Methods for diagnosing and predicting renal dysfunction

Abstract

The present disclosure provides methods for identifying human subjects at risk for developing progressive renal decline by determining the level(s) of protective protein(s) in a sample from the subject. The level(s) of the protein(s) identified in the present disclosure are associated with protection from progressive renal failure and end-stage kidney disease (ESKD). Examples of such protective proteins include FGF20, ANGPT1, and TNFSF12.

Description

[Background technology]

[0001] Related Applications This application claims priority to U.S. Provisional Application No. 63 / 172,541, filed April 8, 2021, and claims priority to U.S. Provisional Application No. 63 / 215,150, filed June 25, 2021. The entire contents of the aforementioned priority applications are incorporated herein by reference.

[0002] U.S. Government Interests This invention was made with United States Government support under Grant DK041526-27 awarded by the National Institutes of Health. The United States Government may have certain rights in the invention.

[0003] Sequence Listing This application contains a Sequence Listing that has been submitted electronically in ASCII format, and is incorporated herein by reference in its entirety. Said ASCII copy, created on April 5, 2022, is named J103021_1090WO_SL.txt and is 24,798 bytes in size.

[0004] Chronic kidney disease (CKD) is the slow, progressive loss of kidney function over many years of a patient's life. The outcome of progressive kidney function decline is permanent kidney failure and ultimately end-stage renal disease (ESRD; also known as end-stage kidney disease, ESKD).

[0005] Chronic kidney disease is widespread and is often associated with other conditions that patients have, such as hypertension or diabetes. Unfortunately, renal decline (RD) is often undetected and undiagnosed until the disease is well advanced. As renal failure progresses, the kidneys' function is significantly impaired, resulting in toxic levels of waste products building up in the patient's body. Treatment of chronic kidney disease aims to stop or slow the progression of the disease. Chronic renal decline can have serious effects on patients and can ultimately lead to ESKD, requiring dialysis and kidney transplantation. Identifying patients at risk for renal decline would improve early treatment and slow the progression of this serious disease. Summary of the Invention

[0006] Given the progressive nature of chronic kidney disease and its severity, it would be beneficial to identify patients at risk for progressive renal function decline.

[0007] The present disclosure is based, at least in part, on the discovery of certain protective proteins whose levels can be used to identify patients / subjects who will progress to End Stage Renal Disease (ESKD; also referred to herein as End Stage Renal Disease or ESRD) and those who will be protected.

[0008] In a first aspect, the disclosure provides a method of identifying a human subject at risk of developing progressive renal decline, comprising: detecting a level of at least one protective protein in a sample(s) from a subject in need thereof, the protective protein being selected from the group consisting of fibroblast growth factor 20 (FGF20), angiopoietin-2 (ANGPT1), and tumor necrosis factor ligand superfamily member 12 (TNFSF12); and comparing the level of the protective protein to a reference level of the protective protein, the reference level being the level of the protective protein in a non-progressor human subject. In certain embodiments, the protective protein is Testican-2. In some embodiments, a lower level of the protective protein compared to the reference level indicates that the human subject is at risk of developing progressive renal decline, or a comparable or higher level of the protective protein compared to the reference level indicates that the human subject is not at risk of developing progressive renal decline.

[0009] In some embodiments of the foregoing aspects, the level of a combination of protective proteins is detected, and the combination of protective proteins is selected from the group consisting of FGF20 and TNFSF12, FGF20 and ANGPT1, and TNFSF12 and ANGPT1, or the combination of protective proteins includes FGF20, TNFSF12, and ANGPT1. In certain embodiments, the combination of protective proteins detected includes Testican-2.

[0010] In another aspect, the disclosure provides a method of identifying a human subject at risk of developing progressive renal decline, comprising: detecting a level of at least one protective protein in a sample(s) from a subject in need thereof, the protective protein being selected from the group consisting of (i) a protective protein from a first group of protective proteins selected from the group consisting of secretory protein rich in acidic cysteine ​​(SPARC), CC motif chemokine 5 (CCL5), amyloid beta A4 protein (APP), platelet factor 4 (PF4), and ANGPT1, and / or (ii) a protective protein from a second group of protective proteins selected from the group consisting of DNAJC19 and TNFSF12, and FGF20; and comparing the level of the protective protein to a reference level of the protective protein, the reference level being the level of the protective protein in a non-progressor human subject. In certain embodiments, the protective protein is Testican-2 in combination with one or more protective proteins described herein. In some embodiments, a lower level of the protective protein compared to the reference level indicates that the human subject is at risk for developing progressive renal function decline, or a similar or higher level of the protective protein compared to the reference level indicates that the human subject is not at risk for developing progressive renal function decline.

[0011] In some embodiments of the foregoing aspects, the level of a combination of protective proteins is detected, and the combination of protective proteins is selected from the group consisting of FGF20 and a group 1 protective protein, FGF20 and a group 2 protective protein, a group 1 protective protein and a group 2 protective protein, and FGF20, a group 1 protective protein, and a group 2 protective protein. In certain embodiments, the protective protein is Testican-2 in combination with one or more protective proteins described herein. In certain embodiments, the non-progressor is a non-diabetic human subject.

[0012] In some embodiments of any of the above aspects, the method further comprises administering a therapy that improves renal function if the subject is identified as at risk for progressive renal function decline. In one embodiment, an SGLT2 inhibitor is administered to the patient if the patient is identified as at risk. In some embodiments, the therapy comprises FGF20 (e.g., recombinant FGF20). In some embodiments, the therapy comprises administering to the subject FGF20, an active fragment of FGF20, an FGF20 mimetic, or a nucleic acid encoding FGF20, or an active fragment thereof, if the subject is identified as at risk for progressive renal function decline. In other embodiments, the therapy comprises TNFSF12 (e.g., recombinant TNFSF12). In some embodiments, the therapy comprises administering to the subject TNFSF12, an active fragment of TNFSF12, a TNFSF12 mimetic, or a nucleic acid encoding TNFSF12, or an active fragment thereof, if the subject is identified as at risk for progressive renal function decline. In yet other embodiments, the therapy comprises ANGPT1 (e.g., recombinant ANGPT1). In some embodiments, the therapy comprises administering ANGPT1, an active fragment of ANGPT1, an ANGPT1 mimetic, or a nucleic acid encoding ANGPT1, or an active fragment thereof, to the subject if the subject is identified as having a risk of progressive renal function decline. In some embodiments, the therapy comprises administering Testican-2, an active fragment of Testican-2, a Testican-2 mimetic, or a nucleic acid encoding Testican-2, or an active fragment thereof, to the subject if the subject is identified as having a risk of progressive renal function decline.

[0013] In some embodiments, the human subject has renal dysfunction, diabetes, or both. In certain embodiments, the diabetes is type I diabetes or type II diabetes. In other embodiments, the human subject is non-diabetic.

[0014] In some embodiments of any of the above aspects, the sample is a plasma sample. In some embodiments, the level of the protective protein is determined using immunoassay, mass spectrometry, liquid chromatography (LC) fractionation, SOMAscam, Mesoscale platform, or electrochemiluminescence detection. In some embodiments, the immunoassay is ELISA or Western blot analysis. In some embodiments, the mass spectrometry is matrix-assisted laser desorption / ionization time-of-flight (MALDI-TOF), inductively coupled plasma mass spectrometry (ICP-MS), triggered-by-offset, multiplexed, accurate-mass, high-resolution, and absolute quantification (TOMAHAQ), direct analysis in real time mass spectrometry (DART-MS), or secondary ion mass spectrometry (SIMS). In some embodiments, the sample is a blood sample, a serum sample, a plasma sample, a lymph sample, a urine sample, a saliva sample, a tear sample, a sweat sample, a semen sample, a vaginal sample, a bronchial sample, a mucosal sample, or a cerebrospinal fluid (CSF) sample.

[0015] In another aspect, the present disclosure provides a protein array for identifying or monitoring progressive renal function decline in a human subject, the protein array comprising antibodies or antigen-binding fragments specific for human FGF20, human TNFSF12, and human ANGPT1.

[0016] In yet another aspect, provided herein is a protein array for identifying or monitoring progressive renal function decline in a human subject, the protein array comprising antibodies or antigen-binding fragments specific to human FGF20, human TNFSF12 and human ANGPT1, human SPARC, human CCL5, human APP, human PF4, human ANGPT1, human DNAJC19, human TNFSF12, Testican-2, or combinations thereof.

[0017] In another aspect, provided herein is an array comprising a plurality of probes for specifically binding to protein biomarkers, wherein the protein biomarkers are at least one or more of human FGF20, human TNFSF12, and human ANGPT1.

[0018] In yet another aspect, provided herein is an array comprising a plurality of probes for specifically binding to protein biomarkers, wherein the protein biomarkers are at least one or more of human FGF20, human TNFSF12 and human ANGPT1, human SPARC, human CCL5, human APP, human PF4, human Testican-2, and human DNAJC19.

[0019] In another aspect, the present disclosure provides a test panel comprising a protein array as disclosed herein.

[0020] In another aspect, the present disclosure provides a kit or assay device comprising a test panel as disclosed herein.

[0021] In another aspect, the disclosure provides a method for inhibiting the progression of progressive renal function decline in a human subject, the method comprising administering to the subject an effective amount of at least one protective protein and / or at least one agonist of a protective protein.

[0022] In another aspect, the disclosure provides a method for preventing a decline in renal function in a human subject, the method comprising administering to the subject an effective amount of at least one agonist of a protective protein and / or at least one agonist of a protective protein.

[0023] In another aspect, the disclosure provides a method of treating reduced renal function in a human subject, the method comprising administering a therapeutically effective amount of at least one agonist of a protective protein and / or an agonist of at least one protective protein to the subject.

[0024] In another aspect, provided herein is a method of determining whether a human subject has an increased risk of developing progressive renal disease, comprising obtaining a sample from the at-risk human subject, detecting the presence and measuring the level of at least one protective protein in the subject sample, comparing the subject level of the protective protein to a reference level of the protective protein, determining whether the subject has an increased risk of developing progressive renal disease based on the comparison of the subject level to the reference level, wherein the presence of the protective protein in the subject sample at a level significantly lower than the reference level indicates that the subject has an increased risk of developing progressive renal disease, and administering a therapy to the subject identified as having a risk of developing progressive renal disease. The method may further comprise monitoring an increase in the protective protein in the identified subject.

[0025] In some embodiments of any of the above aspects, the at least one protective protein is one or more of FGF20, TNFSF12, ANGPT1, SPARC, CCL5, APP, PF4, Testican-2, and DNAJC19. In other embodiments, the at least one protective protein is FGF20, an active fragment of FGF20, an FGF20 mimetic, or a nucleic acid encoding FGF20, or an active fragment thereof. In various other embodiments, the at least one protective protein is TNFSF12, an active fragment of TNFS12, a TNFSF12 mimetic, or a nucleic acid encoding TNFSF12, or an active fragment thereof. In certain other embodiments, the at least one protective protein is ANGPT1, an active fragment of ANGPT1, an ANGPT1 mimetic, or a nucleic acid encoding ANGPT1, or an active fragment thereof. In other embodiments, the at least one protective protein is SPARC, an active fragment of SPARC, a SPARC mimetic, or a nucleic acid encoding SPARC, or an active fragment thereof. In other embodiments, at least one protective protein is CCL5, an active fragment of CCL5, a CCL5 mimetic, or a nucleic acid encoding CCL5, or an active fragment thereof. In certain other embodiments, at least one protective protein is APP, an active fragment of APP, an APP mimetic, or a nucleic acid encoding APP, or an active fragment thereof. In other embodiments, at least one protective protein is PF4, an active fragment of PF4, a PF4 mimetic, or a nucleic acid encoding PF4, or an active fragment thereof. In other embodiments, at least one protective protein is DNAJC19, an active fragment of DNAJC19, a DNAJC19 mimetic, or a nucleic acid encoding DNAJC19, or an active fragment thereof. In certain embodiments, at least one protective protein is Testican-2, an active fragment of Testican-2, a Testican-2 mimetic, or a nucleic acid encoding Testican-2, or an active fragment thereof.

[0026] In yet other embodiments, the nucleic acid is in a vector.In other embodiments, the human subject has previously been identified as a progressor at risk for developing progressive renal decline.

[0027] In another aspect, the disclosure provides a method of determining an approximate risk of renal decline in a human subject within a defined time period, comprising: a) obtaining a biological sample from the human subject; b) detecting a level of at least one protective protein in the biological sample, where the at least one protective protein is selected from the group consisting of FGF20, TNFSF12, ANGPT1, SPARC, CCL5, APP, PF4, Testican-2, and DNAJC19; c) combining data regarding the level of the protective protein with clinical data characteristics of the human subject (eGFR, uACR, clinical chemistry laboratory measurements, vital signs, patient demographics, etc.); and d) determining an approximate risk of renal decline (RD) for the human subject as determined using a prognostic risk score algorithm by machine learning or statistical modeling (e.g., KidneyIntelX testing platform). In certain embodiments, the sample from the human subject is contacted with an antibody or antigen-binding fragment thereof that specifically binds to the protective protein, and binding of the antibody to the protective protein is measured to determine the level of binding between the protective protein and the antibody.

[0028] In some embodiments of any of the above aspects, the method further comprises comparing the level of the at least one protective protein in the biological sample to a non-progressor control level or a normoalbuminuric control level. In some embodiments, the biological sample is obtained from the human subject at a first time point and a second time point. In other embodiments, the second time point is obtained from the human subject about 6 months, about 12 months, about 18 months, about 24 months, about 3 years, about 4 years, about 5 years, about 10 years, or about 15 years after the first time point. In certain other embodiments, the method further comprises comparing the level of the at least one protective protein in the biological sample obtained from the human subject at the first time point to a biological sample obtained from the human subject at the second time point. [Brief description of the drawings]

[0029] [Figure 1] A and B provide histograms showing the distribution of the top three potential protective proteins, FGF20, TNFSF12, and ANGPT1, after log10 transformation. A provides a histogram showing the distribution of FGF20, TNFSF12, and ANGPT1, after log10 transformation, in the combined T1D discovery and T2D replication cohorts. B provides a histogram showing the distribution of FGF20, TNFSF12, and ANGPT1, after log10 transformation, in the T1D validation cohort. [Diagram 2] Graph showing distribution of eGFR slope (ml / min / 1.73m2 / year) in Joslin Kidney Study cohorts with T1D and T2D. Slow decliners were defined as eGFR loss < 3.0ml / min / 1.73m2 / year, and fast decliners were defined as eGFR loss ≥ 3.0ml / min / 1.73m2 / year or ESKD progressors. In each cohort, only ESKD cases occurring in the first 10 years after study entry were considered in the study. The dashed line indicates eGFR loss equal to 3.0ml / min / 1.73m2 / year. [Diagram 3]1 is a schematic representation of the study design showing how the discovery and replication panel study participants and candidate protective proteins were selected. [Figure 4A] Provides graphs showing candidate circulating proteins associated with protection from rapidly progressive renal function decline. Graphs showing Spearman's rank correlation coefficients (rS) between baseline concentrations of 19 plasma proteins and slope of eGFR in the Joslin cohort with T1D (N=214) and T2D (N=144). Shaded bars are graphical representations of effect sizes. Corresponding two-sided P values ​​are provided. *Significance thresholds used: FDR adjusted P<0.005 in the T1D discovery cohort and nominal P<0.05 in the T2D replication cohort. [Figure 4B] Graphs are provided showing candidate circulating proteins associated with protection from rapidly progressive renal function decline. Graphs show odds ratios (95% CI) for 19 candidate protective proteins and rapidly progressive renal function decline (eGFR loss > 3.0 ml / min / year) in a combined T1D and T2D cohort in univariate and adjusted logistic regression models. Effects are shown as odds ratios (95% CI) per quartile increase in circulating baseline concentration of the specific protein. Final models were adjusted for baseline eGFR, HbA1c and ACR, stratified by diabetes type. The eight selected markers are shown in red. PKM2 included in the analysis was based on previous publications. [Figure 5A]Graphs are provided showing the association of eight confirmed protective proteins with clinical covariates and with the risk of rapidly progressive renal decline. Graphs show Spearman rank correlation matrices between eight candidate protective proteins, including TNF-R1, and key clinical covariates in two cohorts adjusted for diabetes type. Correlation coefficients (rs) are presented as shades of red (positive; indicated by #) and blue (negative; indicated by ##) corresponding to the magnitude of the effect size. The effects of eGFR and HbA1c on rapidly progressive renal decline are estimated per 10 ml / min / 1.73 m2 increase and per 1% increase, respectively. The effect of ACR on rapidly progressive renal decline is estimated as a 1-unit increase in log10 ACR. The effect of each protein is shown as odds ratio (95% CI) per quartile increase in the circulating baseline concentration of the corresponding protein. *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001; ns, not significant. [Figure 5B] Figure 1 provides graphs showing the association of eight identified protective proteins with clinical covariates and risk of rapidly progressive renal decline. Figure 2 shows hierarchical cluster analysis in the combined Joslin cohort. The effects of eGFR and HbA1c on rapidly progressive renal decline are estimated per 10 ml / min / 1.73 m2 increase and per 1% increase, respectively. The effect of ACR on rapidly progressive renal decline is estimated as a 1 unit increase in log10ACR. The effect of each protein is shown as odds ratio (95% CI) per quartile increase in the circulating baseline concentration of the corresponding protein. [Figure 5C]Graphs are provided showing the association of eight confirmed protective proteins with clinical covariates and with the risk of rapidly progressive renal decline. Graphs show odds ratios (95% CI) for covariates selected from retrospective covariate selection using a significance criterion of α=0.1. The effects of eGFR and HbA1c on rapidly progressive renal decline are estimated per 10 ml / min / 1.73 m2 increase and per 1% increase, respectively. The effect of ACR on rapidly progressive renal decline is estimated as a one-unit increase in log10ACR. The effect of each protein is shown as odds ratio (95% CI) per quartile increase in the circulating baseline concentration of the corresponding protein. [Figure 6] FIG. 13 is a graph of the Spearman rank correlation matrix between ACR and 11 candidate protective proteins adjusted for diabetes type. Correlation coefficients (rs) are presented as shades of red (positive) and blue (negative; indicated by #) corresponding to the magnitude of the effect size. [Figure 7A] Graphs are provided showing the combined effect of protective proteins (FGF20, TNFSF12 and ANGPT1) on the risk of rapid progressive renal decline and progression to ESKD. Graphs showing odds ratios of rapid progressive renal decline according to the protective index considered as a discrete covariate in a combined exploratory and replication cohort (N=358) with both types of diabetes and abnormalities of renal function (also called "kidney function"). Protective index: values ​​above the median of each protein were scored as 1 and values ​​below the median were scored as 0; by summing these scores, subjects could have a total protective index that varies between 0 (all proteins below the median) and 3 (all proteins above the median). *P<0.05; ****P<0.0001; ns, not significant. [Figure 7B]Graphs are provided showing the combined effect of protective proteins (FGF20, TNFSF12 and ANGPT1) on the risk of rapid progressive renal function decline and progression to ESKD. Graphs showing the cumulative incidence (%) of ESKD according to discrete values ​​of protective index in the combined exploration and replication cohort. Protective index: values ​​above the median of each protein were scored as 1 and values ​​below the median were scored as 0; by summing these scores, subjects could have a total protective index that ranges between 0 (all proteins below the median) and 3 (all proteins above the median). *P<0.05; ****P<0.0001; ns, not significant. [Figure 7C] Graphs are provided showing the combined effect of protective proteins (FGF20, TNFSF12 and ANGPT1) on the risk of rapid progressive renal decline and progression to ESKD. Graphs showing odds ratios of rapid progressive renal decline according to protective index considered as a discrete covariate in a validation cohort of T1D subjects with normal renal function (N=294). Protective index: values ​​above the median of each protein were scored as 1 and values ​​below the median were scored as 0; by summing these scores, subjects may have a total protective index that varies between 0 (all proteins below the median) and 3 (all proteins above the median). *P<0.05; ****P<0.0001; ns, not significant. [Figure 7D] 1 provides a graph showing the combined effect of protective proteins (FGF20, TNFSF12 and ANGPT1) on the risk of rapid progressive renal function decline and progression to ESKD. Graph showing cumulative incidence (%) of ESKD according to discrete values ​​of protective index in validation cohort. Protective index: values ​​above the median of each protein were scored as 1 and values ​​below the median were scored as 0; by summing these scores, subjects can have a total protective index that varies between 0 (all proteins below the median) and 3 (all proteins above the median). *P<0.05; ****P<0.0001; ns, not significant. [Figure 8]Extracted ion chromatogram of FGF20 tryptic peptide GGPGAAQLAHLHGILR (SEQ ID NO: 9) (amino acids 50-65). FGF20 SOMAmer plasma pulldown in the presence (top) or absence (bottom) of recombinant FGF20. [Figure 9] Graphs are provided showing plasma concentrations of exemplary protective proteins ANGPT1 (left panel), TNFSF12 (middle panel), FGF20 (right panel) in the combined Joslin cohort for non-progressors and progressors compared to non-diabetics. Bars indicate mean ± standard deviation. One-way ANOVA with Dunn's multiple comparison test. **P<0.01; ***P<0.001; ****P<0.0001; ns, not significant. [Figure 10] 1 is a histogram showing data comparing Testican-2 (SPOCK2) plasma levels (RFU) between non-ESKD progressors and ESKD progressors. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0030] I. Definition Before describing the invention in detail, definitions of certain terms used herein are provided. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.

[0031] The terms "subject" and "patient" are used interchangeably herein and refer to a human.

[0032] The term "sample" as used herein refers to plasma, serum, cells, or tissue obtained from a subject. The source of the tissue or cell sample can be solid tissue (from fresh, frozen, and / or preserved organ or tissue samples or biopsies or aspirates), whole blood or any blood component, or a bodily fluid such as serum, plasma, urine, saliva, sweat, or synovial fluid. In one embodiment, the sample is a plasma sample obtained from a human subject.

[0033] The term "level" or "amount" of a biomarker, as used herein, refers to a measurable quantity of the biomarker, e.g., the protein level of the biomarker. This amount can be either (a) an absolute amount, measured in molecules, moles, or weight per unit volume or cell, or (b) a relative amount, measured, for example, by densitometric analysis.

[0034] As used herein, the terms "known standard level", "reference level" or "control level" are used interchangeably and refer to an accepted or predefined level of a biomarker used to compare biomarker levels derived from a patient sample. In one embodiment, when compared to a reference level of a particular biomarker (protective protein), deviation from the reference level generally indicates either an improvement or worsening of a disease state or future disease state. In one embodiment, when compared to a reference level of a protective protein, deviation from the reference level generally indicates an increased or decreased likelihood of disease progression in a subject. Reference levels can be generated from samples taken from healthy (e.g., non-diabetic) individuals or individuals known to have a predisposition to ESKD. In one embodiment, the reference level of a protective protein described herein is the level of the protein in a non-diabetic subject.

[0035] As used herein, the term "equivalent level" refers to a level of one biomarker that is substantially similar to the level of another biomarker, e.g., a control level. In one embodiment, two biomarkers have equivalent levels if the level of the biomarker is within one standard deviation of the control biomarker level. In another embodiment, two biomarkers have equivalent levels if the level of the biomarker is 20% or less of the level of the control biomarker level.

[0036] As used herein, the term "estimated glomerular filtration rate" or "eGFR" refers to a means for estimating renal function. In one embodiment, eGFR can be determined based on measuring serum creatinine levels. In another embodiment, eGFR can be determined based on measuring serum cystatin C levels. In yet another embodiment, eGFR can be determined using the CKD-EPI creatinine equation.

