Prediction of renal failure and uses thereof

Abstract

The present disclosure includes biomarkers, methods, devices, reagents, systems, and kits for assessing the risk of renal failure within a certain time frame, for example, within 4 years. In one aspect, the present disclosure provides biomarkers that can be used alone or in various combinations to assess the risk of renal failure within 4 years. In another aspect, a method for assessing the risk of renal failure within 4 years in an individual is provided, the method comprising detecting at least one biomarker value corresponding to at least one biomarker selected from the group of biomarkers shown in Table 8 in a biological sample from the individual.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority to U.S. Provisional Application No. 63 / 210,600, filed June 15, 2021, which is incorporated by reference in its entirety for all purposes.

[0002] The present application relates generally to methods for detecting biomarkers and assessing future risk of renal failure in an individual, and more specifically to one or more biomarkers, methods, devices, reagents, systems, and kits for use in assessing an individual to predict their risk of developing renal failure within four years. [Background technology]

[0003] The following discussion provides a brief summary of information related to the present application and is not an admission that any of the information provided herein or publications referenced are prior art to the present application.

[0004] Chronic kidney disease (CKD) is defined as a disease with abnormalities in kidney structure or function for more than 3 months (Table 1), affects approximately 13% of adults in the United States, and risk factors for the disease are heterogeneous and include genetic and demographic predisposition as well as diabetes. The kidneys serve three main functions: filtering metabolic by-products from the blood, producing urine, thereby regulating blood pressure and fluid and electrolyte balance, and secreting hormones. Depending on the stage of kidney disease (Figure 1), various symptoms and pathologies may occur, including hypertension, peripheral vascular disease, atherosclerosis, chronic anemia, chronic fatigue, uremia, and cardiovascular disease. Chronic kidney disease is often asymptomatic in the early stages of the disease (stages 1 and 2), which are advanced stages of the disease where kidney function can still be maintained, so identifying patients with early disease may help slow or prevent disease progression. Stages 3a to 4 are considered moderate to severe kidney disease, and stage 5 is end-stage kidney disease. Progression through the stages of kidney disease varies depending on the underlying cause of the disease, the presence of comorbidities, treatment, genetics, socioeconomic factors, and other factors. TIFF2024525146000002.tif72170

[0005] Currently, the standard of care for kidney disease prognosis is based on current laboratory parameters (e.g., eGFR, albuminuria, packed cell volume) and comorbidities, or by using the Kidney Failure Risk Equation (KFRE, Equation 1). (Tangri N, Stevens LA, Griffith J, et al. A predictive model for progression of chronic kidney disease to kidney failure. JAMA 2011;305:1553-1559. doi:1510.1001 / jama.2011.1451. Epub 2011 Apr 1511.) The KFRE was developed for patients with moderate to severe kidney disease (stage 3a to stage 4), but does not include early-stage kidney disease, where more kidney function needs to be preserved.

[0006] Using current clinical parameters as prognostic tools is imprecise and may not identify all patients who would benefit from more aggressive treatment to prevent disease progression. Recommendations for the management of kidney disease include those shown in Table 2 (Chapter 2: Definition, identification, and prediction of CKD progression. 2011) 2013;3:63-72. doi:10.1038 / kisup.2012.1065.) TIFF2024525146000003.tif235170

[0007] Chronic kidney disease could be prevented by aggressive treatment if the tendency for such disease could be accurately determined. Existing multi-marker tests require collection of multiple samples from an individual or require the sample to be divided among multiple assays. Optimally, improved tests are needed that require only a single blood, urine, or other sample and a single assay. Thus, there is a need for biomarkers, methods, devices, reagents, systems, and kits that allow prediction of the onset of kidney disease within a specified time frame, for example, a four-year period. Summary of the Invention

[0008] The present application includes biomarkers, methods, reagents, devices, systems, and kits for predicting the risk of developing renal failure within a specific time frame, such as 4 years. In certain embodiments, the risk of developing renal failure is predicted by a 50% decrease in estimated glomerular filtration rate (eGFR), a diagnosis of need for dialysis, an eGFR < 15 ml / min / 1.73 m 2 The present invention discloses a kidney disease progression test that predicts the occurrence within four years of at least one of the following: the development of idiopathic kidney disease, the development of end-stage renal disease (ESRD), or a diagnosis of the need for a kidney transplant.

[0009] In one aspect, the kidney disease progression test disclosed herein is intended to provide a 4-year prognosis of progressive chronic renal failure (PCRI) and includes patients with early stage (stage 1-stage 2) kidney disease compared to the population used to generate the KFRE, who are candidates for aggressive treatment to prevent disease progression (Table 2). In a further aspect, the test disclosed does not require calculation of eGFR, measurement of proteinuria, or reliance on patient characteristics such as age or sex.

[0010] Advantages of the kidney disease progression test disclosed herein include the convenience of a prognostic test for people with diagnosed chronic kidney disease without the need to estimate current kidney function (by eGFR), measure proteinuria, or enter age or gender; identification of high-risk patients for PCRI early in the disease process; and a metric (relative risk) provided to patients that provides context for the reported value so that their risk of severe kidney function decline can be understood compared to an "average" or "typical" person with the same disease process.

[0011] The following numbered paragraphs

[0012] ~

[0122] includes a description of the broad combinations of technical features of the present invention disclosed in this specification:

[0012] 1. a) measuring the level of COL28A1 protein and the levels of at least one, two, three, four, five, six, seven, eight, or nine proteins selected from the group consisting of HAVCR1, FSTL3, RGMB, UBE2G2, REG1A, REG1B, COL6A3, CST3, and TNFRSF1A in a sample from a human subject; b) identifying the human subject as having a relative risk of developing progressive chronic renal failure within a four year period based on the level of COL28A1 and the levels of at least one, two, three, four, five, six, seven, eight, or nine proteins.

[0013] 2. a) measuring the level of UBE2G2 protein and the levels of at least one, two, three, four, five, six, seven, eight, or nine proteins selected from the group consisting of HAVCR1, FSTL3, RGMB, COL28A1, REG1A, REG1B, COL6A3, CST3, and TNFRSF1A in a sample from a human subject; b) identifying the human subject as having a relative risk of developing progressive chronic renal failure within a four year period based on the level of UBE2G2 and the levels of at least one, two, three, four, five, six, seven, eight, or nine proteins.

[0014] 3. a) measuring the level of REG1B protein and the levels of at least one, two, three, four, five, six, seven, eight, or nine proteins selected from the group consisting of HAVCR1, FSTL3, RGMB, COL28A1, UBE2G2, REG1A, COL6A3, CST3, and TNFRSF1A in a sample from a human subject; b) identifying the human subject as having a relative risk of developing progressive chronic renal failure within a four-year period based on the level of REG1B and the levels of at least one, two, three, four, five, six, seven, eight, or nine proteins.

[0015] 4. The method of embodiment 1, wherein said method comprises measuring COL28A1 and HAVCR1, COL28A1 and FSTL3, COL28A1 and RGMB, COL28A1 and UBE2G2, COL28A1 and REG1A, COL28A1 and REG1B, COL28A1 and COL6A3, COL28A1 and CST3, or COL28A1 and TNFRSF1A.

[0016] 5. The method comprises the steps of: COL28A1, HAVCR1, and FSTL3; COL28A1, HAVCR1, and RGMB; COL28A1, HAVCR1, and UBE2G2; COL28A1, HAVCR1, and REG1A; COL28A1, HAVCR1, and REG1B; COL28A1, HAVCR1, and COL6A3; COL28A1, HAVCR1, and CST3; COL28A1, HAVCR1, and TNFRSF1A; COL28A1, FSTL3, and RG MB;COL28A1, FSTL3, and UBE2G2;COL28A1, FSTL3, and REG1A;COL28A1, FSTL3, and REG1B;COL28A1, FSTL3, and COL6A3;COL28A1, FSTL3, and CST3;COL28A1, FSTL3, and TNFRSF1A;COL28A1, RGMB, and UBE2G2;COL28A1, RGMB, and REG1A;COL28A1, RGMB, and REG1B;COL28A1, RGM B, and COL6A3;COL28A1, RGMB, and CST3;COL28A1, RGMB, and TNFRSF1A;COL28A1, UBE2G2, and REG1A;COL28A1, UBE2G2, and REG1B;COL28A1, UBE2G2, and COL6A3;COL28A1, UBE2G2, and CST3;COL28A1, UBE2G2, and TNFRSF1A;COL28A1, REG1A, and REG1B;COL28A1, REG1A, and COL 6A3; COL28A1, REG1A, and CST3; COL28A1, REG1A, and TNFRSF1A; COL28A1, REG1B, and COL6A3; COL28A1, REG1B, and CST3; COL28A1, REG1B, and TNFRSF1A; COL28A1, COL6A3, and CST3; COL28A1, COL6A3, and TNFRSF1A; or COL28A1, CST3, and TNFRSF1A.

[0017] 6. The method of embodiment 2, wherein the method comprises measuring UBE2G2 and HAVCR1, UBE2G2 and FSTL3, UBE2G2 and RGMB, UBE2G2 and COL28A1, UBE2G2 and REG1A, UBE2G2 and REG1B, UBE2G2 and COL6A3, UBE2G2 and CST3, or UBE2G2 and TNFRSF1A.

[0018] 7. The method comprises the steps of: UBE2G2, HAVCR1, and FSTL3; UBE2G2, HAVCR1, and RGMB; UBE2G2, HAVCR1, and COL28A1; UBE2G2, HAVCR1, and REG1A; UBE2G2, HAVCR1, and REG1B; UBE2G2, HAVCR1, and COL6A3; UBE2G2, HAVCR1, and CST3; UBE2G2, HAVCR1, and TNFRSF1A; UBE2G2, FSTL3, and RGM B;UBE2G2, FSTL3, and COL28A1;UBE2G2, FSTL3, and REG1A;UBE2G2, FSTL3, and REG1B;UBE2G2, FSTL3, and COL6A3;UBE2G2, FSTL3, and CST3;UBE2G2, FSTL3, and TNFRSF1A;UBE2G2, RGMB, and COL28A1;UBE2G2, RGMB, and REG1A;UBE2G2, RGMB, and REG1B;UBE2G2, RGMB, and COL6A3;UBE2G2, RGMB, and CST3;UBE2G2, RGMB, and TNFRSF1A;UBE2G2, COL28A1, and REG1A;UBE2G2, COL28A1, and REG1B;UBE2G2, COL28A1, and COL6A3;UBE2G2, COL28A1, and CST3;UBE2G2, COL28A1, and TNFRSF1A;UBE2G2, REG1A, and REG1B;UBE2G2, REG1A, and CO The method of claim 2, comprising measuring L6A3; UBE2G2, REG1A, and CST3; UBE2G2, REG1A, and TNFRSF1A; UBE2G2, REG1B, and COL6A3; UBE2G2, REG1B, and CST3; UBE2G2, REG1B, and TNFRSF1A; UBE2G2, COL6A3, and CST3; UBE2G2, COL6A3, and TNFRSF1A; or UBE2G2, CST3, and TNFRSF1A.

[0019] 8. The method of embodiment 3, wherein said method comprises measuring REG1B and HAVCR1, REG1B and FSTL3, REG1B and RGMB, REG1B and COL28A1, REG1B and UBE2G2, REG1B and REG1A, REG1B and COL6A3, REG1B and CST3, or REG1B and TNSF1A.

[0020] 9. The method comprises the steps of: REG1B, HAVCR1, and FSTL3; REG1B, HAVCR1, and RGMB; REG1B, HAVCR1, and COL28A1; REG1B, HAVCR1, and UBE2G2; REG1B, HAVCR1, and REG1A; REG1B, HAVCR1, and COL6A3; REG1B, HAVCR1, and CST3; REG1B, HAVCR1, and TNFRSF1A; REG1B, FSTL3, and RGMB. ;REG1B, FSTL3, and COL28A1;REG1B, FSTL3, and UBE2G2;REG1B, FSTL3, and REG1A;REG1B, FSTL3, and COL6A3;REG1B, FSTL3, and CST3;REG1B, FSTL3, and TNFRSF1A;REG1B, RGMB, and COL28A1;REG1B, RGMB, and UBE2G2;REG1B, RGMB, and REG1A;REG1B, RGMB, and COL6A3;REG1B, RGMB, and CST3;REG1B, RGMB, and TNFRSF1A;REG1B, COL28A1, and UBE2G2;REG1B, COL28A1, and REG1A;REG1B, COL28A1, and COL6A3;REG1B, COL28A1, and CST3;REG1B, COL28A1, and TNFRSF1A;REG1B, UBE2G2, and REG1A;REG1B, UBE2G2, and COL The method of embodiment 3, comprising measuring REG1B, UBE2G2, and CST3; REG1B, UBE2G2, and TNFRSF1A; REG1B, REG1A, and COL6A3; REG1B, REG1A, and CST3; REG1B, REG1A, and TNFRSF1A; REG1B, COL6A3, and CST3; REG1B, COL6A3, and TNFRSF1A; or REG1B, CST3, and TNFRSF1A.

[0021] 10. The method of embodiment 1, wherein the method comprises measuring at least one of the proteins selected from COL28A1 and UBE2G2, and HAVCR1, FSTL3, RGMB, REG1A, REG1B, COL6A3, CST3, and TNFRSF1A.

[0022] 11. The method of embodiment 1, wherein the method comprises measuring at least one of COL28A1 and REG1B, and proteins selected from HAVCR1, FSTL3, RGMB, UBE2G2, REG1A, COL6A3, CST3, and TNFRSF1A.

[0023] 12. The method of embodiment 2, wherein the method comprises measuring at least one of UBE2G2 and REG1B, and proteins selected from HAVCR1, FSTL3, RGMB, COL28A1, REG1A, COL6A3, CST3, and TNFRSF1A.

[0024] The method of any one of aspects 1-12, wherein progressive chronic renal failure within a 13.4 year period indicates the occurrence of one or more of the following: a 50% decrease in estimated glomerular filtration rate (eGFR), a diagnosis of the need for dialysis, the occurrence of an eGFR<15 ml / min / 1.73 m2, the development of end stage renal disease (ESRD), or a diagnosis of the need for a kidney transplant.

[0025] 14. The method according to any one of aspects 1 to 13, wherein said measuring is carried out using mass spectrometry, an aptamer-based assay, and / or an antibody-based assay.

[0026] 15. The method of any one of aspects 1-14, wherein the sample is selected from blood, plasma, serum, or urine.

[0027] 16. a) contacting a sample from a human subject with a set of capture reagents, where each capture reagent has affinity for a different protein of a set of proteins that includes a COL28A1 protein and at least one, two, three, four, five, six, seven, eight, or nine proteins selected from the group consisting of HAVCR1, FSTL3, RGMB, UBE2G2, REG1A, REG1B, COL6A3, CST3, and TNFRSF1A; b) measuring the level of each protein of said set of proteins using said set of capture reagents.

[0028] 17. a) contacting a sample from a human subject with a set of capture reagents, where each capture reagent has affinity for a different protein of the set of proteins that includes UBE2G2 protein and at least 1, 2, 3, 4, 5, 6, 7, 8, or 9 proteins selected from the group consisting of HAVCR1, FSTL3, RGMB, COL28A1, REG1A, REG1B, COL6A3, CST3, and TNFRSF1A; b) measuring the level of each protein of said set of proteins using said set of capture reagents.

[0029] 18. a) contacting a sample from a human subject with a set of capture reagents, where each capture reagent has affinity for a different protein of a set of proteins that includes REG1B protein and at least 1, 2, 3, 4, 5, 6, 7, 8, or 9 proteins selected from the group consisting of HAVCR1, FSTL3, RGMB, COL28A1, UBE2G2, REG1A, COL6A3, CST3, and TNFRSF1A; b) measuring the level of each protein of said set of proteins using said set of capture reagents.

[0030] 19. The method of embodiment 16, wherein said method comprises measuring COL28A1 and HAVCR1, COL28A1 and FSTL3, COL28A1 and RGMB, COL28A1 and UBE2G2, COL28A1 and REG1A, COL28A1 and REG1B, COL28A1 and COL6A3, COL28A1 and CST3, or COL28A1 and TNFRSF1A.

[0031] 20. The method comprises the steps of: COL28A1, HAVCR1, and FSTL3; COL28A1, HAVCR1, and RGMB; COL28A1, HAVCR1, and UBE2G2; COL28A1, HAVCR1, and REG1A; COL28A1, HAVCR1, and REG1B; COL28A1, HAVCR1, and COL6A3; COL28A1, HAVCR1, and CST3; COL28A1, HAVCR1, and TNFRSF1A; COL28A1, FSTL3, and RG MB;COL28A1, FSTL3, and UBE2G2;COL28A1, FSTL3, and REG1A;COL28A1, FSTL3, and REG1B;COL28A1, FSTL3, and COL6A3;COL28A1, FSTL3, and CST3;COL28A1, FSTL3, and TNFRSF1A;COL28A1, RGMB, and UBE2G2;COL28A1, RGMB, and REG1A;COL28A1, RGMB, and REG1B;COL28A1, RGM B, and COL6A3;COL28A1, RGMB, and CST3;COL28A1, RGMB, and TNFRSF1A;COL28A1, UBE2G2, and REG1A;COL28A1, UBE2G2, and REG1B;COL28A1, UBE2G2, and COL6A3;COL28A1, UBE2G2, and CST3;COL28A1, UBE2G2, and TNFRSF1A;COL28A1, REG1A, and REG1B;COL28A1, REG1A, and COL 17. The method of claim 16, comprising measuring COL28A1, REG1A, and CST3; COL28A1, REG1A, and TNFRSF1A; COL28A1, REG1B, and COL6A3; COL28A1, REG1B, and CST3; COL28A1, REG1B, and TNFRSF1A; COL28A1, COL6A3, and CST3; COL28A1, COL6A3, and TNFRSF1A; or COL28A1, CST3, and TNFRSF1A.

[0032] 21. The method of embodiment 17, wherein said method comprises measuring UBE2G2 and HAVCR1, UBE2G2 and FSTL3, UBE2G2 and RGMB, UBE2G2 and COL28A1, UBE2G2 and REG1A, UBE2G2 and REG1B, UBE2G2 and COL6A3, UBE2G2 and CST3, or UBE2G2 and TNFRSF1A.

[0033] 22. The method comprises the steps of: UBE2G2, HAVCR1, and FSTL3; UBE2G2, HAVCR1, and RGMB; UBE2G2, HAVCR1, and COL28A1; UBE2G2, HAVCR1, and REG1A; UBE2G2, HAVCR1, and REG1B; UBE2G2, HAVCR1, and COL6A3; UBE2G2, HAVCR1, and CST3; UBE2G2, HAVCR1, and TNFRSF1A; UBE2G2, FSTL3, and RGM B;UBE2G2, FSTL3, and COL28A1;UBE2G2, FSTL3, and REG1A;UBE2G2, FSTL3, and REG1B;UBE2G2, FSTL3, and COL6A3;UBE2G2, FSTL3, and CST3;UBE2G2, FSTL3, and TNFRSF1A;UBE2G2, RGMB, and COL28A1;UBE2G2, RGMB, and REG1A;UBE2G2, RGMB, and REG1B;UBE2G2, RGMB, and COL6A3;UBE2G2, RGMB, and CST3;UBE2G2, RGMB, and TNFRSF1A;UBE2G2, COL28A1, and REG1A;UBE2G2, COL28A1, and REG1B;UBE2G2, COL28A1, and COL6A3;UBE2G2, COL28A1, and CST3;UBE2G2, COL28A1, and TNFRSF1A;UBE2G2, REG1A, and REG1B;UBE2G2, REG1A, and CO 18. The method of embodiment 17, comprising measuring L6A3; UBE2G2, REG1A, and CST3; UBE2G2, REG1A, and TNFRSF1A; UBE2G2, REG1B, and COL6A3; UBE2G2, REG1B, and CST3; UBE2G2, REG1B, and TNFRSF1A; UBE2G2, COL6A3, and CST3; UBE2G2, COL6A3, and TNFRSF1A; or UBE2G2, CST3, and TNFRSF1A.

