject, wherein the clinical variable is selected from the group consisting of those set forth in Tables 3A, 4A, 5A, 6A, 7A, 8A, 9A, 10A, 11A, 12A, 13A, 14A, 15A, 16A, 17A, 18A, 19A, 20A, 21A, 22A, 23A, 24A, 25A, 26A, 27A, 28A, 29A, 30A, 31A, 32A, 33A, 34A, 35A, 36A, 37A, 38A, 39A, 40A, 41A, 42A, 43A, 44A, 45A, 46A, 47A, 48A, 49A, 50A, 51A, 52A, 53A, 54A, 55A, 56A, 57A, 58A, 59A, 60A, 61A, 62A, 63A, 64A, 65A, 66A, 67A, 68A (iv) correlating the assay result(s) produced by the assay device, and optionally the status of the clinical variables, with a cardiovascular condition of the subject, said correlating step comprising correlating the assay result(s) with one or more of risk stratification, prognosis, diagnosis, classification, and monitoring of the cardiovascular condition of the subject, said correlating step comprising assigning a likelihood of a positive or negative diagnosis, or one or more future changes in the cardiovascular condition to the subject based on the assay result(s); and (v) treating the patient based on the predetermined subpopulation of individuals to which the patient was assigned, wherein the treatment comprises a therapeutic or diagnostic intervention regimen.

[0095] An embodiment of the invention provides a method for diagnosing obstructive coronary artery disease in a subject, comprising: (i) obtaining a sample from a subject selected for assessment; (ii) performing one or more assays configured to detect a biomarker selected from the group consisting of those set forth in Tables 1A and 1B by (a) contacting the sample with one or more antibodies that specifically bind to detect the biomarker(s) being assayed; and (b) introducing the sample obtained from the subject into an assay device that generates one or more assay results indicative of binding of each biomarker being assayed to a respective antibody to provide one or more assay results; and (iii) optionally administering to the subject a sample comprising one or more antibodies that specifically bind to the biomarker(s) being assayed. (iv) correlating the assay result(s) produced by the assay device, and optionally the status of the clinical variable, with obstructive coronary artery disease, said correlating step comprising correlating the assay result(s) and optionally the clinical variable(s) with a diagnostic score, said correlating step comprising assigning a score to a positive or negative result; and (v) treating the patient based on the positive or negative result, wherein the treatment comprises a therapeutic or diagnostic intervention regimen.

[0096] In yet other embodiments, the disclosure provides a method for prognosis of cardiac outcome in a subject within a time endpoint, comprising: (i) obtaining a sample from a subject selected for assessment; (ii) performing one or more assays configured to detect a biomarker selected from the group consisting of those set forth in Tables 2A and 2B by (a) contacting the sample with one or more antibodies that specifically bind to detect the biomarker(s) being assayed; and (b) introducing the sample obtained from the subject into an assay device that generates one or more assay results indicative of binding of each biomarker being assayed to the respective antibodies to provide one or more assay results; and (iii) optionally, measuring the cardiac outcome of the subject. (iv) correlating the assay result(s) produced by the assay device and optionally the status of the clinical variable(s) with a likelihood of a cardiac outcome in the subject, said correlating step comprising correlating the assay result(s) and optionally the clinical variable(s) with a prognostic score, said correlating step comprising assigning a score to a positive or negative result; and (v) treating the patient based on the positive or negative result, wherein the treatment comprises a therapeutic or diagnostic intervention regimen.

[0097] In certain embodiments, the biomarkers, optionally used in conjunction with clinical variables, may be used in the methods of the present invention to diagnose obstructive coronary artery disease. In other embodiments, the biomarkers, optionally used in conjunction with clinical variables, may be used in the prognosis of cardiovascular outcomes, including but not limited to cardiovascular death, myocardial infarction, and stroke. The method may also be used to predict the risk of a composite endpoint, which is a combination of various possible clinical events, such as cardiovascular death, myocardial infarction, or stroke, and any one of these events may be counted as part of the composite endpoint. The composite may include all-cause mortality, including cardiovascular mortality.

[0098] In certain embodiments, the biomarkers and / or clinical variables used in accordance with the methods of the invention include those listed in Tables 1A, 1B, 2A, 2B, 3A, 3B, 4A, and 4B, particularly those associated with a p-value of less than 0.1, less than 0.05, less than 0.01, or less than 0.001.

[0099] In some embodiments, at least one, at least two, at least three, or at least four biomarkers are used in the methods described herein. In other embodiments, the number of biomarkers used may vary and include at least five, six, seven, eight, nine, ten or more. In still other embodiments, the number of biomarkers may include at least 15, 20, 25, or 50 or more.

[0100] In more specific embodiments, the biomarkers used in the diagnostic methods of the invention are selected from adiponectin, apolipoprotein CI, decorin, interleukin-8, kidney injury molecule-1, matrix metalloproteinase 9, midkine, myoglobin, pulmonary surfactant-associated protein D, stem cell factor, and troponin.

[0101] In other specific embodiments, the biomarkers used in the prognostic methods of the invention are selected from apolipoprotein A-II, kidney injury molecule-1, midkine, N-terminal prohormone brain natriuretic protein (NT-proBNP), osteopontin, tissue inhibitor of metalloproteinase-1 (TIMP-1), and vascular cell adhesion molecule.

[0102] In some embodiments, the diagnostic or prognostic model will provide a numerical or categorical score for the patient's level of likelihood of CAD, including, but not limited to, positive predictive value (PPV), negative predictive value (NPV), sensitivity (Sn), or specificity (Sp), or risk of cardiovascular events occurring within a certain time period. The number of levels used by the diagnostic model can be as few as two ("positive" vs. "negative") or as many as are considered clinically relevant, for example, a diagnostic model for CAD may provide a score of five levels, with higher scores indicating a higher likelihood of disease. Specifically, a score of 1 indicates strong confidence in a low likelihood of CAD (as determined by the NPV of the test), a score of 5 indicates strong confidence in a high likelihood of CAD (as determined by the PPV of the test), and a score of 3 indicates a moderate likelihood of CAD.

[0103] Furthermore, in certain embodiments, upon making a positive diagnosis for obstructive coronary artery disease in a subject according to the methods disclosed herein, a physician may advantageously use the diagnosis to identify a need for therapeutic, diagnostic, or other intervention in the subject, particularly an intervention selected from one or more of diagnostic cardiac catheterization, percutaneous coronary intervention (balloon angioplasty with or without stenting), coronary artery bypass grafting (CABG), and administration of a pharmacological agent selected from nitrates, beta blockers, ACE inhibitors, and lipid-lowering agents.

[0104] Furthermore, in certain related embodiments, upon making a negative diagnosis for obstructive coronary artery disease in a subject according to the methods disclosed herein, a physician may advantageously use the information obtained thereby to identify a need for intervention in the subject, e.g., an intervention selected from one or more of the following: ongoing monitoring and management of coronary risk factors including hypertension, diabetes, and smoking, as well as lifestyle modifications selected from dietary modification, exercise, and smoking cessation.

[0105] In still other embodiments, the prognostic information obtained thereby may be advantageously used by a physician when making a positive prognosis of cardiac outcome (e.g., cardiovascular death, myocardial infarction (MI), stroke, all-cause death, or a composite thereof) according to the methods disclosed herein. The information obtained thereby may be advantageously used to identify the need for an intervention in a subject, for example, one or more of the following: stress test or myocardial perfusion imaging with ECG response, coronary computed tomography angiography, diagnostic cardiac catheterization, percutaneous coronary (e.g., balloon angioplasty with or without stenting), coronary artery bypass graft (CABG), clinical trial enrollment, and administration or monitoring of the effect of a drug selected from, but not limited to, a drug selected from nitrates, beta blockers, ACE inhibitors, antiplatelet agents, and lipid-lowering agents.

[0106] In further related embodiments, in making a negative prognosis of cardiac outcome in accordance with the methods described herein, a physician may advantageously use the information obtained thereby to identify the need for intervention, in particular an intervention selected from one or more of the following: ongoing monitoring and management of coronary risk factors including hypertension, diabetes, dyslipidemia, and smoking, as well as lifestyle modifications selected from dietary modification, exercise, and smoking cessation.

[0107] As used herein, a "biological sample" encompasses essentially any sample type obtained from a subject that can be used in the diagnostic or prognostic methods described herein. A biological sample can be any bodily fluid, tissue, or any other sample in which a clinically relevant biomarker level can be determined. This definition encompasses blood and other liquid samples of biological origin, solid tissue samples, such as biopsy specimens or tissue cultures or cells derived therefrom, as well as their progeny. This definition also includes samples that have been manipulated in any way after their acquisition, such as by treatment with reagents, solubilization, or enrichment for certain components, such as polynucleotides. The term "biological sample" encompasses clinical samples, but also includes cells in culture, cell supernatants, cell lysates, blood, serum, plasma, urine, cerebrospinal fluid, bodily fluids, and tissue samples, in some cases. The sample can be pretreated by dilution in an appropriate buffer solution, or concentrated as needed. Any of several standard aqueous buffer solutions can be used, preferably using one of a variety of buffers, such as phosphate, Tris, etc., at physiological pH. Biological samples may be obtained from a patient using well known techniques, such as venipuncture, lumbar puncture, a fluid sample such as saliva or urine, or a tissue biopsy.

[0108] In certain embodiments, the biological sample used in the method of the present disclosure includes, but is not limited to, whole blood, plasma, serum, or urine. In some embodiments, the sample is whole blood. In some embodiments, the sample is plasma. In other embodiments, the sample is serum or urine.

[0109] Determining the level of a biomarker in a sample obtained from a subject can be accomplished according to standard techniques available and known to those of skill in the art. In many cases, this will involve performing a protein detection method that provides a quantitative measure of the protein biomarker present in the biological sample.

[0110] Many embodiments of the present disclosure are based, in part, on the use of binding agents that specifically bind to the biomarkers described herein, thereby allowing for the determination of the level of the biomarker in a biological sample. Any of a variety of binding agents can be used, including, for example, antibodies, polypeptides, sugars, and nucleic acids.

[0111] In one particular embodiment of the disclosure, the binding agent is an antibody or fragment thereof that specifically binds to a biomarker of the disclosure and is effective for determining the level of the biomarker to which it is bound in a biological sample.

