Prediction of cardiovascular event risk

Abstract

There is a need for biomarkers, methods, devices, reagents, systems, and kits that can predict cardiovascular events within a one-year period. [Solution] To predict the risk of developing cardiovascular (CV) events, the present invention provides biomarkers, methods, devices, reagents, systems, and kits used to evaluate individuals with preserved ejection fraction (HFpEF) or reduced ejection fraction (HFrEF) over a period of 90 days, 180 days, or 1 year.

Description

[Technical Field]

[0001] The present application generally relates to methods for detecting biomarkers and assessing the risk of future cardiovascular events in individuals with chronic heart failure, and more specifically to one or more biomarkers, methods, devices, reagents, systems, and kits used to assess an individual for prediction of the risk of developing cardiovascular (CV) events. Such events include, but are not limited to, hospitalization and death. [Background Art]

[0002] The leading cause of death in the United States is cardiovascular disease. Primary events (D’Agostino, R et al., “General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study” Circulation 117:743-53(2008); and Ridker, P. et al., “Development and Validation of Improved Algorithms for the Assessment of Global Cardiovascular Risk in Women” JAMA 297(6):611-619(2007)) and secondary events (Shlipak, M. et al. “Biomarkers Numerous existing and important predictors for the risk of Recurrent Cardiovascular Disease (as described in "Predict Recurrent Cardiovascular Disease: The Heart & Soul Study" Am.J.Med. 121:50-57 (2008)) exist and are widely used in clinical practice and therapeutic trials. Unfortunately, receiver operating characteristic curves, hazard ratios, and concordances indicate that the performance of existing risk factors and biomarkers is not very high (an AUC of approximately 0.75 means that these factors are only somewhere between a coin toss and perfect). In addition to the need for improved diagnostic performance, there is a need for risk products that are short-term and allow individuals to respond to beneficial (and harmful) interventions and lifestyle changes on their own. The widely used Framingham equation has three main problems. First, it is too long-term. Firstly, while the Framingham equation provides a 10-year risk calculation, people tend to downplay future risks and are reluctant to change their behavior and lifestyles based on them. Secondly, they are not very responsive to interventions. The Framingham equation is highly dependent on chronological age, which cannot be reduced, and sex, which cannot be changed. Thirdly, in the high-risk population assumed here, the Framingham factor does not distinguish well between high and low risk. The hazard ratio between high and low quartiles is only 2, and when attempting to use the Framingham score to individualize risk by stratifying subjects into finer strata (e.g., deciles), the observed event rates are similar across many deciles.

[0003] Cardiovascular disease risk factors have been widely used to improve the intensity and nature of treatment, and their use has undoubtedly contributed to the reduction in cardiovascular disease morbidity and mortality observed over the past 20 years. While these factors have been routinely incorporated into algorithms, unfortunately, they do not capture all risk (the most common first manifestation of heart disease is still death). In fact, they probably only capture half of the risk. The area under the ROC curve for such risk factors in primary prevention is typically around 0.76, and the performance is much worse in secondary prevention (typically 0.62), representing only about 1 / 4 to 1 / 2 of the performance between a 0.5 coin toss and a perfect 1.0.

[0004] Furthermore, the Framingham Study (Wang et al., “Multipl e Biomarkers for the Prediction of First In "Major Cardiovascular Events and Death" N.Eng.J.Med.355:2631-2637(2006), the addition of 10 biomarkers (CRP, BNP, NT-proBNP, aldosterone, renin, fibrinogen, D-dimer, plasminogen activator inhibitor 1, homocysteine, and urinary albumin to creatinine ratio) did not significantly improve the AUC when added to existing risk factors. The AUC for events from 0 to 5 years was 0.76 when using age, sex, and conventional risk factors, and 0.77 when the best combination of biomarkers was added to this combination, indicating that the situation worsens for secondary prevention.

[0005] Early identification of patients at higher risk of cardiovascular events within a 1-5 year period is important, as aggressive intervention can improve outcomes in individuals at higher risk. Therefore, while aggressive intervention is necessary for optimal management to reduce the risk of cardiovascular events in patients considered to be at higher risk, patients at lower risk may be spared expensive and potentially invasive procedures that may not provide a beneficial effect.

[0006] The selection of biomarkers to predict the risk of developing a specific pathological or disease state within a given period first involves identifying markers that have a measurable and statistically significant relationship with the probability and / or timing of an event for a specific medical application. Biomarkers may include molecules secreted or released either downstream, in parallel with, or both in the causal pathway leading to the state of interest, or downstream of or in parallel with the onset or progression of the disease or state. They may be released into the bloodstream from cardiovascular tissue or other organs, as well as surrounding tissues and circulating cells, in response to biological processes that predispose individuals to cardiovascular events, or they may reflect downstream pathophysiological effects such as impaired renal function. Biomarkers may include small molecules, peptides, proteins, and nucleic acids. Some important issues affecting biomarker identification include overfitting and bias in available data.

[0007] Various methods have been used in attempts to identify biomarkers and diagnose or predict the risk of developing a disease or condition. For protein-based markers, these methods include two-dimensional electrophoresis, mass spectrometry, and immunoassays. For nucleic acid markers, these methods include mRNA expression profiling, microRNA profiling, FISH, gene expression linkage analysis (SAGE), large-scale gene expression arrays, gene sequencing, and genotyping (SNP or small variant analysis).

[0008] The usefulness of two-dimensional electrophoresis is limited by its low detection sensitivity; issues related to protein solubility, charge, and hydrophobicity; gel reproducibility; and the possibility that a single spot may represent multiple proteins. The central limitations of mass spectrometry, depending on the format used, include sample handling and separation, sensitivity to low-abundance proteins, consideration of the signal-to-noise ratio, and the inability to immediately identify detected proteins. Limitations in immunoassay approaches to biomarker discovery center on the inability to perform antibody-based multiplex assays for measuring numerous analytes. Some might simply create arrays of high-quality antibodies and measure analytes bound to those antibodies without sandwiching them (this would formally be equivalent to using the entire genome's nucleic acid sequence to measure all DNA or RNA sequences in an organism or cell by hybridization; hybridization experiments are effective because hybridization can be a stringent test for identity). However, However, even very good antibodies typically lack sufficient stringency to function effectively in the context of blood or even cell extracts, in selecting their binding partners. This is because the protein populations in their matrices vary widely in abundance, which can lead to a poor signal-to-noise ratio. Therefore, a different approach must be used for immunoassay-based approaches to biomarker discovery; namely, a multiplexed ELISA assay (i.e., a sandwich) would be necessary to measure multiple analytes simultaneously and obtain sufficient stringency to determine which analytes are true biomarkers. Sandwich immunoassays cannot be scaled to high content levels, and therefore, biomarker discovery using stringent sandwich immunoassays with standard array formats is impossible. Finally, antibody reagents are susceptible to large lot-to-lot variability and reagent instability. The platform of the present invention for protein biomarker discovery overcomes these problems.

[0009] Many of these methods rely on, or require, several types of sample fractionation before analysis. Therefore, sample preparation required to conduct powerful studies designed to identify and discover statistically relevant biomarkers in a defined sample population is extremely difficult, costly, and time-consuming. During fractionation, a wide range of variations can occur in different samples. For example, candidate markers may be unstable to processing, marker concentrations may change, improper aggregation or dissociation may occur, and accidental sample contamination may occur, obscuring subtle changes predicted in the early stages of disease.

[0010] Methods for discovering and detecting biomarkers using these techniques are widely recognized to have serious limitations for identifying diagnostic or predictive biomarkers. These limitations include the inability to detect low-abundance biomarkers, the inability to consistently cover the entire dynamic range of the proteome, non-reproducibility in sample handling and fractionation, and the overall non-reproducibility and lack of robustness of these methods. Furthermore, the data in these studies are biased and do not adequately address the complexity of sample populations, including appropriate controls, in terms of distribution and randomization required to identify and validate biomarkers within target disease populations.

[0011] Attempts to discover novel and effective biomarkers have continued for decades, but most of these attempts have been unsuccessful. Biomarkers for various diseases have generally always been identified in university laboratories through incidental discoveries made during basic research on several disease processes. Based on these discoveries, papers suggesting the identification of novel biomarkers have been published using small amounts of clinical data. However, most of these proposed biomarkers have not been confirmed as true or useful biomarkers. The main reason for this is that testing on small clinical samples provides only weak statistical evidence that an effective biomarker has actually been discovered. In other words, the initial identifications were not rigorous with respect to the fundamental elements of statistics.

[0012] Based on the history of unsuccessful biomarker discovery attempts, theories have been proposed that further reinforce the general understanding that the discovery of biomarkers for diagnosing, predicting, or predicting the risk of developing diseases and conditions is rare and difficult. Biomarker studies based on two-dimensional gels or mass spectrometry support these ideas. Very few useful biomarkers have been identified by these approaches. However, it is often overlooked that two-dimensional gels and mass spectrometry measure proteins present in the blood at concentrations of approximately 1 nM or higher, and that this protein population is unlikely to change with the onset of disease or a particular condition. Apart from the biomarker discovery platform of the present invention, accurately measuring protein expression levels at very low concentrations is not possible. There is no existing platform for discovering proteomics biomarkers.

[0013] Much is known about the complex biochemical pathways of human biology. Numerous biochemical pathways result in, or initiate, the secretion of proteins that function locally in pathology. For example, growth factors are secreted to stimulate the replication of other cells in pathology, and other factors are secreted to evade the immune system. Many of these secreted proteins function paracrinely, but some act distally within the body. Those skilled in the art with a basic understanding of biochemical pathways will understand that numerous pathology-specific proteins should be present in the blood at concentrations below the detection limits of two-dimensional gels and mass spectrometry (and even below that). Prior to identifying this relatively large number of disease biomarkers, what is needed is a proteomics platform capable of analyzing proteins at concentrations below the detection limits of two-dimensional gels or mass spectrometry.

[0014] Chronic heart failure, also known as congestive heart failure (CHF), occurs when the heart is unable to pump enough blood and oxygen to support the normal function of other organs and is the most common cause of hospitalization in individuals over 65 years of age. Left ventricular ejection fraction (LVEF), measured by echocardiography, indicates how much blood the left ventricle pumps with each contraction. Based on the ejection fraction (EF) level, heart failure (HF) can be classified into HFrEF (LVEF < 40%), where the ejection fraction is reduced; HFpEF (LVEF ≥ 50%), where the ejection fraction is preserved; and HFmrEF (LVEF 40%–49%), where the ejection fraction is moderate.

[0015] As mentioned above, cardiovascular events can be prevented by proactive interventions if the tendencies of such events can be accurately determined, and the efficiency of healthcare resource allocation can be improved and costs reduced by targeting such interventions to those who need them most and / or away from those who do not need them most. Furthermore, if patients have accurate and short-term information about their individualized likelihood of cardiovascular events, it will be harder to dismiss than population-based long-term information, leading to improved medication adherence that will result in improved lifestyle choices and benefits. Existing multi-marker tests require the collection of multiple samples from an individual or require samples to be split between multiple assays. Improved tests requiring only a single blood, urine, or other sample type and only one assay would be ideal. Thus, there is a need for biomarkers, methods, devices, reagents, systems, and kits that enable the prediction of cardiovascular events within a one-year period. [Overview of the project]

[0016] This application includes biomarkers, methods, reagents, apparatus, systems, and kits for predicting the risk of an individual with chronic heart failure, e.g., stable chronic heart failure, of developing a cardiovascular (CV) event within 90, 180, or 1 year. The biomarkers of this application were identified using slow-dissociation-rate multiple aptamer-based assays described in detail herein. By using the slow-dissociation-rate multiple aptamer-based biomarker identification methods described herein, this application describes a set of biomarkers useful in predicting the likelihood of a cardiovascular event within 90, 180, or 1 year in patients with chronic heart failure, e.g., stable chronic heart failure.

[0017] In some embodiments, the individual has stable chronic heart failure with preserved ejection fraction (HFpEF). In some embodiments, the individual has reduced ejection fraction (HFrEF). In some embodiments, the prognosis of the individual's one-year mortality is predicted.

[0018] Cardiovascular disease involves multiple biological processes and tissues. Examples of biological systems and processes associated with cardiovascular disease include inflammation, thrombosis, disease-related angiogenesis, platelet activation, and macrophages. These include lophage activation, acute hepatic response, extracellular matrix remodeling, and renal function. These processes may be observed depending on sex, menopausal status, and age, as well as the state of coagulation and vascular function. Since these systems communicate with each other, partly through protein-based signaling pathways, and multiple proteins can be measured in a single blood sample, the present invention provides a single-sample, single-assay multi-protein-based test that focuses on proteins derived from specific biological systems and processes involved in chronic heart failure.

[0019] In some embodiments, a method is provided for screening subjects for the risk of cardiovascular (CV) events, the method comprising forming a biomarker panel having N biomarker proteins and detecting the level of each of the N biomarker proteins in a sample derived from the subject, where N is at least 2, and a) at least 2 of the N biomarker proteins are selected from HCC-1, RNAS6, PAP1, SVEP1, and ATL2, or b) at least 1 of the N biomarker proteins is selected from HCC-1, RNAS6, PAP1, SVEP1, and ATL2, and at least 1 of the N biomarker proteins is selected from N-terminal pro-BNP, RSPO4, BNP, MIC-1, FABPA, ILRL1, ANGP2, HE4, TAGL, RNAS1, and TSP2.

[0020] In some embodiments, a method is provided to predict the likelihood of a subject undergoing a CV event, the method comprising forming a biomarker panel having N biomarker proteins and detecting the level of each of the N biomarker proteins in a sample derived from the subject, where N is at least 2, and a) at least 2 of the N biomarker proteins are selected from HCC-1, RNAS6, PAP1, SVEP1, and ATL2, or b) at least 1 of the N biomarker proteins is selected from HCC-1, RNAS6, PAP1, SVEP1, and ATL2, and at least 1 of the N biomarker proteins is selected from N-terminal pro-BNP, RSPO4, BNP, MIC-1, FABPA, ILRL1, ANGP2, HE4, TAGL, RNAS1, and TSP2.

[0021] In some embodiments, at least two of the N biomarker proteins are RNAS6 and PAP1. In some embodiments, at least two of the N biomarker proteins are RNAS6 and ATL2. In some embodiments, at least two of the N biomarker proteins are HCC-1 and PAP1. In some embodiments, at least two of the N biomarker proteins are HCC-1 and ATL2. In some embodiments, at least two of the N biomarker proteins are HCC-1 and RNAS6. In some embodiments, at least two of the N biomarker proteins are PAP1 and SVEP1. In some embodiments, at least two of the N biomarker proteins are HCC-1 and SVEP1. In some embodiments, at least two of the N biomarker proteins are RNAS6 and SVEP1. In some embodiments, at least two of the N biomarker proteins are PAP1 and ATL2. In some embodiments, all N biomarker proteins are selected from HCC-1, RNAS6, PAP1, SVEP1, ATL2, N-terminal pro-BNP, RSPO4, BNP, MIC-1, FABPA, ILRL1, ANGP2, HE4, TAGL, RNAS1, and TSP2. In some embodiments, one of the N biomarker proteins is MIC-1. In some embodiments, one of the N biomarker proteins is RNAS1. In any of the embodiments described above, N is 2, N is 3, N is 4, N is 5, N is 6, N is 7, N is 8, N is 9, N is 10, N is 11, N is 12, N is 13, N is 14, N is 15, or N is 16. In some embodiments, the subject has heart failure with reduced ejection fraction. In some embodiments, cardiovascular The present invention provides a method for predicting the risk of event-dependent (CV) events, comprising forming a biomarker panel having N biomarker proteins and detecting the levels of each of the N biomarker proteins in a sample derived from the subject, where N is at least 2, at least one of the N biomarker proteins is selected from RET and CRDL1, and at least one of the N biomarker proteins is selected from tetranectin, N-terminal pro-BNP, TNNT2, CA125, MIC-1, SLPI, HE4, MMP-12, HSPB6, WISP-2, GHR, and IGFBP-2.

[0022] In some embodiments, a method is provided to predict the likelihood of a subject undergoing a CV event, the method comprising forming a biomarker panel containing N biomarker proteins and detecting the level of each of the N biomarker proteins in a sample derived from the subject, where N is at least 2, at least one of the N biomarker proteins is selected from RET and CRDL1, and at least one of the N biomarker proteins is selected from tetranectin, N-terminal pro-BNP, TNNT2, CA125, MIC-1, SLPI, HE4, MMP-12, HSPB6, WISP-2, GHR, and IGFBP-2.

[0023] In some embodiments, at least two of the N biomarker proteins are RET and CRDL1. In some embodiments, one of the N biomarker proteins is MIC-1. In some embodiments, one of the N biomarker proteins is HE4. In some embodiments, one of the N biomarker proteins is tetranectin. In some embodiments, one of the N biomarker proteins is GHR. In some embodiments, one of the N biomarker proteins is CA125. In some embodiments, one of the N biomarker proteins is N-terminal proBNP. In some embodiments, one of the N biomarker proteins is IGFBP-2. In some embodiments, one of the N biomarker proteins is WISP-2. In some embodiments, one of the N biomarker proteins is TNNT2. In some embodiments, one of the N biomarker proteins is HSPB6. In some embodiments, one of the N biomarker proteins is SLPI. In some embodiments, one of the N biomarker proteins is MMP-12. In some embodiments, all of the N biomarker proteins are selected from RET, CRDL1, tetranectin, N-terminal proBNP, TNNT2, CA125, MIC-1, SLPI, HE4, MMP-12, HSPB6, WISP-2, GHR, and IGFBP-2. In any of the foregoing variations, N is 2, N is 3, N is 4, N is 5, N is 6, N is 7, N is 8, N is 9, N is 10, N is 11, N is 12, N is 13, or N is 14. In some embodiments, the subject has heart failure with preserved ejection fraction.

[0024] In various embodiments, the CV event is death.

[0025] In some embodiments, the risk or possibility that a subject will develop a CV event within one year from the date of collecting a sample from the subject is screened or predicted. In some embodiments, the risk or possibility that a subject will develop a CV event within 180 days from the date of collecting a sample from the subject is screened or predicted. In some embodiments, the risk or possibility that a subject will develop a CV event within 90 days from the date of collecting a sample from the subject is screened or predicted. In some embodiments, when the level of each of at least 2 of N biomarker proteins is abnormal compared to the control level of the corresponding biomarker protein, 1 year, 180 days, or 90 days from the date of collecting a sample from the subject the subject has a high risk or possibility of developing a CV event within the above period. In some embodiments, when the level of each of N biomarker proteins is abnormal compared to the control level of the corresponding biomarker protein, the subject has a high risk or possibility of developing a CV event within 1 year, 180 days, or 90 days from the date of collecting a sample from the subject. In some embodiments, the risk or possibility that a subject will develop a CV event within 1 year, 180 days, or 90 days from the date of collecting a sample from the subject is calculated as the survival probability of 1 year, 180 days, or 90 days from the date of collecting a sample from the subject.