[0037] As used herein, the term "disorders associated with chronic kidney disease" or "disorders associated with chronic kidney disease" refers to diseases or conditions associated with abnormal kidney function that may cause kidney damage over time. Examples of disorders associated with chronic kidney disease include, but are not limited to, type 1 diabetes, type 2 diabetes, hypertension, glomerulonephritis, interstitial nephritis, polycystic kidney disease, long-term obstruction of the urinary tract (e.g., due to conditions such as prostate enlargement, kidney stones, and some cancers), vesicoureteral reflux, and recurrent kidney infections. Chronic kidney disease and its stages (CKD 1-5) are usually characterized by kidney damage (albuminuria) or abnormalities in estimated glomerular filtration rate (GFR<60 [ml / min / 1.73 m with or without kidney damage). 2 ]) may be appropriately characterized or classified.

[0038] As used herein, the term "ESKD progressor", "progressor" or "rapid progressor" refers to a subject with a disorder associated with chronic kidney disease who has been identified as being at high risk of developing ESKD (also referred to herein as ESRD). An ESKD progressor has a disorder associated with chronic kidney disease that may place the subject at risk of developing ESKD, but the term is intended to include subjects who have been identified as being at elevated risk beyond the risk normally associated with a disorder associated with chronic kidney disease. In one embodiment, a progressor has a level of any one or more of FGF20, TNFSF12, ANGPT1, SPARC, CCL5, APP, PF4, Testican-2, and / or DNAJC19 that is statistically significantly lower than non-progressor control levels or normal albuminuric controls, and therefore has an increased risk of developing ESKD. In another embodiment, progressors have levels of any one or more of FGF20, TNFSF12, and / or ANGPT1 that are statistically significantly lower than non-progressor control levels or normoalbuminuric controls and therefore have an increased risk of developing ESKD.

[0039] As used herein, the term "non-progressor" refers to a subject with a disorder associated with chronic kidney disease who has a reduced risk of developing ESKD. In one embodiment, a non-progressor is a subject with a disorder associated with chronic kidney disease who is in stage 1 or 2 of CKD (chronic kidney disease) but has a lower risk of progressing to ESKD, at least in part due to elevated or equivalent levels of protective proteins (e.g., compared to normal albuminuric controls). In one embodiment, a non-progressor is defined as a subject who has a level of any one or more of FGF20, TNFSF12, ANGPT1, SPARC, CCL5, APP, PF4, Testican-2, and / or DNAJC19 that is statistically significantly higher than the progressor control level, or higher or equivalent than normal albuminuric controls. In another embodiment, a non-progressor is defined as a subject who has a level of any one or more of FGF20, TNFSF12, and / or ANGPT1 that is statistically significantly higher than the progressor control level, or higher or equivalent than normal albuminuric controls. In another embodiment, a non-progressor is defined as a subject having a level of Testican-2 that is statistically significantly higher than the progressor control level, or higher than or equal to the normal albuminuric control level.In one embodiment, a non-progressor is a non-diabetic human subject.Non-diabetic refers to a person who has not been diagnosed with diabetes (type II).

[0040] As used herein, the term "protective protein" refers to a protein whose levels in a human subject are associated with renal function decline and / or increased or decreased risk of progressing to ESKD. As used herein, a protective protein is a protein whose presence or elevated levels provide demonstrable protection from progressive renal function decline. Examples of protective proteins include FGF20, TNFSF12, ANGPT1, SPARC, CCL5, APP, PF4, Testican-2, and / or DNAJC19.

[0041] As used herein, the term "renal decline" or "RD" (also referred to herein as "kidney decline" (KD)) refers to a condition associated with abnormal kidney function. In one embodiment, renal decline is defined as a change in estimated glomerular filtration rate (eGFR) of at least -3 ml / min / year (i.e., eGFR loss > 3.0 ml / min / year). In one embodiment, renal decline is defined as a change in estimated glomerular filtration rate (eGFR) of at least -5 ml / min / year (i.e., eGFR loss > 5.0 ml / min / year). In one embodiment, renal decline is defined as a sustained decline in eGFR of > 40% from baseline (confirmed for at least 3 months).

[0042] The term "therapeutically effective amount" or "effective amount" refers to an amount that, when administered to a living subject, achieves a desired effect on the living subject. The exact amount depends on the purpose of the treatment and can be ascertained by one of skill in the art using known techniques. As is known in the art, adjustments may be necessary for systemic versus local delivery, age, weight, general health, sex, diet, time of administration, drug interactions, and severity of the condition, which can be ascertained by one of skill in the art using routine experimentation. For example, an effective amount of an agent described herein administered to a living subject is an amount that prevents and / or treats ESKD. For example, in the case of a nephroprotectant, a therapeutically effective amount can be an amount that has been shown to provide an observable therapeutic benefit compared to baseline clinically observable signs and symptoms of chronic kidney disease.

[0043] As used herein, the term "nephroprotective agent" refers to an agent that can prevent or delay the progression of nephropathy in subjects with moderately elevated albuminuria or diabetic nephropathy. Examples of nephroprotective agents include, but are not limited to, angiotensin-converting enzyme (ACE) inhibitors and angiotensin-II receptor blockers (ARBs). In one embodiment, the nephroprotective agent is a protective protein as described herein, or an equivalent thereof, such as an active fragment.

[0044] II. Defense Proteins The present disclosure is based, at least in part, on the discovery of certain biomarkers whose protein levels can be used to identify subjects / patients who will progress to ESKD (also referred to herein as ESRD) and those who will be protected.

[0045] Disclosed herein is a method for identifying whether a human subject is at risk of developing progressive renal function decline. The method includes detecting the level of at least one protective protein in a sample(s) from a subject in need thereof. Studies herein have identified secretory protein rich in acidic cysteine ​​(SPARC), CC motif chemokine 5 (CCL5), amyloid beta A4 protein (APP), platelet factor 4 (PF4), DNAJC19, angiopoietin-2 (ANGPT1), tumor necrosis factor ligand superfamily member 12 (TNFSF12), fibroblast growth factor 20 (FGF20), and Testican-2 (SPOCK2) as protective proteins whose levels correlate with non-progression of kidney disease. These levels are higher than patients with lower levels of these proteins, which indicate progressive disease.

[0046] The levels of the one or more protective proteins in one or more samples from a subject can be compared to the levels of the one or more protective proteins with a reference level of the protective protein to determine the risk of the patient to develop progressive renal function decline and ultimately ESKD (also referred to herein as ESRD).

[0047] At least one, at least two, at least three, at least four, at least five, at least six, at least seven, or all eight levels of the protective proteins may be used in the methods disclosed herein.

[0048] In one embodiment, the levels of fibroblast growth factor 20 (FGF20), angiopoietin-2 (ANGPT1), and tumor necrosis factor ligand superfamily member 12 (TNFSF12), or a combination thereof, are compared to a reference level to determine the risk of a patient developing or continuing progressive renal function decline. In one embodiment, the level of Testican-2 is compared to a reference level to determine the risk of a patient developing or continuing progressive renal function decline. In another embodiment, levels of FGF20 and TNFSF12; FGF20 and ANGPT1; TNFSF12 and ANGPT1; and FGF20, TNFSF12, and ANGPT1, FGF20 and Testican-2; ANGPT1 and Testican-2; TNFSF12 and Testican-2; FGF20, ANGPT1, and Testican-2; ANGPT1, TNFSF12 and Testican-2; FGF20, TNFSF12 and Testican-2; or each of FGF20, ANGPT1, TNFSF12 and Testican-2 are used in the methods disclosed herein.

[0049] In one embodiment, the levels of fibroblast growth factor 20 (FGF20); a protective protein from a first group of protective proteins (group 1 protective proteins) including SPARC, CCL5, APP, PF4 and ANGPT1; a protective protein from a second group of protective proteins (group 2 protective proteins) including DNAJC19 and TNFSF12, or a combination thereof, e.g., group 1 and group 2 protective proteins, or FGF20 and either group 1 or group 2 protective proteins, are compared to a reference level to determine a patient's risk of developing or continuing progressive renal function decline.

[0050] Provided below is a table describing the nine protective proteins identified herein. [Table 1]

[0051] Once a protective protein level is detected in a sample from a subject, the level is compared to a reference level to determine whether the level is consistent with a progressor profile (risk) or a non-progressor (protective).

[0052] The development of progressive renal decline begins when a patient has normal renal function that progresses approximately linearly to ESKD, but the rate of decline, expressed as the slope of the estimated glomerular filtration rate (eGFR), varies from individual to individual, ranging from −72 to 3.0 ml / min / year.

[0053] In one embodiment, the reference level of the protective protein is the level in a non-diabetic human subject, and a lower level of the protective protein compared to the reference level indicates that the human subject is at risk for developing progressive renal decline, or a similar or higher level of the protective protein compared to the reference level indicates that the human subject is not at risk for developing progressive renal decline.

[0054] In one embodiment, the human subject who provides the sample for testing is a subject with a condition related to progressive renal function decline, such as diabetes or hypertension.In another embodiment, the subject may have renal function abnormality, and it is desirable to determine the risk of further renal function decline in order to reduce renal destruction.In one embodiment, the subject has type I diabetes or type II diabetes.

[0055] For subjects with diabetes, the risk of chronic kidney disease and ESKD remains relatively high despite improvements in glycemic control and advances in renal protective therapies for the prevention and treatment of DKD over the past two decades (Rosolowsky et al., J Am Soc Nephrol 22:545-553 (2011); de Boer et al., JAMA 305:2532-2539 (2011)). Findings from the Joslin Kidney Study, a longitudinal study of over 3,000 diabetic subjects, demonstrate that progressive renal function decline is the primary clinical sign of DKD underlying progression to ESKD (Perkins et al., N Engl J Med 348:2285-2293(2003); Perkins et al., J Am Soc Nephrol 18:1353-1361(2007); Krolewski, Diabetes Care 38,954-962(2015); Krolewski et al., Kidney International 91:1300-1311(2017)).

[0056] Despite improvements in glycemic control and advances in almost universally implemented renoprotective therapies, the incidence of ESKD in diabetic patients continues to increase.

[0057] Diabetic kidney disease (DKD) and its key clinical manifestation, progressive renal function decline leading to end-stage renal disease (ESKD; also referred to herein as ESRD), are a major health burden for subjects with diabetes. The disease process underlying progressive renal function decline includes factors / pathways that prevent progressive renal function decline as well as factors / pathways that increase the risk of this outcome. Using nontargeted proteomic profiling of circulating proteins from three independent cohorts of subjects with longitudinal type 1 and type 2 diabetes and various stages of DKD followed for 7 to 15 years, three elevated plasma proteins were identified that were associated with protection from progressive renal function decline and progression to ESKD: fibroblast growth factor 20 (FGF20; OR=0.69; 95% CI:0.54-0.88), angiopoietin-1 (ANGPT1; OR=0.72; 95% CI:0.57-0.91), and tumor necrosis factor ligand superfamily member 12 (TNFSF12; OR=0.75; 95% CI:0.59-0.95). The combined effect of these three protective proteins was well documented by a high cumulative risk of ESKD in subjects with low concentrations of these proteins (below the median) at the start of follow-up, whereas the cumulative risk of ESKD was very low in subjects with high baseline concentrations of all three proteins (above the median). This protective effect was robust independent of circulating inflammatory proteins and important clinical covariates and was confirmed in an independent cohort of diabetic subjects with normal renal function. The three protective proteins may serve as biomarkers to stratify diabetic subjects according to their risk of progression to ESKD.

[0058] In one embodiment, the test sample from the subject is a plasma sample. Multiple samples may be used to test for one or more protective proteins. Alternatively, one sample may be used to test for one or more protective proteins.

[0059] Detection of the protective protein can be determined according to standard immunoassays, such as ELISA or electrochemiluminescence detection (e.g. Meso Sector S600 (Meso Scale Diagnostics)).

[0060] Also included herein is a protein array for identifying or monitoring progressive renal function decline in a human subject. In one embodiment, the protein array comprises antibodies or antigen-binding fragments specific for human FGF20, human TNFSF12, human ANGPT1, and / or human Testican-2.

[0061] In another embodiment, the present disclosure provides a protein array for identifying or monitoring progressive renal function decline in a human subject, wherein the protein array comprises antibodies or antigen-binding fragments specific for human FGF20, human TNFSF12 and human ANGPT1, human SPARC, human CCL5, human APP, human PF4, human DNAJC19, human Testican-2, or combinations thereof.

[0062] In one embodiment, the array comprises a plurality of probes for specifically binding to protein biomarkers, the protein biomarkers being at least one or more of human FGF20, human TNFSF12, and human ANGPT1.

[0063] In one embodiment, the array comprises a plurality of probes for specific binding to protein biomarkers, the protein biomarkers being at least one or more of human FGF20, human TNFSF12, human ANGPT1, human SPARC, human CCL5, human APP, human PF4, human DNAJC19, and human Testican-2.

[0064] The studies described herein identify nine protective proteins (i.e., secreted protein rich in acidic cysteine ​​(SPARC), CC motif chemokine 5 (CCL5), amyloid beta A4 protein (APP), platelet factor 4 (PF4), DNAJC19, angiopoietin-2 (ANGPT1), tumor necrosis factor ligand superfamily member 12 (TNFSF12), fibroblast growth factor 20 (FGF20), and Testican-2) that, depending on their levels in a sample, can be used to identify patients who are likely to develop ESKD or have ongoing progressive kidney disease leading to ESKD, or who are protected from progression to ESKD.

[0065] SPARC One of the defense proteins of the present disclosure is secreted protein rich in acidic cysteine ​​(SPARC).

[0066] The term "acidic cysteine-rich secreted protein" gene, or "SPARC" gene, also known as "osteonectin," "ONT," "basement membrane protein 40," "BM-40," and "OI17," refers to a gene that is expressed at high levels in tissues undergoing morphogenesis, remodeling, and wound repair. The SPARC gene encodes a protein called SPARC. SPARC is a 32-35 kD Ca2+-binding matricellular glycoprotein with a phylogenetically conserved modular organization (Martinek, et al. Dev. Genes Evol. 212:124-133). SPARC binds to type I collagen in the extracellular space (Mendozo-Londono, et al. Am J Hum Genet. 2015 Jun 4;96(6):979-985). Biochemical studies have shown that SPARC binds to several collagenous and non-collagenous ECM molecules, including a Ca2+-dependent interaction with network-forming collagen IV. SPARC protein contains three domains: a Follistin-like domain, a Kazal-like domain, and an EF-hand domain, and contains two calcium-binding sites. The Follistin-like acidic domain binds five to eight Ca with low affinity. 2+ The EF-hand loop binds Ca with high affinity. 2+ It binds ions. In bone, SPARC is expressed by osteoblasts. SPARC null mice develop progressive osteoporosis due to defects in bone formation (Delany, et al. J. Clin. Invest. 2000;105:915-923).

[0067] SPARC polymorphisms, especially those in the 3'UTR, affect SPARC accumulation in bone and are associated with variability in bone formation, variability in bone mass, and may play a role in the pathogenesis of osteoporosis in adults (Delany,et al.(2016)Osteoporos. Int.2008;19:969-978, Dole,et al.(2016)J.Bone Miner. Res.2015;30:723-732). Homozygous mutations in SPARC can cause severe bone fragility in humans (Mendozo-Londono,et al.Am J Hum Genet.2015 Jun 4;96(6):979-985).

[0068] The nucleotide sequence of the genomic region of the human chromosome carrying the SPARC gene can be found, for example, in Genome Reference Consortium Human Build 38 (also referred to as Human Genome build 38 or GRCh38), available at GenBank. The nucleotide sequence of the genomic region of human chromosome 5 carrying the SPARC gene can also be found, for example, in GenBank accession number NC_000005.10, which corresponds to nucleotides 151,661,096-151,686,975 of human chromosome 5. Three transcript variants encoding different isoforms have been found for this gene. Exemplary nucleotide and amino acid sequences of SPARC can be found, for example, in GenBank accession number NM_003118.4 (Homo sapiens SPARC transcript variant 1). The amino acid sequence of human SPARC transcript variant 1 is shown below. MRAWIFFLLCLAGRALAAPQQEALPDETEVVEETVAEVTEVSVGANPVQVEVGEFDDGAEETEEEVVAENPCQNHHCKHGKVCELDENNTPMCVCQDPTSCPA IGEFEKVCSNDNKTFDSSCHFFATKCTLEGTKKGHKLHLDYIGPCKYIPPCLDSELTEFPLRMRDWLKNVLVTLYERDEDNNLLTEKQKLRVKKIHENEKRLEAGDHPVELLARDFEKNYNMYIFPVHWQFGQLDQHPIDGYLSHTELAPLRAPLIPMEHCTTRFFETCDLDNDKYIALDEWAGCFGIKQKDIDKDLVI (SEQ ID NO: 1)

[0069] Further examples of SPARC sequences can be found in publicly available databases, such as GenBank, OMIM, and UniProt (P09486). Additional information regarding SPARC can be found, for example, at the NCBI website, which references gene 6678. The term SPARC as used herein also refers to variations of the SPARC gene, including variants provided in clinical variant databases, for example, at the NCBI clinical variant website, which references the term NM_003118.4.

[0070] CCL5 One of the protective proteins of the present disclosure is CC motif chemokine ligand 5 (CCL5).

[0071] The term "CC motif chemokine ligand 5" gene, or "CCL5" gene, also known as "RANTES", "SCYA5", "SISd", "EoCP" and "D17S136E", refers to the gene encoding the CCL5 protein, a chemotactic factor for T cells, eosinophils, and basophils that functions actively in recruiting leukocytes to sites of inflammation. The CCL5 protein is an 8 kD protein with a single domain. CCL5 is a chemotactic factor for blood monocytes, memory T helper cells, and eosinophils. CCL5 is known to cause the release of histamine from basophils, activate eosinophils, and activate several chemokine receptors, including CCR1, CCR3, CCR4, and CCR5. CCL5 and one of its cognate receptors, CCR5, are best known as one of the major HIV suppressors produced by CD8+ T cells, and recombinant CCL5 protein induces dose-dependent inhibition of various strains of HIV-1, HIV-2, and simian immunodeficiency virus (SIV). At high concentrations, CCL5 is thought to activate T cells through a tyrosine kinase pathway (Wong et al. J Biol Chem 273:309-314 (1998); Bacon et al. Science 269:1727-1730 (1995)), leading to the production of IFNγ by T cells (Appay et al. Int Immunol 12:1173-1182 (2000)), and to induce maturation of dendritic cells (Fischer, et al. J Immunol 167:1637-1643 (2001)). Synovial CD8+ T cells have been shown to have high levels of CCL5 protein, which is rapidly released from synovial CD8+ T cells upon T cell receptor triggering (Pharoah et al. Arthritis Res Ther 8(2):R50(2006)). CCL5 directly signals to cancer cells to promote survival, invasion, and stem cell renewal. In breast cancer, CCL5 expressed by MSCs acts on breast cancer cells to promote invasion and metastasis (Karnoub et al. Nature 449(7162):557-63(2007)).

[0072] The nucleotide sequence of the genomic region of the human chromosome carrying the CCL5 gene can be found, for example, in Genome Reference Consortium Human Build 38 available at GenBank. The CCL5 gene is one of several chemokine genes that form a cluster on the q arm of chromosome 17. The nucleotide sequence of the genomic region of human chromosome 17 carrying the CCL5 gene can also be found, for example, in GenBank accession number NC_000017.11, which corresponds to nucleotides 35871491-35880360 of human chromosome 17. Four transcript variants have been found in this gene that encode different isoforms. Exemplary nucleotide and amino acid sequences of CCL5 can be found, for example, in GenBank accession number NM_002985.3 (Homo sapiens CCL5 transcript variant 1). The amino acid sequence of human CCL5 transcript variant 1 is shown below. MKVSAAALAVILIATALCAPASASPYSSDTTPCCFAYIARPLPRAHIKEYFYTSGKCSNPAVVFVTRKNRQVCANPEKKWVREYINSLEMS (SEQ ID NO: 2)

[0073] Further examples of CCL5 sequences can be found in publicly available databases, such as GenBank, OMIM, and UniProt (P13501). Additional information regarding CCL5 can be found, for example, at the NCBI website, which references gene 6352. The term CCL5 as used herein also refers to variations of the CCL5 gene, including variants provided in clinical variant databases, for example, at the NCBI clinical variant website, which references the term NM_002985.3.

[0074] APP Another protective protein of the present disclosure is amyloid beta precursor protein (APP).

[0075] The term "amyloid beta precursor protein" gene, or "APP" gene, also known as "ABPP", "A4", "AD1", "peptidase nexin-II" and "PreA4", refers to the gene encoding the amyloid beta A4 protein. APP is a type I transmembrane protein with a short cytoplasmic tail and a large extracellular domain that contains copper binding sites in its E1 and E2 domains (Kong et al. Eur Biophys J 37(3):269-79(2008); Dahms et al. J Mol Biol 416(3):438-52(2012)). APP protein plays a central role in the pathogenesis of Alzheimer's disease (Masters et al. Brain 129(Pt 11):2823-39(2006)). APP is also essential for synaptic processes including cell-cell synaptic adhesion as a cell surface receptor, neurite outgrowth, neuronal adhesion, axon formation, synaptogenesis, promotion of cell migration, and transcriptional regulation via protein-protein interactions (Muller et al. Cold Spring Harb Perspect Med 2(2):a006288(2012)). App is involved in copper homeostasis / oxidative stress through reduction of copper ions. In vitro, copper-metallated APP directly induced neuronal death or inhibited the induction of Cu-induced neuronal death. 2+ This is enhanced by α-mediated oxidation of low density lipoprotein (White et al. J Neurosci 19(21):9170-9(1999); Maynard et al. J Biol Chem 277(47):44670-6(2002)). APP knockout mice exhibit cognitive impairment, and inactivation of APP in an APLP2 knockout background in either the presynaptic or postsynaptic compartment caused neuromuscular synaptic defects (Muller et al. Cold Spring Harb Perspect Med 2(2):a006288(2012)).

[0076] The nucleotide sequence of the genomic region of the human chromosome carrying the APP gene can be found, for example, in Genome Reference Consortium Human Build 38 available at GenBank. The nucleotide sequence of the genomic region of human chromosome 21 carrying the APP gene can also be found, for example, in GenBank Accession No. NC_000021.9, which corresponds to nucleotides 25880550-26171128 of human chromosome 21. Multiple transcript variants encoding different isoforms have been found for this gene. Exemplary nucleotide and amino acid sequences of APP can be found, for example, in GenBank Accession No. NM_000484.4 (Homo sapiens APP transcript variant 1). The amino acid sequence of human APP transcript variant 1 is shown below. (SEQ ID NO:3)

[0077] Further examples of APP sequences can be found in publicly available databases, such as GenBank, OMIM, and UniProt (P05067). Additional information regarding APP can be found, for example, at the NCBI website, which refers to gene 351. The term APP as used herein also refers to variations of the APP gene, including those provided in clinical variant databases, for example, at the NCBI clinical variant website, which refers to the term NM_000484.4.

[0078] PF4 One of the protective proteins of the present disclosure is platelet factor 4 (PF4).

[0079] The term "platelet factor 4" gene, or "PF4" gene, also known as "CXCL4", "chemokine (CXC motif) ligand 4", "oncostatin-A", "SCYB4" and "Iroplact", refers to the gene encoding the PF4 protein. PF4 is a chemokine that is primarily released from the alpha granules of activated platelets in the form of a homotetramer with high affinity for heparin and is involved in platelet aggregation. PF4 is known to be secreted by various immune cells (Levine et al. J Biol Chem 251(2):324-8(1976), Bon et al. N Engl J Med 370(5):433-43(2014)). PF4 is chemotactic for a number of other cell types and also functions as an inhibitor of hematopoiesis, angiogenesis, and T cell function. The protein also exhibits antibacterial activity against Plasmodium falciparum. PF4 has also been implicated in the pathology of various inflammatory diseases, including myelodysplastic syndromes, malaria, HIV-1, atherosclerosis, inflammatory bowel disease, and rheumatoid arthritis (Affandi et al. Eur J Immunol 48(3):522-531(2018); Yeo et al. Ann Rheum Dis 75(4):763-71(2016)).

