Biomarker composition for early diagnosis of kidney disease and method for providing information necessary for early diagnosis of kidney disease using the same

Abstract

The present invention relates to a biomarker for the early diagnosis of kidney disease and its use, and a composition, kit, and method according to one embodiment enable early diagnosis of kidney disease or the risk of kidney disease with higher accuracy, sensitivity, and specificity. In particular, the present invention enables classification into a normal group, a risk group (a stage with inherent risk factors), chronic kidney disease stage 1 (IRIS stage 1), or chronic kidney disease stages 2 to 4 (IRIS stages 2 to 4) based on the International Renal Interest Society (IRIS) chronic kidney disease (CKD) staging guidelines with a sensitivity of 90% or more and a specificity of 95% or more.

Description

[Technical Field]

[0001] The present invention relates to a biomarker composition for the early diagnosis of kidney disease and a method for providing information necessary for the early diagnosis of kidney disease using the same. [Background technology]

[0002] The kidneys are located in two places on either side of the spine in the lower back. In addition to their primary function of filtering metabolic products and waste products from the body and excreting them as urine, they also have the function of maintaining homeostasis by keeping the body's water content, electrolytes, and acidity constant within narrow ranges, and the endocrine function of producing and activating various hormones that are important for maintaining blood pressure, improving anemia, and calcium and phosphorus metabolism.

[0003] Kidney diseases that cause a decline in kidney function include glomerulonephritis, chronic renal failure, acute renal failure, nephrotic syndrome, pyelonephritis, kidney stones, kidney cancer, etc. Depending on the rate at which kidney function deteriorates, they can be broadly divided into acute kidney injury (AKI) and chronic kidney disease (CKD).

[0004] In chronic kidney disease, kidney function declines gradually over several months and is usually irreversible and progressive, often progressing to end-stage renal disease, which requires dialysis or kidney transplantation. In contrast, acute kidney injury (AKI) is a sudden deterioration of kidney function within days or weeks, commonly caused by dehydration or hypotension, nephrotoxic substances or drugs, or urinary tract obstruction. Conservative treatment, usually involving intravenous fluid administration to correct dehydration and eliminating the underlying cause of kidney strain, usually restores kidney function, but depending on the severity of the underlying disease, some cases can progress to chronic renal failure.

[0005] Despite advances in modern medicine, many hospitalized patients suffer greatly from declining kidney function, and patients with particularly severe illnesses often require renal replacement therapy due to declining kidney function. The prevalence of acute kidney injury has been reported to range from approximately 5% of hospitalized patients to approximately 30-50% of patients admitted to intensive care units, and these prevalence rates have been steadily increasing despite the development of new treatments (Lameire et al., Lancet, 2005; Devarajan, Contrib Nephrol, 2007).

[0006] While there may be several reasons for the high mortality rate of acute kidney injury, the lack of early diagnostic methods for acute kidney injury (AKI) may be a major contributing factor, leading to missed treatment windows. Traditional methods for assessing renal function indirectly reflect the degree of renal function by measuring serum creatinine. However, serum creatinine is affected by factors such as individual body weight, age, sex, muscle mass, protein intake, and medications, making it difficult to reflect changes in renal function in real time. In other words, serum creatinine does not increase until renal function declines by more than 50%, limiting the diagnostic method's ability to diagnose acute kidney injury early (Belcher et al., Am J Kidney Dis, 2011; Endre and Westhuyzen, Nephrology, 2008). Fortunately, innovative cutting-edge technologies such as functional genomics and proteomics have recently been developed and applied, demonstrating the potential of various proteins and gene products as biomarkers. However, their clinical efficacy remains limited.

[0007] NGAL (Neutrophil gelatinase-associated lipocalin) is a 25kDa glycoprotein that binds to neutrophils and renal tubular epithelium. It is a biomarker that rapidly increases in patients with acute kidney injury due to various causes. It is currently used primarily to diagnose renal dysfunction and to determine the prognosis of kidney transplant patients.

[0008] In addition, KIM-1 (Kidney Injury Molecule-1) is a protein that is not expressed in normal kidneys, but is strongly expressed in renal tubules of patients with renal ischemia-reperfusion injury, nephrotoxic drugs, or kidney disease from several hours after kidney injury. KIM-1 consists of a cytoplasmic domain and an extracellular domain, and the ectodomain is excreted in urine and has been extensively studied as a diagnostic biomarker for kidney disease.

[0009] However, conventional clinical pathological kidney disease diagnostic techniques using not only NGAL and / or KIM-1 but also serum creatinine and SDMA (Symmetric Dimethylarginine) were unable to differentiate between normal and at-risk kidney disease groups.

[0010] Therefore, the present inventors have conducted research and development to develop an optimal index that can diagnose kidney disease early, and in particular, can distinguish between normal and at-risk kidney disease groups. The inventors measured the concentrations of NGAL and KIM-1 in individual body fluid samples, and then performed a crude logistic regression analysis to determine the degree to which each variable, including the concentrations of NGAL and KIM-1, affects the prevalence. Only variables that were significant at a significance level of 0.05 were combined, and multiple logistic regression analysis was performed to grasp and estimate the functional relationships between the variables. An optimal model (function) with the highest pseudo R2 (explanatory power, strength of the relationship between the dependent variable and the independent variable) was derived from the estimated values. The inventors have confirmed that the accuracy, sensitivity, and specificity of the early diagnosis of kidney disease are significantly high when the derived optimal model is used, thereby completing the present invention. [Prior art documents] [Patent documents]

[0011] [Patent Document 1] Republic of Korea Publication No. 10-2013-0089474 [Patent Document 2] Republic of Korea Patent No. 10-1657881 Summary of the Invention [Problem to be solved by the invention]

[0012] One aspect is to provide a diagnostic composition for kidney disease, comprising a preparation capable of measuring the expression levels of NGAL (Neutrophil gelatinase-associated lipocalin) protein, KIM-1 (Kidney injury molecule-1) protein, or a combination thereof, or the genes encoding them.

[0013] Another aspect is to provide a kit for diagnosing kidney disease, comprising the composition.

[0014] Another aspect provides a method for providing information for diagnosing kidney disease, comprising measuring the expression level of NGAL (Neutrophil gelatinase-associated lipocalin) protein, KIM-1 (Kidney injury molecule-1) protein, or a combination thereof, or genes encoding them, from a biological sample obtained from an individual, and comparing the measured expression level with the expression level of a normal group of proteins, or a combination thereof, or genes encoding the same.

[0015] Another aspect is to provide a method of treating kidney disease comprising measuring the expression level of NGAL (Neutrophil gelatinase-associated lipocalin) protein, KIM-1 (Kidney injury molecule-1) protein, or a combination thereof, or the genes encoding them, from a biological sample obtained from an individual.

[0016] Another aspect is to provide a method for diagnosing kidney disease, comprising measuring the expression level of NGAL (Neutrophil gelatinase-associated lipocalin) protein, KIM-1 (Kidney injury molecule-1) protein, or a combination thereof, or genes encoding them, from a biological sample obtained from an individual, and comparing the measured expression level with the expression level of a normal population of proteins, or a combination thereof, or genes encoding the same.

[0017] Another embodiment is an input unit for inputting the concentration of one or more markers selected from NGAL (Neutrophil gelatinase-associated lipocalin) and KIM-1 (Kidney injury molecule-1) measured from a body fluid sample of an individual, or inputting the concentration of one or more markers selected from SDMA, creatinine, inorganic phosphorus, amylase, and BUN together with the concentration of the marker; a variable setting unit for setting the input marker concentration as one or more independent variables, and setting the presence or absence of chronic kidney disease risk group (stage with inherent risk factors), chronic kidney disease stage 1 (IRIS stage 1), or chronic kidney disease stage 2 to 4 (IRIS stage 2 to 4) according to the International Renal Interest Society (IRIS) chronic kidney disease (CKD) staging guideline criteria as a dependent variable; and a variable setting unit for analyzing the relationship between the plurality of independent variables and the dependent variable by logistic regression analysis. and a diagnostic unit that derives a value by substituting data on markers input from the input unit into each independent variable of the inferred model formula, and, when the derived value is equal to or greater than a predetermined cutoff value, classifies the individual into a kidney disease risk group (a stage with inherent risk factors), chronic kidney disease stage 1 (IRIS stage 1), or chronic kidney disease stage 2 to 4 (IRIS stage 2 to 4).

[0018] Another aspect is to provide a computer-readable recording medium having recorded thereon a computer program for executing the method on a computer. [Means for solving the problem]

[0019] One aspect provides a diagnostic composition for kidney disease, comprising a preparation capable of measuring the expression levels of NGAL (Neutrophil gelatinase-associated lipocalin) protein, KIM-1 (Kidney injury molecule-1) protein, or a combination thereof, or the genes encoding them.

[0020] Neutrophil gelatinase-associated lipocalin (lipocalin-2) is a 25 kDa glycoprotein that binds to neutrophils and the epithelium of renal tubules and is known to play an important role in assessing kidney health or kidney damage. NGAL may be expressed in renal proximal tubule damage or nephron damage.

[0021] KIM-1 (Kidney Injury Molecule-1) is a protein that is not expressed in normal kidneys, but is strongly expressed in the renal tubules of patients with renal ischemia-reperfusion injury, nephrotoxic drugs, or kidney disease from several hours after renal injury. It is one of the proteins that indicates renal injury and is primarily used as a biomarker for diagnosing or monitoring acute kidney injury. KIM-1 is composed of a cytoplasmic domain and an extracellular domain, of which the extracellular domain is known to be excreted in urine. Expression of KIM-1 may be increased during proximal tubular injury.

[0022] As used herein, the term "marker" or "biomarker" refers to a substance that can be used to distinguish between normal individuals and diseased individuals, and can include any organic biomolecules such as polypeptides, proteins, nucleic acids, genes, lipids, glycolipids, glycoproteins, or sugars that show an increase in individuals with kidney-related diseases of the present invention.

[0023] In one embodiment, the NGAL or KIM-1 may be used as a biomarker for the early diagnosis of kidney disease.

[0024] The composition may further comprise a preparation for measuring the expression level of one or more proteins or genes encoding them selected from the group consisting of SDMA, BUN, creatinine, inorganic phosphorus, amylase, inulin, and cystatin C.

[0025] The SDMA, BUN, creatinine, inulin, and cystatin C can be used in practice as biomarkers for evaluating glomerular filtration rate.

[0026] The term "glomerular filtration rate (GFR)" as used herein is an index of kidney function and refers to the rate at which the kidney filters specific substances from the blood. The glomerular filtration rate may indicate the kidney's ability to filter waste products and substances contained in the blood and excrete them in urine.

[0027] The preparation capable of measuring the expression level of the protein may be selected from the group consisting of a monoclonal antibody, a polyclonal antibody, a chimeric antibody, a ligand, a peptide nucleic acid (PNA), an aptamer, and a nanoparticle that specifically binds to the protein, but is not limited thereto.

[0028] Methods for measuring the level of protein expression include, but are not limited to, protein chip analysis, immunoassay, ligand binding, MALDI-TOF (Matrix Desorption / Ionization Time of Flight Mass Spectrometry) analysis, SELDI-TOF (Surface Enhanced Laser Desorption / Ionization Time of Flight Mass Spectrometry) analysis, radioimmunoassay, radial immunodiffusion, Ouchterlony diffusion, rocket immunoelectrophoresis, immunohistochemistry, complement fixation analysis, two-dimensional electrophoresis, liquid chromatography-mass spectrometry (LC-MS), LC-MS / MS (liquid chromatography-mass spectrometry / mass immunosorbent assay), Western blotting, and enzyme-linked immunosorbent assay (ELISA). Therefore, the preparation for measuring the protein level may contain an antibody that specifically binds to NGAL protein or KIM-1 protein.