[0034] 23. The method of embodiment 18, wherein said method comprises measuring REG1B and HAVCR1, REG1B and FSTL3, REG1B and RGMB, REG1B and COL28A1, REG1B and UBE2G2, REG1B and REG1A, REG1B and COL6A3, REG1B and CST3, or REG1B and TNSF1A.

[0035] 24. The method comprises the steps of: REG1B, HAVCR1, and FSTL3; REG1B, HAVCR1, and RGMB; REG1B, HAVCR1, and COL28A1; REG1B, HAVCR1, and UBE2G2; REG1B, HAVCR1, and REG1A; REG1B, HAVCR1, and COL6A3; REG1B, HAVCR1, and CST3; REG1B, HAVCR1, and TNFRSF1A; REG1B, FSTL3, and RGMB. ;REG1B, FSTL3, and COL28A1;REG1B, FSTL3, and UBE2G2;REG1B, FSTL3, and REG1A;REG1B, FSTL3, and COL6A3;REG1B, FSTL3, and CST3;REG1B, FSTL3, and TNFRSF1A;REG1B, RGMB, and COL28A1;REG1B, RGMB, and UBE2G2;REG1B, RGMB, and REG1A;REG1B, RGMB, and COL6A3;REG1B, RGMB, and CST3;REG1B, RGMB, and TNFRSF1A;REG1B, COL28A1, and UBE2G2;REG1B, COL28A1, and REG1A;REG1B, COL28A1, and COL6A3;REG1B, COL28A1, and CST3;REG1B, COL28A1, and TNFRSF1A;REG1B, UBE2G2, and REG1A;REG1B, UBE2G2, and COL 20. The method of claim 18, comprising measuring REG1B, UBE2G2, and CST3; REG1B, UBE2G2, and TNFRSF1A; REG1B, REG1A, and COL6A3; REG1B, REG1A, and CST3; REG1B, REG1A, and TNFRSF1A; REG1B, COL6A3, and CST3; REG1B, COL6A3, and TNFRSF1A; or REG1B, CST3, and TNFRSF1A.

[0036] 25. The method of embodiment 16, wherein the method comprises measuring at least one of the proteins selected from COL28A1 and UBE2G2, and HAVCR1, FSTL3, RGMB, REG1A, REG1B, COL6A3, CST3, and TNFRSF1A.

[0037] 26. The method of embodiment 16, wherein the method comprises measuring at least one of COL28A1 and REG1B, and proteins selected from HAVCR1, FSTL3, RGMB, UBE2G2, REG1A, COL6A3, CST3, and TNFRSF1A.

[0038] 27. The method of embodiment 17, wherein the method comprises measuring at least one of UBE2G2 and REG1B, and proteins selected from HAVCR1, FSTL3, RGMB, COL28A1, REG1A, COL6A3, CST3, and TNFRSF1A.

[0039] 28. The method according to any one of aspects 16 to 27, wherein said protein levels are used to identify human subjects at high relative risk of developing progressive chronic renal failure within a 4 year period.

[0040] The method of embodiment 28, wherein progressive chronic renal failure within a 29.4 year period indicates the occurrence of one or more of: a 50% decrease in estimated glomerular filtration rate (eGFR), a diagnosis of the need for dialysis, the occurrence of an eGFR<15 ml / min / 1.73 m2, the development of end stage renal disease (ESRD), or a diagnosis of the need for a kidney transplant.

[0041] 30. The method according to any one of aspects 16 to 29, wherein the set of capture reagents is selected from aptamers, antibodies, and combinations of aptamers and antibodies.

[0042] 31. The method according to any one of aspects 16 to 30, wherein the sample is selected from blood, plasma, serum, or urine.

[0043] 32. a) contacting a sample from a human subject with two capture reagents, where one capture reagent has affinity for COL28A1 protein and a second capture reagent has affinity for UBE2G2 protein; b) A method comprising measuring the level of each protein using two capture reagents.

[0044] 33. a) contacting a sample from a human subject with two capture reagents, where one capture reagent has affinity for COL28A1 protein and a second capture reagent has affinity for REG1B protein; b) A method comprising measuring the level of each protein using two capture reagents.

[0045] 34. a) contacting a sample from a human subject with two capture reagents, where one capture reagent has affinity for UBE2G2 protein and a second capture reagent has affinity for REG1B protein; b) measuring the level of each protein using said two capture reagents.

[0046] 35. The method according to any one of aspects 32 to 34, further comprising measuring the level of said HAVCR1 protein using a capture reagent having affinity for said HAVCR1 protein.

[0047] 36. The method according to any one of aspects 32 to 35, further comprising measuring the level of said FSTL3 protein using a capture reagent having affinity for the FSTL3 protein.

[0048] 37. The method of any one of aspects 32 to 36, further comprising measuring the level of RGMB protein using a capture reagent having affinity for the RGMB protein.

[0049] 38. The method according to any one of aspects 32 to 37, further comprising measuring the level of said REG1A protein using a capture reagent having affinity for the REG1A protein.

[0050] 39. The method according to any one of aspects 32 to 38, further comprising measuring the level of said COL6A3 protein using a capture reagent having affinity for the COL6A3 protein.

[0051] 40. The method according to any one of aspects 32-39, further comprising measuring the level of said CST3 protein using a capture reagent having affinity for the CST3 protein.

[0052] 41. The method according to any one of aspects 32 to 40, further comprising measuring the level of said TNFRSF1A protein using a capture reagent having affinity for said TNFRSF1A protein.

[0053] 42. a) measuring the levels of COL28A1 and UBE2G2 in a sample from a human subject; b) identifying the human subject as having a relative risk of developing progressive chronic renal failure within a four year period based on the levels of COL28A1 and UBE2G2.

[0054] 43. a) measuring the levels of COL28A1 and REG1B in a sample from a human subject; b) identifying the human subject as having a relative risk of developing progressive chronic renal failure within a four year period based on the levels of COL28A1 and REG1B.

[0055] 44. a) measuring the levels of UBE2G2 and REG1B in a sample from a human subject; b) identifying the human subject as having a relative risk of developing progressive chronic renal failure within a four year period based on the levels of UBE2G2 and REG1B.

[0056] 45. A method according to any one of embodiments 42 to 44, further comprising measuring the level of HAVCR1 protein.

[0057] 46. ​​A method according to any one of aspects 42 to 45, further comprising measuring the level of FSTL3 protein.

[0058] 47. A method according to any one of aspects 42 to 46, further comprising measuring the level of RGMB protein.

[0059] 48. A method according to any one of aspects 42 to 47, further comprising measuring the level of REG1A protein.

[0060] 49. The method according to any one of aspects 42 to 48, further comprising measuring the level of COL6A3 protein.

[0061] 50. The method according to any one of embodiments 42 to 49, further comprising measuring the level of CST3 protein.

[0062] 51. A method according to any one of aspects 42 to 50, further comprising measuring the level of TNFRSF1A protein.

[0063] 52. The method according to any one of aspects 42 to 51, wherein said measuring is performed using mass spectrometry, an aptamer-based assay, and / or an antibody-based assay.

[0064] 53. a) contacting a sample from a human subject with three capture reagents, wherein each of the three capture reagents has an affinity for a protein selected from COL28A1, UBE2G2, and REG1B; b) measuring the levels of each of the proteins using the three capture reagents.

[0065] 54. The method of embodiment 53, further comprising measuring the level of the HAVCR1 protein using a capture reagent having affinity for the HAVCR1 protein.

[0066] 55. The method according to embodiment 53 or 54, further comprising measuring the level of said FSTL3 protein using a capture reagent having affinity for the FSTL3 protein.

[0067] 56. The method of any one of aspects 53 to 55, further comprising measuring the level of RGMB protein using a capture reagent having affinity for the RGMB protein.

[0068] 57. The method according to any one of aspects 53 to 56, further comprising measuring the level of said REG1A protein using a capture reagent having affinity for the REG1A protein.

[0069] 58. The method according to any one of aspects 53 to 57, further comprising measuring the level of said COL6A3 protein using a capture reagent having affinity for the COL6A3 protein.

[0070] 59. The method according to any one of aspects 53-58, further comprising measuring the level of said CST3 protein using a capture reagent having affinity for CST3 protein.

[0071] 60. A method according to any one of aspects 53 to 59, further comprising measuring the level of said TNFRSF1A protein using a capture reagent having affinity for said TNFRSF1A protein.

[0072] 61. a) measuring the levels of COL28A1, UBE2G2, and REG1B in a sample from a human subject; b) identifying the human subject as having a relative risk of developing progressive chronic renal failure within a four year period based on the levels of COL28A1, UBE2G2, and REG1B.

[0073] 62. The method of embodiment 61, further comprising measuring the level of HAVCR1 protein.

[0074] 63. The method of embodiment 61 or 62, further comprising measuring the level of FSTL3 protein.

[0075] 64. A method according to any one of aspects 61 to 63, further comprising measuring the level of RGMB protein.

[0076] 65. A method according to any one of aspects 61 to 64, further comprising measuring the level of REG1A protein.

[0077] 66. The method according to any one of embodiments 61 to 65, further comprising measuring the level of COL6A3 protein.

[0078] 67. The method according to any one of embodiments 61 to 66, further comprising measuring the level of CST3 protein.

[0079] 68. A method according to any one of aspects 61 to 67, further comprising measuring the level of TNFRSF1A protein.

[0080] 69. The method according to any one of aspects 61 to 68, wherein said measuring is performed using mass spectrometry, an aptamer-based assay, and / or an antibody-based assay.

[0081] 70. a) measuring the levels of at least 3, 4, 5, 6, 7, 8, 9, or 10 proteins selected from the group consisting of HAVCR1, FSTL3, RGMB, COL28A1, UBE2G2, REG1A, REG1B, COL6A3, CST3, and TNFRSF1A in a sample from a human subject; b) identifying said human subject as having a relative risk of developing progressive chronic renal failure within a four year period based on the levels of said at least three, four, five, six, seven, eight, nine, or ten proteins.

[0082] 71. The method of embodiment 70, wherein the measuring is performed using mass spectrometry, an aptamer-based assay, and / or an antibody-based assay.

[0083] 72. The method of embodiment 70 or 71, wherein the sample is selected from blood, plasma, serum, or urine.

[0084] 73. The method comprises the steps of: HAVCR1, FSTL3, and RGMB; HAVCR1, FSTL3, and COL28A1; HAVCR1, FSTL3, and UBE2G2; HAVCR1, FSTL3, and REG1A; HAVCR1, FSTL3, and REG1B; HAVCR1, FSTL3, and COL6A3; HAVCR1, FSTL3, and CST3; HAVCR1, FSTL3, and TNFRSF1; HAVCR1, RGMB, and COL28A1; HAVCR1, RGMB, and UBE2G2; HAVCR1, RGMB, and REG1A; CR1, RGMB, and REG1B; HAVCR1, RGMB, and COL6A3; HAVCR1, RGMB, and CST3; HAVCR1, RGMB, and TNFRSF1A; HAVCR1, COL28A1, and UBE2G2; HAVCR1, COL28A1, and REG1A; HAVCR1, COL28A1, and REG1B; HAVCR1, COL28A1, and COL6A3; HAVCR1, COL28A1, and CST3; HAVCR1, COL28A1, and TNFRSF1A; HAVCR1, UBE2G2, and REG1A; HAV CR1, UBE2G2, and REG1B; HAVCR1, UBE2G2, and COL6A3; HAVCR1, UBE2G2, and CST3; HAVCR1, UBE2G2, and TNFRSF1A; HAVCR1, REG1A, and REG1B; HAVCR1, REG1A, and COL6A3; HAVCR1, REG1A, and CST3; HAVCR1, REG1A, and TNFRSF1A; HAVCR1, REG1B, and COL6A3; HAVCR1, REG1B, and CST3; HAVCR1, REG1B, and TNFRSF1A; HAVCR1 , COL6A3, and CST3;HAVCR1, COL6A3, and TNFRSF1A;HAVCR1, CST3, and TNFRSF1A;FSTL3, RGMB, and COL28A1;FSTL3, RGMB, and UBE2G2;FSTL3, RGMB, and REG1A;FSTL3, RGMB, and REG1B;FSTL3, RGMB, and COL6A3;FSTL3, RGMB, and CST3;FSTL3, RGMB, and TNFRSF1A;FSTL3, COL28A1, and UBE2G2;FSTL3, COL28A1, and REG1A;FSTL3, COL28A1, and REG1B;FSTL3, COL28A1, and COL6A3;FSTL3, COL28A1, and CST3;FSTL3, COL28A1, and TNFRSF1A;FSTL3, UBE2G2, and REG1A;FSTL3, UBE2G2, and REG1B;FSTL3, UBE2G2, and COL6A3;FSTL3, UBE2G2, and CST3;FSTL3, UBE2G2, and TNFRSF1A;FSTL3, REG1A, and REG1B;FSTL3, REG1A, and COL6A3;FSTL3, REG1A and CST3;FSTL3, REG1A and TNFRSF1A;FSTL3, REG1B and COL6A3;FSTL3, REG1B and CST3;FSTL3, REG1B and TNFRSF1A;FSTL3, COL6A3 and CST3;FSTL3, COL6A3 and TNFRSF1A;FSTL3, CST3 and TNFRSF1A;RGMB, COL28A1 and UBE2G2;RGMB, COL28A1 and REG1A;RGMB, COL28A1 and REG1B;RGMB, COL28A1 and COL 6A3;RGMB, COL28A1, and CST3;RGMB, COL28A1, and TNFRSF1A;RGMB, UBE2G2, and REG1A;RGMB, UBE2G2, and REG1B;RGMB, UBE2G2, and COL6A3;RGMB, UBE2G2, and CST3;RGMB, UBE2G2, and TNFRSF1A;RGMB, REG1A, and REG1B;RGMB, REG1A, and COL6A3;RGMB, REG1A, and CST3;RGMB, REG1A, and TNFRSF1A;RGMB, REG1B, and COL 6A3;RGMB, REG1B, and CST3;RGMB, REG1B, and TNFRSF1A;RGMB, COL6A3, and CST3;RGMB, COL6A3, and TNFRSF1A;RGMB, CST3, and TNFRSF1A;COL28A1, UBE2G2, and REG1A;COL28A1, UBE2G2, and REG1B;COL28A1, UBE2G2, and COL6A3;COL28A1, UBE2G2, and CST3;COL28A1, UBE2G2, and TNFRSF1A;COL28A1, REG1A, and REG1B;COL28A1, REG1A, and COL6A3;COL28A1, REG1A, and CST3;COL28A1, REG1A, and TNFRSF1A;COL28A1, REG1B, and COL6A3;COL28A1, REG1B, and CST3;COL28A1, REG1B, and TNFRSF1A;COL28A1, COL6A3, and CST3;COL28A1, COL6A3, and TNFRSF1A;COL28A1, CST3, and TNFRSF1A;UBE2G2, REG1A, and REG1B;UBE2G2, REG1A, and COL6A3;UBE2G2, REG1A, and CST3;UBE2G2, REG1A, and TNFRSF1A;UBE2G2, REG1B, and COL6A3;UBE2G2, REG1B, and CST3;UBE 2G2, REG1B, and TNFRSF1A; UBE2G2, COL6A3, and CST3; UBE2G2, COL6A3, and TNFRSF1A; UBE2G2, CST3, and TNFRSF1A; REG1A, REG1B, and COL6A3; REG1A, REG1B, and CST3; REG1A, REG1B, and TNFRSF1A; REG1A, COL6A3, and CST3; REG1A, COL6A3, and TNFRSF1A; REG1A, CST3, and TNFRSF1A; REG1B, COL6A3, and CST3; REG1B, COL6A3, and TNFRSF1A; REG1B, COL6A3, and CST3; REG1B, COL6A3, and TNFRSF1A; REG1B, CST3, and TNFRSF1A; or COL6A3, CST3, and TNFRSF1A.

[0085] 74. The method according to any one of aspects 70-73, further comprising measuring one or more of COL28A1, UBE2G2, and REG1B.

[0086] 75. a) contacting a sample from a human subject with a set of capture reagents, where each capture reagent has affinity for a different protein of a set of proteins that includes 3, 4, 5, 6, 7, 8, 9, or 10 proteins selected from the group consisting of HAVCR1, FSTL3, RGMB, COL28A1, UBE2G2, REG1A, REG1B, COL6A3, CST3, and TNFRSF1A in the sample from the subject; b) measuring the level of each protein of the set of proteins using said set of capture reagents.

[0087] 76. The method of embodiment 75, wherein the set of capture reagents is selected from aptamers, antibodies, and combinations of aptamers and antibodies.

[0088] 77. The method of embodiment 75, wherein the sample is selected from blood, plasma, serum, or urine.