[0080] The nucleotide sequence of the genomic region of the human chromosome carrying the APP gene can be found, for example, in Genome Reference Consortium Human Build 38 available at GenBank. The nucleotide sequence of the genomic region of human chromosome 4 carrying the PF4 gene can also be found, for example, in GenBank Accession No. NC_000004.12, which corresponds to nucleotides 73,980,811-73,982,027 of human chromosome 4. One transcript has been identified for this gene. Exemplary nucleotide and amino acid sequences of PF4 can be found, for example, in GenBank Accession No. NM_002619.4 (Homo sapiens PF4 transcript variant 1). The amino acid sequence of human PF4 transcript variant 1 is shown below. MSSAAGFCASRPGLLFLGLLLLPLVVAFASAEAEEDGDLQCLCVKTTSQVRPRHITSLEVIKAGPHCPTAQLIATLKNGRKICLDLQAPLYKKIIKKLLES (SEQ ID NO: 4)

[0081] Further examples of PF4 sequences can be found in publicly available databases, such as GenBank, OMIM, and UniProt (P02776). Additional information regarding PF4 can be found, for example, at the NCBI website, which references gene 5196. The term PF4 as used herein also refers to variations of the PF4 gene, including variants provided in clinical variant databases, for example, at the NCBI clinical variant website, which references the term NM_002619.4.

[0082] DNAJC19 One of the protective proteins of the present disclosure is DnaJ heat shock protein family (Hsp40) member C19 (DNAJC19).

[0083] The term "DnaJ heat shock protein family (Hsp40) member C19" gene, or "DNAJC19" gene, also known as "TIMM14", "TIM14", "PAM18", and "mitochondrial import inner membrane translocase subunit TIM14", refers to the gene encoding the DNAJC19 protein. The DNAJC19 protein is a 6.29 kDa protein composed of 59 amino acids with an unusual structure compared to the rest of the DNAJ protein family. The DNAJ domain of DNAJC19 is located at the C-terminus rather than the N-terminus, and its transmembrane domain confers membrane-bound localization to DNAJC19, whereas other DNAJ proteins are cytosolic (Zong et al. Circulation Research 113(9):1043-53). DNAJC19 is required for ATP-dependent import of mitochondrial preproteins into the mitochondrial matrix. The J-domain of DNAJC19 stimulates mtHsp70 ATPase activity to drive this transport (Mokranjac et al. EMBO J 22(19):4945-56). Defects in DNAJC19 have been associated with dilated cardiomyopathy with ataxia (DCMA), growth failure, microcytic anemia, and male genital abnormalities. DNAJC19 was first implicated in DCMA in a study of a consanguineous Hutterite population, which has since been confirmed in other European populations (Ojala et al. Pediatric Research 72(4):432-7). In the clinic, DNAJC19 mutations have been detected by screening for elevated 3-methylglutaconic acid levels, mitochondrial distress, dilated cardiomyopathy, prolonged QT interval on electrocardiogram, and cerebellar ataxia (Ojala et al. Pediatric Research 72(4):432-7, Koutras et al. Frontiers in Cellular Neuroscience 8:191).

[0084] The nucleotide sequence of the genomic region of the human chromosome carrying the DNAJC19 gene can be found, for example, in Genome Reference Consortium Human Build 38 available at GenBank. The nucleotide sequence of the genomic region of human chromosome 3 carrying the DNAJC19 gene can also be found, for example, in GenBank Accession No. NC_000003.12, which corresponds to nucleotides 180983709-180989838 of human chromosome 3. An exemplary nucleotide sequence and amino acid sequence of DNAJC19 can be found, for example, in GenBank Accession No. NM_145261.4 (Homo sapiens DnaJ heat shock protein family (Hsp40) member C19 (DNAJC19) transcript variant 1). The amino acid sequence of human DNAJC19 is shown below. MASTVVAVGLTIAAAGFAGRYVLQAMKHMEPQVKQVFQSLPKSAFSGGYYRGGFEPKMTKREAALILGVSPTANKGKIRDAHRRIMLLNHPDKGGSPYIAAKINEAKDLLEGQAKK (SEQ ID NO: 5)

[0085] Further examples of DNAJC19 sequences can be found in publicly available databases, such as GenBank, OMIM, and UniProt (Q96DA6). Additional information on DNAJC19 can be found, for example, on the NCBI website, which refers to gene 131118. The term DNAJC19 as used herein also refers to variations of the DNAJC19 gene, including variants provided in clinical variant databases, for example, on the NCBI clinical variant website, which refers to the term NM_145261.4.

[0086] ANGPT1 One of the protective proteins of the present disclosure is angiopoietin 1 (ANGPT1).

[0087] The term "angiopoietin 1" gene, or "ANGPT1" gene, also known as "KIAA0003", "ANG-1", "AGP1" and "AGPT", refers to the gene encoding the ANGPT1 protein. ANGPT1 is a secreted 70 kDa glycoprotein and a member of the angiopoietin family of growth factors. ANGPT1 is the primary agonist of the tyrosine kinase receptor Tek, found primarily in endothelial cells. ANGPT1 is produced by vascular support cells and specialized pericytes such as podocytes in the kidney and ITO cells in the liver (Satchell et al. J Am Soc Nephrol 13(2):544-550(2002)). ANGPT1 plays a key role in controlling angiogenesis, endothelial cell survival, proliferation, migration, adhesion and cell spreading, reorganization of the actin cytoskeleton, and maintaining vascular quiescence (Jeansson et al. J Clin Invest 121(6):2278-2289(2011)). The ANGPT1 / Tek pathway is important for normal development, as conventional ANGPT1 or Tek knockout mice exhibit lethality between E9.5 and E12.5 with similar abnormal vascular phenotypes and loss of cardiac trabeculation (Suri et al. Cell 87(7):1171-80(1996), Tachibana et al. Mol Cell Biol 25(11):4693-702 (2005)).

[0088] The nucleotide sequence of the genomic region of the human chromosome carrying the ANGPT1 gene can be found, for example, in Genome Reference Consortium Human Build 38 available at GenBank. The nucleotide sequence of the genomic region of human chromosome 8 carrying the ANGPT1 gene can also be found, for example, in GenBank Accession No. NC_000008.11, which corresponds to nucleotides 107249482-107497918 of human chromosome 8. Exemplary nucleotide and amino acid sequences of ANGPT1 can be found, for example, in GenBank Accession No. NM_001146.5 (Homo sapiens angiopoietin 1 (ANGPT1), transcript variant 1). The amino acid sequence of human ANGPT1 is shown below. MTVFLSFAFLAAILTHIGCSNQRRSPENSGRRYNRIQHGQCAYTFILPEHDGNCRESTTDQYNTNALQRDAPHVEPDFSSQKLQHLEHVMENYTQWLQKLENYIVENMKSEMAQIQQNAVQNHTAT MLEIGTSLLSQTAEQTRKLTDVETQVLNQTSRLEIQLLENSLSTYKLEKQLLQQTNEILKIHEKNSLLEHKILEMEGKHKEELDTLKEEKENLQGLVTRQTYIIQELEKQLNRATTNNSVLQKQQL ELMDTVHNLVNLCTKEGVLLKGGKREEEKPFRDCADVYQAGFNKSGIYTIYINNMPEPKKVFCNMDVNGGGWTVIQHREDGSLDFQRGWKEYKMGFGNPSGEYWLGNEFIFAITSQRQYMLRIELMDWEGNRAYSQYDRFHIGNEKQNYRLYLKGHTGTAGKQSSLILHGADFSTKDADNDNCMCKCALMLTGGWWFDACGPSNLNGMFYTAGQNHGKLNGIKWHYFKGPSYSLRSTTMMIRPLDF (SEQ ID NO: 6)

[0089] Further examples of ANGPT1 sequences can be found in publicly available databases, such as GenBank, OMIM, and UniProt (Q15389). Additional information on ANGPT1 can be found, for example, on the NCBI website, which refers to gene 284. The term ANGPT1 as used herein also refers to variations of the ANGPT1 gene, including those provided in clinical variant databases, for example, on the NCBI clinical variant website, which refers to the term NM_001146.5.

[0090] TNFSF12 One of the protective proteins of the present disclosure is tumor necrosis factor superfamily member 12 (TNFSF12).

[0091] The term "tumor necrosis factor superfamily member 12" gene, or "TNFSF12" gene, also known as "APO3L", "DR3LG", "TWEAK", and "TNLG4A", refers to the gene encoding the TNFSF12 protein. TNFSF12 is a member of the tumor necrosis factor (TNF) family of proteins that play an important role in regulating the immune system. TNFSF12 is widely expressed in many tissues and induces interleukin-8 synthesis in several cell lines (Chicheportiche et al. Cell Biology and Metabolism 272(51):32401-32410(1997)). The human adenocarcinoma cell line HT29 underwent apoptosis in the presence of both TNFSF12 and interferon gamma. Leukocytes are the main source of TNFSF12 in humans, including resting and activated monocytes, dendritic cells, and natural killer cells (Maecker et al. Cell 123(5):931-44). TNFSF12 suppresses the production of IFN-γ and IL-12, suppressing the innate response and its shift towards adaptive TH1 immunity. TNFSF12 also promotes endothelial cell proliferation and migration, acting as a regulator of angiogenesis.

[0092] The nucleotide sequence of the genomic region of the human chromosome carrying the TNFSF12 gene can be found, for example, in Genome Reference Consortium Human Build 38 available at GenBank. The nucleotide sequence of the genomic region of human chromosome 17 carrying the TNFSF12 gene can also be found, for example, in GenBank Accession No. NC_000017.11, which corresponds to nucleotides 7549058-7557881 of human chromosome 17. Exemplary nucleotide and amino acid sequences of TNFSF12 can be found, for example, in GenBank Accession No. NM_003809.3 (Homo sapiens TNF superfamily member 12 (TNFSF12), transcript variant 1). The amino acid sequence of human TNFSF12 is shown below. MAARRSQRRRGRRGEPGTALLVPLALGLGLALACLGLLLAVVSLGSRASLSAQEPAQEELVAEEDQDPSELNPQTEESQDPAPFLNRLVRPRRSAPKGRKTRARRAIAAHYEVHPRPGQDGAQAGVDGTVSGWEEARINSSSPLRYNRQIGEFIVTRAGLYYLYCQVHFDEGKAVYLKLDLLVDGVLALRCLEEFSATAASSLGPQLRLCQVSGLLALRPGSSLRIRTLPWAHLKAAPFLTYFGLFQVH (SEQ ID NO: 7)

[0093] Further examples of TNFSF12 sequences can be found in publicly available databases, such as GenBank, OMIM, and UniProt (O43508). Additional information on TNFSF12 can be found, for example, on the NCBI website, which refers to gene 8742. The term TNFSF12 as used herein also refers to variations of the TNFSF12 gene, including those provided in clinical variant databases, for example, on the NCBI clinical variant website, which refers to the term NM_003809.3.

[0094] FGF20 Another protective protein of the present disclosure is fibroblast growth factor 20 (FGF20).

[0095] The term "fibroblast growth factor 20" gene, or "FGF20" gene, also known as "RHDA2", refers to the gene encoding the FGF20 protein. FGF20 is expressed primarily in the normal brain, especially in the cerebellum. The rat homolog is preferentially expressed in the brain and can enhance the survival of midbrain dopaminergic neurons in vitro. FGF20 is a member of the fibroblast growth factor (FGF) family that has broad mitogenic and cell survival activities and is involved in a variety of biological processes, including cell growth, morphogenesis, tissue repair, tumor growth, invasion, and embryonic development (Koga et al. Biochemical and Biophysical Research Communications 261(3):756-65). Genetic polymorphisms in FGF20 have been implicated in Parkinson's disease (Zhao et al. Neurol Sci 37(7):1119-26(2016), Zhu et al. Neurol Sci35(12)(2014)).

[0096] The nucleotide sequence of the genomic region of the human chromosome carrying the FGF20 gene can be found, for example, in Genome Reference Consortium Human Build 38 available at GenBank. The nucleotide sequence of the genomic region of human chromosome 8 carrying the FGF20 gene can also be found, for example, in GenBank Accession No. NC_000008.11, which corresponds to nucleotides 16992181-17002345 of human chromosome 8. Exemplary nucleotide and amino acid sequences of FGF20 can be found, for example, in GenBank Accession No. NM_019851.3 (Homo sapiens fibroblast growth factor 20 (FGF20)). The amino acid sequence of human FGF20 is shown below. MAPLAEVGGFLGGLEGLGQQVGSHFLLPPAGERPPLLGERRSAAERSARGGPGAAQLAHLHGILRRRQLYCRTGFHLQILPDGSVQGTRQDHSLFGILEFISVAVGLVSIRGVDSGLYLGMNDKGELYGSEKLTSECIFREQFEENWYNTYSSNIYKHGDTGRRYFVALNKDGTPRDGARSKRHQKFTHFLPRPVDPERVPELYKDLLMYT (SEQ ID NO: 8)

[0097] Further examples of FGF20 sequences can be found in publicly available databases, such as GenBank, OMIM, and UniProt (Q9NP95). Additional information regarding FGF20 can be found, for example, at the NCBI website, which refers to gene 26281. The term FGF20 as used herein also refers to variations of the FGF20 gene, including those provided in clinical variant databases, for example, at the NCBI clinical variant website, which refers to the term NM_019851.3.

[0098] Testican-2 Another defense protein that can be used as a marker in the methods and compositions described herein is Testican-2.

[0099] Human Testican-2 protein is encoded by the SPOCK2 gene, also known as TICN2 or KIAA0275. Testican-2 binds glycosaminoglycans to form part of the extracellular matrix. The protein contains a thyroglobulin type 1 domain, a follistatin-like domain, and a calcium-binding domain, and has a glycosaminoglycan attachment site in the acidic C-terminal region. SPOCK (SPARC / osteonectin CWCV and Kazal-like domain) encodes a secreted proteoglycan and has three known homologs, SPOCK1, SPOCK2, and SPOCK3. SPOCK was initially characterized as a precursor form of seminal plasma GAG-containing peptide and was later cloned and identified as a chondroitin / heparin sulfate proteoglycan (HSPG). SPOCK1 and SPOCK2 proteoglycans inhibit neuronal cell attachment and neurite outgrowth. Furthermore, polymorphisms in SPOCK2 have recently been found to be a genetic trait associated with susceptibility to bronchopulmonary dysplasia, a chronic respiratory disease common in premature infants (Hadchouel et al., Am J Respir Crit Care Med., 2011, 184(10):1164-70), and function as a defense barrier against viral infection of lung epithelial cells (Ahn et al., J Virol., 2019, 93(20):e00662-19).

[0100] The nucleotide sequence of the genomic region of the human chromosome carrying the Testican-2 gene (SPOCK2) can be found, for example, in Genome Reference Consortium Human Build 38 available at GenBank. The nucleotide sequence of the genomic region of human chromosome 10 carrying the Testican-2 gene can also be found, for example, in GenBank Accession No. NC_000010.11, which corresponds to nucleotides 72059034-72095313 of human chromosome 10. Exemplary nucleotide and amino acid sequences of Testican-2 can be found, for example, in GenBank Accession No. NM_001244950.2 (Homo sapiens SPARC / osteonectin, cwcv and kazal-like domain proteoglycan 2 (SPOCK2), transcript variant 3). The amino acid sequence of human Testican-2 (isoform 2 precursor) is shown below. MRAPGCGRLVLPLLLLAAAALAEGDAKGLKEGETPGNFMEDEQWLSSISQYSGKIKHWNRFRDEVEDDYIKSWEDNQQGDEALDTTKDPCQKVKCSRHKVCIAQGYQR AMCISRKKLEHRIKQPTVKLHGNKDSICKPCHMAQLASVCGSDGHTYSSVCKLEQQACLSSKQLAVRCEGPCPCPTEQAATSTADGKPETCTGQDLADLGDRLRDWFQ LLHENSKQNGSASSVAGPASGLDKSLGASCKDSIGWMFSKLDTSADLFLDQTELAAINLDKYEVCIRPFFNSCDTYKDGRVSTAEWCFCFWREKPPCLAELERIQIQEAAKKKPGIFIPSCDEDGYYRKMQCDQSSGDCWCVDQLGLELTGTRTHGSPDCDDIVGFSGDFGSGVGWEDEEEKETEEAGEEAEEEEGEAGEADDGGYIW (SEQ ID NO: 11)

[0101] Testican-2 sequences can also be found in publicly available databases, such as GenBank, OMIM, and UniProt (Q92563). Additional information regarding Testican-2 (SPOCK2) can be found, for example, at the NCBI website, which references gene 9806. The term Testican-2, as used herein, also refers to variations of the SPOCK2 gene, including variants provided in clinical variant databases, for example, at the NCBI clinical variant website, which references the term NM_001244950.2.

[0102] The entire contents of each of the foregoing GenBank Accession Numbers and Gene Database Numbers are incorporated herein by reference as of the filing date of this application.

[0103] III. Methods and compositions for determining risk of RD and ESRD based on protective proteins The present disclosure is based at least in part on the discovery that the level of a particular protective protein can be used to identify human subjects at risk of progression to progressive kidney disease or end-stage renal disease.Compared to individuals without progressive renal failure, lower levels of the protective proteins identified herein indicate those who are protected from progression to end-stage renal disease and those who are not.Another embodiment described herein is the treatment of a human patient identified as at risk for ESKD, for example, administering a protective protein or a combination thereof reduces the patient's risk of progressive kidney disease.

[0104] Examples of protection proteins that can be used in the methods and compositions described herein are provided herein. As described herein, the term protection protein is intended to include protection proteins and functional fragments thereof. Functional fragments, for example, retain the capabilities ascribed to the corresponding full-length (or non-fragment) equivalent.

[0105] The expression level of one or more protective proteins can be determined in a biological sample from a subject. A sample from a subject originates from and is obtained from a subject. Such a sample can be further processed after being obtained from a subject. For example, proteins can be isolated from the sample. In one embodiment, the protein isolated from the sample is also a sample from a subject. Since protein expression has been reported in cells, tissues, and body fluids throughout the body, biological samples useful for determining the level of one or more protective proteins can be obtained from essentially any source. However, in one aspect of the present disclosure, the level of one or more protective proteins indicative of a subject having renal impairment and / or ESKD, or a subject having renal impairment and / or at risk of developing ESKD, can be detected in a sample obtained non-invasively from a subject.

[0106] In certain embodiments, the biological sample used to determine the level of one or more protective proteins is a sample containing circulating protein biomarkers. Extracellular protein biomarkers circulate freely in a wide range of biological materials, including bodily fluids, such as fluids from the circulatory system, for example, blood samples or lymph samples, or other bodily fluids, such as cerebrospinal fluid (CSF), urine or saliva. Thus, in some embodiments, the biological sample used to determine the level of one or more protective proteins is a bodily fluid, such as blood, a fraction thereof, serum, plasma, urine, saliva, tears, sweat, semen, vaginal secretions, lymph, bronchial secretions, CSF, etc. In some embodiments, the sample is a non-invasively obtained sample. In one particular embodiment, the sample is a urine sample. In another embodiment, the sample is a plasma sample. In another embodiment, the sample is a serum sample.

[0107] In some embodiments, the biological sample used to determine the level of one or more protective proteins may contain cells. In other embodiments, the biological sample may be free or substantially free of cells (e.g., a serum sample). In some embodiments, the sample containing the circulating protein biomarkers is a blood-derived sample. Exemplary blood-derived sample types include, for example, blood samples, plasma samples, serum samples, etc. In other embodiments, the sample containing the circulating protein biomarkers is a lymph sample. Circulating protein biomarkers are also found in urine and saliva, and biological samples derived from these sources are similarly suitable for determining the level of one or more protective proteins.

[0108] Compositions for determining protective protein levels Also disclosed herein are arrays (e.g., protein arrays) or compositions comprising antibodies or antigen-binding fragments thereof specific for any one or more of FGF20, TNFSF12, ANGPT1, SPARC, CCL5, APP, PF4, DNAJC19, and Testican-2 for carrying out the methods described herein. Such arrays may include carriers or substrates for attachment of any one or more of antibodies or antigen-binding fragments thereof specific for any one or more of FGF20, TNFSF12, ANGPT1, SPARC, CCL5, APP, PF4, DNAJC19, and Testican-2. Such carriers and substrates are known in the art and include covalent and non-covalent interactions. For example, diffusion of applied proteins (e.g., antibodies or antigen-binding fragments thereof) over a porous surface such as a hydrogel allows for non-covalent attachment of unmodified proteins within the hydrogel structure. Covalent attachment methods provide stable attachment and may be applied to a variety of proteins. Biological capture methods utilizing a tag (e.g., hexahistidine (SEQ ID NO: 10) / Ni-NTA or biotin / avidin) on a protein (e.g., biomarker) and a partner reagent immobilized on the surface of a substrate provide stable binding and bind to the protein (e.g., biomarker) in a specific and reproducible orientation.

[0109] In one embodiment, an antibody or antigen-binding fragment thereof specific for any one or more of FGF20, TNFSF12, ANGPT1, SPARC, CCL5, APP, PF4, DNAJC19, and Testican-2 described herein is coated or spotted onto a support or substrate, such as chemically derivatized glass, or a glass plate coated with a protein binding agent, such as, but not limited to, nitrocellulose.

[0110] In another embodiment, the antibody or antigen-binding fragment thereof specific for any one or more of FGF20, TNFSF12, ANGPT1, SPARC, CCL5, APP, PF4, DNAJC19, and Testican-2 is provided in the form of an array, such as a microarray. Protein microarrays are known in the art and are reviewed, for example, by Hall et al. (2007) Mech Ageing Dev 128:161-167 and Stoevesandt et al. (2009) Expert Rev Proteomics 6:145-157, the disclosures of which are incorporated herein by reference. Microarrays can be prepared by immobilizing purified antigens on a substrate, such as a treated microscope slide, using a contact spotter or a non-contact microarrayer. Microarrays can also be generated by in situ cell-free synthesis directly from the corresponding DNA array. Microarrays can be included in a test panel for carrying out the methods disclosed herein. The generation of the microarray is, in certain circumstances, performed using commercially available printing buffers designed to maintain the three-dimensional shape of the antigen. In one embodiment, the microarray substrate is a nitrocellulose-coated glass slide.

[0111] The assay is carried out by methods known in the art, by contacting one or more antibodies or antigen-binding fragments specific for any one or more of FGF20, TNFSF12, ANGPT1, SPARC, CCL5, APP, PF4, DNAJC19, and Testican-2 with a biological sample under conditions that allow the formation of immune complexes with any one or more of FGF20, TNFSF12, ANGPT1, SPARC, CCL5, APP, PF4, DNAJC19, and Testican-2, allowing detection of the immune complexes. The presence and amount of immune complexes can be detected by methods known in the art, including label-based and label-free detection. For example, label-based detection methods include the addition of a secondary antibody conjugated to an indicator reagent that includes a signal-generating compound. The secondary antibody can be an anti-human IgG antibody. Indicator reagents include colorants, catalysts such as enzyme conjugates, fluorescent compounds such as fluorescein and rhodamine, chemiluminescent compounds such as dioxetanes, acridinium, phenanthridinium, ruthenium, and luminol, radioactive elements, direct visual labels, as well as cofactors, inhibitors, and magnetic particles. Examples of enzyme conjugates include alkaline phosphatase, horseradish peroxidase, and beta-galactosidase. Label-free detection methods include surface plasmon resonance, carbon nanotubes and nanowires, and interferometry. Label-based and label-free detection methods are known in the art and are disclosed, for example, by Hall et al. (2007) and Ray et al. (2010) Proteomics 10:731-748. Detection can be achieved by scanning methods known in the art and appropriate for the label used, and associated analysis software.

