WO2017040488A1 - Prediction of kidney disease, severity and related treatment approaches - Google Patents

Abstract

Methods of treatment for subjects at risk of developing a chronic kidney disease and methods of identifying subjects at risk of developing a chronic kidney disease are provided.

Description

PREDICTION OF KIDNEY DISEASE, SEVERITY AND RELATED

TREATMENT APPROACHES

RELATED APPLICATIONS

[0001] This application claims the benefit of US Provisional Application No. 62/212,370, filed August 31 , 2015 which is incorporated by reference herein in their entirety.

FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] This invention was made with Government support under Grant No. RO1 DK101350, awarded by National Institutes of Health. The Government has certain rights in the invention.

BACKGROUND

1. Technical Field

[0003] Methods of treating subjects at risk of developing a chronic kidney disease and methods of identifying subjects at risk of developing a chronic kidney disease are provided.

2. Background

[0004] Chronic kidney disease (CKD) and progressive loss of kidney function constitute a major public health problem affecting 1 1 % of the US population.¹ The three most common diseases associated with CKD are diabetes mellitus, hypertension, and glomerulonephritis.² Patients with CKD are at high risk of cardiovascular disease (CVD) and mortality.³ It is thus important to identify patients at high risk for CKD and treat underlying disease processes that drive kidney injury.⁴ In clinical practice, methods of screening for kidney disease are limited to measurement of proteinuria, and estimation of glomerular filtration rate (eGFR). Proteinuria and decline in eGFR are relative insensitive indices of early injury and have limited utility in mass screening for CKD.⁴ Hence, more sensitive biomarkers are required to identify patients at risk earlier in the disease process, which is essential to design and study interventions aimed at preventing the progression to CKD. [0005] Soluble urokinase-type plasminogen activator receptor (suPAR) is the circulating form of a glycosyl-phosphatidylinositol (GPI)-anchored three-domain membrane protein expressed on a variety of cells including immunologically active cells, endothelial cells and podocytes.^7"9 Both forms are directly involved in the regulation of cell adhesion and migration through binding of integrins.⁷

SuPAR is produced by cleavage of membrane-bound uPAR, and is readily detected in plasma, serum, urine, and other bodily fluids.^10"12 As a biomarker of immune system activation and inflammation, elevated suPAR levels are

associated with poor outcomes in the general population including those with CVD,^13"17 infectious conditions,¹⁸ cancer,¹⁹ and diabetes.^20,21

[0006] More recently, suPAR has been implicated in the pathogenesis of kidney disease, specifically focal and segmental glomerulosclerosis (FSGS) and diabetic nephropathy, through interference with podocyte migration and

apoptosis.⁸'²⁰'²²'²³ These findings suggest a broader role for suPAR as a mediator of kidney injury beyond being a biomarker for a specific glomerular disease entity. Therefore, in a large prospective cohort of patients with CVD, the hypothesis that plasma suPAR levels predict risk of developing new-onset CKD was tested.

BRIEF SUMMARY

[0007] A method of treating a subject at risk of developing a chronic kidney disease (CKD) is provided. The method includes obtaining a biological sample from the subject where the subject has an estimated glomerular filtration rate (eGFR) of >60 mL/min/1.73m² and assaying a soluble urokinase-type

plasminogen activator receptor (suPAR) level in the sample. The method also includes determining the suPAR level in the sample relative to a control sample, identifying the subject having an elevated suPAR level relative to the control and stratifying the subject having the elevated suPAR level to a treatment group for treating CKD.

[0008] A method of identifying a subject at risk of developing chronic kidney disease (CKD) is provided. The method includes obtaining a biological sample from the subject where the subject has an estimated glomerular filtration rate (eGFR) of >60 mL/min/1 .73m² and assaying a level of soluble urokinase-type plasminogen activator receptor (suPAR) in the sample. The method also includes identifying the subject having a suPAR level of about 3000 pg/ml or greater as being at risk for developing CKD.

BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 illustrates suPAR levels and decline in eGFR

[0010] Percent change in estimated glomerular filtration rate (eGFR) over time stratified by suPAR quartiles in 2292 subjects. Q4 and Q3 had significantly higher rates of decline compared to Q1 and Q2 (P<0.001 , P=0.0002 respectively). There were no significant differences in rate of decline between Q1 and Q2 (P=0.91 ).

The statistical significance for the difference between Q3 and Q4 was marginal

(P=0.08).

[0011] Figures 2A and 2B illustrate suPAR-related decline in eGFR stratified by relevant subgroups.

[0012] Figure 2(A) SuPAR-related percent change in eGFR per year during follow-up stratified by diabetes status, African American race, and baseline of eGFR <60 or >60 mL/min/1 .73 m2. P-values reflect the results of the interaction analysis. Figure 2(B) SuPAR-related percent decline in eGFR stratified by baseline eGFR. Note that subjects with eGFR within the normal range (90-120) had the highest suPAR-related percent eGFR decline.

[0013] Figure 3 illustrates suPAR levels and incident CKD.

[0014] Kaplan-Meier curves demonstrating cumulative incidence of CKD, defined as eGFR<60 mL/min/1 .73 rri2, for subjects with baseline eGFR>60 mL/min/1 .73 rri2 (N=1331 ) stratified by suPAR level quartiles. The shaded areas represent the 95% confidence intervals. The P-value was the test result of log- rank test to compare the survival distributions among the four suPAR quartile groups. The log-rank statistic is approximately a χ2 with 3 df for large samples.

[0015] Figure 4 is a flow diagram showing eGFR measurements. [0016] Figures 5A-5D illustrate the relationship between suPAR and eGFR at baseline. Bar graphs depicting (A) mean suPAR levels and (B) percentage of subjects with baseline suPAR level >3040 pg/mL stratified according to eGFR categories. Panels C and D are scatterplots showing the relationship between baseline suPAR levels and eGFR stratified according to eGFR lower (C) or higher (D) than 90 mL/min/1.73m2. The correlation between suPAR and eGFR was strongest in those with eGFR <90 mL/min/1.73 m2 (r=-0.47, P<0.001 ), and weaker for those with eGFR≥90 mL/min/1.73 m2 (r=-0.07, P=0.041 ) with a P- value for the interaction of <0.001.

[0017] Figures 6A-6B illustrate the relationship between suPAR and

proteinuria at baseline. Bar graphs depicting (A) mean suPAR levels stratified by semi-quantitative proteinuria grade and (B) percentage of subjects with baseline suPAR level >3040 pg/mL stratified according to proteinuria grade. Note the positive correlation between suPAR and proteinuria (r=0.22, P<0.001 ). Nearly half of the subjects without proteinuria had suPAR levels above 3040 pg/mL.