[0029] The term "antibody" as used herein can refer to a specific protein molecule directed against an antigenic site. For purposes of the present invention, antibody refers to an antibody that specifically binds to the NGAL protein or KIM-1 protein, and includes all polyclonal, monoclonal, and recombinant antibodies. Antibodies can be easily produced using techniques well known in the art. Furthermore, the term "antibody" as used herein includes intact forms having two full-length light chains and two full-length heavy chains, as well as functional fragments of antibody molecules. Functional fragments of antibody molecules refer to fragments that retain at least the antigen-binding function, and include Fab, F(ab'), F(ab')2, and Fv.

[0030] The preparation capable of measuring the expression level of the gene encoding the protein may be selected from the group consisting of a primer pair, a probe, and an antisense nucleotide that specifically binds to the gene, but is not limited thereto.

[0031] The term "primer pair" as used herein includes all combinations of primer pairs consisting of forward and reverse primers that recognize target gene sequences, and specifically refers to primer pairs that provide analytical results with specificity and sensitivity. High specificity can be achieved when the nucleic acid sequence of the primer is a sequence that is incompatible with non-target sequences present in the sample, and the primer amplifies only the target gene sequence containing the complementary primer binding site without inducing non-specific amplification.

[0032] The term "probe" as used herein refers to a substance capable of specifically binding to a target substance to be detected in a sample and capable of specifically confirming the presence of the target substance in the sample through this binding. The types of probe molecules are not limited to those commonly used in the art, but may preferably be peptide nucleic acid (PNA), locked nucleic acid (LNA), peptides, polypeptides, proteins, RNA, or DNA. More specifically, the probes include biological substances derived from or similar to living organisms, or those produced in vitro. For example, the probes may be enzymes, proteins, antibodies, microorganisms, animal and plant cells and organs, nerve cells, DNA, and RNA. DNA may include cDNA, genomic DNA, and oligonucleotides; RNA may include genomic DNA, mRNA, and oligonucleotides; and proteins may include antibodies, antigens, enzymes, peptides, etc.

[0033] The term "antisense oligonucleotide" as used herein refers to DNA, RNA, or their derivatives containing a nucleic acid sequence complementary to a specific mRNA sequence, which binds to the complementary sequence in the mRNA and inhibits translation of the mRNA into protein. An antisense oligonucleotide sequence refers to a DNA or RNA sequence that is complementary to the mRNA of the gene and capable of binding to the mRNA. This can inhibit essential activities for the translation, translocation into the cytoplasm, maturation, or all other biological functions of the gene mRNA. The length of an antisense oligonucleotide can be 6 to 100 bases, preferably 8 to 60 bases, and more preferably 10 to 40 bases. Antisense oligonucleotides can be synthesized in vitro using conventional methods and administered in vivo, or antisense oligonucleotides can be synthesized in vivo. One example of synthesizing antisense oligonucleotides in vitro is by using RNA polymerase I. One example of synthesizing antisense RNA in vivo is by transcribing antisense RNA using a vector with a multiple cloning site (MCS) origin in the opposite direction. The antisense RNA preferably contains a translation stop codon in its sequence so that it is not translated into a peptide sequence.

[0034] In one embodiment, the kidney disease can be acute kidney injury (AKI) or chronic kidney disease (CKD).

[0035] The acute kidney injury is not particularly limited, and may be any one selected from the group consisting of acute renal failure, acute tubular necrosis, acute tubulointerstitial nephritis, ischemic acute kidney injury, acute pyelonephritis, acute progressive nephritis, and toxic acute kidney injury, but is not limited thereto.

[0036] The chronic kidney disease is not particularly limited, and may be any selected from the group consisting of nephritic syndrome, tubular damage, renal hypertension, uremia, chronic glomerulonephritis, renal failure, and chronic renal failure, but is not limited thereto.

[0037] The kidney disease may include, but is not limited to, one or more diseases selected from the group consisting of diabetic nephropathy, hypertensive nephropathy, glomerulonephritis, polycystic kidney disease, urinary tract obstruction, renal fibrosis, nephritis, pyelitis, kidney cancer, hydronephrosis, hemorrhagic fever with renal syndrome, renal tuberculosis, focal glomerulosclerosis, diabetic nephropathy, membranous nephropathy, membranous proliferative glomerulonephritis, and nephrotic syndrome.

[0038] In one embodiment, the expression level of said protein or the gene encoding it may be measured in a body fluid sample of the individual.

[0039] In one embodiment, the individual may be a mammal, such as, but not limited to, a human, dog, cat, cow, horse, pig, sheep, or goat.

[0040] The individual may be a non-human animal.

[0041] The individual may be selected for early diagnosis of kidney disease based on the pre-existence of one or more risk factors selected from prerenal, intrinsic renal, and postrenal kidney disorders.

[0042] As used herein, the term "prerenal kidney injury" refers to kidney damage caused by extrarenal factors. It mainly occurs when there is a problem with blood circulation, resulting in insufficient blood supply to the kidneys and impaired renal function. Prerenal kidney injury may generally be caused by factors such as decreased blood pressure, decreased blood volume, and changes in blood viscosity.

[0043] The term "intrinsic kidney injury" as used herein refers to kidney injury caused by problems in the kidney itself, which can result in damage to the kidney tissue itself and reduced function. Intrinsic kidney injury can be caused by cell injury, inflammation, exposure to toxic substances, infection, hemodynamic abnormalities, and the like that directly affect kidney tissue.

[0044] The term "postrenal kidney injury" as used herein refers to kidney damage caused by problems in the renal excretory system, and may occur when urine produced by the kidneys is abnormally blocked and accumulates. Postrenal kidney injury may occur when urine is not properly excreted due to urinary tract infection, ureteral obstruction, bladder obstruction, prostate enlargement, etc.

[0045] The risk factors may be, but are not limited to, one or more pre-existing diagnoses selected from congestive heart failure, pre-eclampsia, seizures, renal diabetes, hypertension, coronary artery disease, proteinuria, renal insufficiency, glomerular filtration below the normal range, serum creatinine above the average range, sepsis, impairment to renal function, decreased renal function, and acute renal failure (ARF); previous surgery selected from major vascular surgery, coronary artery bypass surgery, and cardiac surgery; or exposure to nonsteroidal anti-inflammatory drugs, cyclosporine, tacrolimus, aminoglycosides, foscarnet, ethylene glycol, hemoglobin, myoglobin, ifosfamide, heavy metals, methotrexate, radiopaque contrast agents, or streptozocin.

[0046] In one embodiment, the individual may be, but is not limited to, not receiving renal replacement therapy.

[0047] In one embodiment, the body fluid sample may be urine or blood, preferably blood, more preferably a plasma or serum sample, but is not limited thereto.

[0048] The composition may be capable of classifying risk groups (stages with inherent risk factors) and each stage of the disease in accordance with the International Renal Interest Society (IRIS) chronic kidney disease (CKD) staging guidelines.

[0049] As used herein, the term "International Renal Interest Society (IRIS) Chronic Kidney Disease (CKD) Staging Guidelines" or "IRIS CKD stage" refers to the kidney disease classification criteria presented by the International Society of Veterinary Nephrology.

[0050] As used herein, the term "early diagnosis of kidney disease" includes distinguishing between kidney disease risk groups (stages with inherent risk factors), chronic kidney disease stage 1 (IRIS stage 1), or chronic kidney disease stages 2 to 4 (IRIS stages 2 to 4), or early diagnosis of kidney damage or kidney disease.

[0051] The term "kidney disease risk group" as used herein refers to a stage in which a person does not yet have kidney disease but has risk factors for kidney disease.

[0052] As used herein, the term "stage 1 kidney disease" refers to a stage in which the glomerular filtration rate (GFR) is still normal or near-normal, but chronic kidney disease is diagnosed based on medical history, a persistent decrease in glomerular filtration rate, diagnostic imaging tests, etc.

[0053] Another aspect provides a kit for diagnosing kidney disease, comprising the composition.

[0054] The composition is as described above.

[0055] The kit may provide information regarding the presence or absence of chronic kidney risk group (stage with inherent risk factors), chronic kidney disease stage 1 (IRIS stage 1), or chronic kidney disease stages 2 to 4 (IRIS stages 2 to 4) according to the International Renal Interest Society (IRIS) chronic kidney disease (CKD) staging guidelines. The kit may also be capable of classifying disease stages according to the International Renal Interest Society (IRIS) chronic kidney disease staging guidelines.

[0056] The kit may be applicable to various types of diagnostic kits using antigen-antibody reactions, such as, but not limited to, Lateral Flow Assay or Indirect Immunofluorescence Assay.

[0057] The kit may include not only a preparation for measuring the expression level of the protein or gene, but also tools, reagents, etc. that are commonly used in immunological analysis.

[0058] Examples of the tool or reagent include, but are not limited to, a suitable carrier, a labeling substance capable of generating a detectable signal, a chromophore, a solubilizing agent, a detergent, a buffer, a stabilizer, etc. If the labeling substance is an enzyme, a substrate capable of measuring the enzyme activity and a reaction stopper may be included. Carriers include soluble and insoluble carriers. Examples of soluble carriers include physiologically acceptable buffers known in the art, such as PBS. Examples of insoluble carriers include polymers such as polystyrene, polyethylene, polypropylene, polyester, polyacrylonitrile, fluororesin, cyclodextrin, polysaccharides, and magnetic particles formed by plating metal on latex, as well as paper, glass, metal, agarose, and combinations thereof.

[0059] The kit may include, but is not limited to, a sample pad, a conjugate pad, a stacking pad, a membrane, an absorbent pad, and a backing card.

[0060] The "sample pad" refers to a pad capable of accommodating a sample to be analyzed and allowing diffusional flow, and is made of a material having sufficient porosity to accommodate and contain the sample to be analyzed. Examples of such porous materials include, but are not limited to, fibrous paper, microporous membranes made of cellulose materials, cellulose derivatives such as cellulose acetate, nitrocellulose, glass fiber, natural cotton, woven fabrics such as nylon, and porous gels.

[0061] The "conjugate pad" refers to a pad that accommodates a sample that diffuses and migrates from a sample pad. The conjugate pad may be configured to allow diffusion flow similar to the sample pad, but is not limited thereto.

[0062] The "membrane" may be formed with a test line for capturing an analyte in a specimen and may be made of any material that allows the passage of sample material. For example, it may be formed from natural, synthetic, or synthetically modified naturally occurring materials, such as polysaccharides (e.g., cellulose materials, paper, cellulose derivatives such as cellulose acetate and nitrocellulose), polyethersulfone, polyethylene, nylon, polyvinylidene fluoride (PVDF), polyester, polypropylene, silica, polymers such as vinyl chloride, vinyl chloride-propylene copolymer, and vinyl chloride-vinyl acetate copolymer, inorganic materials uniformly dispersed in a porous polymer matrix, such as deactivated alumina, diatomaceous earth, MgSO4, or other inorganic fine powder materials, natural (e.g., cotton) and synthetic (e.g., nylon or rayon) fabrics, porous gels such as silica gel, agarose, dextran, and gelatin, and polymer films such as polyacrylamide. In one embodiment, the membrane may be, but is not limited to, a nitrocellulose (NC) membrane, a glass fiber membrane, a polyethersulfone (PES) membrane, a cellulose membrane, a nylon membrane, or a combination thereof.

[0063] On the membrane, a test area and a control area may be sequentially formed in a direction from the conjugate pad to the absorbent pad, but the invention is not limited thereto.

[0064] The "absorbent pad" can be located adjacent to or near the end of the membrane. The absorbent pad generally receives the fluid sample that migrates throughout the membrane. The absorbent pad can help promote capillary action and diffusive flow of fluid through the membrane.

[0065] The solid support may be made of any material capable of supporting and transporting the sample pad, conjugate pad, membrane, stacking pad, and absorbent pad. In one embodiment, the support is liquid-impermeable so that sample fluids diffusing through the membrane do not leak from the support. Examples include, but are not limited to, glass and polymeric materials such as polystyrene, polypropylene, polyester, polybutadiene, polyvinyl chloride, polyamide, polycarbonate, epoxide, methacrylate, polymelamine, etc.

[0066] The kit may have the sample pad, conjugate pad, stacking pad, membrane and absorbent pad arranged in that order on the same solid support.