[0112] As described herein, protective proteins indicative of renal function decline and / or ESKD and / or protective proteins indicative of increased risk of renal function decline and / or increased risk of progression to ESKD are disclosed. Thus, it is contemplated that the level of protective proteins can be assayed from a sample from a subject, such as a test subject, to determine whether the test subject (e.g., a subject suspected of having renal function decline and / or ESKD, or a subject with increased risk of having renal function decline and / or ESKD) has renal function decline and / or ESKD, or whether the test subject has increased risk of renal function decline and / or increased risk of progression to ESKD. In certain embodiments, protective proteins were identified by comparing, for example, the levels of a particular protein (e.g., circulating protein) in samples from subjects who developed renal function decline and / or ESKD or from subjects with diabetes (T1D, T2D) who were at risk for rapid progression to renal function decline and ESKD, compared to the levels of a particular protein (e.g., circulating protein) in samples from subjects who did not develop renal function decline and / or ESKD or from subjects with diabetes (T1D, T2D) who were determined to have stable renal function (i.e., were non-progressors), or from healthy control subjects, or from standard control or reference levels. In other embodiments, protective proteins were identified by comparing levels of particular proteins (e.g., circulating proteins) in samples from subjects who had, for example, developed renal function decline and / or ESKD or samples from subjects with diabetes (T1D, T2D) who were at risk for rapid progression to renal function decline and ESKD, compared to known baseline concentrations of the protein (e.g., circulating or plasma proteins) as seen or measured, for example, by a proteomics platform (e.g., the SOMAscan platform, and / or the OLINK platform).Several differentially present protein biomarkers have been identified in this manner and determined to be indicative of subjects with renal function decline and / or ESKD and to be indicative of an increased risk of progression to renal function decline and / or ESKD, including, but not limited to, FGF20, TNFSF12, ANGPT1, SPARC, CCL5, APP, PF4, DNAJC19, and / or Testican-2.

[0113] The protective proteins identified herein can be used to determine whether a subject, such as a subject with T1D or T2D and previously unknown risk of developing renal decline and / or ESKD, has or is at risk of developing renal decline and / or ESKD. This can be accomplished by determining the levels of one or more of FGF20, TNFSF12, ANGPT1, SPARC, CCL5, APP, PF4, DNAJC19, and / or Testican-2, or a combination thereof, in a biological sample from the subject. A difference in the levels of one or more of these protective proteins compared to levels in a biological sample from a normal subject (i.e., a subject known not to have renal decline and / or ESKD, or a normal albuminuric control level, or a healthy control level, or a standard control level) can have predictive power as to whether the subject is at risk of developing renal decline and / or ESKD.

[0114] The level of one or more protective proteins in a biological sample can be determined by any suitable method. Any reliable method for measuring or detecting the level or amount of a protein in a sample can be used. Thus, routine techniques in the field of molecular biology can be utilized to implement the methods disclosed herein. Basic documents that disclose the general methods of use in this disclosure include Sambrook and Russell, Molecular Cloning, A Laboratory Manual (3rd ed. 2001), Kriegler, Gene Transfer and Expression: A Laboratory Manual (1990), and Current Protocols in Molecular Biology (Ausubel et al., eds., 1994).

[0115] The present disclosure relates to methods (e.g., in vitro methods) for measuring or detecting the amount of specific protein levels found in a subject's cells, tissues, or samples (e.g., plasma or serum samples) as a means for detecting the presence of, assessing the risk of, diagnosing, prognosing, and / or monitoring the progression of, and / or monitoring the effectiveness of treatment for, renal function decline and / or ESKD. Thus, in certain embodiments, the first step in carrying out the methods of the present disclosure (e.g., in vitro methods using specific identified biomarkers for diagnosing, prognosing, and / or monitoring renal function decline and / or ESKD) is to obtain cells, tissues, or samples (e.g., urine samples, plasma samples, or serum samples) from a test subject and extract proteins from the samples.

[0116] Samples can be prepared according to methods known in the art. Cell, tissue or blood samples (e.g., plasma or serum samples) from subjects can be obtained using well-known methods suitable for the present disclosure and as described herein. In certain embodiments of the present disclosure, plasma samples are the preferred sample type. In other embodiments of the present disclosure, serum samples are the preferred sample type.

[0117] In some embodiments, biological samples (e.g., cells, tissues, plasma samples, or serum samples) are obtained from subjects to be tested or monitored for renal impairment and / or ESKD as described herein. The same type of biological samples should be taken from both test subjects (e.g., subjects suspected of having renal impairment and / or ESKD and / or subjects at risk of developing renal impairment and / or ESKD) and control subjects (e.g., subjects not suffering from renal impairment and / or ESKD; e.g., samples from normoalbuminuric or healthy control subjects, or of known / standard control levels). Collection of biological samples from subjects, such as test subjects, may be performed according to standard protocols commonly followed by hospitals or clinics. An appropriate amount of biological sample (e.g., cells, tissues, or plasma samples) may be collected and stored according to standard procedures before further preparation.

[0118] Analysis of the specific protective proteins described herein found in a biological sample of a subject (e.g., a test subject) according to the methods disclosed herein can be performed, in certain embodiments, using, for example, cells, tissues, urine samples, plasma samples, or serum samples. Methods for preparing biological samples for protein extraction are well known to those skilled in the art. For example, a cell population or tissue sample of a subject (e.g., a test subject) should first be treated to disrupt cell membranes to release proteins contained within the cells.

[0119] For purposes of detecting the presence of the specific protective proteins disclosed herein or for purposes of assessing whether a test subject has or is at risk for developing renal decline and / or ESKD, a biological sample can be collected from the subject and the levels of the specific protective proteins disclosed herein can be measured and then compared to normal levels of these same specific protective proteins (e.g., compared to the levels of the specific protective proteins disclosed herein in the same type of biological sample in the subject prior to the onset of renal decline and / or ESKD, and / or compared to the levels of the specific protective proteins disclosed herein in the same type of biological sample from a healthy control subject (e.g., a subject not having T1D or T2D), and / or compared to a known control standard of baseline levels of the specific protective proteins disclosed herein). If the levels of one or more of the specific protective proteins disclosed herein are statistically significantly lower compared to the normal levels of one or more of the specific protective proteins disclosed herein, the test subject is considered to have renal decline and / or ESKD or to be at increased risk for developing renal decline and / or ESKD. For the purpose of monitoring the progression of the disease or for the purpose of evaluating the efficacy of treatment in patients with renal impairment and / or ESKD, biological samples from the test subject can be taken at different time points so that measuring the level of the specific protective protein disclosed herein over time (i.e., serial testing) can provide information indicative of the state of the disease. For example, if the level of the specific protective protein disclosed herein from the test subject shows a general trend of increasing or stabilizing to normal levels over time, the severity of renal impairment and / or ESRD in the test subject is deemed to be improved or stabilized, or the therapy being received by the patient is deemed to be effective. If the level of the specific protective protein disclosed herein from the test subject does not increase or stabilize, or if the level of the specific protective protein disclosed herein from the test subject shows a continuous trend of decreasing, this indicates a worsening of the disease state and the ineffectiveness of the therapy given to the patient.Generally, relatively low levels of certain protective proteins disclosed herein found in a test subject are indicative of the test subject having renal impairment and / or ESKD and / or of a worsening condition of renal impairment and / or ESKD in the test subject or of progression of renal impairment and / or ESKD.

[0120] Proteins with certain unique characteristics, such as the protection protein(s) as disclosed herein, can be detected using a variety of immunological assays. In some embodiments, a sandwich assay can be performed by capturing the protection protein(s) from the test sample with antibody(s) having specific binding affinity for the protection protein(s). The protection protein(s) can then be detected, for example, using a labeled antibody with specific binding affinity for the protection protein(s). One common detection method uses radioisotopes (e.g. 3 H, 125 I, 35 S, 14 C, or 32 P, 99mThe use of autoradiography by using a radiolabeled detection agent (e.g., a radiolabeled anti-protection protein-specific antibody) labeled with a radioisotope such as Tc. The choice of radioisotope depends on the preference in the study due to the ease of synthesis, stability, and half-life of the selected isotope. Other labels that can be used for labeling the detection agent (e.g., for labeling the anti-biomarker-specific antibody) include compounds (e.g., biotin and digoxigenin) that bind to anti-ligands or antibodies labeled with fluorophores, chemiluminescent agents, fluorophores, and enzymes (e.g., HRP). Such immunological assays can be performed using microfluidic devices such as microarray protein chips. The protein of interest (e.g., the protection protein(s) as disclosed herein) can also be detected by gel electrophoresis (such as two-dimensional gel electrophoresis) and Western blot analysis using a specific antibody (e.g., an anti-protection protein-specific antibody). In some embodiments, standard ELISA techniques can be used to detect a given protein (e.g., a protection protein as disclosed herein) using an appropriate antibody(s), e.g., an anti-protection protein-specific antibody. In other embodiments, standard Western blot analysis techniques can be used to detect a given protein (e.g., a protection protein as disclosed herein) using an appropriate antibody. Alternatively, standard immunohistochemistry (IHC) techniques can be used to detect a given protection protein using an appropriate antibody(ies), e.g., an anti-protection protein-specific antibody. Both monoclonal and polyclonal antibodies (including antibody fragments with the desired binding specificity) can be used for the specific detection of the protection protein(s). Such antibodies and binding fragments thereof that have specific binding affinity for a particular protein (e.g., a protection protein(s) as disclosed herein) can be generated by known techniques.

[0121] In some embodiments, a protection protein as disclosed herein may be detected (e.g., detected in a detection assay) using an antibody that binds to the protection protein, such as an anti-protection protein-specific antibody, or an antigen-binding fragment thereof. In certain embodiments, an anti-protection protein-specific antibody is used as a detection agent, such as a detection antibody, that binds to a protection protein(s) as disclosed herein and detects the protection protein(s) (e.g., from a biological sample), e.g., detects the protection protein(s) in a detection assay (e.g., Western blot analysis, immunohistochemistry analysis, autoradiography analysis, and / or ELISA). In certain embodiments, an anti-protection protein-specific antibody is used as a capture agent that binds to a protection protein and detects the protection protein (e.g., from a biological sample), e.g., detects the protection protein in a detection assay (e.g., Western blot analysis, immunohistochemistry analysis, autoradiography analysis, and / or ELISA). In some embodiments, an anti-protection protein-specific antibody, or an antigen-binding fragment thereof, is labeled to facilitate detection. In some embodiments, an anti-protection protein-specific antibody, or an antigen-binding fragment thereof, is radiolabeled (e.g., labeled with a radioisotope, e.g., 3 H, 125 I, 35 S, 14 C, or 32 P, 99m Tc), enzymatically labeled (e.g., labeled with an enzyme such as horseradish peroxidase (HRP)), fluorescently labeled (e.g., labeled with a fluorophore), chemiluminescently labeled, and / or labeled with a compound (e.g., biotin and digoxigenin).

[0122] In carrying out the present disclosure, other methods may be used to measure or detect the level of protective proteins as disclosed herein. For example, various methods based on mass spectrometry have been developed to rapidly and accurately quantify target proteins in a large number of samples. These methods use highly sophisticated instruments such as triple quadrupole (Triple Q) instruments using multiple reaction monitoring (MRM) technology, matrix-assisted laser desorption / ionization time-of-flight tandem mass spectrometers (MALDI TOF / TOF), ion trap instruments using selected ion monitoring (SIM) mode, and electrospray ionization (ESI)-based QTOP mass spectrometers. See, for example, Pan et al., J Proteome Res 2009 February;8(2):787-797.

[0123] In other embodiments, expression of a protection protein as disclosed herein is assessed by assessing a protection protein as disclosed herein. In some embodiments, an anti-protection protein specific antibody, or a fragment thereof, may be used to assess the protection protein. Such methods may include the use of IHC, Western blot analysis, ELISA, immunoprecipitation, autoradiography, or antibody arrays. In certain embodiments, the protection protein is assessed using IHC. The use of IHC may allow for quantification and characterization of the protection protein. IHC may also allow for an immunoreactivity score of the sample in which expression of the protection protein is determined. The term "immunoreactivity score" (IRS) refers to a number calculated based on a scale reflecting the percentage of positive cells (on a scale of 1-4, 0=0%, 1=<10%, 2=10%-50%, 3=50%-80%, 4=>80%) multiplied by the intensity of staining (on a scale of 1-3, 1=weak, 2=moderate, 3=strong). The IRS may range from 0-12.

[0124] In certain other embodiments, SOMAscan, an aptamer-based proteomics platform, can be used to determine the levels of protective proteins as disclosed herein. This platform technology is based on the recognition that unique single-stranded sequences of DNA and RNA, called aptamers, can recognize folded protein epitopes with high affinity and specificity. This property has been further advanced by using the SOMAscan platform to assay protein concentrations (one aptamer per protein). This platform is characterized by high throughput capabilities (more than 1000 proteins in one sample) with reproducibility and sensitivity.

[0125] In certain other embodiments, an OLINK-Proximity Extension Assay-based proteomics platform may be used to determine the levels of protective protein(s) as disclosed herein. The OLINK Proximity Extension Assay is a molecular technology that merges antibody-based immunoassays with the powerful properties of PCR and quantitative real-time PCR (qPCR), resulting in a multiplexable and highly specific method (e.g., using two antibodies per protein) that can simultaneously quantify a large number of protective proteins using as little as 1 μL of plasma / serum. These assays have been fully validated and grouped as panels designed to focus on specific diseases or biological processes, and optimized to the expected dynamic range of target protein concentrations in clinical samples.

[0126] As described herein, estimated glomerular filtration rate (eGFR) refers to a means for estimating renal function. In some embodiments, the methods described herein include measuring the slope of the estimated glomerular function rate (eGFR) of a human subject and determining whether the slope of the eGFR of the human subject indicates that the human subject has or is at risk of developing reduced renal function. In some embodiments, the eGFR is determined based on measuring serum creatinine levels. In other embodiments, the eGFR is determined based on measuring serum cystatin C levels. In other embodiments, the eGFR is determined using ordinary least squares assuming linear regression with at least three serum creatinine values ​​available and measured at least six months apart. In other embodiments, the eGFR is determined using ordinary least squares assuming linear regression with at least three serum creatinine values ​​available and measured at least one year apart. In yet other embodiments, the eGFR is determined using ordinary least squares assuming linear regression with at least three serum creatinine values ​​available and measured at least two years apart. In other embodiments, the eGFR is estimated by visual inspection.

[0127] In some embodiments, an eGFR slope of at least <-3 ml / min / year (i.e., eGFR loss > 3.0 ml / min / year) indicates that the human subject has or is at risk for developing reduced renal function. In other embodiments, an eGFR slope of at least <-5 ml / min / year indicates that the human subject has or is at risk for developing reduced renal function. In yet other embodiments, an eGFR slope of at least <-10 ml / min / year indicates that the human subject has or is at risk for developing reduced renal function. In yet another embodiment, an eGFR slope of at least <-15 ml / min / year indicates that the human subject has or is at risk for developing reduced renal function. In other embodiments, a sustained decline in eGFR of > 40% from baseline (confirmed over at least 3 months) indicates that the human subject has or is at risk for developing reduced renal function.

[0128] In yet another embodiment, eGFR may be determined using the CKD-EPI creatinine equation. In some embodiments, the estimate of the slope of the GFR may depend on the subject's race, sex, and serum creatinine level. For example, in one embodiment, the slope of the eGFR for an African-American woman with a serum creatinine concentration (μmol / dL) of ≦62 (≦0.7) is determined using the following formula: GFR=166×(Scr / 0.7). -0.329 ×(0.993) Age In another embodiment, the slope of eGFR for women of African descent with serum creatinine (μmol / dL) >62 (>0.7) is determined using the formula: GFR=166×(Scr / 0.7). -1.209 ×(0.993) Age In another embodiment, the slope of eGFR for men of African descent with serum creatinine concentrations (μmol / dL) ≦80 (≦0.9) is determined using the formula: GFR=163×(Scr / 0.9). -0.411 ×(0.993) Age In another embodiment, the slope of eGFR for men of African descent with serum creatinine concentrations (μmol / dL) >80 (>0.9) is determined using the formula: GFR=163×(Scr / 0.9). -1.209 ×(0.993) Age In another embodiment, the slope of eGFR for non-African women with serum creatinine concentrations (μmol / dL) ≦62 (≦0.7) is determined using the formula: GFR=144×(Scr / 0.7). -0.329 ×(0.993) Age In another embodiment, the slope of eGFR for non-African women with serum creatinine concentrations (μmol / dL) >62 (>0.7) is determined using the formula: GFR=144×(Scr / 0.7). -1.209 ×(0.993) Age In another embodiment, the slope of eGFR for non-African men with serum creatinine concentrations (μmol / dL) ≦80 (≦0.9) is determined using the formula: GFR=141×(Scr / 0.9). -0.411 ×(0.993) AgeIn another embodiment, the slope of eGFR for men of African descent with serum creatinine concentrations (μmol / dL) >80 (>0.9) is determined using the formula: GFR=141×(Scr / 0.9). -1.209 ×(0.993) Age .

[0129] Additional methods for determining an estimated glomerular filtration rate are known to those of skill in the art.

[0130] The methods described herein may further include combining electronic health records (EHRs) with biomarkers (e.g., one or more of SPARC, CCL5, APP, PF4, DNAJC19, ANGPT1, TNFSF12, FGF20, and Testican-2) using a machine learning prognostic risk score assay as an in vitro diagnostic to enable accurate risk prediction of progressive kidney function decline.

[0131] In some embodiments, the machine learning prognostic risk score assay is KIDNEYINTELX™. For that, a random forest model can be trained and the performance (e.g., area under the curve (AUC), positive and negative predictive values ​​(PPV / NPV), and net reclassification index (NRI)) can be compared to clinical models and KDIGO categories for predicting a composite outcome of estimated glomerular filtration rate (eGFR) decline ≧5 ml / min / year, sustained decline of ≧40%, or renal failure within 5 years. In some embodiments, an observational cohort study of patients with prevalent diabetic kidney disease (DKD) / banked plasma from two HER-associated biobanks can be used. KIDNEYINTELX™ can provide improved prediction of renal outcomes over KDIGO (Kidney Disease: Improving Global Outcomes) guidelines and clinical models for individuals with early stage DKD. In some embodiments, machine learning models such as those described in PCT Application No. PCT / US2021 / 018030 (Publication No. WO / 2021 / 163619; the methods and compositions of which are incorporated herein by reference) are used in the methods described herein.

[0132] The eight protective protein biomarkers can be measured in a proprietary, analytically validated multiplex format using the Mesoscale platform (MesoScale Diagnostics, Gaithersburg, Maryland, USA), which allows for assay multiplexing using electrochemiluminescence detection combined with patterned arrays. Each sample can be run in duplicate, along with quality control samples containing known low, medium, and high concentrations of each biomarker on each plate. Assay precision can be assessed using a panel of reference samples spanning the measurement range. Levey-Jennings plots can be used and Westguard rules can be followed for sample reruns. Laboratory personnel performing the biomarker assays can be blinded to all clinical information.

[0133] eGFR can be determined using the CKD-EPI creatinine equation, for example, as described by Levey et al. (Ann Intern Med 150(9):604-61221(2009)). A linear mixed model can be used with an unstructured variance-covariance matrix, and a random intercept / slope can be used for each individual to estimate the slope of eGFR, for example, as described by Leffondre et al. (Nephrol Dial Transplant 30(8):1237-1243(2015)). The primary composite outcome, progressive decline in renal function, can include: a decrease in the slope of eGFR of ≥ 5 ml / min / 1.73 m 2 RKFD defined as a decline / year; a sustained (confirmed after at least 3 months) ≥ 40% decline from baseline in eGFR; or an eGFR < 15 ml / min / 1.73 m2 confirmed after at least 30 days. 2 or receipt of long-term maintenance dialysis, or receipt of a kidney transplant (KDIGO, Kidney Int Suppl 3:1-163 (2012); Levey et al. Am J Kidney Dis 64(6):821-835 (2014)). In addition, a nephrologist (SC / GNN) can be employed to independently adjudicate all outcomes, examining each individual patient over time and taking into account changes in eGFR (ensuring a decline of ≥ 5 ml / min annualized or a sustained decline of ≥ 40%), corresponding ICD / CPT codes, and medications to ensure outcomes represented true declines rather than temporary changes that were situation-dependent (e.g., due to medications / hospitalizations). Follow-up periods can be censored after loss to follow-up, after the date the non-slope component of the composite renal endpoint was met, or 5 years from baseline.

[0134] The dataset can be randomized into a derivation set (60%) and a validation set (40%). The validation dataset can be fully blinded and isolated from the entire derivation dataset. Using only the derivation set, a supervised random forest algorithm on the combined biomarkers and all structured EHR features can be evaluated without prior feature selection to identify candidate feature sets. The derivation set can then be randomly split into a secondary training and testing set for model optimization with 70%-30% spitting and 10-fold cross-validation of AUC. Both raw values ​​and proportions of biomarkers can be considered. Missing uACR values ​​can be imputed with 10 mg / g (Nelson et al. JAMA (2019)), missing blood pressure (BP) values ​​can be imputed using multiple predictors (age, sex, race, and antihypertensive medications) (De Silva et al. BMC Med Res Methodol 17(1):114 (2017)), and median values ​​can be used for other features if missingness was less than 30%.

[0135] Further iterations of the model can be performed by tuning individual hyperparameters. Hyperparameters are parameters used to control the learning process (e.g., number of RF trees) as opposed to parameters whose weights (e.g., variable weights) are learned during training. Tuning hyperparameters refers to the iteration of the model architecture after setting the parameter weights to achieve ideal performance. Hyperparameter optimization can be done using a grid search approach. K-fold cross-validation based AUC can be evaluated for all possible combinations of hyperparameters. The combination of hyperparameters that optimizes the AUC can be selected for model construction. The following hyperparameters can be considered for optimization: the number of variables randomly selected as candidates for splitting a node; the average number of unique cases (data points) in the terminal nodes of the forest; and the maximum depth to which the tree should be grown.

[0136] Furthermore, to improve reproducibility and transparency, the code for hyperparameter optimization can be deposited in the github repository (https: / / github.com / girish-nadkarni / KidneyIntelX_hyperparameter_tuning). The final model can be selected based on AUC performance.

[0137] The final model in the derivation set, scaled in increments of 5 to fit a continuous score of 5-100, can be used to generate a risk probability for the composite renal endpoint, which can then be applied to the validation set. Risk cutoffs can be selected in the derivation set to encompass the top 15% as a high-risk group (score 90-100), the bottom 45% as a low-risk group (score 5-45), and the middle 40% as an intermediate-risk group (score 50-85). Primary performance criteria can be the AUC at the predefined cutoffs, the positive predictive value for the high-risk group, and the negative predictive value for the low-risk group (PPV and NPV, respectively). The selected model and associated cutoffs can then be validated by an independent biostatistician (MK) in a segregated validation cohort.

[0138] In addition to these traditional test statistics, calibration can be assessed by examining the slope of the plot of observed versus expected outcomes of the KIDNEYINTELX score versus the observed outcome alone. Kaplan-Meier curves can also be constructed for the time-dependent outcomes of 40% decline and renal failure with hazard ratios using the Cox proportional hazards method. The discrimination of the KIDNEYINTELX model can be compared to a recently validated comprehensive clinical model that includes age, sex, race, eGFR, cardiovascular disease, smoking, hypertension, BMI, UACR, insulin, antidiabetic medications, and HbA1c and was developed to predict a 40% decline in eGFR in T2D (Nelson et al. JAMA (2019)). Utility metrics (PPV, NPV) can be compared to both the comprehensive clinical model and the KDIGO risk strata.