[0018] Figures 7A-7B illustrate the Kaplan Meier curves with eGFR<45 mL/min and eGFR<30 mL/min as outcomes. Kaplan-Meier curves demonstrating cumulative incidence of CKD, defined as (A) eGFR<45 mL/min/1.73 m², or (B) eGFR<30 mL/min/1.73 m², for subjects with baseline eGFR≥60 mL/min/1.73 m² (N=1331 ) stratified by suPAR level quartiles. The shaded areas represent the 95% confidence intervals.

[0019] Figure 8 illustrates suPAR levels and decline in eGFR in the WIHS subset. Percent change in estimated glomerular filtration rate (eGFR) over time stratified by median plasma suPAR (3000 pg/mL) in 347 subjects. Over five years, subjects with suPAR≥3000 pg/mL (N=175) had a 3.0% decline in eGFR (95% CI [1.6%, 4.4%]), compared to only a 0.7% (95% CI [0.6%, 2.0%]) decline in those with suPAR<3000 (N=172).

DETAILED DESCRIPTION

[0020] The embodiments disclosed below are not intended to be exhaustive or to limit the scope of the disclosure to the precise form in the following description. Rather, the embodiments are chosen and described as examples so that others skilled in the art may utilize its teachings.

[0021] Embodiments of the present invention relate to methods of treating a subject at risk of developing a chronic kidney disease and methods of identifying subjects at risk of developing chronic kidney disease. In some embodiments, the methods include determining a suPAR level in a subject having an eGFR of >60 mL/min/1.73m².

[0022] As used herein, chronic kidney disease (CKD) is defined as having an eGFR of <60 mL/min/1.73m².

[0023] "Treating", "treat", or "treatment" within the context of the instant invention, means an alleviation of symptoms associated with a disorder or disease, or halt or delay of progression to a chronic disease or worsening of the symptoms associated with disease progression, or prevention or prophylaxis of the disease or disorder. In some embodiments, successful treatment may include halt or delay of progression related to a chronic kidney disease. Likewise, a therapeutically effective amount of a compound is a quantity sufficient to delay, diminish or alleviate at least one symptom associated with the conditions being treated. "Therapeutically effective amount" refers to the quantity of a component which is sufficient to yield a desired therapeutic response without undue adverse side effects (such as toxicity, irritation, or allergic response) commensurate with a reasonable benefit/risk ratio when used in the manner of this invention.

[0024] Methods of treatment of a subject at risk for developing a CKD are provided. Identifying the subjects may include obtaining a biological sample and assaying a suPAR level in the subject. In some embodiments, the biological sample may be isolated from the subject by collecting a blood sample, urine sample or other tissue sample from the subject. In a blood sample, plasma or serum may be isolated using any technique known to one skilled in the art. In some embodiments, the subject may be stratified to a treatment group if the subject has an elevated suPAR level. In some embodiments, the subject may have an eGFR of >60 mL/min/1.73m² and an elevated suPAR level and be stratified to a treatment group. In some embodiment, subjects may be stratified to a treatment group for optimal blood pressure treatment. In some embodiments, the subjects in the blood pressure treatment group may receive treatment in the form of angiotensin-converting enzyme (ACE) inhibitors, angiotensin II receptor blockers (ARBs), diuretics, beta-blockers, calcium channel blockers, alpha-blockers, alpha-agonists, renin inhibitors, combination

medications and other therapies for blood pressure. In some embodiments, the subjects may be stratified to a clinical trial group.

[0025] In some embodiments, the treatment may include administration to of an agent to the subject at risk of developing a CKD. In some embodiments, the agent may be an antibody, aptamer, antisense oligonucleotide, a natural agent, a synthetic agent or combinations thereof. In some embodiments, the agent is a chemical compound, natural or synthetic, in particular an organic or inorganic molecule of plant, bacterial, viral, animal, eukaryotic, synthetic or semisynthetic origin, capable of reducing, delaying or inhibiting progression to a chronic kidney disease in a subject having an elevated suPAR level.

[0026] In some embodiments, the treatment may include an oral administration of a compound. In some embodiments, the dose of the compound administered to the subject may be in the range from about 500 mg to 2000 mg per day for patients. In some embodiments, the dose of the compound to be administered alone or in combination therapy warm-blooded animals, for example humans, is preferably from approximately 0.01 mg/kg to approximately 1000 mg/kg, more preferably from approximately 1 mg/kg to approximately 100 mg/kg, per day, divided preferably into 1 to 3 single doses which may, for example, be of the same size. Usually children receive half of the adult dose, and thus the

preferential dose range for the inhibitor in children is 0.5 mg/kg to approximately 500 mg/kg, per day, divided preferably into 1 to 3 single doses that may be of the same size.

[0027] A compound can be administered alone or in combination with another autophagy activators, possible combination therapy taking the form of fixed combinations or the administration of a compound and another inhibitor being staggered or given independently of one another. Long-term therapy is equally possible as is adjuvant therapy in the context of other treatment strategies, as described above. Other possible treatments are therapy to maintain the subject's status after symptom amelioration, or even preventive therapy, for example in subjects at risk.

[0028] Effective amounts of the compounds described herein generally include any amount sufficient to detectably ameliorate one or more symptoms of a neurodegenerative disorder, or by detecting an inhibition or alleviation of symptoms of a kidney disease or disorder. The amount of active ingredient that may be combined with the carrier materials to produce a single dosage form will vary depending upon the host treated and the particular mode of administration.

It will be understood, however, that the specific dose level for any particular subject will depend upon a variety of factors including the activity of the specific compound employed, the age, body weight, general health, sex, diet, time of administration, route of administration, rate of excretion, drug combination, and the severity of the particular disease undergoing therapy. The therapeutically effective amount for a given situation can be readily determined by routine experimentation and is within the skill and judgment of the ordinary clinician.

[0029] According to the methods of treatment of the present invention, a chronic kidney disease is reduced or prevented in a subject such as a human or lower mammal by stratifying the subject to a treatment group, wherein the treatment may include administering to the subject an amount of an agent, in such amounts and for such time as is necessary to achieve the desired result.

[0030] It will be understood, however, that the total daily usage of the compounds and compositions of the present invention will be decided by the attending physician within the scope of sound medical judgment. The specific therapeutically effective dose level for any particular subject will depend upon a variety of factors including the disorder being treated and the severity of the disorder; the activity of the specific compound employed; the specific composition employed; the age, body weight, general health, sex and diet of the subject; the time of administration, route of administration, and rate of excretion of the specific compound employed; the duration of the treatment; drugs used in combination or coincidental with the specific compound employed; and like factors well known in the medical arts.