[0026] In some embodiments, the sample is selected from the group consisting of a blood sample, a serum sample, a plasma sample, and a urine sample. In some embodiments, the sample is a blood sample. In some embodiments, the method is performed in vitro.

[0027] In some embodiments, the method involves contacting a biomarker protein from a sample derived from a target with a set of capture reagents, each of which specifically binds to one biomarker protein to be detected. In some embodiments, two of the capture reagents bind to the same biomarker protein to be detected. In some embodiments, two capture reagents specifically bind to SVEP1, and the two capture reagents are aptamers containing different sequences. In some embodiments, the method involves contacting a biomarker protein from a sample derived from a target with a set of capture reagents, each of which specifically binds to a different biomarker protein to be detected. In some embodiments, each capture reagent is an antibody or an aptamer. In some embodiments, each biomarker capture reagent is an aptamer. In some embodiments, at least one of the aptamers is an aptamer with a slow dissociation rate. In some embodiments, at least one slow dissociation rate aptamer comprises a nucleotide having at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten modifications. In some embodiments, each slow dissociation rate aptamer comprises a dissociation rate (t) of ≥30 min, ≥60 min, ≥90 min, ≥120 min, ≥150 min, ≥180 min, ≥210 min, or ≥240 min. 1 / 2 ) binds to the target protein.

[0028] In some embodiments, the risk or likelihood of a CV event is determined by the biomarker level to be detected and a) information corresponding to the subject's physical descriptor, b) information corresponding to the subject's weight change, c) information corresponding to the subject's ethnicity, d) information corresponding to the subject's sex, e) information corresponding to the subject's smoking history, f) information corresponding to the subject's drinking history, g) information corresponding to the subject's occupational history, h) information corresponding to the subject's family history of cardiovascular disease or other circulatory conditions, i) information corresponding to the presence or absence of at least one gene marker associated with a higher risk of cardiovascular disease in the subject or their family, j) information corresponding to the subject's clinical symptoms, k) information corresponding to other clinical tests, and l) information corresponding to the subject's gene expression levels. The report is based on, and at least one additional biomedical information selected from the following: m) information corresponding to the intake of known cardiovascular risk factors of the subject, such as high saturated fat diet, high salt diet, and high cholesterol diet; n) information corresponding to the subject's imaging results obtained by techniques selected from the group consisting of electrocardiogram, echocardiogram, carotid artery ultrasound of intima-media thickness, flow-dependent vasodilation response test, pulse wave velocity, ankle-brachial index, stress echocardiogram, myocardial perfusion imaging, coronary artery calcium examination by CT, high-resolution CT angiography, MRI imaging, and other imaging techniques; o) information on the subject's drug treatment; p) information corresponding to the subject's age; and q) information on the subject's renal function.

[0029] In some embodiments, at least one item of additional biomedical information is information corresponding to the age of the subject. In some embodiments, the method is used to calculate medical insurance premiums or life insurance premiums. This includes determining the risk or likelihood of a CV event in order to make a decision. In some embodiments, the method further includes determining the coverage or premiums of a medical or life insurance policy. In some embodiments, the method further includes using the information obtained by the method to predict and / or manage the use of medical resources. In some embodiments, the method further includes using the information obtained by the method to enable a decision to acquire or purchase a medical business, hospital, or enterprise.

[0030] In some embodiments, a kit is provided comprising N biomarker protein capture reagents, where N is at least 2, and at least one of the capture reagents binds to HCC-1, RNAS6, PAP1, SVEP1, or ATL2, and at least one of the capture reagents binds to N-terminal pro-BNP, RSPO4, BNP, MIC-1, FABPA, ILRL1, ANGP2, HE4, TAGL, RNAS1, or TSP2. In some embodiments, two of the capture reagents bind to SVEP1, and each of the remaining capture reagents binds to a different protein selected from HCC-1, RNAS6, PAP1, ATL2, N-terminal pro-BNP, RSPO4, BNP, MIC-1, FABPA, ILRL1, ANGP2, HE4, TAGL, RNAS1, and TSP2. In some embodiments, a kit is provided comprising N biomarker protein capture reagents, where N is at least 2, at least one of the capture reagents binds to RET or CRDL1, and at least one of the capture reagents binds to tetranectin, N-terminal pro-BNP, TNNT2, CA125, MIC-1, SLPI, HE4, MMP-12, HSPB6, WISP-2, GHR, or IGFBP-2. In some embodiments, each capture reagent binds to a different biomarker protein. In some embodiments, N is 2, N is 3, N is 4, N is 5, N is 6, N is 7, N is 8, N is 9, N is 10, N is 11, N is 12, N is 13, N is 14, N is 15, N is 16, or N is 17. In some embodiments, N is 2, N is 3, or N is 4, or N is 5, or N is 6, or N is 7, or N is 8, or N is 9, or N is 10, or N is 11, or N is 12, or N is 13, or N is 14.

[0031] In some embodiments, each of the N biomarker protein capture reagents specifically binds to a biomarker protein selected from Table 1. In some embodiments, each of the N biomarker protein capture reagents specifically binds to a biomarker protein selected from Table 2. In some embodiments, each of the N biomarker capture reagents is an antibody or an aptamer. In some embodiments, each biomarker capture reagent is an aptamer. In some embodiments, at least one aptamer is a slow-dissociation aptamer. In some embodiments, at least one slow-dissociation aptamer contains at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 modified nucleotides. In some embodiments, each slow-dissociation aptamer has a dissociation rate (t) of ≥30 min, ≥60 min, ≥90 min, ≥120 min, ≥150 min, ≥180 min, ≥210 min, or ≥240 min. 1 / 2 ) binds to the target protein. In some embodiments, the kit is intended for use in detecting N types of biomarker proteins in a sample derived from a subject. In some embodiments, the kit is intended for use in determining the risk or likelihood of a subject experiencing a CV event within one year from the date of sampling from a subject with heart failure. In some embodiments, the CV event is death. In some embodiments, the subject has heart failure with reduced ejection fraction. In some embodiments, the subject has heart failure with preserved ejection fraction. [Brief explanation of the drawing]

[0032] [Figure 1] This shows the observed Kaplan-Meier survival probabilities for the HFrEF model training dataset, where individuals are divided into quartiles based on their predicted event probabilities over 365 days. The first to fourth quartiles are shown in the top row (quartile 1), the second row (quartile 2), the third row (quartile 3), and the bottom row (quartile 4). [Figure 2A]The Kaplan-Meier survival curves for each quartile in the validation dataset of the HFrEF model are shown. [Figure 2B] The Kaplan-Meier survival curves for each quartile in the validation dataset of the HFrEF model are shown. The shaded areas represent the 95% confidence intervals for the Kaplan-Meier estimates. [Figure 3] This shows the observed Kaplan-Meier survival probabilities for the HFpEF model training dataset, with individuals divided into quartiles based on their predicted event probabilities over 365 days. The first to fourth quartiles are shown in the top row (quartile 1), second row (quartile 2), third row (quartile 3), and bottom row (quartile 4). The shaded areas represent the 95% confidence intervals for the Kaplan-Meier estimates. [Figure 4] This figure shows the Kaplan-Meier survival curves for each quartile in the validation dataset of the HFpEF model. The shaded areas represent the 95% confidence intervals for the Kaplan-Meier estimates. [Figure 5] This specification provides a non-limiting, exemplary computer system for use with the various computer implementations described herein. [Figure 6] This describes a non-limiting, exemplary aptamer assay that can be used to detect one or more biomarkers in a biological sample. [Figure 7] This shows specific exemplary modified pyrimidines that can be incorporated into aptamers, such as aptamers with slow dissociation rates. [Figure 8] This shows specific exemplary modified pyrimidines that can be incorporated into aptamers, such as aptamers with slow dissociation rates. [Figure 9] This shows specific exemplary modified pyrimidines that can be incorporated into aptamers, such as aptamers with slow dissociation rates. [Modes for carrying out the invention]

[0033] While the present invention is described with reference to certain representative embodiments, it should be understood that the present invention is defined by the claims and is not limited to those embodiments.

[0034] Those skilled in the art will recognize many methods and materials similar to or equivalent to those described herein that may be used in the implementation of the present invention. The present invention is by no means limited to the methods and materials described herein.

[0035] Unless otherwise defined, technical and scientific terms used herein have meanings that are generally understood by those skilled in the art to which the present invention pertains. Any methods, apparatus, and materials similar or equivalent to those described herein may be used in the practice of the present invention, but specific methods, apparatus, and materials are described herein.

[0036] All publications, published patent documents, and patent applications cited herein are incorporated by reference to the extent that each individual publication, published patent document, or patent application is specifically and individually indicated as being incorporated by reference herein.

[0037] As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “contains,” “containing,” and any variations thereof are intended to encompass non-exclusive inclusions, thereby including, any process, method, product-by-process, or composition of substance that includes, includes, or contains an element or set of elements may include other elements not expressly listed.

[0038] This application includes biomarkers, methods, apparatus, reagents, systems, and kits for predicting the risk of short-term cardiovascular events within a specified period, for example, within 90 days, 180 days, or 1 year.

[0039] As used herein, “cardiovascular event” or “CV event” broadly encompasses stroke, transient ischemic attack (TIA), myocardial infarction (MI), death, and / or hospitalization due to heart failure in subjects with chronic heart failure. In some embodiments, a “cardiovascular event” is hospitalization due to heart failure or death. In some embodiments, a “cardiovascular event” is hospitalization due to heart failure. In some embodiments, a “cardiovascular event” is death.

[0040] As used herein, the term “heart failure” or “HF” refers to a complex clinical syndrome resulting from structural or functional impairment of ventricular filling or ejection. Typical symptoms of HF include dyspnea and fatigue, which may limit exercise tolerance and fluid retention, and may result in pulmonary and / or visceral congestion and / or peripheral edema. The clinical syndrome of HF may be due to damage to the pericardium, myocardium, endocardium, heart valves, or great vessels, or certain metabolic abnormalities. Many HF patients exhibit symptoms due to left ventricular (LV) myocardial dysfunction. (Yancy et al.) al.,2013 ACCF / AHA guideline for the management of heart failure:A report of the American college of cardiology foundation / American heart association task force on practice guidelines. J Amer College Cardiol, 62(16), e147-e239. (2013). As used herein, the term “chronic heart failure” or “CHF” means stable chronic heart failure unless otherwise indicated.

[0041] As used herein, the term “ejection fraction” or “EF” refers to the percentage of blood pumped out of the heart with each contraction. This is typically measured by echocardiography and serves as a general measure of cardiac function in question. It can be measured as the amount of blood pumped out of the left ventricle of the heart with each contraction. It can also be measured as the amount of blood pumped out of the right ventricle of the heart to the lungs. As used herein, the term “ejection fraction” or “EF” refers to left ventricular ejection fraction unless otherwise indicated. Patients with an ejection fraction of 50% or greater are classified as having “heart failure with preserved ejection fraction” (HFpEF), and patients with an ejection fraction of less than 40% are classified as having “heart failure with reduced ejection fraction” (HFrEF). (Ponikowski P, Voors A, Anker S, et al. 2016 ESC Guidelines for the diagnostic and treatment of acute and chronic heart failure. Eur J Heart) Fail.2016;37:2129-200).

[0042] In some embodiments, biomarkers are provided that can be used alone or in various combinations to assess the risk or likelihood of future cardiovascular events within a one-year period, where a cardiovascular event is defined as hospitalization due to heart failure or death. Representative embodiments, as described below, include the biomarkers listed in Table 1 or Table 2.

[0043] Some of the CV event biomarkers described may be useful on their own for assessing the risk or likelihood of a CV event, but methods for grouping multiple subsets of CV event biomarkers are also described herein, where each grouping or subset selection is useful as a panel of two or more biomarkers, and is referred herein interchangeably to as a “biomarker panel” and “panel.” In some embodiments, The CV event is death. Accordingly, various embodiments provide combinations of at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, or all sixteen of the biomarkers in Table 1. Other various embodiments provide combinations of at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, or all fourteen of the biomarkers in Table 2.

[0044] "Biological sample," "sample," and "test sample" are used interchangeably herein to mean any substance, biological fluid, tissue, or cell obtained from or otherwise derived from an individual. Examples include blood (e.g., whole blood, leukocytes, peripheral blood mononuclear cells, buffy coat, plasma, and serum), sputum, tears, mucus, nasal lavage fluid, nasal aspirate, urine, saliva, peritoneal lavage fluid, ascites, cystic fluid, glandular fluid, lymph, bronchial aspirate, synovial fluid, joint aspirate, organ secretions, cells, cell extracts, and cerebrospinal fluid. Examples also include fractions separated for all of the above experiments. For example, a blood sample may be fractionated into serum, plasma, or fractions containing a specific type of blood cell, e.g., red blood cells or leukocysts (white blood cells). In some embodiments, a blood sample is a dried blood spot. In some embodiments, a plasma sample is a dried plasma spot. In some embodiments, the sample may be a combination of samples derived from an individual, such as a combination of tissue and liquid samples. The term “biological sample” also includes substances containing homogenized solid material derived from, for example, stool samples, tissue samples, or tissue biopsies. The term “biological sample” also includes substances derived from tissue cultures or cell cultures. Any preferred method for obtaining a biological sample may be used, exemplary methods including, for example, venotomy, cotton swabs (e.g., oral swabs), and microneedle aspiration biopsy. Exemplary tissues that can be microneedle aspiration include lymph nodes, lungs, thyroid gland, breast, pancreas, and liver. Samples may also be collected by, for example, microdissection (e.g., laser capture microdissection (LCM) or laser microdissection (LMD)), bladder lavage, smears (e.g., PAP smears), or mammary duct lavage. A “biological sample” obtained from or derived from an individual also includes any such sample that has been processed in any preferred method after being obtained from that individual. In some embodiments, the biological sample is a plasma sample.

[0045] Furthermore, in some embodiments, biological samples may be obtained by taking biological samples from a large number of individuals and pooling them, or by pooling aliquots of biological samples from each individual. Pooled samples may be processed as described herein as single-individual-derived samples, for example, if a poor prognosis is confirmed in the pooled samples, biological samples from each individual may be retested to determine which individual(s) have a high or low risk of CV events, such as death.

[0046] For the purposes of this specification, the phrase “data attributable to a biological sample of an individual” is intended to mean that any form of such data is obtained from or generated using a biological sample of that individual. The data may be reformatted, modified, or have its numerical values ​​altered to some extent, such as by conversion from units in one measurement system to units in another, after it has been generated, but the data is understood to be obtained from or generated using a biological sample.

[0047] "Target," "target molecule," and "analyte" are used interchangeably herein to refer to any molecule of interest that may be present in a biological sample. "Molecule of interest" refers to any minor alteration of a particular molecule, for example, in the case of a protein, e.g., amino acid sequence, disulfide. Minor changes in phytobond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other arbitrary operation or modification, such as conjugation with a labeled component, which do not substantially alter the identity of the molecule. "Target molecule," "target," or "analyte" refers to one type or a set of copies of one type of molecule or multimolecular structure. Exemplary target molecules include proteins, polypeptides, nucleic acids, carbohydrates, lipids, polysaccharides, glycoproteins, hormones, receptors, antigens, antibodies, aphibodies, antibody mimetics, viruses, pathogens, toxic substances, substrates, metabolites, transition state analogs, cofactors, inhibitors, drugs, dyes, nutrients, growth factors, cells, tissues, and any fragment or part of any of the foregoing. In some embodiments, the target molecule is a protein, in which case the target molecule may be referred to as the "target protein."

[0048] As used herein, “scavenger” or “scavenger” refers to a molecule capable of specifically binding to a biomarker. “Target protein scavenger” refers to a molecule capable of specifically binding to a target protein. Non-limiting exemplary scavengers include aptamers, antibodies, adonectin, ankyrin, other antibody mimetics and other protein scaffolds, autoantibodies, chimeras, small molecules, nucleic acids, lectins, ligand-binding receptors, imprinted polymers, avimers, peptidomimetics, hormone receptors, cytokine receptors, synthetic receptors, and modified or fragmented versions of any of the above scavengers. In some embodiments, the scavenger is selected from aptamers and antibodies.

[0049] The term "antibody" refers to full-length antibodies of any species, as well as fragments and derivatives of such antibodies, including Fab fragments, F(ab')2 fragments, single-chain antibodies, Fv fragments, and single-chain Fv fragments. The term "antibody" also refers to antibodies obtained by synthesis, such as antibodies and fragments obtained by phage display, affibodies, nanobodies, etc.

[0050] As used herein, “marker” and “biomarker” are used interchangeably to refer to a target molecule that indicates or is a sign of a normal or abnormal process in an individual, or indicates or is a sign of a disease or other condition in an individual. More specifically, a “marker” or “biomarker” is an anatomical, physiological, biochemical, or molecular parameter associated with the presence of a particular physiological condition or process, whether normal or abnormal, and if abnormal, whether chronic or acute. Biomarkers can be detected and measured by various methods, such as laboratory assays and medical imaging. In some embodiments, the biomarker is a target protein.

[0051] As used herein, “biomarker level” and “level” refer to measured values ​​obtained using any analytical method for detecting a biomarker in a biological sample, indicating the presence, absence, absolute or concentration, relative or concentration, titer, level, expression level, or ratio of measured levels of the biomarker in the biological sample, relating to or corresponding to the biomarker in the biological sample. The exact nature of “level” depends on the specific design and components of the particular analytical method used to detect the biomarker.

[0052] If a biomarker indicates or is a symptom of an abnormal process, disease, or other condition in an individual, that biomarker is generally described as either overexpressed or underexpressed compared to the expression level or value of a biomarker that indicates or is a symptom of a normal process, disease, or other condition in the individual. “Upregulated,” “upregulated,” “overexpressed,” “overexpressed,” and any variations thereof refer to the value or level of a biomarker in a biological sample. The term is used interchangeably to refer to a value or level that exceeds the value or level (or range of values ​​or levels) of the biomarker that is normally detected in similar biological samples from healthy or normal individuals. The term may also refer to a value or level of a biomarker in a biological sample that exceeds the value or level (or range of values ​​or levels) of the biomarker that may be detected at different stages of a particular disease.

[0053] The terms “downregulated,” “downregulated,” “underexpressed,” “underexpressed,” and any variations thereof are used interchangeably to refer to a value or level of a biomarker in a biological sample that is below the value or level (or range of values ​​or levels) of the biomarker that is normally detected in similar biological samples from healthy or normal individuals. The term may also refer to a value or level of a biomarker in a biological sample that is below the value or level (or range of values ​​or levels) of the biomarker that may be detected at different stages of a particular disease.