[0139] Finally, the net reclassification index (NRI) of events and non-events compared to the KDIGO risk strata can be calculated (Pencina et al. Stat Med 27(2):157-172(2008); Pencina et al. Stat Med 30(1):11-21(2010)). All a priori significance levels can be <0.05. All hypothesis tests can be two-sided. 95% confidence intervals can be calculated by bootstrapping. All analyses were performed using R software (www.rproject.org), dplyr, randomForestSRC, and CARET packages (Hadley et al. (2020) dplyr: A Grammar of Data Manipulation. R Package version 0.7.6. Available at cran.r-project.org / web / packages / dplyr / index.html; Hemant Ishwaran UBK (2020) randomForestSRC: Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC). Available at cran.rproject.org / web / packages / randomForestSRC / index).

[0140] Utilizing plasma samples and associated EHR data of T2D patients from two biobanks, a risk score combining clinical data and plasma biomarkers can be developed and validated by a random forest algorithm for predicting progressive renal decline, a composite renal outcome consisting of RKFD, a sustained 40% decline in eGFR, and renal failure over a 5-year period. KIDNEYINTELX can be demonstrated to outperform models using only standard clinical variables, including KDIGO risk categories (KDIGO, Kidney Int Suppl 3:1-163 (2012)). A notable improvement in discriminatory ability over clinical models can be seen, as measured by improvements in AUC, NRI, and PPV compared to KDIGO risk categories. Furthermore, KIDNEYINTELX can accurately identify patients experiencing an event by over 40% more than the KDIGO risk strata. Finally, KIDNEYINTELX may provide superior risk stratification with respect to the approved FDA endpoint of a sustained 40% decline in eGFR or renal failure, with a 15-fold risk difference between high- and low-risk strata for this clinical and objective endpoint.

[0141] DKD has become an increasingly complex and common problem threatening modern healthcare systems. In real-world practice, prediction of DKD progression is difficult, especially for early disease with preserved renal function, and therefore the implementation of improved prognostic tests is crucial. Integrated risk scores have short-term clinical significance, especially when associated with clinical decision support (CDS) and embedded care pathways. The current standard for clinical risk stratification (KDIGO risk strata) (KDIGO KDIGO, Kidney Int Suppl 3:1-163 (2012)) has three risk strata that overlap with the DKD patient population that can be included in the KIDNEYINTELX study. Risk scores with three risk strata (low, intermediate, and high) can be created by incorporating the KDIGO classification components (eGFR and uACR) as well as adding other clinical variables and the three blood-based biomarkers. In this way, the ability to accurately risk stratify DKD patients can be enhanced, thereby enabling improved patient management.

[0142] Care for low-risk patients with DKD will continue with their current PCP or diabetologist and may require less intense treatment unless otherwise indicated by local arrangements for re-examination, change in clinical status, or referral to a specialist. For those with high-risk scores, oversight may include further referral to a nephrologist (Smart et al. The Cochrane database of systematic reviews(6):CD007333(2014); Smart and Titus, Am J Med 124(11):1073-1080 e1072(2011)), increased monitoring intervals, increased awareness of kidney health, referral to a dietitian, enhanced use of antagonists of the renin-angiotensin-aldosterone system, and increased motivation to initiate recently approved agents including SGLT2 inhibitors and GLP-1 receptor agonists to slow progression (Kristensen et al. Lancet Diabetes Endocrinol 7(10):776-785(2019); Sarafidis et al. Nephrol Dial Transplant 34(2):208-230(2019)). Adoption of these new therapies has been particularly slow in patients considered “low risk” by standard criteria, with cost of treatment and the presence of adverse events being limiting factors. Early engagement with nephrologists may allow more time to advise and educate patients about home dialysis and preemptive or early kidney transplantation as patient-centered kidney replacement options if more aggressive measures ultimately fail to prevent DKD progression. Using risk scores as part of the enrollment process in future RCTs may increase trial participants with a higher likelihood of events, reducing the probability of type II error, or minimize the sample size required to detect a statistically significant difference between treatment and control. Interventions that prevent or slow DKD progression and promote patient-centered kidney replacement modalities support the goals of the U.S. Department of Health and Human Services’ Advancing American Kidney Health initiative (Mehrotra, Clin J Am Soc Nephrol 14(12):1788(2019)).

[0143] KIDNEYINTELX included input from biomarkers examined in several settings, including patients with DKD. Soluble TNFR1 and 2 and plasma KIM-1 have shown reliable and independent prognostic signals for renal function decline and ESKD (Niewczas et al. J Am Soc Nephrol 23(3):507-515(2012); Coca et al. J Am Soc Nephrol 28(9):2786-2793(2017); Nadkarni et al. Kidney Int 93(6):1409-1416(2018); Tummalapalli et al. Curr Opin Nephrol Hypertens 25(6):480-486(2016); Gohda et al. J Am Soc Nephrol 23(3):516-524(2012); Krolewski et al. Diabetes care 37(1):226-234(2014); Bhatraju et al. al. J Am Soc Nephrol 29(11):2713-2721(2018)). A previous study found that including biomarkers in clinical data from EHRs at a single institution improved predictive performance over clinical models alone (Chauhan et al. Kidney360(2020)). However, this study included few patients with prevalent CKD (roughly 1 / 3 in the T2D cohort and 1 / 4 in the APOL1 high-risk cohort). However, the methods described above in this specification can provide a multidimensional representation of a patient's risk for DKD by incorporating biomarker concentrations and EHR data into machine learning algorithms, generating improved prognostic estimates of future progression (Tangri et al. JAMA 315(2):164-174 (2016); Tangri et al. JAMA 305(15):1553-1559 (2011)).Other combined tests incorporating multiple plasma biomarkers and limited clinical data features have been shown to accurately predict incident CKD in individuals with T2D, but predicting progressive decline in kidney function is an ongoing challenge (Peters et al. J Clin Med 9(10)(2020); Peters et al. J Diabetes Complicat 33(12)(2019)). However, the goal of the KIDNEYINTELX test is to determine which patients with established DKD are at highest risk for progressive decline in kidney function due to renal failure and which have CKD that is less likely to progress over time.

[0144] Thus, machine learning models combining plasma biomarkers and EHR data can significantly improve prediction of progressive decline in kidney function compared with standard clinical models in patients with T2 DKD from a large academic medical center.

[0145] Machine learning prognostic risk score assays for use in the methods of the present disclosure can be used, for example, as described in U.S. Patent Application No. 62 / 976,767, U.S. Patent Application No. 62 / 976,761, and U.S. Patent Application No. 63 / 016,868, each of which is incorporated by reference in its entirety.

[0146] IV. Methods of Treatment or Prevention Methods and compositions for treating or preventing renal impairment and / or ESKD (also referred to herein as ESRD) in a subject in need thereof are also a feature of the present disclosure. In one embodiment, the present disclosure provides a method of treating a subject having renal impairment and / or ESKD, a subject suspected of having renal impairment and / or ESKD, or a subject at risk of developing renal impairment and / or ESKD. In other embodiments, a subject having a disorder associated with renal impairment and / or ESKD may be treated using the methods described herein without being identified by the predictive methods of the present disclosure. In certain embodiments, the methods of treatment disclosed herein improve renal function (also referred to herein as "kidney function") in such a subject.

[0147] In some embodiments, the method of treatment described herein comprises administering a therapy of the present disclosure to a subject. The therapy of the present disclosure may comprise a therapeutically effective amount of a protein or nucleic acid molecule that increases the expression and / or function of one or more of the protective proteins described herein above. For example, the therapy of the present disclosure may comprise a therapeutically effective amount of one or more protective proteins (e.g., a therapeutically effective amount of a recombinant SPARC, a recombinant CCL5, a recombinant APP, a recombinant PF4, a recombinant DNAJC19, a recombinant ANGPT1, a recombinant TNFSF12, a recombinant FGF20, and / or a recombinant Testican-2). Alternatively, the therapy of the present disclosure may comprise a therapeutically effective amount of one or more analogs of protective proteins (e.g., a therapeutically effective amount of a SPARC analog, a CCL5 analog, an APP analog, a PF4 analog, a DNAJC19 analog, an ANGPT1 analog, a TNFSF12 analog, a FGF20 analog, and / or a Testican-2 analog). An analog of a protective protein can be a mutant polypeptide (e.g., a mutant SPARC polypeptide, a mutant CCL5 polypeptide, a mutant APP polypeptide, a mutant PF4 polypeptide, a mutant DNAJC19 polypeptide, a mutant ANGPT1 polypeptide, a mutant TNFSF12 polypeptide, a mutant FGF20 polypeptide, and / or a mutant Testican-2 polypeptide). Alternatively, an analog of a protective protein can be a fusion protein, such as a chimeric protein, that contains a protective protein (e.g., a SPARC polypeptide, a CCL5 polypeptide, an APP polypeptide, a PF4 polypeptide, a DNAJC19 polypeptide, an ANGPT1 polypeptide, a TNFSF12 polypeptide, an FGF20 polypeptide, and / or a Testican-2 polypeptide) and one or more polypeptide moieties that improve in vivo stability, in vivo half-life, and / or uptake / administration. Alternatively, the analog of a protective protein can be a mimetic (e.g., a non-peptide mimetic) of one or more protective proteins (e.g., a mimetic of SPARC, CCL5, APP, PF4, DNAJC19, ANGPT1, TNFSF12, FGF20, and / or Testican-2).In other cases, the analog of a protection protein can be an agonist of one or more protection proteins (e.g., a SPARC agonist, a CCL5 agonist, an APP agonist, a PF4 agonist, a DNAJC19 agonist, an ANGPT1 agonist, a TNFSF12 agonist, an FGF20 agonist, and / or a Testican-2 agonist). Agonists for use in the present disclosure can be agonist antibodies, such as antibodies against receptors for protective proteins (e.g., agonist SPARC receptor antibodies, agonist CCL5 receptor antibodies, agonist APP receptor antibodies, agonist PF4 receptor antibodies, agonist DNAJC19 receptor antibodies, agonist ANGPT1 receptor antibodies, agonist TNFSF12 receptor antibodies, agonist FGF20 receptor antibodies, and / or agonist Testican-2 receptor antibodies. In yet other embodiments, therapies of the present disclosure can include a therapeutically effective amount of a nucleic acid molecule encoding one or more proteins (e.g., a DNA or RNA molecule encoding one or more of SPARC, CCL5, APP, PF4, DNAJC19, ANGPT1, TNFSF12, FGF20, and / or Testican-2).

[0148] ANGPT1 In some embodiments, the methods of treatment described herein include therapeutic uses of ANGPT1, such as administering to a subject a therapeutically effective amount of a protein or nucleic acid molecule that increases the expression and / or function of ANGPT1. For example, the methods of treatment described herein may include administering to a subject a therapeutically effective amount of recombinant ANGPT1 (e.g., of human or mouse origin), an ANGPT1 analog (e.g., a mutant ANGPT1 polypeptide, or an ANGPT1 fusion protein, such as a chimeric protein that contains an ANGPT1 polypeptide and one or more polypeptide moieties that improve in vivo stability, in vivo half-life, and / or uptake / administration), an ANGPT1 mimetic (e.g., a non-peptide mimetic of ANGPT1), an ANGPT1 agonist (e.g., an agonist ANGPT1 receptor antibody), and / or a nucleic acid molecule encoding ANGPT1.

[0149] Such therapeutic uses of ANGPT1 may include, for example, those described in WO2018067991A1, which describes methods of modulating T cell dysfunction used to treat conditions such as cancer and chronic infections by contacting dysfunctional T cells with one or more modulators that promote the expression, activity and / or function of angiopoietin or angiopoietin-like proteins, such as ANGPT1.

[0150] Alternatively, the therapeutic use of ANGPT1 may include, for example, those described in US20090304680A1, which describes a pharmaceutical composition for the treatment, prevention or diagnosis of Kawasaki disease in an individual, the composition comprising a molecule comprising ANGPT1 or a modulator thereof.

[0151] TNFSF12 In some embodiments, the methods of treatment described herein include therapeutic use of TNFSF12 or TWEAK, such as administering to a subject a therapeutically effective amount of a protein or nucleic acid molecule that increases the expression and / or function of TNFSF12. For example, the methods of treatment described herein may include administering to a subject a therapeutically effective amount of a recombinant TNFSF12 (e.g., of human or mouse origin), a TNFSF12 analog (e.g., a mutant TNFSF12 polypeptide, or a TNFSF12 fusion protein, such as a chimeric protein that contains a TNFSF12 polypeptide and one or more polypeptide moieties that improve in vivo stability, in vivo half-life, and / or uptake / administration), a TNFSF12 mimetic (e.g., a non-peptide mimetic of TNFSF12), a TNFSF12 agonist (e.g., an agonist TNFSF12 receptor antibody), and / or a nucleic acid molecule encoding TNFSF12.

[0152] Such therapeutic uses of TNFSF12 may include, for example, those described in WO2010088534A1. As described in WO2010088534A1, TNFSF12 can increase the population of human and rodent pancreatic cells and induce the emergence of endocrine lineage-committed progenitor cells in the pancreas. Thus, agonists of the TNFSF12 receptor (TNFSF12-R) can be used in methods for regenerating pancreatic tissue and increasing the population of pancreatic cells in vivo and in vitro. These methods can be used to treat diseases or conditions, including diabetes, in which an increase in pancreatic progenitor cells is desirable for cell replacement therapy, and conditions that result in the loss of all or part of the pancreas. When used in such methods, TNFSF12-R agonists can be TNFSF12 (e.g., TNFSF12 polypeptides of human or murine origin), TNFSF12 analogs (e.g., mutant TNFSF12 polypeptides, or TNFSF12 fusion proteins, such as chimeric proteins containing a TNFSF12 polypeptide and one or more polypeptide moieties that improve in vivo stability, in vivo half-life, and / or uptake / administration), TNFSF12 mimetics (e.g., non-peptide mimetics of TNFSF12), and agonistic TNFSF12-R antibodies.

[0153] Alternatively, the therapeutic use of TNFSF12 may include, for example, therapeutic use as described in WO2001085193A2. WO2001085193A2 describes the use of synergistically effective amounts of TNFSF12 agonists and angiogenic factors in a method for enhancing angiogenic activity to promote neovascularization. Such TNFSF12 agonists include soluble recombinant TNFSF12 proteins and TNFSF12 agonists taught in WO98 / 05783, WO98 / 35061 and WO99 / 19490.

[0154] FGF20 In some embodiments, the methods of treatment described herein include therapeutic uses of FGF20, such as administering to a subject a therapeutically effective amount of a protein or nucleic acid molecule that increases expression and / or function of FGF20. For example, the methods of treatment described herein may include administering to a subject a therapeutically effective amount of a recombinant FGF20 (e.g., of human or murine origin), an FGF20 analog (e.g., a mutant FGF20 polypeptide or an FGF20 fusion protein, such as a chimeric protein containing an FGF20 polypeptide and one or more polypeptide moieties that improve in vivo stability, in vivo half-life, and / or uptake / administration), an FGF20 mimetic (e.g., a non-peptide mimetic of FGF20), an FGF20 agonist (e.g., an agonistic FGF20 receptor antibody), and / or a nucleic acid molecule encoding FGF20.

[0155] Such therapeutic uses of FGF20 may include, for example, those described in WO2005019427A2. WO2005019427A2 describes a method for treating a hyperphosphatemic condition by administering a therapeutically effective amount of an isolated FGF20 polypeptide (e.g., an FGF20 polypeptide having a mutation that confers increased stability to the FGF20 polypeptide). WO2005019427A2 also describes a method for treating a hyperphosphatemic condition by administering a therapeutically effective amount of an agent that increases the level of an FGF20 polypeptide. WO2005019427A2 also describes a method for treating a condition involving calcium and phosphate deposition in arteries or soft tissues of a subject by administering to the subject a therapeutically effective amount of FGF20 or an agent that increases the level of an FGF20 polypeptide.

[0156] Alternatively, the therapeutic use of FGF20 may include, for example, therapeutic uses as described in WO2020160468A1, which describes a method of treating a patient diagnosed with a neurocognitive disorder (NCD) by providing the patient with one or more agents that collectively increase the expression and / or activity of two or more proteins selected from the group including FGF20.

[0157] SPARC In some embodiments, the methods of treatment described herein include therapeutic uses of SPARC, such as administering to a subject a therapeutically effective amount of a protein or nucleic acid molecule that increases SPARC expression and / or function. For example, the methods of treatment described herein can include administering to a subject a therapeutically effective amount of a recombinant SPARC (e.g., of human or murine origin), a SPARC analog (e.g., a mutant SPARC polypeptide or a SPARC fusion protein, such as a chimeric protein containing a SPARC polypeptide and one or more polypeptide moieties that improve in vivo stability, in vivo half-life, and / or uptake / administration), a SPARC mimetic (e.g., a non-peptide mimetic of SPARC), a SPARC agonist (e.g., an agonist SPARC receptor antibody), and / or a nucleic acid molecule encoding SPARC.

[0158] Such therapeutic uses of SPARC may include, for example, those described in WO2008128169A1, which describes a composition for treating mammalian tumors comprising a therapeutically effective amount of a SPARC polypeptide and a therapeutically effective amount of a hydrophobic chemotherapeutic agent (e.g., a microtubule inhibitor such as a taxane) in the absence or presence of an angiogenesis inhibitor. The SPARC polypeptide used in the composition of WO2008128169A1 is either exogenous wild-type SPARC or exogenous mutant SPARC (having a mutation corresponding to the deletion of the third glutamine in the mature form of the human SPARC protein).

[0159] Therapeutic uses of SPARC may also include those described, for example, in WO2013170365A1, which discloses a method for sensitizing cancer cells by administration of a SPARC polypeptide and GRP78. The SPARC polypeptide used in the method of WO2013170365A1 refers to the full-length 303 amino acid SPARC protein sequence and any fragments or variants thereof known in the art that retain chemosensitizing activity, including some of the SPARC polypeptides described in Rahman et al. (PLOS ONE 10.1371 / journal.pone.0026390 Published:1 November 2011) and the SPARC fragments tested in WO / 2008 / 000079.

[0160] Alternatively, therapeutic uses of SPARC can include those described, for example, in Chlenski et al. (Mol Cancer 9:138 (2010)), who describe that SPARC peptides corresponding to the follistatin domain of the protein (FS-E), and in particular the peptide FSEC, corresponding to the C-terminal loop of FS-E, have potent antiangiogenic and antitumorigenic effects in neuroblastoma.

[0161] CCL5 In some embodiments, the methods of treatment described herein include therapeutic uses of CCL5, such as administering to a subject a therapeutically effective amount of a protein or nucleic acid molecule that increases expression and / or function of CCL5. For example, the methods of treatment described herein may include administering to a subject a therapeutically effective amount of a recombinant CCL5 (e.g., of human or murine origin), a CCL5 analog (e.g., a mutant CCL5 polypeptide or a CCL5 fusion protein, such as a chimeric protein containing a CCL5 polypeptide and one or more polypeptide moieties that improve in vivo stability, in vivo half-life, and / or uptake / administration), a CCL5 mimetic (e.g., a non-peptide mimetic of CCL5), a CCL5 agonist (e.g., an agonistic CCL5 receptor antibody), and / or a nucleic acid molecule encoding CCL5.

[0162] Such therapeutic uses of CCL5 may include, for example, those described in Bhat et al. (Front Immunol, 11:1849 (2020)) and / or Xie et al. (PNAS 118(9)e2017282118 (2021)). Bhat et al. describe robust CCL5 production following arenavirus lymphocytic choriomeningitis virus (LCMV) treatment. Xie et al. show widespread expression of the chemokine CCL5 following ciliary neurotrophic factor (CNTF) gene therapy.

[0163] Alternatively, the therapeutic use of CCL5 may include therapeutic uses such as those described in WO2020068261A1, which describes an immunomodulatory fusion protein comprising a collagen binding domain operably linked to an immunomodulatory domain, the immunomodulatory domain comprising one or more chemokines, such as CCL5, and methods of using same, for example to treat cancer.

[0164] In other cases, therapeutic uses of CCL5 may include therapeutic uses such as those described in WO2020146857A1, which describes a protease-released chemokine protein (PARK) comprising a prochemokine moiety comprising a propeptide moiety fused to a chemokine moiety, where the chemokine moiety comprises an N-terminus and a C-terminus, and the chemokine moiety comprises a chemokine amino acid sequence having at least 90% similarity to CCL5, and a targeting moiety linked to the prochemokine moiety, where the targeting moiety has binding specificity for an antigen or receptor associated with tumors, fibrosis, or Alzheimer's disease.

[0165] APP In some embodiments, the methods of treatment described herein include therapeutic uses of APP, such as administering to a subject a therapeutically effective amount of a protein or nucleic acid molecule that increases expression and / or function of APP. For example, the methods of treatment described herein may include administering to a subject a therapeutically effective amount of a recombinant APP (e.g., of human or murine origin), an APP analog (e.g., a mutant APP polypeptide, or an APP fusion protein, such as a chimeric protein containing an APP polypeptide and one or more polypeptide moieties that improve in vivo stability, in vivo half-life, and / or uptake / administration), an APP mimetic (e.g., a non-peptide mimetic of APP), an APP agonist (e.g., an agonist APP receptor antibody), and / or a nucleic acid molecule encoding APP.

[0166] Such therapeutic uses of APP may include, for example, therapeutic uses as described in WO2020201471A1. WO2020201471A1 describes a compound for use in treating or preventing liver disease, the compound being an amyloid beta-related protein, the amyloid beta-related protein being selected from the group consisting of amyloid beta protein, amyloid beta peptides derived therefrom, amyloid precursor protein (APP), a compound involved in the generation of amyloid beta peptides from APP, or a compound inhibiting the degradation of amyloid beta protein or amyloid peptides derived therefrom. Amyloid precursor protein or "APP" refers to an integral membrane protein expressed in many tissues and concentrated at neuronal synapses. APP is known as a precursor molecule that generates beta amyloid (Ab) by proteolysis. In particular, the amyloid beta peptides derived from the amyloid beta protein are selected from the group consisting of amyloid beta 40, amyloid beta 42, and amyloid beta 38. Furthermore, the compound involved in the generation of amyloid beta peptides from APP may be an enzyme selected from alpha-secretase, beta-secretase (BACE1), gamma-secretase, and preferably presenilin.

[0167] Alternatively, the therapeutic use of APP may include therapeutic uses such as those described in WO2020160468A1. WO2020160468A1 describes compositions and methods for treating patients with or at risk of developing neurocognitive disorders such as Alzheimer's disease, Parkinson's disease, and / or frontotemporal dementia by providing the patient with one or more agents that collectively increase the expression and / or activity of two or more proteins selected from a group including APP. APP and amyloid beta A4 proteins include wild-type forms of the APP gene or protein, as well as variants of the wild-type APP protein (e.g., splice variants, truncations, concatemers, and fusion constructs, among others) and nucleic acids encoding same.

[0168] PF4 In some embodiments, the methods of treatment described herein include therapeutic uses of PF4, such as administering to a subject a therapeutically effective amount of a protein or nucleic acid molecule that increases expression and / or function of PF4. For example, the methods of treatment described herein may include administering to a subject a therapeutically effective amount of a recombinant PF4 (e.g., of human or murine origin), a PF4 analog (e.g., a mutant PF4 polypeptide or a PF4 fusion protein, such as a chimeric protein containing a PF4 polypeptide and one or more polypeptide moieties that improve in vivo stability, in vivo half-life, and / or uptake / administration), a PF4 mimetic (e.g., a non-peptide mimetic of PF4), a PF4 agonist (e.g., an agonist PF4 receptor antibody), and / or a nucleic acid molecule encoding PF4.

[0169] Such therapeutic uses of PF4 may include, for example, those described in WO2009117710A2, which describes a method of treating a MIF-mediated disorder by administering to a subject an active agent that inhibits (i) the binding of MIF to CXCR2 and CXCR4, and / or (ii) MIF activation of CXCR2 and CXCR4; (iii) the ability of MIF to form homomultimers; or a combination thereof, which active agent may be recombinant PF4.