[0031] Compositions for administration of the active agent in the method of the invention may be prepared by means known in the art for the preparation of compositions (such as in the art of veterinary and pharmaceutical compositions) including blending, grinding, homogenising, suspending, dissolving, emulsifying, dispersing and where appropriate, mixing of the active agent, together with selected excipients, diluents, carriers and adjuvants.

[0032] For oral administration, the composition may be in the form of tablets, lozenges, pills, troches, capsules, elixirs, powders, including lyophilised powders, solutions, granules, suspensions, emulsions, syrups and tinctures. Slow-release, or delayed-release, forms may also be prepared, for example in the form of coated particles, multi-layer tablets or microgranules.

[0033] Solid forms for oral administration may contain binders acceptable in human and veterinary pharmaceutical practice, sweeteners, disintegrating agents, diluents, flavourings, coating agents, preservatives, lubricants and/or time delay agents. Suitable binders include gum acacia, gelatine, corn starch, gum tragacanth, sodium alginate, carboxymethylcellulose or polyethylene glycol.

Suitable sweeteners include sucrose, lactose, glucose, aspartame or saccharine. Suitable disintegrating agents include corn starch, methyl cellulose,

polyvinylpyrrolidone, guar gum, xanthan gum, bentonite, alginic acid or agar. Suitable diluents include lactose, sorbitol, mannitol, dextrose, kaolin, cellulose, calcium carbonate, calcium silicate or dicalcium phosphate. Suitable flavouring agents include peppermint oil, oil of wintergreen, cherry, orange or raspberry flavouring. Suitable coating agents include polymers or copolymers of acrylic acid and/or methacrylic acid and/or their esters, waxes, fatty alcohols, zein, shellac or gluten. Suitable preservatives include sodium benzoate, vitamin E, alpha- tocopherol, ascorbic acid, methyl paraben, propyl paraben or sodium bisulphite. Suitable lubricants include magnesium stearate, stearic acid, sodium oleate, sodium chloride or talc. Suitable time delay agents include glyceryl monostearate or glyceryl distearate. [0034] Liquid forms for oral administration may contain, in addition to the above agents, a liquid carrier. Suitable liquid carriers include water, oils such as olive oil, peanut oil, sesame oil, sunflower oil, safflower oil, arachis oil, coconut oil, liquid paraffin, ethylene glycol, propylene glycol, polyethylene glycol, ethanol, propanol, isopropanol, glycerol, fatty alcohols, triglycerides or mixtures thereof.

[0035] Suspensions for oral administration may further include dispersing agents and/or suspending agents. Suitable suspending agents include sodium carboxymethylcellulose, methylcellulose, hydroxypropylmethyl-cellulose, poly- vinyl-pyrrolidone, sodium alginate or acetyl alcohol. Suitable dispersing agents include lecithin, polyoxyethylene esters of fatty acids such as stearic acid, polyoxyethylene sorbitol mono- or di-oleate, -stearate or -laurate,

polyoxyethylene sorbitan mono- or di-oleate, -stearate or -laurate and the like.

[0036] The emulsions for oral administration may further include one or more emulsifying agents. Suitable emulsifying agents include dispersing agents as exemplified above or natural gums such as guar gum, gum acacia or gum tragacanth.

[0037] Examples

[0038] Emory Cardiovascular Biobank cohort characteristics at baseline

[0039] Subject demographic and clinical characteristics in the total cohort and dichotomized according to median suPAR level (3040 pg/mL (2373, 4019 [IQR]) are shown in Table 1. Elevated plasma suPAR level were independently associated with gender, a history of smoking, hypertension, hyperlipidemia, diabetes mellitus, myocardial infarction, hs-CRP levels, proteinuria and eGFR (Table 3).

[0040] Relationship between, eGFR, proteinuria, and suPAR at baseline.

[0041] After multivariable regression analysis adjusting for the aforementioned covariates, a lower eGFR at baseline was independently associated with increasing age, male gender, African American race, higher BMI, hypertension, smoking, proteinuria, lack of RAS inhibitors, and suPAR levels (Table S3, Figure

5). The correlation between suPAR and eGFR varied depending on baseline eGFR (P-value<0.001 for interaction), and was weak in subjects with eGFR >90 mL/min/1.73 m² (r=-0.07, P=0.041 , Figure 5 Panels C and D). Note that more than 30% of subjects with eGFR≥90/min/1.73 m² had suPAR>3040 pg/mL (Figure 5, Panel B). In addition, we found a positive correlation between proteinuria and suPAR levels (r=0.22, P<0.001 ) that was independent of the aforementioned covariates including eGFR (Table 3, Figure 6). Of note, over half of the subjects without proteinuria (52%) had suPAR levels above 3040 pg/mL (Figure 6, Panel B)

[0042] Relationship between suPAR levels and change in eGFR during follow- up.

[0043] The relationship between the plasma suPAR level at baseline and the change in eGFR in 2292 (62%) subjects with follow-up eGFR measurements was determined. The decline in eGFR was greater in subjects with higher suPAR levels (Table 4, Figure 1 ). Subjects in the two higher quartiles of suPAR levels

(>3040 pg/mL) had a significantly greater decline in eGFR compared to the lower two quartiles (<3040 pg/mL) after adjusting for age, gender, race, the use of RAS inhibitors, and serum hs-CRP (Figure 1A). Thus, over 5-years, the decline in eGFR in those in the lower two quartiles was 7.3% (95% CI [5.0%, 9.5%]) whereas it was 14.5% (95% CI [10.7%, 18.2%]) and 20.4% (95% CI [12.9%,

27.3%]) in those in the third and fourth highest quartiles, respectively (Figure 1 ).