[0089] 78. The method comprises the steps of: HAVCR1, FSTL3, and RGMB; HAVCR1, FSTL3, and COL28A1; HAVCR1, FSTL3, and UBE2G2; HAVCR1, FSTL3, and REG1A; HAVCR1, FSTL3, and REG1B; HAVCR1, FSTL3, and COL6A3; HAVCR1, FSTL3, and CST3; HAVCR1, FSTL3, and TNFRSF1; HAVCR1, RGMB, and COL28A1; HAVCR1, RGMB, and UBE2G2; HAVCR1, RGMB, and REG1A; CR1, RGMB, and REG1B; HAVCR1, RGMB, and COL6A3; HAVCR1, RGMB, and CST3; HAVCR1, RGMB, and TNFRSF1A; HAVCR1, COL28A1, and UBE2G2; HAVCR1, COL28A1, and REG1A; HAVCR1, COL28A1, and REG1B; HAVCR1, COL28A1, and COL6A3; HAVCR1, COL28A1, and CST3; HAVCR1, COL28A1, and TNFRSF1A; HAVCR1, UBE2G2, and REG1A; HAV CR1, UBE2G2, and REG1B; HAVCR1, UBE2G2, and COL6A3; HAVCR1, UBE2G2, and CST3; HAVCR1, UBE2G2, and TNFRSF1A; HAVCR1, REG1A, and REG1B; HAVCR1, REG1A, and COL6A3; HAVCR1, REG1A, and CST3; HAVCR1, REG1A, and TNFRSF1A; HAVCR1, REG1B, and COL6A3; HAVCR1, REG1B, and CST3; HAVCR1, REG1B, and TNFRSF1A; HAVCR1 , COL6A3, and CST3;HAVCR1, COL6A3, and TNFRSF1A;HAVCR1, CST3, and TNFRSF1A;FSTL3, RGMB, and COL28A1;FSTL3, RGMB, and UBE2G2;FSTL3, RGMB, and REG1A;FSTL3, RGMB, and REG1B;FSTL3, RGMB, and COL6A3;FSTL3, RGMB, and CST3;FSTL3, RGMB, and TNFRSF1A;FSTL3, COL28A1, and UBE2G2;FSTL3, COL28A1, and REG1A;FSTL3, COL28A1, and REG1B;FSTL3, COL28A1, and COL6A3;FSTL3, COL28A1, and CST3;FSTL3, COL28A1, and TNFRSF1A;FSTL3, UBE2G2, and REG1A;FSTL3, UBE2G2, and REG1B;FSTL3, UBE2G2, and COL6A3;FSTL3, UBE2G2, and CST3;FSTL3, UBE2G2, and TNFRSF1A;FSTL3, REG1A, and REG1B;FSTL3, REG1A, and COL6A3;FSTL3, REG1A and CST3;FSTL3, REG1A and TNFRSF1A;FSTL3, REG1B and COL6A3;FSTL3, REG1B and CST3;FSTL3, REG1B and TNFRSF1A;FSTL3, COL6A3 and CST3;FSTL3, COL6A3 and TNFRSF1A;FSTL3, CST3 and TNFRSF1A;RGMB, COL28A1 and UBE2G2;RGMB, COL28A1 and REG1A;RGMB, COL28A1 and REG1B;RGMB, COL28A1 and COL 6A3;RGMB, COL28A1, and CST3;RGMB, COL28A1, and TNFRSF1A;RGMB, UBE2G2, and REG1A;RGMB, UBE2G2, and REG1B;RGMB, UBE2G2, and COL6A3;RGMB, UBE2G2, and CST3;RGMB, UBE2G2, and TNFRSF1A;RGMB, REG1A, and REG1B;RGMB, REG1A, and COL6A3;RGMB, REG1A, and CST3;RGMB, REG1A, and TNFRSF1A;RGMB, REG1B, and COL 6A3;RGMB, REG1B, and CST3;RGMB, REG1B, and TNFRSF1A;RGMB, COL6A3, and CST3;RGMB, COL6A3, and TNFRSF1A;RGMB, CST3, and TNFRSF1A;COL28A1, UBE2G2, and REG1A;COL28A1, UBE2G2, and REG1B;COL28A1, UBE2G2, and COL6A3;COL28A1, UBE2G2, and CST3;COL28A1, UBE2G2, and TNFRSF1A;COL28A1, REG1A, and REG1B;COL28A1, REG1A, and COL6A3;COL28A1, REG1A, and CST3;COL28A1, REG1A, and TNFRSF1A;COL28A1, REG1B, and COL6A3;COL28A1, REG1B, and CST3;COL28A1, REG1B, and TNFRSF1A;COL28A1, COL6A3, and CST3;COL28A1, COL6A3, and TNFRSF1A;COL28A1, CST3, and TNFRSF1A;UBE2G2, REG1A, and REG1B;UBE2G2, REG1A, and COL6A3;UBE2G2, REG1A, and CST3;UBE2G2, REG1A, and TNFRSF1A;UBE2G2, REG1B, and COL6A3;UBE2G2, REG1B, and CST3;UBE 78. The method of any one of aspects 75 to 77, comprising measuring COL6A3, REG1B, and TNFRSF1A; UBE2G2, COL6A3, and CST3; UBE2G2, COL6A3, and TNFRSF1A; UBE2G2, CST3, and TNFRSF1A; REG1A, REG1B, and COL6A3; REG1A, REG1B, and CST3; REG1A, REG1B, and TNFRSF1A; REG1A, COL6A3, and CST3; REG1A, COL6A3, and TNFRSF1A; REG1A, CST3, and TNFRSF1A; REG1B, COL6A3, and CST3; REG1B, COL6A3, and TNFRSF1A; REG1B, CST3, and TNFRSF1A; or COL6A3, CST3, and TNFRSF1A.

[0090] 79. The method according to any one of aspects 75-78, further comprising measuring one or more of COL28A1, UBE2G2, and REG1B.

[0091] 80. a) measuring the level of HAVCR1 protein and the levels of at least one, two, three, four, five, six, seven, eight, or nine proteins selected from the group consisting of FSTL3, RGMB, COL28A1, UBE2G2, REG1A, REG1B, COL6A3, CST3, and TNFRSF1A in a sample from a human subject; b) identifying the human subject as having a relative risk of developing progressive chronic renal failure within a four year period based on the level of HAVCR1 and the levels of at least one, two, three, four, five, six, seven, eight, or nine proteins.

[0092] 81. a) measuring the level of FSTL3 protein and the levels of at least one, two, three, four, five, six, seven, eight, or nine proteins selected from the group consisting of HAVCR1, RGMB, COL28A1, UBE2G2, REG1A, REG1B, COL6A3, CST3, and TNFRSF1A in a sample from a human subject; b) identifying the human subject as having a relative risk of developing progressive chronic renal failure within a four year period based on the level of FSTL3 and the levels of at least one, two, three, four, five, six, seven, eight, or nine proteins.

[0093] 82. a) measuring the level of RGMB protein and the levels of at least one, two, three, four, five, six, seven, eight, or nine proteins selected from the group consisting of HAVCR1, FSTL3, COL28A1, UBE2G2, REG1A, REG1B, COL6A3, CST3, and TNFRSF1A in a sample from a human subject; b) identifying the human subject as having a relative risk of developing progressive chronic renal failure within a four year period based on the level of RGMB and the levels of at least one, two, three, four, five, six, seven, eight, or nine proteins.

[0094] 83. a) measuring the level of REG1A protein and at least one, two, three, four, five, six, seven, eight, or nine proteins selected from the group consisting of HAVCR1, FSTL3, RGMB, COL28A1, UBE2G2, REG1B, COL6A3, CST3, and TNFRSF1A in a sample from a human subject; b) identifying the human subject as having a relative risk of developing progressive chronic renal failure within a four-year period based on the level of REG1A and the levels of at least one, two, three, four, five, six, seven, eight, or nine proteins.

[0095] 84. a) measuring the level of COL6A3 protein and the levels of at least one, two, three, four, five, six, seven, eight, or nine proteins selected from the group consisting of HAVCR1, FSTL3, RGMB, COL28A1, UBE2G2, REG1A, REG1B, CST3, and TNFRSF1A in a sample from a human subject; b) identifying the human subject as having a relative risk of developing progressive chronic renal failure within a four year period based on the level of COL6A3 and the levels of at least one, two, three, four, five, six, seven, eight, or nine proteins.

[0096] 85. a) measuring the level of CST3 protein and the levels of at least one, two, three, four, five, six, seven, eight, or nine proteins selected from the group consisting of HAVCR1, FSTL3, RGMB, COL28A1, UBE2G2, REG1A, REG1B, COL6A3, and TNFRSF1A in a sample from a human subject; b) identifying said human subject as having a relative risk of developing progressive chronic renal failure within a four year period based on the level of CST3 and the levels of at least one, two, three, four, five, six, seven, eight, or nine proteins.

[0097] 86. a) measuring the level of TNFRSF1A protein and the levels of at least one, two, three, four, five, six, seven, eight, or nine proteins selected from the group consisting of HAVCR1, FSTL3, RGMB, COL28A1, UBE2G2, REG1A, REG1B, COL6A3, and CST3 in a sample from a human subject; b) identifying the human subject as having a relative risk of developing progressive chronic renal failure within a four year period based on the level of TNFRSF1A and the levels of at least one, two, three, four, five, six, seven, eight, or nine proteins.

[0098] 87. The method according to any one of aspects 80 to 86, wherein said measuring is performed using mass spectrometry, an aptamer-based assay, and / or an antibody-based assay.

[0099] 88. The method according to any one of aspects 80 to 87, wherein the sample is selected from blood, plasma, serum, or urine.

[0100] 89. a) contacting a sample from a human subject with a set of capture reagents, where each capture reagent has affinity for a different protein of a set of proteins that includes HAVCR1 protein and at least 1, 2, 3, 4, 5, 6, 7, 8, or 9 proteins selected from the group consisting of FSTL3, RGMB, COL28A1, UBE2G2, REG1A, REG1B, COL6A3, CST3, and TNFRSF1A; b) measuring the level of each protein of said set of proteins using said set of capture reagents.

[0101] 90. a) contacting a sample from a human subject with a set of capture reagents, where each capture reagent has affinity for a different protein of the set of proteins that includes FSTL3 protein and at least 1, 2, 3, 4, 5, 6, 7, 8, or 9 proteins selected from the group consisting of HAVCR1, RGMB, COL28A1, UBE2G2, REG1A, REG1B, COL6A3, CST3, and TNFRSF1A; b) measuring the level of each protein of said set of proteins using said set of capture reagents.

[0102] 91. a) contacting a sample from a human subject with a set of capture reagents, where each capture reagent has affinity for a different protein of the set of proteins that includes RGMB protein and at least 1, 2, 3, 4, 5, 6, 7, 8, or 9 proteins selected from the group consisting of HAVCR1, FSTL3, COL28A1, UBE2G2, REG1A, REG1B, COL6A3, CST3, and TNFRSF1A; b) measuring the level of each protein of said set of proteins using said set of capture reagents.

[0103] 92. a) contacting a sample from a human subject with a set of capture reagents, where each capture reagent has affinity for a different protein of a set of proteins that includes REG1A protein and at least 1, 2, 3, 4, 5, 6, 7, 8, or 9 proteins selected from the group consisting of HAVCR1, FSTL3, RGMB, COL28A1, UBE2G2, REG1B, COL6A3, CST3, and TNFRSF1A; b) measuring the level of each protein of said set of proteins using said set of capture reagents.

[0104] 93. a) contacting a sample from a human subject with a set of capture reagents, where each capture reagent has affinity for a different protein of the set of proteins that includes COL6A3 protein and at least 1, 2, 3, 4, 5, 6, 7, 8, or 9 proteins selected from the group consisting of HAVCR1, FSTL3, RGMB, COL28A1, UBE2G2, REG1A, REG1B, CST3, and TNFRSF1A; b) measuring the level of each protein of said set of proteins using said set of capture reagents.

[0105] 94. a) contacting a sample from a human subject with a set of capture reagents, where each capture reagent has affinity for a different protein of the set of proteins comprising CST3 protein and at least 1, 2, 3, 4, 5, 6, 7, 8, or 9 proteins selected from the group consisting of HAVCR1, FSTL3, RGMB, COL28A1, UBE2G2, REG1A, REG1B, COL6A3, and TNFRSF1A; b) measuring the level of each protein of said set of proteins using said set of capture reagents.

[0106] 95. a) contacting a sample from a human subject with a set of capture reagents, where each capture reagent has affinity for a different protein of a set of proteins that includes TNFRSF1A protein and at least 1, 2, 3, 4, 5, 6, 7, 8, or 9 proteins selected from the group consisting of HAVCR1, FSTL3, RGMB, COL28A1, UBE2G2, REG1A, REG1B, COL6A3, and CST3; b) measuring the level of each protein of said set of proteins using said set of capture reagents.

[0107] 96. The method according to any one of aspects 89 to 95, wherein the set of capture reagents is selected from aptamers, antibodies, and combinations of aptamers and antibodies.

[0108] 97. The method of any one of aspects 89-96, wherein the sample is selected from blood, plasma, serum, or urine.

[0109] 98. The method of any one of aspects 16-27, 32-41, 53-60, 75-79, and 89-97, further comprising identifying the subject as having an increased relative risk of developing progressive chronic renal failure within a four year period based on the measured levels of each of the proteins.

[0110] 99. The method of any one of aspects 1-15, 28-31, 42-52, 61-74, 80-88, and 98, wherein the relative risk of developing progressive chronic renal failure within the four year period is based on inputting the levels of each measured protein in a statistical model.

[0111] 100. The method of claim 99, wherein the model is a linear regression model.

[0112] 101. The method of aspect 99 or 100, wherein the model has an area under the curve (AUC) selected from 0.65, 0.7, 0.75, 0.77, or greater.

[0113] 102. The method of any one of aspects 99 to 101, wherein the model provides a binary prediction and / or a relative risk prediction for developing progressive chronic renal failure within a four year period.

[0114] 103. The method according to any one of aspects 99 to 102, wherein the model is based on the levels of each protein selected from HAVCR1, FSTL3, RGMB, COL28A1, UBE2G2, REG1A, REG1B, COL6A3, CST3, and TNFRSF1A.

[0115] 104. The method of embodiment 103, wherein the model provides a binary prediction with a probability cut-point X, where X<0.3, 0.31, 0.32, 0.32, 0.33, 0.34, 0.35, or 0.3533 predicts no risk of developing progressive chronic renal failure within a four year period, and X≧0.3, 0.31, 0.32, 0.33, 0.34, 0.35, or 0.3533 predicts at risk of developing progressive chronic renal failure within a four year period.

[0116] 105. The method of embodiment 104, wherein the risk of developing progressive chronic renal failure indicates the risk of an event selected from a 50% decrease in estimated glomerular filtration rate (eGFR), a diagnosis of need for dialysis, the development of an eGFR < 15 ml / min / 1.73 m2, the development of end-stage renal disease (ESRD), and a diagnosis of need for a kidney transplant.

[0117] 106. The method of aspect 103, wherein the model provides a relative risk prediction for developing progressive chronic renal failure within a four year period based on the levels of each protein selected from HAVCR1, FSTL3, RGMB, COL28A1, UBE2G2, REG1A, REG1B, COL6A3, CST3, and TNFRSF1A.

[0118] 107. The method according to embodiment 98, wherein the level of each of the measured proteins is determined from relative fluorescence units (RFU) or protein concentration.

[0119] 108. The method of embodiment 103, wherein the model provides a relative risk of developing progressive chronic renal failure within four years.

[0120] 109. The method of embodiment 103, wherein the relative risk is selected from mild and severe.

[0121] 110. The method of aspect 103, wherein the relative risk is a probability calculation.

[0122] 111. The method of embodiment 103, wherein the relative risk is a range of values ​​used to predict developing progressive chronic renal failure within a four-year period. [Brief description of the drawings]

[0123] [Figure 1] Figure 1 shows prediction of development of end-stage renal disease in CKD patients based on clinical parameters. G1-G5 correspond to stages 1-5 of CKD. [Diagram 2] 95% CIs of observed event rates by relative risk quintiles in the training data are shown. [Diagram 3] Box plots of predicted relative risks sorted by true PCRI class are shown, with risk bin boundaries indicated by dashed lines. [Figure 4] 1 illustrates an exemplary computer system for use with various computer-implemented methods described herein. [Diagram 5] 1 is a flow chart illustrating a method for assessing risk of renal failure according to one embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0124] Reference will now be made in detail to exemplary embodiments of the present invention. While the invention will be described in conjunction with the enumerated embodiments, it will be understood that it is not intended that the invention be limited to those embodiments. On the contrary, the invention is intended to cover all alternatives, modifications, and equivalents which may be included within the scope of the present invention as defined by the claims.

[0125] One skilled in the art will recognize many methods and materials similar or equivalent to those described herein, which could be used in the practice of the present invention and are within the scope of the present invention, and is in no way intended to be limited to the methods and materials described.

[0126] Unless otherwise defined, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods, devices, and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, the preferred methods, devices, and materials are now described.

[0127] All publications, published patent documents, and patent applications cited in this application are indicative of the level of skill in the technical field(s) to which this application pertains. All publications, published patent documents, and patent applications cited in this specification are incorporated by reference into this specification to the same extent as if each individual publication, published patent document, or patent application was specifically and individually indicated to be incorporated by reference into this specification.

[0128] As used in this application, including the appended claims, the singular forms "a," "an," and "the" include plural references and are used synonymously with "at least one" and "one or more," unless the context clearly dictates otherwise. Thus, reference to a "SOMAmer" includes mixtures of SOMAmers, reference to a "probe" includes mixtures of probes, and so forth.

[0129] As used herein, the term "about" refers to a small modification or variation of a numerical value that does not change the basic function of the item to which the numerical value is associated.

[0130] As used herein, the words "comprises," "comprising," "includes," "including," "contains," "containing," and any variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, product of a process, or composition that comprises, includes, or contains an element or list of elements may include not only those elements, but also other elements that are not expressly listed or that are inherent in such process, method, product of a process, or composition.

[0131] This application includes biomarkers, methods, devices, reagents, systems, and kits for predicting the risk of renal failure within a defined period, such as four years.

[0132] "Progressive chronic renal failure" or "PCRI" or "renal failure" means a composite endpoint treated as a categorical endpoint (yes / no in a given time frame) defined by the occurrence of at least one of the following within a time frame from the test result: A 50% decrease in estimated glomerular filtration rate (eGFR) A diagnosis that requires dialysis; · eGFR<15ml / min / 1.73m 2 The occurrence of Onset of end-stage renal disease (ESRD), or · A diagnosis that a kidney transplant is needed.

[0133] "End stage renal disease" or "ESRD" means that at least one of the following conditions is met: a glomerular filtration rate of 15 ml / min / 1.73 m 2 less than 18 years of age, require chronic kidney dialysis, or require a kidney transplant.

[0134] "Relative risk" refers to the risk of developing PCRI in a given time frame compared to the average risk in a reference population. Relative risks range from 0.01 to 3.24. In one embodiment, the relative risk can be calculated

number

[0135] "Biological sample," "sample," and "test sample" are used interchangeably herein and refer to any material, biological fluid, tissue, or cell obtained from or otherwise derived from an individual. This includes blood (including whole blood, white blood cells, peripheral blood mononuclear cells, buffy coat, plasma, and serum), dried blood spots (e.g., from infants), sputum, tears, mucus, nasal washings, nasal aspirates, exhaled breath, urine, semen, saliva, peritoneal washings, ascites, cyst fluid, cerebrospinal fluid, glandular fluid, pancreatic juice, lymphatic fluid, pleural fluid, nipple aspirate, bronchial aspirate, bronchial scraping, synovial fluid, joint aspirate, organ secretions, cells, cell extracts, and cerebrospinal fluid. This also includes all of the experimentally separated fractions of the foregoing. For example, a blood sample can be fractionated into serum, plasma, or fractions containing specific types of blood cells, e.g., red blood cells or white blood cells (leukocytes). Optionally, the sample may be a combination of samples from an individual, such as a combination of tissue and liquid samples. The term "biological sample" also includes homogenized solid material from, for example, a stool sample, a tissue sample, or a tissue biopsy. The term "biological sample" also includes material from tissue culture or cell culture. Any suitable method for obtaining a biological sample may be used, and exemplary methods include, for example, phlebotomy, swabs (e.g., buccal swabs), and fine needle aspiration cytology procedures. Exemplary tissues that can be aspirated include lymph nodes, lung, lung lavage, BAL (bronchoalveolar lavage), thyroid, breast, pancreas, and liver. Samples may also be collected, for example, by microdissection (e.g., laser capture microdissection (LCM) or laser microdissection (LMD)), bladder washing, smear (e.g., PAP smear), or ductal lavage. A "biological sample" obtained or derived from an individual includes any such sample that has been obtained from the individual and then processed in any suitable manner.

[0136] It should further be understood that the biological sample may be obtained by collecting and pooling biological samples from multiple individuals or by pooling aliquots of each individual's biological sample. The pooled sample may be treated as a sample from a single individual, and if an increased or decreased risk of renal failure is identified in the pooled sample, each individual biological sample may be retested to determine which individuals have increased or decreased risk of renal failure.

[0137] As mentioned above, the biological sample can be urine. Urine samples offer certain advantages over blood or serum samples. Collecting blood or plasma samples by venipuncture is more complicated than desired, can result in variable volumes, can be a concern for patients, and carries some (low) risk of infection. Also, phlebotomy requires skilled personnel. The ease of collecting urine samples allows the method of the present invention to be more widely applicable.

[0138] For purposes of this specification, the phrase "data attributable to a biological sample from an individual" is intended to mean data in any form that is derived from or generated using a biological sample from an individual. The data may have been reformatted, modified, or have their values ​​altered to some extent after generation, such as by conversion from units in one measurement system to units in another, but the data is understood to have been obtained from or generated using a biological sample.