[0170] Alternatively, the therapeutic use of PF4 may include, for example, therapeutic uses as described in WO1994013321A1, which describes a process of suppressing myeloid cells by administering a synergistic combination of chemokines that suppress myeloid cells, the synergistic combination comprising at least one chemokine selected from the group consisting of PF4. The PF4 used in the methods and compositions of WO1994013321A1 is naturally occurring human PF4.

[0171] DNAJC19 In some embodiments, the methods of treatment described herein include therapeutic uses of DNAJC19, such as administering to a subject a therapeutically effective amount of a protein or nucleic acid molecule that increases the expression and / or function of DNAJC19. For example, the methods of treatment described herein may include administering to a subject a therapeutically effective amount of a recombinant DNAJC19 (e.g., of human or murine origin), a DNAJC19 analog (e.g., a mutant DNAJC19 polypeptide or a DNAJC19 fusion protein, such as a chimeric protein containing a DNAJC19 polypeptide and one or more polypeptide moieties that improve in vivo stability, in vivo half-life, and / or uptake / administration), a DNAJC19 mimetic (e.g., a non-peptide mimetic of DNAJC19), a DNAJC19 agonist (e.g., an agonist DNAJC19 receptor antibody), and / or a nucleic acid molecule encoding DNAJC19.

[0172] Such therapeutic uses of DNAJC19 may include, for example, those described in WO2016170348A2, which describes small activating RNAs for regulating the expression of a target gene for therapeutic purposes, and the target gene may be DNAJC19.

[0173] Alternatively, the therapeutic use of DNAJC19 may include therapeutic uses such as those described in WO2017191274A2, which describes RNAs containing coding sequences useful for preparing compositions used as medicaments for use in gene therapy in diseases, disorders or conditions such as metabolic or endocrine disorders, cancer, infectious diseases or immune deficiencies, wherein the encoded peptide or protein comprises a therapeutic protein selected from the group including, but not limited to, DNAJC19, or a fragment or variant thereof.

[0174] Testican-2 In some embodiments, the methods of treatment described herein include therapeutic uses of Testican-2, such as administering to a subject a therapeutically effective amount of a protein or nucleic acid molecule that is Testican-2 or that increases the expression and / or function of Testican-2. For example, the methods of treatment described herein may include administering to a subject a therapeutically effective amount of recombinant Testican-2, a Testican-2 analog (e.g., a mutant Testican-2 polypeptide or a Testican-2 fusion protein, such as a chimeric protein containing a Testican-2 polypeptide and one or more polypeptide moieties that improve in vivo stability, in vivo half-life, and / or uptake / administration), a Testican-2 mimetic (e.g., a non-peptide mimetic of Testican-2), a Testican-2 agonist (e.g., an agonist Testican-2 receptor antibody), and / or a nucleic acid molecule encoding Testican-2.

[0175] In certain embodiments, the methods and compositions disclosed herein are used to identify human subjects at risk of developing progressive renal decline (the subject may already have renal decline, in which case the risk is assessed for further progression), and human subjects identified as at risk are administered a therapy to improve renal function (i.e., slow the progression of kidney disease). Examples of therapies include, but are not limited to, weight loss, medications to control high blood pressure, and / or medications to control high cholesterol levels. Such medications may be used to treat problems that may cause progressive kidney disease and the complications that may result, such as high blood pressure. The methods disclosed herein, in certain embodiments, also include administering an additional medication, such as an anti-fibrotic agent, to the subject. Exemplary agents include, but are not limited to, angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor type 1 blockers (ARBs), renin inhibitors (aliskiren, enalkiren, zalkirene), mineralocorticoid receptor blockers (spironolactone, eplerenone), vasopeptidase inhibitors (e.g., AVE7688, omapatrilat). In certain embodiments, a statin, such as atorvastatin or simvastatin, is administered to reduce cholesterol levels in a human subject.

[0176] Additionally, the nucleic acid molecules (e.g., DNA and / or mRNA nucleic acid molecules) useful in the therapeutic methods described herein may be synthetic. The term "synthetic" means that the nucleic acid molecule is isolated and is not identical in sequence (overall sequence) and / or chemical structure to a naturally occurring nucleic acid molecule, such as an endogenous precursor mRNA molecule. In some embodiments, the nucleic acids of the invention do not have an entire sequence identical to that of a naturally occurring nucleic acid, but such molecules may include all or part of a naturally occurring sequence. However, it is contemplated that a synthetic nucleic acid administered to a cell may then be modified or altered within the cell such that its structure or sequence is the same as a non-synthetic or naturally occurring nucleic acid, such as a mature mRNA sequence. For example, a synthetic nucleic acid may have a sequence that is different from that of a precursor mRNA, but the sequence may be altered once in the cell to be the same as an endogenous processed mRNA. The term "isolated" means that the nucleic acid molecules of the present disclosure are first separated from undesired nucleic acid molecules that differ (in sequence or structure), such that the population of isolated nucleic acids is at least about 90% homogeneous, and may be at least about 95, 96, 97, 98, 99, or 100% homogeneous with respect to other polynucleotide molecules. In many embodiments of the present disclosure, the nucleic acid is isolated by being synthesized in vitro apart from the endogenous nucleic acid in the cell. However, it will be understood that the isolated nucleic acids may then be mixed or pooled together.

[0177] Nucleic acids can be produced by any technique known to those of skill in the art, such as, for example, chemical synthesis, enzymatic production, or biological production.

[0178] Nucleic acid synthesis is carried out according to standard methods. See, for example, Itakura and Riggs (1980). In addition, U.S. Patent No. 4,704,362, U.S. Patent No. 5,221,619, and U.S. Patent No. 5,583,013 each describe various methods for preparing synthetic nucleic acids. Non-limiting examples of synthetic nucleic acids (e.g., synthetic oligonucleotides) include nucleic acids made by in vitro chemical synthesis using phosphotriester, phosphite, or phosphoramidite chemistry and solid-phase techniques, as described in EP 266,032, which is incorporated herein by reference, or via deoxynucleoside H-phosphonate intermediates, as described in Froehler et al., 1986 and U.S. Patent No. 5,705,629, each of which is incorporated herein by reference. One or more oligonucleotides may be used in the methods of the present invention. A variety of different oligonucleotide synthesis mechanisms are disclosed, for example, in U.S. Pat. Nos. 4,659,774, 4,816,571, 5,141,813, 5,264,566, 4,959,463, 5,428,148, 5,554,744, 5,574,146, and 5,602,244, each of which is incorporated herein by reference.

[0179] Non-limiting examples of enzymatically produced nucleic acids include those produced by enzymes in amplification reactions such as PCR (see, e.g., U.S. Pat. Nos. 4,683,202 and 4,682,195, each of which is incorporated herein by reference), or those produced by synthesis of oligonucleotides as described in U.S. Pat. No. 5,645,897, which is incorporated herein by reference.

[0180] Oligonucleotide synthesis is well known to those skilled in the art.A variety of different oligonucleotide synthesis mechanisms are disclosed in, for example, U.S. Patent No. 4,659,774, U.S. Patent No. 4,816,571, U.S. Patent No. 5,141,813, U.S. Patent No. 5,264,566, U.S. Patent No. 4,959,463, U.S. Patent No. 5,428,148, U.S. Patent No. 5,554,744, U.S. Patent No. 5,574,146, U.S. Patent No. 5,602,244, each of which is incorporated herein by reference.

[0181] Recombinant methods for producing nucleic acids in cells are well known to those skilled in the art. These include the use of vectors, plasmids, cosmids, and other vehicles to deliver nucleic acids (to produce large amounts of the desired RNA molecule) to cells, which may be target cells or simply host cells. Alternatively, such vehicles may be used in a cell-free environment, so long as the reagents for producing the RNA molecule are present. Such methods include those described in Sambrook, 2003, Sambrook, 2001, and Sambrook, 1989, which are incorporated herein by reference.

[0182] In certain embodiments, the nucleic acid molecules of the present disclosure are not synthetic. In some embodiments, the nucleic acid molecules have the chemical structure and sequence of a naturally occurring nucleic acid. In addition to using recombinant techniques, such non-synthetic nucleic acids can also be produced chemically, such as by utilizing techniques used to make oligonucleotides.

[0183] Administration or delivery of a therapeutic agent (e.g., a protective protein) according to the present disclosure can be via any route so long as the target tissue is available via that route. For example, administration can be by intradermal, subcutaneous, intramuscular, intraperitoneal or intravenous injection, or by direct injection into the target tissue (e.g., cardiac tissue). Pharmaceutical compositions containing a polypeptide or polynucleotide, or an expression construct containing a polypeptide or polynucleotide sequence, can also be administered by a catheter system for delivering the therapeutic agent to the heart or a system that isolates the coronary circulation. A variety of catheter systems for delivering therapeutic agents to the heart and coronary vasculature are known in the art. Some non-limiting examples of catheter-based delivery or coronary vessel isolation methods suitable for use in the present invention are disclosed in U.S. Pat. No. 6,416,510, U.S. Pat. No. 6,716,196, U.S. Pat. No. 6,953,466, WO2005 / 082440, WO2006 / 089340, U.S. Patent Publication No. 2007 / 0203445, U.S. Patent Publication No. 2006 / 0148742, and U.S. Patent Publication No. 2007 / 0060907, all of which are incorporated by reference herein in their entireties.

[0184] The therapeutic agent (e.g., protective protein) may be administered parenterally or intraperitoneally. For example, solutions of the conjugate as a free base or pharmacologically acceptable salt may be prepared in water suitably mixed with a surfactant, such as hydroxypropylcellulose. Dispersions may also be prepared in glycerol, liquid polyethylene glycols, and mixtures thereof, and in oils. Under ordinary conditions of storage and use, these preparations generally contain a preservative to prevent the growth of microorganisms.

[0185] Therapeutic agents (e.g., protective proteins) suitable for injection use or catheter delivery include, for example, sterile aqueous solutions or dispersions and sterile powders for the extemporaneous preparation of sterile injectable solutions or dispersions. Generally, these preparations are sterile and have a fluidity to the extent that they can be easily syringable. Preparations must be stable under the conditions of manufacture and storage and must be preserved against the contaminating action of microorganisms, such as bacteria and fungi. Suitable solvents or dispersion media may contain, for example, water, ethanol, polyol (e.g., glycerol, propylene glycol, and liquid polyethylene glycol, etc.), suitable mixtures thereof, and vegetable oils. Proper fluidity can be maintained, for example, by the use of a coating such as lecithin, by the maintenance of the required particle size in the case of dispersion, and by the use of surfactants. Prevention of the action of microorganisms can be brought about by various antibacterial and antifungal agent(s), for example, parabens, chlorobutanol, phenol, sorbic acid, thimerosal, and the like. In many cases, it will be preferable to include isotonic agents, for example, sugars or sodium chloride. Prolonged absorption of the injectable compositions can be brought about by the use in the compositions of agents delaying absorption, for example, aluminum monostearate and gelatin.

[0186] This disclosure is further illustrated by the following examples, which should not be construed as limiting. EXAMPLES

[0187] Described herein are studies to identify biomarkers useful for the diagnosis, prognosis, and identification of subjects having, or suspected of having, or likely to develop progressive renal function decline and / or ESKD. The following examples are included for illustrative purposes only and are not intended to be limiting.

[0188] Over the past decades, considerable research efforts have been made to understand the mechanisms of diabetic kidney disease (DKD) in humans with type 1 diabetes (T1D) and type 2 diabetes (T2D). In the studies, the main focus was on factors and markers associated with a high risk of developing various manifestations of DKD (Parving et al., Diabetic Nephropathy. In: Brenner BM, ed. Brenner and Rector's The Kidney. 7th ed. Philadelphia. (Elsevier, 2004); JAMA 290: 2159-2167 (2003); Lancet 352: 837-853 (1998); Nowak et al., Kidney International 93: 1198-1206 (2018); Niewczas et al., Nat Med 25: 805-813 (2019); Ahluwalia et al., Editorial: Novel Biomarkers for Type 2 Diabetes. Front Endocrinol (Lausanne) 10: 649 (2019)). Recently, there has been interest in searching for factors and biomarkers associated with protection from DKD. It has been hypothesized that subjects who have had diabetes for a long time but have not developed late complications, so-called long-term diabetes survivors, may be enriched for such protective factors / biomarkers. This approach has not only led to the development of new hypotheses regarding DKD, but has already provided findings that led to the identification of pyruvate kinase M2 (PKM2) as a new therapeutic target to prevent DKD (Qi et al., Nat Med 23:753-762(2017)).

[0189] Materials and Methods Subjects for the study described herein were selected from participants in the Joslin Kidney Study (JKS). The Joslin Diabetes Center Committee on Human Studies approved the informed consent, recruitment, and testing protocols for the JKS, a longitudinal observational study investigating the determinants and natural history of kidney function decline in both types of diabetes.

[0190] Joslin Kidney Study (JKS) Briefly, JKS includes two components: type 1 diabetes (T1D) and type 2 diabetes (T2D). Subjects for the T1D component were consecutively recruited from among 3,500 adults aged 18-64 years with T1D who attended the Joslin Clinic between 1991 and 2009. Subjects were classified into three subgroups according to the median ACR obtained in the 2 years prior to enrollment (baseline survey): macroalbuminuric (ACR ≥ 300 μg / mg), microalbuminuric (30 ≤ ACR < 300 μg / mg), and normoalbuminuric (ACR < 30 μg / mg). The aim was to recruit all macroalbuminuric and microalbuminuric subjects, as well as a similar number of normoalbuminuric subjects, into JKS. A total of 1884 subjects were enrolled: 526 macroalbuminuric subjects, 563 microalbuminuric subjects, and 795 normoalbuminuric subjects.

[0191] Subjects for the T2D cohort were consecutively recruited from among 4500 adults aged 35-64 years with T2D attending the Joslin Clinic between 2003 and 2009. Subjects were classified into three subgroups as described above for T1D according to the median ACR obtained in the 2 years prior to enrollment (baseline survey). The aim was to recruit all macroalbuminuric and microalbuminuric subjects, as well as a similar number of normoalbuminuric subjects, to the JKS. A total of 1476 subjects were enrolled: 261 macroalbuminuric, 482 microalbuminuric, and 733 normoalbuminuric.

[0192] All subjects enrolled in the JKS were surveyed twice a year during routine clinic visits, or were invited to special visits or surveyed at home. These surveys were conducted until the subjects developed end-stage kidney disease (ESKD), died, were lost to follow-up, or the end of follow-up in 2015. Biospecimens obtained during the surveys were stored at -85°C. Serum creatinine was used to determine renal function at baseline and its change during follow-up visits. Serum creatinine measurements were calibrated over time using a protocol described by Skupien et al. (Kidney international 82:589-597(2012)). Estimates of glomerular filtration rate (GFR) were obtained using the Chronic Kidney Disease Epidemiology Collaboration formula described by Levey et al. (Ann Intern Med 150:604-612(2009)).

[0193] The first step in classifying patterns of trajectories of renal function change during follow-up was to determine whether they were linear or nonlinear. Although most estimated glomerular filtration rate (eGFR) trajectories appeared linear at the time of examination, this impression was statistically verified by fitting both linear and spline models to each patient's renal function trajectory. By applying the approach described by Jones and Molitoris (Anal Biochem 141:287-290 (1984)) and used by Shah and Levey (J Am Soc Nephrol 2:1186-1191 (1992)), the continuous renal function change of individuals during follow-up was examined. Participants in this study had 5 or more eGFR determinations during 7 to 15 years of follow-up. This method represents each participant's renal function trajectory as a simple linear model and as a spline model containing linear segments connected at the individually determined points. Linear and spline models were compared, and linear models were rejected with a nominal significance of 0.05 and degrees of freedom determined by the number of spline segments (n-1). Most had linear slopes. To determine the slope of eGFR decline, a distribution of the slope of the overall eGFR change during follow-up was generated by extracting the linear component of each individual's trajectory. Details of this approach are described below and in Skupien et al. (Kidney international 82:589-597(2012)).

[0194] All subjects included in the JKS were cross-referenced every two years with the United States Renal Data System (USRDS) and the National Death Index (NDI) to identify patients who developed or died from ESKD, with the last queries being performed in 2015. The USRDS maintains a registry of US patients receiving renal replacement therapy, including dates of dialysis and transplantation.

[0195] Discovery, Replication, and Validation Cohorts This study includes three JKS cohorts. An exploratory cohort of 214 T1D subjects and a replication cohort of 144 T2D subjects were previously enrolled in our study to determine the cut-point values ​​of serum TNF-R1 concentrations for predicting the development of ESKD in T1D and T2D (Yamanouchi et al., Kidney International 92:258-266(2017)). Unlike previous studies that included subjects with chronic kidney disease (CKD) stages 3 and 4, this study included JKS subjects who had CKD stage 3 at baseline survey. The validation cohort consisted of 294 T1D subjects who had CKD stages 1 and 2 at baseline and was used to examine the significance of three exemplary protective proteins observed in the late diabetic kidney disease (DKD) cohort of early stage DKD subjects. The primary objective was to search for protective proteins against progressive renal function decline and progression to ESKD not only in T1D patients with abnormal renal function but also in any diabetic patients with any stage of DKD. Therefore, to demonstrate the robustness of the findings, we selected three very different cohorts with different baseline characteristics: T1D discovery cohort (T1D patients with late stage DKD), T2D replication cohort (T2D patients with late stage DKD), and T1D validation cohort (T1D patients with early stage DKD).

[0196] Subjects with T1D and T2D had macroalbuminuria (ACR ≥ 300 μg / mg) and microalbuminuria (ACR ≥ 30 μg / mg). These subjects were followed for 7 to 15 years to determine the rate of eGFR decline (eGFR slope) and to confirm the occurrence of ESKD. All clinical data and plasma samples from these subjects were available for this study. Detailed descriptions of these cohorts, measurements of clinical characteristics, determination of eGFR slope from serial measurements of serum creatinine, and confirmation of the occurrence of ESKD are described, for example, in Niewczas et al. (Nat Med 25:805-813 (2019)) and Yamanouchi et al. (Kidney International 92:258-266 (2017)). In all three cohorts, eGFR loss <3.0 ml / min / year was chosen as the threshold for defining individuals with slow (non-progressors) or fast (progressors) progressive renal function decline. The rationale for such a threshold has been well documented and used in previous publications (Perkins et al., J Am Soc Nephrol 18:1353-1361 (2007); Krolewski et al., Diabetes Care 37:226-234 (2014)) and corresponds to the 2.5th percentile of the distribution of annual renal function loss in the general population (Lindeman et al., J Am Geriatr Soc 33:278-285 (1985)).

[0197] Healthy non-diabetic parents of T1D subjects In the Joslin Kidney Study, living parents of subjects with T1D were also examined. The group of nondiabetic parents of T1D subjects was derived from a genetic study of determinants of DKD in T1D. The baseline survey of parents was conducted according to the same protocol as for all participants in the JKS. Biospecimens obtained in the survey were stored at -85°C. In this study, 79 Caucasian nondiabetic parents aged 50–69 years at the time of the baseline survey were selected to serve as nondiabetic controls. Forty parents had children who did not develop renal complications despite a long duration of diabetes, and 39 parents had children with advanced DKD (dyslipidemia or ESKD). The clinical phenotype of the T1D offspring of nondiabetic parents was either normoalbuminuria (n=40), or ESKD or proteinuria (n=39). Plasma samples obtained in the baseline survey were subjected to SOMAscan analysis.

[0198] SOMAscan proteomic analysis The SOMAscan proteomics platform uses single-stranded DNA aptamers to measure the concentration of 1129 proteins in as little as 50 μl of plasma, serum, or a variety of other biological matrices in similarly small volumes. A complete list of proteins is shown in Table 1. The SOMAscan platform leverages a new generation of SOMAMER (Slow Off-rate Modified Aptamer) reagents that take advantage of aptamer technology developed over the past 20 years (Tuerk et al., Science 249:505-510 (1990); Ellington et al., Nature 346:818-822 (1990)). SOMAmer reagents are selected against proteins in their native folded conformation and bind to folded proteins and thus three-dimensional shape epitopes rather than linear peptide sequences. The SOMAscan platform offers a high dynamic range, and this large dynamic range is due to the detection range of each SOMAMER reagent in combination with three serial dilutions of the sample of interest. The dilutions are split into three pools: 40% (most concentrated sample to detect the least abundant proteins: fM-pM in 100% sample), 1% (mid-range), and 0.005% (least concentrated sample design to detect the most abundant proteins: ~μM in 100% sample). The assay readout is reported in relative fluorescence units (RFU) and is directly proportional to the amount of target protein in the original sample. Details of the SOMAscan proteomics platform are described elsewhere (Gold et al., PLoS One 5:e15004 (2010); Hathout et al., Proc Natl Acad Sci USA 112:7153-7158 (2015)).

[0199] Proteomic profiling was performed using the SOMAscan platform based at the SomaLogic laboratory (Boulder, CO). A Human Plasma SOMAscan 1.1k kit with a set of calibration and normalization samples was used according to the manufacturer's recommended protocol. Data normalization was performed according to the data quality control protocol of the SOMAscan platform. To normalize SOMAscan assay results, raw SOMAscan assay data were first normalized to remove within-run hybridization variability (hybridization normalization), then removed other assay bias within the run by median signal normalization of all samples, and finally calibrated to remove between-run assay differences. Acceptance criteria for scale factors for hybridization and median signal normalization are expected to be in the range of 0.4-2.5. The median of the calibration scale factors is expected to be within ±0.2 of 1.0, and a minimum of 95% of the individual SOMAmer reagents in all arrays should be within ±0.4 of the median. SOMAscan data for all samples passed quality control standards and were suitable for analysis.

[0200] Technical validation of SOMAmer specificity by LC-MS / MS To systematically evaluate the specificity of the SOMAscan platform, a protocol was developed using SOMAmers to perform peptide digestion after affinity pulldown of intact proteins and analysis by untargeted mass spectrometry. FGF20 SOMAmer reagents were thawed, vortexed and spun down for 2 min, heated to 100°C in a PCR machine for 5 min, then cooled slowly in a water bath at 25°C. FGF20 SOMAmers were diluted in 50 mM AB buffer (40 mM HEPES, 100 mM NaCl, 5 mM KCl, 5 mM MgCl2, 0.05% Tween-20, pH 7.5) and then cooled to 25°C in a water bath for 20 min. Streptavidin agarose beads were diluted from 50 mM to 7.5% and then spun at 1000×g for 2 min. The 7.5% streptavidin agarose beads were washed with AB buffer, vortexed and centrifuged at 1000×g for 2 min. The liquid was removed by vacuuming and the wash repeated once more for a total of 2 times. SOMAmer was added to the beads and incubated at 25°C with shaking for 20 minutes. The tubes were spun at 1000xg for 2 minutes and the liquid removed by vacuum. The beads were washed twice with 0-W buffer and then twice with AB buffer. AB buffer, plasma and serum samples, and recombinant protein were added to the appropriate tubes along with 30μl of SOMAmer-bound beads. The tubes were shaken at room temperature for 1.5 hours. After the incubation was complete, the tubes were spun down for 1 minute and the liquid removed. The samples were washed once with 1-B blocker, shaken at 800rpm for 5 minutes and the liquid removed. The samples were washed 6 times with AB buffer and then frozen at -80°C. Four times the sample volume of -20°C acetone was added to each tube. The tubes were vortexed quickly and incubated at -20°C for 1 hour. The tubes were centrifuged at 13,000 xg for 10 minutes and the supernatant was removed by vacuum.