[0044] Sensitivity analyses were performed to test whether race, presence of diabetes, proteinuria, baseline eGFR or baseline hs-CRP values influenced the relationship between the suPAR level and change in eGFR. SuPAR remained an independent predictor of eGFR decline regardless of race or presence of diabetes (Figure 2A). However, the interaction with baseline eGFR was

significant (P<0.001 ): there was no relationship between suPAR levels and eGFR decline in subjects with baseline eGFR<60 mL/min/1.73 m², whereas the suPAR level was predictive of eGFR change in those with eGFR>60 mL/min/1.73 m² at baseline (Figure 2A). Importantly, subjects with normal eGFR>90 mL/min/1.73 m²

(n=921 ) at baseline had the largest suPAR-related eGFR decline (P<0.001 for interaction, Figure 2B). We did not find a significant interaction with baseline proteinuria (P=0.29) nor baseline serum hs-CRP levels (P=0.89) [0045] Relationship between plasma suPAR levels and incident CKD

[0046] In 1335 participants with eGFR≥60 mL/min/1.73 m² at baseline, we determined whether baseline suPAR level predicted progression to clinical CKD (defined as eGFR<60 mL/min/1.73 m²). A total of 320 (24%) subjects developed CKD during follow-up. Independent predictors of incident CKD were age, baseline eGFR, hypertension, and plasma suPAR level (Table 2). Higher baseline suPAR level was associated with a significantly greater incidence of CKD (P<0.001 ) (Figure 3). Subjects with a suPAR level in the third and fourth quartiles had a 2.0- and 3.13-fold increased hazard of incident CKD compared to those in the lowest quartile (Table 2). Thus, at 1 year and 5 years, 7% and 41 % of subjects with suPAR>3040 ng/mL (third and fourth quartiles) developed CKD respectively, compared to 1 % and 12% of those with suPAR<3040 ng/mL (1 st and second quartiles) at the respective follow-up times. Even after excluding subjects with potential acute kidney injury (N=208), defined as a 50% decline in eGFR compared to the prior eGFR measurement regardless of time we found both the 3rd (OR 2.20 95%CI [1.33-3.06) and 4th highest quartiles (OR 2.93 95%CI [1.86-4.62]) of suPAR remained predictive of incident CKD compared to the lowest quartile, suggesting that suPAR is associated with a progressive decline in kidney function.

[0047] Risk Discrimination: The incremental value of adding suPAR to a model with traditional risk factors in predicting incident CKD was tested. The C-statistic for incident CKD at both 1 and 5 years of follow-up increased with the addition of suPAR quartile groups (W=0.08, 95% CI [0.01 , 0.15] and W=0.03, 95% CI [0.01 ,

0.06]), respectively (Table 5). The continuous NRI metric shows significant reclassification of subject risk by addition of suPAR to the model at both 1 (0.40,

95%CI [0.15, 0.53]) and 5 years (0.34, 95%CI [0.19, 0.41]) (Table 5). Addition of suPAR to the model with conventional risk factors was associated with a change in R2 of 0.33 that was larger than all other variables (Table 6). Validation analyses: We repeated the longitudinal analyses in (1 ) two randomly selected subsets of the Emory Cardiovascular Biobank cohort and (2) in a subset of subjects enrolled in the WIHS cohort. In both randomly selected subsets of the Emory Cardiovascular Biobank, elevated baseline suPAR level remained an independent predictor of eGFR decline during follow-up and of incident CKD, findings consistent with the primary analysis (Tables 4, 5). suPAR was then measured using a different assay (human uPAR QuantikineR ELISA kit (R&D, Minneapolis MN) in a separate replication cohort of 347 women (mean age 40, 62% African American, 31 % HIV+, Table 8) with follow-up eGFR (median 26 measurements over at least 10 years). Over five years, subjects with

suPAR≥3000 pg/mL (N=175) had a 3.0% decline in eGFR (95% CI [1.6%, 4.4%]) compared to only a 0.7% (95% CI [0.6%, 2.0%]) decline in those with

suPAR<3000 (ISM 72) (Table 9, Figure 8).

[0048] DISCUSSION

[0049] In this prospective cohort of adults with CVD, an association between elevated plasma suPAR levels and both the decline in eGFR and the

development of new-onset CKD has been identified. This relationship was observed in patients with normal baseline kidney function, and was independent of conventional kidney and cardiovascular disease risk factors, including baseline eGFR, age, race, diabetes, hypertension, and hs-CRP levels. Moreover, the lack of association between hs-CRP and eGFR decline or incident CKD suggests that the effect of suPAR on kidney dysfunction is unlikely to be the result of acute inflammatory processes. Importantly, suPAR significantly improved discrimination of future risk of CKD over a standard clinical model as evidenced by significant improvement in the C-statistic and NRI. This risk re-classification is greater than well-established biomarkers such as CRP and BNP that are used to predict cardiovascular events and heart failure, respectively.³⁰ We also show that suPAR remains associated with renal function decline in younger subjects with a significantly lower CVD risk factor burden, suggesting the effect of suPAR is truly independent of traditional CVD/CKD risk factors. The estimate in the WIHS subset was unsurprisingly lower than the one observed in the Emory

Cardiovascular Biobank cohort (3% versus 20% decline over 5 years,

respectively), likely due to the older age and greater burden of CVD/CKD risk factors in the Biobank cohort. [0050] High suPAR levels have typically been attributed to a state of inflammation or decreased renal clearance.¹⁸'³⁸ While we found a negative correlation between plasma suPAR and the baseline eGFR levels, this

association was weak at baseline eGFR>90 mL/min/1.73 m², with a substantial proportion (>30%) of subjects with normal kidney function having suPAR levels >3000 pg/mL in the absence of sepsis or cancer. This suggests that elevated suPAR levels are unlikely to simply result from a decrease in renal clearance, and may be related to the underlying pathogenic mechanisms initiating kidney disease.

[0051] Evidence of suPAR's pathogenic role in kidney disease has emerged mainly from studies of FSGS, where suPAR was found to activate ανβ3 integrin on podocytes, leading to effacement of foot processes and proteinuria.22

Elevated suPAR levels have also been associated with diabetic nephropathy and with progression of lupus nephritis.²⁹'²¹ In animal models of diabetic kidney disease, blockade of β3 integrin using a monoclonal antibody was

protective.²¹'³⁵'³⁶ In addition to activation of ανβ3 integrin on podocytes, SuPAR- mediated kidney injury may be due to several molecular mechanisms. For example, CD40 auto-antibodies have been implicated in modifying the effect of suPAR in FSGS,³⁷ whereas levels of acid sphingomyelinase-like