[0139] "Target", "target molecule", and "analyte" are used interchangeably herein and refer to any molecule of interest that may be present in a biological sample. "Molecule of interest" includes any minor change in a particular molecule, such as in the case of a protein, such as minor changes in amino acid sequence, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification, such as conjugation with a labeling moiety, that does not substantially change the identity of the molecule. "Target molecule", "target", or "analyte" is a type or set of copies of a molecule or multi-molecular structure. "Target molecule", "target", and "analyte" refer to a set of multiple such molecules. Exemplary target molecules include proteins, polypeptides, nucleic acids, carbohydrates, lipids, polysaccharides, glycoproteins, hormones, receptors, antigens, antibodies, affibodies, antibody mimetics, viruses, pathogens, toxic substances, substrates, metabolites, transition state analogs, cofactors, inhibitors, drugs, dyes, nutrients, growth factors, cells, tissues, and any fragments or portions of any of the foregoing.

[0140] As used herein, "polypeptide", "peptide" and "protein" are used interchangeably herein to refer to polymers of amino acids of any length. The polymers may be linear or branched, may contain modified amino acids, and may be interrupted by non-amino acids. These terms also encompass amino acid polymers that are modified naturally or by intervention, such as disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification, such as conjugation with a labeling component. For example, polypeptides containing one or more analogs of an amino acid (including, for example, unnatural amino acids, etc.), and other modifications known in the art, are also included in this definition. Polypeptides can be single chains or associated chains. This definition also includes protein precursors and intact mature proteins, peptides or polypeptides derived from mature proteins, protein fragments, splice variants, recombinant proteins, protein variants with amino acid modifications, deletions, or substitutions, digests, and post-translational modifications such as glycosylation, acetylation, phosphorylation.

[0141] As used herein, "marker" and "biomarker" and "feature" are used interchangeably and refer to a target molecule that is indicative of or indicative of a normal or abnormal process in an individual, or a disease or other pathology in an individual. More specifically, a "marker" or "biomarker" or "feature" is an anatomical, physiological, biochemical, or molecular parameter that is associated with the presence of a particular physiological state or process, whether normal or abnormal, and if abnormal, whether chronic or acute. Biomarkers can be detected and measured by a variety of methods, including laboratory assays and medical imaging. When a biomarker is a protein, the expression of the corresponding gene can also be used as a surrogate measure of the amount or presence or absence of the corresponding protein biomarker in a biological sample, or the methylation status of the gene encoding the biomarker or the protein that controls the expression of the biomarker. In certain embodiments, the feature is an analyte / SOMAmer reagent of other predictors in a statistical model.

[0142] As used herein, "biomarker value," "value," "biomarker level," "characteristic level," and "level" are used interchangeably and refer to a measurement obtained using any analytical method to detect a biomarker in a biological sample and indicating the presence, absence, absolute amount or concentration, relative amount or concentration, titer, level, expression level, ratio of measured levels, etc. of a biomarker in a biological sample, relating to, or corresponding to, a biomarker in a biological sample. The exact nature of a "value" or "level" will depend on the specific design and components of the particular analytical method used to detect the biomarker.

[0143] If a biomarker is indicative of or is an indication of an abnormal process or disease or other condition in an individual, the biomarker is generally described as being either overexpressed or underexpressed compared to a level or value of expression of the biomarker that is indicative of or is an indication of the absence of a normal process or disease or other condition in an individual. "Upregulation," "upregulated," "overexpression," "overexpressed," and any variations thereof are used interchangeably and refer to a value or level of the biomarker in a biological sample that is greater than the value or level (or range of values ​​or levels) of the biomarker that is normally detected in a similar biological sample from a healthy or normal individual. This term may also refer to a value or level of the biomarker in a biological sample that is greater than the value or level (or range of values ​​or levels) of the biomarker that can be detected at different stages of a particular disease.

[0144] "Downregulation," "downregulated," "underexpression," "underexpressed," and any variations thereof, are used interchangeably and 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 a similar biological sample from a healthy or normal individual. The terms 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 can be detected at different stages of a particular disease.

[0145] Furthermore, a biomarker that is either overexpressed or underexpressed may also be referred to as being "differentially expressed" or having a "differential level" or "differential value" compared to a "normal" expression level or value of the biomarker that is indicative of or symptomatic of a normal process or the absence of a disease or other pathology in an individual. Thus, "differential expression" of a biomarker may also be referred to as a variation from the "normal" expression level of the biomarker.

[0146] The terms "differential gene expression" and "differential expression" are used interchangeably and refer to a gene (or its corresponding protein expression product) whose expression is activated to a higher or lower level in a subject suffering from a particular disease or condition, compared to its expression in a normal or control subject. The term also includes a gene (or its corresponding protein expression product) whose expression is activated to a higher or lower level in different stages of the same disease or condition. It is also understood that a differentially expressed gene may also be activated or inhibited at the nucleic acid or protein level, or may undergo alternative splicing to produce different polypeptide products. Such differences may be indicated by a variety of changes, including mRNA levels, surface expression, secretion, or other partitioning of the polypeptide. Differential gene expression may include a comparison of expression between two or more genes or their gene products, or a comparison of the ratio of expression between two or more genes or their gene products, or even a comparison of two differently processed same gene products that differ between normal and diseased subjects, or between different stages of the same disease. Differential expression includes both quantitative and qualitative differences in the temporal or cellular expression patterns of a gene or its expression products, for example, between normal and diseased cells, or between cells undergoing different disease events or stages of a disease.

[0147] As used herein, "individual" refers to a subject or patient. An individual may be a mammal or a non-mammal. In various embodiments, an individual is a mammal. A mammalian individual may be a human or a non-human. In various embodiments, an individual is a human. A healthy or normal individual is one in which a disease or condition of interest (including, for example, renal failure) is not detected by conventional diagnostic methods.

[0148] "Diagnosing", "diagnosis", "diagnosis" and variations thereof refer to detecting, quantifying, or recognizing the health or condition of an individual based on one or more signs, symptoms, data, or other information associated with the individual. An individual's health status may be diagnosed as healthy / normal (i.e., diagnosing the absence of a disease or condition) or disease / abnormal (i.e., diagnosing the presence or characterization of a disease or condition). The terms "diagnosing", "diagnosis", "diagnosis" and the like, with respect to a particular disease or condition, encompass early detection of disease, characterization or classification of disease, detection of disease progression, remission, or recurrence, and detection of disease response after administering treatment or therapy to an individual. Predicting risk of renal failure includes distinguishing between individuals at elevated risk of renal failure and those who are not.

[0149] "Prognostic," "prognosing," "prognosis," and variations thereof refer to predicting the future course of a disease or condition in an individual who has the disease or condition (e.g., predicting patient survival), and such terms encompass assessing the response of a disease or condition after administering a treatment or therapy to an individual.

[0150] "Assess", "assessing", "assessment" and variations thereof encompass both "diagnosis" and "prognosis", and include the determination or prediction of the future course of a disease or condition in a disease-free individual, and the determination or prediction of the risk of recurrence of a disease or condition in an individual who has apparently been cured of the disease or remission of the condition. The term "assess" also includes evaluating an individual's response to a treatment, e.g., predicting whether an individual is likely to respond favorably to a treatment or not (e.g., to suffer toxic or other undesirable side effects), selecting a treatment for administration to the individual, or monitoring or determining an individual's response to a treatment administered to the individual. Thus, "assessing" renal failure risk can include, for example, any of the following: predicting the future risk of renal failure in an individual, predicting the risk of renal failure in an individual who does not appear to have renal failure problems, or determining or predicting an individual's response to a renal failure treatment based on measuring biomarker values ​​from an individual's biological sample, or selecting a renal failure treatment for administration to an individual. The risk assessment of renal failure may include, for example, assessment of the risk of renal failure on a continuous scale, or classification of the risk of renal failure in a graded ascending classification. Classification of risk may include classification into two or more classifications, such as "no elevated risk of renal failure" and "elevated risk of renal failure". The risk assessment of renal failure is performed for a predetermined period, such as, for example, 4 years.

[0151] As used herein, "additional biomedical information" refers to one or more evaluations of an individual using other than any of the biomarkers described herein that are associated with risk of renal failure. "Additional biomedical information" includes the individual's physical description, including the individual's height and / or weight, the individual's age, the individual's sex, weight change, the individual's ethnicity, employment history, family history of renal failure, the presence of genetic marker(s) that is associated with high risk of renal failure in the individual, clinical symptoms such as abdominal pain, weight gain, or loss of gene expression value, the individual's physical description, including physical description observed in radiological imaging, smoking status, alcohol drinking history, employment history, dietary habits-salt, saturated fat, and cholesterol intake, caffeine consumption, and imaging information. Combination of the examination of biomarker levels with the evaluation of any additional biomedical information, including other clinical examinations, may improve the sensitivity, specificity, and / or AUC of renal failure prediction, for example, compared to only the biomarker examination or only the evaluation of any particular item of additional biomedical information (e.g., only imaging of carotid intima thickness). The additional biomedical information may be obtained from the individual using routine techniques known in the art, such as from the individual themselves using routine patient or health history questionnaires, or from a medical professional, etc. Combining testing of biomarker levels with evaluation of any additional biomedical information may improve the sensitivity, specificity, and / or threshold for predicting renal failure, for example, compared to biomarker testing alone or evaluation of any particular item of additional biomedical information alone (e.g., CT imaging alone).

[0152] As used herein, "detecting" or "determining" with respect to a biomarker value includes the use of both the equipment needed to observe and record a signal corresponding to the biomarker value as well as the substance / substances needed to generate that signal. In various embodiments, the biomarker value is detected using any suitable method, including fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic waves, mass spectrometry, infrared spectroscopy, Raman spectroscopy, atomic force microscopy, scanning tunneling microscopy, electrochemical detection methods, nuclear magnetic resonance, quantum dots, and the like.

[0153] As used herein, "solid support" refers to any substrate having a surface to which molecules can be directly or indirectly attached, either covalently or non-covalently. "Solid support" can have a variety of physical forms, including, for example, membranes; chips (e.g., protein chips); slides (e.g., glass slides or cover slips); columns; hollow, solid, semi-solid, particles with holes or cavities, such as beads; gels; fibers, including fiber optic materials; matrices; and sample containers. Exemplary sample containers include sample wells, tubes, capillaries, vials, and any other container, groove or depression that can hold a sample. Sample containers can be included in multi-sample platforms, such as microtiter plates, glass slides, microfluidic devices, and the like. Supports can be composed of natural or synthetic materials, organic or inorganic materials. The composition of the solid support to which the capture reagent is attached generally depends on the method of attachment (e.g., covalent attachment). Other exemplary containers include microdroplets, microfluidic controlled, or bulk oil-in-water emulsions in which assays and related operations can be performed. Suitable solid supports include, for example, plastics, resins, polysaccharides, silica or silica-based materials, functionalized glass, modified silicon, carbon, metals, inorganic glass, membranes, nylon, natural fibers (e.g., silk, wool, and cotton), polymers, etc. The material that constitutes the solid support may contain reactive groups, such as, for example, carboxy, amino, or hydroxyl groups, which are used to bind the capture reagent. Polymeric solid supports include, for example, polystyrene, polyethylene glycol tetraphthalate, polyvinyl acetate, polyvinyl chloride, polyvinylpyrrolidone, polyacrylonitrile, polymethylmethacrylate, 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.

[0154] As used herein, "maximum likelihood adaptive normalization" refers to the process of normalizing analytes to reduce site bias.

[0155] As used herein, an "analyte" is a protein target of a capture reagent. In certain embodiments, the capture reagent is an aptamer. In certain further embodiments, the capture reagent is a SOMAmer.

[0156] As used herein, "Lin's CCC" refers to the concordance correlation coefficient that measures the agreement between a new test and an existing test that is considered the gold standard.

[0157] As used herein, a "test" refers to a set of sample and clinical data that is analyzed to derive a test.

[0158] As used herein, "training data set" means a subset of data from a study used to fit a model.

[0159] As used herein, "validation dataset" means the final subset of data used to evaluate the performance of the final model developed based on the design validation dataset.

[0160] As used herein, "design validation dataset" means a separate subset of data used to provide an unbiased assessment of model fitting to the training dataset while adjusting model parameters.

[0161] As used herein, the terms "need" or "required" refer to a judgment made by a health care provider regarding the treatment of a patient that the health care provider deems beneficial to the patient's health condition.

[0162] In one aspect, disclosed herein is an objective kidney disease progression test that provides a model predictive of development of at least one of the following within four years: (1) A 50% decrease in estimated glomerular filtration rate (eGFR) (2) A diagnosis of need for dialysis; (3)eGFR<15ml / min / 1.73m 2 The occurrence of (4) development of end-stage renal disease (ESRD), or (5) Diagnosis of need for a kidney transplant.

[0163] In certain embodiments, the composite endpoint provided by conditions (1)-(5) is treated as a categorical endpoint (yes / no within a specific time frame) and is referred to herein as Progressive Chronic Renal Insufficiency (PCRI). In certain embodiments, the specified time frame is 4 years.

[0164] In certain embodiments, the test was developed using the Chronic Renal Insufficiency Cohort (CRIC), which was divided into a training dataset (70%), a design validation dataset (15%), and a validation dataset (15%). TIFF2024525146000005.tif65170

[0165] The results of the CRIC have resulted in over 200 peer-reviewed publications, providing significant contributions to the understanding of CKD progression. (Hannan M, Ansari S, Meza N, et al. Risk Factors for CKD Progression: Overview of Findings from the CRIC Study. Clin J Am Soc Nephrol 2020;11:07830520). The components of the PCRI composite endpoint include the most relevant clinical features that explain chronic kidney disease progression.

[0166] The kidney disease progression test disclosed herein is useful for testing adults diagnosed with mild to severe chronic kidney disease as defined in Table 1.

[0167] In a particular embodiment, a logistic regression model is disclosed with 10 features, and the optimal probability cut point for PCRI is 0.3533 (≥0.3533 for PCRI=yes, 0.3533 for PCRI=no). The model output can be reported as the relative risk (RR) of developing PCRI over 4 years compared to the average risk in a reference population. The RR ranges from 0.01 to 3.24.

[0168] In certain embodiments, the minimum performance requirement for a kidney disease progression test is an area under the curve (AUC) at least equivalent to the Kidney Failure Risk Equation (KFRE) applied to the CRIC dataset (AUC=0.77, 95% CI: 0.75, 0.78). Validation exceeds the performance metric of AUC≧0.77.

[0169] In certain embodiments, the kidney disease progression test is intended for use based on medical necessity for individual patients with any stage of diagnosed chronic kidney disease. In certain further embodiments, the results are reported as a relative risk to a reference population of chronic kidney disease patients who experience a composite endpoint within four years at an average rate of 27%. In certain embodiments, the reference population is aged 23-75 years, has CKD stages I-V (80% have stages III-IV), and has an eGFR range of 10.6-86.4 ml / min / 1.73 m 2 , and reported relative risks can range from 0.01 to 3.24.

[0170] In certain embodiments, kidney disease progression testing is applied in a research setting to predict the onset of PCRI within 4 years. In certain further embodiments, in the research setting, benefits and risks are relevant to study decision making for participant monitoring, stratification, and enrichment.

[0171] In certain embodiments, EDTA plasma, chronic kidney disease diagnosed, 10.6 to 86.4 ml / min / 1.73 m 2 Validation was performed using samples from individuals with an eGFR range of 100-150,000 and from multiple racial / ethnic individuals living in North America aged 23-75 years. In certain embodiments, the sample matrix is ​​human EDTA plasma.

[0172] The risk analysis characteristics can be described as shown in Table 4. TIFF2024525146000006.tif245170

[0173] The testing methods disclosed herein provide convenience to healthcare providers in assessing and monitoring the risk of PCRI and help identify patients at risk of developing PCRI early in the process, where intervention may slow or prevent progression to end-stage renal disease (ESRD).

[0174] The Chronic Renal Insufficiency Cohort (CRIC) is enriched for late kidney disease stages (85% stage 3a or later) compared to the distribution of kidney disease stages in the US CKD patient population (approximately 50% stage 3a or later). This leads to a higher incidence of ESRD in the CRIC compared to the US CKD patient population. To improve accuracy, the tests disclosed herein can be supplemented with additional assessments including, but not limited to, health status including comorbidities such as diabetes, clinical pathology, laboratory tests (e.g., eGFR and albuminuria), renal imaging, and histology to assess risk of ESRD and reduce the risk of erroneous test results. TIFF2024525146000007.tif33170

[0175] Performance thresholds were set based on the performance of KFRE. Accuracy, sensitivity, and specificity are calculated but are not part of the performance requirement thresholds. Sensitivity and specificity depend on where the cut point is placed on the receiver operating curve.

[0176] The KFRE is an equation commonly used in clinical practice to predict the risk of CKD progression to ESRD. The equation has been validated in a cohort of patients with all stages of CKD (stages 1-5) (Major RW, Shepherd D, Medcalf JF, et al. The Kidney Failure Risk Equation for prediction of end stage renal disease in UK primary care: An external validation and clinical impact projection cohort study. PLoS Med 2019;16:e1002955.doi:1002910.1001371 / journal.pmed.1002955.eCollection 1002019 Nov.), so we applied it to the entire cohort to ensure valid comparisons. In addition, we used the measured components of the KFRE (eGFR and proteinuria) to assess renal function at all stages of CKD (Figure 1).

[0177] When KFRE was applied to a subset of the CRIC population equivalent to that used to develop KFRE (i.e., stage 3a or greater with ESRD outcome), an AUC of 0.83 was obtained, which is consistent with the published validation AUC from the initial development of KFRE, which was 0.83. This result suggests that the performance of this formula in the cohort examined is comparable to the published performance (in other cohorts).

[0178] Model performance requirements (AUC≧0.77) were based on the performance of KFRE (Equation 1) on the full CRIC dataset, which included expanded endpoint definitions to identify individuals earlier in the disease process and at risk of progression at a point where medical intervention may slow the loss of renal function.

[0179] To demonstrate test intercomparability to the developed KFRE formula, the AUC of the proteomic model was analyzed, and the KFRE was analyzed in a subset of populations and outcomes equivalent to the population originally used to develop the KFRE (Table 6). This analysis is not related to performance requirements, but is presented here to demonstrate that the renal failure prognostic test is comparable to the KFRE in the population used to derive the KFRE formula, and the intended use population grown for this test. TIFF2024525146000008.tif70170Equation 1: Kidney failure risk equation Pr(ESRD) = 100 * (1-0.924 exp[(0.2694 (男性)-0.2167 (年齢 / 10)-0.55418 (eGFR / 5) + 0.45608 (ln(24時間タンパク尿(mg / g))) + 2.96774] ) During the ceremony, Pr(ESRD) = Probability of End-Stage Renal Disease eGFR = estimated glomerular filtration rate (ml / min / 1.73m 2 )

[0180] In one aspect, one or more biomarkers are provided for use alone or in various combinations to assess the risk of renal failure within a four year period. As described in more detail below, exemplary embodiments include the biomarkers shown in Table 8, which were identified using a multiplex SOMAmer-based assay.

[0181] In a preferred embodiment, the model has 10 features (Table 8) and predicts PCRI over 4 years. The model output is the relative risk of PCRI compared to the average person with CKD. RR ranges from 0.01 to 3.24. Validation exceeds the performance metric of AUC≧0.77.