[0201] An equal volume of 0.5 M ammonium carbonate pH 10.5 was added to each set of washed beads. Another equal volume of reduction / alkylation cocktail consisting of 2% (v / v) iodoethanol and 0.5% (v / v) triethylphosphine in 97.5% acetonitrile was then added to each sample. The solutions were capped and incubated at 37°C for 1 h, after which they were speed vacuumed to dryness. The resulting pellets were then redissolved in trypsin solution (Pierce Trypsin Protease MS-Grade, in 100 mM Tris-HCl, pH 8.0). Digestion was carried out overnight at 37°C, after which the solution was desalted using μC18 ZipTips (Millipore). The digested samples were analyzed by Thermo Q-Exactive mass spectrometry using a Thermo EASY-nLC HPLC system. Separation was carried out using a 75 μm x 15 cm Thermo EASY-Spray C18 column. MS data were collected in data-dependent acquisition mode using a full high-resolution MS scan followed by an MS / MS scan of the top 10 most intense precursor ions (within the mass range of 350–2000 m / z). [Table 2-1] [Table 2-2] [Table 2-3] [Table 2-4] [Table 2-5] [Table 2-6] [Table 2-7] [Table 2-8]

Table 2-9

Table 2-10

Table 2-11

Table 2-12

Table 2-13

Table 2-14

Table 2-15

Table 2-16

Table 2-17

Table 2-18

Table 2-19

Table 2-20

Table 2-21

Table 2-22

Table 2-23

Table 2-24

Table 2-25

Table 2-26

Table 2-27

Table 2-28

Table 2-29

Table 2-30

Table 2-31

Table 2-32

Table 2-33

Table 2-34

Table 2-35

Table 2-36

Table 2-37

Table 2-38

Table 2-39

Table 2-40

Table 2-41

Table 2-42

[0202] statistical analysis All statistical analyses were performed using SAS version 9.4 for Windows (SAS Institute, Cary, NC). All data were presented as either mean and standard deviation, median (25th and 75th percentiles), or count (proportion) measures, where applicable. Spearman rank correlation (r s) was used to assess the correlation of circulating plasma concentrations with the slope of eGFR, TNF-R1, and clinical covariates. Hierarchical cluster analysis (Ward method) was used to identify clusters of protective proteins. Baseline protein RFU concentrations (n=1,129) were natural log transformed and then classified into quartiles of their distribution before association tests. The distribution of the top three protective proteins after natural log transformation in the combined discovery and replication cohort as well as in the validation cohort is shown in Figures 1A-1B. Univariate and multivariate logistic regression models were used to test the association between the relevant circulating plasma proteins measured at baseline and outcome measures (progressors in case of eGFR loss ≥ 3.0 ml / min / year or progression to ESKD) and expressed as odds ratios per one quartile increase in circulating plasma concentrations of the relevant proteins with the corresponding 95% confidence intervals. The cumulative incidence of ESKD according to the protective index, the combined effect of the three exemplary protective proteins, was analyzed using PROC LIFETEST in SAS software. Comparisons between plasma protein concentrations in non-diabetic, non-progressor, and progressor subjects were examined using one-way ANOVA and Dunn's multiple comparison test. Significance was determined by the mean 1.0-fold difference. * P < 0.05, ** P < 0.01, *** P < 0.001, and **** P was defined as <0.0001.

[0203] Example 1. Characteristics of the Joslin Kidney Study Discovery and Replication Cohorts The study disclosed herein included subjects participating in the ongoing Joslin Kidney Study. Two independent cohorts of subjects with diabetes and abnormal kidney function (CKD stage 3) were constructed: the exploratory Joslin cohort of 214 T1D subjects and the replication Joslin cohort of 144 T2D subjects. These cohorts were followed for 7 to 15 years to determine the slope of eGFR and to ascertain the time of ESKD occurrence. Clinical characteristics of these cohorts are shown in Table 2. All study participants included in the Joslin T1D cohort and 92% of study participants included in the T2D cohort were white. At baseline, compared with T1D subjects, T2D subjects were older, had shorter duration of diabetes, higher body mass index (BMI), lower hemoglobin A1c (HbA1c), and lower urinary albumin to creatinine ratio (ACR), but had similar abnormalities in eGFR.

[0204] During 7–15 years of follow-up, the majority of subjects in both cohorts had progressive renal function decline. However, the eGFR slopes varied widely between subjects, with the slopes of T1D subjects being slightly steeper than those of T2D subjects. The distribution of eGFR slopes in the T1D and T2D Joslin cohorts is shown in Figure 2. The number of slow decliners (referred to as nonprogressors), defined as eGFR loss <3.0 ml / min / year, was 71 (33%) and 69 (48%) in the T1D exploration and T2D replication cohorts, respectively (Table 2). These nonprogressors, who are the focus of this study, had very shallow eGFR slopes, with medians (25th, 75th percentiles) of −1.6 ml / min / year (−2.3, −1.0) and −0.9 ml / min / year (−2.0, 0.4) in the T1D and T2D cohorts, respectively. None of these subjects progressed to ESKD during 7–15 years of follow-up. In contrast, as shown in Table 2, the majority of fast decliners (referred to as progressors) defined as eGFR loss ≥ 3.0 ml / min / year progressed to ESKD during 10 years of follow-up (61% of the combined cohort). [Table 3]

[0205] Example 2. Profiling of plasma proteins that prevent progressive renal function decline Using the SOMAscan proteomics platform, we measured 1129 plasma proteins, listed in Table 1 above. These plasma proteins were examined for elevated concentrations in non-progressors at baseline. A schematic representation of the study is summarized in Figure 3. In the Joslin Discovery T1D cohort, baseline plasma concentrations of 73 proteins were positively and significantly correlated with the slope of eGFR at false discovery rate (FDR) adjusted P<0.005 (Table 3), and thus elevated baseline concentrations of these proteins were associated with slow or minimal renal function decline during follow-up. These proteins can be considered as candidate protective factors / biomarkers against progressive renal function decline. Proteins that were negatively correlated with the slope of eGFR can be considered as candidate factors / biomarkers for increasing the risk of progressive renal function decline and progression to ESKD. On the contrary, a separate study, using the same SOMAscan proteomics platform, published an association between 194 inflammatory circulating proteins and risk of progression to ESKD in these two Joslin cohorts (Niewczas et al., Nat Med 25:805-813 (2019)).

[0206] The 73 plasma proteins positively correlated with eGFR slope in T1D subjects were further analyzed in a replication cohort of T2D subjects. Eighteen proteins were found to be positively correlated with eGFR slope with a nominal P<0.05 (Table 3). As described herein, elevated concentrations of PKM2 in renal tissue and plasma were recently demonstrated to be a novel biomarker and potential therapeutic target to prevent DKD in subjects with long duration of T1D (Qi et al., Nat Med 23:753-762(2017)). To determine whether this protein is also involved in protection from progressive renal decline in subjects with abnormal renal function, PKM2 was included in further analysis along with the 18 candidate proteins, even though PKM2 was not significantly correlated with eGFR slope in T2D subjects. The names of the 19 plasma proteins, the correlation coefficients, and the P values ​​of each protein that was positively correlated with the slope of eGFR in the T1D and T2D cohorts, respectively, are presented in Figure 4 A. In the T2D group, all 18 proteins were positively and significantly correlated with the slope of eGFR, although the correlations were generally slightly weaker. [Table 4-1] [Table 4-2] [Table 4-3]

[0207] Example 3. Plasma proteins that prevent progressive kidney function decline Because both Joslin cohorts had abnormal renal function (CKD stage 3) at baseline and had associations of uniform strength with the slope of eGFR, the SOMAscan results from both cohorts were combined. Logistic regression analysis was used to analyze the association between baseline plasma concentrations of each of the 19 proteins and the rate of progressive renal function decline. Subjects in the combined Joslin cohort were divided into groups: (1) progressors, subjects with fast renal function decline (eGFR loss ≥ 3.0 ml / min / year) or progression to ESKD, or (2) non-progressors, subjects with slow or minimal renal function decline (eGFR loss < 3.0 ml / min / year). To assess the statistical independence of the protective effect from clinical characteristics and risk factors associated with progressive renal function decline, univariate logistic models were first performed, followed by multivariate logistic models adjusted for baseline clinical covariates. The list of potential confounders included age, sex, ethnicity / race, diabetes duration, insulin treatment, renal protection treatment, BMI, systolic and diastolic blood pressure, HbA1c, eGFR, and ACR. The main covariates of HbA1c, eGFR, and ACR were included in the final logistic model. Information on the selection of covariates to enter into the logistic model is shown in Table 4. The results of univariate and multivariate analyses are shown in Figure 4B. All models were adjusted for type of diabetes. Effects are shown as odds ratios (OR) with 95% confidence intervals (95% CI) for each quartile increase in baseline plasma concentration of the specific protein. In the univariate model, all 19 proteins, including PKM2 (shown as Figure 4B-##), protected against progressive renal function decline (OR was < 1.0). In the final model adjusted for baseline clinical covariates including eGFR, HbA1c, ACR, and type of diabetes, elevated plasma concentrations of eight proteins remained associated with protection from progressive renal function decline (Figure 4B and Table 5). These eight plasma proteins were referred to as "confirmed" protective proteins and included TNFSF12, SPARC, CCL5, APP, PF4, DNAJC19, ANGPT1, and FGF20 (denoted by # in Figure 4B). Baseline concentrations of PKM2 were not associated with protection from progressive renal function decline after further adjustment for clinical covariates.The effect of PKM2 was significant (P<0.05) in univariate analysis but was no longer statistically significant after adjustment for clinical covariates. [Table 5] [Table 6]

[0208] To investigate which identified protective proteins independently contributed to protection from progressive renal decline, we first analyzed the relationship at baseline between the eight proteins and key clinical covariates using Spearman's rank correlation. The correlation matrix shown in Figure 5A indicates that while variation in baseline HbA1c did not affect the variation in the eight protective proteins, variation in baseline eGFR was weakly correlated with TNFSF12 and FGF20. In contrast, baseline ACR was weakly correlated with all proteins except FGF20 (Figures 5A and 6). Furthermore, all of the protective proteins were negatively correlated with plasma tumor necrosis factor receptor 1 (TNF-R1) concentrations, which we previously reported as one of the circulating inflammatory proteins associated with increased risk of progression to ESKD (Niewczas et al., Nat Med 25:805-813 (2019)), indicating a decrease in plasma TNF-R1 concentrations with increasing concentrations of the protective proteins. The identified protective proteins were divided into three subgroups according to their correlation coefficients with each other (Figure 5A). Subgroup (A) included five highly intercorrelated proteins: SPARC, CCL5, APP, PF4, and ANGPT1. Subgroup (B) included two proteins: DNAJC19 and TNFSF12, which were moderately correlated with each other and with the proteins in subgroup (A). Subgroup (C) included FGF20, a protein that did not correlate with any of the other proteins except for a moderate correlation with TNFSF12. This pattern of protein grouping was maintained and confirmed in hierarchical cluster analysis, as shown in Figure 5B. This finding suggests that the plasma concentrations of the proteins in these three subgroups are regulated by different mechanisms. This is in contrast to the five proteins in subgroup (A), which showed such strong intercorrelation that it could be hypothesized that they are regulated by the same mechanism.

[0209] To further examine which of these eight proteins (three subgroups) independently contributed to protection from progressive renal function decline, a multivariable logistic regression analysis with backward deletion of proteins and clinical covariates with no or weak effects (α>0.1) was performed (Table 6). All relevant clinical characteristics and the eight confirmed protective proteins were included in the analysis. In the final model, three baseline clinical variables, eGFR, HbA1c, and ACR, significantly increased the risk of progressive renal function decline, and three baseline plasma proteins, ANGPT1 (an example of subgroup A), TNFSF12 (an example of subgroup B), and FGF20 (subgroup C), significantly protected against progressive renal function decline. Odds ratios (95% CI) obtained from the multivariable logistic regression analysis for clinical covariates and exemplary protective proteins are shown in Figure 5C. [Table 7]

[0210] Example 4. Combined Effect of Three Exemplary Protective Proteins To estimate the combined effect of the three exemplary protective proteins on progressive renal function decline and the risk of progression to ESKD, a "protection index" was developed. Plasma concentrations of the three exemplary protective proteins (ANGPT1, TNFSF12, and FGF20) were assessed in each subject. Values ​​above the median for each protein were scored as 1, and values ​​below the median were scored as 0; by summing these scores, a subject could have a total protective index that ranged between 0 (all proteins below the median) and 3 (all proteins above the median). The association between the protective index and progressive renal function decline is shown in Figure 7A. The odds ratios (95%CI) of progressive renal function decline were 0.69 (0.28, 1.69), 0.34 (0.14, 0.83), and 0.19 (0.1, 0.52) for subjects with a total protective index of 1, 2, and 3, respectively, compared to subjects with a protective index value of 0. To visualize the combined effect of the three protective proteins, the cumulative risk of progression to ESKD was analyzed in the combined study cohort according to the protective index. Figure 7B shows the cumulative incidence of ESKD during 7.5 years of follow-up according to the value of the protective index. Subjects with values ​​of all three protective proteins above the median had a very low risk of developing ESKD, with a cumulative incidence of 16% during 7.5 years of follow-up. In contrast, subjects with a protective index value of 0, i.e., values ​​of all three protective proteins below the median, had a very high cumulative incidence of ESKD of 80%. The statistically significant P value (P = 2.7 × 10 -10 ) showed strong evidence of significant differences in the cumulative incidence of ESKD among the four subgroups.

[0211] To examine whether the results shown in Figure 7A may have been confounded by inflammatory circulating proteins (e.g., high TNF-R1 plasma concentrations) or clinical covariates, we performed a logistic regression analysis in the combined Joslin cohort (T1D and T2D). In this analysis, the protective index was considered as a continuous variable rather than a discrete variable as in Figure 7A. As shown in Table 7, the effect of the protective index was highly significant (P<0.0001), with an odds ratio of 0.47 (95% CI: 0.32-0.60). As we previously reported (5), the inclusion of one inflammatory protein, TNF-R1, in the model attenuated the protective effect of the index, increasing the odds ratio to 0.60 (95% CI: 0.45-0.78), but still remaining statistically significant (P<0.0002). It is significant that the odds ratio of the protective index did not change substantially when many clinical covariates were added to the model. [Table 8]

[0212] Example 5. Validation of three exemplary protective proteins in early CKD To demonstrate the robustness of the findings, a validation study was performed in an independent Joslin cohort of 294 T1D subjects with albuminuria but normal renal function at baseline. This cohort was followed for 7 to 15 years to determine the slope of eGFR and to ascertain the time of incident ESKD. Plasma samples from the validation study of 294 T1D subjects were profiled for proteins of interest using the same SOMAscan platform. Unlike the discovery and replication cohorts, who had abnormal renal function at baseline (CKD stage 3), the validation cohort had normal renal function at baseline (CKD stages 1 and 2; median eGFR (25th, 75th percentiles): 100 (82, 114) ml / min / 1.73 m 2 The clinical characteristics of the validation cohort are shown in Table 8. [Table 9]

[0213] Plasma concentrations of three exemplary protective proteins (ANGPT1, TNFSF12, and FGF20) were assessed in each subject, and a protective index was developed. The association between protective index and progressive renal function decline is shown in Figure 7C. The odds ratios (95%CI) of progressive renal function decline were 0.48 (0.24, 0.95), 0.46 (0.24, 0.89), and 0.11 (0.05, 0.27) for subjects with a total protective index of 1, 2, and 3, respectively, compared with subjects with a protective index value of 0. The cumulative risk of progression to ESKD was also analyzed in the validation cohort according to the protective index. Figure 7D shows the cumulative incidence of ESKD during 7.5 years of follow-up according to the value of the protective index. None of the subjects with values ​​of all three protective proteins above the median value progressed to ESKD during the 7.5 years of follow-up. Compared with subjects with a protection index value of 0 and a cumulative incidence of 33% during 7.5 years of follow-up, lower cumulative incidences of ESKD were observed for subjects with protection index values ​​of 1 and 2, respectively, with a statistically significant P value (P = 1.7 × 10 -5 ) suggested strong evidence of significant differences in the cumulative incidence of ESKD among the four subgroups.

[0214] Additionally, two of the three exemplary protective proteins (ANGPT1 and FGF20) were validated using a different platform. ANGPT1 measurements were validated in a subset of samples (n=32) using the Human Ang-1 MSD R-Plex Assay (F21YQ-3, Meso Scale Diagnostics) according to the manufacturer's protocol. Briefly, MSD GOLD Small Spot Streptavidin plates were coated with 100 μl of biotinylated Ang-1 capture antibody in Coating Diluent 100 and incubated for 1 hour at room temperature. Plates were washed with 150 μl / well wash buffer (1×PBS-Tween 20) and 25 μl of serially diluted standards in duplicate, ranging from 100,000 pg to 24 pg / ml, and 32 plasma samples from our study were loaded, all on the same plate. After 1 hour incubation at room temperature with shaking, the plate was washed and incubated with 50 μl of bound detection antibody (MSD GOLD SULFO-TAG™) for 1 hour at room temperature, then washed and finally 150 μl / well of read buffer was added to the plate. The plate was loaded into the MSD instrument and a voltage was applied to the plate electrodes to measure the intensity of the emitted light, providing a quantitative measurement of the analyte in the sample.

[0215] Correlation between antibody-based (MSD) measurements and aptamer-based (SOMAscan) results was very good. The Spearman rank correlation coefficient between SOMAscan and MSD ANGPT1 measurements was r s= 0.76, P < 0.0001. To analytically validate SOMAmer specificity, we formulated a protocol to integrate DNA-based affinity pulldown of intact protein with mass spectrometry. Fourteen FGF20 tryptic peptides spanning amino acids (aa) 50-211 of the FGF20 protein sequence were identified in FGF20 SOMAmer plasma pulldowns spiked with recombinant FGF20, whereas no FGF20 peptides were identified in FGF20 SOMAmer plasma pulldowns without added recombinant FGF20. An example of an extracted ion chromatogram for the FGF20 tryptic peptide GGPGAAQLAHLHGILR (aa 50-65; SEQ ID NO: 9) is shown in Figure 8. This FGF20 peptide was identified in plasma pulldowns spiked with recombinant FGF20 but not detected in plasma pulldowns without added recombinant FGF20, thereby confirming the specificity of the FGF20 SOMAmer on the SOMAscan platform.

[0216] Example 6. Plasma concentrations of protective proteins in non-diabetic and diabetic subjects There are two possibilities how to explain the elevated concentrations of protective proteins in non-progressors compared to those who were at risk for progressive renal decline at study entry. The first possibility is that diabetes and associated kidney damage may cause a decrease in the plasma concentration of the putative protective proteins. As a result, progressors will have lower protein concentrations than non-progressors due to a higher degree of underlying kidney damage, which was not recognized by the clinical covariates and was not taken into account in the multivariable model. If this is correct, it is hypothesized that protective proteins will be more elevated in non-diabetics compared to diabetics who have a lower rate of decline. The second possibility is that diabetes may not be a determinant of the concentration of putative protective proteins, but that elevated concentrations of these proteins at baseline may protect against progressive renal decline. Thus, subjects with elevated plasma concentrations of these proteins mainly constitute non-progressors, whereas those with low concentrations of these proteins will be at risk for progressive renal decline. If this is correct, it is hypothesized that concentrations of the putative protective protein should be higher in non-progressors compared with non-diabetic individuals, whereas in progressors, protein concentrations would be lower than or similar to controls.

[0217] To distinguish between the two possibilities mentioned above, the same aptamer-based SOMAscan platform was used to compare plasma concentrations of protective proteins between healthy nondiabetic parents of T1D subjects, nonprogressors, and progressors with T1D and T2D. The baseline clinical characteristics and baseline values ​​of protective proteins among the three study subgroups are shown in Table 9. Nondiabetic subjects were older, had normal HbA1c, normal ACR, and near-normal eGFR compared to diabetic subjects. By design, nonprogressors and progressors had similar renal function abnormalities at baseline, but the slope of eGFR during 7–15 years of follow-up was dramatically different. For the eight confirmed protective proteins, the lowest baseline concentrations were observed in nondiabetic subjects and the highest in nonprogressors, while the concentrations in progressors fell between the two other subgroups. Comparison of the three exemplary protective proteins among the three subgroups is shown in Figure 9 and mainly supports the role of these protective proteins against progressive renal function decline. [Table 10]

[0218] To investigate whether the plasma concentrations of protective proteins preceded the onset of diabetic state and early renal decline, a comparative analysis was performed on the plasma concentrations of three exemplary protective proteins (ANGPT1, TNFSF12, and FGF20) in non-diabetic parents of T1D probands in two categories: normoalbuminuria or ESKD (or proteinuria). The baseline characteristics and baseline values ​​of the three protective proteins in non-diabetic parents of T1D probands in two categories are shown in Table 10. Interestingly, as shown in Table 10, parents of children with renal complications (ESKD or proteinuria) had significantly lower circulating FGF20 concentrations than parents of children who did not develop renal complications despite a longer duration of diabetes. [Table 11]

[0219] Considerations of Examples 1 to 6 The present study, described in the above examples, identified circulating plasma proteins that are specifically associated with protection from progressive renal function decline and progression to ESKD in two independent cohorts of subjects with diabetes and moderate renal dysfunction by unbiased proteomic profiling. Eight circulating proteins were identified that have protective effects from progressive renal function decline independent of clinical covariates such as baseline eGFR, HbA1c, ACR, and type of diabetes. These proteins could be classified into three subgroups: (A) SPARC, CCL5, APP, PF4, ANGPT1, (B) DNAJC19, TNFSF12, and (C) FGF20. Considering the eight confirmed protective proteins together, it is meaningful to note that only three proteins representative of each subgroup, e.g., ANGPT1, TNFSF12, and FGF20, showed strong and independent protective effects against progressive renal function decline. The combined effect of these three exemplary protective proteins was well demonstrated by the very low cumulative risk of ESKD in subjects who had values ​​above the median for all three proteins at the beginning of follow-up.Furthermore, the fact that the concentrations of these protective proteins were much higher in non-progressors than in non-diabetics provides strong evidence that these proteins or the pathways they represent are causally involved in protection from progressive renal function decline.The importance of these three exemplary protective proteins was confirmed in three independent cohorts of study participants with different types of diabetes, T1D and T2D, and different stages of DKD, early and late stages of DKD, who were prospectively followed for 10 years, making these findings highly generalizable.