[0112] In the context of binding interactions between two molecules, the term "specifically binds" or "binds specifically" refers to high avidity and / or high affinity binding of an antibody (or other binding agent) to a particular polypeptide subsequence or epitope of a biomarker. Antibody binding to an epitope in a particular biomarker sequence (also referred to herein as an "epitope") is preferably stronger than the binding of the same antibody to any other epitope, particularly one that may be present on a molecule associated with or in the same sample as the particular biomarker of interest. An antibody that specifically binds to a biomarker of interest may be capable of binding other polypeptides at weak, even detectable levels (e.g., 10% or less, 5% or less, 1% or less of the binding exhibited by the polypeptide of interest). Such weak or background binding is easily discernible from specific antibody binding to a compound or polypeptide of interest, for example, by the use of appropriate controls. Generally, the binding is greater than 10%. 7 mol / L or more, preferably 10 8 Antibodies used in the compositions and methods of the invention that bind to a specific biomarker protein with a binding affinity of mol / L or better are said to specifically bind to the specific biomarker protein.

[0113] In one embodiment, the specific binding affinity of an antibody or other binding agent for a biomarker is greater than about 2-fold above background binding, greater than about 5-fold above background binding, greater than about 10-fold above background binding, greater than about 20-fold above background binding, greater than about 50-fold above background binding, greater than about 100-fold above background binding, or greater than about 1000-fold above background binding, or more.

[0114] In another embodiment, the affinity of specific binding of an antibody or other binding agent to a biomarker is from about 2-fold to about 1000-fold greater than background binding, from about 2-fold to about 500-fold greater than background binding, from about 2-fold to about 100-fold greater than background binding, from about 2-fold to about 50-fold greater than background binding, from about 2-fold to about 50-fold greater than background binding, from about 2-fold to about 20-fold greater than background binding, or from about 2-fold to about 10-fold greater than background binding, or any intervening range of affinities.

[0115] The term "antibody" is used herein in the broadest sense and specifically includes, but is not limited to, monoclonal antibodies, polyclonal antibodies, multispecific antibodies (e.g., bispecific antibodies) formed from at least two intact antibodies, single-chain antibodies (e.g., scFv), and antibody fragments so long as they exhibit the desired biological specificity.

[0116] The term "monoclonal antibody" as used herein refers to an antibody obtained from a population of substantially homogeneous antibodies, i.e., the individual antibodies comprising the population are identical except for the possibility of naturally occurring variants that may be present in minor amounts. In certain embodiments, the monoclonal antibody is an antibody specific for a biomarker described herein.

[0117] Monoclonal antibodies are highly specific, being directed against a single antigenic site. Moreover, in contrast to conventional (polyclonal) antibody preparations which typically contain different antibodies directed against different determinants (epitopes), each monoclonal antibody is directed against a single determinant on an antigen. In addition to their specificity, monoclonal antibodies are advantageous in that they are synthesized by the hybridoma culture and are uncontaminated by other immunoglobulins. The modifier "monoclonal" indicates the character of the antibody as being obtained from a substantially homogeneous population of antibodies, and is not to be construed as requiring production of the antibody by any particular method. For example, the monoclonal antibodies used in accordance with the present invention may be made by the hybridoma method first described in Kohler et al., Nature, 256:495 (1975), or may be made by recombinant DNA methods (see, e.g., U.S. Pat. No. 4,816,567), or any other suitable methods available known in the art. "Monoclonal antibody" also refers to the hybridoma method described, for example, in Clackson et al. Antibody fragments can be isolated from phage antibody libraries using the techniques described in Marks et al., Nature, 352:624-628 (1991) and Marks et al., J. Mol. Biol., 222:581-597 (1991).

[0118] As used herein, monoclonal antibodies specifically include "chimeric" antibodies in which a portion of the heavy and / or light chain is identical to or homologous to corresponding sequences of antibodies derived from a particular species or belonging to a particular antibody class or subclass, while the remainder of the chain(s) is identical to or homologous to corresponding sequences of antibodies derived from another species or belonging to another antibody class or subclass, as well as fragments of those antibodies, so long as they exhibit the desired biological activity and / or specificity (e.g., U.S. Pat. No. 4,816,567; Morrison et al., Proc. Natl. Acad. Sci. USA, 81:6851-6855 (1984)). Methods for producing chimeric antibodies are known in the art.

[0119] A "functional fragment" of an antibody can also be used and include those fragments that retain sufficient binding affinity and specificity for a biomarker to permit determination of the level of the biomarker in a biological sample. In some cases, functional fragments will bind to a biomarker with substantially the same affinity and / or specificity as the intact, full chain molecule from which they are derived.

[0120] An "isolated" antibody is one that has been identified, separated and / or recovered from components of its natural environment. Contaminant components of its natural environment are materials that would interfere with diagnostic or prognostic uses for the antibody, and may include enzymes, hormones, and other proteinaceous or nonproteinaceous solutes. In certain embodiments, the antibodies will be greater than 95% purified by weight, and most preferably greater than 99% purified by weight, as determined, for example, by the Lowry method.

[0121] The term "detectably labeled antibody" refers to an antibody (or antibody fragment) that retains binding specificity for a biomarker described herein and has a detectable label attached thereto. The detectable label can be attached by any suitable means, such as chemical conjugation or genetic engineering techniques. Methods for producing detectably labeled proteins are known in the art. The detectable label can be selected from a variety of such labels known in the art, including, but not limited to, haptens, radioisotopes, fluorophores, paramagnetic labels, enzymes (e.g., horseradish peroxidase), or other moieties or compounds that emit a detectable signal (e.g., radioactivity, fluorescence, color) or emit a detectable signal after exposure of the label to its substrate. A variety of detectable label / substrate pairs (e.g., horseradish peroxidase / diaminobenzidine, avidin / streptavidin, luciferase / luciferin), methods for labeling antibodies, and methods for using labeled antibodies are known in the art (see, e.g., Harlow and Lane, eds. (Antibodies: A Laboratory Manual (1988) Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY)).

[0122] In certain embodiments, the levels of protein biomarkers of the present disclosure are determined using an assay or format, including, but not limited to, for example, immunoassays, ELISA sandwich assays, lateral flow assays, flow cytometry, mass spectroscopic detection, calorimetric assays, binding to protein arrays (e.g., antibody arrays), single molecule detection methods, nanotechnology-based detection methods, or fluorescence activated cell sorting (FACS). In some embodiments, the approach involves the use of labeled affinity reagents (e.g., antibodies, small molecules, etc.) that recognize epitopes of one or more biomarker proteins in ELISA, antibody-labeled fluorescent bead arrays, antibody arrays, or FACS screens. As mentioned above, any of the many exemplary methods for producing, evaluating, and / or using antibodies to detect and quantify the biomarkers herein are known and available in the art. It will also be understood that detection and quantification of proteins according to the methods described herein may be performed in single assay formats, multiplex formats, or other known formats.

[0123] Many suitable high-throughput multiplex formats exist for evaluating the disclosed biomarkers.Typically, the term "high-throughput" refers to a format that performs a large number of assays per day, for example, at least 100 assays, 1000 assays, at most 10,000 assays or more per day.When listing assays, the number of samples or the number of markers that are assayed can be seen.

[0124] In some embodiments of the invention, the samples are analyzed on an assay device. For example, the assay device can be a multiplex analyzer that simultaneously measures multiple analytes, e.g., proteins, in a single microplate well. The assay formats can be receptor-ligand assays, immunoassays, and enzyme assays. One example of such an analyzer is the Luminex® 100 / 200 system, which is a combination of three xMAP® technologies. The first is the xMAP Microspheres, a series of fluorescently dyed, micron-sized polystyrene microspheres that act as both identifiers and solid surfaces to build the assay. The second is the Luminex® 100 / 200 Analyzer, a flow cytometry-based device that integrates the key xMAP® detection elements, such as lasers, vision, fluidics, and high-speed digital signal processors. The third element is the xPONENT® software, designed for protocol-based data acquisition with robust data regression analysis.

[0125] By determining the levels of biomarkers and optionally the status of clinical variables for a subject, a dataset can be generated and used to classify the biological sample into one or more of risk stratification, prognosis, diagnosis, and monitoring of the subject's cardiovascular condition (as further described herein), and further assign a positive or negative diagnostic probability, outcome, or one or more future changes in cardiovascular condition to the subject, thereby constructing a diagnosis and / or prognosis of cardiovascular disease and / or outcome, as described herein. Of course, the dataset can be obtained via automated or manual methods.

[0126] statistical analysis By analyzing a combination of biomarkers and optionally clinical variables as described herein, the methods of the present invention can distinguish between different endpoints. Endpoints can include, for example, obstructive coronary artery disease (CAD), cardiovascular mortality (CVD), myocardial infarction (MI), stroke, composites thereof, and further composites of all-cause mortality. The identity of the markers and their corresponding characteristics (e.g., concentration, quantitative level) is used in developing and implementing an analytical process or processes that distinguish between clinically relevant classes of patients.

[0127] The datasets of biomarkers and clinical variables may be used in an analytical process to correlate the assay result(s) generated by the assay device, and optionally the status of the clinical variables, with a cardiovascular condition of the subject, said correlating step comprising correlating the assay result(s) with one or more of risk stratification, prognosis, diagnosis, classification, and monitoring of the cardiovascular condition of the subject, said correlating step comprising assigning a likelihood of a positive or negative diagnosis, or one or more future changes in the cardiovascular condition for the subject, based on the assay result(s).

[0128] The data set of biomarkers and clinical variables can be used in an analytical process to generate a diagnostic and / or prognostic result or score. For example, an exemplary analytical process can include a linear model with one term for each component (protein level or clinical factor). The result of the model is a number that generates a diagnosis and / or prognosis. The result can also provide a multi-level or continuous score, with a higher number indicating a higher likelihood of disease or risk of an event.

[0129] The following examples show that data analysis algorithms can be used to construct many such analytical processes. Each of the data analysis algorithms described in the examples uses characteristics (e.g., quantitative protein levels and / or clinical factors) of a subset of markers identified herein across a training population. Specific data analysis algorithms for constructing an analytical process or multiple analytical processes that distinguish differences between subjects disclosed herein will be described in the following subsections. Once an analytical process is constructed using these exemplary data analysis algorithms or other techniques known in the art, the analytical process can be used to classify a test subject into one or more of two or more phenotypic classes and / or predict survival / mortality or severe medical events within a certain period of time after a blood test is obtained. This is accomplished by applying one or more analytical processes to one or more marker profile(s) obtained from a test subject. Thus, such analytical processes have a great deal of value as diagnostic or prognostic indicators.