[0182] In one embodiment, the number of biomarkers useful in a biomarker subset or panel is based on the sensitivity and specificity values ​​for a particular combination of biomarker values. The terms "sensitivity" and "specificity" are used herein with respect to the ability to correctly classify an individual as having an elevated risk of renal failure within four years or as not having an elevated relative risk of renal failure within the same time period based on one or more biomarker values ​​detected in their biological sample. "Sensitivity" refers to the performance of a biomarker(s) in correctly classifying individuals at elevated risk of renal failure. "Specificity" refers to the performance of a biomarker(s) in correctly classifying individuals at not elevated relative risk of renal failure.

[0183] Alternatively, the score may be reported on a continuous range, with thresholds for high, moderate, or low risk of renal failure determined based on clinical findings.

[0184] Another factor that may influence the number of biomarkers used in a biomarker subset or panel is the procedure used to collect the biological samples from the individuals being evaluated for risk of renal failure. In a carefully controlled sample procurement environment, the number of biomarkers required to meet the desired sensitivity and specificity and / or thresholds will be fewer than in a situation where there may be a lot of variation in sample collection, handling, and storage.

[0185] Exemplary Uses of Biomarkers In various exemplary embodiments, a method for assessing the risk of renal failure in an individual is provided by detecting one or more biomarker values ​​corresponding to one or more biomarkers present in the circulation of an individual, such as serum or plasma, by any number of analytical methods, including any of the analytical methods described herein.These biomarkers are, for example, differentially expressed in individuals with elevated risk of renal failure compared to individuals without elevated risk of renal failure.Detecting the differential expression of biomarkers in an individual can be used to enable prediction of the risk of renal failure, for example, within a 4-year period.

[0186] In addition to testing biomarker levels as a stand-alone diagnostic test, biomarker levels can be determined in conjunction with determining SNPs or other genetic lesions or genetic variability that indicate an increased risk of susceptibility to a disease or condition (see, e.g., Amos et al., Nature Genetics 40, 616-622 (2009)).

[0187] In addition to testing biomarker levels as a stand-alone diagnostic test, biomarker levels can also be used in combination with radiological screening. Biomarker levels can be used with associated symptoms or genetic testing. Detection of any of the biomarkers described herein can be useful in guiding the appropriate clinical care of an individual after the risk of renal failure has been assessed, including, for example, increasing the degree of treatment of high-risk individuals to a more aggressive one after the risk of renal failure has been determined. In addition to testing biomarker levels with associated symptoms or risk factors, information about the biomarker can be evaluated with other types of data, particularly data indicative of an individual's risk of renal failure (e.g., the patient's medical history, symptoms, family history, smoking or drinking history, risk factors such as the presence of genetic marker(s), and / or the status of other biomarkers, etc.). These various data can be evaluated by automated methods, such as computer programs / software that can be embodied in a computer or other apparatus / device.

[0188] In addition to examining biomarker levels in high-risk individuals in conjunction with radiological screening (e.g., evaluating biomarker levels in conjunction with blockages detected on a coronary angiogram), information regarding the biomarkers can be evaluated in conjunction with other types of data, particularly data indicative of the individual's risk of developing renal failure (e.g., the patient's medical history, symptoms, family history of cardiovascular disease, risk factors such as whether the individual is a smoker, heavy alcohol drinker, and / or other biomarker status, etc.). These various data can be evaluated by automated methods, such as computer programs / software that may be embodied in a computer or other apparatus / device.

[0189] Any of the described biomarkers may also be used in imaging studies. For example, an imaging agent may bind to any of the described biomarkers, which may be used to aid in predicting risk of renal failure, to monitor responses to therapeutic interventions, and to select target populations in clinical trials, among other uses.

[0190] DETECTION AND DETERMINATION OF BIOMARKERS AND BIOMARKER VALUES The biomarker values ​​of the biomarkers described herein can be detected using any of a variety of known analytical methods. In one embodiment, a capture reagent is used to detect the biomarker value. As used herein, a "capture agent" or "capture reagent" refers to a molecule that can specifically bind to a biomarker. In various embodiments, the capture reagent can be exposed to the biomarker in solution or can be exposed to the biomarker while the capture reagent is immobilized on a solid support. In other embodiments, the capture reagent has a feature that reacts with a second feature on the solid support. In these embodiments, the capture reagent can be exposed to the biomarker in solution and then the feature on the capture reagent can be used in conjunction with the second feature on the solid support to immobilize the biomarker on the solid support. The capture reagent is selected based on the type of analysis to be performed. Capture reagents include, but are not limited to, SOMAmers, antibodies, adnectins, ankyrins, other antibody mimetics, and other protein scaffolds, autoantibodies, chimeras, small molecules, F(ab') 2 These include fragments, single chain antibody fragments, Fv fragments, single chain Fv fragments, nucleic acids, lectins, ligand binding receptors, affibodies, nanobodies, imprinted polymers, avimers, peptidomimetics, hormone receptors, cytokine receptors, and synthetic receptors, as well as modified versions and fragments thereof.

[0191] In some embodiments, a biomarker / capture reagent complex is used to detect the biomarker level.

[0192] In other embodiments, the biomarker value is obtained from the biomarker / capture reagent complex and is indirectly detected, e.g., as a result of a reaction following biomarker / capture reagent interaction, but is dependent on the formation of the biomarker / capture reagent complex.

[0193] In some embodiments, the biomarker value is detected directly from the biomarker in a biological sample.

[0194] In one embodiment, biomarkers are detected using a multiplexing format that allows for simultaneous detection of two or more biomarkers in a biological sample. In one embodiment of the multiplexing format, capture reagents are immobilized directly or indirectly, covalently or non-covalently, at separate locations on a solid support. In another embodiment, the multiplexing format uses separate solid supports, each solid support having a unique capture reagent bound to the solid support, e.g., quantum dots. In another embodiment, a separate device is used for the detection of each of the multiple biomarkers to be detected in a biological sample. The separate device can be configured to allow for simultaneous processing of each biomarker in a biological sample. For example, a microtiter plate can be used, whereby each well in the plate is used to uniquely analyze one of the multiple biomarkers to be detected in a biological sample.

[0195] In one or more of the foregoing embodiments, a fluorescent tag can be used to label a component of a biomarker / capture complex to detect a biomarker value. In various embodiments, a fluorescent label can be conjugated to a capture reagent specific for any of the biomarkers described herein using known techniques, and the fluorescent label can then be used to detect the corresponding biomarker value. 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.

[0196] In one embodiment, the fluorescent label is a fluorescent dye molecule. In some embodiments, the fluorescent dye molecule comprises at least one substituted indolium ring system, in which the substituent at the carbon at the 3-position of the indolium ring contains a chemically reactive group or a conjugated substance. In some embodiments, the dye molecule comprises an AlexFluor molecule, such as AlexaFluor 488, AlexaFluor 532, AlexaFluor 647, AlexaFluor 680, or AlexaFluor 700. In other embodiments, the dye molecule comprises a first type of dye molecule and a second type of dye molecule, such as two different AlexaFluor molecules. In other embodiments, the dye molecule comprises a first type of dye molecule and a second type of dye molecule, in which the two types of dye molecules have different emission spectra.

[0197] Fluorescence can be measured by a variety of instrumentation methods that are adaptable to 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 J.R. Lakowicz, Springer Science + Business Media, Inc., 2004. See Bioluminescence & Chemiluminescence: Progress & Current Applications; Philip E. Stanley and Larry J. Kricka editors, World Scientific Publishing Company, January 2002.

[0198] In one or more of the foregoing embodiments, a chemiluminescent tag can optionally be used to label a component of the biomarker / capture complex and detect the biomarker value. Suitable chemiluminescent materials include any of oxalyl chloride, rhodamine 6G, Ru(bipy)32+, TMAE (tetrakis(dimethylamino)ethylene), pyrogallol (1,2,3-trihydroxybenzene), lucigenin, peroxyoxalates, aryloxalates, acridinium esters, dioxetanes, and the like.

[0199] In yet other embodiments, the detection method involves an enzyme / substrate combination that generates a detectable signal corresponding to the biomarker value. Generally, the enzyme catalyzes a chemical change of a chromogenic substrate, which can be measured using a variety of techniques, including spectrophotometry, fluorescence, and chemiluminescence. Suitable enzymes include, for example, luciferase, luciferin, malate dehydrogenase, urease, horseradish peroxidase (HRPO), alkaline phosphatase, β-galactosidase, glucoamylase, lysozyme, glucose oxidase, galactose oxidase, and glucose-6-phosphate dehydrogenase, uricase, xanthine oxidase, lactoperoxidase, microperoxidase, and the like.

[0200] In yet other embodiments, the detection method may be a combination of fluorescent, chemiluminescent, radionuclide, or enzyme / substrate combinations that generate a measurable signal. Multiplexed signal generation may be a unique and advantageous feature in biomarker assay formats.

[0201] More specifically, the biomarker values ​​of the biomarkers described herein can be detected using known analytical methods, including singleplex SOMAmer assays, multiplex SOMAmer assays, singleplex or multiplex immunoassays, mRNA expression profiling, miRNA expression profiling, mass spectrometry, histological / cytological methods, etc., as detailed below.

[0202] Determining Biomarker Values ​​Using SOMAmer-Based Assays Assays aimed at the detection and quantification of physiologically significant molecules in biological and other samples are important tools in scientific research and the health care field. One class of such assays involves the use of microarrays that contain 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. Pat. No. 5,475,096, entitled "Nucleic Acid Ligands." See also, for example, U.S. Pat. Nos. 6,242,246, 6,458,543, and 6,503,715, each entitled "Nucleic Acid Ligand Diagnostic Biochip." When a sample is contacted with the microarray, the aptamers bind to the respective target molecules present in the sample, thereby allowing the biomarker value corresponding to the biomarker to be measured.

[0203] As used herein, "aptamer" refers to a nucleic acid that has a specific binding affinity to a target molecule. It is recognized that affinity interaction is a matter of degree, however, in this respect, the "specific binding affinity" of an aptamer to its target generally means that the aptamer binds to its target with a much higher degree of binding affinity than it binds to other components in the test sample. An "aptamer" is a set of copies of one type or type of nucleic acid molecule with a specific nucleotide sequence. An aptamer can contain any suitable number of nucleotides, including any number of chemically modified nucleotides. An "aptamer" refers to a set of multiple such molecules. Different aptamers can have either the same or different number of nucleotides. An aptamer can be DNA or RNA or a chemically modified nucleic acid, and can be single-stranded, double-stranded, or can contain double-stranded regions and can include higher order structures. Aptamers can also be photoaptamers, which contain photoreactive or chemically reactive functional groups to allow the aptamer to be covalently linked to its corresponding target.Any of the aptamer methods disclosed herein can include the use of two or more aptamers that specifically bind to the same target molecule.As described further below, aptamers can contain tags.When an aptamer contains a tag, it is not necessary that all copies of the aptamer have the same tag.Furthermore, when different aptamers each contain a tag, these different aptamers can have either the same tag or different tags.

[0204] Aptamers can be identified using any known method, including the SELEX process. Once identified, aptamers can be prepared or synthesized according to any known method, including chemical synthesis and enzymatic synthesis.

[0205] As used herein, "SOMAmers" or modified slow off-rate aptamers refer to aptamers with improved off-rate properties. SOMAmers can be generated using the improved SELEX method described in U.S. Publication No. 2009 / 0004667, entitled "Method for Generating Aptamers with Improved Off-Rates."

[0206] The terms "SELEX" and "SELEX process" are used interchangeably herein and generally refer to the combination of (1) the selection of aptamers that interact with a target molecule in a desired manner, e.g., bind to a protein with high affinity, and (2) the amplification of those selected nucleic acids. The SELEX process can be used to identify aptamers with high affinity for a specific target or biomarker.

[0207] SELEX generally involves preparing a mixture of candidate nucleic acids, binding the mixture to a desired target molecule to form an affinity complex, separating the affinity complex from unbound candidate nucleic acids, separating and isolating the nucleic acids from the affinity complex, purifying the nucleic acids, and identifying specific aptamer sequences. To further improve the affinity of the selected aptamers, the process may include multiple rounds. The process may include an amplification step at one or more points in the process. See, for example, U.S. Patent No. 5,475,096, entitled "Nucleic Acid Ligands." The SELEX process can be used to generate aptamers that bind covalently to targets as well as aptamers that bind non-covalently to targets. See, for example, U.S. Patent No. 5,705,337, entitled "Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Chemi-SELEX."

[0208] 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 properties. Examples of such modifications include chemical substitutions at the ribose and / or phosphate and / or base positions. Aptamers containing modified nucleotides identified by the SELEX process are described in U.S. Pat. No. 5,660,985, entitled "High Affinity Nucleic Acid Ligands Containing Modified Nucleotides," which describes oligonucleotides containing nucleotide derivatives chemically modified at the 5' and 2' positions of the pyrimidines. U.S. Pat. No. 5,580,737 (see above) describes highly specific aptamers containing one or more nucleotides modified with 2'-amino (2'-NH2), 2'-fluoro (2'-F), and / or 2'-O-methyl (2'-OMe). See also U.S. Patent Application Publication No. 20090098549, entitled "SELEX and PHOTOSELEX," which describes nucleic acid libraries with enhanced physical and chemical properties and their use in SELEX and PHOTOSELEX.

[0209] SELEX can also be used to identify aptamers with desired off-rate properties. See US Patent Publication No. 20090004667, entitled "Method for Generating Aptamers with Improved Off-Rates," which describes an improved SELEX process for generating aptamers capable of binding to target molecules. As mentioned above, these slow off-rate aptamers are known as "SOMAmers." A method for generating aptamers or SOMAmers and photoaptamers or SOMAmers with slower off-rates from their respective target molecules is described. The method includes contacting a candidate mixture with a target molecule, forming a nucleic acid-target complex, and performing a process of enrichment for slow off-rates, where the nucleic acid-target complexes with fast off-rates dissociate and do not reform, while the complexes with slow off-rates remain intact. In addition, the method includes using modified nucleotides in the generation of the candidate nucleic acid mixture to produce aptamers or SOMAmers with improved off-rate performance.

[0210] A variation of this assay uses aptamers that contain photoreactive functional groups that allow the aptamer to covalently bind or photocrosslink to its target molecule. See, e.g., U.S. Patent No. 6,544,776, entitled "Nucleic Acid Ligand Diagnostic Biochip." These photoreactive aptamers are also referred to as photoaptamers. See, e.g., U.S. Patent Nos. 5,763,177, 6,001,577, and 6,291,184, entitled "Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Photoselection of Nucleic Acid Ligands and Solution SELEX," respectively. See also, e.g., U.S. Patent No. 6,458,539, entitled "Photoselection of Nucleic Acid Ligands." After contacting the microarray with a sample and giving the photoaptamers an opportunity to bind to their target molecules, the photoaptamers are photoactivated and the solid support is washed to remove any non-specifically bound molecules. Stringent washing conditions may be used because target molecules bound to the photoaptamers are not typically removed due to the covalent bond generated by the photoactivated functional group(s) on the photoaptamers. In this manner, the assay allows for detection of biomarker values ​​corresponding to the biomarkers in the test sample.

[0211] In both of these assay formats, the aptamer or SOMAmer is immobilized on a solid support before contacting with the sample. However, under certain circumstances, immobilizing the aptamer or SOMAmer before contacting with the sample may not provide an optimal assay. For example, pre-immobilization of the aptamer or SOMAmer may result in inefficient mixing of the aptamer or SOMAmer with the target molecule on the solid support surface, which may result in prolonged reaction times, and thus extended incubation times allow for efficient binding of the aptamer or SOMAmer with its target molecule. Furthermore, when photoaptamers or photoSOMAmers are used in the assay, and depending on the material utilized as the solid support, the solid support may tend to scatter or absorb the light used to form covalent bonds between the photoaptamer or photoSOMAmer and its target molecule. Furthermore, depending on the method used, the surface of the solid support may also be exposed to and affected by any labeling agent used, making the detection of the target molecule bound to the aptamer or photoSOMAmer prone to inaccuracies. Finally, immobilization of an aptamer or SOMAmer on a solid support generally involves an aptamer or SOMAmer preparation step (i.e., immobilization) prior to exposure of the aptamer or SOMAmer to a sample, which preparation step may affect the activity or functionality of the aptamer or SOMAmer.

[0212] SOMAmer assays have also been described that allow the SOMAmer to capture its target in solution and then use a separation step designed to remove certain components of the SOMAmer-target mixture prior to detection (see U.S. Patent Application Publication No. 20090042206, entitled "Multiplexed Analyses of Test Samples"). The described SOMAmer assays allow for the detection and quantification of non-nucleic acid targets (e.g., protein targets) in a test sample by detecting and quantifying nucleic acids (i.e., SOMAmers). The described methods create nucleic acid surrogates (i.e., SOMAmers) for detecting and quantifying non-nucleic acid targets, thus allowing a wide variety of nucleic acid techniques, including amplification, to be applied to a wider range of desired targets, including protein targets.

[0213] SOMAmers can be constructed to facilitate separation of assay components from the SOMAmer-biomarker complex (or photo-SOMAmer-biomarker covalent complex) and allow isolation of the SOMAmer for detection and / or quantification. In one embodiment, these constructs can include a cleavable or releasable element within the SOMAmer sequence. In other embodiments, additional functionality can be introduced into the SOMAmer, such as a label or detectable moiety, a spacer moiety, or a specific binding tag or immobilization element. For example, a SOMAmer can include a tag linked to the SOMAmer via a cleavable moiety, a label, a spacer component separating the labels, and a cleavable moiety. In one embodiment, the cleavable element is a photocleavable linker. The photocleavable linker can be attached to a biotin moiety and a spacer moiety and can include an NHS group for derivatization of an amine, which can be used to introduce a biotin group into the SOMAmer and thereby release the SOMAmer later in the assay method.

[0214] Homogeneous assays, performed with all assay components in solution, do not require separation of sample and reagents prior to signal detection. These methods are rapid and easy to use. These methods generate signals based on molecular capture or binding reagents that react with specific targets. In predicting renal failure, the molecular capture reagents would be SOMAmers or antibodies, etc., and the specific targets would be renal failure biomarkers, such as those in Table 8.

[0215] In one embodiment, the signal generation method utilizes the anisotropic signal change resulting from the interaction of a fluorophore-labeled capture reagent with its specific biomarker target. When the labeled capture reagent reacts with its target, the increased molecular weight causes the rotational motion of the fluorophore bound to the complex to become very slow, resulting in a change in anisotropy value. By monitoring the anisotropy change, the binding event can be used to quantitatively measure the biomarker in solution. Other methods include fluorescence polarization assays, molecular beacon methods, time-resolved fluorescence quenching, chemiluminescence, fluorescence resonance energy transfer, etc.

[0216] An exemplary solution-based SOMAmer assay that can be used to detect a biomarker value corresponding to a biomarker in a biological sample includes: (a) preparing a mixture by contacting the biological sample with a SOMAmer that includes a first tag and has a specific affinity for the biomarker, such that if the biomarker is present in the sample, a SOMAmer affinity complex is formed; (b) exposing the mixture to a first solid support that includes a first capture element, such that the first tag is associated with the first capture element; (c) removing any components of the mixture that are not associated with the first solid support; and (d) binding a second tag to the SOMAmer affinity complex. (e) releasing the SOMAmer affinity complex from the first solid support; (f) exposing the released SOMAmer affinity complex to a second solid support comprising a second capture element, allowing the second tag to associate with the second capture element; (g) removing any uncomplexed SOMAmer from the mixture by separating any uncomplexed SOMAmer from the SOMAmer affinity complex; (h) eluting the SOMAmer from the solid support; and (i) detecting the biomarker by detecting the SOMAmer component of the SOMAmer affinity complex.