phosphodiesterase 3b have been implicated in modulating the effect of suPAR in diabetic nephropathy.²⁰ Thus, suPAR may interact with other molecules to induce podocyte dysfunction and mediated progression to CKD in a broad range of conditions. Alternatively, given the stability of the suPAR protein both in vivo and in vitro,³³ it is possible that suPAR acts as a marker of underlying low-grade inflammatory processes leading to disease. That said, in our cohort, hs-CRP, a well-accepted marker of chronic inflammation was predictive of cardiovascular events (similarly to suPAR), but not of decline in kidney function, suggesting that role of suPAR in CKD goes beyond inflammatory processes. SuPAR could thus satisfy the need for a long-sought biomarker for the prediction of CKD. Early identification and management of CKD is highly cost-effective and can reduce the risk of progression of CKD and CVD by up to 50%.³¹ Currently, proteinuria is considered the most sensitive marker for CKD progression in clinical practice, especially when combined with decline in eGFR. However, there are significant shortfalls in the diagnostic accuracy and sensitivity of these parameters for detection of CKD in the population. While multiple biomarkers are currently being explored as predictors of kidney disease progression and associated risk of accelerated CVD, none have been demonstrated to predict the incidence of CKD in patients with normal eGFR.³² Our results suggest that suPAR meets critical requirements for a CKD biomarker. First, suPAR levels are relatively stable in plasma,³³ and elevated levels are predictive of a heightened risk of developing CKD before there is any decline in eGFR. Second, it adds prognostic value in all patients and in the subgroups of patients who have diabetes or hypertension, the two most prevalent diseases associated with CKD in the U.S. Third, it is predictive of CKD in Caucasians and African Americans, despite the marked differences in likelihood of CKD in these two racial groups. Finally, measurement of suPAR may allow for more accurate stratification of patients early in their disease course enabling rational targeting of preventive health resources and enrollment into potential trials of novel renoprotective therapies. Importantly, we and others have shown that elevated suPAR levels are associated with increased risk of adverse cardiovascular events in subjects with or without mild to moderate CKD.¹⁷'³⁴ Thus, suPAR may be a common mediator of both CKD and CVD, conditions that often co-exist and are associated with poor prognosis and outcome.

[0052] Elevated plasma suPAR levels are associated with incident CKD and a more rapid decline in eGFR in subjects with normal baseline kidney function. Measurement of plasma suPAR level may be a practical and cost-effective tool for identification of adults at higher risk of developing CKD early in the course of disease.

[0053] METHODS

[0054] Study population.

[0055] Study subjects were recruited from the Emory Cardiovascular Biobank, a prospective registry of patients undergoing cardiac catheterization at three Emory Healthcare sites in Atlanta, GA between 2003 and 2009.¹⁷ Subjects with congenital heart disease, severe valvular heart disease, severe anemia, recent blood transfusion, myocarditis, or history of active inflammatory disease and cancer were excluded. Subjects aged 20 to 90 years were interviewed to collect information on demographic characteristics, medical history, medication use, and behavioral habits. Coronary artery disease was defined as the presence of an obstructive lesion (>50%) on coronary angiogram. Cardiovascular and kidney risk-factor prevalence was determined by physician diagnosis and treatment for hypertension, hyperlipidemia, and diabetes. Medical records were reviewed to confirm self-reported medical history. The study was approved by the Institutional Review Board at Emory University (Atlanta, GA). All subjects provided written informed consent at the time of enrollment.

[0056] Study design.

[0057] The relationship between baseline suPAR levels and kidney function (eGFR and semi-quantitative assessment of proteinuria) was examined in 3683 subjects. To investigate the relationship between suPAR levels and change in eGFR during follow-up, 2292 (62%) subjects had at least one additional measure of eGFR (median number of measurements=7) during a median follow-up period of 1337 days. Finally, the relationship between suPAR levels and the

development of incident CKD, defined as eGFR<60 mL/min/1.73m² beyond 30 days, was determined in 1335 subjects with a baseline eGFR>60 mL/min/1.73rri2 (Figure S1 ).²⁴

[0058] Sample collection and measurement of suPAR and hs-CRP.

[0059] Fasting arterial blood samples were collected and serum and plasma stored at -80°C for a mean duration of 4.9 years. Serum high sensitivity CRP (hs- CRP) concentrations were determined using a particle-enhanced

immunoturbidimetry assay with a lower detection limit of 0.03 mg/L (FirstMark, Division of GenWay Biotech Inc, San Diego, CA).²⁵ Plasma levels of suPAR were measured by Virogates (suPARnostic kit; Copenhagen, Denmark) with a lower detection limit of 100 pg/mL and intra- and inter-assay variation of 2.75% and

9.17%, respectively as determined by the assay manufacturer. [0060] Parameters of kidney function.

[0061] Serum creatinine measurements at enrollment and all subsequent values acquired during routine follow-up clinic visits or hospitalizations within the Emory Healthcare system were collected. eGFR was calculated using the CKD- EPI equation.²⁶ Semi-quantitative random urine protein excretion by dipstick testing was available for 1477 subjects at the time of enrollment. Subjects with subsequent dipstick tests showing trace or no proteinuria (N=815) were assumed to have trace or no proteinuria at enrollment, for a total of N=2292.

[0062] Statistical analysis.

[0063] Continuous variables are presented as means (standard deviation) or as median (interquartile range), and categorical variables as proportions (%). Independent sample t-tests and chi-square tests were used to compare continuous and categorical variables, respectively. Proteinuria was dichotomized as "no proteinuria" (n=2188) which included negative or trace, and "proteinuria" (n=104) that included grades 1 + and above. Estimated GFR values >120 mL/min/1 .73 m² (<1 % of measurements) were set at 120 mL/min/1 .73 m². Both suPAR and eGFR were log transformed prior to analyses.

[0064] SuPAR and baseline parameters of kidney function: Independent determinants of baseline suPAR and eGFR were determined using linear regression after adjustment for age, gender, race (African American vs. others), body mass index (BMI), history of smoking, diabetes, proteinuria (>1 + vs.

negative or trace protein on dipstick), hypertension, hyperlipidemia, coronary artery disease, history of myocardial infarction, hs-CRP, and use of renin- angiotensin system (RAS) inhibitors.

[0065] SuPAR and change in eGFR at follow-up: The association between baseline suPAR levels and change in eGFR over time was investigated in 2292 subjects with follow-up eGFR measurements using linear mixed effects modeling with random subject-specific intercept and random time effect, by regressing log(eGFR) on log(suPAR), follow-up time (years since baseline), log(suPAR) * time, baseline eGFR, in addition to the aforementioned covariates. Three-way interaction terms were incorporated into the model to determine whether eGFR, race, and diabetes modified the association between suPAR and change in eGFR. Estimated decline in eGFR in each subgroup was derived accordingly.