[0130] Thus, the present invention further provides algorithms that can be used to convert the levels of a panel of biomarkers, as described above, into a score that can be used to determine whether a patient will be diagnosed with obstructive coronary artery disease or have a prognosis of risk for developing an adverse cardiovascular event.

[0131] This data is processed before the analytical process. The data in each data set is collected, usually in duplicate or triplicate or multiple replicates, by measuring the value for each marker. The data may be manipulated, for example, raw data may be transformed using a standard curve and the average of repeated measurements used to calculate the mean and standard deviation for each patient. These values ​​may be transformed, for example log transformed, normalized to a standard scale, such as Winsorized, before being used in the model. The data is transformed via computer software. This data may then be input into the analytical process using defined parameters.

[0132] Direct protein levels (after log transformation and normalization), clinical factors expressed in bivariate form (e.g., gender) and / or the presence / absence of clinical factors in quantitative form (e.g., BMI, age) provide values ​​that are input into the diagnostic model provided by the software, and the results are evaluated against one or more cutoffs to determine a diagnosis or prognosis.

[0133] Below are examples of the types of statistical analysis methods available to those skilled in the art to aid in the implementation of the disclosed methods, panels, assays, and kits. Statistical analysis can be applied to one or both of two tasks. First, these and other statistical methods can be used to identify preferred subsets of markers and other indicators that will form a preferred data set. Furthermore, these and other statistical methods can be used to generate an analytical process that will be used with the data set to generate a result. Some statistical methods represented herein or otherwise available in the art will perform both of these tasks and result in a model that is suitable for use as an analytical process for the practice of the methods disclosed herein.

[0134] Before analysis, the data is divided into a training set and a validation set. The training set is used to train, evaluate, and build the final diagnostic or prognostic model. The validation set is not used at all during the training process, but is used only to validate the final diagnostic or prognostic model. Unless explicitly stated, all the following processes involve the use of only the training set.

[0135] The creation of training and validation sets is done through random sampling. Once these sets are determined, a balance of different outcomes (e.g., presence of occlusions ≥ 70%, occurrence of MI within 1 year, etc.) is believed to ensure that outcomes of interest are adequately represented in each data set.

[0136] Diagnostic and / or prognostic model features (e.g., proteins and / or clinical factors) are selected for each result using a combination of analytical processes, including least angle regression (LARS, a procedure based on stepwise forward selection), shrinkage in statistical learning methods such as least absolute shrinkage and selection operator (LASSO), significance testing, and expert opinion.

[0137] The statistical learning method used to generate the result (classification, survival / mortality within a particular time period, etc.) can be any type of process that can provide a useful result for classifying the sample (e.g., a linear model, a probabilistic model, a decision tree algorithm, or a comparison of the obtained dataset with a reference dataset).

[0138] The diagnostic or prognostic signal in the signature is evaluated using these statistical learning methods using a cross-validation procedure. For each cross-validation fold, the training set is further divided into a training set and a validation set (referred to herein as the CV-training data set and the CV-validation data set).

[0139] For each fold of the cross-validation, a diagnostic or prognostic model is constructed using the CV-training data and evaluated on the CV-validation data.

[0140] Models during the cross-validation process are evaluated using standard metrics of classification accuracy, such as area under the ROC curve (AUC), sensitivity (Sn), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV).

[0141] Once a set of features (eg, quantitative protein levels and optionally clinical factors) have been selected to comprise the final diagnostic or prognostic panel, a final predictive model is constructed using all of the training data.

[0142] Application of patient data (e.g., quantitative protein levels and / or clinical factors) to the final predictive model yields classification results, which can be compared against thresholds for classifying samples within a particular class (e.g., positive or negative diagnosis and / or prognosis, or severity / likelihood scores).

[0143] The final model is evaluated on a validation dataset. In order to respect the authority of the validation dataset, feeding information back into the training process is not used in an iterative manner. It is used only as a stop in the analysis pipeline.

[0144] Models are evaluated on the validation dataset using metrics of accuracy, including AUC, sensitivity, specificity, positive predictive value, and / or negative predictive value. Other metrics of accuracy, such as hazard ratio, relative risk, and net reclassification index, are considered separately for the models of interest.

[0145] This final model, or a model optimized for a particular biomarker platform, when used in a clinical context, may be implemented as a software system running either directly on the assay hardware platform or on a proprietary system. The model may accept protein level or concentration data directly from the assay platform or other means of data transfer, and patient clinical data may be accepted via electronic, manual, or other query of the patient's medical record or by interactive input by an operator. This patient data may be processed to summarize the final model, which will provide results to clinicians and medical staff for decision support purposes.

[0146] Panels, Assays, and Kits The present invention further provides panels, assays, and kits comprising at least one, at least two, at least three, at least four, or more than four biomarkers and / or clinical variable(s) to aid or facilitate diagnostic or prognostic discovery in accordance with the present disclosure. For example, in some embodiments, a diagnostic or prognostic panel or kit comprises one or more biomarkers set forth in Tables 1A, 1B, 2A, and 2B, and one or more suitable clinical variables set forth in Tables 3A, 3B, 4A, and 4B.

[0147] It will be understood that in many embodiments, the panels, assays, and kits described herein include antibodies, binding fragments thereof, and / or other types of binding agents that are specific for the biomarkers of Tables 1A, 1B, 2A, and 2B and are useful for determining the levels of the corresponding biomarkers in a biological sample according to the methods described herein. Thus, in each of the panels, assays, or kits described herein that include one or more biomarkers, it will be understood that the very same panel, assay, or kit may advantageously include, in addition to, or instead of, one or more antibodies, binding fragments thereof, and / or other types of binding agents that are specific for the biomarkers of Tables 1A, 1B, 2A, and 2B. Of course, the panels, assays, and kits may further include, include, or recommend the determination of one or more appropriate clinical variables, such as those described in Tables 3A, 3B, 4A, and 4B.

[0148] In certain embodiments, the biomarkers and / or clinical variables used in conjunction with the panels, assays, or kits include those listed in Tables 1A, 1B, 2A, 2B, Tables 3A, 3B, 4A, and 4B, respectively, particularly those associated with a p-value of less than 0.1, less than 0.05, less than 0.01, or less than 0.001.

[0149] In some embodiments, the panels, assays, and kits may include at least one, at least two, at least three, or at least four biomarkers described herein. In other embodiments, the number of biomarkers used may include at least five, six, seven, eight, nine, or ten or more. In still other embodiments, the number of biomarkers used may include at least 15, 20, 25, or 50 or more.

[0150] In addition to the biomarkers disclosed in Tables 1A, 1B, 2A, and 2B, the panels, assays, or kits may include additional biomarkers (or binding agents in addition thereto) not specifically described herein, in particular the biomarkers in the panel themselves, that provide statistically significant information regarding a diagnosis or prognosis of interest, e.g., whether a patient has coronary artery disease or may be at increased risk of cardiovascular disease and / or adverse cardiovascular events within one year, at one year, or more than one year.

[0151] The disclosed panels, assays, and kits described herein may be used to identify the presence of cardiovascular disease, particularly obstructive coronary artery disease, and / or predict cardiac events in a subject. In some embodiments, the diagnostic panel, assay, or kit identifies the presence of 70% or greater obstruction in any major epicardial vessel in a subject.

[0152] In other embodiments, the prognostic panel, assay, or kit is used to predict risk of cardiovascular disease or event within 1 year, about 1 year, about 2 years, about 3 years, about 4 years, about 5 years or more from the date the sample is taken. Time endpoint is defined as from sample collection, including less than 1 year, 1 year, and more than 1 year. Less than 1 year or within 1 year can be any time from the time of sample collection up to 365 days (including the same day). For example, the panel results can predict risk of cardiovascular disease or event up to 30 days, 60 days, 90 days, 120 days, 150 days, 180 days, 210 days, 240 days, 270 days, 300 days, 330 days, 360 days, 365 days from the time of sample collection. In yet other embodiments, the time endpoint is defined as from 3 days to 30, 60, 90, 120, 150, 180, 210, 240, 270, 300, 330, 360, or 365 days after sample collection.

[0153] In certain embodiments, the panels, assays, and kits for the diagnosis of obstructive CAD include at least one, at least two, at least three, at least four, or more than four biomarkers, or antibodies, binding fragments thereof, or other types of binding agents specific for the biomarkers, selected from the group consisting of adiponectin, apolipoprotein CI, decorin, interleukin-8, kidney injury molecule-1, matrix metalloproteinase 9, midkine, myoglobin, pulmonary surfactant-associated protein D, stem cell factor, and troponin. In some embodiments, at least one clinical variable described herein is used with the determined biomarker levels. In other embodiments, the clinical variable is selected from the group consisting of age, history of coronary artery bypass graft (CABG), history of type 2 diabetes, history of hemodialysis, history of myocardial infarction (MI), history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stent placement), and gender.

[0154] In certain embodiments, a panel, assay, or kit for diagnosis of 70% or greater occlusion in any major epicardial vessel includes biomarkers for adiponectin, apolipoprotein C-1, kidney injury molecule-1, and midkine, and the clinical variables of history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stenting) and gender. This combination of biomarkers and clinical variables is represented by panel FM139 / 685 in Table 25, Example 1, and Figures 1-4.

[0155] In certain embodiments, a panel, assay, or kit for diagnosis of 70% or greater occlusion in any major epicardial vessel includes biomarkers for adiponectin, apolipoprotein C-1, kidney injury molecule-1, and midkine, and the clinical variables of history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stenting), sex, and age. This combination of biomarkers and clinical variables is represented by panel FM144 / 696 in Table 25 and Figure 5.

[0156] In another embodiment, a panel, assay, or kit for diagnosis of 70% or greater occlusion in any major epicardial vessel includes biomarkers for adiponectin, apolipoprotein CI, kidney injury molecule-1, and midkine, and the clinical variables of sex and age. This combination of biomarkers and clinical variables is represented by panel FM145 / 701 in Table 25 and Figure 6.

[0157] In another embodiment, a panel, assay, or kit for diagnosis of 70% or greater occlusion in any major epicardial vessel comprises biomarkers for adiponectin, apolipoprotein CI, kidney injury molecule-1, and midkine. This combination of biomarkers is represented by panel FM146 / 690 in Table 25 and FIG. 7.