[0217] Any means known in the art can be used to detect the SOMAmer components of the SOMAmer affinity complex and thereby detect the biomarker value. Many different detection methods can be used to detect the SOMAmer components of the affinity complex, such as hybridization assays, mass spectrometry, or QPCR. In some embodiments, nucleic acid sequencing can be used to detect the SOMAmer components of the SOMAmer affinity complex and thus detect the biomarker value. In summary, the test sample can be subjected to any type of nucleic acid sequencing method to identify and quantify one or more SOMAmer sequences or sequences present in the test sample. In some embodiments, the sequence includes the entire SOMAmer molecule or any portion of the molecule that can be used to uniquely identify the molecule. In other embodiments, the identifying sequence is a specific sequence added to the SOMAmer, such sequences are often referred to as "tags," "barcodes," or "zip codes." In some embodiments, the sequencing method includes an enzymatic step to amplify the SOMAmer sequence or to convert any type of nucleic acid, including RNA and DNA containing chemical modifications at any position, into any other type of nucleic acid suitable for sequencing.

[0218] In some embodiments, the sequencing method comprises one or more cloning steps, while in other embodiments, the sequencing method comprises a direct sequencing method without cloning.

[0219] In some embodiments, the sequencing method comprises a directed approach using specific primers that target one or more SOMAmers in the test sample. In other embodiments, the sequencing method comprises a shotgun approach that targets all SOMAmers in the test sample.

[0220] In some embodiments, the sequencing method includes an enzymatic step to amplify the molecule targeted for sequencing. In other embodiments, the sequencing method directly sequences a single molecule. An exemplary nucleic acid sequencing-based method that can be used to detect biomarker values ​​corresponding to biomarkers in biological samples includes: (a) converting a mixture of SOMAmers containing chemically modified nucleotides using an enzymatic step into unmodified nucleic acids; (b) shotgun sequencing the resulting unmodified nucleic acids using a massively parallel sequencing platform, such as 454 Sequencing System (454 Life Sciences / Roche), Illumina Sequencing System (Illumina), ABI SOLiD Sequencing System (Applied Biosystems), HeliScope 1 Molecule Sequencer (Helicos Biosciences) or Pacific BioSciences Real-Time 1 Molecule Sequencing System (Pacific BioSciences), or Polonator G Sequencing Systems (Dover Systems); and (c) identifying and quantifying the SOMAmers present in the mixture by specific sequences and sequence counts.

[0221] Determining biomarker values ​​using immunoassays Immunoassay methods are based on the reaction of antibodies to corresponding targets or analytes, and can detect the analytes in a sample depending on the specific assay format. To improve the specificity and sensitivity of immunoreactivity-based assay methods, monoclonal antibodies are frequently used due to their specific epitope recognition. Polyclonal antibodies have also been successfully used in various immunoassays due to their higher affinity for targets compared to monoclonal antibodies. Immunoassays are designed for use with a wide range of biological sample matrices. Immunoassay formats are designed to provide qualitative, semi-quantitative, and quantitative results.

[0222] Quantitative results are obtained by using a standard curve prepared with known concentrations of the specific analyte to be detected. The response or signal of an unknown sample is plotted against the standard curve to determine the amount or value corresponding to the target in the unknown sample.

[0223] Numerous immunoassay formats have been designed. ELISA or EIA can be quantitative for the detection of analytes. The method is based on the binding of a label to either the analyte or the antibody, the label component comprising an enzyme, either directly or indirectly. ELISA tests can be in formats for direct, indirect, competitive, or sandwich detection of analytes. Other methods are based on labels such as, for example, radioisotopes (I125) or fluorescence. Additional techniques include, for example, agglutination, nephelometry, turbidimetry, Western blot, immunoprecipitation, immunocytostaining, immunohistostaining, flow cytometry, Luminex assays, etc. (see ImmunoAssay: A Practical Guide, edited by Brian Law, published by Taylor & Francis, Ltd., 2005 edition).

[0224] Exemplary assay formats include enzyme-linked immunosorbent assays (ELISAs), radioimmunoassays, fluorescence, chemiluminescence, and fluorescence resonance energy transfer (FRET) or time-resolved FRET (TR-FRET) immunoassays. Exemplary techniques for detecting biomarkers include biomarker immunoprecipitation followed by quantitative methods that allow size and peptide level discrimination, such as gel electrophoresis, capillary electrophoresis, planar electrochromatography, and the like.

[0225] The method of detecting and / or quantifying the detectable label or signal generating substance depends on the nature of the label. The products of the reaction catalyzed by the appropriate enzyme (in this case, the detectable label is an enzyme; see above) can be, but are not limited to, fluorescent, luminescent, or radioactive, or they can absorb visible or ultraviolet light. Examples of detectors suitable for detecting such detectable labels include, but are not limited to, X-ray film, radioactivity counters, scintillation counters, spectrophotometers, colorimeters, fluorometers, luminometers, and densitometers.

[0226] Any of the detection methods can be carried out in any format that allows any suitable preparation, processing, and analysis of the reaction.The detection methods can be carried out, for example, in multi-well assay plates (e.g., 96-well or 384-well) or using any suitable array or microarray.Stock solutions of various agents can be made manually or robotically, and all subsequent pipetting, dilution, mixing, dispensing, washing, incubation, sample reading, data collection, and analysis can be carried out robotically using commercially available analysis software, robots, and detection equipment that can detect detectable labels.

[0227] Determining Biomarker Values ​​Using Gene Expression Signatures Measuring mRNA in a biological sample may be used as a proxy to detect the level of the corresponding protein in the biological sample. Thus, the biomarker or biomarker panel described herein can also be detected by detecting the appropriate RNA.

[0228] mRNA expression levels are measured by reverse transcription quantitative polymerase chain reaction (RT-PCR followed by qPCR). RT-PCR is used to generate cDNA from the mRNA. The cDNA can be used in a qPCR assay to generate fluorescence as the DNA amplification process proceeds. In qPCR, absolute measurements such as the number of copies of mRNA per cell can be obtained by comparison to a standard curve. Northern blots, microarrays, invader assays, and RT-PCR in combination with capillary electrophoresis have all been used to measure mRNA expression levels in samples. See Gene Expression Profiling: Methods and Protocols, Richard A. Shimkets, editor, Humana Press, 2004.

[0229] miRNA molecules are small RNAs that are non-coding but can regulate the expression of genes. Any method suitable for measuring mRNA expression levels can be used for the corresponding miRNA. In recent years, the use of miRNAs as biomarkers for disease has been investigated in many laboratories. Many diseases involve widespread transcriptional regulation, so it is not surprising that miRNAs find a role as biomarkers. The association between miRNA concentrations and disease is often even less clear than the association between protein levels and disease, but the value of miRNA biomarkers can be substantial. Naturally, as with any RNA that is differentially expressed during disease, problems faced in developing in vitro diagnostic products will include that the miRNA must survive in diseased cells and be easily extracted for analysis, or that the miRNA must remain long enough to be released into the blood or other matrix and measured there. Protein biomarkers have similar requirements, but many potential protein biomarkers are purposefully secreted and function at the site of pathology, in a paracrine manner, during disease. Many potential protein biomarkers are designed to function outside the cells in which these proteins are synthesized.

[0230] Detection of biomarkers using in vivo molecular imaging techniques Any of the biomarkers described (Table 8) may also be used in molecular imaging. For example, an imaging agent may be bound to any of the biomarkers described, which may be used, among other uses, to help predict risk of renal failure within 4 years, to monitor response to therapeutic interventions, and to select populations for clinical trials.

[0231] In vivo imaging techniques provide a non-invasive method for determining the state of a particular disease or condition within an individual's body. For example, all parts of the body or the entire body may be displayed as a three-dimensional image, thereby providing useful information about the morphology and structure of the body. Such techniques may be combined with the detection of biomarkers as described herein to provide information about the kidney health of an individual.

[0232] Various technological advances have led to the development of in vivo molecular imaging techniques. These advances include the development of new contrast agents or labels, such as radiolabels and / or fluorescent labels, that can generate strong signals within the body, as well as 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. The contrast agents can be visualized with an appropriate imaging system to provide an image of the part or parts of the body in which they are present. The contrast agents can be bound to or associated with, for example, capture agents such as SOMAmers or antibodies, and / or peptides or proteins or oligonucleotides (e.g., for detecting gene expression), or complexes that include any of these with one or more macromolecules and / or other particulate forms.

[0233] Contrast agents may be characterized by radioactive atoms useful in imaging. Suitable radioactive atoms include technetium-99m or iodine-123 for scintigraphy examinations. Other easily detectable moieties include, for example, spin labels for magnetic resonance imaging (MRI), such as iodine-123, iodine-131, indium-111, fluorine-19, carbon-13, nitrogen-15, oxygen-17, gadolinium, manganese, or iron. Such labels are well known in the art and can be easily selected by those skilled in the art.

[0234] Standard imaging techniques include, but are not limited to, magnetic resonance imaging, computed tomography (coronary calcium score), positron emission tomography (PET), single photon emission computed tomography (SPECT), computed tomography angiography, etc. In in vivo diagnostic imaging, the type of detection instrument available is an important factor in the selection of a given contrast agent, e.g., a given radionuclide and the specific biomarker (protein, mRNA, etc.) that is to be targeted with it. The radionuclide typically selected exhibits a certain type of decay that is detectable by a given type of instrument. In addition, when selecting a radionuclide for in vivo diagnosis, its half-life should be long enough to allow detection when maximally taken up by the target tissue, and short enough to minimize harmful radiation to the host.

[0235] Exemplary imaging techniques include, but are not limited to, PET and SPECT, which are imaging techniques in which radionuclides are administered synthetically or locally to an individual. Subsequent radiotracer uptake is measured over time and used to obtain information about target tissues and biomarkers. Depending on the high-energy (gamma-ray) emission of the particular isotope used, and the sensitivity and accuracy of the equipment used to detect it, the two-dimensional distribution of radioactivity can be estimated from outside the body.

[0236] Commonly used positron-emitting nuclides in PET include, for example, carbon-11, nitrogen-13, oxygen-15, and fluorine-18. SPECT uses isotopes that decay by electron capture and / or gamma emission, including, for example, iodine-123 and technetium-99m. An exemplary method for labeling amino acids with technetium-99m is the reduction of pertechnetate ions in the presence of a chelating precursor to form an unstable technetium-99m-precursor complex, which then reacts with the metal binding group of a bifunctionally modified chemotactic peptide to form a technetium-99m-chemotactic peptide conjugate.

[0237] For such in vivo imaging diagnostic methods, antibodies are frequently used. Preparation and use of antibodies for in vivo diagnosis is well known in the art. A labeled antibody that specifically binds to any of the biomarkers in Table 8 can be injected into an individual suspected of having an elevated risk of renal failure, detectable by the particular biomarker used, to diagnose or evaluate the individual's disease state or condition. The label used is selected according to the imaging modality used, as described above. The localization of the label can determine tissue damage or other signs related to the risk of renal failure. The amount of label in an organ or tissue can also determine the contribution of the renal failure biomarker to the risk of renal failure in that organ or tissue.

[0238] Similarly, SOMAmers may be used in such imaging diagnostic methods. For example, SOMAmers used to identify (and therefore specifically bind to) a particular biomarker listed in Table 8 can be appropriately labeled and injected into an individual to be evaluated for renal failure detectable by the particular biomarker to diagnose or evaluate the level of tissue damage, atherosclerotic plaque, components of the inflammatory response, and other factors related to the risk of renal failure in the individual. The label used is selected according to the imaging modality used, as described above. The localization of the label allows the determination of the site of the process leading to an increased risk. The amount of label in an organ or tissue also allows the infiltration of the pathological process in that organ or tissue to be determined. SOMAmer-directed imaging agents may have unique and advantageous properties compared to other imaging agents in terms of tissue penetration, biodistribution, kinetics, clearance, potency, and selectivity.

[0239] Such techniques can be carried out to detect gene expression, optionally using labeled oligonucleotides, for example, by imaging with antisense oligonucleotides. These methods are used, for example, for in situ hybridization, using fluorescent molecules or radionuclides as labels. Other methods for detecting gene expression include, for example, detecting the activity of reporter genes.

[0240] Another common type of imaging technique is optical imaging, in which fluorescent signals within a subject are detected by optical devices external to the subject. These signals can be due to actual fluorescence and / or bioluminescence. Improvements in the sensitivity of optical detection devices have increased the usefulness of optical imaging for in vivo diagnostic assays.

[0241] The use of in vivo molecular biomarker imaging, including for clinical trials, is increasing, for example, to more rapidly measure clinical efficacy in trials of treatments for new diseases or conditions, and / or to avoid long-term placebo treatment of diseases where such long-term treatment may be considered ethically questionable, e.g., multiple sclerosis.

[0242] For a review of other techniques, see N. Blow, Nature Methods, 6, 465-469, 2009.

[0243] Determining Biomarker Values ​​Using Mass Spectrometry Mass spectrometers of various configurations can be used to detect biomarker values. Several types of mass spectrometers are available or can be manufactured in various configurations. Generally, mass spectrometers have the following main components: sample inlet, ion source, mass analyzer, detector, vacuum system, and instrument control system, and data system. The differences in the sample inlet, ion source, and mass analyzer generally define the type of instrument and its capabilities. For example, the inlet can be a capillary column liquid chromatography source, or a direct probe or stage such as used in matrix-assisted laser desorption. Common ion sources are, for example, electrospray, including nanospray and microspray, or matrix-assisted laser desorption. Common mass analyzers include quadrupole mass filters, ion trap mass analyzers, and time-of-flight mass analyzers. Additional mass spectrometry methods are well known in the art (see Burlingame et al., Anal. Chem. 70:647R-716R (1998); Kinter and Sherman, New York (2000)).

[0244] Protein biomarkers and biomarker values ​​can be detected and measured by any of the following: electrospray ionization mass spectrometry (ESI-MS), ESI-MS / MS, ESI-MS / (MS)n, matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption / ionization time-of-flight mass spectrometry (SELDI-TOF-MS), silicon-assisted desorption / ionization (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), a tandem time-of-flight (TOF / TOF) technology called UltraFlex III TOF / TOF, atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS / MS, APCI-(MS)N, atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS / MS, and APPI-(MS)N, quadrupole mass spectrometry, Fourier transform mass spectrometry (FTMS), quantitative mass spectrometry, and ion trap mass spectrometry.

[0245] Sample preparation strategies are used to label and enrich samples before characterizing protein biomarkers and measuring biomarker values ​​by mass spectrometry. Labeling methods include, but are not limited to, iso-mass tags for relative or absolute quantification (iTRAQ) and stable isotope labeling with amino acids in cell culture (SILAC). Capture reagents used to selectively enrich potential biomarker proteins in samples prior to mass spectrometry analysis include, but are not limited to, SOMAmers, antibodies, nucleic acid probes, chimeras, small molecules, F(ab')2 fragments, single-chain antibody fragments, Fv fragments, single-chain Fv fragments, nucleic acids, lectins, ligand-binding receptors, affibodies, nanobodies, ankyrin, domain antibodies, alternative antibody scaffolds (e.g., diabodies, etc.), imprinted polymers, avimers, peptidomimetics, peptoids, peptide nucleic acids, threose nucleic acids, hormone receptors, cytokine receptors, and synthetic receptors, as well as modified versions and fragments thereof.

[0246] Determining biomarker values ​​using proximity ligation assays Proximity ligation assays can be used to determine biomarker values. In summary, a test sample is contacted with a pair of affinity probes, which can be a pair of antibodies or a pair of SOMAmers, with each member of the pair extended with an oligonucleotide. The targets of a pair of affinity probes can be two different determinants on one protein, or one determinant each on two different proteins that can exist as homo- or heteromultimeric complexes. When the probes bind to the target determinants, the free ends of the oligonucleotide extensions are brought close enough together to hybridize together. Hybridization of the oligonucleotide extensions is facilitated by a common connector oligonucleotide that serves to bridge the oligonucleotide extensions together if they are placed close enough together. Once the oligonucleotide extensions of the probes are hybridized, the ends of the extensions are joined together by enzymatic DNA ligation.

[0247] Each oligonucleotide extension contains a primer site for PCR amplification. When the oligonucleotide extensions are ligated together, the oligonucleotides form a continuous DNA sequence, which by PCR amplification reveals information about the identity and amount of the target protein, as well as information about protein-protein interactions if the target determinant is present on two different proteins. Proximity ligation can provide a highly sensitive and specific assay for real-time protein concentration and interaction information by using real-time PCR. Probes that do not bind to the determinant of interest will not bring the corresponding oligonucleotide extension into proximity, and ligation or PCR amplification cannot proceed, resulting in no signal generation.

[0248] The aforementioned assays allow for the detection of biomarker values ​​useful in a method of predicting renal failure, comprising detecting in a biological sample from an individual biomarker values ​​each corresponding to a biomarker selected from the group of biomarkers shown in Table 8, where classification using the biomarker values ​​indicates whether the individual is at high risk of developing renal failure within four years, as described in detail below. Some of the renal failure biomarkers described are useful alone for predicting risk of renal failure, but methods are also described herein for grouping multiple subsets of renal failure biomarkers, each of which is useful as a panel of three or more biomarkers. According to any of the methods described herein, biomarker values ​​can be detected and classified individually, or can be detected and classified together, such as in a multiplex assay format.

[0249] A biomarker "signature" of a given diagnostic or predictive test includes a set of markers, each of which has different levels in a population of interest. In this context, different levels may refer to different means of marker levels for individuals in two or more groups, or different variances in two or more groups, or a combination of both. In the simplest form of diagnostic testing, these markers can be used to assign unknown samples from individuals to one of two groups: either at elevated risk for renal failure or not. Assigning a sample to one of two or more groups is known as classification, and the techniques used to achieve this assignment are known as classifiers or classification methods. Classification methods can also be referred to as scoring methods. There are many classification methods that can be used to build diagnostic classifiers from a set of biomarker values. In general, classification methods are most easily performed using supervised learning techniques, where a dataset is collected using samples obtained from individuals from two (or more in the case of multiple classification conditions) separate groups that one wishes to distinguish. The class (group or population) to which each sample belongs is known in advance for each sample, and a classification method is obtained to obtain the desired classification response. Unsupervised learning techniques can also be used to generate diagnostic classifiers.

[0250] Common techniques for developing diagnostic classifiers include decision trees; bagging, boosting, forests and random forests; learning based on inference rules; Parzen windows; linear models; logistic curves; neural network methods; unsupervised clustering; K-means; hierarchical ascending / descending classification; semi-supervised learning; prototype methods; nearest neighbor methods; kernel density estimation; support vector machines; hidden Markov models; Boltzmann learning, and classifiers may be combined simply or in a way that minimizes a particular objective function. For a general discussion, see, for example, Pattern Classification, R.O.Duda, et al., editors, John Wiley & Sons, 2nd edition, 2001. See also The Elements of Statistical Learning-Data Mining, Inference, and Prediction, T.Hastie, et al., editors, Springer Science+Business Media, LLC, 2nd edition, 2009 (each of which is incorporated by reference in its entirety).