[0054] Furthermore, a biomarker that is either overexpressed or underexpressed may also be described as having a "differentially expressed" or "differential level" or "differential value" compared to the "normal" expression level or value of the biomarker, which indicates or is a sign of the absence of a normal process or disease or other condition in an individual. Thus, "differential expression" of a biomarker may also be described as a variation from the "normal" expression level of the biomarker.

[0055] The “control level” of a target molecule refers to the level of the target molecule in the same type of sample derived from individuals without disease or condition, individuals not suspected of having disease or condition, or individuals without risk of having disease or condition, or individuals who have experienced a primary or first cardiovascular event but have not experienced a secondary cardiovascular event, or individuals with stable cardiovascular disease. The control level may refer to the mean level of the target molecule in samples derived from a population of individuals without disease or condition, individuals not suspected of having disease or condition, or individuals without risk of having disease or condition, or individuals who have experienced a primary or first cardiovascular event but have not experienced a secondary cardiovascular event, or individuals with stable cardiovascular disease, or a combination thereof.

[0056] As used herein, “individual,” “subject,” and “patient” are used interchangeably to refer to mammals. Mammalian individuals may be human or non-human. In various embodiments, the individual is human. A healthy or normal individual is one in which the disease or condition of interest (e.g., chronic heart failure or cardiovascular events, e.g., myocardial infarction, stroke, and hospitalization due to heart failure) is not detected by conventional diagnostic methods.

[0057] The terms “to diagnose,” “to make a diagnosis,” and their variations refer to detecting, determining, or identifying an individual’s health status or condition based on one or more signs, symptoms, data, or other information relating to that individual. An individual’s health status may be diagnosed as healthy / normal (i.e., a diagnosis of the absence of disease or condition) or as disease / abnormal (i.e., a diagnosis of the presence of disease or condition, or an assessment of the characteristics of disease or condition). The terms “to diagnose,” “to make a diagnosis,” and “diagnosis” include, with respect to a particular disease or condition, the initial detection of such disease; the characterization or classification of such disease; the detection of progression, remission, or relapse of such disease; and the detection of disease responses after treatment or therapy has been administered to the individual. Predicting CV event risk includes distinguishing between individuals with and without a high CV event risk.

[0058] "Prognose," "prognosing," "prognosis," and their variations refer to predicting a disease or condition. This refers to predicting the future course of the disease or condition in an individual (e.g., predicting patient survival), and such terminology includes evaluating the response of the disease or condition to the individual after treatment or therapy has been administered.

[0059] The terms “evaluate,” “assess,” and their variations encompass both “diagnosis” and “prognosis prediction,” and include decisions or predictions about the future course of a disease or condition in an individual without the disease, as well as decisions or predictions about the risk of recurrence of the disease or condition in an individual who has clearly recovered from the disease or whose condition has subsided. The term “evaluate” also includes assessing an individual’s response to treatment, for example, predicting whether an individual is likely to respond well to a drug or not (or, for example, experience toxic effects or other undesirable side effects), selecting a drug to administer to an individual, or monitoring or determining an individual’s response to treatment given to an individual. Therefore, “assessing” the risk of a CV event may include, for example, any of the following: predicting the future risk of a CV event in an individual; predicting the risk of a CV event in an individual that is clearly free from CV problems; predicting a specific type of CV event; predicting when a CV event will occur; or determining or predicting an individual’s response to a CV treatment, or selecting a CV treatment to be administered to an individual based on the determination of biomarker values ​​derived from the individual’s biological sample. The assessment of the risk of a CV event may include embodiments such as, for example, the assessment of the risk of a CV event on a continuous scale, or the classification of the risk of a CV event in a progressively increasing classification. The classification of the risk may include, for example, two or more classifications, such as “no increased risk of a CV event,” “increased risk of a CV event,” and / or “below average CV event risk.” In some embodiments, the assessment of the risk of a CV event is for a predetermined period. Non-limiting exemplary such predetermined periods include 90 days, 180 days, and 1 year. In various embodiments, the CV event is death.

[0060] As used herein, “additional biomedical information” refers to one or more assessments of individuals using biomarkers other than those described herein, relating to CV risk, or more specifically, CV event risk. "Additional biomedical information" may include any of the following: physical descriptors of the individual, including the individual's height and / or weight; the individual's age; the individual's sex; weight changes; the individual's ethnicity; occupational history; family history of cardiovascular disease (or other circulatory disorders); the presence of any genetic marker(s) associated with a higher risk of cardiovascular disease (or other circulatory disorders) in the individual or family; changes in carotid intima thickness; clinical symptoms such as chest pain, weight gain, or decreased gene expression levels; physical descriptors of the individual, including physical descriptors observed by radiographic imaging; smoking status; drinking history; occupational history; dietary habits, i.e., intake of salt, saturated fat, and cholesterol; caffeine intake; and imaging information, e.g., electrocardiogram, echocardiogram, carotid ultrasound of intima-media thickness, flow-dependent vasodilation response testing, pulse wave velocity, ankle-brachial index, stress echocardiogram, myocardial perfusion imaging, coronary calcium testing by CT, high-resolution CT angiography, MRI imaging, and other imaging techniques; and the individual's drug treatment. A combination of biomarker-level testing and evaluation of any additional biomedical information, including other clinical tests (e.g., HDL, LDL, CRP levels, Nt-proBNP, BNP, high-sensitivity troponin, galectin-3, serum albumin, creatine), may improve the sensitivity, specificity, and / or AUC of CV event prediction compared to, for example, biomarker testing alone or evaluation of any specific item among the additional biomedical information (e.g., carotid intima imaging alone). The additional biomedical information may be obtained from the individual using standardized techniques known in the art, for example, from the individual themselves using standardized patient questionnaires or health history questionnaires, or from healthcare professionals, etc. Compared to evaluating any specific item alone (e.g., CT imaging only), this approach may improve sensitivity, specificity, and / or thresholds for predicting CV events (or other cardiovascular applications).

[0061] As used herein, “detect” or “determine” a biomarker value includes the use of both instruments used to observe and record a signal corresponding to a biomarker level, and the substance / substance(s) required to generate that signal. In various embodiments, the biomarker level is detected using any preferred method, including fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic waves, mass spectrometry, infrared spectroscopy, Raman spectroscopy, atomic force microscopy, scanning tunneling microscopy, electrochemical detection methods, nuclear magnetic resonance, quantum dots, and the like.

[0062] In this specification, “solid support” refers to any substrate having a surface to which molecules can be directly or indirectly bound by either covalent or non-covalent bonds. “Solid supports” can take various physical forms, including, for example, membranes; tips (e.g., protein tips); slides (e.g., slide glasses or coverslips); columns; hollow, solid, semi-solid, porous or cavity-containing particles, e.g., beads; gels; fibers containing optical fiber materials; matrices; and sample containers. Exemplary sample containers include sample wells, tubes, capillaries, vials, and any other containers, grooves or depressions capable of holding samples. Sample containers may be included in multi-sample platforms, e.g., microtiter plates, slide glasses, microfluidic devices, etc. Supports may consist of natural or synthetic materials, organic or inorganic materials. The composition of the solid support to which the capture reagent is bound generally depends on the method of binding (e.g., covalent bonding). Other exemplary containers include microdroplets, microfluidically controlled or bulk oil-in-water emulsions, in which assays and related operations may be performed. Suitable solid supports include, for example, plastics, resins, polysaccharides, silica or silica-based materials, functionalized glass, modified silicon, carbon, metals, inorganic glass, membranes, nylon, natural fibers (e.g., silk, wool, and cotton), and polymers. The materials constituting the solid support may contain reactive groups such as carboxy, amino, or hydroxyl groups, which are used to bind the capture reagent. Examples of polymer solid supports include polystyrene, polyethylene glycol tetraphthalate, polyvinyl acetate, polyvinyl chloride, polyvinylpyrrolidone, polyacrylonitrile, polymethyl methacrylate, polytetrafluoroethylene, butyl rubber, styrene-butadiene rubber, natural rubber, polyethylene, polypropylene, (poly)tetrafluoroethylene, (poly)vinylidene fluoride, polycarbonate, and polymethylpentene. Suitable solid support particles that can be used include, for example, coded particles, such as Luminex® type coded particles, magnetic particles, and glass particles.

[0063] Exemplary use of biomarkers In various exemplary embodiments, a method is provided for assessing the risk of cardiovascular events in an individual by detecting the value of one or more biomarkers corresponding to one or more biomarkers present in the individual's circulation, for example, in blood, serum, or plasma, by a number of analytical methods, for example, including any of the analytical methods described herein. These biomarkers are expressed differentially, for example, in individuals with a high risk of cardiovascular events compared to individuals without a high risk of cardiovascular events. Detection of differential expression of biomarkers in an individual is used, for example, to enable prediction of the risk of cardiovascular events within a period of 90 days, 180 days, or 1 year. In various embodiments, a cardiovascular event is hospitalization or death due to heart failure. In various embodiments, a cardiovascular event is death.

[0064] In addition to testing biomarker levels as independent diagnostic tests, biomarker levels may be performed in conjunction with the determination of single nucleotide polymorphisms (SNPs) or other gene damage or genetic variability indicating an increased risk of susceptibility to a disease or condition. (e.g., Amos et al.) See al., Nature Genetics 40, 616-622 (2009).

[0065] Biomarker levels may be used in conjunction with radiological screening. Biomarker levels may be used in conjunction with relevant symptoms or genetic testing. Detection of any of the biomarkers described herein may be useful in appropriately managing the clinical care of an individual after the risk of cardiovascular events has been assessed, including, for example, increasing the degree of treatment for high-risk individuals after the risk of cardiovascular events has been determined. In addition to testing biomarker levels in conjunction with relevant symptoms or risk factors, information on biomarkers may be evaluated in conjunction with other types of data, particularly data indicating the risk of cardiovascular events in an individual (e.g., patient medical history, symptoms, family history of cardiovascular disease, smoking or drinking history, risk factors, e.g., presence of genetic markers, and / or the status of other biomarkers). These various data may be evaluated by automated methods, such as computer programs / software that can be embodied in a computer or other device / apparatus.

[0066] Biomarker testing may be combined with guidelines and cardiovascular risk algorithms currently used in clinical practice. For example, the Framingham Risk Score uses risk factors to derive a risk score, such as LDL-cholesterol and HDL-cholesterol levels, abnormal glucose levels, smoking, systolic blood pressure, and diabetes. The frequency of high-risk patients increases with age, and men account for a higher proportion of high-risk patients than women.

[0067] Any of the biomarkers described may also be used in imaging studies. For example, contrast agents may bind to any of the described biomarkers, which may be used, among other uses, to aid in predicting the risk of cardiovascular events, to monitor responses to therapeutic interventions, and to select target populations in clinical trials.

[0068] Detection and determination of biomarkers and biomarker levels The biomarker levels of the biomarkers described herein can be detected using any of a variety of known analytical methods. In one embodiment, the biomarker value is detected using a capture reagent. In various embodiments, the capture reagent can be exposed to the biomarker in solution, or the capture reagent can be exposed to the biomarker while immobilized on a solid support. In other embodiments, the capture reagent has a feature portion which reacts with a second feature portion on a solid support. In these embodiments, the capture reagent can be exposed to the biomarker in solution, and then the feature portion on the capture reagent can be used together with the second feature portion on the solid support to immobilize the biomarker on the solid support. The capture reagent is selected based on the type of analysis to be performed. Examples of capture reagents include, but are not limited to, aptamers, antibodies, adnectin, ankyrin, other antibody mimetics, and other protein scaffolds, autoantibodies, chimeras, small molecules, F(ab')2 fragments, single-chain antibody fragments, Fv fragments, single-chain Fv fragments, nucleic acids, lectins, ligand-binding receptors, aphibodies, nanobodies, imprinted polymers, avimers, peptidomimetics, hormone receptors, cytokine receptors, and synthetic receptors, as well as modified versions and fragments thereof.

[0069] In some embodiments, biomarker levels are detected using a biomarker / capture reagent complex.

[0070] In some embodiments, the biomarker level is obtained from the biomarker / capture reagent complex and is detected indirectly, for example, as a result of a reaction following the interaction of the biomarker / capture reagent, but depends on the formation of the biomarker / capture reagent complex.

[0071] In some embodiments, the biomarker level is detected directly from the biomarker in the biological sample.

[0072] In some embodiments, biomarkers are detected using a multiplexing format that allows for the simultaneous detection of two or more biomarkers in a biological sample. In some embodiments of the multiplexing format, the capture reagents are immobilized directly or indirectly by covalent or non-covalent bonding at separate locations on a solid support. In some embodiments, the multiplexing format uses separate solid supports, where each solid support has its own capture reagent bonded to that solid support, e.g., quantum dots. In some embodiments, separate devices are used for the detection of each of the multiple biomarkers to be detected in the biological sample. The separate devices may be configured to allow each biomarker in the biological sample to be processed simultaneously. For example, a microtiter plate may be used, so that each well in the plate is used to analyze one or more biomarkers to be detected in the biological sample.

[0073] In one or more of the embodiments described above, components of the biomarker / capture reagent complex may be labeled using fluorescent tags to enable detection at biomarker levels. In various embodiments, the fluorescent label may be conjugated using known techniques to a capture reagent specific to one of the biomarkers described herein, and the corresponding biomarker level may then be detected using the fluorescent label. Suitable fluorescent labels include rare earth chelates, fluorescein and its derivatives, rhodamine and its derivatives, dansyl, allophycocyanin, PBXL-3, Qdot 605, Lissamine, phycoerythrin, Texas Red, and other similar compounds.

[0074] In some embodiments, the fluorescent label is a fluorescent dye molecule. In some embodiments, the fluorescent dye molecule comprises at least one substituted indolium ring system in which the substituent on the carbon at position 3 of the indolium ring contains a chemically reactive group or a conjugated substance. In some embodiments, the dye molecule is an AlexFluor molecule, e.g., Alexafluor 488, Alexafluor 532, Alexafluor 647, Alexafluor 680, or Alexafluor 700. In other embodiments, the dye molecule comprises a first type of dye molecule and a second type of dye molecule, e.g., two different Alexafluor molecules. In some embodiments, the dye molecule comprises a first type of dye molecule and a second type of dye molecule, and these two types of dye molecules have different emission spectra.

[0075] Fluorescence can be measured using a variety of measurement methods that are compatible with a wide range of assay formats. For example, spectrofluorometers are designed to analyze microtiter plates, microscope slides, printed arrays, cuvettes, etc. See Principles of Fluorescence Spectroscopy by JRLakowicz, Springer Science + Business Media, Inc., 2004. See Bioluminescence Chemiluminescence: Progress Current Applications; Philip E. Stanley Larry J. Kricka editors, World Scientific Publishing Company, January 2002.

[0076] In one or more embodiments, components of the biomarker / capture complex may be labeled with chemiluminescent tags to enable detection at the biomarker level. Suitable chemiluminescent substances include oxalyl chloride, rhodamine 6G, and Ru(bipy)3. 2+Examples include TMAE (tetrakis(dimethylamino)ethylene), pyrogallol (1,2,3-trihydroxybenzene), lucigenin, peroxyoxalate, aryloxalate, acridinium ester, and dioxetane.

[0077] In some embodiments, the detection method includes an enzyme / substrate combination that generates a detectable signal corresponding to a biomarker level. Generally, the enzyme catalyzes a chemical change in a chromogenic substrate, and this chemical change can be measured using a variety of techniques, including spectrophotometrics, fluorescence, and chemiluminescence. Suitable enzymes include, for example, luciferase, luciferin, malate dehydrogenase, urease, wasabi peroxidase (HRPO), alkaline phosphatase, β-galactosidase, glucoamylase, lysozyme, glucose oxidase, galactose oxidase, and glucose-6-phosphate dehydrogenase, uricase, xanthine oxidase, lactoperoxidase, and microperoxidase.

[0078] In some embodiments, the detection method may be a combination of fluorescence, chemiluminescence, radionuclides, or enzyme / substrate combinations that generate a measurable signal. In some embodiments, the generation of diverse signals may have unique and advantageous features in the biomarker assay format.

[0079] In some embodiments, the biomarker levels of the biomarkers described herein may be detected using any analytical method, including, as described below, singleplex aptamer assays, multiplex aptamer assays, singleplex or multiplex immunoassays, mRNA expression profiling, miRNA expression profiling, mass spectrometry, histological / cytological methods, etc.

[0080] Determination of biomarker levels using aptamer-based assays Assays aimed at detecting and quantifying physiologically important molecules in biological and other samples are essential tools in scientific research and healthcare. One type of such assay involves the use of microarrays containing one or more aptamers immobilized on a solid support. Each aptamer can bind to a target molecule in a highly specific manner and with extremely high affinity. See, for example, U.S. Patent No. 5,475,096, titled "Nucleic Acid Ligands." Also see, for example, U.S. Patents No. 6,242,246, 6,458,543, and 6,503,715, each titled "Nucleic Acid Ligand Diagnostic Biochip." When the microarray comes into contact with a sample, the aptamers bind to their respective target molecules present in the sample, thereby enabling the measurement of biomarker levels corresponding to biomarkers.

[0081] As used herein, “aptamer” refers to a nucleic acid that has a specific binding affinity to a target molecule. While affinity interactions are recognized as a matter of degree, in this context, “specific binding affinity” of an aptamer to its target generally means that the aptamer binds to its target with a much higher degree of binding affinity than it binds to other components in the test sample. An “aptamer” is a set of copies of one type or species of nucleic acid molecule containing a specific nucleotide sequence. An aptamer may contain any number of nucleotides, including any number of chemically modified nucleotides. “Aptamer” refers to two or more such molecules. Different aptamers are the same Aptamers may have either a number of nucleotides or different numbers of nucleotides. Aptamers may be DNA or RNA or chemically modified nucleic acids, and may be single-stranded, double-stranded, or contain double-stranded regions, and may contain higher-order structures. Aptamers may be photoaptamers, in which a photoreactive or chemically reactive functional group is included in the aptamer to enable the aptamer to be covalently linked to its corresponding target. Any of the aptamer methods disclosed herein may involve the use of two or more aptamers that specifically bind to the same target molecule. As further described below, aptamers may include tags. If an aptamer includes a tag, all copies of the aptamer do not need to have the same tag. Furthermore, if different aptamers each include a tag, these different aptamers may have either the same tag or different tags.

[0082] Aptamers can be identified using any known method, including the SELEX process. Once identified, aptamers can be prepared or synthesized according to any known method, including chemical and enzymatic synthesis.

[0083] The terms “SELEX” and “SELEX process” are used interchangeably herein to generally refer to a combination of (1) the selection of aptamers that interact with a target molecule in a desired manner, for example, those that bind with high affinity to proteins, and (2) the amplification of those selected nucleic acids. The SELEX process may be used to identify aptamers that have high affinity for a specific target or biomarker.