[0158] In another embodiment, a panel, assay, or kit for diagnosis of 70% or greater occlusion in any major epicardial vessel includes biomarkers for adiponectin, kidney injury molecule-1, and midkine, and the clinical variables of type 2 diabetes mellitus history, sex, and age. This combination of biomarkers and clinical variables is represented by panel FM152 / 757 in Table 25 and Figure 8.

[0159] Embodiments of the present invention include panels, assays, and kits for the diagnosis of 70% or greater occlusion in any major epicardial vessel comprising at least one biomarker and one or more clinical variables.

[0160] In one particular embodiment, a panel, assay, or kit for diagnosis of 70% or greater occlusion in any major epicardial vessel includes a biomarker for midkine and the clinical variables of history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stenting) and gender. This combination of biomarkers and clinical variables is represented by panel FM117a / 657 in Table 25 and Figure 9.

[0161] In one particular embodiment, a panel, assay, or kit for the diagnosis of 70% or greater occlusion in any major epicardial vessel includes a biomarker for adiponectin and the clinical variables of history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stenting) and gender. This combination of biomarkers and clinical variables is represented by panel FM139CLa / 658 in Table 25 and Figure 10.

[0162] In one particular embodiment, a panel, assay, or kit for the diagnosis of 70% or greater occlusion in any major epicardial vessel includes a biomarker for apolipoprotein C-1 and the clinical variables of history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stenting) and gender. This combination of biomarkers and clinical variables is represented by panel FM139CLb / 750 in Table 25 and FIG. 11.

[0163] In one particular embodiment, a panel, assay, or kit for diagnosis of 70% or greater occlusion in any major epicardial vessel includes a biomarker for kidney injury molecule-1 and the clinical variables of history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stenting) and gender. This combination of biomarkers and clinical variables is represented by panel FM139CLc / 751 in Table 25 and FIG. 12.

[0164] In yet another embodiment, a panel, assay, or kit for diagnosis of 70% or greater occlusion in any major epicardial vessel includes biomarkers for adiponectin and midkine, and the clinical variables of history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stenting) and gender. This combination of biomarkers and clinical variables is represented by panel FM117b / 663 in Table 25 and Figure 13.

[0165] In another embodiment, a panel, assay, or kit for diagnosis of 70% or greater occlusion in any major epicardial vessel includes biomarkers for apolipoprotein CI and midkine, and the clinical variables of history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stenting) and gender. This combination of biomarkers and clinical variables is represented by panel FM139CLd / 752 in Table 25 and FIG. 14.

[0166] In another embodiment, a panel, assay, or kit for diagnosis of 70% or greater occlusion in any major epicardial vessel includes biomarkers for kidney injury molecule-1 and midkine, and the clinical variables of history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stenting) and gender. This combination of biomarkers and clinical variables is represented by panel FM139Cle / 753 in Table 25 and FIG. 15.

[0167] In a further embodiment, a panel, assay, or kit for diagnosis of 70% or greater occlusion in any major epicardial vessel includes biomarkers for adiponectin, apolipoprotein CI, and midkine, and the clinical variables of history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stenting) and gender. This combination of biomarkers and clinical variables is represented by panel FM139CLf / 754 in Table 25 and Figure 16.

[0168] In yet another embodiment, a panel, assay, or kit for diagnosis of 70% or greater occlusion in any major epicardial vessel includes biomarkers for adiponectin, kidney injury molecule-1, and midkine, and the clinical variables of history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stenting) and gender. This combination of biomarkers and clinical variables is represented by panel FM139CLg / 755 in Table 25 and FIG. 17.

[0169] In yet another embodiment, a panel, assay, or kit for diagnosis of 70% or greater occlusion in any major epicardial vessel includes biomarkers for adiponectin, decorin, and midkine, and the clinical variables of history of myocardial infarction (MI), history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stenting), and gender. This combination of biomarkers and clinical variables is represented by panel FM46 / 572 in Table 25, Example 2, and Figures 18-20.

[0170] In yet another embodiment, a panel, assay, or kit for diagnosis of 70% or greater occlusion in any major epicardial vessel includes biomarkers for adiponectin and midkine, and the clinical variables of history of myocardial infarction (MI), history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stenting), and gender. This combination of biomarkers and clinical variables is represented by panel FM46Fd / 586 in Table 25 and Figure 21.

[0171] In yet another embodiment, a panel, assay, or kit for diagnosis of 70% or greater occlusion in any major epicardial vessel includes biomarkers for decorin and midkine, and the clinical variables of history of myocardial infarction (MI), history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stenting), and gender. This combination of biomarkers and clinical variables is represented by panel FM46Fe / 587 in Table 25 and Figure 22.

[0172] In yet another embodiment, a panel, assay, or kit for diagnosis of 70% or greater occlusion in any major epicardial vessel includes biomarkers for adiponectin and decorin, and the clinical variables of history of myocardial infarction (MI), history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stenting), and gender. This combination of biomarkers and clinical variables is represented by panel FM46Ff / 588 in Table 25 and Figure 23.

[0173] In yet another embodiment, a panel, assay, or kit for diagnosis of 70% or greater occlusion in any major epicardial vessel includes biomarkers for adiponectin, interleukin-8, kidney injury molecule-1, and stem cell factor, and the clinical variables of history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stenting), sex, and age. This combination of biomarkers and clinical variables is represented by panel FM186 / 796 in Table 25 and Figure 24.

[0174] In yet another embodiment, a panel, assay, or kit for diagnosis of 70% or greater occlusion in any major epicardial vessel includes biomarkers for adiponectin, interleukin-8, kidney injury molecule-1, and stem cell factor, and the clinical variables of history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stenting) and gender. This combination of biomarkers and clinical variables is represented by panel FM189 / 798 in Table 25 and Figure 25.

[0175] In yet another embodiment, a panel, assay, or kit for diagnosis of 70% or greater occlusion in any major epicardial vessel includes biomarkers for adiponectin, apolipoprotein C-1, interleukin-8, kidney injury molecule-1, and stem cell factor, and the clinical variables of history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stenting), sex, and age. This combination of biomarkers and clinical variables is represented by panel FM187 / 792 in Table 25 and Figure 26.

[0176] In yet another embodiment, a panel, assay, or kit for diagnosis of 70% or greater occlusion in any major epicardial vessel includes biomarkers for adiponectin, apolipoprotein C-1, interleukin-8, kidney injury molecule-1, and stem cell factor, and the clinical variables of history of percutaneous coronary intervention (e.g., balloon angioplasty with or without stenting) and gender. This combination of biomarkers and clinical variables is represented by panel FM188 / 794 in Table 25 and Figure 27.

[0177] In yet another embodiment, a panel, assay, or kit for diagnosis of 70% or greater occlusion in any major epicardial vessel includes biomarkers for adiponectin, apolipoprotein C-1, matrix metalloproteinase 9, midkine, myoglobin, and pulmonary surfactant-associated protein D, and the clinical variables of coronary artery bypass graft (CABG) history, percutaneous coronary intervention (e.g., balloon angioplasty with or without stenting), and gender. This combination of biomarkers and clinical variables is represented by panel FM02 / 410 in Table 25, Example 3, and FIG. 28.

[0178] In yet another embodiment, a panel, assay, or kit for the diagnosis of 70% or greater occlusion in any major epicardial vessel includes biomarkers for adiponectin, midkine, pulmonary surfactant-associated protein D, and troponin, and the clinical variables of prior coronary artery bypass graft (CABG), prior hemodialysis, prior myocardial infarction, and gender. This combination of biomarkers and clinical variables is represented by panel FM01 / 390 in Table 25 and Figure 29.

[0179] The present invention also provides panels, assays, and kits for the prognosis of a composite of cardiovascular death, myocardial infarction, or stroke, where the panel comprises one or more biomarkers or antibodies, binding fragments thereof, or other types of binding agents specific to the biomarkers disclosed herein. Such panels, assays, and kits can be used to determine the prognosis of a composite of, for example, cardiovascular death, myocardial infarction, or stroke risk within a certain time period, for example, within one year, or within three years, in a subject. In some embodiments, the time endpoint is defined as starting from sample collection. In other embodiments, the time endpoint is defined as starting from three days after sample collection.

[0180] In certain embodiments, a panel, assay, or kit for the composite prognosis of cardiovascular mortality, myocardial infarction, or stroke includes the biomarkers kidney injury molecule-1, N-terminal prohormone brain natriuretic protein (NT-proBNP), osteopontin, and tissue inhibitor of metalloproteinase-1 (TIMP-1). In some embodiments, the time endpoint is defined as beginning 3 days after sample collection. This combination of biomarkers is represented by panel FM160 / 02 in Table 25, Example 4, and Figure 30.

[0181] In certain embodiments, a panel, assay, or kit for the composite prognosis of cardiovascular mortality, myocardial infarction, or stroke includes the biomarkers N-terminal prohormone brain natriuretic protein (NT-proBNP), osteopontin, and tissue inhibitor of metalloproteinase-1 (TIMP-1). In some embodiments, the time endpoint is defined as beginning from sample collection (as described in Table 25, Example 5, and panel FM96 / 04 in FIG. 31). In other embodiments, the time endpoint is defined as beginning 3 days after sample collection. This combination of biomarkers is represented by panel FM190 / 33 in Table 25 and FIG. 32.

[0182] In another specific embodiment, the panel, assay, or kit for the composite prognosis of cardiovascular mortality, myocardial infarction, or stroke comprises at least NT-proBNP and osteopontin. In some embodiments, the time endpoint is defined as beginning from the time of sample collection. This combination of biomarkers is represented by panel FM98 / 03 in Table 25 and Figure 33.

[0183] The present invention also provides panels, assays, and kits for the prognosis of a composite endpoint of all-cause death, myocardial infarction, or stroke, which panels include one or more biomarkers or antibodies, binding fragments thereof, or other types of binding agents specific to the biomarkers disclosed herein. Such panels, assays, and kits can be used, for example, to determine the prognosis of a composite endpoint of all-cause death, myocardial infarction, or stroke risk within a certain time period, for example, within one year, or within three years, in a subject. In some embodiments, the time endpoint is defined as starting from sample collection. In other embodiments, the time endpoint is defined as starting from three days after sample collection.