[0251] To generate a classifier using supervised learning techniques, a set of samples, called training data, is obtained. For diagnostic testing, training data includes samples from different groups (classes) to which unknown samples are subsequently assigned. For example, samples taken from individuals of a control population and individuals of a population with a particular disease, condition, or event can constitute training data for developing a classifier that can classify unknown samples (or more specifically, the individuals from whom the samples are obtained) as either having a disease, condition, or increased risk of the event, or not suffering from a disease, condition, or increased risk of the event. The development of a classifier from training data is known as training the classifier. The specific details of classifier training will vary depending on the nature of the supervised learning method (see, e.g., Pattern Classification, R.O.Duda, et al., editors, John Wiley & Sons, 2nd edition, 2001; see also, The Elements of Statistical Learning - Data Mining, Inference, and Prediction, T. Hastie, et al., editors, Springer Science+Business Media, LLC, 2nd edition, 2009).

[0252] Usually, there may be many higher biomarker values ​​compared to the samples of training set, so care must be taken to avoid overfitting.Overfitting occurs when statistical model represents random error or noise instead of the underlying relationship.Overfitting can be avoided in various ways, including, for example, limiting the number of markers used during classifier development, assuming that the responses of markers are independent of each other, limiting the complexity of the underlying statistical model used, and ensuring that the underlying statistical model fits data.

[0253] To identify a set of biomarkers associated with the occurrence of an event, the combined set of control and early event samples was analyzed using principal component analysis (PCA). PCA shows samples on the axis defined by the strongest variation among all samples, regardless of case or control outcome, thus reducing the risk of overfitting the distinction between cases and controls. Since the occurrence of severe thrombotic events has a strong element of chance and requires the reporting of the rupture of unstable plaques in living vessels, one would not expect to see a clear separation between the control and event sample sets. The separation observed between cases and controls is not large, but this occurs in the second principal component and corresponds to about 10% of the total variation in this set of samples, indicating that the quantification of the underlying biological variation is relatively simple.

[0254] In the next set of analyses, the biomarkers can be analyzed for components of the differences between samples that were specific to the separation between control and early event samples. One method that can be used is to use DSGA (Bair, E. and Tibshirani, R. (2004) Semi-supervised methods to predict patient survival from gene expression data. PLOS Biol., 2, 511-522) to remove (shrink) the first three main directions of variation between samples in the control set. To find out, we perform dimensionality reduction on the control set, but also perform PCA on both samples in the control and samples from the early event samples. The separation of cases from early events can be observed along the horizontal axis. Cross-validation of selected proteins associated with renal failure

[0255] To avoid overfitting the predictive power of proteins to the idiosyncratic features of a particular selection of samples, cross-validation and dimensionality reduction approaches can be taken. Cross-validation involves multiple selections of a set of samples to monitor the ability of the method to be applied to samples that were not used to create the risk model, in combination with using unselected samples to determine the association of risk with proteins (The Elements of Statistical Learning-Data Mining, Inference and Prediction, and Prediction, T., Hastie, et al., editors, Springer Science+ Business Media, LLC, 2nd edition, 2009). We applied the supervised PCA method of Tibshirani et al. (Bair, E. and Tibshirani, R. (2004) Semi-supervised methods to predict patient survival from gene expression data. PLOS Biol., 2, 511-522.), which is applicable to high-dimensional datasets in modeling the risk of renal failure. The supervised PCA (SPCA) method involves the univariate selection of a set of proteins that are statistically associated with the event hazard observed in the data, and the determination of correlated components that combine the information from all of these proteins. This determination of correlated components is a dimensionality reduction step that not only combines information across proteins, but also mitigates the likelihood of overfitting by reducing the number of independent variables from a full protein menu of over 1000 proteins down to a small number of principal components (in this study, only the first principal component is examined). Univariate and multivariate analyses of the association of individual proteins with survival time

[0256] The Cox proportional hazard model (Cox, David R (1972) "Regression Models and Life-Tables". Journal of the Royal Statistical Society. Series B (Methodological) 34 (2): 187-220.)) is widely used in medical statistics. Cox regression avoids fitting a specific time function to the cumulative survival rate, and instead uses a relative risk model that refers to a baseline hazard function (which can change over time). The baseline hazard function represents the common shape of the survival time distribution for all individuals, whereas the relative risk gives the level of hazard for a set of covariates (such as a single individual or a group) as a multiple of the baseline hazard. The relative risk is constant over time in the Cox model.

[0257] kit For example, a suitable kit for use in practicing the methods disclosed herein can be used to detect any combination of the biomarkers in Table 8. Additionally, any kit can include one or more detectable labels as described herein, such as, for example, fluorescent moieties.

[0258] In one embodiment, the kit comprises (a) one or more capture reagents (e.g., at least one SOMAmer or antibody) for detecting one or more biomarkers in a biological sample, the biomarkers including any of the biomarkers listed in Table 8, and optionally (b) one or more software or computer program products for classifying an individual from whom the biological sample was obtained as either having or not having an elevated risk of renal failure, or for determining the likelihood of the individual having an elevated risk of renal failure. Alternatively, rather than one or more computer program products, one or more instructions for a human to manually perform the above steps can be provided.

[0259] The combination of the solid support and the corresponding capture reagent with a signal generating agent is referred to herein as a "detection device" or "kit." The kit may also include instructions for use of the device and reagents, sample handling, and data analysis. Additionally, the kit may be used with a computer system or software for analyzing biological samples and reporting the results of the analysis.

[0260] The kits may also include one or more reagents for processing the biological sample (e.g., solubilization buffer, detergent, wash solution, or buffer). Any of the kits described herein may also include, for example, buffers, blocking agents, matrix materials for mass spectrometry, antibody capture agents, positive control samples, negative control samples, software, and information, such as protocols, guidelines, and reference data.

[0261] In one aspect, the present invention provides a kit for analyzing risk status of renal failure. The kit includes PCR primers for one or more SOMAmers specific for a biomarker selected from Table 8. The kit may further include instructions for use and correlation of the biomarkers with risk prediction of renal failure risk. The kit may also include a DNA array including the complement of one or more Somamers specific for a biomarker selected from Table 8, reagents and / or enzymes for amplifying or isolating sample DNA. The kit may include reagents for real-time PCR, such as TaqMan probes and / or primers, and enzymes.

[0262] For example, the kit may include (a) at least a capture reagent for quantifying one or more biomarkers in a test sample, the biomarkers including those listed in Table 8, or any other biomarker or panel of biomarkers described herein, and optionally (b) one or more algorithms or computer programs for performing the steps of: comparing the amount of each quantified biomarker in the test sample to one or more pre-defined cut-offs, assigning a score to each quantified biomarker based on said comparison, combining the scores assigned to each quantified biomarker to obtain a total score, comparing the total score to a pre-defined score, and using said comparison to determine whether the individual is at elevated risk for renal failure. Alternatively, rather than one or more algorithms or computer programs, one or more instructions for a human to manually perform the above steps may be provided.

[0263] Computer Methods and Software After selecting a biomarker or panel of biomarkers, a method for diagnosing an individual may include: 1) collecting or harvesting a biological sample, 2) performing an analytical method to detect and measure the biomarkers in the panel in the biological sample, 3) performing data normalization or standardization required for the method used to collect biomarker values, 4) calculating marker scores, 5) combining the marker scores to obtain an overall diagnostic or predictive score, and 6) reporting the individual's diagnostic or predictive score. In this approach, the diagnostic or predictive score may be a single number determined from the sum of all marker calculations compared to a pre-set threshold indicating the presence or absence of disease. Alternatively, the diagnostic or predictive score may be a series of bars, each representing a biomarker value, whose response pattern may be compared to a pre-set pattern to determine the presence or absence of a disease, condition, or elevated (or not elevated) risk of an event.

[0264] At least some embodiments of the methods described herein can be implemented using a computer. An example of a computer system 100 is shown in FIG. 4. As shown in FIG. 4, the system 100 is shown to be composed of hardware elements electrically connected via a bus 108, including a processor 101, an input device 102, an output device 103, a storage device 104, a computer-readable storage medium reader 105a, a communication system 106, an accelerated processing device (e.g., DSP or special purpose processor) 107, and a memory 109. The computer-readable storage medium reader 105a is further connected to a computer-readable storage medium 105b, and this combination collectively represents a storage medium, memory, etc., in addition to remote, local, fixed, and / or removable storage devices, for temporarily and / or more persistently containing computer-readable information, and this combination includes the storage device 104, the memory 109, and / or any other such accessible system 100 resources. System 100 also includes software elements (shown here as residing in working memory 191) that include an operating system 192 and other code 193, eg, programs, data, and the like.

[0265] As shown in FIG. 4, the system 100 has a wide range of flexibility and configurability. Thus, for example, one or more servers may be implemented using a single architecture, and such servers may be further configured according to generally desired protocols, protocol modifications, extensions, and the like. However, it will be apparent to one skilled in the art that embodiments may be utilized according to more specific application requirements. For example, one or more system elements may be implemented as sub-elements within the components of the system 100 (e.g., within the communication system 106). Customized hardware may be utilized, and / or specific elements may be implemented in hardware, software, or both. Additionally, connections to other computing devices, such as network input / output devices (not shown), may be utilized, although it should be understood that wired, wireless, modem, and / or other connections to other computing devices may also be utilized.

[0266] In one embodiment, the system may include a database that includes biomarker features that indicate predictive characteristics of risk of renal failure. Biomarker data (or biomarker information) can be utilized as input to a computer for use as part of a computer-implemented method. Biomarker data may include data described herein.

[0267] In one aspect, the system further includes one or more devices for providing input data to the one or more processors.

[0268] The system further includes a memory for storing the dataset of ranked data elements.

[0269] In another embodiment, the device for providing input data includes a detector for detecting characteristics of the data elements, such as, for example, a mass spectrometer or a gene chip reader.

[0270] The system may additionally include a database management system. A user's request or query may be formatted in an appropriate language understood by the database management system, which processes the query and extracts relevant information from the training set database.

[0271] The system may be connectable to a network to which a network server and one or more clients are connected. The network may be a local area network (LAN) or a wide area network (WAN), as is well known in the art. Preferably, the server includes the necessary hardware to execute a computer program product (e.g., software) for accessing data from a database to process user requests.

[0272] The system may include an operating system (e.g., UNIX or Linux) for executing instructions from a database management system. In one embodiment, the operating system is operable over a global communications network, such as the Internet, and may utilize a global communications network server for connecting to such a network.

[0273] The system may include one or more devices that include a graphical display interface that includes interface elements such as buttons, pull-down menus, scroll bars, fields for text entry, etc., commonly found in graphical user interfaces known in the art. Requests entered at the user interface may be transmitted to application programs in the system for formatting for searching for relevant information in one or more system databases. Requests or queries entered by a user may be formulated in any suitable database language.

[0274] 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 input data. The results of processed data may be displayed in the interface, printed on a printer in communication with the system, stored on a storage device, and / or transmitted over a network, or provided in the form of a computer readable medium.

[0275] The system can be in communication with an input device for providing data regarding the data elements (e.g., expression values) to the system. In one aspect, the input device can include a gene expression profiling system, including, for example, a mass spectrometer, a gene chip, or an array reader.

[0276] According to various embodiments, the method and device for analyzing biomarker information for renal failure risk prediction may be implemented in any suitable manner, for example, using a computer program running on a computer system.A conventional computer system including a processor and random access memory may be used, such as a remotely accessible application server, a network server, a personal computer, or a workstation.Additional computer system elements may include a storage device or information storage system, for example, a mass storage system, and a user interface, for example, a conventional monitor, keyboard, and tracking device.The computer system may be a stand-alone system or part of a network of computers, including a server and one or more databases.

[0277] The renal failure risk predictive biomarker analysis system may provide functions and operations to complete data analysis, such as data collection, processing, analysis, reporting, and / or diagnosis. For example, in one embodiment, a computer system may execute a computer program that may receive, store, retrieve, analyze, and report information about renal failure risk predictive biomarkers. The computer program may include multiple modules that perform various functions or operations, such as a processing module for processing raw data and generating supplemental data, and an analysis module for analyzing the raw data and supplemental data to generate a predicted status and / or diagnosis or risk calculation of renal failure risk. The calculation of the risk status of renal failure may optionally include generating or collecting any other information, including additional biomedical information regarding the individual's status associated with a disease, condition, or event, identifying whether further testing may be desirable, or otherwise evaluating the health status of the individual.

[0278] An example of a computer-implemented method according to the principles of the disclosed embodiments can be seen in FIG. 5. FIG. 5 shows a flow chart 3000. In block 3004, biomarker information can be obtained for an individual. For example, the biomarker information can be obtained from a computer database after performing testing of the individual's biological sample. The biomarker information can include biomarker values ​​that each correspond to one or more of the biomarkers in Table 8. In block 3008, a computer can be used to classify each biomarker value. Then, in block 3012, a determination can be made regarding the likelihood of an increased risk of renal failure for the individual based on the plurality of classifications. This representation can be output to a display or other display device for human viewing. Thus, it can be displayed, for example, on a display screen of a computer or other output device.

[0279] Some embodiments described herein may be implemented to include a computer program product, which may include a computer readable medium having computer readable program code embodied in the medium for executing an application program on a computer having a database.

[0280] As used herein, a "computer program product" refers to a set of instructions, organized in the form of natural or programming language statements, contained in a physical medium of any nature (e.g., written, electronic, magnetic, optical, or otherwise) and usable by a computer or other automated data processing system. Such programming language statements, when executed by a computer or data processing system, cause the computer or data processing system to operate according to the specific content of the statements. Computer program products include, but are not limited to, programs in source and object code, and / or test or data libraries stored on a computer-readable medium. Furthermore, computer program products that enable a computer system or data processing device to operate in a preselected manner may be provided in a number of forms, including, but not limited to, original source code, assembly code, object code, machine language, encrypted or compressed versions of the foregoing, and any equivalents.

[0281] In one aspect, a computer program product for assessing risk of renal failure is provided, the computer program product comprising a computer readable medium embodying program code executable by a processor of a computing device or system, the program code comprising: code for retrieving data pertaining to biological samples from an individual, each comprising biomarker values ​​corresponding to one or more biomarkers in Table 8, and code for performing a classification method that indicates the individual's renal failure risk status as a function of the biomarker values.

[0282] In yet another aspect, a computer program product for indicating likelihood of risk of renal failure is provided. The computer program product includes a computer readable medium having program code embodied therein executable by a processor of a computing device or system, the program code including: code for retrieving data pertaining to a biological sample from an individual, the data including biomarker values ​​corresponding to one or more biomarkers in Table 8, and code for performing a classification method to indicate the individual's renal failure risk status as a function of the biomarker values.

[0283] Although various embodiments have been described as methods or apparatus, it should be understood that the embodiments may be implemented via code in conjunction with a computer, e.g., code resident in or accessible by a computer. For example, software and databases may be utilized to implement many of the above methods. Thus, in addition to embodiments that are implemented by hardware, it should also be noted that these embodiments may be implemented by using an article of manufacture consisting of a computer usable medium having computer readable program code embodied therein that provides for the execution of the functions disclosed herein. Thus, it is desirable to consider the embodiments to be protected by this patent in their program code means as well. Furthermore, the embodiments may be embodied as code stored in virtually any type of computer readable memory, including but not limited to RAM, ROM, magnetic media, optical media, or magneto-optical media. Even more generally, the embodiments may be implemented in software, including but not limited to software running on a general purpose processor, microcode, PLA, or ASIC, or in any combination thereof, or in hardware.

[0284] It is further contemplated that embodiments may be achieved as a computer signal embodied in a carrier wave and a signal propagated through a transmission medium (e.g., electrical and optical). Thus, the various types of information described above may be formatted in structures, such as data structures, and transmitted as electrical signals over a transmission medium or stored on a computer-readable medium.

[0285] It should also be noted that many of the structures, materials, and acts recited herein may be recited as a means for performing a function or a step for performing a function, and such language should therefore be understood to be entitled to cover all such structures, materials, or acts disclosed within this specification, including those incorporated by reference, and their equivalents.

[0286] The biomarker identification process, uses of the biomarkers, and various methods for determining biomarker values ​​disclosed herein have been detailed above with respect to assessing renal failure risk. However, the application of the process, the use of the identified biomarkers, and the methods for determining biomarker values ​​are fully applicable to other specific types of diseases or medical conditions, or to identifying individuals who may or may not benefit from adjunctive medical treatment. EXAMPLES

[0287] The following examples are provided for illustrative purposes only and are not intended to limit the scope of the present application, which is defined by the appended claims. All examples described herein were carried out using standard techniques that are well known and routine to those skilled in the art. The routine molecular biology techniques described in the following examples can be carried out 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).

[0288] Example 1. Model specifications 1.1 Endpoint Description. Incidence of PCRI (as a binary status of yes or no) within 4 years of blood sample. PCRI is defined as at least one of the following events: (1) a 50% decline in estimated glomerular filtration rate (eGFR), (2) a diagnosis of the need for dialysis, (3) the occurrence of eGFR < 15 ml / min / 1.73 m2, (4) the development of end-stage renal disease (ESRD), or (5) a diagnosis of the need for a kidney transplant.

[0289] 1.2 Model Information. The model is a logistic regression model that includes 10 features with nonzero coefficients. The model was trained with PCRI within 4 years as the endpoint. The model provides two predictions: (1) PCRI as a binary yes / no variable for each 4 years: In model development, an optimal probability score of 0.3533 was identified as the threshold for classifying PCRI into "yes" and "no" status within the following 4 years. (2) Relative Risk: This is a continuous value that allows the model prediction to be interpreted such that a higher prediction indicates a higher likelihood of developing a PCRI in the following 4 years. The model generates a probability that an individual will develop a PCRI, which is represented by p*. This probability is used to calculate the relative risk

number

[0290] 1.3 Reporting Business Rules TIFF2024525146000013.tif114170

[0291] 1.4 Hypothetical Patient The hypothetical patient has a predicted probability based on the proteomic model equal to 0.72. The relative risk for this patient is 2.33, and therefore this patient's bin is in the fourth quintile.

number

[0292] Further AUC values ​​for selected Table 8 features and combinations of Table 8 features are shown in Tables 9a-9f. TIFF2024525146000015.tif46170TIFF2024525146000016.tif193170TIFF2024525146000017.tif173170 TIFF2024525146000018.tif73170TIFF2024525146000019.tif153170TIFF2024525146000020.tif140170

[0293] Example 2. Test development and validation data sets 2.1 Development and Validation Cohort(s).CRIC is a multi-center, observational study that was initiated to examine the relationship between chronic renal failure and cardiovascular disease and has since been expanded to measure many outcomes thought to be related to renal failure, such as cognitive decline and frailty. CRIC enrolled patients aged 21-74 years, half of whom had diabetes. Participants attended annual in-person follow-up visits (urine and plasma samples were collected and stored) and six-monthly telephone interviews in which study results and general health were ascertained. Recruitment for the study began in 2003, and recruitment continued for approximately 2.5 years at 13 clinical sites in the United States, with investigators continuing to monitor the cohort. The SomaLogic CRIC dataset contains clinical data and second annual visit samples (collected July 2003-December 2009) on 3413 kidney disease patients who had not progressed to end-stage renal disease by the second annual visit.

[0294] 2.2 Dataset stratification: In this study, the cohort was split independently into training (70%), design validation (15%), and validation (15%) sets to allow for the identification of robust models while mitigating the overfitting problem. The validation dataset was not used in the POC or refinement phases.

[0295] 2.2.1 Model development data TIFF2024525146000021.tif232170TIFF2024525146000022.tif231170

[0296] 2.2.2 Model validation data TIFF2024525146000023.tif225170

[0297] Example 3. Development results 3.1 Data QC and Pre-Analytical Results Previous data QC and feasibility POC have been conducted in the CRIC cohort for this endpoint. Samples for this test were run with assay version 4.0 from 21 January 2019 to 30 September 2019.