[0084] SELEX generally involves preparing a candidate nucleic acid mixture, binding the candidate mixture to a desired target molecule to form an affinity complex, separating the affinity complex from unbound candidate nucleic acids, separating and isolating nucleic acids from the affinity complex, purifying the nucleic acids, and identifying specific aptamer sequences. To further improve the affinity of the selected aptamers, this process may be performed in multiple rounds. This process may include amplification steps at one or more time points in the process. See, for example, U.S. Patent No. 5,475,096, titled "Nucleic Acid Ligands". The SELEX process can also be used to generate aptamers that do not covalently bind to a target, as well as aptamers that do not covalently bind to a target. See, for example, U.S. Patent No. 5,705,337, titled "Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Chemi-SELEX".

[0085] The SELEX process can be used to identify high-affinity aptamers containing modified nucleotides that confer improved properties to the aptamer, such as improved in vivo stability or improved delivery characteristics. Examples of such modifications include chemical substitutions at the ribose and / or phosphate and / or base positions. Aptamers containing modified nucleotides identified by the SELEX process are described in U.S. Patent No. 5,660,985, titled "High Affinity Nucleic Acid Ligands Containing Modified Nucleotides," which describes oligonucleotides containing nucleotide derivatives chemically modified at the 5' and 2' positions of a pyrimidine. U.S. Patent No. 5,580,737 (see above) describes highly specific aptamers containing one or more nucleotides modified with 2'-amino (2'-NH2), 2'-fluoro (2'-F), and / or 2'-O-methyl (2'-OMe). See also U.S. Patent Application Publication No. 20090098549, titled "SELEX and PHOTOSELEX," which describes nucleic acid libraries with enhanced physical and chemical properties, and their use in SELEX and photoSELEX.

[0086] SELEX can also be used to identify aptamers with desired dissociation rate characteristics. See U.S. Patent Application Publication 20090004667, entitled "Method for Generating Aptamers with Improved Off-Rates," which describes an improved SELEX process for producing aptamers that can bind to target molecules. A method for producing aptamers and photoaptamers with slower dissociation rates from each target molecule is described. The method includes a process of contacting a candidate mixture with a target molecule, forming a nucleic acid-target complex, and enriching slow-dissociation rate aptamers, wherein the nucleic acid-target complex with a fast dissociation rate dissociates and does not reform, while the complex with a slow dissociation rate remains intact. In addition, the method includes using modified nucleotides in the generation of the candidate nucleic acid mixture to produce aptamers with improved dissociation rate performance. Non-limiting exemplary modified nucleotides include, for example, the modified pyrimidines shown in Figures 7-9. In some embodiments, the aptamer comprises at least one nucleotide having modifications such as base modifications. In some embodiments, the aptamer comprises at least one nucleotide having hydrophobic modifications such as hydrophobic base modifications that enable hydrophobic contact with a target protein. In some embodiments, such hydrophobic contact contributes to higher hydrophilic and / or slower dissociation rate binding by the aptamer. Non-limiting exemplary nucleotides with hydrophobic modifications are shown in Figure 7. In some embodiments, the aptamer comprises at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten hydrophobic modifications in the aptamer, where each hydrophobic modification may be identical or different from one another. In some embodiments, at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten hydrophobic modifications in the aptamer may be selected independently of the hydrophobic modifications shown in Figure 7.

[0087] In some embodiments, the aptamer is a slow-dissociation aptamer. In some embodiments, a slow-dissociation aptamer (including an aptamer containing at least one nucleotide with hydrophobic modification) has a dissociation rate of ≥30 min, ≥60 min, ≥90 min, ≥120 min, ≥150 min, ≥180 min, ≥210 min, or ≥240 min (t 1 / 2 ) has.

[0088] In some embodiments, the assay uses aptamers containing photoreactive functional groups that allow the aptamer to covalently bond to or be “photocrosslinked” to its target molecule. See, for example, U.S. Patent No. 6,544,776, titled “Nucleic Acid Ligand Diagnostic Biochip.” These photoreactive aptamers are also called photoaptamers. See, for example, U.S. Patents No. 5,763,177, 6,001,577, and 6,291,184, titled “Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Photoselection of Nucleic Acid Ligands and Solution SELEX,” respectively. See also U.S. Patent No. 6,458,539, titled “of Nucleic Acid Ligands”. After the microarray is brought into contact with the sample and the photoaptamer is given the opportunity to bind to its target molecule, the photoaptamer is photoactivated, the solid support is washed, and any nonspecifically bound molecules are removed. Strict washing conditions may be used because the target molecule bound to the photoaptamer is not usually removed due to covalent bonds generated by the photoactivated functional group(s) on the photoaptamer. In this way, the assay enables the detection of biomarker levels corresponding to biomarkers in the test sample.

[0089] In some assay formats, aptamers are immobilized on a solid support before contact with the sample. However, under certain circumstances, immobilizing aptamers before contact with the sample may not provide an optimal assay. For example, pre-immobilization of aptamers can lead to inefficient mixing of the aptamer with the target molecule on the solid support surface, potentially prolonging the reaction time. Therefore, extending the incubation time allows for efficient binding of the aptamer to its target molecule. Furthermore, when photoaptamers are used in the assay, depending on the material used as the solid support, the solid support may tend to scatter or absorb the light used to influence the formation of covalent bonds between the photoaptamer and its target molecule. Additionally, depending on the method used, the surface of the solid support may be exposed to and affected by any labeling agents used, making the detection of the target molecule bound to the aptamer prone to inaccuracies. Finally, immobilization of aptamers onto a solid support generally involves an aptamer preparation step (i.e., immobilization) prior to exposure of the aptamer to the sample, and this preparation step may affect the activity or functionality of the aptamer.

[0090] Aptamer assays are also described that utilize a separation step designed to allow the aptamer to capture its target in solution, and then to remove specific components of the aptamer-target mixture before detection (see U.S. Patent Application Publication No. 20090042206, entitled “Multiplexed Analyses of Test Samples”). The described aptamer assays enable the detection and quantification of non-nucleic acid targets (e.g., protein targets) in test samples by detecting and quantifying nucleic acids (i.e., aptamers). The described methods create nucleic acid substitutes (i.e., aptamers) for detecting and quantifying non-nucleic acid targets, thereby enabling a wide variety of nucleic acid technologies, including amplification, to be applied to a broader range of desired targets, including protein targets.

[0091] Aptamers can be constructed to facilitate the separation of assay components from aptamer biomarker complexes (or covalent complexes of photoaptamer biomarkers) and to enable the isolation of aptamers for detection and / or quantification. In one embodiment, these constructs may include cleavable or releaseable elements within the aptamer sequence. In other embodiments, further functionality can be introduced to the aptamer, such as labeling or detectable components, spacer components, or specific binding tags or immobilization elements. For example, an aptamer may include a tag linked to the aptamer via a cleavable moiety, a label, a spacer component separating the label, and a cleavable moiety. In one embodiment, the cleavable element is a photocleavable linker. The photocleavable linker may be bound to a biotin moiety and a spacer moiety, may contain an NHS group for amine derivatization, and may be used to introduce a biotin group into the aptamer, thereby enabling the subsequent release of the aptamer in the assay.

[0092] Homogeneous assays performed using all assay components in solution do not require separation of the sample and reagents before signal detection. These methods are rapid and easy to use. These methods generate a signal based on molecular capture or a binding reagent that reacts with a specific target. In some embodiments of the methods described herein, the molecular capture reagent includes one or more aptamers and / or antibodies, and each of these specific targets may be biomarkers shown in Table 1 or Table 2.

[0093] In some embodiments, the signal generation method utilizes an anisotropic signal change resulting from the interaction of a fluorophore-labeled capture reagent with a specific biomarker target. When the labeled capture reagent reacts with its target, the molecular weight increases, and the complex is bound to the target. The rotational motion of the fluorophore becomes very slow, and the anisotropy value changes. By monitoring the change in anisotropy, the binding event can be used to quantitatively measure biomarkers in solution. Other methods include fluorescence polarization assays, molecular beacon methods, time-resolved fluorescence quenching, chemiluminescence, and fluorescence resonance energy transfer.

[0094] An exemplary solution-based aptamer assay that may be used to detect biomarker levels in a biological sample comprises: (a) preparing a mixture by contacting the biological sample with an aptamer comprising a first tag and having a specific affinity for the biomarker, such that an aptamer affinity complex is formed if the biomarker is present in the sample; (b) exposing the mixture to a first solid support comprising a first capture element to associate the first tag with the first capture element; (c) removing any components of the mixture that are not associated with the first solid support; and (d) attaching a second tag to the aptamer (e) binding to a biomarker component of a aptamer affinity complex; (f) releasing the aptamer affinity complex from a first solid support; (g) exposing the released aptamer affinity complex to a second solid support containing a second capture element, thereby associating the second tag with the second capture element; (h) separating any uncomplexed aptamers from the aptamer affinity complex, thereby removing any uncomplexed aptamers from the mixture; (i) eluting the aptamers from the solid support; and (e) detecting the biomarker by detecting the aptamer component of the aptamer affinity complex.

[0095] Any means known in the art may be used to detect biomarker values ​​by detecting the aptamer components of aptamer affinity complexes. Numerous different detection methods, such as hybridization assays, mass spectrometry, or QPCR, may be used to detect the aptamer components of affinity complexes. In some embodiments, nucleic acid sequencing methods may be used to detect the aptamer components of aptamer affinity complexes and thereby to detect biomarker values. In summary, a test sample may be subjected to any type of nucleic acid sequencing method to identify and quantify the sequences of one or more aptamers present in the test sample. In some embodiments, the sequence includes the entire aptamer molecule or any part of the molecule that can be used to uniquely identify the molecule. In other embodiments, the identification sequence is a specific sequence attached to the aptamer, such sequences are often referred to as “tags,” “barcodes,” or “zip codes.” In some embodiments, the sequencing method includes an enzymatic step for amplifying aptamer sequences or for converting any type of nucleic acid, including RNA and DNA containing chemical modifications at arbitrary positions, into any other type of nucleic acid suitable for sequencing.

[0096] In some embodiments, the sequencing method includes one or more cloning steps. In other embodiments, the sequencing method includes a direct sequencing method that does not involve cloning.

[0097] In some embodiments, the sequencing method includes a directed approach using specific primers that target one or more aptamers in the test sample. In other embodiments, the sequencing method includes a shotgun approach that targets all aptamers in the test sample.

[0098] In some embodiments, the sequencing method includes an enzymatic step for amplifying the molecule to be sequenced. In other embodiments, the sequencing method directly sequences a single molecule. Exemplary nucleic acid sequencing-based methods that may be used to detect biomarker values ​​corresponding to biomarkers in biological samples include: (a) converting a mixture of aptamers containing chemically modified nucleotides into unmodified nucleic acids using an enzymatic step; (b) large-scale parallel sequencing platforms, e.g., 454 sequencing system (454 Life Sciences / Roche), Illumina sequencing system (c) Shotgun sequencing of unmodified nucleic acids obtained using a Stem (Illumina), ABI SOLiD sequencing system (Applied Biosystems), HeliScope 1 molecular sequencer (Helicos Biosciences), Pacific BioSciences real-time single molecular sequencing system (Pacific BioSciences), or Polonator G sequencing systems (Dover Systems), etc.; and (c) Identifying and quantifying the aptamers present in the mixture by specific sequences and sequence counts.

[0099] A non-limiting, exemplary method for detecting biomarkers in biological samples using aptamers is described in Example 1. See also Kraemer et al., 2011, PLoS One 6(10):e26332.

[0100] Determination of biomarker levels using immunoassays Immunoassays are based on the reaction of an antibody to its corresponding target or analyte, and depending on the specific assay format, they can detect analytes in a sample. To improve the specificity and sensitivity of immunoreactivity-based assays, monoclonal antibodies and their fragments are frequently used for their specific epitope recognition. Polyclonal antibodies have also been successfully used in various immunoassays due to their higher affinity for targets compared to monoclonal antibodies. Immunoassays are designed for use with a wide range of biological sample matrices. Immunoassay formats are designed to yield qualitative, semi-quantitative, and quantitative results.

[0101] Quantitative results are obtained by using a calibration curve created with specific analytes to be detected at known concentrations. The reaction or signal from the unknown sample is plotted on the calibration curve, and the amount or level corresponding to the target in the unknown sample is determined.

[0102] Numerous immunoassay formats have been designed. ELISA or EIA can be quantitative for the detection of an analyte. This method is based on the binding of a label to either the analyte or the antibody, and the label components include enzymes, either directly or indirectly. ELISA tests can be formats for direct, indirect, competitive, or sandwich detection of an analyte. Other methods include, for example, radioisotopes (I) 125 ) or based on labeling such as fluorescence. Additional techniques include, for example, agglutination, nephelometry, turbidimetry, Western blotting, immunoprecipitation, immunocytochemistry, immunohistochemistry, flow cytometry, and Luminex assay (see ImmunoAssay: A Practical Guide, edited by Brian Law, published by Taylor & Francis, Ltd., 2005 edition).

[0103] Exemplary assay formats include enzyme-linked immunosorbent assay (ELISA), radioimmunoassay, fluorescence, chemiluminescence, and fluorescence resonance energy transfer (FRET) or time-resolved FRET (TR-FRET) immunoassays. Examples of techniques for detecting biomarkers include biomarker immunoprecipitation followed by quantitative methods that allow for size and peptide level identification, such as gel electrophoresis, capillary electrophoresis, and planar electrochromatography.

[0104] Methods for detecting and / or quantifying detectable labels or signal-producing substances depend on the nature of the label. The product of a reaction catalyzed by a suitable enzyme (where the detectable label is the enzyme; see above) may be fluorescent, luminescent, or radioactive, or they may absorb visible light or ultraviolet light. Examples of detectors suitable for detecting such detectable labels include, but are not limited to, X-ray films. Examples include radioactivity counters, scintillation counters, spectrophotometers, colorimeters, fluorescein meters, luminometers, and concentration meters.

[0105] Any of the detection methods can be performed in any format that allows for the analysis of any suitable preparation, processing, and reaction. The detection method can be performed, for example, in a multi-well assay plate (e.g., 96-well or 386-well) or using any suitable array or microarray. Storage solutions of various drugs can be prepared manually or robotically, and all subsequent pipetting, dilution, mixing, distribution, washing, incubation, sample reading, data acquisition, and analysis can be performed robotically using commercially available analytical software, robots, and detection equipment capable of detecting detectable labels.

[0106] Determination of biomarker levels using gene expression profiling In some embodiments, mRNA measurement in the biological sample may be used as an alternative to detect the level of the corresponding protein in the biological sample. Thus, in some embodiments, the biomarkers or biomarker panels described herein may be detected by detecting the appropriate RNA.

[0107] In some embodiments, mRNA expression levels are measured by reverse transcription quantitative polymerase chain reaction (RT-PCR followed by qPCR). RT-PCR is used to produce cDNA from mRNA. cDNA can be used in a qPCR assay to generate fluorescence as the DNA amplification process progresses. qPCR can provide absolute measurements, such as the copy number of mRNA per cell, by comparison with a calibration curve. Northern blotting, microarrays, Invader assays, and combinations of RT-PCR and capillary electrophoresis have all been used to measure mRNA expression levels in samples. See Gene Expression Profiling: Methods and Protocols, Richard A. Shimkets, editor, Humana Press, 2004.

[0108] Biomarker detection using in vivo molecular imaging technology In some embodiments, the biomarkers described herein may be used in molecular imaging studies. For example, a contrast agent may be bound to a capture reagent that can be used to detect the biomarker in vivo.

[0109] In vivo imaging techniques provide non-invasive methods for determining the state of a particular disease in an individual's body. For example, all parts or the entire body may be displayed as a three-dimensional image, thereby providing useful information about the body's morphology and structure. Such techniques can be combined with biomarker detection, as described herein, to provide information about biomarkers in vivo.

[0110] Various technological advancements have led to the development of in vivo molecular imaging techniques. These advancements include the development of novel contrast agents or labels, such as radiolabeling and / or fluorescent labeling, that can generate strong signals within the body; and the development of powerful new imaging techniques that can detect and analyze these signals from outside the body with sufficient sensitivity and accuracy to provide useful information. Contrast agents can be visualized in appropriate imaging systems, thereby providing images of part or more parts of the body where the contrast agent is present. Contrast agents can be bound to or associated with capture reagents such as aptamers or antibodies, and / or peptides, proteins, or oligonucleotides (for example, to detect gene expression), or complexes containing any of these together with one or more macromolecules and / or other particle forms.

[0111] The contrast agent may feature radioactive atoms useful in imaging. Suitable radioactive atoms include technetium-99m or iodine-123 for scintigraphy. Other readily detectable parts include, for example, spin labels for magnetic resonance imaging (MRI), such as iodine-123, iodine-131, indium-111, fluorine-19, carbon-13, nitrogen-15, oxygen-17, gadolinium, manganese, or iron. Such labels are well known in the art and can be readily selected by those skilled in the art.

[0112] Standard imaging techniques, though not limited to them, include magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and single-photon emission computed tomography (SPECT). In in vivo diagnostic imaging, the types of available detection instruments are important factors in the selection of a given contrast agent, such as a given radionuclide and the specific biomarker (protein, mRNA, etc.) targeted using it. The radionuclides typically selected exhibit a certain degree of decay that is detectable by a given type of instrument. Furthermore, when selecting a radionuclide for in vivo diagnosis, its half-life should be long enough to allow detection when it is maximally taken up by the target tissue, and short enough to minimize harmful radiation to the host.

[0113] Exemplary imaging techniques include, but are not limited to, PET and SPECT, which involve the whole-body or local administration of radionuclides to an individual. The subsequent uptake of the radioactive tracer is measured over time and used to obtain information about the target tissue and biomarkers. Depending on the high-energy (gamma-ray) emission of the specific isotope used, and the sensitivity and sophistication of the instruments used to detect it, a two-dimensional distribution of radioactivity can be estimated from outside the body.

[0114] Commonly used positron-emitting radionuclides in PET include, for example, carbon-11, nitrogen-13, oxygen-15, and fluorine-18. SPECT uses isotopes that decay by electron capture and / or gamma radiation, such as iodine-123 and technetium-99m. An exemplary method for labeling amino acids with technetium-99m involves reducing pertechnetium ions in the presence of a chelate precursor to form an unstable technetium-99m-precursor complex, which then reacts with the metal-binding group of a bifunctionally modified chemotactic peptide to form a technetium-99m-chemotactic peptide conjugate.

[0115] Antibodies are frequently used in such in vivo imaging diagnostic methods. The preparation and use of antibodies for in vivo diagnostics are well known in the art. Similarly, aptamers may be used in such in vivo imaging diagnostic methods. For example, an aptamer used to identify a specific biomarker described herein may be appropriately labeled and injected into an individual for in vivo detection of the biomarker. The label used is selected according to the imaging technique used, as described above. Aptamer-directed contrast agents may have unique and advantageous properties compared to other contrast agents with respect to tissue permeability, in vivo distribution, kinetics, removal, potency, and selectivity.