[0184] In certain embodiments, a panel, assay, or kit for prognosis of risk of a composite endpoint of all-cause mortality, myocardial infarction, or stroke includes biomarkers for kidney injury molecule-1, N-terminal prohormone brain natriuretic protein (NT-proBNP), osteopontin, and tissue inhibitor of metalloproteinase-1 (TIMP-1). In some embodiments, the time endpoint is defined as beginning 3 days after sample collection. This combination of biomarkers is represented by panel FM209 / 02 in Table 25 and Figure 34.

[0185] In certain embodiments, a panel, assay, or kit for prognosis of risk of a composite endpoint of all-cause mortality, myocardial infarction, or stroke includes N-terminal prohormone brain natriuretic protein (NT-proBNP), osteopontin, and tissue inhibitor of metalloproteinase-1 (TIMP-1). In some embodiments, the time endpoint is defined as beginning from sample collection. (As described in panel FM111 / 05 in Table 25 and Figure 35). In other embodiments, the time endpoint is defined as beginning 3 days after sample collection. This combination of biomarkers is represented by panel FM210 / 03 in Table 25 and Figure 36.

[0186] In another specific embodiment, a panel, assay, or kit for the prognosis of a composite endpoint of all-cause mortality, myocardial infarction, or stroke comprises NT-proBNP and osteopontin. In some embodiments, the time endpoint is defined as beginning from the time of sample collection. This combination of biomarkers and clinical variables is represented by panel FM110 / 04 in Table 25 and Figure 37.

[0187] The present invention also provides a panel, assay, or kit for the prognosis of a composite endpoint of cardiovascular death or myocardial infarction, which comprises one or more biomarkers or antibodies, binding fragments thereof, or other types of binding agents specific for the biomarkers disclosed herein. Such panels, assays, and kits can be used, for example, to determine the prognosis of the risk of, for example, cardiovascular death or myocardial infarction within a certain time period, for example, within one year, or within three years, in a subject. In some embodiments, the time endpoint is defined as starting from sample collection. In other embodiments, the time endpoint is defined as starting from three days after sample collection.

[0188] In certain embodiments, a panel, assay, or kit for prognosis of a composite endpoint of cardiovascular death or myocardial infarction includes biomarkers for apolipoprotein A-II, N-terminal prohormone brain natriuretic protein (NT-proBNP), and osteopontin. In some embodiments, the time endpoint is defined as beginning 3 days after sample collection. This combination of biomarkers is represented by panel FM211 / 03 in Table 25 and Figure 38.

[0189] In other specific embodiments, a panel, assay, or kit for prognosis of a composite endpoint of cardiovascular death or myocardial infarction includes biomarkers for apolipoprotein A-II, midkine, N-terminal prohormone brain natriuretic protein (NT-proBNP), and osteopontin. In some embodiments, the time endpoint is defined as beginning from sample collection (as described in panel FM77 / 26 in Table 25 and Figure 39). In other embodiments, the time endpoint is defined as beginning 3 days after sample collection. This combination of biomarkers is represented by panel FM212 / 02 in Table 25 and Figure 40.

[0190] The present invention also provides panels, assays, and kits for prognosis of myocardial infarction (MI), which include one or more biomarkers or antibodies, binding fragments thereof, or other types of binding agents specific for the biomarkers disclosed herein. These can be used, for example, to determine the prognosis of risk of myocardial infarction within a certain time period, for example, within one year, or within three years, in a subject. In some embodiments, the time endpoint is defined as starting from sample collection. In other embodiments, the time endpoint is defined as starting from three days after sample collection.

[0191] In certain embodiments, the panel, assay, or kit for prognosis of myocardial infarction includes biomarkers for N-terminal prohormone brain natriuretic protein (NT-proBNP) and osteopontin. In some embodiments, the time endpoint is defined as beginning 3 days after sample collection. This combination of biomarkers is represented by panel FM201 / MI002 in Table 25 and Figure 41.

[0192] In certain embodiments, the panel, assay, or kit for prognosis of myocardial infarction includes biomarkers for N-terminal prohormone brain natriuretic protein (NT-proBNP), osteopontin, and vascular cell adhesion molecule. In some embodiments, the time endpoint is defined as beginning 3 days after sample collection. This combination of biomarkers is represented by panel FM204 / MI003 in Table 25 and Figure 42.

[0193] In certain embodiments, the panel, assay, or kit for prognosis of myocardial infarction includes biomarkers for kidney injury molecule-1, N-terminal prohormone brain natriuretic protein (NT-proBNP), and vascular cell adhesion molecule. In some embodiments, the time endpoint is defined as beginning 3 days after sample collection. This combination of biomarkers is represented by panel FM202 / MI005 in Table 25 and Figure 43.

[0194] In certain embodiments, a panel, assay, or kit for prognosis of myocardial infarction includes biomarkers for kidney injury molecule-1, N-terminal prohormone brain natriuretic protein (NT-proBNP), and osteopontin. In some embodiments, the time endpoint is defined as beginning 3 days after sample collection. This combination of biomarkers is represented by panel FM205 / MI007 in Table 25 and Figure 44.

[0195] In certain embodiments, the panel, assay, or kit for prognosis of myocardial infarction comprises a biomarker for N-terminal prohormone brain natriuretic protein (NT-proBNP). In some embodiments, the time endpoint is defined as starting from the time of sample collection. This combination of biomarkers is represented by panel FM63 / 64 in Table 25 and Figure 45.

[0196] The present invention further provides panels, assays, and kits for the prognosis of cardiovascular mortality, which include one or more biomarkers or antibodies, binding fragments thereof, or other types of binding agents specific for the biomarkers disclosed herein. Such panels, assays, and kits can be used, for example, to determine the prognosis of cardiovascular mortality within a certain time period, for example, within one year, or within three years, in a subject. In some embodiments, the time endpoint is defined as starting from sample collection. In other embodiments, the time endpoint is defined as starting from three days after sample collection. In some embodiments, at least one clinical variable described herein is used with the determined biomarker level. In one particular embodiment, the clinical variable is type 2 diabetes mellitus history.

[0197] In certain embodiments, the panel, assay, or kit for prognosis of cardiovascular mortality comprises biomarkers for apolipoprotein A-II and osteopontin. In some embodiments, the time endpoint is defined as beginning from sample collection. In some embodiments, the time endpoint is defined as beginning from sample collection (as described in panel FM52 / 244 in Table 25 and Figure 46). In some embodiments, the time endpoint is defined as beginning 3 days after sample collection. This combination of biomarkers is represented by panel FM194 / CVD001 in Table 25 and Figure 47. In one particular embodiment, the time endpoint is defined as beginning 3 days after sample collection and further comprises the clinical variable of type 2 diabetes mellitus history. This combination of biomarkers and clinical variables is represented by panel FM193 / R08 in Table 25 and Figure 48.

[0198] In certain embodiments, a panel, assay, or kit for prognosis of cardiovascular mortality comprises biomarkers for apolipoprotein A-II, midkine, and osteopontin. In some embodiments, the time endpoint is defined as beginning from sample collection. This combination of biomarkers is represented by panel FM53 / 237 in Table 25 and Figure 49. In some embodiments, the time endpoint is defined as beginning 3 days after sample collection. This is represented by panel FM195 / CVD002 in Table 25 and Figure 50.

[0199] In certain embodiments, a panel, assay, or kit for prognosis of cardiovascular mortality includes biomarkers for apolipoprotein A-II, N-terminal prohormone brain natriuretic protein (NT-proBNP), osteopontin, and tissue inhibitor of metalloproteinase-1. In some embodiments, the time endpoint is defined as beginning 3 days after sample collection. This combination of biomarkers is represented by panel FM207 / R04 in Table 25 and Figure 51.

[0200] In certain embodiments, a panel, assay, or kit for prognosis of cardiovascular mortality includes biomarkers for N-terminal prohormone brain natriuretic protein (NT-proBNP), osteopontin, and tissue inhibitor of metalloproteinase-1. In some embodiments, the time endpoint is defined as beginning 3 days after sample collection. This combination of biomarkers is represented by panel FM208 / R05 in Table 25 and Figure 52.

[0201] In certain embodiments, a panel, assay, or kit comprises at least one, at least two, at least three, at least four, or more than four antibodies or binding fragments thereof, or other types of binding agents, which antibodies, binding fragments, or other types of binding agents are specific for the biomarkers of Tables 1A, 1B, 2A, and 2B.

[0202] It will be understood that the panels, assays, and kits of the present disclosure may further include virtually any other compounds, compositions, components, instructions, etc., which may be necessary or desirable in facilitating a diagnosis or prognosis determination in accordance with the present disclosure. These may include instructions for using the panel, assay, or kit, instructions for making a diagnosis or prognosis determination (e.g., by calculating a diagnostic or prognostic score), instructions or other suggestions for a physician as to preferred or desired modes of therapeutic or diagnostic intervention in a subject in light of the diagnosis or prognosis determination, and the like.

[0203] In some embodiments, the panels, assays, and kits of the invention will facilitate detection of the biomarkers disclosed herein. Means for measuring such blood, plasma, and / or serum levels are known in the art and include, for example, the use of immunoassays. Standard techniques that may be used include, for example, enzyme-linked immunosorbent assays ("ELISA") or Western blots.

[0204] In addition to the methods described above, any method known in the art for quantitatively measuring the levels of proteins in a sample, e.g., non-antibody based methods, can be used in the methods and kits of the invention. For example, mass spectrometry based (e.g., multiple reaction monitoring (MRM) mass spectrometry, etc.) or HPLC based methods can be used. Protein quantification methods are described, for example, in Ling-Na Zheng et al., 2011, J. of Analytical Atomic Spectrometry, 26, 1233-1236; Vaudel, M., et al., 2010, Proteomics, Vol. 10:4; Pan, S., 2009 J. Proteome Research, February; 8(2):787-97; Westermeier and Marouga, 2005, Bioscience Reports, Vol. 25, Nos. 1 / 2; Carr and Anderson, 2008, Clinical Chemistry. 54:1749-1752; and Aebersold and Mann, 2003, Nature, Vol. 422.