[0067] Another aspect provides a method for providing information for diagnosing kidney disease, comprising measuring the expression level of NGAL (Neutrophil gelatinase-associated lipocalin) protein, KIM-1 (Kidney injury molecule-1) protein, or a combination thereof, or genes encoding them, from a biological sample obtained from an individual, and comparing the measured expression level with the expression level of a normal population of proteins, or a combination thereof, or genes encoding the same.

[0068] In one embodiment, the method may further include measuring the expression level of one or more proteins selected from the group consisting of SDMA, serum creatinine (sCr), inorganic phosphorus, amylase, and BUN, or genes encoding the same, and setting the measured expression level as the independent variable.

[0069] The method may further include the steps of setting the measured protein or gene expression levels as independent variables, and setting the occurrence of chronic kidney disease risk group (stage with inherent risk factors), chronic kidney disease stage 1 (IRIS stage 1), or chronic kidney disease stages 2 to 4 (IRIS stages 2 to 4) according to the International Renal Interest Society (IRIS) chronic kidney disease (CKD) staging guidelines as dependent variables; modeling the relationship between the independent variables and the dependent variable using logistic regression analysis to infer a model equation; and determining the individual as being in the kidney disease risk group (stage with inherent risk factors), chronic kidney disease stage 1 (IRIS stage 1), or chronic kidney disease stages 2 to 4 (IRIS stages 2 to 4) if the value derived from the model equation is equal to or greater than a predetermined cutoff value.

[0070] In one embodiment, the logistic regression analysis is a dichotomous algorithm for modeling the relationship between data on markers related to kidney disease and kidney disease risk group (stage with inherent risk factors), chronic kidney disease stage 1 (IRIS stage 1), or chronic kidney disease stages 2-4 (IRIS stages 2-4), and the basic formula is as follows:

number

[0071] In this case, the dependent variable P in the basic formula is the probability value of falling into a chronic kidney disease risk group (a stage with inherent risk factors), chronic kidney disease stage 1 (IRIS stage 1), or chronic kidney disease stages 2 to 4 (IRIS stages 2 to 4), the dependent variable 1-P is the probability value of falling into a normal group, and the independent variables X1 to Xn are variables related to markers associated with kidney disease in the individual.

[0072] The markers associated with kidney disease in the individual can be used by replacing the concentrations of NGAL, KIM-1, SDMA, creatinine, inorganic phosphorus, amylase, and BUN with the corresponding units.

[0073] That is, the relationship of probability values ​​of the markers related to kidney disease of the individual to the chronic kidney disease risk group (stage with inherent risk factors), chronic kidney disease stage 1 (IRIS stage 1), or chronic kidney disease stages 2 to 4 (IRIS stages 2 to 4) is modeled through logistic regression analysis, and estimated coefficients of the basic formula are derived to create a model formula.

[0074] In one embodiment, the model formula may be the formula with the highest explanatory power (crystal similarity coefficient; Pseudo R2) in logistic regression analysis.

[0075] In one embodiment, the cutoff value can be determined by converting the point at which "sensitivity x specificity" shows the maximum value according to the concordance probability method in a receiver operating characteristic (ROC) curve.

[0076] In one embodiment, the model formula may be any one selected from the following calculation formulas 1 to 6.

[0077] [Formula 1] RNK(y)=1.648×pNGAL(ng / mL)+3.287×pKIM-1(ng / mL)-12.2 [Formula 2] RNKC(y)=1.71×pNGAL(ng / mL)+3.306×pKIM-1(ng / mL)+0.9716×sCr(mg / dl)-13.22 [Formula 3] RNKS(y)=1.928×pNGAL(ng / mL)+3.948×pKIM-1(ng / mL)+0.4207×SDMA(μg / dl)-19.09 [Formula 4] RNKA=1.398×pNGAL(ng / mL)+3.989×pKIM-1(ng / mL)+0.5979×age(year)-17.02 [Formula 5] RNKR=2.832×pNGAL(ng / mL)+4.726×pKIM-1(ng / mL)+8.756×CRP(mg / dl)-21.36 [Formula 6] RNKCS=2.15×pNGAL(ng / mL)+4.178×pKIM-1(ng / mL)+1.798×sCr(mg / dl)+0.4377×SDMA(μg / dl)-21.97

[0078] In one embodiment, the cutoff value is a value determined so that a value derived from any one of Formulas 1 to 6 exceeds the level observed in a normal individual without kidney disease, and if the value is equal to or greater than the cutoff value, it means that kidney damage has occurred and that the individual has kidney disease or is at risk of kidney disease. Specifically, it means that the individual has risk factors for chronic kidney disease according to the International Renal Interest Society (IRIS) chronic kidney disease (CKD) staging guidelines or is in chronic kidney disease stages 1 to 4 (IRIS stages 1 to 4).

[0079] In a preferred embodiment, the range of the cutoff value in the calculation formula 1 (RNK) is any number selected from -1.94 to 1.58, preferably any number selected from -1.50 to 1.58, more preferably any number selected from 0 to 1.58, more preferably any number selected from 1.40 to 1.58, more preferably 1.58.

[0080] In a preferred embodiment, the range of the cutoff value in the calculation formula 2 (RNKC) is any number selected from -2.02 to 1.49, preferably any number selected from -1.00 to 1.49, more preferably any number selected from 0.00 to 1.49, more preferably any number selected from 1.26 to 1.49, more preferably 1.49.

[0081] In a preferred embodiment, the range of the cutoff value of the above-mentioned formula 3 (RNKS) is any number selected from -3.57 to 2.09, preferably any number selected from 0.00 to 2.09, more preferably any number selected from 1.93 to 2.09, more preferably 2.085.

[0082] In a preferred embodiment, the cutoff value range of the calculation formula 4 (RNKA) is any number selected from -3.33 to 0.54, preferably any number selected from -2.77 to 0.53, more preferably any number selected from -0.36 to 0.52, more preferably 0.51.

[0083] In a preferred embodiment, the range of the "cutoff value" in the above-mentioned formula 5 (RNKR) is any number selected from -3.49 to -0.04, preferably any number selected from -0.30 to -0.05, and more preferably -0.077.

[0084] In a preferred embodiment, the range of the cutoff value of the calculation formula 6 (RNKCS) is any number selected from -4.27 to 2.50, preferably any number selected from -2.35 to 2.40, more preferably any number selected from 2.03 to 2.30, more preferably any number selected from 2.03 to 2.30, more preferably 2.11.

[0085] In one embodiment, any of the values ​​derived from the above formulas 1 to 6 can be converted to converge to the values ​​of 1 and 0 using an exponential function. In this case, a prediction formula for classifying subjects into kidney disease risk group and IRIS stages 1 to 4 (indicating a value of 1) and normal subjects (indicating a value of 0) using an exponential function is shown in the following formula 7. In the following formula 7, the p value indicates a distribution from 0 to 1 as a percentage, and if it is less than 0.5, it can be determined that there is no risk of kidney disease, and if it is 0.5 or more, it can be determined that the subject is in the kidney disease risk group or higher.

[0086] [Formula 7] p=exp(y) / [exp(y)+1]

[0087] In the above formula, y is a value derived from the above calculation formulas 1 to 6.

[0088] In one embodiment, the model formula may be any one selected from the following formulas 8 to 13.

[0089] [Formula 8] SNK(y)=0.5541×pNGAL(ng / mL)+0.3766×pKIM-1(ng / mL)-2.614 [Formula 9] SNKC(y)=0.5522×pNGAL(ng / mL)+0.3146×pKIM-1(ng / mL)+0.4417×sCr(mg / dl)-2.792 [Formula 10] SNKS(y)=0.442×pNGAL(ng / mL)+0.001992×pKIM-1(ng / mL)+0.2562×SDMA(μg / dl)-4.079 [Formula 11] SNKA(y)=0.447×pNGAL(ng / mL)+0.2079×pKIM-1(ng / mL)+0.2108×age(year)-3.55 [Formula 12] SNKP(y)=0.4599×pNGAL(ng / mL)+0.3363×pKIM-1(ng / mL)+1.004×Inorganic phosphorus(mg / dl)-5.678 [Formula 13] SNKCS(y)=0.4406×pNGAL(ng / mL)+0.007931×pKIM-1(ng / mL)-0.07766×sCr(mg / dl)+0.258×SDMA(μg / dl)-4.048

[0090] In one embodiment, the cutoff value is determined so that the value derived from any one of the formulas selected from Formulas 8 to 13 exceeds the level observed in normal individuals without kidney disease or individuals in a kidney disease risk group. If the value is equal to or greater than the cutoff value, this indicates that the glomerular filtration rate is normal or close to normal, but kidney damage has occurred and kidney disease is present. Specifically, this indicates that the subject is in chronic kidney disease stages 1 to 4 (IRIS stages 1 to 4) according to the International Renal Interest Society (IRIS) chronic kidney disease (CKD) staging guidelines. The cutoff value for the value derived from any one of the formulas selected from Formulas 8 to 13 for determining IRIS stages 1 to 4 of the present invention is a number selected from the range of -1.67 to 4.26.

[0091] In a preferred embodiment, the cutoff value range of the calculation formula 8 (SNK) is any number selected from -0.74 to 4.40, preferably any number selected from 0.00 to 4.00, more preferably any number selected from 0.30 to 1.00, more preferably 0.89.

[0092] In a preferred embodiment, the cutoff value range of the formula 9 (SNKC) is any number selected from −0.70 to 4.26, more preferably 0.83.

[0093] In a preferred embodiment, the cutoff value range of the formula 10 (SNKS) is any number selected from -1.25 to 3.30, more preferably 0.49.

[0094] In a preferred embodiment, the cutoff value range of the formula 11 (SNKA) is any number selected from −1.67 to 4.10, and more preferably 0.96.

[0095] In a preferred embodiment, the cutoff value range of the formula 12 (SNKP) is any number selected from the range of −0.76 to 3.65, and more preferably 0.76.

[0096] In a preferred embodiment, the cutoff value range of the above-mentioned formula 13 (SNKCS) is any number selected from -1.26 to 3.30, more preferably 0.48.

[0097] In one embodiment, any of the values ​​derived from the above formulas 8 to 13 can be converted to converge to values ​​of 1 and 0 using an exponential function. In this case, a prediction formula for classifying into chronic kidney disease stages 1 to 4 (IRIS stages 1 to 4, indicating a value of 1) and normal subjects and kidney disease risk group (indicating a value of 0) using an exponential function is shown in the following formula 14. In the following formula 14, the p value indicates a distribution from 0 to 1 as a percentage, and if it is less than 0.5, it can be determined that there is no kidney disease or that it is in the risk group (a stage with inherent risk factors), and if it is 0.5 or more, it can be determined that it is in chronic kidney disease stages 1 to 4.

[0098] [Formula 14] p=exp(y) / [exp(y)+1] In the above formula, y is a value derived from the above formulas 8 to 13. In one embodiment, the model formula may be any one selected from the following formulas 15 to 23. [Formula 15] TNK(y)=0.03031×pNGAL(ng / mL)+1.187×pKIM-1(ng / mL)-5.538 [Formula 16] TNKC(y)=-0.01621×pNGAL(ng / mL)+0.9737×pKIM-1(ng / mL)+3.773×sCr(mg / dl)-8.309 [Formula 17] TNKS(y) = -0.01627 × pNGAL (ng / mL) + 0.631 × pKIM-1 (ng / mL) + 0.4914 × sCr (mg / dl) - 10.55 [Equation 18] TNKA(y) = 0.02812 × pNGAL (ng / mL) + 1.078 × pKIM-1 (ng / mL) + 0.2033 × age (year) - 7.344 [Equation 19] TNKP(y) = -0.1125 × pNGAL (ng / mL) + 1.42 × pKIM-1 (ng / mL) + 0.7162 × inorganic phosphorus (mg / dl) - 8.453 [Equation 20] TNKAm(y) = -0.05275 × pNGAL (ng / mL) + 1.001 × pKIM-1 (ng / mL) + 0.001492 × amylase (U / L) - 5.468 [Equation 21] TNKCS(y) = -0.05199 × pNGAL (ng / mL) + 0.2173 × pKIM-1 (ng / mL) + 9.823 × sCr (mg / dl) + 0.9584 × SDMA (μg / dl) - 26.57 [Equation 22] TNKB(y) = -0.0266 × pNGAL (ng / mL) + 1 × pKIM-1 (ng / mL) + 0.11 × BUN (mg / dl) - 7.155 [Equation 23] TNKCSB(y) = -0.08838 × pNGAL (ng / mL) + 0.2262 × pKIM-1 (ng / mL) + 8.739 × sCr (mg / dl) + 0.961 × SDMA (μg / dl) + 0.04618 × BUN (mg / dl) - 26.28

[0099] In one embodiment, the cutoff value is determined so that the value derived from any one of the formulas selected from Formulas 15 to 23 exceeds the level observed in normal individuals without kidney disease, individuals in the risk group, or individuals in IRIS stage 1. A value equal to or greater than the cutoff value indicates the occurrence of kidney damage and kidney disease. Specifically, this indicates that the subject corresponds to chronic kidney disease stages 2 to 4 (IRIS stages 2 to 4) according to the International Renal Interest Society (IRIS) chronic kidney disease (CKD) staging guidelines. The cutoff value for a value derived from any one of the formulas selected from Formulas 15 to 23 for determining IRIS stages 2 to 4 of the present invention is a number selected from the range of -5.17 to 2.23.