[0116] Such techniques may optionally be performed using labeled oligonucleotides to detect gene expression, for example, by imaging using antisense oligonucleotides. These methods are used, for example, in in situ hybridization using fluorescent molecules or radionuclides as labels. Other methods for detecting gene expression include, for example, the detection of reporter gene activity.

[0117] Another common type of imaging technique involves detecting a fluorescence signal within a subject from light outside the subject. Optical imaging is detected by optical instruments. These signals may be due to actual fluorescence and / or bioluminescence. Improved sensitivity of optical detection devices is increasing the usefulness of optical imaging for in vivo diagnostic assays.

[0118] For a general overview of other techniques, see N. Blow, Nature Methods, 6, 465-469, 2009.

[0119] Determination of biomarker levels using mass spectrometry Various configurations of mass spectrometers can be used to detect biomarker levels. Several types of mass spectrometers are available or can be manufactured in various configurations. Generally, a mass spectrometer has the following main components: sample inlet, ion source, mass spectrometer, detector, vacuum system, instrument control system, and data system. Differences in the sample inlet, ion source, and mass spectrometer generally define the type of instrument and its capabilities. For example, the inlet may be a capillary column liquid chromatography source, or a direct probe or stage, as used in matrix-assisted laser desorption. Common ion sources include electrospray, which includes nanosprays and microsprays, or matrix-assisted laser desorption. Common mass spectrometers include quadrupole mass filters, ion trap mass spectrometers, and time-of-flight mass spectrometers. Further mass spectrometry methods are well known in the art (see Burlingame et al., Anal. Chem. 70:647R-716R (1998); Kinter and Sherman, New York (2000)).

[0120] Protein biomarkers and biomarker levels can be detected and measured by any of the following methods: electrospray ionization mass spectrometry (ESI-MS), ESI-MS / MS, ESI-MS / (MS)n, matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption / ionization time-of-flight mass spectrometry (SELDI-TOF-MS), silicon-assisted desorption / ionization (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), tandem time-of-flight (TOF / TOF) techniques called UltraFlex III TOF / TOF, atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS / MS, APCI-(MS)N, atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS / MS, and APPI-(MS)N, quadrupole mass spectrometry, Fourier transform mass spectrometry (FTMS), quantitative mass spectrometry, and ion trap mass spectrometry.

[0121] Prior to characterizing protein biomarkers and determining biomarker levels by mass spectrometry, sample preparation strategies are used to label and concentrate the sample. Labeling methods include, but are not limited to, isomass tagging (iTRAQ) for relative or absolute quantification, and stable isotope labeling (SILAC) using amino acids in cell culture medium. Capture reagents used to selectively concentrate candidate biomarker proteins in the sample before mass spectrometry include, but are not limited to, aptamers, antibodies, nucleic acid probes, chimeras, small molecules, F(ab')2 fragments, single-chain antibody fragments, Fv fragments, single-chain Fv fragments, nucleic acids, lectins, ligand-bound receptors, aphibodies, nanobodies, ankyrins, domain antibodies, alternative antibody scaffolds (e.g., diabodies), imprinted polymers, avimers, peptidomimetics, peptoids, peptide nucleic acids, threose nucleic acids, hormone receptors, cytokine receptors, and synthetic receptors, as well as their modifications and fragments.

[0122] Determination of biomarker levels using proximity ligation assays Proximity ligation assays may be used to determine biomarker values. In short, a test sample is brought into contact with a pair of affinity probes, which may be a pair of antibodies or a pair of aptamers, each of which is an extension of an oligonucleotide. The targets of the pair of affinity probes may be two different determinants on one protein, or one determinant on each of two different proteins, which may exist as a homo- or hetero-multimer complex. When the probes bind to the target determinants, the free ends of the oligonucleotide extensions are brought close enough to hybridize together. Hybridization of the oligonucleotide extensions is facilitated by a common connector oligonucleotide that helps crosslink them together when the oligonucleotide extensions are positioned close enough. Once the oligonucleotide extensions of the probes have hybridized, the ends of the extensions are joined together by enzymatic DNA ligation.

[0123] Each oligonucleotide extension contains a primer region for PCR amplification. When oligonucleotide extensions are ligated together, they form a continuous DNA sequence, which, through PCR amplification, reveals information about the identity and quantity of the target protein, as well as information about protein-protein interactions if the target determinant is present on two different proteins. Proximity ligation can provide a highly sensitive and specific assay for real-time protein concentration and interaction information using real-time PCR. Probes that do not bind to the determinant of interest will not bring the corresponding oligonucleotide extensions into proximity, and ligation or PCR amplification cannot proceed, resulting in no signal generation.

[0124] The aforementioned assay enables the detection of biomarker values ​​useful for predicting the risk or likelihood of a CV event, the method comprising detecting at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, or all sixteen biomarkers selected from Table 1, or at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, or all fourteen biomarkers selected from Table 2, the classification using biomarker values ​​as described below, indicates whether the individual is at high risk of a CV event occurring within a period of 90 days, 180 days, or one year. According to any of the methods described herein, biomarker values ​​may be detected and classified individually, or they may be detected and classified collectively, for example, in a multiplex assay format.

[0125] Biomarker classification and disease score calculation In some embodiments, a given diagnostic test biomarker "signature" comprises a set of biomarkers, each biomarker having a characteristic level in the population of interest. In some embodiments, the characteristic level may refer to the mean or average value of the biomarkers for individuals within a particular group. In some embodiments, the diagnostic methods described herein may be used to assign an unknown sample from an individual to one of two groups: one with a high risk of a cardiovascular event and one without.

[0126] Assigning a sample to one of two or more groups is known as classification, and the techniques used to achieve this assignment are known as classifiers or classification methods. A classification method may also be called a scoring method. There are numerous classification methods that can be used to construct a diagnostic classifier from a set of biomarker levels. In some cases, a classification method is performed using supervised learning techniques, in which a dataset is collected using samples obtained from individuals of two (or more in the case of a multi-classification state) distinct groups that you want to distinguish. Each sample Since the class (group or population) to which each sample belongs is known in advance, a classification method can be trained to obtain the desired classification response. It is also possible to generate diagnostic classifiers using unsupervised learning techniques.

[0127] Common methods for developing diagnostic classifiers include decision trees; bagging + boosting + forests; learning based on inference rules; Parsen windows; linear models; logistic curves; neural network methods; unsupervised clustering; K-means algorithm; hierarchical ascending / descending classification; semi-supervised learning; prototype methods; nearest neighbors; kernel density estimation; support vector machines; hidden Markov models; and Boltzmann learning. Classifiers can be simply combined or combined in a way that minimizes a specific objective function. For general information, see, for example, Pattern Classification, RODuda, et al. See also *The Elements of Statistical Learning - Data Mining, Inference, and Prediction*, T. Hastie, et al., editors, Springer Science+Business Media, LLC, 2nd edition, 2009.

[0128] To generate a classifier using supervised learning techniques, a set of samples called training data is acquired. In the context of diagnostic testing, training data includes samples from different groups (classes) to which unknown samples are later assigned. For example, samples collected from individuals in a control population and samples collected from individuals in a specific disease population may constitute training data for developing a classifier that can classify unknown samples (or, more specifically, individuals from which samples are taken) as either having the disease or not having the disease. Developing a classifier from training data is known as classifier training. Specific details regarding classifier training depend on the nature of the supervised learning technique. Training a Naive Bayesian classifier is an example of such supervised learning techniques (see, for example, Pattern Classification, RODuda, et al., editors, John Wiley & Sons, 2nd edition, 2001; also see The Elements of Statistical Learning - Data Mining, Inference, and Prediction, T. Hastie, et al., editors, Springer Science+Business Media, LLC, 2nd edition, 2009). Training a Naive Bayesian classifier is described, for example, in U.S. Patent Publications 2012 / 0101002 and 2012 / 0077695.

[0129] Typically, there may be many higher biomarker levels compared to the training set samples, so care must be taken to avoid overfitting. Overfitting occurs when a statistical model represents random error or noise instead of the underlying relationship. Overfitting can be avoided in various ways, including, for example, limiting the number of biomarkers used during classifier development, assuming that the biomarker responses are independent of each other, limiting the complexity of the underlying statistical model used, and ensuring that the underlying statistical model fits the data.

[0130] A concrete example of developing a diagnostic test using a set of biomarkers is the application of a simple Bayesian classifier, which is a simple probabilistic classifier based on Bayes' theorem with strictly independent processing of biomarkers. Each biomarker is described by a class-dependent probability density function (pdf) with respect to the measured RFU value or log-RFU (relative fluorescence unit) value in each class. The combined pdf for a set of biomarkers in a class is estimated to be the product of the individual class-dependent pdfs for each biomarker. Training a simple Bayesian classifier in a pulse is equivalent to assigning parameters ("parameterization") to characterize class-dependent PDFs. While any underlying model can be used for class-dependent PDFs, the model must generally fit the data observed in the training set.

[0131] The performance of a Naive Bayesian classifier depends on the number and quality of biomarkers used to build and train the classifier. A single biomarker will work according to the Kolmoborov-Smirnov (KS) distance. Subsequent additions of biomarkers with good KS distances (e.g., >0.3) will generally improve classification performance, provided that the subsequently added biomarkers are independent of the first biomarker. By using specificity in addition to sensitivity as the classifier score, a large number of high-scoring classifiers can be generated using a type of greedy algorithm. (A greedy algorithm is an arbitrary algorithm that follows a metaheuristic for problem solving, making locally optimal choices at each stage with the aim of finding a globally optimal solution.)

[0132] Another method for describing classifier performance is by receiver operating characteristics (ROC), or simply by ROC curves or ROC plots. ROC is a graphical plot of the sensitivity or true positive rate versus false positive rate (1-specificity or 1-true negative rate) of a binary classifier system as the discrimination threshold of the binary classifier system changes. This ROC can also be expressed by plotting the ratio of true positives among positives (TPR) against the ratio of false positives among negatives (FPR). Because this is a comparison of two operating characteristics (TPR and FPR) as the criteria change, it is also known as the relative operating characteristic curve. The area under the ROC curve (AUC) is commonly used as an aggregate measure of diagnostic accuracy. It can take values ​​between 0.0 and 1.0. AUC has important statistical properties. In other words, the AUC of a classifier is equal to the probability that the classifier ranks randomly selected positive cases higher than randomly selected negative cases (Fawcett T, 2006. An introduction to ROC analysis. Pattern Recognition Letters. 27:861-874). This corresponds to Wilcoxon's rank test (Hanley, JA, McNeil, BJ, 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29-36). Another way to express the performance of a diagnostic test against a known reference criterion is net reclassification improvement, which is the ability of a new test to accurately increase or decrease risk compared to a reference criterion test. See, for example, Pencina et al., 2011, Stat. Med. 30:11-21. While AUC under the ROC curve is best suited to evaluating the performance of a two-class classifier, stratification and personalized medicine rely on the inference that the population contains more than two classes. For such comparisons, the hazard ratio of upper quartiles versus lower quartiles (or other stratifications such as deciles) may be more appropriate.

[0133] The risk and probability predictions enabled by the present invention may be applied to individuals undergoing initial treatment or to subjects in specialized cardiovascular centers, or even to consumers. In some embodiments, the classifiers used to predict events may require some calibration for the population to which they apply, for example, because variations due to ethnicity or region may exist. In some embodiments, such calibrations may be established in advance by large-scale trials and are therefore incorporated before risk prediction is made when applied to individual patients. Venous blood samples are collected, appropriately processed, and analyzed as described herein. Once the analysis is complete, risk predictions may be made mathematically, with or without incorporating other metadata from medical records described herein, such as genetics or demographics. Depending on the consumer's level of expertise, various forms of information may be used. Information can be output. For consumers seeking the simplest type of output, in some embodiments, the information may be "Yes / No, is this person likely to experience an event in the next x days (where x is 90 to 365)?", or alternatively, it may be similar to the red / orange / green of a "traffic light", or it may be words or written equivalents such as high / medium / low risk. For consumers seeking more detail, in some embodiments, the risk may be output as a number or diagram showing the probability of an event per unit time as a continuous score, or as a larger number of layers (e.g., deciles), and / or as the average time to the event and / or the most likely type of event. In some embodiments, the output may include treatment recommendations. Longitudinal monitoring of the same patient over time would allow for graphing of responses to interventions or changes in lifestyle. In some embodiments, two or more types of output may be provided simultaneously to meet the needs of the patient and the individual members of a healthcare management team with different levels of expertise.

[0134] In some embodiments, biomarkers shown in Table 1 or Table 2 in a blood sample derived from the subject (e.g., a plasma or serum sample) are detected using aptamers, such as aptamers with slow dissociation rates. The risk or likelihood of an individual experiencing a cardiovascular event, or a prognostic index (PI), is calculated using logarithmic RFU values.

[0135] Given a PI, the probability that a subject will experience a cardiovascular event (CV event) on the next "t" days can be obtained using the following formula.

number

[0136] kit For example, any combination of the biomarkers described herein can be detected using a suitable kit for use in carrying out the methods disclosed herein. Furthermore, any kit may include one or more detectable labels, such as fluorescent moieties, as described herein.

[0137] In some embodiments, the kit includes (a) one or more biomarkers in a biological sample, wherein the biomarkers are at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, or a total of 16 biomarkers selected from the biomarkers in Table 1; or at least two, at least three, at least four, at least five, at least six, or fewer biomarkers selected from the biomarkers in Table 2. (b) one or more capture reagents (e.g., at least one aptamer or antibody) for detecting one or more biomarkers, including seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, or a total of fourteen; and optionally, (b) one or more software or computer program products for classifying the individual from which the biological sample was obtained as having or not having a high risk of CV events, as further described herein, or for determining the likelihood that the individual has a high risk of CV events. Alternatively, instead of one or more computer program products, one or more instructions for use are provided for a person to perform the above steps manually. It is possible.

[0138] In some embodiments, the kit includes a solid support, at least one capture reagent, and a substance that generates a signal. The kit may also include instructions for use regarding the use of the apparatus and reagents, sample handling, and data analysis. Furthermore, the kit may be used in conjunction with a computer system or software for analyzing biological samples and reporting the results of the analysis.

[0139] The kit may also include one or more reagents for processing biological samples (e.g., solubilizing buffer, surfactant, washing solution, or buffer). Any of the kits described herein may also include, for example, buffers, blocking agents, matrix materials for mass spectrometry, antibody capture agents, positive control samples, negative control samples, software, and information such as protocols, guidelines, and reference data.

[0140] In some embodiments, a kit is provided for analyzing the status of CV event risk, wherein the kit includes PCR primers for one or more aptamers specific to the biomarkers described herein. In some embodiments, the kit may further include instructions for use of the biomarkers and instructions regarding their relationship to the prediction of CV event risk. In some embodiments, the kit may also include a DNA array containing complements for one or more aptamers specific to the biomarkers described herein, reagents, and / or enzymes for amplifying or isolating sample DNA. In some embodiments, the kit may include reagents for real-time PCR, e.g., TaqMan probes and / or primers, and enzymes.

[0141] For example, the kit may include (a) a reagent comprising at least one capture reagent for determining the level of one or more biomarkers in a test sample, and optionally, (b) one or more algorithms or computer programs for performing the step of comparing the amount of each quantified biomarker in the test sample with one or more predetermined cutoff values. In some embodiments, the algorithm or computer program assigns a score to each quantified biomarker based on the above comparison, and in some embodiments, the assigned scores of each quantified biomarker are combined to obtain a total score. Furthermore, in some embodiments, the algorithm or computer program uses this comparison to compare the total score with a predetermined score and to determine whether the individual has a high risk of a cardiovascular event. Alternatively, instead of one or more algorithms or computer programs, one or more instructions for manual operation of the above steps may be provided.

[0142] Biomarker panel In some embodiments, one or more of the biomarkers listed in Table 1 are detected. In some embodiments, one or more of the biomarkers listed in Table 1 are detected in samples from individuals having HFrEF. In some embodiments, all of the biomarkers listed in Table 1 are detected. In some embodiments, the level of each protein listed in Table 1 is detected. In some embodiments, the detection of one or more biomarkers or all of the biomarkers is performed to determine the risk or likelihood that the subject will have a CV event within a predetermined period. In some such embodiments, the predetermined period is 90 days, 180 days, or 1 year. In some embodiments, the predetermined period is 1 year. In some embodiments, the CV event is death. [Table 1]

[0143] In some embodiments, one or more of the biomarkers listed in Table 2 are detected. In some embodiments, one or more of the biomarkers listed in Table 2 are detected in samples from individuals having HFpEF. In some embodiments, all of the biomarkers listed in the following table are detected. In some embodiments, the level of each protein listed in Table 2 is detected. In some embodiments, detection of one or more biomarkers or all biomarkers is performed to determine the risk or likelihood of a subject experiencing a CV event within a predetermined period. In some such embodiments, the predetermined period is 90 days, 180 days, or 1 year. In some embodiments, the predetermined period is 1 year. In some embodiments, the CV event is death. [Table 2]

[0144] Computer methods and software Methods for assessing the risk or likelihood of CV events in an individual may include: 1) obtaining a biological sample; 2) performing an analytical method to detect and measure a panel of biomarkers or sets of biomarkers in the biological sample; 3) optionally performing normalization or standardization of any data; 4) determining the level of each biomarker; and 5) reporting the results. In some embodiments, the results are calibrated for the ethnicity of the population / subject. In some embodiments, the biomarker levels are combined in some way. A single value is reported for the combined and combined biomarker levels. In this approach, in some embodiments, the score may be a single numerical value determined by the aggregation of all biomarkers, compared to a predetermined threshold that is an indicator of the presence or absence of the disease. Alternatively, the diagnostic or predictive score may be a series of bars, each representing a biomarker value, and the response pattern may be compared to a predetermined pattern for determining the presence or absence of a high (or low) risk of the disease, condition, or event.

[0145] At least some embodiments of the methods described herein may be carried out using a computer. An example of a computer system 100 is shown in Figure 5. Referring to Figure 5, it is shown that the system 100 consists of hardware elements electrically connected via a bus 108, including a processor 101, an input device 102, an output device 103, a storage device 104, a computer-readable storage medium reader 105a, a communication system 106, an acceleration processing unit (e.g., a DSP or special-purpose processor) 107, and memory 109. The computer-readable storage medium reader 105a is further connected to a computer-readable storage medium 105b, and this combination comprehensively corresponds to a storage medium, memory, etc., with remote, local, fixed, and / or removable storage devices for temporarily and / or more persistently storing computer-readable information, and this combination includes the storage device 104, memory 109, and / or any other such accessible system 100 resources. System 100 also includes an operating system 192 and other code 193, such as software elements (shown here as residing in working memory 191), including programs, data, etc.