[0205] Additionally, techniques such as those used in the proteomic field and other areas may also be embodied in the methods, kits, and other aspects of the present invention. Such techniques include, for example, those described in U.S. Patent Application Nos. 2008 / 0202927, 2014 / 0256573, 2016 / 0153980, WO2016 / 001795, U.S. Patent Application Nos. 2008 / 0185295, 2010 / 0047901, 2010 / 0231242, 2011 / 0154648, 2013 / 0306491, 2010 / 0329929, 2013 / 0261009, Sorger, 2008, Nature Biotechnol. 26:1345-1346, Li et al., 2002, Mol. Cell. Proteomics 1.2:157, Hou et al. al., 2006, J. Proteome Res.5(10):2754-2759, Li et al., 2001, Proteomics1(8):975-986, Ramsey et al. These include the use of micro- and nano-fluidic chips, biosensors, and other techniques described in (e.g., Armenta et al., 2009, Electrophoresis 30(7):1145-1156; Lynch et al., 2004, Proteomics 4(6):1695-1702; Kingsmore et al., 2003, Curr. Opin. Biotechnol. 14(1):74-81). EXAMPLES

[0206] Example 1: Clinical and biomarker scoring system for diagnosing obstructive coronary artery disease (CAD), panel FM139 / 685 A convenience sample of 1251 patients undergoing coronary and / or peripheral angiography with or without intervention between 2008 and 2011 was prospectively enrolled. Patients undergoing only peripheral angiography or no catheterization at all were excluded from this analysis (N=244). Additionally, the final 153 patients in the chronological subset who had undergone either coronary or peripheral catheterization were excluded from this analysis due to their potential use in further validation of these models. Patients were referred for these procedures for many reasons, including angiography after acute processes such as myocardial infarction (MI), unstable angina, and heart failure (HF), but also for non-acute processes, e.g., diagnostic evaluation of stable chest pain, failed stress testing, or pre-operatively prior to valvular heart surgery.

[0207] After informed consent was obtained, detailed clinical and historical variables, as well as the reason for referral for angiography, were recorded at the time of the procedure. Coronary angiography results were also recorded with the highest percent stenosis within each major coronary artery or their branches. For the purposes of this analysis, significant coronary stenosis was characterized by 70% or greater luminal obstruction.

[0208] Medical records were reviewed from the time of enrollment to the end of follow-up. Clinical endpoints were identified through medical record review and patient follow-up by telephone and / or physician management. Vital status was ascertained using the Social Security Death Index and / or death announcement postings. The following clinical end events were identified, adjudicated, and recorded by study investigators: death, nonfatal MI, HF, stroke, transient ischemic attack, peripheral arterial complications, and cardiac arrhythmias. Each discrete event was recorded for any relapse events. Additionally, death was adjudicated for the presence / absence of cardiovascular death.

[0209] 15 mL of blood was obtained before and immediately after the angiographic procedure through a centrally located vascular access sheath. Blood was immediately centrifuged for 15 minutes, and serum and plasma were aliquoted on ice and frozen at -80°C until biomarker measurement. Only blood obtained immediately before the procedure was used for this analysis.

[0210] After a single freeze-thaw cycle, 200ul of plasma was purified using Luminex 100 / 200 Over 100 protein biomarkers were analyzed on the xMAP technology platform. This technology uses multiplexed, microsphere-based assays in a single reaction vessel. It combines optical sorting schemes, biochemical assays, flow cytometry, and advanced digital signal processing hardware and software. Multiplexing is achieved by assigning each protein-specific assay to a set of microspheres that are labeled with a unique fluorescent signature. An assay-specific capture antibody is covalently conjugated to each unique set of microspheres. The assay-specific capture antibody on each microsphere binds to the protein of interest. A mixture of assay-specific biotinylated detection antibodies is reacted with the microsphere mixture, followed by a streptavidin-labeled fluorescent "reporter" molecule. As with a flow cytometer, as each individual microsphere passes through a series of excitation beams, it is analyzed for size and encoded fluorescent signatures, and the amount of fluorescence generated is proportional to protein levels. A minimum of 100 individual microspheres from each unique set are analyzed and the median protein-specific fluorescence is logarithmic. Using a known amount of internal control, sensitivity and quantitative results are achieved with precision enhanced by the analysis of 100 microspheres per data point.

[0211] The patients selected for analysis consisted of 927 chronologically young patients who underwent coronary angiography and who also underwent concomitant peripheral angiography.

[0212] The 927 patients selected for analysis were randomly divided into a training set (70%, or N=649) and a hold-out validation set (30%, or N=278). Baseline clinical characteristics and protein concentrations between those with and without coronary stenosis ≥70% in at least one major epicardial coronary artery were compared (Tables 1A, 3A, 5, and 6), with dichotomous variables compared using a two-tailed Fisher exact test, whereas continuous variables were compared using a two-tailed two-sample T-test. The biomarkers compared were tested with Wilcoxon rank sum tests because their concentrations were not normally distributed. For any non-measurable marker results, a standard approach of imputing concentrations 50% below the detection limit was utilized.

[0213] All work for biomarker selection and development of diagnostic models was done solely on the training set. Level or concentration values ​​for all proteins underwent the following transformations to facilitate predictive analysis: (a) they were log-transformed to achieve a normal distribution, (b) outliers were clipped at a value three times the median absolute deviation, and (c) values ​​were rescaled to a distribution with zero mean and unit variance. Candidate panels of proteins and clinical features were selected via least angle regression (LARS) and models were generated using least absolute shrinkage and selection operator (LASSO) with logistic regression and Monte Carlo cross-validation with 400 iterations. Candidates were subjected to further evaluation of discrimination via iterative model building, evaluating the change in area under the curve (AUC) with the addition of biomarkers to the base model, along with evaluation of the improvement in calibration from their addition by minimization of Akaike or Bayesian information criterion (AIC, BIC) and goodness of fit by Hosmer-Lemeshow test.

[0214] Once the final panel was selected, a final model was constructed using all of the training data. Multivariate logistic regression assessed the performance of the model in the training set in general and in several relevant subgroups to determine how well the model performed in men vs. women, in those with a history of CAD vs. those without a history of CAD, and in those showing that they had and did not have a myocardial infarction. Diagnostic odds ratios (ORs) with 95% confidence intervals (CIs) were generated. The final model was then evaluated in the validation set: in doing so, a score distribution was generated within the validation cohort, followed by a receiver operating characteristic (ROC) test using the score values ​​as a function of the AUC. The operating characteristics of the score were calculated using the generated sensitivity (Sn), specificity (Sp), positive and negative predictive values ​​(PPV, NPV). We also considered a method to convert the single diagnostic score into a level of probability (e.g., a 5-level score, where a score of 1 means that the patient does not have CAD at all and a score of 5 means that the patient is extremely likely to have CAD), and evaluated each of these levels with the above operating characteristics (Figure 3 and Table 9). To evaluate the prognostic significance of the CAD score, we performed age- and CAD-score-adjusted Cox proportional hazards analysis to assess whether scores above the optimal threshold for CAD diagnosis also predicted future acute MI, estimating the hazard ratio (HR) for elevated CAD score as well as the per unit score increase with 95% CI. Finally, the time to the first acute MI event as a function of elevated CAD score was calculated and presented as Kaplan-Meier survival curves and compared using the log-rank test (Figure 4).

[0215] All statistics were performed using R software, version 3.3 (R Foundation for Statistical Computing, Vienna, AT), p values ​​were two-sided, and values ​​less than 0.05 were considered significant.

[0216] Table 1A shows biomarker concentrations and their diagnostic relevance that differed between those who had at least one coronary artery stenosis of 70% or greater (N=428) and those who did not in a training set (N=649) in a cohort of subjects who received a coronary artery catheter with or without any peripheral catheter.

[0217] Notably, of all protein biomarkers measured in the training set (Table 1A), individuals with severe CAD had lower concentrations of adiponectin and apolipoprotein CI (apo CI), and higher concentrations of kidney injury molecule-1 and midkine.

[0218] Baseline clinical variables of subjects in the validation set (N=278) were similar to those in the training set and are included in Table 5 (for diagnostic protein biomarkers) and Table 6 (for clinical variables). Table 5 below shows protein biomarker concentrations and their diagnostic relevance that differ between those who had at least one coronary artery stenosis of 70% or greater (N=178) and those who did not in the validation set (N=278) in a cohort of subjects who received a coronary artery catheter with or without any peripheral catheter.

[0219] [Table 5-1] [Table 5-2] [Table 5-3] [Table 5-4] [Table 5-5] [Table 5-6]

[0220] Table 6 below shows baseline clinical variables and their diagnostic relevance that differed between those with at least one coronary artery stenosis of 70% or greater (N=178) and those who did not in the validation set (N=278) in a cohort of subjects who received a coronary artery catheter with or without any peripheral catheter.

[0221] [Table 6-1] [Table 6-2] [Table 6-3] [Table 6-4]

[0222] According to the described method, from the training cohort (N=649), independent predictors of 70% or greater CAD in any one vessel included four biomarkers (adiponectin, apolipoprotein CI, kidney injury molecule-1, and midkine) and clinical variables (percutaneous coronary intervention, e.g., history of balloon angioplasty with or without stent placement, and gender). This combination of protein biomarkers and clinical variables is represented by panel FM139 / 685, shown in Table 25 and Figures 1-4.

[0223] Model fitting is shown in Table 7, which shows that the addition of each individual biomarker to the clinical variables improves discrimination while at the same time improving the calibration for coronary artery stenosis ≥ 70%, as demonstrated by minimization of AIC or BIC and joint goodness of fit by the Hosmer-Lemeshow test. For biomarkers, candidates were retained if they enhanced the model and / or improved the calibration.

[0224] [Table 7]

[0225] In multivariate logistic regression, our score strongly predicted severe CAD in all subjects in the training cohort (OR = 9.74, 95% CI 6.05-16.1, P < 0.001). To better understand the performance of the score in various subgroups, we tested the score performance in men (OR = 7.88, 95% CI = 4.31-14.9, P < 0.001), women (OR = 24.8, 95% CI = 7.11-111.6, P < 0.001), as well as those without a history of CAD (OR = 8.67, 95% CI = 4.38-17.9, P < 0.001).

[0226] In the validation cohort, individual scores were calculated and the results expressed as a function of the presence of CAD. Doing so revealed a bimodal score distribution (Figure 2), with a higher prevalence of severe CAD in those with higher scores and a lower prevalence in those with lower scores. In a receiver operating characteristic test, for the gold standard diagnosis of ≥70% stenosis of any major epicardial coronary artery, the generated score had an AUC of 0.87 (Figure 1, P<0.001).