[0066] SuPAR and incident CKD: In 1335 participants with eGFR > 60 mL/min/1 .73 m² at baseline, we compared the progression to clinical CKD (eGFR < 60 mL/min/1 .73 m²) among the suPAR quartile groups using the log-rank test and a Cox proportional hazard model adjusting for the aforementioned

covariates. In addition, the C-statistic and category free net reclassification improvement (NRI) for risk discrimination were calculated.27

[0067] To validate our results, the analysis was repeated (1 ) after randomly dividing the entire study sample into two groups, and (2) in a subset of 347 subjects enrolled as part of the Women's Interagency HIV Study (WIHS). The Women's Interagency HIV Study (WIHS) is a longitudinal study of HIV infected and uninfected at-risk women that enrolled 2054 HIV-infected women and 569 uninfected women at six sites, Chicago, San Francisco Bay Area, Brooklyn and Bronx/Manhattan, New York, Washington, DC and Los Angeles from October 1994 through November 1995. From October 2001 through September 2002, an additional 737 HIV-infected women and 406 uninfected women were enrolled. Informed consent was obtained from all participants in accordance with the US Department of Health and Human Services (DHHS) guidelines and the

institutional review boards of participating institutions. Women are seen semiannually for interview, physical exam and collection of blood and genital specimens. The cohort was designed to reflect the demographics of the HIV epidemic among US women. Details of cohort recruitment, retention and demographics are published elsewhere. (1 ,2)

[0068] For the purpose of replicating the findings of the Emory Cardiovascular

Biobank cohort, we measured plasma suPAR in 374 randomly selected WIHS participants with eGFR>30 mL/min (N=135 or 36% with HIV) who had follow-up eGFR measurements available (median 26 measurements over at least 10 years) and determined whether suPAR levels were predictive of eGFR decline as described in the manuscript. A cutoff of 3000 pg/mL (median) for plasma suPAR was used to dichotomize the cohort subset into high and low suPAR. Plasma suPAR was measured in a blinded fashion using the human uPAR Quantikine® ELISA kit (R&D, Minneapolis, MN) following the manufacturer's instruction. The minimum detectable dose of suPAR is typically less than 33 pg/mL. The intra- and inter-assay variation is less than 5% respectively. ^28,29 Two-tailed P values≤ 0.05 were considered statistical significant. All analyses were performed using SAS 9.3 (Cary, NC, USA).

[0069] The above Figures and disclosure are intended to be illustrative and not exhaustive. This description will suggest many variations and alternatives to one of ordinary skill in the art. All such variations and alternatives are intended to be encompassed within the scope of the attached claims. Those familiar with the art may recognize other equivalents to the specific embodiments described herein which equivalents are also intended to be encompassed by the attached claims.

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2004;291 :844-50. Table 1. Demographics and clinical characteristics of the Emory Cardiovascular Biobank cohort

Variables Entire Cohort suPAR<3040 pg/mL suPAR>3040 pg/mL P Value

(N=3683) (N=1839) (N=1844)

Age, years 63 (12) 60 (1 1) 65 (12) O.001 Male, n (%) 2404 (65%) 1324 (72%) 1108 (59%) O.001

African American, n (%) 654 (18%) 334 (18%) 320 (17%) 0.55

2

Body mass index, kg/m 30 (6) 29 (6) 30 (7) 0.07 Clinical Characteristics

History of smoking, n (%) 2063 (58%) 968 (55%) 1095 (61%) O.001

Hypertension, n (%) 2601 (72%) 1220 (67%) 1381 (76%) O.001

Diabetes mellitus, n (%) 1 168 (32%) 423 (23%) 745 (41%) O.001

Hy erlipidemia, n (%) 2494 (69%) 1220 (67%) 1274 (70%) 0.06

History of myocardial infarction, n (%) 1296 (36%) 568 (32%) 728 (40%) O.001

Coronary artery disease, n (%) 2279 (66%) 1092 (63%) 1 187 (69%) O.001

Renin-angiotensin system antagonists, n 2046 (63%) 998 (62%) 1048 (64%) 0.09

Urine dipstick protein >1+, n (%) 137 (9%) 32 (5%) 105 (13%) O.001 eGFR, mL/min/1.73m² 73 (23) 81 (18) 64 (24) O.001 hs-CRP, ng/mL 6.85 (12.86) 5.23 (10.83) 8.46 (14.42) O.001 suPAR, pg/mL 3493 (1938) 2330 (452) 4654 (2145) O.001

Values are mean (SD), or n (%) as noted. Coronary artery disease is defined as the presence of a >50% obstructive lesion in any of the major vessels on coronary angiogram. P value denotes differences between suPAR >3040 and <3040 pg/mL.

Table 2. Predictors of incident chronic kidney disease

HR 95% CI P Value

Baseline eGFR, per mL/min/1.73 m² increase 0.96 (0.95,0.97) <0.001

Age, per year increase 1.01 (1.00,1.02) 0.05

Gender (female) 0.94 (0.72,1.23) 0.66

African American 1.23 (0.90,1.70) 0.20

Body mass index 1.01 (0.99,1.03) 0.39

History of smoking 1.16 (0.91,1.47) 0.24

Hypertension 1.34 (1.00,1.78) 0.05

Diabetes 1.18 (0.91,1.52) 0.22

Proteinuria 1.55 (0.86,2.81) 0.15

Hyperlipidemia 1.03 (0.78,1.35) 0.86

History of myocardial infarction 0.96 (0.76,1.23) 0.76

RAS antagonists 0.84 (0.65,1.09) 0.18

hs-CRP, > 2.8 vs. < 2.8 ng/niL 1.08 (0.84,1.38) 0.55

suPAR

Per quartile increase (1236 pg/mL) 1.40 (1.26,1.55) <0.001

> 3040 vs. < 3040 pg/mL 1.97 (1.53,2.54) <0.001

2nd vs. 1st quartile 1.18 (0.81,1.73) 0.39

3rd vs. 1st quartile 2.00 (1.38,2.89) <0.001

4th vs. 1st quartile 3.13 (2.11,4.65) <0.001

4th vs. 3rd quartile 1.51 (1.11,2.06) 0.01

CI: confidence interval, eGFR: estimated glomerular filtration rate. HR: hazard ratio, hs-CRP: high sensitivity c-reactive protein; RAS: renin- angiotensin system; suPAR: soluble urokinase-type plasminogen activator receptor. Except for baseline eGFR, age, hs- CRP, and suPAR, all variables were dichotomous (e.g., presence vs. abscense). The effect of suPAR is reported as a continuous variable, dichotomized by median (3040 pg/mL) as well as by quartiles. Subjects with a suPAR level in the third (Q3) and fourth (Q4) quartiles had a 2.0- and 3.13-fold increased hazard of incident CKD compared to those in the lowest quartile (Ql).