[0146] Referring to Figure 5, System 100 has a wide range of flexibility and configurability. Therefore, for example, a single architecture may be used to implement one or more servers, and such servers may be further configured according to generally preferred protocols, protocol modifications, extensions, etc. However, it will be apparent to those skilled in the art that embodiments will be used according to more specific application requirements. For example, one or more system elements may be implemented as sub-elements within the components of System 100 (e.g., within the communication system 106). Customized hardware may be used, and / or specific elements may be implemented in hardware, software, or both. Furthermore, connections to other computing devices, such as network input / output devices (not shown), may be used, but it should be understood that wired, wireless, modem, and / or other connections or multiple connections to other computing devices may be used.

[0147] In one embodiment, the system may include a database containing biomarker features that indicate predictive characteristics of the risk of a CV event. Biomarker data (or biomarker information) may be used as input to a computer for use as part of a computerized procedure. Biomarker data may include the data described herein.

[0148] In one embodiment, the system further includes one or more devices for providing input data to one or more processors.

[0149] The system further includes memory for storing datasets of ranked data elements.

[0150] In another embodiment, the apparatus for providing input data includes, for example, a detector for detecting features of data elements, such as a mass spectrometer or a gene chip reader.

[0151] The system may additionally include a database management system. (Based on user requests or inquiries) The query can be formatted in a language that is understood by the database management system, which processes the query and extracts relevant information from the training set database.

[0152] The system may be connectable to a network to which a network server and one or more clients are connected. The network may be a local area network (LAN) or a wide area network (WAN), as is well known in the art. Preferably, the server includes hardware necessary to run computer program products (e.g., software) for accessing database data to process user requests.

[0153] The system may include an operating system (e.g., UNIX or Linux) for executing instructions from the database management system. In one embodiment, the operating system may run on a global communication network such as the Internet, and may utilize a global communication network server to connect to such a network.

[0154] The system may include one or more devices, including a graphical display interface, which includes interface elements such as buttons, pull-down menus, scroll bars, and text input fields, as commonly found in graphical user interfaces known in the art. Requests entered through the user interface may be sent to application programs in the system for formatting to retrieve relevant information in one or more system databases. Requests or queries entered by the user may be constructed in any preferred database language.

[0155] A graphical user interface may be generated by graphical user interface code as part of an operating system and may be used to input data and / or display the input data. The results of the processed data may be displayed on the interface, printed by a printer communicating with the system, stored in a storage device, and / or transmitted over a network, or provided in the form of computer-readable media.

[0156] The system may communicate with an input device for providing the system with data (e.g., expression values) related to data elements. In one embodiment, the input device may include a gene expression profiling system, such as a mass spectrometer, a gene chip, or an array reader.

[0157] According to various embodiments, methods and apparatus for analyzing biomarker information for predicting CV event risk can be implemented in any preferred manner, for example, using a computer program running on a computer system. Conventional computer systems including a processor and random access memory, such as remotely accessible application servers, network servers, personal computers, or workstations, may be used. Additional computer system elements may include storage or information storage systems, such as mass storage systems, and user interfaces, such as conventional monitors, keyboards, and tracking devices. The computer system may be a standalone system or part of a network of computers including servers and one or more databases.

[0158] A CV event risk prediction biomarker analysis system may provide functions and operations to complete data analysis, such as data acquisition, processing, analysis, reporting, and / or diagnosis. Example For example, in one embodiment, a computer system may run a computer program capable of receiving, storing, retrieving, analyzing, and reporting information regarding biomarkers predicting CV event risk. The computer program may include a number of modules that perform various functions or operations, such as a processing module for processing raw data and generating supplemental data, and an analysis module for analyzing the raw and supplemental data to generate a predicted status and / or diagnosis or risk calculation of CV event risk. The calculation of the CV event risk status may optionally include generating or retrieving any other information, which may include additional biomedical information about the individual's condition associated with the disease, condition, or event, determining whether further testing may be desirable, or otherwise assessing the individual's health status.

[0159] Some embodiments described herein may be implemented to include a computer program product. The computer program product may include a computer-readable medium having computer-readable program code embodied in a medium for running an application program on a computer having a database.

[0160] As used herein, “computer program product” means a set of instructions organized in the form of natural or programming language statements, contained in a physical medium of any nature (e.g., document, electronic, magnetic, optical, or other form) and usable by a computer or other automated data processing system. When such programming language statements are executed by a computer or data processing system, they cause the computer or data processing system to operate according to statements of a particular content. Examples of computer program products include, but are not limited to, programs in source and object code, and / or test or data libraries stored on computer-readable media. Furthermore, computer program products that enable computer systems or data processing devices to operate in a pre-selected manner may be provided in a number of forms, but are not limited to, original source code, assembly code, object code, machine language, the aforementioned encrypted or compressed versions, and any equivalents.

[0161] In one embodiment, a computer program product is provided for assessing the risk or likelihood of a CV event. The computer program product includes a computer-readable medium that embodies program code executable by the processor of a computing device or system, the program code including code for extracting data derived from a biological sample of an individual, each including a biomarker level corresponding to one of the biomarkers in Table 1 or Table 2; and code for performing a classification method that indicates the CV event risk status of the individual as a function of the biomarker values.

[0162] In yet another embodiment, a computer program product is provided for indicating the likelihood or risk of a CV event. The computer program product includes a computer-readable medium that embodies program code executable by the processor of a computing device or system, the program code including code for extracting data derived from an individual biological sample, which includes biomarker values ​​in the biological sample corresponding to at least one biomarker selected from the biomarkers provided in Table 1 or 2; and code for performing a classification method that indicates the CV event risk status of the individual as a function of the biomarker values.

[0163] Although various embodiments have been described as methods or apparatus, it should be understood that embodiments may be implemented via code used in conjunction with a computer, such as code inherent in or accessible by a computer. For example, software. And databases may be used to carry out many of the above methods. Therefore, it should be noted that, in addition to embodiments achieved by hardware, these embodiments may also be achieved by using manufactured articles consisting of computer-readable media embodying computer-readable program code that brings about the functionality disclosed herein. Therefore, it is desirable that embodiments of such program code means also be considered to be similarly protected by this patent. Furthermore, embodiments may be embodied as code stored in substantially any type of computer-readable memory, including but not limited to RAM, ROM, magnetic media, optical media, or magneto-optical media. More generally, embodiments may be carried out in software, hardware, or any combination thereof, including, but not limited to, software, microcode, programmable logic arrays (PLAs), or application-specific integrated circuits (ASICs) operating on a general-purpose processor.

[0164] Furthermore, it is conceivable that embodiments may be achieved as computer signals embodied in carrier waves, and signals (e.g., electrical and optical) propagated through a transmission medium. Accordingly, the various types of information described above may be formatted in structures such as data structures, transmitted as electrical signals through a transmission medium, or stored in a computer-readable medium.

[0165] It should also be noted that many of the structures, materials, and actions listed herein may be listed as means for performing a function or steps for performing a function. Therefore, it should be understood that such terms may encompass all such structures, materials, or actions disclosed herein, and their equivalents, including those incorporated by reference.

[0166] The use of biomarkers disclosed herein, and the various methods for determining biomarker values, are described in detail with respect to the assessment of the risk of cardiovascular events. However, the applications of the processes, the use of identified biomarkers, and the methods for determining biomarker values ​​are also fully applicable to the identification of individuals who may or may not benefit from other specific types of cardiovascular conditions, any other disease or medical condition, or supplemental medical treatments.

[0167] Other methods In some embodiments, the biomarkers and methods described herein are used to determine medical insurance premiums or coverage and / or life insurance premiums or coverage. In some embodiments, the results of the methods described herein are used to determine medical insurance premiums and / or life insurance premiums. In some such examples, an organization providing medical or life insurance requests or otherwise obtains information regarding the risk or likelihood of a subject CV event and uses that information to determine an appropriate medical or life insurance premium for the subject. In some embodiments, the examination is requested and paid for by the organization providing medical or life insurance. In some embodiments, the examination is used by a business or insurance scheme or a potential acquirer of a company to forecast future liabilities or costs and to determine whether to proceed with the acquisition.

[0168] In some embodiments, the biomarkers and methods described herein are used to predict and / or manage the utilization of medical resources. In some such embodiments, the method is not performed for such predictive purposes, but the information obtained by the method is used in such predicting and / or managing the utilization of medical resources. For example, a laboratory or hospital may use the method to collect information on a large number of subjects in order to predict and / or manage the utilization of medical resources in a particular facility or a particular geographical area. [Examples]

[0169] The following examples are provided for illustrative purposes only and are not intended to limit the scope of this application as defined by the appended claims. The typical molecular biology techniques described in the following examples can be performed as described in standard laboratory manuals such as Sambrook et al., Molecular Cloning: A Laboratory Manual, 3rd ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, (2001).

[0170] Example 1: Exemplary biomarker detection using aptamers Exemplary methods for detecting one or more biomarker proteins in a sample are described, for example, in Kraemer et al., PLoS One 6(10):e26332, and are described below. Three different quantification methods are described: microarray-based hybridization, a Luminex bead-based method, and qPCR.

[0171] reagent HEPES, NaCl, KCl, EDTA, EGTA, MgCl2, and Tween-20 can be purchased, for example, from Fisher Biosciences. Dextran sulfate sodium salt (DxSO4) with a nominal molecular weight of 8000 can be purchased, for example, from AIC and dialyzed against deionized water for at least 20 hours in a single exchange. KOD EX DNA polymerase can be purchased, for example, from VWR. Tetramethylammonium chloride and CAPSO can be purchased, for example, from Sigma-Aldrich, and streptavidin-phycoerythrin (SAPE) can be purchased, for example, from Moss Inc. 4-(2-aminoethyl)-benzenesulfonyl fluoride hydrochloride (AEBSF) can be purchased, for example, from Gold Biotechnology. Streptavidin-coated 96-well plates can be purchased, for example, from Thermo Scientific (Pierce Streptavidin Coated Plates HBC, clear, 96 wells, product number 15500 or 15501). NHS-PEO4-Biotin can be purchased, for example, from Thermo Scientific (EZ-Link NHS-PEO4-Biotin, product number 21329), dissolved in anhydrous DMSO, and frozen in single-use aliquots. IL-8, MIP-4, Lipocalin-2, RANTES, MMP-7, and MMP-9 can be purchased, for example, from R&D. Resistin and MCP-1 can be purchased from, for example, PeproTech, and tPA can be purchased from, for example, VWR.

[0172] nucleic acid Conventional oligodeoxynucleotides (including amine-substituted and biotin-substituted) can be purchased, for example, from Integrated DNA Technologies (IDT). Z-blocks are single-stranded oligodeoxynucleotides of the sequence 5'-(AC-BnBn)7-AC-3', where Bn represents a benzyl-substituted deoxyuridine residue. Z-blocks can be synthesized using conventional phosphoramidite chemistry. Aptamer capture reagents can be synthesized by conventional phosphoramidite chemistry and purified, for example, using a 21.5 × 75 mm PRP-3 column operating at 80°C in a Waters Autopurification 2767 system (or Waters 600 series semi-automated system), with, for example, a timberline TL-600 or TL-150 heater and a triethylammonium bicarbonate (TEAB) / ACN gradient to elute the product. Detection is performed at 260 nm, and after fractions are collected across the main peak, the best fractions are pooled.

[0173] buffer solution Buffer SB18 consists of 40 mM HEPES, 101 mM NaCl, 5 mM KCl, 5 mM MgCl2, and 0.05% (v / v) Tween 20, and is adjusted to pH 7.5 with NaOH. Buffer SB17 is SB18 supplemented with 1 mM trisodium EDTA. Buffer PB1 consists of 10 mM HEPES, 101 mM NaCl, 5 mM KCl, 5 mM MgCl2, 1 mM trisodium EDTA, and 0.05% (v / v) Tween-20, and is adjusted to pH 7.5 with NaOH. CAPSO elution buffer consists of 100 mM CAPSO (pH 10.0) and 1 M NaCl. The neutralization buffer contains 500 mM HEPES, 500 mM HCl, and 0.05% (v / v) Tween-20. Agilent Hybridization Buffer is a proprietary formulation supplied as part of the kit (Oligo aCGH / ChIP-on-chip Hybridization Kit). Agilent Wash Buffer 1 is a proprietary formulation (Oligo aCGH / ChIP-on-chip Wash Buffer 1, Agilent). Agilent Wash Buffer 2 is a proprietary formulation (Oligo aCGH / ChIP-on-chip Wash Buffer 2, Agilent). The TMAC hybridization solution consists of 4.5 M tetramethylammonium chloride, 6 mM trisodium EDTA, 75 mM Tris-HCl (pH 8.0), and 0.15% (v / v) sarcosyl. KOD buffer (10x concentrated) consists of 1200 mM Tris-HCl, 15 mM MgSO4, 100 mM KCl, 60 mM (NH4)2SO4, 1% v / v Triton-X100, and 1 mg / mL BSA.

[0174] Sample preparation Thaw the serum (stored at -80°C in 100 μL aliquots) in a 25°C water bath for 10 minutes, then store on ice before sample dilution. Mix the samples by gently vortexing for 8 seconds. Prepare a 6% serum sample solution by diluting in 0.94 × SB17 supplemented with 0.6 mM MgCl2, 1 mM trisodium EGTA, 0.8 mM AEBSF, and 2 μM Z-Block. Prepare a 0.6% serum stock by diluting a portion of the 6% serum stock 10-fold in SB17. In some embodiments, the 6% and 0.6% stocks are used to detect high-abundance and low-abundance analytes, respectively.

[0175] Preparation of capture reagents (aptamers) and streptavidin plates The aptamers are classified into two mixtures according to the relative abundance of their associated analytes (or biomarkers). The storage solution concentration is 4 nM for each aptamer, and the final concentration of each aptamer is 0.5 nM. The aptamer storage solution mixture is diluted four-fold in SB17 buffer and heated to 95°C for 5 minutes, then cooled to 37°C over 15 minutes before use. This denaturation-regeneration cycle is intended to normalize the conformational isomer distribution of the aptamers, thereby ensuring reproducible aptamer activity regardless of historical variations. The streptavidin plate is washed twice with 150 μL of buffer PB1 before use.

[0176] Incubation and capture in plates Combine the heated and cooled 2× aptamer mixture (55 μL) with equal volumes of 6% or 0.6% serum dilutions to create mixtures containing 3% and 0.3% serum. Seal the plate with a silicone sealing mat (Axymat silicone sealing mat, VWR) and incubate at 37°C for 1.5 hours. Then transfer the mixture to the wells of a washed 96-well streptavidin plate and incubate for a further 2 hours on an Eppendorf Thermomixer set to 37°C with shaking at 800 rpm.

[0177] Manual assay Unless otherwise specified, liquids are removed by discarding them and then tapping twice on stacked paper towels. The washing volume is 150 μL, and all shaking incubations are performed on an Eppendorf Thermomixer set to 25°C and 800 rpm. Remove the mixture by pipetting and wash the plate twice for 1 minute each with Buffer PB1 supplemented with 1 mM dextran sulfate and 500 μM biotin, then wash four times with Buffer PB1 for 15 seconds each. Add a freshly prepared solution of 1 mM NHS-PEO4-biotin in Buffer PB1 (150 μL / well) and incubate the plate for 5 minutes with shaking. Remove the NHS-biotin solution and wash the plate three times with Buffer PB1 supplemented with 20 mM glycine, then wash three times with Buffer PB1. Next, add 85 μL of buffer PB1 supplemented with 1 mM DxSO4 to each well, and irradiate the plate with a BlackRay UV lamp (indicated wavelength 365 nm) at a distance of 5 cm with shaking for 20 minutes. Transfer the sample to a new, washed streptavidin-coated plate, or to an unused well of an existing washed streptavidin plate, and combine the high and low dilution sample mixtures into a single well. Incubate the sample at room temperature for 10 minutes with shaking. Remove any unadsorbed material and wash eight times with buffer PB1 supplemented with 30% glycerol for 15 seconds each. Then, wash the plate once with buffer PB1. Elute the aptamers with 100 μL of CAPSO elution buffer at room temperature for 5 minutes. Transfer 90 μL of the eluate to a 96-well HybAid plate and add 10 μL of neutralizing buffer.

[0178] Semi-automated assay Place the streptavidin plate containing the adsorbed equilibrium mixture onto the deck of the BioTek EL406 plate washer. The washer is programmed to perform the following steps: Remove any unadsorbed material by aspiration and wash the wells four times with 300 μL of buffer PB1 supplemented with 1 mM dextran sulfate and 500 μM biotin. Then wash the wells three times with 300 μL of buffer PB1. Add a solution of 1 mM NHS-PEO4-biotin in 150 μL of buffer PB1 (from a newly prepared stock solution of 100 mM in DMSO). Incubate the plate for 5 minutes with shaking. Aspirate the liquid and wash the wells eight times with 300 μL of buffer PB1 supplemented with 10 mM glycine. Add 100 μL of buffer PB1 supplemented with 1 mM dextran sulfate. After these automated steps, the plate was removed from the plate washer and placed 5 cm away from a thermoshaker mounted under a UV light source (BlackRay, displayed wavelength 365 nm) for 20 minutes. The thermoshaker was set to 800 rpm and 25°C. After 20 minutes of irradiation, the samples were manually transferred to a new, washed streptavidin plate (or an unused well on an existing washed plate). The high-abundance (3% serum + 3% aptamer mixture) and low-abundance reaction mixture (0.3% serum + 0.3% aptamer mixture) were combined into a single well at this point. This "Catch-2" plate was placed on the deck of a BioTek EL406 plate washer. The washer was programmed to perform the following steps: Incubate the plate for 10 minutes with shaking. Aspirate the liquid and wash the wells 21 times with 300 μL of buffer PB1 supplemented with 30% glycerol. Wash the wells five times with 300 μL of buffer PB1, and aspirate the final wash solution. Add 100 μL of CAPSO elution buffer and elute the aptamers for 5 minutes with shaking. After these automated steps, remove the plate from the plate washer deck and manually transfer 90 μL aliquots of the sample into the wells of a HybAid 96-well plate containing 10 μL of neutralizing buffer.

[0179] Hybridization to a custom-made Agilent 8x15k microarray Transfer 24 μL of neutralized eluate to a new 96-well plate and apply 10 types of Cy3 applicators. Add 6 μL of 10×Agilent Block (Oligo aCGH / ChIP-on-chip hybridization kit, high volume, Agilent 5188-5380), containing a set of hybridization controls consisting of aptamers, to each well. Add 30 μL of 2×Agilent hybridization buffer to each sample and mix. Manually add 40 μL of the resulting hybridization solution to each "well" of the Hybridization Gasket Slide (8 microarrays per slide format, Agilent) using a pipette. Place custom Agilent microarray slides, each containing 10 probes per array complementary to a random 40-nucleotide region of each aptamer, along with a 20×dT linker, onto the gasket slides according to the manufacturer's protocol. Clamp the assembly (Hybridization Chamber Kit, SureHyb compatible, Agilent) and incubate it at 60°C for 19 hours while rotating it at 20 rpm.