[0227] Table 8 shows the operating characteristics of the FM139 / 685 CAD algorithm across various scores. For sensitivity and specificity, 95% confidence intervals are listed in parentheses. At the optimal score cut point, we found a sensitivity of 77%, a specificity of 84%, a PPV of 90%, and an NPV of 67% for severe CAD. In subjects with a history of CAD, the score had a sensitivity of 84%, a specificity of 66%, a PPV of 90%, and an NPV of 53% for predicting CAD. In subjects without a history of CAD, the score had a sensitivity of 78%, a specificity of 80%, a PPV of 80%, and an NPV of 78% for predicting CAD. The CAD score was also tested for performance in patients presenting with and without MI. For predicting severe CAD in subjects presenting without acute MI, the AUC score was 0.87 (P<0.001).

[0228] [Table 8-1] [Table 8-2]

[0229] Table 9 below shows a scoring model using a 5-level scoring system, showing the performance of the model when the raw diagnostic value is divided into 5 levels of scores, each optimized for different operational characteristics and diagnostic confidence, with higher scores indicating increased risk for the presence of CAD. A score of 1-2 indicates a negative diagnosis, a score of 4-5 indicates a positive diagnosis, and a score of 3 indicates a moderate risk diagnosis. Cutoffs for score levels 1 and 2 were optimized for NPV (0.9 and 0.8, respectively) and tested in the validation set with NPV values ​​of 0.91 and 0.76, respectively. Cutoffs for score levels 4 and 5 were optimized for PPV (0.9 and 0.95, respectively) and tested in the validation set with PPV values ​​of 0.85 and 0.93, respectively. This is also shown in Figure 3.

[0230] [Table 9]

[0231] In particular, among those with data available to calculate the Framingham risk score (N=577), we found the CAD algorithm to have a consistent and superior AUC over the ability of the Framingham risk score to predict the presence of CAD (0.87 vs. 0.52, P<0.001). Of 649 in the training set, 154 had exercise stress testing without imaging and 174 had nuclear cardiology studies, and of 278 patients in the validation set, 47 had exercise stress testing without imaging and 61 had nuclear cardiology studies. Among patients undergoing cardiac stress testing per standard of care, the CAD score was substantially more accurate for predicting severe CAD by angiography (again, 0.87 vs. 0.52; P<0.001 for the difference in AUC).

[0232] During the 3.6-year follow-up period, in the entire cohort of subjects, the CAD diagnostic scoring system independently predicted subsequent incident acute MI in an age- and score-adjusted model (HR=2.39, 95% CI=1.65-3.47, P<0.001). When modeled as a continuous variable, the score was similarly predictive, with higher scores predicting a higher risk for incident acute MI (HR=1.19 / unit score increase, 95% CI=1.09-1.31, P<0.001). Those with dichotomously elevated scores had a shorter time to first event than those with lower CAD scores, as evidenced by the rapid and persistent divergence of the Kaplan-Meier survival curves (Figure 4, log-rank p-value<0.001).

[0233] A novel scoring system for diagnosing the presence of severe epicardial CAD (≥70% stenosis in at least one major vessel) is described using patients referred for coronary angiography for a wide range of indications. The score consisted of a combination of clinical variables and concentrations of four biologically relevant biomarkers. In diagnosing ≥70% stenosis of any major epicardial coronary artery, the generated score had an area under the receiver operating characteristic curve of 0.87 in the validation set, and at the optimal cut point, the score was both highly sensitive (77%) and specific (84%) for diagnosing CAD, with a PPV of 90%. Importantly, the CAD score performed particularly well in women, while one component of the score is percutaneous coronary intervention (also referred to as PCI) history, and score performance was similar to subjects without a history of CAD or those without MI at the time of diagnosis. Among those with data available to calculate the Framingham risk score, the CAD score had a significantly higher AUC in predicting the presence of CAD than the Framingham risk score, and such data are similar to Pen and colleagues, who found that the Framingham score was less accurate in determining general coronary plaque burden detected by CT angiography. The score also performed significantly better for diagnosing CAD than stress testing.

[0234] The clinical and biomarker scoring strategies disclosed herein can reliably diagnose the presence of severe epicardial CAD. The advantages of a robust clinical and biomarker score to diagnose the presence of CAD include the fact that such techniques can be widely deployed in a cost-effective manner, easily interpreted, and are associated with a well-defined course of treatment.

[0235] Example 2: Clinical and biomarker scoring system for diagnosing obstructive coronary artery disease (CAD), panel FM46 / 572

[0236] This example illustrates yet another non-invasive method using a clinical and biomarker scoring system that provides, among other things, high accuracy in diagnosing the presence of anatomically significant CAD and in providing a prognosis of cardiovascular events. This example utilized the same described methods as Example 1 (study design and participants, data acquisition, follow-up, biomarker testing, statistics, and results (Tables 1A, 3A, 5, and 6, Figures 18-20). The main differences between Example 1 and Example 2 are the clinical variables and proteins utilized.

[0237] According to the described method, from the training cohort (N=649), independent predictors of 70% or greater CAD in any one vessel included three biomarkers (adiponectin, decorin, and midkine) and three clinical variables (history of myocardial infarction, history of percutaneous coronary intervention, and gender). This combination of biomarkers and clinical variables is represented by panel FM46 / 572, shown in Table 25 and Figures 18-20.

[0238] Model fitting is shown in Table 10, which shows that the addition of each individual biomarker to the clinical variables improves discrimination while at the same time improving the calibration for coronary artery stenosis 70% or greater, as demonstrated by minimization of AIC or BIC and joint goodness of fit by the Hosmer-Lemeshow test. For biomarkers, candidates were retained if they enhanced the model and / or improved the calibration.

[0239] [Table 10-1] [Table 10-2]

[0240] Notably, those with severe CAD had lower concentrations of adiponectin and higher concentrations of midkine and decorin. The biomarkers and their concentrations in subjects in the training set with and without coronary stenosis are shown in Table 1A, and baseline clinical variable values ​​in the training set are found in Table 3A. The baseline biomarkers and clinical variables of subjects in the validation set (N=278) are included in Tables 5 and 6, respectively.

[0241] In the validation cohort, the ROC test showed that for the gold standard diagnosis of 70% or greater stenosis of any major epicardial coronary artery, the generated score had an AUC of 0.84 (Figure 18, P<0.001). Individual scores were calculated in the validation cohort and the results expressed as a function of the presence of CAD. Doing so revealed a bimodal score distribution (Figure 19), with a higher prevalence of severe CAD in those with higher scores and a lower prevalence among those with lower scores.

[0242] Table 11 below shows the operating characteristics of the FM46 / 572 CAD algorithm across various scores. For sensitivity and specificity, 95% confidence intervals are listed in parentheses. At the optimal score cut point, we found a sensitivity of 69.7%, a specificity of 81.0%, a PPV of 86.7%, and an NPV of 60.0% for severe CAD.

[0243] [Table 11-1] [Table 11-2]

[0244] Table 12 below shows a scoring model using a three-level scoring system, showing the performance of the model when the raw diagnostic value is divided into three levels of scores, each optimized for different operational characteristics and diagnostic confidence, with higher scores indicating increased risk for the presence of CAD. In the three-level score, a score of 1 indicates a negative or low probability of a CAD diagnosis, a score of 3 indicates a positive or high probability of a CAD diagnosis, and a score of 2 indicates a moderate probability of a CAD diagnosis. The cutoff for score level 1 was optimized for an NPV of 0.8 in the training set, and the cutoff for score level 3 was optimized for a PPV of 0.95 in the training set.

[0245] [Table 12-1] [Table 12-2]

[0246] In particular, among those with data available to calculate the Framingham risk score (N=577), we found the CAD algorithm to have a consistent and superior AUC over the Framingham risk score's ability to predict the presence of CAD (0.84 vs. 0.52, P<0.001). Of 649 in the training set, 154 had exercise stress testing without imaging and 174 had nuclear cardiology studies, and of 278 patients in the validation set, 47 had exercise stress testing without imaging and 61 had nuclear cardiology studies. Among patients undergoing cardiac stress testing per standard of care, the CAD score was substantially more accurate for predicting severe CAD by angiography (again, 0.84 vs. 0.52; P<0.001 for the difference in AUC).

[0247] During the 3.6-year follow-up period, in the entire cohort of subjects, the CAD scoring system independently predicted subsequent incident acute MI in an age- and score-adjusted model (HR=1.85, 95% CI=1.28-2.67, P<0.001). When modeled as a continuous variable, the score was similarly predictive, with higher scores predicting a higher risk for incident acute MI (HR=1.19 / unit score increase, 95% CI=1.08-1.30, P<0.001). Those with dichotomously elevated scores had a shorter time to first event than those with lower CAD scores, as evidenced by the rapid and persistent divergence of the Kaplan-Meier survival curves (Figure 20, log-rank p-value<0.001).

[0248] Example 3: Clinical and Biomarker Scoring System for Diagnosing Obstructive Coronary Artery Disease (CAD), Panel FM02 / 410

[0249] This example illustrates yet another non-invasive method using a clinical and biomarker scoring system that provides, inter alia, high accuracy in diagnosing the presence of anatomically significant CAD and in providing a prognosis of a cardiovascular event.

[0250] This example utilized the same descriptive methods (study design, data acquisition, follow-up, biomarker testing, statistics, and results) as Examples 1 and 2, with the exception of subjects. This example included subjects who had coronary catheterization only, N=809 (vs. subjects who had coronary catheterization and optionally peripheral catheterization (Tables 1B, 3B, 13, 14, 15, and 16 and FIG. 28).

[0251] Patients selected for analysis consisted of 809 chronologically young patients who underwent coronary angiography alone. Patients who may have concomitantly undergone peripheral angiography were excluded.

[0252] The 809 patients selected for analysis were randomly divided into a training set (70%, or N=566, Tables 1B and 3B) and a hold-word validation set (30%, or N=243, Tables 13 and 14). Baseline protein and clinical characteristics between those with and without coronary stenosis ≥70% in at least one major epicardial coronary artery were compared, with dichotomous variables compared using a two-tailed Fisher exact test, whereas continuous variables were compared using a two-tailed two-sample T-test. The biomarkers compared were tested with Wilcoxon rank sum tests because their concentrations were not normally distributed. For any non-measurable marker results, a standard approach of imputing concentrations 50% below the detection limit was utilized.