[0100] In a preferred embodiment, the cutoff value range of the above-mentioned formula 15 (TNK) is any number selected from -5.17 to -4.08, and more preferably -4.83.

[0101] In a preferred embodiment, the cutoff value range of the above formula 16 (TNKC) is any number selected from the range of −3.72 to 1.31, and more preferably −0.14.

[0102] In a preferred embodiment, the cutoff value range of the above formula 17 (TNKS) is any number selected from -3.57 to -0.05, and more preferably -0.08.

[0103] In a preferred embodiment, the cutoff value range of the formula 18 (TNKA) is any number selected from the range of −4.28 to 1.65, more preferably −0.30.

[0104] In a preferred embodiment, the cutoff value range of the above formula 19 (TNKP) is any number selected from the range of −1.78 to 1.31, and more preferably −0.52.

[0105] In a preferred embodiment, the cutoff value range of the formula 20 (TNKAm) is any number selected from −2.12 to 1.37, more preferably −0.51.

[0106] In a preferred embodiment, the cutoff value range of the above formula 21 (TNKCS) is any number selected from −0.76 to 2.23, more preferably −0.76.

[0107] In a preferred embodiment, the cutoff value range of the formula 22 (TNKB) is any number selected from the range of −4.18 to 1.73, and more preferably 0.13.

[0108] In a preferred embodiment, the cutoff value range of the above formula 23 (TNKCSB) is any number selected from −1.66 to 1.51, and more preferably −0.52.

[0109] In one embodiment, any of the values ​​derived from the above formulas 15 to 23 can be converted using an exponential function so as to converge to the values ​​of 1 and 0. In this case, a prediction formula for classifying chronic kidney disease stages 2 to 4 (IRIS stages 2 to 4, showing a value of 1), normal subjects, high-risk renal disease groups (stages with inherent risk factors), and chronic kidney disease stage 1 (IRIS stage 1, showing a value of 0) using an exponential function is shown in the following formula 24. In the following formula 24, the p-value indicates a distribution from 0 to 1 as a percentage, and a value less than 0.5 may be used to determine that kidney disease is stage 1 or lower, and a value of 0.5 or higher may be used to determine that kidney disease is stage 2 or higher.

[0110] [Formula 24] p=exp(y) / [exp(y)+1]

[0111] In the above formula, y is a value derived from the above formulas 15 to 23. When the dependent variable is a chronic kidney disease risk group (stage with inherent risk factors), the information providing method of the present invention can distinguish between a normal group and a chronic kidney disease risk group with a sensitivity of 90% or more and a specificity of 95% or more. Therefore, the present invention can enable early detection of an individual's renal function abnormality, particularly a chronic kidney disease risk group (stage with inherent risk factors), and enable prompt treatment.

[0112] When the dependent variable is the presence or absence of IRIS stage 1, the information providing method of the present invention can distinguish between a normal group, a chronic kidney disease risk group, and IRIS stage 1 with a sensitivity of 85% or more and a specificity of 90% or more. Therefore, the present invention can enable early detection of an individual's renal dysfunction, particularly chronic kidney disease stage 1 (IRIS stage 1), and enable prompt treatment.

[0113] Furthermore, when the dependent variable is the presence or absence of IRIS stages 2 to 4, the information provision method of the present invention can distinguish between a normal group, a chronic kidney disease risk group (a stage with inherent risk factors), and chronic kidney disease stage 1 (IRIS stage 1), and IRIS stages 2 to 4 with a sensitivity of 90% or more and a specificity of 90% or more.

[0114] Another aspect provides a method for diagnosing kidney disease, comprising measuring the expression level of NGAL (Neutrophil gelatinase-associated lipocalin) protein, KIM-1 (Kidney injury molecule-1) protein, or a combination thereof, or the genes encoding them, from a biological sample obtained from an individual, and comparing the measured expression level with the expression level of a normal population of the protein, or a combination thereof, or the genes encoding same.

[0115] Another aspect provides a method of treating kidney disease comprising measuring the expression level of NGAL (Neutrophil gelatinase-associated lipocalin) protein, KIM-1 (Kidney injury molecule-1) protein, or a combination thereof, or the genes encoding them, from a biological sample obtained from the individual.

[0116] Another embodiment is an input unit for inputting the concentration of one or more markers selected from NGAL (Neutrophil gelatinase-associated lipocalin) and KIM-1 (Kidney injury molecule-1) measured from a body fluid sample of an individual, or inputting the concentration of one or more markers selected from SDMA, creatinine, inorganic phosphorus, amylase, and BUN together with the concentration of the marker; a variable setting unit for setting the input marker concentration as one or more independent variables and for setting a chronic kidney disease risk group (stage with inherent risk factors), chronic kidney disease stage 1 (IRIS stage 1), or chronic kidney disease stage 2 to 4 (IRIS stage 2 to 4) according to the International Renal Interest Society (IRIS) chronic kidney disease (CKD) staging guideline criteria as a dependent variable; and a variable setting unit for analyzing the relationship between the plurality of independent variables and the dependent variable by logistic regression analysis. and a diagnostic unit that derives a value by substituting data on markers input from the input unit into each independent variable of the inferred model formula, and classifies the individual into a kidney disease risk group (a stage with inherent risk factors), chronic kidney disease stage 1 (IRIS stage 1), or chronic kidney disease stage 2 to 4 (IRIS stage 2 to 4) when the derived value is equal to or greater than a predetermined cutoff value.

[0117] Another aspect provides a computer-readable recording medium having recorded thereon a computer program for executing the method on a computer.

[0118] The recording medium may be implemented by an application (or a program) and may be readable by a terminal device (or a computer). Specifically, the recording medium may include any type of recording device or medium on which data readable by a computing system is stored. [Effects of the Invention]

[0119] According to the present invention, kidney disease or the risk of kidney disease can be diagnosed early with higher accuracy, sensitivity, and specificity. In particular, according to the International Renal Interest Society (IRIS) chronic kidney disease (CKD) staging guidelines, the present invention can distinguish between a normal group, a chronic kidney disease at-risk group (a stage with inherent risk factors), chronic kidney disease stage 1 (IRIS stage 1), and chronic kidney disease stages 2 to 4 (IRIS stages 2 to 4) with a sensitivity of 85% or more and a specificity of 90% or more. [Brief explanation of the drawings]

[0120] [Figure 1] Figure 1 shows the results of a log-scale scatter plot analysis of the correlation between NGAL (pNGAL) and KIM-1 (pKIM-1) concentrations in dog plasma and the concentrations of the existing biomarkers creatinine (Cr) and SDMA. [Figure 2] Figure 2 shows the results of statistical analysis after log transformation of the scatter plot in Figure 1. [Figure 3] FIG. 3 shows the results of ROC (receiver operating characteristic) curve analysis for kidney disease risk groups and IRIS stages 1 to 4 for the evaluation of diagnostic accuracy. [Figure 4] FIG. 4 shows the results of ROC (receiver operating characteristic) curve analysis for IRIS stages 1 to 4 to evaluate diagnostic accuracy according to the reference stage of the disease state. [Figure 5] FIG. 5 shows the results of ROC (receiver operating characteristic) curve analysis for IRIS stages 2 to 4 to evaluate diagnostic accuracy according to the reference stage of the disease state. [Figure 6] FIG. 6A is a schematic diagram simply illustrating the structure of a kidney disease diagnostic kit according to one embodiment of the present invention, and FIG. 6B is a diagram simply illustrating the operating principle of a kidney disease diagnostic kit according to one embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0121] Preferred embodiments are presented below to aid in understanding the present invention. However, the following embodiments are provided to facilitate understanding of the present invention, and the contents of the present invention are not limited to the following embodiments. Various modifications can be made to the embodiments, and the embodiments are not limited to the embodiments disclosed below, and can be implemented in various forms.

[0122] Example 1. Diagnosis of kidney disease 1.1. Selection of experimental subjects Plasma samples and medical records from dogs admitted to Konkuk University Veterinary Hospital between July 1, 2018, and August 31, 2022, were used. To minimize the influence of dog weight and breed during the study period and reflect the characteristics of Korea, where small dogs are primarily bred, only small dogs weighing less than 10 kg were included in the study. Furthermore, plasma samples with insufficient analytical volume or hemolysis were excluded because they may affect the results of enzyme-linked immunosorbent assay (ELISA). Next, dogs from this small dog population were included that underwent plasma SDMA, BUN, and creatinine (Cr) level testing. Finally, dogs that did not undergo SDMA testing but had no significant abnormalities in clinical symptoms or clinical pathological test results, no long-term medication use, no underlying diseases, and were visited for general checkups or mild lameness served as normal controls.

[0123] 1.2. Sample processing and biomarker analysis The concentrations of pNGAL and pKIM-1 were analyzed using plasma samples remaining after clinical examination of dogs that met the conditions for selecting test subjects in Example 1.1.

[0124] Specifically, venous blood collected in lithium heparin tubes was centrifuged at 3,000 rpm for 6 minutes at room temperature to separate plasma. Creatinine, BUN, SDMA, inorganic phosphorus, and amylase levels in the plasma were measured immediately after sample collection using a Catalyst One chemistry analyzer (IDEXX Laboratories). The remaining plasma samples were then frozen within 6 hours and stored at -78°C. SDMA concentrations in the control group were measured using the Catalyst One analyzer.

[0125] pNGAL and pKIM-1 concentrations were measured using a canine-specific sandwich ELISA (enzyme-linked immunosorbent assay) kit (ab205084 and ab205085, respectively; Abcam, Cambridge, UK) according to the manufacturer's instructions. To do this, the freshly frozen plasma samples were quickly thawed in a 37°C water bath. For pKIM-1 concentration measurements, the plasma samples were diluted 1:10 with diluent, and for pNGAL concentration measurements, the plasma samples were diluted 1:50 with diluent. The absorbance of the diluted samples was then measured at 450 nm using a microplate reader (Tecan, Zurich, Switzerland) according to the manufacturer's instructions. The concentrations of pNGAL and pKIM-1 were then calculated from a standard curve (4-parameter logistic curve) calculated using the absorbance measurements of the standards.

[0126] 1.3. Kidney disease stages and groups The study population selected for the conditions in Example 1.1 was classified into four stages based on the International Society of Nephrology (IRIS) chronic kidney disease (CKD) staging guidelines published in 2019. For reference, the risk group consisted of individuals with risk factors for kidney disease who did not meet the criteria for the control group but had normal plasma creatinine and SDMA concentrations. The risk group was determined based on factors such as mitral valve regurgitation (MMVD), portosystemic shunt, chronic heart failure, hyperadrenocorticism (HAC), diabetes mellitus, and babesiosis. The underlying diseases, including MMVD and HAC, were confirmed using medical records, such as physical examination, clinical pathological analysis, and / or radiological examination. Furthermore, because the number of stage 4 subjects was significantly smaller, they were combined into the stage 3–4 group. Thus, 15 were classified as the risk group, 43 as IRIS stage 1, 33 as IRIS stage 2, and 16 as IRIS stages 3–4.