Table 3. Independent predictors of baseline suPAR and eGFR suPAR eGFR

Variables Change (%) 95 % CI P Value Change (%) 95 % CI P Value

Age, per year increase 0.1 (0.0,0.2) 0.19 -0.6 (-0.8,-0.5) O.001

Female gender 14.8 (11.7,18.0) O.001 5.1 (2.1,8.2) O.01

African American 2.1 (-1.5,5.8) 0.25 -5.2 (-8.6,-1.7) O.01

Body Mass Index, per unit

0.2 (0.0,0.4) 0.03 0.3 (0.1,0.5) 0.01 increase

History of Smoking 6.2 (3.5,9.0) <0.001 5.3 (2.5,8.2) <0.001

Hypertension 3.3 (0.2,6.4) 0.04 -5.6 (-8.5,-2.6) O.001

Diabetes Mellitus 14.5 (11.3,17.8) O.001 0.4 (-2.6,3.4) 0.82

Proteinuria 20.5 (12.4,29.2) O.001 -20.8 (-26.3,-15.0) O.001

Hyperlipidemia -6.4 (-9.1,-3.6) O.001 -2.1 (-5.0,0.9) 0.17

History of Myocardial Infarction 4.4 (1.4,7.4) 0.004 -1.7 (-4.6,1.2) 0.24

Coronary Artery Disease 1.2 (-1.9,4.4) 0.45 2.9 (-0.3,6.3) 0.08

RAS Antagonists -1.1 (-5.0,0.9) 0.42 3.2 (0.3,6.1) 0.03 hsCRP, per unit increase 0.4 (0.3,0.5) O.001 0.0 (-0.1,0.1) 0.88 eGFR, per unit increase -0.8 (-0.9,-0.8) <0.001 — — — suPAR, per 100% higher value — — — -14.4 (-15.1 ,-13.6) O.001 hsCRP: high sensitivity C-reactive protein; eGFR: estimated glomerular filtration rate; RAS: renin-angiotensin system; suPAR: soluble urokinase plasminogen-type activator receptor. Given both suPAR and eGFR were both log-transformed for the purpose of this analysis, estimated changes are expressed in %; i.e. female gender was independently associated with a 14.8% (95% CI [1 1.7,18.0]) increase in suPAR levels.

Table 4. Percent change in eGFR per year associated with different baseline suPAR levels for (1) all study participants and (2) validation cohorts

Overall (N=2292) Validation Cohort 1 (N= =1160) Validation Cohort 2 (N= =1132)

eGFR; estimated glomerular filtration rate. AeGFR (%): percent change in eGFR per year. Note the incrementally larger absolute decrease in eGFR associated with higher baseline suPAR values in the overall as well as the validation cohorts.

Table 5. Risk prediction metrics for suPAR for the outcome of incident CKD

Model C-statistic (95% CI) AC-statistic (95% CI) Continuous NRI (95% CI)

One Year ofFollow-Up

Model 0 0.75 (0.64, 0.85)

Model 1 (+suPAR pg/mL) 0.80 (0.71, 0.89) 0.05 (0.01, 0.10) 0.43 (0.18, 0.58)

Model 2 (+suPAR quartiles) 0.82 (0.76, 0.89) 0.08 (0.01, 0.15) 0.40 (0.15, 0.53)

Five Years of Follow-Up

Model 0 0.71 (0.67, 0.76)

Model 1 (+suPAR pg/mL) 0.74 (0.70, 0.78) 0.03 (0.01, 0.06) 0.29 (0.10, 0.38)

Model 2 (+suPAR quartiles) 0.75 (0.70, 0.79) 0.03 (0.01, 0.06) 0.34 (0.19, 0.41)

Model 0 includes baseline eGFR, age, gender, race (African American vs. others), body mass index (BMI), history of smoking, diabetes, proteinuria (>1+ vs. negative or trace protein on dipstick), hypertension, hyperlipidemia, history of myocardial infarction, hsCRP, and use of renin-angiontensin system (RAS) inhibitors. Model 1 includes all variables in Model 0 plus suPAR as a continuous variable. Model 2 include all variables in Model 0 plus suPAR quartile groups. CI = confidence interval, NRI = net reclassification index. The change in C- statistic as well as the NRI shows the addition of suPAR to the model has improved predictive accuracy of the model.

Table 6. R² change with addition of each variable to the prediction model

Variable Adjusted R² R² Increase in R²

BMI 0.0022 0.0025 0.0025

Age 0.0733 0.0739 0.0714

Female Gender 0.0753 0.0761 0.0022

History Smoking 0.0795 0.0806 0.0045

African American 0.0821 0.0835 0.0029

Hypertension 0.0924 0.0941 0.0106

Diabetes Mellitus 0.1023 0.1041 0.0100

Hyperlipidemia 0.1020 0.1042 0.0001

Proteinuria 0.1314 0.1338 0.0296

Myocardial Infarction 0.1331 0.1357 0.0019

Coronary Artery Disease 0.1327 0.1358 0.0001

RAS Antagonists 0.1333 0.1368 0.0010

hsCRP 0.1371 0.1409 0.0041

Log(suPAR) 0.3289 0.3322 0.1913

Adjusted R-square, R-square, and increase in R-square of the linear regression models are shown with log(eGFR) as the response variable using baseline data. Adjusted R-square and R-square values are reported for the model with the variable on the same row and all the variables above tha row. Increase in R-square is the change in R-square for adding the variable to the model, compared to the model using all variables above that row. Note that changing the order of the variable will affect the change in R-square. Note that the increase in R² with the addition of suPAR to the model is by far the largest compared to all other variables.

Table 7. Predictors of incident CKD and associated hazard ratios in two validation cohorts

Validation Cohort 1 (N = 661) Validation Cohort 2 (N = 674)

Effect HR 95% CI P Value HR 95% CI P Value

Baseline eGFR, per unit increase 0.96 (0.94,0.97) <0.001 0.97 (0.95,0.98) <0.001

Age, per year increase 1.01 (0.99, 1.03) 0.36 1.01 (1.00,1.03) 0.09

Gender (female) 0.88 (0.59, 1.31) 0.53 0.97 (0.67,1.40) 0.85

African American 0.97 (0.59, 1.61) 0.91 1.46 (0.94,2.27) 0.09

Body Mass Index 1.01 (0.98, 1.05) 0.50 1.01 (0.98,1.03) 0.64

History of Smoking 1.43 (1.00,2.06) 0.05 0.97 (0.69,1.36) 0.84

Hypertension 1.76 (1.14,2.71) 0.01 1.05 (0.71,1.55) 0.80

Diabetes 1.10 (0.75, 1.61) 0.63 1.29 (0.90,1.84) 0.16

Proteinuria 2.79 (1.06,7.31) 0.04 1.00 (0.46,2.20) 0.99

Hyperlipidemia 0.92 (0.62, 1.35) 0.66 1.12 (0.76,1.65) 0.56

History of Myocardial Infarction 1.01 (0.71, 1.43) 0.97 0.88 (0.62,1.26) 0.49

RAS Antagonists 0.77 (0.53, 1.1 1) 0.16 0.93 (0.64,1.35) 0.70 hsCRP, per unit increase 0.98 (0.95, 1.01) 0.22 1.00 (0.98,1.01) 0.50 suPAR