[0180] Washing after hybridization Pour approximately 400 mL of Agilent Wash Buffer 1 into each of two separate glass staining dishes. Disassemble and separate the slides (no more than two at a time) while immersing them in Wash Buffer 1, then transfer them to the slide rack of a second staining dish containing Wash Buffer 1. Incubate the slides in Wash Buffer 1 for a further 5 minutes with stirring. Transfer the slides to Wash Buffer 2, which has been pre-equilibriumized to 37°C, and incubate for 5 minutes with stirring. Transfer the slides to a fourth staining dish containing acetonitrile and incubate for 5 minutes with stirring.

[0181] Microarray imaging Microarray slides are imaged using the Agilent G2565CA microarray scanner system with a resolution of 5 μm, 100% PMT setting, Cy3-channel, and an XRD option enabled at 0.05. The resulting TIFF images are processed using Agilent feature extraction software (version 10.5.1.1) according to the GE1_105_Dec08 protocol.

[0182] Luminex probe design The probe, immobilized on a bead, contains 40 deoxynucleotides complementary to a random 40-nucleotide region at the 3' end of the target aptamer. The aptamer complementation region is bound to the Luminex microsphere via a hexaethylene glycol (HEG) linker with a 5' amino terminus. The biotinylated detection deoxyoligonucleotide contains 17–21 deoxynucleotides complementary to the 5' primer region of the target aptamer. The biotin moiety is added to the 3' end of the detection oligo.

[0183] Probe coupling to Luminex microspheres The probe is attached to the Luminex microsphere mostly according to the manufacturer's instructions, but modified as follows: the amount of amino-terminal oligonucleotide is 2.5 × 10⁻⁶. 6 The concentration is 0.08 nmol per microsphere, and the second EDC addition is 5 μL at 10 mg / mL. The coupling reaction is carried out in an Eppendorf ThermoShaker set to 25°C and 600 rpm.

[0184] Microsphere hybridization The microsphere storage solution (approximately 40,000 microspheres / μL) is vortexed and sonicated for 60 seconds using a Health Sonics ultrasonic cleaner (model: T1.9C) to suspend the microspheres. The suspended microspheres are then diluted in 1.5 × TMAC hybridization solution to a concentration of 2,000 microspheres per reaction.Mix by vortexing and sonication. Transfer 33 μL of the bead mixture per reaction to a 96-well HybAid plate. Add 7 μL of 15 nM biotinylated detection oligonucleotide stock solution in 1 × TE buffer to each reaction and mix. Add 10 μL of neutralized assay sample and seal the plate with a silicone cap mat seal. Incubate the plate initially at 96°C for 5 minutes, then incubate overnight at 50°C without stirring in a standard hybridization oven. Pre-moisten a filter plate (Dura pore, Millipore part number MSBVN1250, 1.2 μm pore size) with 75 μL of 1 × TMAC hybridization solution supplemented with 0.5% (w / v) BSA. Transfer the entire sample volume from the hybridization reaction to the filter plate. Rinse the hybridization plate with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA, and transfer any remaining material to the filter plate. Filter the sample with 150 μL of buffer under slow reduced pressure and evacuate for approximately 8 seconds. Wash the filter plate once with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA, and resuspend the microspheres in the filter plate in 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. Protect the filter plate from light and incubate on an Eppendorf Thermalmixer R at 1000 rpm for 5 minutes. Then wash the filter plate once with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. Add 75 μL of streptavidin phycoerythrin (SAPE-100, MOSS, Inc.) at a concentration of 10 μg / mL to each reaction in a 1×TMAC hybridization solution, and incubate on an Eppendorf Thermalmixer R at 25°C at 1000 rpm for 60 minutes. Wash the filter plate twice with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA, and resuspend the microspheres in the filter plate in 75 μL of 1×TMAC hybridization solution containing 0.5% BSA.Next, the filter plate is protected from light and incubated on an Eppendorf Thermalmixer R at 1000 rpm for 5 minutes. Then, the filter plate is washed once with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. The microspheres are resuspended in 75 μL of 1×TMAC hybridization solution supplemented with 0.5% BSA and analyzed on a Luminex 100 instrument running XPonent 3.0 software. At least 100 microspheres per bead type are counted under high PMT calibration and doublet discriminator settings of 7500–18000.

[0185] QPCR reading Prepare a qPCR calibration curve sample in water at a 10-fold dilution to a range of 10⁸–10⁻² copies, and prepare a control without a template. Dilute the neutralized assay sample 40-fold in diH₂O. Prepare the qPCR master mix at 2× final concentration (2× KOD buffer, 400 μM dNTP mix, 400 nM forward and reverse primer mix, 2× SYBR Green I, and 0.5 U KOD EX). Add 10 μL of the 2× qPCR master mix to 10 μL of the diluted assay sample. Perform qPCR using a BioRad MyIQ iCycler for 2 minutes at 96°C, followed by 40 cycles of 5 seconds at 96°C and 30 seconds at 72°C.

[0186] Example 2. HFrEF model and prediction of cardiovascular events To predict the risk or likelihood of individuals with HFrEF experiencing a cardiovascular event within one year, a model was developed that included a panel of 16 biomarker proteins. A cardiovascular event was defined as death. A training analysis was developed using the Bristol Myers Squibb (BMS) Penn Heart Failure Study (PHFS) dataset. The PHFS dataset included 1,345 patients and 360 events. The Henry Ford Heart Failure (HFHF) study included 620 patients and 222 events. The Atherosclerosis Risk in Communities (ARIC) study included 44 patients and 25 events. Data from ARIC visit 5 and the Henry Ford dataset were used to validate improvements.

[0187] Table 3A shows the hierarchical structure of datasets used for the development (training and validation) and validation of the HFrEF model. [Table 3]

[0188] The HFrEF model is an accelerated failure time (AFT) survival model based on a Weibull distribution. This model features 17 aptamers. In total, the 17 aptamers bind to 16 different biomarker proteins, as shown in Table 1. The output of this model is the probability of survival at a given time, which is (1 - p(all causes of death at that time)). Therefore, the output is a number between 0 and 1, where 0 represents the lowest probability of survival (highest risk) and 1 represents the highest probability of survival (lowest risk). The feature list was refined using several iterations of the iterative Lasso AFT survival model, and the final model was trained using a penaltyless AFT regression method.

[0189] Overall results Each model was fitted using an accelerated failure time regression model with a Weibull distribution, employing a linear combination fitting of the values ​​of each reagent. The concordance index (C-index) was calculated by comparing the concordance of the one-year risk probability predicted from each model with the time to death or censoring in the training data.

[0190] Table 3B below shows the C-index and area under the ROC curve (AUC) values ​​for using the model to predict mortality within a specific time frame. [Table 4]

[0191] Model Development Table 4A shows the number of deaths at 6 months, 1 year, and any arbitrary time point in this study, as well as the number of individuals that did not die at any time point in this study, categorized by training / validation / validation and by dataset. [Table 5]

[0192] Tables 4B-4D below show some of the demographic data from the PHFS developmental cohort used in the training dataset, the ARIC VISIT 5 dataset and Henry Ford dataset used for validation, and the PHFS and Henry Ford (HFHF) datasets used for validation. [Table 6-1] [Table 6-2] [Table 6-3]

[0193] To ensure data quality, preprocessing steps were performed before data analysis. These preprocessing steps included data quality control (QC) and preliminary analysis.

[0194] Data QC for PHFS data showed that 29 samples failed row checking, meaning at least one hybridization or three median scale coefficients were outside the range of 0.4–2.5, indicating a technical issue (e.g., blockage) with that particular sample that would not be corrected by rerunning the sample. Furthermore, at least 5% of the measurements were 6MAD above the signal median. Two outlier samples were found. These 41 samples (1.1%) were excluded from further analysis. Finally, all analytes that failed the target confirmation specificity test were removed from the dataset.

[0195] Data quality control (QC) of the Henry Ford dataset showed that 24 samples failed row checking and were removed. This represents 3.7% of the 644 HFrEF patients in the set. No outlier samples were found in this dataset.

[0196] Data QC for the ARIC Visit 5 data did not identify any row check failures or outliers, so no samples were removed.

[0197] The preliminary analysis did not show evidence of a strong relationship between any of the clinical variables examined (age, sex, ethnicity, diabetes status, BMI, HFpEF / HFrEF status, event status, and eGFR) and the normalization scale coefficients of any of the datasets.

[0198] After data quality control and preliminary analysis, model development was completed in two steps: 1) proof of concept (POC) and 2) refinement.

[0199] In the POC step, only training data was used. The preliminary models considered were Cox with elastic net regularization and AFT with elastic net regularization, using Weibull and log-logistic distributions, all with 10 iterations of 5-fold cross-validation. In the POC analysis, sex and age were included in the development of the model for the first composite endpoint, HFrEF only.

[0200] The improved model used the PFHS, ARIC visit 5, and Henry Ford datasets. Similar to the POC, the PHFS data was split into 80 / 20 training / validation sets. All ARIC data (44 samples) was used for validation only. The Henry Ford data was split into 20 / 80 validation / validation sets.

[0201] The final model is an AFT model with a Weibull distribution, possessing 17 features, which are 17 aptamers. This model type was chosen for its performance in the proof-of-concept (POC).

[0202] The feature list was developed using several rounds of iterative Lasso with an AFT elastic network model, where λ=100 and α=0.125 were cross-validated three times. This model started with the top 100 univariate features per rank from the POC analysis. After each round of model fitting, a threshold for coefficient size was manually identified, and features with the smallest coefficients (in absolute value) were removed from the feature list. This was repeated until the resulting model metrics (C-exponent and AUC over 1 year (365 days)) began to decline in the validation set. The final list of 17 aptamers was then used to fit a standard AFT survival model without penalty parameters.

[0203] POC results The initial model performance criteria met the HFrEF model with all-cause mortality as the endpoint, and provided sufficient evidence to move the trial to model improvement. Neither the all-comers model nor the composite endpoint HFrEF model met the initial performance criteria, and it was not recommended to move to improvement.

[0204] The POC results showed that, for the Cox model, many analytes were statistically significant at p-value levels adjusted for different false-detection rates (FDRs).

[0205] These numbers and percentages can be observed in Table 5 for the heart failure prognosis-ejection fraction reduction study for death from any cause. [Table 7]

[0206] The best-performing model was a Cox model with an elastic net regression penalty parameter, achieving a C-exponent of 0.751. The best-performing AFT log-logistic model also achieved a C-exponent of 0.71. Both of these exceed the feasibility threshold of a C-exponent > 0.67. Due to the interpretability of the AFT model, this was the preferred method to use for improvement. The AUC of this model after one year was 0.782.

[0207] Adding age and gender to the models in the proof-of-concept (POC) did not improve model performance and was not included in any of the improved models.

[0208] Improvement results The final model developed in improving the HFrEF population is a 17-aptamer AFT survival model using a Weibull distribution. The final model did not include regularization parameters (α and λ).

[0209] The model with 31 features achieved slightly better metrics on the training and validation data, but was rejected because it was largely unstable during model strengthening.

[0210] The model was trained using 80% of the PHFS data used in the POC. Validation metrics were calculated for all HFrEF patients in ARIC visit 5 and 20% of HFrEF Henry Ford patients. The remaining data (20% of PHFS and 80% of Henry Ford) were reserved for validation. The C-index and AUC results when the model was used to predict mortality or the 1-year composite endpoint are shown in Table 3B above.

[0211] Figure 1 shows the observed Kaplan-Meier probabilities for the training dataset, with individuals divided into quartiles based on their predicted event probabilities over 365 days. As expected for a good-performing model, the lines are sufficiently separated at 180 and 365 days.

[0212] Validation Model validation was performed using 20% ​​of the PHFS not used in model development and Henry The evaluation was performed using 80% of the Ford data. The prediction is the probability of survival at one year. A minimum C-index and AUC of 0.7 is required to pass validation.

[0213] For training, validation, and validation sets, 1 year (365 days) and 6 months ( Table 6 shows the AUC and C index at 180 days. All validation metrics are above the values ​​required to pass validation (C index > 0.7 and AUC > 0.7 at 1 year and 6 months). As expected, all three metrics from the training data to the validation data are slightly lower. [Table 8]

[0214] Figure 2A shows the observed Kaplan-Meier probabilities for the validation dataset, where individuals are divided into quartiles by their predicted event probability at 365 days. The lines are well separated at 180 and 365 days, suggesting a good-performing model. Furthermore, the lines do not cross after approximately 45 days, which is another indicator that the model is performing as expected. Figure 2B shows the observed survival probabilities in the validation data when stratified by predicted risk quartiles, with a 95% confidence interval for the different Kaplan-Meier curves. The survival probability cutoffs for each quartile are Q4: p<0.813, Q3: p<0.9, Q2<0.942, and Q1: >0.942.

[0215] Interference test data were evaluated for estimated interference using the final model. Albumin at 1000 and 2000 mg / dL, hemoglobin at 500 and 1000 mg / dL, cholesterol, and valsartan failed the first step of the interference test at 365 and 180 days, but had no measurable impact on the model's C-index. Out-of-range RFU values ​​were attributed by Windsorization during model development. Overall, the results of model enhancement tools on validation data (such as interference tests and assay noise simulations) fully met the targets for both the C-index and AUC metrics at both 365 and 180 days.

[0216] Example 3: Analysis of the HFrEF biomarker panel model Model biomarker panels containing various combinations of the biomarkers listed in Table 1 were analyzed to determine the C index for all causes of death within one year for each panel. Tables 7 and 8 below show the model results when various combinations containing 1 to 8 biomarker proteins were measured.

[0217] The results in Table 7 show that panels containing at least two of CCL14, RNASE6, REG3A, and SVEP1, or at least two of CCL14, RNASE6, REG3A, and ADAMTSL2, performed well with a C index greater than 0.700.

[0218] The results in Table 8 also show that many panels, including ADAMTSL2, CCL14, REG3A, RNASE6, or SVEP1, and at least one of GDF15, THBS2, SVEP1, RNASE1, TAGLN, RSPO4, and WFDC2, performed well with a C index greater than 0.700. In panels including SVEP1 twice, SVEP1 was measured using two different aptamers that bind to SVEP1. [Table 9] [Table 10-1] [Table 10-2] [Table 10-3] [Table 10-4] [Table 10-5] [Table 10-6] [Table 10-7] [Table 10-8] [Table 10-9] [Table 10-10] [Table 10-11] [Table 10-12] [Table 10-13] [Table 10-14] [Table 10-15]

[0219] Example 4. HFpEF model and prediction of cardiovascular events To predict the risk or probability that an individual with HFpEF will experience a CV event within one year, a model comprising a panel of 14 biomarker proteins was developed. A CV event was defined as death. Training analysis was developed using the BMS Penn Heart Failure Study (PHFS) dataset. PHFS includes 1,345 patients and 360 events. A portion of the Henry Ford dataset from the University of Arizona (AZHF) and the ARIC visit 5 dataset were used for validation. The validation dataset is a holdout from the PHFS, AZHF, and ARIC datasets.

[0220] Table 9A shows the stratification of datasets for training, validation, and confirmation of the HFpEF model. [Table 11]

[0221] The HFpEF model is an accelerated failure time (AFT) survival model based on the Weibull distribution. This model has 14 aptamers as its features. The 14 aptamers bind to 14 different biomarker proteins shown in Table 2. The output of this model is the survival probability after one year, and conventional metrics are traditionally evaluated as the inverse probability (i.e., the probability of an event). The feature list was improved using univariate association in training and validation datasets, cross-validated elastic net for feature selection models, and removal of analytes with high CV. The final model was trained using unpenalized AFT regression. Model metrics are shown in Table 9B below. The results in Table 9B show that the model exceeds the performance criteria for one-year risk prediction in the training, validation, and confirmation datasets. [Table 12]

[0222] Model Development Table 10 below shows some of the demographics of the PHFS development cohort used in the training dataset. The AZHF cohort and ARIC visit used in the validation dataset. Some demographic data for the five cohorts are shown in Tables 11A and 11B, respectively. Some demographic data for the PHFS, AZHF, and ARIC visit five cohorts used in the validation dataset are shown in Table 12, respectively. [Table 13] [Table 14] [Table 15] [Table 16]

[0223] To ensure data quality, preprocessing steps were performed before data analysis. These preprocessing steps included data quality control (QC) and preliminary analysis.

[0224] Data QC revealed that 29 samples from the original combination dataset failed row checking, meaning that at least one hybridization or three median scale coefficients were outside the range of 0.4–2.5, indicating a technical issue (e.g., blockage) with those particular samples that would not be corrected by rerunning the samples. Additionally, there were 12 outlier samples where at least 5% of the measurements were 6MAD above the signal median. These 41 samples (1.1%) were excluded from further analysis. Finally, all analytes that failed the target confirmation specificity test were removed from the dataset.

[0225] The preliminary analysis did not show evidence of a strong relationship between any of the clinical variables examined (age, sex, ethnicity, diabetes status, BMI, HFpEF / HFrEF status, event status, and eGFR) and the normalization scale coefficients.

[0226] After data quality control and preliminary analysis, model development was completed in two steps: 1) proof of concept (POC) and 2) refinement.

[0227] In the POC step, only the training data was used. The preliminary models considered were Cox with elastic network regularization and AFT with elastic network regularization, using Weibull and log-logistic distributions, and all were performed with 10 iterations of 5-fold cross-validation.

[0228] The initial model performance criteria were met for both the composite mortality endpoint and the all-cause mortality endpoint. Furthermore, the HFpEF model trained on all causes of mortality performed nearly identically to the model trained on the composite endpoint in predicting it. The improved HFpEF was then trained on all causes of mortality to align with the improvements to the HFrEF model (see Example 2).

[0229] The improved model used the PFHS, AZHF, and ARIC visit 5 datasets. The PHHS dataset was split into 80 / 20 training / validation sets, similar to the POC. The AZHF dataset was split into 20 / 80 validation / validation sets to retain a substantial candidate validation dataset. The ARIC dataset was split into 50 / 50 sets.

[0230] The final model is the Weibull AFT model, which has 14 features. This model type was selected because of its performance in proof-of-concept (POC), consistency with the HFrEF model, and its ability to provide estimated risk probabilities.

[0231] POC results The POC results showed a significant number of analytes at different FDR levels for the univariate Cox association with the endpoint. These numbers and proportions are shown in Table 13 below for HFpEF prognosis for all-cause mortality POC.