[0127] 1.4. Statistical analysis and derivation of the best-fit model Statistical analysis and derivation of the optimal diagnostic model were performed using GraPhPad Prism (version 9.3.1; GraphPad software, San Diego, CA, USA).

[0128] Categorical variables, such as gender and physical condition score, were analyzed using the chi-square test. Patient age was considered a continuous variable, converted to a decimal point based on the date of sample collection. Differences between the four groups for normally distributed variables, including log-transformed data, were analyzed using one-way analysis of variance followed by a Bonferroni post-hoc test.

[0129] The optimal diagnostic model was derived by using univariate logistic regression analysis (simple logistic regression analysis, crude logistic regression analysis) to determine the degree to which multiple independent variables affect the dependent variable, and then combining only variables that were significant at a significance level of 0.05 to model them using multiple logistic regression analysis to infer a calculation formula.

[0130] The diagnostic accuracy of the four kidney disease biomarkers and the derived model was analyzed using receiver operating characteristic (ROC) curves, and the optimal cutoff value was selected using the highest concordance probability method (sensitivity × specificity).

[0131] Experimental Example 1. Comparison of NGAL and KIM-1 with existing biomarkers The plasma concentrations of SDMA and creatinine in dogs were compared with those of NGAL and KIM-1, and the correlations with the concentrations measured in normal dogs without kidney disease (control group) were analyzed.

[0132] Specifically, the equivalence and difference between the plasma NGAL (pNGAL) and KIM-1 (pKIM-1) concentrations and the existing biomarkers creatinine (Cr) and SDMA concentrations were analyzed on a log scale via scatter plots (Figures 1 and 2).

[0133] Figure 1 is a scatter plot showing the correlation between the concentrations of NGAL (pNGAL) and KIM-1 (pKIM-1) in pet plasma and the concentrations of the existing biomarkers creatinine (Cr) and SDMA. Figure 2 is a graph showing the differences in biomarker concentrations by stage of kidney disease, converted from the scatter plot in Figure 1 to a log scale.

[0134] As shown in Figures 1 and 2, the pNGAL and pKIM-1 of the present invention did not show significant differences from the existing biomarkers sCr and SDMA in IRIS stages 3 to 4, but showed significant differences in the risk group and IRIS stage 1. In particular, while the existing biomarkers did not show significant differences between the normal control group and the risk group, pNGAL and pKIM-1 of the present invention showed significant differences.

[0135] These results indicate that pNGAL and pKIM-1 of the present invention can be used as biomarkers for the early diagnosis of kidney disease, which can distinguish between normal control groups and kidney disease risk groups, or IRIS stage 1, which could not be distinguished using existing biomarkers.

[0136] Experimental Example 2. Optimal model for diagnosing kidney disease risk groups Biomarkers that show statistical significance in two groups, a normal group and a kidney disease risk group, can be combined to provide meaningful information, and an algorithm has been invented that can distinguish the differences between the two groups using a formula. The mathematical model used in this invention is based on dichotomous logistic regression analysis. The inventors have confirmed that various regression analysis models can be obtained using various combinations of biomarkers, which show high sensitivity and specificity in distinguishing between the normal group and the kidney disease risk group, making it possible to develop a technology for early diagnosis of kidney damage or kidney disease.

[0137] Experimental Example 2.1. Derivation of a diagnostic model for kidney disease risk groups - Differentiation of normal vs. risk groups and IRIS stages 1-4 We aimed to derive an optimal index for distinguishing kidney disease risk groups (normal vs. high-risk groups and IRIS stages 1-4). Specifically, we set kidney disease-related markers (including chemical test results or clinical indicators) as independent variables, and risk abnormalities as the dependent variable. We then performed univariate logistic regression analysis in Graphpad Prism to determine the extent to which these independent variables affected the dependent variable, and combined only variables that were significant at the 0.05 significance level (Table 1). Univariate logistic regression analysis revealed that, with the exception of sCr and BUN, the dependent variables were correlated with the independent variables NGAL, KIM-1, SDMA, and CRP. Therefore, we constructed an optimal combination using these six variables.

[0138] TIFF2025535454000003.tif76170In the top item of the table above, n is the number of observations, SE is the standard error, 95% CI (confidence interval) is the lower and upper limits of the 95% confidence interval of the estimated coefficient, z is the estimated coefficient divided by SE and is the t-distribution statistic, p is the test statistic that can reject the null hypothesis, and Pesudo R2 is the degree to which the dependent variable is explained by the independent variables, in other words, the explanatory power (predictive power).

[0139] The functional relationships between the combined independent and dependent variables were modeled and a formula inferred using multiple logistic regression analysis in the Graphpad Prism program, where logistic regression analysis is a dichotomous algorithm for modeling the relationship between risk factor data and kidney disease risk.

[0140] From the modeling, optimal models (functions) with the highest pseudo R2 (explanatory power, the strength of the relationship between the dependent variable and the independent variable) were derived: RNK, RNKC, RNKS, RNKA, RNKR, and RNKCS (Tables 2 to 7). For reference, RNK is a function using pNGAL and pKIM-1, RNKC is a function using pNGAL, pKIM-1, and sCr, RNKS is a function using pNGAL, pKIM-1, and age, and RNKR, pKIM-1, sCr, and SDMA.

[0141] TIFF2025535454000004.tif42170In the top item of the table above, Coef. is the regression coefficient (estimated coefficient) that indicates the magnitude of the influence that the independent variable has on the dependent variable, SE is the standard error, 95% CI (confidence interval) is the lower and upper limits of the 95% confidence interval of the estimated coefficient, z is the estimated coefficient divided by SE and is a t-distribution statistic, p is a test statistic that can reject the null hypothesis, and Pesudo R2 indicates the degree to which the dependent variable is explained by the independent variable, in other words, explanatory power (predictive power).

[0142] The estimated function formula by logistic regression analysis of Model 1 (RNK) is as follows:

[0143] [Formula 1] RNK(y)=1.648×pNGAL(ng / mL)+3.287×pKIM-1(ng / mL)-12.2

[0144] TIFF2025535454000005.tif63170The estimated function equation by logistic regression analysis of Model 2 (RNKC) is as follows:

[0145] [Formula 2] RNKC(y)=1.71×pNGAL(ng / mL)+3.306×pKIM-1(ng / mL)+0.9716×sCr(mg / dl)-13.22

[0146] TIFF2025535454000006.tif63170The estimated function equation by logistic regression analysis of Model 3 (RNKCS) is as follows:

[0147] [Formula 3] RNKS(y)=1.928×pNGAL(ng / mL)+3.948×pKIM-1(ng / mL)+0.4207×SDMA(μg / dl)-19.09

[0148] TIFF2025535454000007.tif62170The estimated function equation by logistic regression analysis of Model 4 (RNKA) is as follows:

[0149] [Formula 4] RNKA=1.398×pNGAL(ng / mL)+3.989×pKIM-1(ng / mL)+0.5979×age(year)-17.02

[0150] TIFF2025535454000008.tif47170The estimated function equation by logistic regression analysis of Model 5 (RNKR) is as follows:

[0151] [Formula 5] RNKR=2.832×pNGAL(ng / mL)+4.726×pKIM-1(ng / mL)+8.756×CRP(mg / dl)-21.36

[0152] TIFF2025535454000009.tif71170The estimated function equation by logistic regression analysis of Model 6 (RNKS) is as follows:

[0153] [Formula 6] RNKCS=2.15×pNGAL(ng / mL)+4.178×pKIM-1(ng / mL)+1.798×sCr(mg / dl)+0.4377×SDMA(μg / dl)-21.97

[0154] Looking at Tables 2 to 7, Model 4 (RNKA), which constructed age together with pNGAL and pKIM-1, showed a high explanatory power (Pseudo R2) of 79.06%, and was found to be the optimal model (function).

[0155] In the above function formula, each coefficient (constant) is a value for deriving a model with the highest explanatory power, and does not in itself indicate which variable is more sensitive or important.

[0156] Then, any of the values ​​derived from the linear equation models, Formulas 1 to 4, can be converted using an exponential function so that they converge to the values ​​of 1 and 0. In this case, the prediction formula for classifying into a kidney disease risk group (IRIS stage 1, showing a value of 1) and a normal person (showing a value of 0) using an exponential function is shown in Formula 7 below. In Formula 5 below, the p value indicates a distribution from 0 to 1 as a percentage, and if it is less than 0.5, it can be determined that there is no kidney disease risk, and if it is 0.5 or more, it can be determined that it is in the kidney disease risk group.

[0157] [Formula 7] p=exp(y) / [exp(y)+1] In the above formula, y is a value derived from the above calculation formulas 1 to 6.

[0158] Experimental Example 2.2. Cutoff value range for diagnosing kidney disease risk groups In order to evaluate the accuracy of the optimal model for diagnosing kidney disease risk groups derived from 2-1 above, the receiver operating characteristic (ROC) curve was analyzed using the GraphPad Prism program, and the cutoff values ​​for diagnosing kidney disease risk groups (normal group vs. risk group and IRIS stages 1 to 4) for the NGAL, KIM-1, and RNK, RNKC, RNKS, RNKA, RNKR, RNKR, and RNKCS models derived from 2-1 above are shown in Tables 8 to 15 below.

[0159] As shown in Table 8, the cutoff value range of pNGAL concentration for distinguishing between kidney disease risk groups can be 1.451 ng / mL (sensitivity 99.02%, specificity 10%) to 4.026 ng / mL (sensitivity 69.61%, specificity 100%). Preferably, the cutoff value can be 3.30 (sensitivity 81.37%, specificity 90%).

[0160] TIFF2025535454000011.tif42170As shown in Table 9, the cutoff value range of pKIM-1 concentration for distinguishing kidney disease risk groups can be 1.705 ng / mL (sensitivity 100%, specificity 10%) to 3.321 ng / mL (sensitivity 66.04%, specificity 100%).

[0161] TIFF2025535454000012.tif41170 As shown in Table 10, the cutoff value range of RNK for distinguishing between kidney disease risk groups can be −1.941 (sensitivity 100%, specificity 22.22%) to 1.584 (sensitivity 92.16%, specificity 100.0%).

[0162] TIFF2025535454000013.tif41170 As shown in Table 11, the cutoff value range of RNKC for distinguishing kidney disease risk groups can be −2.024 (sensitivity 100%, specificity 22.22%) to 1.490 (sensitivity 93.14%, specificity 100.0%).

[0163] TIFF2025535454000014.tif42170 As shown in Table 12, the cutoff value range of RNKS for distinguishing kidney disease risk groups can be −3.574 (sensitivity 100%, specificity 11.11%) to 2.085 (sensitivity 93.14%, specificity 100.0%).

[0164] TIFF2025535454000015.tif50170 As shown in Table 13, the cutoff value range of RNKA for distinguishing between kidney disease risk groups can be −3.334 (sensitivity 100%, specificity 0%) to 0.5082 (sensitivity 99.02%, specificity 100.0%).

[0165] TIFF2025535454000016.tif41170 As shown in Table 14 above, the cutoff value range of RNKR for differentiating the high-risk group of kidney disease can be -3.490 (sensitivity 100%, specificity 14.29%) to -0.07702 (sensitivity 97.96%, specificity 100.0%).

[0166] TIFF2025535454000017.tif51170 As shown in Table 15 above, the cutoff value range of RNKCS for differentiating the high-risk group of kidney disease can be -4.266 (sensitivity 100%, specificity 11.11%) to 2.110 (sensitivity 93.14%, specificity 100.0%).