2nd vs. 1st Quartile 0.98 (0.57,1.70) 0.95 1.39 (0.81,2.40) 0.24

3rd vs. 1st Quartile 1.77 (1.07,2.93) 0.03 2.26 (1.31,3.90) <0.001

4th vs. 1st Quartile 2.76 (1.55,4.91) <0.001 3.90 (2.23,6.82) O.001

CI: confidence interval, eGFR: estimated glomerular filtration rate. HR: hazard ratio, hsCRP: high sensitivity c-reactive protein; RAS: renin-angiotensin system; suPAR: soluble urokinase plasminogen activator receptor. Note that in both validation cohorts, suPAR was independently predictive of incident CKD, with both 3 and 4* having significantly higher hazard ratios compared to the 1^st quartile

Table 8. Demographics and clinical characteristics of the WIHS subset

Entire Cohort suPAR <3000 pg/mL suPAR > 3000 pg/mL P Value

Variables

(N -374) (N -172) (N -175)

Age, years 39.5 (8.2) 39.0 (8.1) 40.0 (8.3) 0.25

African American, n (%) 214 (57%) 110 (64%) 104 (59%) 0.39

Body mass index, kg/m² 28.9 (8.0) 29.5 (7.7) 28.3 (8.2) 0.18

Clinical Characteristics

HIV positive, n (%) 135 (36%) 24 (14%) 111 (63%) <0.001

History of smoking, n (%) 278 (74%) 141 (82%) 137 (78%) 0.33

Hypertension, n (%) 94 (25%) 39 (23%) 55 (31%) 0.07

Diabetes mellitus, n (%) 24 (6%) 10 (6%) 14 (8%) 0.42

HIV viral load, copies/mL of

0 (0, 80) 0 (0, 0) 80 (0, 610) <0.001 blood

eGFR, niL/min/1.73 m² 94.2 (20.0) 97.7 (19.2) 90.8 (20.1) 0.001

3023 (2085,

suPAR, pg/mL 2079 (1740, 2753) 4310 (3616, 5529) <0.001

4334)

Values are mean (SD), n (%), or median (first quartile, third quartile) as noted. P value denotes statistical significance for differences between suPAR >3000 and <3000 pg/mL using appropriate tests.

Table 9. Predictors of eGFR decline in the WIHS subset

95% Confidence

Effect Estimated AeGFR P Value

Limits

Baseline eGFR, per 1 unit

0.3% 0.2% 0.4% O.001 increase

Baseline Age, per 1 year

-0.2% -0.4% -0.1% 0.002 increase

BMI, per 1 unit increase 0.0% -0.2% 0.1% 0.76

African American race 1.3% -0.9% 3.5% 0.26

Hypertension -1.5% -4.0% 1.0% 0.24

Diabetes mellitus -1.9% -6.0% 2.4% 0.38

History of smoking 0.3% -2.3% 2.9% 0.83

HIV viral load, per 100%

-0.1% -0.3% 0.2% 0.53 increase

Follow-up Time, year -0.1% -0.4% 0.1% 0.29 suPAR: >3000 pg/mL vs.

0.4% -1.9% 2.9% 0.72 <3000 pg/mL

Follow-up Time x suPAR

-0.5% -0.8% -0.1% 0.02 >3000 pg/mL

Results of the linear mixed model with eGFR at follow-up visits as the outcome variable and binary suPAR (>3000 vs. <3000 pg/mL) at baseline as the independent variable along with the covariates listed.

Claims

1. A method of treating a subject at risk of developing a chronic kidney disease (CKD), the method comprising: obtaining a biological sample from the subject, the subject having an estimated glomerular filtration rate (eGFR) of >60 mL/min/1.73m²; assaying a soluble urokinase-type plasminogen activator receptor (suPAR) level in the sample; determining the suPAR level in the sample relative to a control sample; identifying the subject having an elevated suPAR level relative to the control; and stratifying the subject having the elevated suPAR level to a treatment group for treating CKD.

2. The method according to claim 1 , comprising stratifying the subject to the treatment group when the suPAR level is about 3000 pg/ml or greater.

3. The method according to claim 1 , comprising stratifying the subject to the treatment group when the suPAR level is about 3040 pg/ml or greater.

4. The method according to claim 1 , comprising stratifying the subject to the treatment group when the suPAR level is about 4020 pg/ml or greater.

5. The method according to any one of claims 1 -4, comprising treating the subject with a therapy to treat CKD or to decrease the suPAR level in the subject.

6. The method according to any one of claims 1 -5, comprising stratifying the subject having the elevated suPAR level to the treatment group for subjects having an eGFR of >90 mL/min/1.73m².

7. The method according to any one of claims 1 -6, wherein the biological sample comprises a blood sample.

8. The method according to claim 7, wherein the blood sample comprises a plasma sample.

9. The method according to any one of claims 1 -8, comprising stratifying the subject having the elevated suPAR level to the treatment group in the absence of proteinuria.

10. The method according to any one of claims 1 -9, wherein the subject has diabetes or hypertension.

1 1. The method according to any one of claims 1 -9, comprising obtaining a second biological sample from the subject after a treatment has been given and determining the suPAR level in the second sample.

12. A method of identifying a subject at risk of developing chronic kidney disease (CKD), the method comprising: obtaining a biological sample from the subject, the subject having an estimated glomerular filtration rate (eGFR) of >60 mL/min/1.73m²; assaying a level of soluble urokinase-type plasminogen activator receptor (suPAR) in the sample; identifying the subject having a suPAR level of about 3000 pg/ml or greater as being at risk for developing CKD.

13. The method according to claim 12, comprising identifying the subject as being at risk for developing CKD when the suPAR level is about 3040 pg/ml or greater.

14. The method according to claim 12 comprising identifying the subject as being at risk for developing CKD when the suPAR level is about 4020 pg/ml or greater.

15. The method according to any one of claims 12-14, comprising stratifying the subject having the elevated suPAR level to the treatment group for subjects having an eGFR of >90 mL/min/1.73m².

16. The method according to any one of claims 12-15, wherein the biological sample comprises a blood sample.

17. The method according to claim 16, wherein the blood sample comprises a plasma sample.