[0298] 3.2 POC Approach and Results The best performing models were logistic regression models, which exceeded the acceptance criteria of AUC ≥ 0.65, 0.7, 0.75, or 0.77.

[0299] 3.3 Refinement approach and results The initial features used were the top 200 aptamers from the univariate results sorted by rank. To increase the flexibility of the model across assay versions, only features with a correlation above 0.75 between assay versions 4.0 and 4.1 were used in the model development. (The actual correlation values ​​for the last 10 features in the model are listed in Table 13). The feature list was further refined by iteratively using elastic net logistic regression. After each round of elastic net regression, features with absolute coefficients below a threshold were removed and the model was refitted. This threshold was initially set to 0.01 and increased to 0.05 over the iterations. Once the final feature set was selected, an unpenalized logistic regression model was fitted to the remaining 10 features. TIFF2024525146000024.tif93170

[0300] The final model selected was a 10-feature logistic regression model, which achieved an AUC of 0.82 on both the training and design validation data. The equation for the logistic regression model is: Probability of CKD = inverse logit(intercept + sum(coefficients * feature levels)). In this model, the intercept = -0.803923, and the feature names and coefficients are shown in Table 8. The feature levels are RFU (relative fluorescence units) measured in the sample by a proteomics assay, e.g., an aptamer-based assay. Comparative performance was evaluated using KFRE, achieving an AUC of 0.77 (95% CI: 0.75, 0.78) for the entire CRIC cohort. Table 14 shows the AUC and 95% CI for the training data and design validation data from the final model. TIFF2024525146000025.tif52170

[0301] The optimal decision threshold was determined by maximizing the F1 score, which is the harmonic mean of sensitivity and specificity, and was found to be p=0.3533. This probability was used as the threshold to determine the "yes" or "no" status for developing PCRI within the following 4 years. A predicted probability less than 0.3533 is labeled as "no" and a predicted probability equal to or greater than 0.3533 is labeled as "yes". The refinement process also included a test for the robustness of the model. In some embodiments, p=0.3, 0.31, 0.32, 0.33, 0.34, 0.35, or 0.3533. TIFF2024525146000026.tif59170

[0302] Example 4. Model validation plan and results 4.1 Clinical validation plan The model was validated by calculating the AUC of PCRI yes / no at the 4-year endpoint on the 15% hold-out validation dataset. Acceptance criteria were AUC ≥ 0.65, 0.7, 0.75, or 0.77. Clinical Results for Validation Data The required acceptance criteria was that the AUC of the model in the full validation cohort had at least the same AUC as KFRE in the full CRIC cohort (AUC≧0.77). Validation results are shown in Table 19. See Figure 3. TIFF2024525146000027.tif58170References All references cited below, or elsewhere throughout this specification, are hereby incorporated by reference in their entirety: 1. Hannan M, Ansari S, Meza N, et al. Risk Factors for CKD Progression: Overview of Findings from the CRIC Study. Clin J Am Soc Nephrol 2020;11:07830520. 2. Feldman HI, Appel LJ, Chertow GM, et al. The Chronic Renal Insufficiency Cohort (CRIC) Study: Design and Methods. J Am Soc Nephrol 2003;14:S148-153. 3. National Institutes of Health NIoDaDaKD.2019 USRDS Annual Data Report:Epidemiology of Kidney Diseases in the United States.2019:64. 4. Levey AS,Coresh J.Chronic kidney disease.Lancet 2012;379:165-180.doi:110.1016 / S0140-6736(1011)60178-60175.Epub 62011 Aug 60115. 5. Chapter 4:Other complications of CKD:CVD,medication dosage,patient safety,infections,hospitalizations,and caveats for investigating complications of CKD.2011)2013;3:91-111.doi:110.1038 / kisup.2012.1067. 6. Chapter 3:Management of progression and complications of CKD.2011)2013;3:73-90.doi:10.1038 / kisup.2012.1066. 7. Chapter 2:Definition,identification,and prediction of CKD progression.2011)2013;3:63-72.doi:10.1038 / kisup.2012.1065. 8. Tangri N,Stevens LA,Griffith J,et al.A predictive model for progression of chronic kidney disease to kidney failure.JAMA 2011;305:1553-1559.doi:1510.1001 / jama.2011.1451.Epub 2011 Apr 1511. 9. Centers for Disease Control and Prevention.Chronic Kidney Disease Surveillance System-United States. http: / / www.cdc.gov / ckd,2021. 10. Major RW,Shepherd D,Medcalf JF,et al.The Kidney Failure Risk Equation for prediction of end stage renal disease in UK primary care:An external validation and clinical impact projection cohort study.PLoS Med 2019;16:e1002955.doi:1002910.1001371 / journal.pmed.1002955.eCollection 1002019 Nov. 11. Lash JP,Go AS,Appel LJ,et al.Chronic Renal Insufficiency Cohort(CRIC)Study:baseline characteristics and associations with kidney function.Clin J Am Soc Nephrol 2009;4:1302-1311. 12. Fuentes AV,Pineda MD,Venkata KCN.Comprehension of Top 200 Prescribed Drugs in the US as a Resource for Pharmacy Teaching,Training and Practice.Pharmacy(Basel)2018;6:43.doi:10.3390 / pharmacy6020043. 13. Song,et al.(2019)“Understanding kidney injury molecule 1:a novel immune factor in kidney pathophysiology”Am J Transl Res 11(3):1219-1229 14. Kralisch,et al.(2017)“FSTL3 is increased in renal dysfunction”Nephrol Dial Transplant 32:1637-1644 15. Liu,et al.(2018)“RGMb protects against acute kidney injury by inhibiting tubular cell necroptosis via an MLKL-dependent mechanism”Proc Natl Acad Sci USA 115(7):E1475-E1484 16. Li,et al.(2017)“Elevated serum level of pancreatic stone protein / regenerating protein(PSP / reg)is observed in diabetic kidney disease”Oncotarget 8(24):38145-38151 17. Yamada,et al.(2018)“Identification of 13 novel susceptibility loci for early-onset myocardial infarction,hypertension,or chronic kidney disease”Int J Mol Med 42(5):2415-2436 18. Peralta,et al.(2011)“Cystatin C identifies chronic kidney disease patients at higher risk for complications”J Am Soc Nephrol 22(1):147-155 19. Nadkarni,et al.(2018)“Plasma biomarkers are associated with renal outcomes in individuals with APOL1 risk variants”Kidney Int.93(6):1409-1416 20. Garrod,et al.(1990)“Early expression of desmosomal components during kidney tubule morphogenesis in human and murine embryos”108(2):313-321 21. Mohammed,et al.(2019)“Cardiac Biomarkers and Cardiovascular Outcome in Children with Chronic Kidney Disease”Iran J Kidney Dis.13(2):120-128 22. Schneider,et al.(1994)“Human cyclophilin C:primary structure,tissue distribution,and determination of binding specificity for cyclosporins”Biochemistry 33(27):8218-8224 23. Friedman,et al.(1994)“An analysis of the expression of cyclophilin C reveals tissue restriction and an intriguing pattern in the mouse kidney”Am J Pathol.144(6):1247-56 24. Jantti,et al.(Aug 28,2020)“Trophic activities of endoplasmic reticulum proteins CDNF and MANF”Cell and Tissue Research 382:83-100 25. Zhang,et al.(2019)“Follicle-stimulating hormone promotes renal tubulointerstitial fibrosis in aging women via the AKT / GSK-3β / β-catenin pathway”Aging Cell 18(5):E12997 26. Tian,et al.(1992)“The variations of PRL,LH,FSH and testosterone in renal disease”Hua Xi Yi Ke Da Xue Xue Bao 23(1):94-97 27. Koyun,et al.(2009)“Evaluation of reproductive functions in male adolescents following renal transplantation”Pediatr Transplant 13(6):697-700 28. Colak,et al.(2014)“The relation between serum testosterone levels and cardiovascular risk factors in patients with kidney transplantation and chronic kidney disease”Saudi J Kidney Dis Transpl.25(5):951-959

Claims

Claim 1 a. Measuring the level of COL28A1 protein in a sample derived from a human subject, and the level of at least 1, 2, 3, 4, 5, 6, 7, 8, or 9 proteins selected from HAVCR1, FSTL3, RGMB, UBE2G2, REG1A, REG1B, COL6A3, CST3, and TNFRSF1A; b. Identifying the human subject as having a relative risk of developing progressive chronic kidney disease within a four-year period based on the level of COL28A1 and the level of the at least 1, 2, 3, 4, 5, 6, 7, 8, or 9 proteins. Claim 2 a. Measuring the level of UBE2G2 protein in a sample derived from a human subject, and the level of at least 1, 2, 3, 4, 5, 6, 7, 8, or 9 proteins selected from HAVCR1, FSTL3, RGMB, COL28A1, REG1A, REG1B, COL6A3, CST3, and TNFRSF1A; b. Identifying the human subject as having a relative risk of developing progressive chronic kidney disease within a four-year period based on the level of UBE2G2 and the level of the at least 1, 2, 3, 4, 5, 6, 7, 8, or 9 proteins. Claim 3 a. Measuring the level of REG1B protein in a sample derived from a human subject, and the level of at least 1, 2, 3, 4, 5, 6, 7, 8, or 9 proteins selected from HAVCR1, FSTL3, RGMB, COL28A1, UBE2G2, REG1A, COL6A3, CST3, and TNFRSF1A; b. Identifying the human subject as having a relative risk of developing progressive chronic kidney disease within a four-year period based on the level of REG1B and the level of the at least 1, 2, 3, 4, 5, 6, 7, 8, or 9 proteins. Claim 4 The method according to claim 1, comprising measuring COL28A1 and HAVCR1; COL28A1 and FSTL3; COL28A1 and RGMB; COL28A1 and UBE2G2; COL28A1 and REG1A; COL28A1 and REG1B; COL28A1 and COL6A3; COL28A1 and CST3; COL28A1 and TNFRSF1A; COL28A1, HAVCR1, and FSTL3; COL28A1, HAVCR1, and RGMB; COL28A1, HAVCR1, and UBE2G2; COL28A1, HAVCR1, and REG1A; COL28A1, HAVCR1, and REG1B; COL28A1, HAVCR1, and COL6A3; COL28A1, HAVCR1, and CST3; COL28A1, HAVCR1, and TNFRSF1A; COL28A1, FSTL3, and RGMB; COL28A1, FSTL3, and UBE2G2; COL28A1, FSTL3, and REG1A; COL28A1, FSTL3, and REG1B; COL28A1, FSTL3, and COL6A3; COL28A1, FSTL3, and CST3; COL28A1, FSTL3, and TNFRSF1A; COL28A1, RGMB, and UBE2G2; COL28A1, RGMB, and REG1A; COL28A1, RGMB, and REG1B; COL28A1, RGMB, and COL6A3; COL28A1, RGMB, and CST3; COL28A1, RGMB, and TNFRSF1A; COL28A1, UBE2G2, and REG1A; COL28A1, UBE2G2, and REG1B; COL28A1, UBE2G2, and COL6A3; COL28A1, UBE2G2, and CST3; COL28A1, UBE2G2, and TNFRSF1A; COL28A1, REG1A, and REG1B; COL28A1, REG1A, and COL6A3; COL28A1, REG1A, and CST3; COL28A1, REG1A, and TNFRSF1A; COL28A1, REG1B, and COL6A3; COL28A1, REG1B, and CST3; COL28A1, REG1B, and TNFRSF1A; COL28A1, COL6A3, and CST3; COL28A1, COL6A3, and TNFRSF1A; or COL28A1, CST3, and TNFRSF1A.

5. The method according to claim 2, comprising measuring UBE2G2 and HAVCR1; UBE2G2 and FSTL3; UBE2G2 and RGMB; UBE2G2 and COL28A1; UBE2G2 and REG1A; UBE2G2 and REG1B; UBE2G2 and COL6A3; UBE2G2 and CST3; UBE2G2 and TNFRSF1A; UBE2G2, HAVCR1, and FSTL3; UBE2G2, HAVCR1, and RGMB; UBE2G2, HAVCR1, and COL28A1; UBE2G2, HAVCR1, and REG1A; UBE2G2, HAVCR1, and REG1B; UBE2G2, HAVCR1, and COL6A3; UBE2G2, HAVCR1, and CST3; UBE2G2, HAVCR1, and TNFRSF1A; UBE2G2, FSTL3, and RGMB; UBE2G2, FSTL3, and COL28A1; UBE2G2, FSTL3, and REG1A; UBE2G2, FSTL3, and REG1B; UBE2G2, FSTL3, and COL6A3; UBE2G2, FSTL3, and CST3; UBE2G2, FSTL3, and TNFRSF1A; UBE2G2, RGMB, and COL28A1; UBE2G2, RGMB, and REG1A; UBE2G2, RGMB, and REG1B; UBE2G2, RGMB, and COL6A3; UBE2G2, RGMB, and CST3; UBE2G2, RGMB, and TNFRSF1A; UBE2G2, COL28A1, and REG1A; UBE2G2, COL28A1, and REG1B; UBE2G2, COL28A1, and COL6A3; UBE2G2, COL28A1, and CST3; UBE2G2, COL28A1, and TNFRSF1A; UBE2G2, REG1A, and REG1B; UBE2G2, REG1A, and COL6A3; UBE2G2, REG1A, and CST3; UBE2G2, REG1A, and TNFRSF1A; UBE2G2, REG1B, and COL6A3; UBE2G2, REG1B, and CST3; UBE2G2, REG1B, and TNFRSF1A; UBE2G2, COL6A3, and CST3; UBE2G2, COL6A3, and TNFRSF1A; or UBE2G2, CST3, and TNFRSF1A.

6. The method according to claim 3, comprising measuring REG1B and HAVCR1; REG1B and FSTL3; REG1B and RGMB; REG1B and COL28A1; REG1B and UBE2G2; REG1B and REG1A; REG1B and COL6A3; REG1B and CST3; REG1B and TNFRSF1A; REG1B, HAVCR1, and FSTL3; REG1B, HAVCR1, and RGMB; REG1B, HAVCR1, and COL28A1; REG1B, HAVCR1, and UBE2G2; REG1B, HAVCR1, and REG1A; REG1B, HAVCR1, and COL6A3; REG1B, HAVCR1, and CST3; REG1B, HAVCR1, and TNFRSF1A; REG1B, FSTL3, and RGMB; REG1B, FSTL3, and COL28A1; REG1B, FSTL3, and UBE2G2; REG1B, FSTL3, and REG1A; REG1B, FSTL3, and COL6A3; REG1B, FSTL3, and CST3; REG1B, FSTL3, and TNFRSF1A; REG1B, RGMB, and COL28A1; REG1B, RGMB, and UBE2G2; REG1B, RGMB, and REG1A; REG1B, RGMB, and COL6A3; REG1B, RGMB, and CST3; REG1B, RGMB, and TNFRSF1A; REG1B, COL28A1, and UBE2G2; REG1B, COL28A1, and REG1A; REG1B, COL28A1, and COL6A3; REG1B, COL28A1, and CST3; REG1B, COL28A1, and TNFRSF1A; REG1B, UBE2G2, and REG1A; REG1B, UBE2G2, and COL6A3; REG1B, UBE2G2, and CST3; REG1B, UBE2G2, and TNFRSF1A; REG1B, REG1A, and COL6A3; REG1B, REG1A, and CST3; REG1B, REG1A, and TNFRSF1A; REG1B, COL6A3, and CST3; REG1B, COL6A3, and TNFRSF1A; or REG1B, CST3, and TNFRSF1A. **Claim 7** The method includes measuring at least one of the proteins selected from COL28A1 and UBE2G2, and HAVCR1, FSTL3, RGMB, REG1A, REG1B, COL6A3, CST3, and TNFRSF1A; or measuring at least one of the proteins selected from COL28A1 and REG1B, and HAVCR1, FSTL3, RGMB, UBE2G2, REG1A, COL6A3, CST3, and TNFRSF1A, the method according to claim 1.

8. The method includes measuring at least one of the proteins selected from UBE2G2 and REG1B, and HAVCR1, FSTL3, RGMB, COL28A1, REG1A, COL6A3, CST3, and TNFRSF1A, the method according to claim 2.

9. Progressive chronic renal insufficiency within a period of 4 years, a 50% decrease in estimated glomerular filtration rate (eGFR), a diagnosis of the need for hemodialysis, eGFR < 15 ml / min / 1.73 m ２ The method according to any one of claims 1 to 8, which indicates the occurrence of one or more of the occurrence of end-stage renal disease (ESRD), or a diagnosis of the need for kidney transplantation.

10. The method according to any one of claims 1 to 8, wherein the measurement is performed using mass spectrometry, an aptamer-based assay, and / or an antibody-based assay.

11. The method according to any one of claims 1 to 8, wherein the sample is selected from blood, plasma, serum, or urine.

12. The method according to any one of claims 1 to 8, wherein the sample is contacted with a set of capture reagents, each capture reagent having an affinity for various proteins of a set of proteins selected from COL28A1, HAVCR1, FSTL3, RGMB, UBE2G2, REG1A, REG1B, COL6A3, CST3, and TNFRSF1A, and the set of capture reagents is selected from aptamers, antibodies, and combinations of aptamers and antibodies.

13. The method according to claim 12, wherein the measurement is performed using mass spectrometry, an aptamer-based assay, and / or an antibody-based assay.

14. The method according to any one of claims 1 to 8, wherein the relative risk of developing progressive chronic kidney disease within the four-year period is based on the input of the levels of each protein measured in a statistical model.

15. The method according to claim 14, wherein the model is a linear regression model.

16. The method according to claim 14, wherein the model has an area under the curve (AUC) selected from 0.65, 0.7, 0.75, 0.77, or more.

17. The method according to claim 14, wherein the model provides a binary prediction and / or a relative risk prediction regarding the onset of progressive chronic kidney disease within a period of 4 years.

18. The method according to claim 14, wherein the model is based on the levels of each protein selected from HAVCR1, FSTL3, RGMB, COL28A1, UBE2G2, REG1A, REG1B, COL6A3, CST3, and TNFRSF1A.

19. The method according to claim 18, wherein the model provides a binary prediction with a probability cut-off point X, where X < 0.3, 0.31, 0.32, 0.32, 0.33, 0.34, 0.35, or 0.3533 is predicted to have no risk of developing progressive chronic kidney disease within a period of 4 years, and X ≧ 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, or 0.3533 is predicted to have a risk of developing progressive chronic kidney disease within a period of 4 years.

20. The method according to claim 18, wherein the risk of developing the progressive chronic kidney disease indicates the risk of an event selected from a 50% decrease in the estimated glomerular filtration rate (eGFR), a diagnosis of the need for hemodialysis, the occurrence of eGFR < 15 ml / min / 1.73 m2, the onset of end-stage renal disease (ESRD), and a diagnosis of the need for kidney transplantation.

21. The method according to claim 18, wherein the model is based on the levels of each protein selected from HAVCR1, FSTL3, RGMB, COL28A1, UBE2G2, REG1A, REG1B, COL6A3, CST3, and TNFRSF1A, and provides a relative risk prediction of developing progressive chronic kidney disease within a period of 4 years.

22. The method according to any one of claims 1 to 8, wherein the level of each measured protein is determined from relative fluorescence units (RFU) or protein concentration.

23. The method according to claim 18, wherein the model provides a relative risk of developing progressive chronic kidney disease within 4 years.

24. The method according to claim 18, wherein the relative risk is selected from mild and severe.

25. The method according to claim 18, wherein the relative risk is a probability calculation.

26. The method according to claim 18, wherein the relative risk is a range of values used to predict the development of progressive chronic kidney disease within a period of 4 years.