Table 17

[0232] The best-performing model was the AFT Weibull model, which achieved a C-index of 0.698, an AUC of 0.81 at 90 days, and an AUC of 0.685 at 365 days. Both of these models were progressed to refinement.

[0233] Although not one of the pre-defined models, evaluation of HFpEF patients trained on all-cause mortality and evaluated for the all-cause mortality endpoint was also completed. The best model was an AFT Weibull distribution that achieved a C-index of 0.718. This model has a C index exceeding 0.67 and passed the POC criteria, so this was also progressed to refinement.

[0234] Refinement Results The final model developed in the refinement for the HFpEF population is a 14-aptamer AFT survival model using the Weibull distribution. The final model does not include regularization parameters (α and λ). The model was trained on 80% of the PHFS data used for POC. Validation metrics were calculated on 20% of AZHF patients with HFpEF and 50% of ARIC visit 5 patients with HFpEF. The remaining data was retained for validation.

[0235] Models were constructed using a reduced feature list of overlapping univariate features from derivative and AZHF validation datasets, and further refined by LASSO feature selection. The best model had an α of 0.2 and a minimum λ (0.1), and the resulting best model with 15 features was observed to have a difference of only 1% in C-index between training and validation, with both sets exceeding 0.80. The refined models were further evaluated by excluding features with high CV, creating a model with 14 features. The final models were evaluated using the C-index and AUC at 1 year to predict the all-cause mortality endpoint. Predictions were also evaluated for concordance and AUC when predicting the composite endpoint of hospitalization or death. The results for the training and validation datasets are shown in Table 14 below. 95% confidence intervals (CI) are shown in parentheses. [Table 18]

[0236] Validation Model validation was evaluated using 20% ​​of the PHFS and 80% of the AZHF data that were not used in model development. The secondary validation dataset was 50% of ARIV visit 5. Predictions are survival probabilities at 1 year. To pass validation, the C-index was met. And a minimum AUC of 0.7 is required.

[0237] The validation results, along with the comparative training and validation results, are shown in Table 15 below. Table 15 also shows the AUC and C index for the 1-year (365-day) and 6-month (180-day) training, validation, and validation sets. All validation metrics are above the values ​​required to pass validation (C index > 0.7 and AUC > 0.7 for both 1 year and 6 months). [Table 19]

[0238] In addition to discrimination metrics (C-index and AUC), the fitting of the predictive model to the observed event rate was observed on the training and validation datasets. Using the fitted AFT model, the survival probability of each sample was predicted at each individual time point from 0 to 365 days.

[0239] Figures 3 and 4 show the observed Kaplan-Meier probabilities for the validation and training datasets, stratified by the 365-day predicted risk quartile, respectively. As expected from a well-performing model, the lines separate at 180 and 365 days. Furthermore, the lines do not cross after approximately 45 days, which is another indicator that the model is performing as expected.

[0240] Interference trial data were evaluated for estimated interference using the final model. Only albumin, hemoglobin, and valsartan failed the first step of the interference trial at both 365 and 180 days, but none of them significantly affected the model's output performance metrics at either 180 or 365 days. The final model has 14 features and predicts the risk of death at either 365 or 180 days in HFpEF patients. The model's output is a risk probability between 0 and 1. The output meets or exceeds the validation criteria for C-index and AUC at each time point in the validation dataset.

[0241] Example 5: Analysis of the HFpEF biomarker panel model Model biomarker panels containing various combinations of biomarkers listed in Table 2 were analyzed to determine the C index for all causes of mortality within one year for each panel. Tables 16A and 16B below show the model results when various combinations containing 1 to 8 biomarker proteins were measured. The results in Table 16A indicate that panels containing at least GDF15 and RET or CHRDL1 functioned well, with a C index above 0.700. Furthermore, panels containing at least 2 to 13 of the following biomarkers functioned well: GDF15, WFDC2, CLEC3B, GHR, MUC16, NPPB, IGFBP2, CCN5, TNNT2, HSPB6, SLPI, MMP12, RET, and CHRDL1. The results in Tables 16A and 16B show that panels including at least three of the following were effective with a C index greater than 0.700: GDF15, WFDC2, CLEC3B, GHR, MUC16, NPPB, IGFBP2, CCN5, TNNT2, HSPB6, SLPI, MMP12, RET, and CHRDL1. [Table 20] [Table 21-1] [Table 21-2]

Claims

1. A screening method for a subject at risk of cardiovascular (CV) events, comprising forming a biomarker panel having N biomarker proteins and detecting the level of each of the N biomarker proteins in a sample derived from the subject, wherein N is at least 2, and a) at least two of the N biomarker proteins are selected from HCC-1, RNAS6, PAP1, SVEP1, and ATL2, or b) at least one of the N biomarker proteins is selected from HCC-1, RNAS6, PAP1, SVEP1, and ATL2, and at least one of the N biomarker proteins is selected from N-terminal pro-BNP, RSPO4, BNP, MIC-1, FABPA, ILRL1, ANGP2, HE4, TAGL, RNAS1, and TSP2.

2. A method for predicting the likelihood of a subject undergoing a CV event, comprising forming a biomarker panel having N types of biomarker proteins, and detecting the level of each of the N types of biomarker proteins in a sample derived from the subject, wherein N is at least 2, and a) at least two of the N types of biomarker proteins are selected from HCC-1, RNAS6, PAP1, SVEP1, and ATL2, or b) at least one of the N types of biomarker proteins is selected from HCC-1, RNAS6, PAP1, SVEP1, and ATL2, and at least one of the N types of biomarker proteins is selected from N-terminal pro-BNP, RSPO4, BNP, MIC-1, FABPA, ILRL1, ANGP2, HE4, TAGL, RNAS1, and TSP2.

3. The method according to claim 1 or claim 2, wherein at least two of the N types of biomarker proteins are RNAS6 and PAP1.

4. The method according to claim 1 or claim 2, wherein at least two of the N types of biomarker proteins are RNAS6 and ATL2.

5. The method according to claim 1 or claim 2, wherein at least two of the N types of biomarker proteins are HCC-1 and PAP1.

6. The method according to claim 1 or claim 2, wherein at least two of the N types of biomarker proteins are HCC-1 and ATL2.

7. The method according to claim 1 or claim 2, wherein at least two of the N types of biomarker proteins are HCC-1 and RNAS6.

8. The method according to claim 1 or claim 2, wherein at least two of the N types of biomarker proteins are PAP1 and SVEP1.

9. The method according to claim 1 or claim 2, wherein at least two of the N types of biomarker proteins are HCC-1 and SVEP1.

10. The method according to claim 1 or claim 2, wherein at least two of the N types of biomarker proteins are RNAS6 and SVEP1.

11. The method according to claim 1 or claim 2, wherein at least two of the N types of biomarker proteins are PAP1 and ATL2.

12. The method according to any one of claims 1 to 11, wherein all of the aforementioned N types of biomarker proteins are selected from HCC-1, RNAS6, PAP1, SVEP1, ATL2, N-terminal pro-BNP, RSPO4, BNP, MIC-1, FABPA, ILRL1, ANGP2, HE4, TAGL, RNAS1, and TSP2.

13. The method according to any one of claims 1 to 12, wherein one of the N types of biomarker proteins is MIC-1.

14. The method according to any one of claims 1 to 13, wherein one of the N types of biomarker proteins is RNAS1.

15. The method according to any one of claims 1 to 14, wherein N is 2, N is 3, N is 4, N is 5, N is 6, N is 7, N is 8, N is 9, N is 10, N is 11, N is 12, N is 13, N is 14, N is 15, or N is 16.

16. The method according to any one of claims 1 to 15, wherein the subject has heart failure accompanied by a decrease in ejection fraction.

17. A screening method for a subject at risk of cardiovascular (CV) events, comprising forming a biomarker panel having N biomarker proteins, and detecting the level of each of the N biomarker proteins in a sample derived from the subject, wherein N is at least 2, at least one of the N biomarker proteins is selected from RET and CRDL1, and at least one of the N biomarker proteins is selected from tetranectin, N-terminal pro-BNP, TNNT2, CA125, MIC-1, SLPI, HE4, MMP-12, HSPB6, WISP-2, GHR, and IGFBP-2.

18. A method for predicting the likelihood of a subject undergoing a CV event, comprising forming a biomarker panel having N types of biomarker proteins, and detecting the level of each of the N types of biomarker proteins in a sample derived from the subject, wherein N is at least 2, at least one of the N types of biomarker proteins is selected from RET and CRDL1, and at least one of the N types of biomarker proteins is selected from tetranectin, N-terminal pro-BNP, TNNT2, CA125, MIC-1, SLPI, HE4, MMP-12, HSPB6, WISP-2, GHR, and IGFBP-2.

19. The method according to claim 17 or claim 18, wherein at least two of the N types of biomarker proteins are RET and CRDL1.

20. The method according to any one of claims 17 to 19, wherein one of the N types of biomarker proteins is MIC-1.

21. The method according to any one of claims 17 to 20, wherein one of the N types of biomarker proteins is HE4.

22. The method according to any one of claims 17 to 21, wherein one of the N types of biomarker proteins is tetranectin.

23. Claims 17-22, one of the N types of biomarker proteins is GHR. The method described in any one of the items.

24. The method according to any one of claims 17 to 23, wherein one of the N types of biomarker proteins is CA125.

25. The method according to any one of claims 17 to 24, wherein one of the N types of biomarker proteins is an N-terminal pro-BNP.

26. The method according to any one of claims 17 to 25, wherein one of the N types of biomarker proteins is IGFBP-2.

27. The method according to any one of claims 17 to 26, wherein one of the N types of biomarker proteins is WISP-2.

28. The method according to any one of claims 17 to 27, wherein one of the N types of biomarker proteins is TNNT2.

29. The method according to any one of claims 17 to 28, wherein one of the N types of biomarker proteins is HSPB6.

30. The method according to any one of claims 17 to 29, wherein one of the N types of biomarker proteins is SLPI.

31. The method according to any one of claims 17 to 30, wherein one of the N types of biomarker proteins is MMP-12.

32. The method according to any one of claims 17 to 31, wherein all of the N types of biomarker proteins are selected from RET, CRDL1, tetranectin, N-terminal pro-BNP, TNNT2, CA125, MIC-1, SLPI, HE4, MMP-12, HSPB6, WISP-2, GHR, and IGFBP-2.

33. The method according to any one of claims 17 to 32, wherein N is 2, N is 3, N is 4, N is 5, N is 6, N is 7, N is 8, N is 9, N is 10, N is 11, N is 12, N is 13, or N is 14.

34. The method according to any one of claims 17 to 33, wherein the subject has heart failure with preserved ejection fraction.

35. The method according to any one of claims 1 to 34, wherein the CV event is death.

36. The method according to any one of claims 1 to 35, for screening or predicting the risk or likelihood of the subject experiencing a CV event within one year from the date on which the sample was taken from the subject.

37. The method according to any one of claims 1 to 35, for screening or predicting the risk or likelihood of the subject experiencing a CV event within 180 days from the date on which the sample was taken from the subject.

38. The method of screening or predicting the risk or likelihood of the subject experiencing a CV event within 90 days from the date on which the sample was taken from the subject, according to any one of claims 1 to 35. Method of description.

39. The method according to any one of claims 36 to 38, wherein if the level of at least two of the N biomarker proteins is abnormal compared to the control level of each of the biomarker proteins, the subject is at risk or likely to develop CV within one year, 180 days, or 90 days from the date the sample was taken from the subject.

40. The method according to any one of claims 36 to 38, wherein if the level of each of the N types of biomarker proteins is abnormal compared to the control level of each of the biomarker proteins, the subject is at risk or likely to develop CV within one year, 180 days, or 90 days from the date the sample was taken from the subject.

41. The method according to any one of claims 36 to 38, wherein the risk or possibility of the subject undergoing a CV event within one year, 180 days, or 90 days from the date of sampling from the subject is calculated as the survival probability from the subject for one year, 180 days, or 90 days from the date of sampling from the subject.

42. The method according to any one of claims 1 to 41, wherein the sample is selected from a blood sample, a serum sample, a plasma sample, and a urine sample.

43. The method according to claim 42, wherein the sample is a blood sample.

44. The method according to any one of claims 1 to 43, performed in vitro.

45. The method according to any one of claims 1 to 44, wherein the method comprises contacting a biomarker protein of the sample derived from the target with a set of capture reagents, and each capture reagent in the set of capture reagents specifically binds to one biomarker protein to be detected.

46. The method according to claim 45, wherein two of the aforementioned capture reagents bind to the same biomarker protein to be detected.

47. The method according to claim 46, wherein two types of capture reagents specifically bind to SVEP1, and the two types of capture reagents are aptamers containing different sequences.

48. The method according to any one of claims 1 to 47, wherein the method comprises contacting a biomarker protein of the sample derived from the target with a set of capture reagents, and each capture reagent in the set of capture reagents specifically binds to a different biomarker protein to be detected.

49. The method according to any one of claims 45 to 48, wherein each capture reagent is an antibody or aptamer.

50. The method according to claim 49, wherein each biomarker capture reagent is an aptamer.

51. The method according to claim 50, wherein at least one aptamer is an aptamer with a slow dissociation rate.

52. At least one slow dissociation rate aptamer is modified to at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least The method according to claim 51, comprising 8, at least 9, or at least 10 nucleotides.

53. Each slow dissociation rate aptamer has a dissociation rate (t) of ≥30 min, ≥60 min, ≥90 min, ≥120 min, ≥150 min, ≥180 min, ≥210 min, or ≥240 min. １／２ The method according to claim 51 or claim 52, wherein the target protein is bound to the target protein.

54. The risk or possibility of a CV event is determined by the detected biomarker level and a) information corresponding to the physical descriptor of the subject. b) Information corresponding to the weight change of the subject, c) Information corresponding to the ethnicity of the subject, d) Information corresponding to the gender of the subject, e) Information corresponding to the smoking history of the subject, f) Information corresponding to the drinking history of the subject, g) Information corresponding to the occupational history of the subject, h) Information corresponding to the family history of the cardiovascular disease or other circulatory system condition of the subject, i) Information relating to the presence or absence of at least one genetic marker associated with a higher risk of cardiovascular disease in the subject or the family of the subject, j) Information corresponding to the clinical symptoms of the subject, k) Information equivalent to other clinical tests, l) Information corresponding to the gene expression value of the subject, and m) Information corresponding to known cardiovascular risk factors of the subject, such as intake of a high-saturated fat diet, a high-salt diet, or a high-cholesterol diet. n) Information corresponding to the imaging results of the subject obtained by techniques selected from the group consisting of electrocardiogram, echocardiogram, carotid artery ultrasound diagnosis of intima-media thickness, flow-dependent vasodilation response test, pulse wave velocity, ankle-brachial index, stress echocardiogram, myocardial perfusion imaging, coronary artery calcium examination by CT, high-resolution CT angiography, MRI imaging, and other imaging techniques. o) Information relating to the drug treatment of the subject, p) Information corresponding to the age of the subject, and q) The method according to any one of the prior claims, based on at least one item of additional biomedical information selected from the information relating to the renal function of the subject.

55. The method according to claim 54, wherein at least one of the additional biomedical information items is information corresponding to the age of the subject.

56. The method according to any one of the prior claims, wherein the method comprises determining the risk or likelihood of the CV event in order to determine a medical insurance premium or a life insurance premium.

57. The method according to claim 56, further comprising determining the scope of coverage or premiums for medical insurance or life insurance.

58. The method according to any one of claims 1 to 57, further comprising using information obtained by the method to predict and / or manage the use of medical resources.

59. The method according to any one of claims 1 to 58, further comprising using information obtained by the method to enable a decision to acquire or purchase a medical business, hospital, or enterprise.

60. A kit containing N types of biomarker protein capture reagents, wherein N is at least 2 The kit wherein at least one of the capture reagents binds to HCC-1, RNAS6, PAP1, SVEP1, or ATL2, and at least one of the capture reagents binds to N-terminal pro-BNP, RSPO4, BNP, MIC-1, FABPA, ILRL1, ANGP2, HE4, TAGL, RNAS1, or TSP2.

61. The kit according to claim 60, wherein two of the capture reagents bind to SVEP1, and each of the remaining capture reagents binds to a different protein selected from HCC-1, RNAS6, PAP1, ATL2, N-terminal pro-BNP, RSPO4, BNP, MIC-1, FABPA, ILRL1, ANGP2, HE4, TAGL, RNAS1, and TSP2.

62. A kit comprising N types of biomarker protein capture reagents, wherein N is at least 2, at least one of the capture reagents is bound to RET or CRDL1, and at least one of the capture reagents is bound to tetranectin, N-terminal pro-BNP, TNNT2, CA125, MIC-1, SLPI, HE4, MMP-12, HSPB6, WISP-2, GHR, or IGFBP-2.

63. The kit according to claim 62, wherein each capture reagent binds to a different biomarker protein.

64. The kit according to claim 60 or claim 61, wherein N is 2, N is 3, N is 4, N is 5, N is 6, N is 7, N is 8, N is 9, N is 10, N is 11, N is 12, N is 13, N is 14, N is 15, N is 16, or N is 17.

65. The kit according to claim 62 or claim 63, wherein N is 2, N is 3, or N is 4, or N is 5, or N is 6, or N is 7, or N is 8, or N is 9, or N is 10, or N is 11, or N is 12, or N is 13, or N is 14.

66. The kit according to any one of claims 60, 61, and 64, wherein each of the N types of biomarker protein capture reagents specifically binds to a biomarker protein selected from Table 1.

67. The kit according to any one of claims 62, 63, or 65, wherein each of the N types of biomarker protein capture reagents specifically binds to a biomarker protein selected from Table 2.

68. The kit according to any one of claims 60 to 67, wherein each of the N types of biomarker capture reagents is an antibody or an aptamer.

69. The kit according to claim 68, wherein each biomarker capture reagent is an aptamer.

70. The kit according to claim 69, wherein at least one aptamer is an aptamer with a slow dissociation rate.

71. The kit according to claim 70, wherein at least one slow-dissociation rate aptamer comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 modified nucleotides.

72. Each slow dissociation rate aptamer has a dissociation rate (t) of ≥30 min, ≥60 min, ≥90 min, ≥120 min, ≥150 min, ≥180 min, ≥210 min, or ≥240 min. １／２ ) A kit according to claim 70 or 71, which binds to a target protein.

73. A kit according to any one of claims 60 to 72 for use in detecting the N types of biomarker proteins in a sample derived from a target.

74. The kit according to claim 73, for use in determining the risk or likelihood of a subject experiencing a CV event within one year from the date on which the sample was taken from the subject having heart failure.

75. The kit according to claim 74, wherein the CV event is death.

76. The kit according to claim 74 or 75, wherein the subject has heart failure accompanied by a reduced ejection fraction.

77. The kit according to claim 74 or 75, wherein the subject has heart failure with preserved ejection fraction.

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