[0253] Table 13 below shows biomarker concentrations and their diagnostic relevance that differ between those who had at least one coronary artery stenosis of 70% or more (N=148) and those who did not in the validation set (N=243) in a cohort of subjects who received only a coronary artery catheter.

[0254] [Table 13-1] [Table 13-2] [Table 13-3] [Table 13-4] [Table 13-5] [Table 13-6]

[0255] Table 14 below shows baseline clinical variables and their diagnostic associations that differed between those with and those without at least one coronary artery stenosis of 70% or greater (N=148) in the validation set (N=243) in a cohort of subjects who received only coronary catheters.

[0256] [Table 14-1] [Table 14-2] [Table 14-3]

[0257] Further differences in Example 3 (as compared in Examples 1 and 2) are the clinical variables and proteins that were utilized in the panel (FM02 / 410). According to the method described, from the training cohort (N=566), independent predictors of 70% or more of CAD in any one vessel included six biomarkers (adiponectin, apolipoprotein C-1, matrix metalloproteinase 9, midkine, myoglobin, and pulmonary surfactant protein D) and three clinical variables (history of coronary artery bypass grafting [CABG], history of percutaneous coronary intervention [e.g., balloon angioplasty with or without stent placement], and gender). This combination of biomarkers and clinical variables is represented by panel FM02 / 410 shown in Table 25 and Figure 28.

[0258] Table 15 below shows the operating characteristics of the FM02 / 410 CAD algorithm across various scores. For sensitivity and specificity, 95% confidence intervals are listed in parentheses. At the optimal score cut point, we found a sensitivity of 84.5%, a specificity of 77.9%, a PPV of 85.6%, and an NPV of 76.3% for severe CAD.

[0259] [Table 15-1] [Table 15-2]

[0260] Table 16 shows the scoring model using a three-level scoring system, showing the performance of the model when the raw diagnostic value is divided into three levels of scores, each optimized for different operational characteristics and diagnostic confidence, with higher scores indicating increased risk for the presence of CAD. With the three-level score, a score of 1 indicates a negative or low probability of CAD diagnosis, a score of 3 indicates a positive or high probability of CAD diagnosis, and a score of 2 indicates a moderate probability of CAD diagnosis. The cutoff for score level 1 was optimized for an NPV of 0.8 in the training set, and the cutoff for score level 3 was optimized for a PPV of 0.95 in the training set.

[0261] [Table 16]

[0262] Example 4: Biomarker scoring system for predicting risk of composite cardiovascular death, myocardial infarction, or stroke (FM160 / 02) 3 days to 1 year (3 to 365 days) after sampling

[0263] This example shows a non-invasive method using a biomarker scoring system that provides, among other things, high accuracy in providing a prognosis of one-year risk of a composite of cardiovascular death, myocardial infarction, or stroke. This example utilized the same described methods as Example 1 (study design and subjects, data acquisition, follow-up, biomarker testing, statistics, and results (Tables 2B, 4B, 17, 18, 19, and 20 and FIG. 30).

[0264] Patients selected for analysis consisted of 927 chronologically young patients who underwent coronary angiography alone. Patients who may have concomitantly undergone peripheral angiography were excluded.

[0265] The 927 patients selected for analysis were randomly divided into a training set (70%, or N=649, Tables 2B and 4B) and a hold-out validation set (30%, or N=278, Tables 17 and 18). The training set had one subject who died after catheterization (i.e., before day 3), so this patient was excluded from the training set, resulting in a population of N=648. Baseline clinical variables between those with and without MACE were compared (Tables 4B and 18), with dichotomous variables being compared using a two-tailed Fisher exact test, while continuous variables were compared using a two-tailed two-sample T-test. The biomarkers compared were tested with Wilcoxon rank sum tests because their concentrations were not normally distributed. For any non-measurable marker results, a standard approach of imputing concentrations 50% below the detection limit was utilized.

[0266] Table 17 below shows the biomarker concentrations and their prognostic associations that differed between those with and those without a major adverse cardiac event (MACE) 3-365 days from blood draw in the validation set (N=278). The numbers in this table were calculated using a composite endpoint of 1-year MACE due to CV death, MI, or major stroke, and these proteins produce similar results using a composite endpoint of 1-year MACE due to any cause death, MI, and / or major stroke.

[0267] [Table 17-1] [Table 17-2] [Table 17-3] [Table 17-4] [Table 17-5] [Table 17-6]

[0268] Table 18 below shows baseline clinical variables and their prognostic associations that differed between those with and those without major adverse cardiac events (MACE) 3-365 days from blood draw in the validation set (N=278). The numbers in this table were calculated using a composite endpoint of 1-year MACE due to CV death, MI, or major stroke, and these proteins produce similar results using a composite endpoint of 1-year MACE due to any cause death, MI, and / or major stroke.

[0269] [Table 18-1] [Table 18-2] [Table 18-3]

[0270] Table 19 below shows the operational characteristics of the FM160 / 02 MACE prognostic algorithm across various scores. For sensitivity and specificity, 95% confidence intervals are listed in parentheses. At the optimal score cut point, we found a sensitivity of 63.9%, a specificity of 77.3%, a PPV of 29.5%, and an NPV of 93.5% for the composite endpoint of 1-year MACE with CV death, MI, or major stroke.

[0271] [Table 19]

[0272] Table 20 below shows the scoring model using a three-level scoring system, showing the performance of the model when raw prognostic values ​​are divided into three levels of scores, each optimized for different operating characteristics and prognostic confidence, with higher scores indicating increased risk for composite MACE. In the three-level score, a score of 1 indicates a low risk or negative prognosis, a score of 3 indicates a high risk or positive prognosis, and a score of 2 indicates a moderate risk prognosis. The cutoff for score level 1 was optimized for an NPV of 0.97 in the training set, and the cutoff for score level 3 was optimized for a PPV of 0.45 in the training set.

[0273] [Table 20]

[0274] Example 5: Biomarker scoring system for predicting 1-year (days 0-365) risk of a composite of cardiovascular death, myocardial infarction, or stroke (FM96 / 04)

[0275] This example shows a non-invasive method using a biomarker scoring system that provides, among other things, high accuracy in providing a prognosis of 1-year risk (0-365 days) of MACE composite of cardiovascular death, myocardial infarction, or stroke. This example utilized the same methods (study design and subjects, data acquisition, follow-up, biomarker testing, statistics, and results) as described in Example 1 (Tables 2A, 4A, 21, 22, 23, and 24 and FIG. 31).

[0276] Patients selected for analysis consisted of 928 chronologically young patients who underwent coronary angiography alone. Patients could also have concomitant peripheral angiography.

[0277] The 928 patients selected for analysis were randomly divided into a training set (70%, or N=649, Table 2A) and a hold-out validation set (30%, or N=279, Table 21).

[0278] Baseline characteristics between those with and without MACE were compared, with dichotomous variables being compared using a two-tailed Fisher exact test, whereas continuous variables were compared using a two-tailed two-sample T-test. Biomarkers compared were tested with the Wilcoxon rank sum test because their concentrations were not normally distributed. For any non-measurable marker results, a standard approach of imputing concentrations 50% below the detection limit was utilized.

[0279] Table 21 below shows the biomarker concentrations and their prognostic associations that differed between those with and those without a 0-365 day major adverse cardiac event (MACE) in the validation set (N=278). The numbers in this table were calculated using a composite endpoint of 1-year MACE due to CV death, MI, or major stroke, and these proteins produce similar results using a composite endpoint of 1-year MACE due to any cause death, MI, and / or major stroke.

[0280] [Table 21-1] [Table 21-2] [Table 21-3] [Table 21-4] [Table 21-5]

[0281] Table 22 below shows baseline clinical variables and their prognostic associations that differ between those with and those without a 0-365 day major adverse cardiac event (MACE) in the validation set (N=278). The numbers in this table are calculated using a composite endpoint of 1-year MACE due to CV death, MI, or major stroke, and these proteins produce similar results using a composite endpoint of 1-year MACE due to any cause death, MI, and / or major stroke.

[0282] [Table 22-1] [Table 22-2] [Table 22-3] [Table 22-4]

[0283] Table 23 below shows the operating characteristics of the FM96 / 04 MACE prognostic algorithm across various scores. For sensitivity and specificity, 95% confidence intervals are listed in parentheses. At the optimal score cut point, we found a sensitivity of 66.7%, a specificity of 76.6%, a PPV of 31.7%, and an NPV of 93.4% for the composite endpoint of MACE at 1 year (0-365) with CV death, MI, or major stroke.

[0284] [Table 23-1] [Table 23-2]

[0285] Table 24 below shows the scoring model using a three-level scoring system, showing the performance of the model when raw prognostic values ​​are divided into three levels of scores, each optimized for different operating characteristics and prognostic confidence, with higher scores indicating increased risk for composite MACE. In the three-level score, a score of 1 indicates a low risk or negative prognosis, a score of 3 indicates a high risk or positive prognosis, and a score of 2 indicates a moderate risk prognosis. The cutoff for score level 1 was optimized for an NPV of 0.97 in the training set, and the cutoff for score level 3 was optimized for a PPV of 0.4 in the training set.

[0286] [Table 24]

[0287] Example 6: Further demonstration of the method using clinical and biomarker assays for diagnosing cardiovascular disease

[0288] Table 25 is a chart of different panels including protein biomarkers and, optionally, clinical variables with corresponding AUC for a given outcome. These reflect the above Examples 1-5, as well as additional panels generated using the methods and analyses provided herein.

[0289] [Table 25-1] [Table 25-2] [Table 25-3] [Table 25-4] [Table 25-5] [Table 25-6]

Table 25-7

Table 25-8

Table 25-9

Table 25-10

Claims

[Claim 1] A method for determining a diagnosis and / or prognosis of a cardiovascular disease or outcome in a subject, comprising: (i) the levels of at least two biomarkers in a biological sample obtained from the subject. determining a biomarker selected from the group consisting of those set forth in Tables 1A, 1B, 2A, and 2B; (ii) optionally determining the status of at least one clinical variable of the subject, wherein the clinical variable is selected from the group consisting of those set forth in Tables 3A, 3B, 4A, and 4B; (iii) calculating a diagnostic or prognostic score based on the levels of said biomarkers determined in step (i) and, optionally, the status of said clinical variable(s) determined in step (ii); (iv) classifying the diagnostic or prognostic score as a positive or negative result; and (v) determining a therapeutic or diagnostic intervention regimen based on the positive or negative result.

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