[0220] Angiopoietin (ANGPT) is a growth factor involved in angiogenesis and vascular inflammation. Among the members of the ANGPT family, angiopoietin-1 (ANGPT1) and angiopoietin-2 (ANGPT2) are both ligands for the Tie-2 receptor (Suri et al., Cell 87:1171-1180 (1996); Maisonpierre et al., Science 277:55-60 (1997)). ANGPT1 is the primary ligand and activator of the Tie-2 receptor and maintains vascular integrity by activating the phosphatidylinositol 3-kinase / protein kinase B (PI3K / Akt) pathway (Brindle et al., Circ Res 98:1014-1023 (2006)), thus protecting the endothelium from excessive activation by growth factors and cytokines (Fiedler et al., Trends Immunol 27:552-558 (2006)). On the other hand, ANGPT2 is thought to be a natural antagonist of ANGPT1 by preventing the binding of ANGPT1 to the Tie-2 receptor, thereby reducing activation of the ANGPT1 / Tie-2 pathway and promoting vascular wall instability and vascular leakage (Maisonpierre et al., Science 277:55-60 (1997); Fiedler et al., Trends Immunol 27:552-558 (2006)). Since ANGPT1 and ANGPT2 compete with each other for the Tie-2 receptor and have opposing effects, it is probably beneficial to measure both angiopoietins to assess the balance of ongoing angiogenesis processes, such that a disturbance in the balance between ANGPT1 and ANGPT2 (e.g., in favor of ANGPT2) may lead to an imbalance of angiopoietins due to diabetes, e.g., destabilization of the vascular wall and promoting inflammation and fibrosis (Gnudi, Diabetologia 59:1616-1620 (2016)). As ANGPT2 was measured on the SOMAscan platform and the results were available for this study, the protective effect of ANGPT1 was compared with the risk effect of ANGPT2 and the effect of the ratio of ANGPT1 / ANGPT2 (in favor of ANGPT1) on the risk of progressive renal decline.Unfortunately, the findings from these analyses did not show a stronger protective effect of the ratio of the two angiopoietins compared with that of ANGPT1 alone (Table 11), supporting a protective role for ANGPT1 alone, but not the ratio of the two angiopoietins, against progressive renal function decline. [Table 12]

[0221] Further studies are being conducted on the protective effects of ANGPT1. ANGPT1 has been shown to exert anti-inflammatory effects and protect endothelial cell permeability from inflammatory factors (Pizurki et al., Br J Pharmacol 139:329-336 (2003)). A variant of ANGPT1 known as cartilage oligomeric matrix protein angiopoietin-1 (COMP-Ang1) was developed to investigate the protective effects of COMP-Ang1 in animal models of unilateral ureteral obstruction-induced renal fibrosis and diabetic nephropathy (Kim et al., J Am Soc Nephrol 17:2474-2483 (2006), Lee et al., Nephrol Dial Transplant 22:396-408 (2007)). Diabetic db / db mice treated with COMP-Ang1 showed reduced albuminuria and fasting blood glucose levels, reduced mesangial expansion, thickened glomerular basement membrane, and enlarged podocyte foot processes (Lee et al., Nephrol Dial Transplant 22:396-408(2007)). The importance of ANGPT1 expression levels in diabetic glomerular disease was further confirmed in studies using genetically modified mice. Overexpression or overabundance of ANGPT1 in diabetic mice, especially in the glomeruli, led to reduced albumin excretion accompanied by reduced diabetes-induced nephrin phosphorylation (Dessapt-Baradez et al., J Am Soc Nephrol 25:33-42(2014)), which resulted in reduced nephrin degradation and enlarged podocyte foot processes, resulting in a more stable and functional glomerular filtration barrier (Zhu et al., Kidney International 73:556-566(2008)). Considering all of these observations together with our robust findings in humans showing that elevated plasma ANGPT1 concentrations prevented progressive renal function decline, it is very clear that ANGPT1 may be a potential therapeutic target for preventing or reducing the risk of progressive renal function decline in diabetes.

[0222] This study demonstrated that ANGPT1 is significantly and highly correlated with four other identified defense proteins (PF4, SPARC, APP, and CCL5), suggesting that these proteins may have similar physiological significance, be part of a common pathway, or be under the same genetic control. The common pathway by which all five of these proteins are expressed and secreted is associated with platelet function. Thrombin is known to induce the release of ANGPT1 from platelets to help stabilize endothelial cells during vascular repair (Li et al., Thromb Haemost 85:204-206(2001)). Platelet factor 4 (PF4) is released from alpha granules of activated platelets and binds with high affinity to heparin. It is a potent chemotactic factor for neutrophils, fibroblasts, and monocytes (Lord et al., J Biol Chem 292:4054-4063(2017)). Secretory protein rich in acidic cysteine ​​(SPARC) is also an alpha granule component of human platelets and is secreted during platelet activation. It is further produced by fibroblasts, endothelial cells, macrophages, and adipocytes. SPARC is involved in cell proliferation, repair of tissue damage, collagen matrix formation, and osteoblast differentiation (Yun et al., Biomed Res Int 2016:9060143(2016)). Platelets are the main source of amyloid beta A4 protein (APP) in the blood circulation (Li et al., Blood 84:133-142(1994)). CC motif chemokine 5 (CCL-5), also known as RANTES, is also released by activated platelet alpha granules, deposited on inflamed endothelium, and mediates monocyte transmigration into the activated endothelium. Low plasma CCL-5 concentrations are an independent predictor of cardiac mortality in patients referred for coronary angiography (Nomura et al., Clin Exp Immunol 121:437-443 (2000)).Previous studies have reported that activated platelets are involved in the development of diabetic nephropathy (Omoto et al., Nephron 81:271-277 (1999); Zhang et al., J Am Soc Nephrol 29:2671-2695 (2018)). The results of this study further point out the importance of platelet-secreted proteins in the progression of diabetic nephropathy. Secretion of platelet-activating proteins may prevent the rapid progression of diabetic nephropathy by preventing vascular damage associated with leukocyte trafficking. The relevance of these proteins in protecting against progressive renal function decline needs to be further investigated.

[0223] Tumor necrosis factor (TNF) ligand superfamily member 12 (TNFSF12), also known as TWEAK, is a member of the large TNF superfamily of ligands and receptors (Chicheportiche et al., J Biol Chem 272:32401-32410 (1997)). Findings from in vitro and in vivo models indicate that administration of TNFSF12 increases inflammatory cytokine production in renal tubular cells, such as increased mRNA and protein expression of monocyte chemoattractant protein-1 and interleukin-6 (IL-6), whereas blocking TNFSF12 prevents tubular chemokine and IL-6 expression, interstitial inflammation, and macrophage infiltration in mice (Sanz et al., J Am Soc Nephrol 19:695-703 (2008)). The role of TNFSF12 in the development / progression of DKD remains unclear. So far, the literature dedicated to this topic is scarce, and several cross-sectional studies have investigated the relationship between circulating TNFSF12 concentrations and DKD. One study reported reduced circulating TNFSF12 concentrations in T2D and ESKD subjects (Kralisch et al., Atherosclerosis 199:440-444 (2008)). The effects of TNFSF12 in other kidney diseases and other forms of diabetes have also been reported (Sanz et al., J Cell Mol Med 13:3329-3342 (2009); Dereke et al., PLoS One 14:e0216728 (2019); Bernardi et al., Clin Sci (Lond):133,1145-1166 (2019)). In experimental folate-induced acute kidney injury, TNFSF12 deficiency reduced renal apoptosis and inflammation and improved renal function. A case-control study of women with and without gestational diabetes mellitus (GBM) reported decreased TNFSF12 concentrations in women with GBM compared with pregnant volunteers without GBM.Unlike the findings in the aforementioned studies, this is the only follow-up observation in which highly robust findings indicate that TNFSF12 is a protective protein against progressive renal function decline, which needs to be further investigated in human and animal studies.

[0224] Fibroblast growth factor 20 (FGF20) is a member of a large family of 22 fibroblast growth factors (FGFs) that includes seven subfamilies consisting of secreted signaling proteins and intracellular non-signaling proteins (Itoh et al., J Biochem 149:121-130(2011)). Seventeen of the 22 FGFs were measured with the SOMAscan proteomics platform, and only FGF20 was robustly associated with protection from progressive renal decline. FGF20 is a novel neurotrophic factor originally identified in rat brain and has been suggested to play an important role in the development of dopaminergic neurons (Ohmachi et al., Biochem Biophys Res Commun 277:355-360(2000); Correia et al., Front Neuroanat 1:4(2007); Shimada et al., J Biosci Bioeng 107:447-454(2009)). Furthermore, although some studies have reported no evidence of an association between FGF20 and Parkinson's disease, numerous studies have reported a correlation between Parkinson's disease susceptibility and FGF20 gene polymorphisms in various ethnicities (Pan et al., Parkinsonism Relat Disord 18:629-631(2012); Sadhukhan et al., Neurosci Lett 675:68-73(2018); van der Walt et al., Am J Hum Genet 74:1121-1127(2004); Clarimon et al., BMC Neurol 5:11(2005); Wider et al., Mov Disord 24:455-459(2009)). Interestingly, a previous study demonstrated an essential role for FGF20 / Fgf20 in kidney development by maintaining the stemness of nephron progenitor cells in both humans and mice (Barak et al., Dev Cell 22:1191-1207(2012)). FGF20 was expressed exclusively in nephron progenitor cells in the kidney.Loss of FGF20 / Fgf20 in humans and mice results in renal agenesis, a condition in which one or both fetal kidneys fail to develop, resulting in the absence of one or both kidneys in newborns.

[0225] FGF20 was first discovered in 2001 when Jeffers et al. identified FGF20 as a novel oncogene that could be a potential target for the treatment of human malignancies (Jeffers et al., Cancer Research 61:3131-3138(2001)). The same authors subsequently demonstrated that FGF-20 (CG53135-05) has therapeutic activity to treat experimental intestinal inflammation (Jeffers et al., Gastroenterology 123:1151-1162(2002)), and another study reported that FGF20 is a novel radioprotectant, such that a single dose of FGF20 administered to mice prior to potentially lethal whole-body radiation reduced the lethal effect of acute radiation exposure and led to a substantial increase in overall survival (Maclachlan et al., Int J Radiat Biol 81:567-579(2005)). Based on these findings, CG53135-05 (renamed Verafermin) was evaluated in a Phase II clinical trial in cancer patients as a defense against the development of oral mucositis, a side effect of chemotherapy (Schuster et al., Support Care Cancer 16:477-483 (2008)). The results of this trial showed that Verafermin had a good safety and tolerability profile, but did not show sufficient efficacy when added to the treatment of oral mucositis.

[0226] This study demonstrates that FGF20 is one of the confirmed protective proteins most strongly associated with protection from progressive renal function decline and progression to ESKD in a combined cohort of T1D and T2D. This association was independent of circulating inflammatory proteins and applicable clinical covariates. Higher plasma concentrations of FGF20 at baseline predicted less renal function decline during 7–15 years of follow-up. This association points to the involvement of FGF20 and its independent role in delaying or reducing the risk of progressive renal function decline and ESKD. Thus, FGF20 may be a useful target to prevent or delay the development of progressive renal function decline and ESKD in diabetes. Another interesting finding obtained in our study was observed in the plasma profiles of nondiabetic parents of T1D probands in two categories, either normoalbuminuria or ESKD / proteinuria. Surprisingly, nondiabetic parents of T1D offspring with ESKD / proteinuria had significantly lower plasma concentrations of FGF20 than parents with T1D offspring without renal complications. These findings prompted the question and / or speculation as to whether genetic predispositions or components inherited from parents could regulate the corresponding protein concentrations in offspring, which, if confirmed in larger studies, may have profound implications in future research on the determinants of progressive renal function decline in T1D (and T2D).

[0227] Recent interest in the study of protective factors against late diabetic complications, including DKD, was initiated by the Joslin Medalist Study. This cross-sectional study enrolled subjects from across the country who had lived with T1D for at least 50 years. Those who did not develop late diabetic complications were compared with their nondiabetic partners and those who developed complications very late in the course of diabetes on a number of features, including various omics profiles of biospecimens. Comparing the proteomic profiles of renal tissue obtained from the three subgroups of subjects, several glucose metabolic enzymes / proteins, including PKM2, were identified in the glomeruli that were highly elevated among those who did not develop DKD despite having a very long duration of diabetes. Following this finding with a series of functional studies, the authors concluded that upregulation of PKM2 could be a means of preventing the development of DKD (Qi et al., Nat Med 23:753-762(2017)).

[0228] The present study also explored protective factors but differed significantly from the Medalist study. While the latter was cross-sectional and searched for candidate protective proteins to investigate in cell and animal studies, the present study was a Joslin Clinic population-based prospective observational study that investigated the association between baseline circulating plasma proteins that prevented progressive renal function decline and rapid progression to ESKD during 7–15 years of follow-up. Moreover, the two studies were based on two different premises. The Medalist study aimed to find protective proteins against the occurrence / development of late diabetic complications, whereas the present study aimed to identify protective proteins against progressive renal function decline in subjects with already existing mild renal impairment. This is likely the reason why we were unable to confirm with statistical significance the PKM2 findings obtained in the Joslin Medalist study.

[0229] Strengths of the study include its prospective design, long-term follow-up observations of three independent study cohorts, consistency of data in T1D and T2D, and use of the SOMAscan proteomics platform to measure protein concentrations in all Joslin cohorts. In addition, the study controlled for findings for major potential confounders and type of diabetes. However, as with any study, the study must be considered in light of potential limitations. First, this is an observational study, and these proteins may directly protect against progression of renal function decline, but may alternatively be indirect reporters of a protective process. To confirm that they have a direct protective potential, a causal explanation for our findings needs to be established by animal models and clinical trials. Second, these findings are limited to Caucasian diabetic individuals with chronic kidney disease and abnormal renal function, and therefore these results may not be generalizable to individuals in other populations and to individuals with other kidney diseases. Third, the baseline plasma samples were not taken at the time of diabetes onset, so whether progressive renal function decline is slow or fast is related to the time of blood collection, not to the onset of disease. This study includes a subset of participants enrolled in the JKS in the 2000s and followed through 2012-2013. Prior to enrollment, these individuals received care at the Joslin clinic for many years (it was not feasible to follow these individuals at the earliest stage of diabetes development), and their inclusion in our prospective study was independent of their unknown future outcomes in subsequent follow-up. Thus, these findings reflect an unbiased and contemporaneous natural history of CKD and the development of ESKD in individuals with diabetes. Regardless of the foregoing, the identification of protective proteins in relation to ESKD and progression to ESKD is noteworthy and offers a wealth of opportunities for both diagnostics and therapy to address a serious diagnosis for any patient.

[0230] Example 7. Circulating levels of Testican-2 are independently associated with protection from ESKD in T1D patients We used SOMAscan to search for additional protective proteins in a small Joslin cohort with T1D. Characteristics of patients who progressed to ESKD and those who did not develop ESKD during 10 years of follow-up are shown in Table 12. Circulating levels of Testican-2 (SPOCK2) were significantly higher in non-progressors than in progressors. This difference is shown in Figure 10. Similar differences were observed for three protective proteins, FGF20, TNFSF12, and ANGPT1, as described in the Examples above. [Table 13]

[0231] Logistic regression analysis was performed to examine the protective effect of circulating SPOCK2 (Testican-2) against progression to ESKD. The results are shown in Table 13 below. In univariate logistic regression (Model 1), all protective proteins had a strong protective effect against progression to ESKD (OR less than 1 indicates a protective effect). In multivariate logistic regression analysis (Model 2), the protective effect of SPOCK2 (Testican-2) was also seen when relevant clinical variables and all protective proteins were analyzed together.

[0232] In conclusion, circulating levels of SPOCK2 (Testican-2) are independently associated with protection from ESKD and can be used together with three previously reported protective proteins (FGF20, ANG1 and TNFSF12) to formulate a so-called "protection index". [Table 14] [Table 15-1] [Table 15-2]

[0233] Incorporation by Reference The entire contents of all references, patents, and published patent applications cited throughout this application are hereby incorporated by reference in their entirety.

Claims

1. 1. A method for identifying a human subject at risk for developing progressive renal function decline, comprising: detecting a level of at least one protective protein in a sample(s) from a subject in need thereof, said protective protein being selected from the group consisting of fibroblast growth factor 20 (FGF20), angiopoietin-2 (ANGPT1), and tumor necrosis factor ligand superfamily member 12 (TNFSF12); comparing the level of the protective protein to a reference level of the protective protein, the reference level being the level of the protective protein in a non-progressor human subject. Including, A lower level of the protective protein compared to a reference level in the non-progressors indicates that the human subject is at risk for developing progressive renal decline; or The method, wherein a similar or higher level of the protective protein compared to the reference level indicates that the human subject is not at risk for developing progressive renal decline.

2. The levels of combinations of protective proteins are detected, said combination of protective proteins is selected from the group consisting of FGF20 and TNFSF12, FGF20 and ANGPT1, and TNFSF12 and ANGPT1; or 2. The method of claim 1, wherein the combination of protective proteins comprises FGF20, TNFSF12, and ANGPT1.

3. 1. A method for identifying a human subject at risk for developing progressive renal function decline, comprising: Detecting a level of at least one protective protein in a sample(s) from a subject in need thereof, said protective protein comprising: a protective protein from a first group of protective proteins selected from the group consisting of Testican-2, secretory protein rich in acidic cysteine ​​(SPARC), C-C motif chemokine 5 (CCL5), amyloid beta A4 protein (APP), platelet factor 4 (PF4), and ANGPT1; a protection protein from a second group of protection proteins selected from the group consisting of DNAJC19 and TNFSF12; and FGF20. comparing the level of the protective protein to a reference level of the protective protein, the reference level being the level of the protective protein in a non-progressor human subject. Including, A lower level of the protective protein compared to the reference level indicates that the human subject is at risk for developing progressive renal function decline; or The method, wherein a similar or higher level of the protective protein compared to the reference level indicates that the human subject is not at risk for developing progressive renal decline.

4. 4. The method of claim 3, wherein the level of a combination of protection proteins is detected, said combination of protection proteins being selected from the group consisting of FGF20 and a group 1 protection protein, FGF20 and a group 2 protection protein, a group 1 protection protein and a group 2 protection protein, and FGF20, a group 1 protection protein, and a group 2 protection protein.

5. The method of claim 1 or 3, wherein the non-progressor is a non-diabetic human subject.

6. A composition for improving renal function in a human subject identified as being at risk of progressive renal decline according to the method of claim 1 or 3.

7. A composition comprising FGF20, an active fragment of FGF20, an FGF20 mimetic, or a nucleic acid encoding FGF20, or an active fragment thereof, for treating a human subject identified as being at risk for progressive renal function decline according to the method of claim 1 or 3.

8. A composition comprising ANGPT1, an active fragment of ANGPT1, an ANGPT1 mimetic, or a nucleic acid encoding ANGPT1, or an active fragment thereof, for treating a human subject identified as being at risk of progressive renal function decline according to the method of claim 1 or 3.

9. A composition comprising TNFSF12, an active fragment of TNFSF12, a TNFSF12 mimetic, or a nucleic acid encoding TNFSF12, or an active fragment thereof, for treating a human subject identified as being at risk of progressive renal function decline according to the method of claim 1 or 3.

10. A composition comprising SPARC, an active fragment of SPARC, a mimetic of SPARC, or a nucleic acid encoding SPARC, or an active fragment thereof, for treating a human subject identified as being at risk of progressive renal decline according to the method of claim 1 or 3.

11. The method of claim 1 or 3, wherein the human subject has impaired renal function, diabetes, or both.

12. 12. The method of claim 11, wherein the diabetes is type I diabetes or type II diabetes.

13. The method of claim 1 or 3, wherein the human subject is non-diabetic.

14. The method of claim 1 or 3, wherein the sample is a plasma sample.

15. 4. The method of claim 1 or 3, wherein the level of the protective protein is determined using immunoassay, mass spectrometry, liquid chromatography (LC) fractionation, SOMAscam, Mesoscale platform, or electrochemiluminescence detection.

16. The method of claim 15, wherein the immunoassay is an ELISA or a Western blot analysis.

17. 16. The method of claim 15, wherein the mass spectrometry is matrix-assisted laser desorption / ionization time-of-flight (MALDI-TOF), inductively coupled plasma mass spectrometry (ICP-MS), offset-triggered multiplexed accurate mass high-resolution absolute quantification (TOMAHAQ), direct analysis in real time mass spectrometry (DART-MS), or secondary ion mass spectrometry (SIMS).

18. 4. The method of claim 1 or 3, wherein the sample is a blood sample, a serum sample, a plasma sample, a lymph sample, a urine sample, a saliva sample, a tear sample, a sweat sample, a semen sample, a vaginal sample, a bronchial sample, a mucosal sample, or a cerebrospinal fluid (CSF) sample.

19. 1. A protein array for identifying or monitoring progressive renal function decline in a human subject, the protein array comprising antibodies or antigen-binding fragments thereof specific for human FGF20, human TNFSF12 and human ANGPT1.

20. 1. A protein array for identifying or monitoring progressive renal function decline in a human subject, comprising an antibody or antigen-binding fragment thereof specific to human FGF20, human TNFSF12, human ANGPT1, human Testican-2, human SPARC, human CCL5, human APP, human PF4, human ANGPT1, human DNAJC19, human TNFSF12, or a combination thereof.

21. 1. An array comprising a plurality of probes for specifically binding to protein biomarkers, the protein biomarkers being at least one of human FGF20, human TNFSF12, and human ANGPT1.

22. 1. An array comprising a plurality of probes for specifically binding to protein biomarkers, the protein biomarkers being at least one or more of human FGF20, human TNFSF12, human ANGPT1, human Testican-2, human SPARC, human CCL5, human APP, human PF4, human DNAJC19, and human TNFSF12.

23. A test panel comprising the protein array according to any one of claims 19 to 22.

24. 24. A kit or assay device comprising the test panel of claim 23.

25. A composition comprising an effective amount of at least one protective protein and / or at least one agonist of a protective protein for inhibiting the progression of progressive renal function decline or preventing renal function decline in a human subject.

26. A composition comprising a therapeutically effective amount of at least one protective protein and / or at least one agonist of a protective protein for treating reduced renal function in a human subject.

27. 27. The composition of claim 25 or 26, wherein the at least one protective protein is one or more of FGF20, TNFSF12, ANGPT1, Testican-2, SPARC, CCL5, APP, PF4, and DNAJC19.

28. 28. The composition of claim 27, wherein the at least one protective protein is FGF20, an active fragment of FGF20, an FGF20 mimetic, or a nucleic acid encoding FGF20, or an active fragment thereof.

29. 28. The composition of claim 27, wherein the at least one protective protein is TNFSF12, an active fragment of TNFS12, a TNFSF12 mimetic, or a nucleic acid encoding TNFSF12, or an active fragment thereof.

30. 28. The composition of claim 27, wherein the at least one protective protein is ANGPT1, an active fragment of ANGPT1, an ANGPT1 mimetic, or a nucleic acid encoding ANGPT1, or an active fragment thereof.

31. 28. The composition of claim 27, wherein the at least one protective protein is SPARC, an active fragment of SPARC, a SPARC mimetic, or a nucleic acid encoding SPARC, or an active fragment thereof.

32. 28. The composition of claim 27, wherein the at least one protective protein is CCL5, an active fragment of CCL5, a CCL5 mimetic, or a nucleic acid encoding CCL5, or an active fragment thereof.

33. 28. The composition of claim 27, wherein the at least one protective protein is APP, an active fragment of APP, an APP mimetic, or a nucleic acid encoding APP, or an active fragment thereof.

34. 28. The composition of claim 27, wherein the at least one protective protein is PF4, an active fragment of PF4, a PF4 mimetic, or a nucleic acid encoding PF4, or an active fragment thereof.

35. 28. The composition of claim 27, wherein the at least one protective protein is DNAJC19, an active fragment of DNAJC19, a DNAJC19 mimic, or a nucleic acid encoding DNAJC19, or an active fragment thereof.

36. 28. The composition of claim 27, wherein the at least one protective protein is Testican-2, an active fragment of Testican-2, a Testican-2 mimetic, or a nucleic acid encoding Testican-2, or an active fragment thereof.

37. The composition of claim 27 , wherein the nucleic acid is in a vector.

38. 28. The composition of claim 27, wherein the human subject has previously been identified as a progressor at risk for developing progressive renal decline.

39. 1. A method for determining an approximate risk of renal function decline in a human subject within a defined time period, comprising: a) obtaining a biological sample from said human subject; b) detecting a level of at least one protective protein in said biological sample, said at least one protective protein being selected from the group consisting of FGF20, TNFSF12, ANGPT1, Testican-2, SPARC, CCL5, APP, PF4, and DNAJC19; c) combining data regarding said levels of said protective proteins with clinical data characteristics of said human subjects (eGFR, uACR, clinical chemistry laboratory measurements, vital signs, patient demographics, etc.); and d) determining the approximate risk of renal decline (RD) for the human subject by using a machine learning or statistical modeling prognostic risk score algorithm (e.g., the KidneyIntelX testing platform).

40. 40. The method of claim 39, further comprising comparing said level of said at least one protective protein in said biological sample to a non-progressor control level or a normoalbuminuric control level.

41. 40. The method of claim 39, wherein the biological sample is obtained from the human subject at a first time point and a second time point.

42. 42. The method of claim 41 , wherein the second time point is obtained from the human subject about 6 months, about 12 months, about 18 months, about 24 months, about 3 years, about 4 years, about 5 years, about 10 years, or about 15 years after the first time point.

43. 42. The method of claim 41, further comprising comparing the level of the at least one protective protein in the biological sample obtained from the human subject at a first time point to the biological sample obtained from the human subject at a second time point.

Images