[0167] 2.3. Evaluation of the diagnostic accuracy of the derived optimal model for the high-risk group of kidney disease The ROC (receiver operating characteristic) curve was analyzed using the GraphPad Prism program to derive the diagnostic accuracy. From the cutoff ranges of pNGAL, pKIM-1, and the indicators RNK, RNKC, RNKS, RNKA, RNKR, RNKCS derived from 2-2 above, the optimal cutoff value with the highest value of sensitivity × specificity was derived by the concordance probability method, and the diagnostic value was evaluated (Figure 3). In addition, the diagnostic sensitivity, specificity, accuracy, and the range of the reference standard for differentiating the normal control group and the high-risk group or IRIS stages 1 to 4 are shown in Table 16 below. According to IRIS stage 2019, 18 < SDMA (μg / dl) < 35 or 1.4 < sCr (mg / dl) < 2.8 represents CKD stage 2, 36 < SDMA < 54 or 2.9 < sCr < 5.0 represents CKD stage 3, and 54 < SDMA or 5.0 < sCr represents CKD stage 4, which means an increased risk of systemic symptoms and uremic crisis.

[0168] [[ID=十六]] Looking at FIG. 3 and Table 16, it was shown that the diagnostic accuracy (AUC, area under curve) for IRIS stages 1 to 4 from the risk group was high in the order of sCr < SDMA < pNGAL = pKIM-1 < RNKC = RNK < RNKS = RNKR = RNKCS < RNKA.

[0169] Specifically, the AUC (area under curve) of RNK of the present invention was 0.97, and the cut-off value below which it was not kidney disease was 1.584. At the said cut-off value, the diagnostic sensitivity was 92.16% and the diagnostic specificity was 100.0%. LR+ (positive rate) was incalculable (infinity), and LR- (negative rate) was 0.08.

[0170] On the other hand, the AUC of pNGAL was 0.90, and the cut-off value was 3.30 ng / mL. At the said cut-off value, the diagnostic sensitivity was 81.37% and the diagnostic specificity was 90%. LR+ (positive rate) was 8.14, and LR- (negative rate) was 0.21.

[0171] Also, the AUC of pKIM-1 was 0.90, and the cut-off value was 3.32 ng / mL. At the said cut-off value, the diagnostic sensitivity was 66.04% and the diagnostic specificity was 100%. LR+ (positive rate) was incalculable (infinity), and LR- (negative rate) was 0.34.

[0172] The above results mean that pNGAL and pKIM-1 of the present invention can distinguish kidney injury or kidney disease risk groups with higher accuracy than the existing indicators sCr and SDMA, and also that RNK can distinguish kidney disease risk groups with significantly higher sensitivity and specificity compared to pNGAL and pKIM-1.

[0173] Furthermore, we conducted an experiment to compare the clinical efficacy of sCr, SDMA, pNGAL, and pKIM-1 by disease state standard stage. When the diagnostic accuracy was confirmed by dividing the disease state into risk groups and IRIS stages 1 to 4 (Figure 3), the AUC, sensitivity, and specificity of pKIM-1 and pNGAL were similar overall and higher than those of SDMA and sCr.

[0174] When the disease state is defined as IRIS stage 1 or higher, the diagnostic sensitivity, specificity, accuracy and reference standard ranges are shown in Table 17 below.

[0175] As shown in Figure 4, the AUC of pNGAL was higher than that of SDMA, and the AUC of pKIM-1 was higher than that of sCR.

[0176] In addition, when the disease state is defined as IRIS stage 2 or higher, the diagnostic sensitivity, specificity, accuracy and reference standard ranges are shown in Table 18 below.

[0177] As shown in Figure 5, we confirmed that sCr and SDMA are used as criteria for IRIS stage, and the accuracy of SDMA and sCr is higher than that of other biomarkers.

[0178] Experimental example 3. Optimal model for diagnosing stage 1 kidney disease Experimental Example 3.1. Derivation of a diagnostic model for kidney disease stage 1 - Differentiation of normal and risk groups vs. IRIS stages 1-4 We attempted to derive an optimal index for differentiating kidney disease stage 1 groups (normal and risk groups vs. IRIS stages 1-4). Specifically, we set kidney disease-related risk factors (including chemical test results or clinical indicators) as independent variables, and set the prevalence (probability of developing IRIS stages 1-4) as the dependent variable. We then performed crude logistic regression analysis in the GraphPad Prism program to determine the extent to which the independent variables affected the dependent variable, and combined only variables that were significant at a significance level of 0.05 (Table 19).

[0179] The functional relationships between the combined independent and dependent variables were modeled and a formula inferred using multiple logistic regression analysis in the Graphpad Prism program, where logistic regression analysis is a dichotomous algorithm for modeling the relationship between risk factor data and the likelihood of developing kidney disease.

[0180] From the modeling, optimal models (functions) with the highest pseudo R2 (explanatory power, the strength of the relationship between the dependent variable and the independent variables) were derived: SNK, SNKC, SNKS, SNKA, SNKP, and SNKCS (Tables 20 to 23). For reference, SNK is a function using pNGAL and pKIM-1, SNKC is a function using pNGAL, pKIM-1, and sCr, SNKS is a function using pNGAL, pKIM-1, and SMDA, SNKA is a function using pNGAL, pKIM-1, and age, SNKP is a function using pNGAL, pKIM-1, and inorganic phosphorus, and SNKCS is a function using pNGAL, pKIM-1, sCr, and SDMA.

[0181] TIFF2025535454000022.tif39170In the top item of the table above, Coef. is the regression coefficient (estimated coefficient) that indicates the magnitude of the influence that the independent variable has on the dependent variable, SE is the standard error, 95% CI (confidence interval) is the lower and upper limits of the 95% confidence interval of the estimated coefficient, z is the estimated coefficient divided by SE and is a t-distribution statistic, p is a test statistic that can reject the null hypothesis, and Pesudo R2 indicates the degree to which the dependent variable is explained by the independent variable, in other words, explanatory power (predictive power).

[0182] The estimated function formula by logistic regression analysis of Model 7 (SNK) is as follows:

[0183] [Formula 8] SNK(y)=0.5541×pNGAL(ng / mL)+0.3766×pKIM-1(ng / mL)-2.614

[0184] TIFF2025535454000023.tif40170The estimated function equation by logistic regression analysis of Model 8 (SNKC) is as follows:

[0185] [Formula 9] SNKC(y)=0.5522×pNGAL(ng / mL)+0.3146×pKIM-1(ng / mL)+0.4417×sCr(mg / dl)-2.792

[0186] TIFF2025535454000024.tif57170The estimated function equation by logistic regression analysis of Model 9 (SNKS) is as follows:

[0187] [Formula 10] SNKS(y)=0.442×pNGAL(ng / mL)+0.001992×pKIM-1(ng / mL)+0.2562×SDMA(μg / dl)-4.079

[0188] TIFF2025535454000025.tif40170The estimated function equation by logistic regression analysis of Model 10 (SNKA) is as follows:

[0189] [Formula 11] SNKA(y)=0.447×pNGAL(ng / mL)+0.2079×pKIM-1(ng / mL)+0.2108×age(year)-3.55

[0190] TIFF2025535454000026.tif40170The estimated function equation by logistic regression analysis of Model 11 (SNKP) is as follows:

[0191] [Formula 12] SNKP(y)=0.4599×pNGAL(ng / mL)+0.3363×pKIM-1(ng / mL)+1.004×Inorganic phosphorus(mg / dl)-5.678

[0192] TIFF2025535454000027.tif71170The estimated function equation by logistic regression analysis of Model 12 (SNKCS) is as follows:

[0193] [Formula 13] SNKCS(y)=0.4406×pNGAL(ng / mL)+0.007931×pKIM-1(ng / mL)-0.07766×sCr(mg / dl)+0.258×SDMA(μg / dl)-4.048

[0194] Looking at Tables 20 to 25, Model 11 (SNKP), which combines pNGAL, pKIM-1, and Phosphorus Inorganic, showed the highest explanatory power (Pseudo R2) of 41.72%, and was found to be the optimal model (function).

[0195] Then, any of the values ​​derived from the linear equation models, Formulas 8 to 13, can be converted using an exponential function so as to converge to the values ​​of 1 and 0. In this case, the prediction formula for classifying into kidney disease stage 1 (IRIS stages 1 to 4, showing a value of 1) and normal subjects and kidney disease risk groups (showing a value of 0) using an exponential function is shown in Formula 14 below. In Formula 14 below, the p value indicates a distribution from 0 to 1 as a percentage, and if it is less than 0.5 it is determined that there is no kidney disease, and if it is 0.5 or more it is determined that it is kidney disease stage 1.

[0196] [Formula 14] p=exp(y) / [exp(y)+1] In the above formula, y is a value derived from the above calculation formulas 1 to 6.

[0197] Experimental Example 3.2. Cutoff value range for diagnosing stage 1 kidney disease In order to evaluate the accuracy of the optimal model for diagnosing kidney disease stage 1 derived from Experimental Example 3.1, the receiver operating characteristic (ROC) curve was analyzed using the GraphPad Prism program, and the cutoff values ​​for diagnosing kidney disease stage 1 (normal group, risk group vs. IRIS stages 1 to 4) for the NGAL and KIM-1 models and the SNK, SNKC, SNKS, SNKA, SNKP, and SNKCS models derived from Experimental Example 3.1 are shown in Tables 26 to 33 below.

[0198] TIFF2025535454000028.tif41170As shown in Table 26, the cutoff value range of pNGAL concentration for distinguishing kidney disease stage 1 can be 1.932 ng / mL (sensitivity 100%, specificity 41.67%) to 9.722 ng / mL (sensitivity 34.09%, specificity 100).

[0199] TIFF2025535454000029.tif42170As shown in Table 27, the cutoff value range of pKIM-1 concentration for distinguishing kidney disease stage 1 can be 1.705 ng / mL (sensitivity 100%, specificity 4.167%) to 5.138 ng / mL (sensitivity 33.7%, specificity 10).

[0200] TIFF2025535454000030.tif42170As shown in Table 28, the cutoff value range of SNK for distinguishing kidney disease stage 1 can be -0.7360 (sensitivity 100%, specificity 8.696%) to 4.229 (sensitivity 36.36%, specificity 100.0%).

[0201] TIFF2025535454000031.tif41170As shown in Table 29 above, the cutoff value range of SNKC for distinguishing kidney disease stage 1 can be from -0.6980 (sensitivity 100%, specificity 8.696%) to 4.256 (sensitivity 35.23%, specificity 100.0%).

[0202] TIFF2025535454000032.tif41170As shown in Table 30 above, the cutoff value range of SNKS for distinguishing kidney disease stage 1 can be -1.246 (sensitivity 100%, specificity 21.74%) to 3.307 (sensitivity 53.41%, specificity 100.0%).

[0203] TIFF2025535454000033.tif41170As shown in Table 31, the cutoff value range of SNKA for distinguishing kidney disease stage 1 can be -1.667 (sensitivity 100%, specificity 13.04%) to 4.106 (sensitivity 39.77%, specificity 100.0%).

[0204] TIFF2025535454000034.tif41170As shown in Table 32 above, the cutoff value range of SNKP for distinguishing kidney disease stage 1 can be from -0.7626 (sensitivity 100%, specificity 25%) to 3.650 (sensitivity 56.36%, specificity 100.0%).

[0205] TIFF2025535454000035.tif41170 As shown in Table 33 above, the cut-off value range of SNKCS for differentiating kidney disease stage 1 can be from -1.258 (sensitivity 100%, specificity 21.74%) to 3.298 (sensitivity 53.41%, specificity 100.0%).

[0206] Experimental Example 3.3. Evaluation of the diagnostic accuracy of the derived optimal model for kidney disease stage 1 Using the GraphPad Prism program, the ROC (receiver operating characteristic) curve was analyzed, and the optimal cut-off value with the highest diagnostic accuracy was derived from the cut-off value ranges of NGAL, KIM-1, SNK, SNKC, SNKS, SNKA, SNKP, and SNKCS derived in Experimental Example 3.2 (Figure 4). Also, the diagnostic sensitivity, specificity, accuracy, and the range of the reference standard for differentiating the normal control group and the risk group from chronic kidney disease stage 1 (IRIS stages 1 to 4) are shown in Table 34 below.

[0207] TIFF2025535454000036.tif137170 From the above Figure 4 and Table 34, it was shown that the diagnostic accuracy (AUC, area under curve) for IRIS stages 1 to 4 was high in the order of sCr < pKIM-1 < SDMA < SNK = SNKC < pNGAL < SNKA < SNKP < SNKS = SNKCS. [[ID=×13]]

[0208] Specifically, the AUC (area under the curve) of SNK of the present invention is 0.87, and the cut-off value below which it is not kidney disease stage 1 was 0.8887. At this cut-off value, the diagnostic sensitivity was 81.82% and the diagnostic specificity was 86.96%. LR+ (positive rate) was 6.27 and LR- (negative rate) was 0.21.

[0209] Note: There seems to be an error in the original text where "×13" is present in the ID. It should be removed or corrected in the original text for a proper translation. The translation above is based on the provided text as much as possible.The AUC (area under the curve) of the SNKS of the present invention was 0.91, and the cutoff value below which kidney disease was not stage 1 was 0.4947. At this cutoff value, the diagnostic sensitivity was 88.64% and the diagnostic specificity was 82.61%. The LR+ (positive rate) was 5.10 and the LR- (negative rate) was 0.14.

[0210] The AUC (area under the curve) of the SNKCS of the present invention was 0.91, and the cutoff value below which kidney disease was not stage 1 was 0.4832. At this cutoff value, the diagnostic sensitivity was 88.64% and the diagnostic specificity was 82.61%. The LR+ (positive rate) was 5.10 and the LR- (negative rate) was 0.14.

[0211] On the other hand, the AUC of pNGAL was 0.88, and the cutoff value was 4.19 ng / mL. At this cutoff value, the diagnostic sensitivity was 76.14% and the diagnostic specificity was 87.5%. The LR+ (positive rate) was 6.09, and the LR- (negative rate) was 0.27.

[0212] The AUC of pKIM-1 was 0.72, and the cutoff value was 3.70 ng / mL. At this cutoff value, the diagnostic sensitivity was 57.61% and the diagnostic specificity was 79.17%. The LR+ (positive rate) was 2.77, and the LR- (negative rate) was 0.54.

[0213] The above results mean that NGAL, SNK, SNKC, SNKS, SNKA, SNKP, and SNKCS of the present invention can distinguish kidney disease stage 1 with higher accuracy, sensitivity, and specificity than sCr and SDMA. In other words, this means that the indicators of the present invention can diagnose kidney disease at a level equivalent to or higher than that of existing indicators.

[0214] Experimental Example 4. Optimal model for diagnosing stage 2 kidney disease Experimental Example 4.1. Derivation of a diagnostic model for kidney disease groups - differentiation of normal group, risk group, and IRIS stage 1 vs. IRIS stage 2-4 We sought to derive an optimal index for differentiating kidney disease groups (normal group and IRIS stage 1 vs. IRIS stages 2-4). Specifically, we defined kidney disease-related risk factors (including chemical test results or clinical indicators) as independent variables, and defined the prevalence (probability of developing kidney disease) of IRIS stages 2-4 as the dependent variable. Univariate logistic regression analysis using the GraphPad Prism program was then performed to determine the extent to which these independent variables affected the dependent variable. Only variables that were significant at the 0.05 significance level were combined (Table 35). Univariate logistic regression analysis revealed that the dependent variable correlated with the independent variables NGAL, KIM-1, SDMA, sCr, age, amylase, inorganic phosphorus, and BUN. Therefore, we constructed an optimal combination using these nine variables.

[0215] The functional relationships between the combined independent and dependent variables were modeled and a formula inferred using multiple logistic regression analysis in the Graphpad Prism program, where logistic regression analysis is a dichotomous algorithm for modeling the relationship between risk factor data and the likelihood of developing kidney disease.

[0216] Through the modeling, the optimal models (functions) with the highest Pseudo R2 (explanatory power, strength of the relationship between the dependent variable and the independent variables) were derived: TNK, TNKC, TNKS, TNKA, TNKP, TNKAm, TNKCS, TNKB, and TNKCSB (Tables 36 to 44). For reference, TNK is a function using pNGAL and pKIM-1, TNKC is a function using pNGAL, pKIM-1, and sCr, TNKS is a function using pNGAL, pKIM-1, and SDMA, TNKA is a function using pNGAL, pKIM-1, and age, TNKAm is a function using pNGAL, pKIM-1, and Amylase, TNKCS is a function using pNGAL, pKIM-1, sCr, and SDMA, TNKB is a function using pNGAL, pKIM-1, and BUN, and TNKCSB is a function using pNGAL, pKIM-1, sCr, SDMA, and BUN.

[0217] TIFF2025535454000038.tif54170In the top item of the table above, Coef. is the regression coefficient (estimated coefficient) that indicates the magnitude of the influence that the independent variable has on the dependent variable, SE is the standard error, 95% CI (confidence interval) is the lower and upper limits of the 95% confidence interval of the estimated coefficient, z is the estimated coefficient divided by SE and is a t-distribution statistic, p is a test statistic that can reject the null hypothesis, and Pesudo R2 indicates the degree to which the dependent variable is explained by the independent variable, in other words, explanatory power (predictive power).

[0218] The estimated function equation by logistic regression analysis of Model 13 (TNK) is as follows:

[0219] [Formula 15] TNK(y)=0.03031×pNGAL(ng / mL)+1.187×pKIM-1(ng / mL)-5.538

[0220] TIFF2025535454000039.tif63170The estimated function equation by logistic regression analysis of Model 14 (TNKC) is as follows:

[0221] [Formula 16] TNKC(y)=-0.01621×pNGAL(ng / mL)+0.9737×pKIM-1(ng / mL)+3.773×sCr(mg / dl)-8.309

[0222] TIFF2025535454000040.tif62170The estimated function equation by logistic regression analysis of Model 15 (TNKS) is as follows:

[0223] [Formula 17] TNKS(y)=-0.01627×pNGAL(ng / mL)+0.631×pKIM-1(ng / mL)+0.4914×sCr(mg / dl)-10.55

[0224] TIFF2025535454000041.tif63170The estimated function equation by logistic regression analysis of Model 16 (TNKA) is as follows:

[0225] [Formula 18] TNKA(y)=0.02812×pNGAL(ng / mL)+1.078×pKIM-1(ng / mL)+0.2033×age(year)-7.344

[0226] TIFF2025535454000042.tif63170The estimated function equation by logistic regression analysis of Model 17 (TNKP) is as follows:

[0227] [Formula 19] TNKP(y)=-0.1125×pNGAL(ng / mL)+1.42×pKIM-1(ng / mL)+0.7162×Inorganic phosphorus(mg / dl)-8.453

[0228] TIFF2025535454000043.tif62170The estimated function equation by logistic regression analysis of Model 18 (TNKAm) is as follows:

[0229] [Formula 20] TNKAm(y)=-0.05275×pNGAL(ng / mL)+1.001×pKIM-1(ng / mL)+0.001492×amylase(U / L)-5.468

[0230] TIFF2025535454000044.tif72170The estimated function equation by logistic regression analysis of Model 19 (TNKCS) is as follows:

[0231] [Formula 21] TNKCS(y)=-0.05199×pNGAL(ng / mL)+0.2173×pKIM-1(ng / mL)+9.823×sCr(mg / dl)+0.9584×SDMA(μg / dl)-26.57

[0232] TIFF2025535454000045.tif63170The estimated function equation by logistic regression analysis of Model 20 (TNKB) is as follows:

[0233] [Formula 22] TNKB(y)=-0.0266×pNGAL(ng / mL)+1×pKIM-1(ng / mL)+0.11×BUN(mg / dl)-7.155

[0234] TIFF2025535454000046.tif80170The estimated function equation by logistic regression analysis of Model 21 (TNKCSB) is as follows:

[0235] [Formula 23] TNKCSB(y)=-0.08838×pNGAL(ng / mL)+0.2262×pKIM-1(ng / mL)+8.739×sCr(mg / dl)+0.961×SDMA(μg / dl)+0.04618×BUN(mg / dl)-26.28

[0236] From Tables 36 to 44, it was found that Model 21 (TNKCSB), which combines pNGAL, pKIM-1, etc. with Creatinine, SDMA, and BUN, showed the highest explanatory power (Pseudo R²) of 84.33% and was the optimal model (function). Subsequently, any of the values ​​derived from Equations 15 to 23, which are linear equation models, can be converted to converge to values ​​of 1 and 0 using an exponential function. In this case, the prediction formula for classifying the patients into the kidney disease group (IRIS stages 2 to 4, showing a value of 1) and the normal group (normal, risk group, and kidney disease stage 1, showing a value of 0) using an exponential function is shown in Equation 24 below. In Equation 24 below, the p-value represents a distribution from 0 to 1 as a percentage, with a value of less than 0.5 indicating no kidney disease and a value of 0.5 or greater indicating the kidney disease group.

[0237] [Formula 24] p=exp(y) / [exp(y)+1] In the above formula, y is a value derived from the above formulas 15 to 23.

[0238] Experimental Example 4.2. Cutoff range for diagnosing stage 2 kidney disease In order to evaluate the accuracy of the optimal model for diagnosing kidney disease groups derived from Experimental Example 4.1, the receiver operating characteristic (ROC) curve was analyzed using the GraphPad Prism program, and the cutoff values ​​for diagnosing kidney disease groups (normal group, risk group, IRIS stage 1 vs. IRIS stages 2 to 4) for NGAL and KIM-1 and the TNK, TNKC, TNKS, TNKA, TNKP, TNKAm, TNKCS, TNKB, and TNKCSB models derived from Experimental Example 3-1 are shown in Tables 45 to 55 below.

[0239] TIFF2025535454000047.tif25170As shown in Table 45, the cutoff value range of pNGAL concentration for distinguishing kidney disease groups can be 2.086 ng / mL (sensitivity 100%, specificity 21.54%) to 40.64 ng / mL (sensitivity 6.383%, specificity 100%).

[0240] TIFF2025535454000048.tif25170As shown in Table 46, the cutoff value range of pKIM-1 concentration for distinguishing kidney disease groups can be 2.360 ng / mL (sensitivity 100%, specificity 16.42%) to 5.323 ng / mL (sensitivity 55.1%, specificity 100.0%).

[0241] TIFF2025535454000049.tif42170As shown in Table 47 above, the cutoff value range of TNK for distinguishing kidney disease groups can be from -5.169 (sensitivity 100%, specificity 12.5%) to -4.081 (sensitivity 23.4%, specificity 100.0%).

[0242] TIFF2025535454000050.tif41170As shown in Table 48 above, the cutoff value range of TNKC for distinguishing kidney disease groups can be −3.718 (sensitivity 100%, specificity 15.63%) to 1.308 (sensitivity 70.21%, specificity 100.0%).

[0243] TIFF2025535454000051.tif42170As shown in Table 49 above, the cutoff value range of TNKS for distinguishing kidney disease groups can be from -3.567 (sensitivity 100%, specificity 39.06%) to -0.08390 (sensitivity 87.23%, specificity 100.0%).

[0244] TIFF2025535454000052.tif42170As shown in Table 50, the cutoff value range of TNKA for differentiating kidney disease groups can be −4.284 (sensitivity 100%, specificity 3.125%) to 1.647 (sensitivity 51.06%, specificity 100.0%).

[0245] TIFF2025535454000053.tif42170As shown in Table 51 above, the cutoff value range of TNKP for distinguishing kidney disease groups can be −1.775 (sensitivity 100%, specificity 54.29%) to 1.308 (sensitivity 70.21%, specificity 100.0%).

[0246] TIFF2025535454000054.tif42170As shown in Table 52 above, the cutoff value range of TNKAm for distinguishing kidney disease groups can be −2.121 (sensitivity 100%, specificity 50%) to 1.374 (sensitivity 47.06%, specificity 100.0%).

[0247] TIFF2025535454000055.tif51170As shown in Table 53 above, the cutoff value range of TNKCS for differentiating kidney disease groups can be from -0.7587 (sensitivity 100%, specificity 92.19%) to 2.234 (sensitivity 80.85%, specificity 100.0%).

[0248] TIFF2025535454000056.tif43170As shown in Table 54 above, the cutoff value range of TNKB for distinguishing kidney disease groups can be -4.175 (sensitivity 100%, specificity 4.688%) to 1.734 (sensitivity 70.21%, specificity 100.0%).

[0249] TIFF2025535454000057.tif43170As shown in Table 55 above, the cutoff value range of TNKCSB for differentiating kidney disease groups can be from -1.657 (sensitivity 100%, specificity 90.63%) to 1.506 (sensitivity 85.11%, specificity 100.0%).

[0250] Experimental Example 4.3. Evaluation of the diagnostic accuracy of the derived optimal model for kidney disease stage 2 The ROC (receiver operating characteristic) curve was analyzed using the GraphPad Prism program, and the optimal cut-off value with the highest diagnostic accuracy was derived from the cut-off value ranges of pNGAL, pKIM-1, indicator TNK, TNKC, TNKS, TNKA, TNKP, TNKAm, TNKCS, TNKB, and TNKCSB derived from the above 4-2 (Figure 5). In addition, the diagnostic sensitivity, specificity, accuracy, and reference standard ranges for distinguishing the normal control group, the risk group, and IRIS stage 1 from IRIS stages 2 to 4 are shown in Table 56 below.

[0251] Looking at Figure 5 and Table 56 above, it was shown that the diagnostic accuracy (AUC, area under curve) of IRIS stages 2 to 4 was high in the order of pNGAL < TNK < pKIM-1 < sCr < TNKAM < TNKA < TNKP = TNKB < TNKC < SDMA = TNKS < TNKCS = TNKCSB.

[0252] Specifically, the AUC (area under curve) of TNK of the present invention was 0.86, and the cut-off value below which it is not a kidney disease was -4.833. At the above cut-off value, the diagnostic sensitivity was 78.72% and the diagnostic specificity was 85.94%. LR+ (positive rate) was 5.60 and LR- (negative rate) was 0.25.

[0253] In addition, the AUC (area under curve) of TNKCS of the present invention was 0.99, and the cut-off value below which it is not a kidney disease was 0.7587. At the above cut-off value, the diagnostic sensitivity was 100% and the diagnostic specificity was 92.19%. LR+ (positive rate) was 12.80 and LR- (negative rate) was 0.

[0254] The AUC (area under the curve) of the TNKCSB of the present invention was 0.99, and the cutoff value below which kidney disease was not detected was -0.5159. At this cutoff value, the diagnostic sensitivity was 97.87% and the diagnostic specificity was 93.75%. The LR+ (positive rate) was 15.66, and the LR- (negative rate) was 0.02.

[0255] On the other hand, the AUC of pNGAL was 0.75, and the cutoff value was 4.14 ng / mL. At this cutoff value, the diagnostic sensitivity was 82.98% and the diagnostic specificity was 60%. The LR+ (positive rate) was 2.07, and the LR- (negative rate) was 0.28.

[0256] The AUC of pKIM-1 was 0.88, and the cutoff value was 4.14 ng / mL. At this cutoff value, the diagnostic sensitivity was 78.72%, and the diagnostic specificity was 85.94%. The LR+ (positive rate) was 5.93, and the LR- (negative rate) was 0.24.

[0257] The above results indicate that the TNKS, TNKCS, and TNKCSB of the present invention can distinguish kidney disease groups with higher accuracy, sensitivity, and specificity than sCr and SDMA, i.e., the indicators of the present invention can diagnose kidney disease at a level equivalent to or higher than that of existing indicators.

[0258] Based on these results, we developed a kit for diagnosing kidney disease. The kit was fabricated as a strip structure as shown in Figure 6A, and included a sample pad, a conjugate pad, a stacking pad, an NC membrane, an absorbent pad, and a backing card.

[0259] As shown in Figure 6B, the presence or absence of chronic kidney disease according to the International Society of Nephrology's chronic kidney disease staging guidelines can be determined by the combination of the control line and the test line (KIM-1 and NGAL). When the presence or absence of the control line is defined as the standard, the absence of KIM-1 and NGAL indicates a normal group, while the presence or absence of KIM-1 and / or NGAL indicates a renal disease group.

[0260] Although the above-described preferred embodiments have been described, various modifications and variations can be made without departing from the spirit and scope of the present invention, and the appended claims are intended to cover such modifications and variations as fall within the spirit and scope of the present invention.

Claims

1. A composition for diagnosing kidney disease, comprising a preparation capable of measuring the expression levels of NGAL (Neutrophil gelatinase-associated lipocalin) protein, KIM-1 (Kidney injury molecule-1) protein, or a combination thereof, or the genes encoding them.

2. 2. The diagnostic composition for kidney disease according to claim 1, wherein the composition is capable of classifying risk groups and stages of each disease according to the staging guidelines for chronic kidney disease (CKD) of the International Renal Interest Society (IRIS).

3. 2. The diagnostic composition for kidney disease according to claim 1, further comprising a preparation capable of measuring the expression level of one or more proteins or genes encoding the same selected from the group consisting of SDMA, creatinine, inorganic phosphorus, amylase, and BUN.

4. 2. The composition for diagnosing kidney disease according to claim 1, wherein the preparation capable of measuring the expression level of the protein or the gene encoding the protein is selected from the group consisting of an antibody, a ligand, a PNA (peptide nucleic acid), an aptamer, and a nanoparticle that specifically binds to the protein, or the group consisting of a primer pair, a probe, and an antisense nucleotide that specifically binds to the gene.

5. The composition for diagnosing kidney disease according to claim 1, wherein the kidney disease is acute kidney injury (AKI) or chronic kidney disease (CKD).

6. The composition for diagnosing kidney disease according to claim 1 , wherein the expression level of the protein or the gene encoding it is measured in a body fluid sample from an individual.

7. A kit for diagnosing kidney disease comprising the composition of claim 1.

8. measuring the expression level of NGAL (Neutrophil gelatinase-associated lipocalin) protein, KIM-1 (Kidney injury molecule-1) protein, or a combination thereof, or the genes encoding them, from a biological sample obtained from the individual; and comparing the measured expression levels with the expression levels of a normal group of proteins or combinations thereof, or the genes encoding them.

9. 9. The method of claim 8, further comprising measuring the expression level of one or more proteins or genes encoding them selected from the group consisting of SDMA, creatinine, inorganic phosphorus, amylase, and BUN.

10. The method includes the steps of setting the measured protein or gene expression levels as independent variables, and setting the presence or absence of kidney disease risk group (stage with inherent risk factors), chronic kidney disease stage 1 (IRIS stage 1), or chronic kidney disease stage 2 to 4 (IRIS stage 2 to 4) according to the International Renal Interest Society (IRIS) chronic kidney disease (CKD) staging guideline criteria as dependent variables; modeling the relationship between the independent variables and the dependent variables through logistic regression analysis to infer a model formula; The method according to claim 8, further comprising a step of classifying the individual into a kidney disease risk group, an IRIS stage 1 group, or a kidney disease group (IRIS stages 2 to 4) when the value derived from the model formula is equal to or greater than a predetermined cutoff value.

11. The method according to claim 10, wherein the model formula is any one selected from the following calculation formulas 1 to 6: [Formula 1] RNK(y) = 1.648 x pNGAL (ng / mL) + 3.287 x pKIM-1 (ng / mL) - 12.2 [Formula 2] RNKC (y) = 1.71 x pNGAL (ng / mL) + 3.306 x pKIM-1 (ng / mL) + 0.9716 x sCr (mg / dl) - 13.22 [Formula 3] RNKS (y) = 1.928 x pNGAL (ng / mL) + 3.948 x pKIM-1 (ng / mL) + 0.4207 x SDMA (μg / dl) - 19.09 [Formula 4] RNKA = 1.398 x pNGAL (ng / mL) + 3.989 x pKIM-1 (ng / mL) + 0.5979 x age (year) - 17.02 [Formula 5] RNKR = 2.832 x pNGAL (ng / mL) + 4.726 x pKIM-1 (ng / mL) + 8.756 x CRP (mg / dl) - 21.36 [Formula 6] RNKCS = 2.15 x pNGAL (ng / mL) + 4.178 x pKIM-1 (ng / mL) + 1.798 x sCr (mg / dl) + 0.4377 x SDMA (μg / dl) - 21.97

12. The method according to claim 11, wherein the cutoff value of the model formula is any number selected from the range of −4.27 to 2.

50.

13. The method according to claim 10, wherein the model formula is any one selected from the following calculation formulas 8 to 13. [Formula 8] SNK(y) = 0.5541 x pNGAL (ng / mL) + 0.3766 x pKIM-1 (ng / mL) - 2.614 [Formula 9] SNKC (y) = 0.5522 x pNGAL (ng / mL) + 0.3146 x pKIM-1 (ng / mL) + 0.4417 x sCr (mg / dl) - 2.792 [Formula 10] SNKS (y) = 0.442 x pNGAL (ng / mL) + 0.001992 x pKIM-1 (ng / mL) + 0.2562 x SDMA (μg / dl) - 4.079 [Formula 11] SNKA (y) = 0.447 x pNGAL (ng / mL) + 0.2079 x pKIM-1 (ng / mL) + 0.2108 x age (year) - 3.55 [Formula 12] SNKP (y) = 0.4599 x pNGAL (ng / mL) + 0.3363 x pKIM-1 (ng / mL) + 1.004 x inorganic phosphorus (mg / dl) - 5.678 [Formula 13] SNKCS (y) = 0.4406 x pNGAL (ng / mL) + 0.007931 x pKIM-1 (ng / mL) - 0.07766 x sCr (mg / dl) + 0.258 x SDMA (μg / dl) - 4.048

14. The method according to claim 13, wherein the cutoff value of the model formula is any number selected from the range of −1.67 to 4.

26.

15. The method according to claim 10, wherein the model formula is any one selected from the following calculation formulas 15 to 23. [Formula 15] TNK(y) = 0.03031 x pNGAL (ng / mL) + 1.187 x pKIM-1 (ng / mL) - 5.538 [Formula 16] TNKC (y) = -0.01621 x pNGAL (ng / mL) + 0.9737 x pKIM-1 (ng / mL) + 3.773 x sCr (mg / dl) -8.309 [Formula 17] TNKS (y) = -0.01627 x pNGAL (ng / mL) + 0.631 x pKIM-1 (ng / mL) + 0.4914 x sCr (mg / dl) - 10.55 [Formula 18] TNKA (y) = 0.02812 x pNGAL (ng / mL) + 1.078 x pKIM-1 (ng / mL) + 0.2033 x age (year) - 7.344 [Formula 19] TNKP (y) = -0.1125 x pNGAL (ng / mL) + 1.42 x pKIM-1 (ng / mL) + 0.7162 x inorganic phosphorus (mg / dl) -8.453 [Formula 20] TNKAm(y) ​​= -0.05275 x pNGAL (ng / mL) + 1.001 x pKIM-1 (ng / mL) + 0.001492 x amylase (U / L) -5.468 [Formula 21] TNKCS (y) = -0.05199 x pNGAL (ng / mL) + 0.2173 x pKIM-1 (ng / mL) + 9.823 x sCr (mg / dl) + 0.9584 x SDMA (μg / dl) -26.57 [Formula 22] TNKB (y) = -0.0266 x pNGAL (ng / mL) + 1 x pKIM-1 (ng / mL) + 0.11 x BUN (mg / dl) -7.155 [Formula 23] TNKCSB(y) = -0.08838 x pNGAL (ng / mL) + 0.2262 x pKIM-1 (ng / mL) + 8.739 x sCr (mg / dl) + 0.961 x SDMA (μg / dl) + 0.04618 x BUN (mg / dl) -26.28

16. The method according to claim 15, wherein the cutoff value of the model formula is any number selected from the range of −5.17 to 2.

23.

17. The method according to claim 8, wherein the method can distinguish between a normal group and a risk group to a suspicious group before IRIS stage 1 with a sensitivity of 90% or more and a specificity of 95% or more.

Images