KR20240056436A - Biomarker composition for early diagnosis of kidney disease and method of providing information for early diagnosis of kidney disease using the same - Google Patents

Abstract

The present invention relates to a biomarker for early diagnosis of kidney disease and its use, and according to a composition, kit, and method according to one aspect, kidney disease or the risk of kidney disease can be diagnosed early with higher accuracy, sensitivity, and specificity. You can. In particular, according to the present invention, based on the International Renal Interest Society (IRIS) staging guidelines for chronic kidney disease (CKD), a normal group and a 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) can be distinguished with a sensitivity of over 90% and specificity of over 95%.

Description

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

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

Two kidneys are located on either side of the spine in the lumbar region of the human body. In addition to their main function of filtering metabolites and waste products from the body and excreting them through urine, they also have the function of maintaining biological homeostasis by maintaining the amount of water, electrolytes, and acidity in the body within a narrow range. , and has an endocrine function that produces and activates various hormones important for maintaining blood pressure, correcting anemia, and metabolism of calcium and phosphorus.

Kidney diseases that cause weakening of kidney function include glomerulonephritis, chronic renal failure, acute renal failure, nephrotic syndrome, pyelonephritis, kidney stones, and kidney cancer. Depending on the rate at which kidney function deteriorates, it can be broadly divided into acute kidney injury (AKI) and chronic kidney disease (CKD).

In chronic kidney disease, the decline in kidney function occurs slowly over several months and progresses to end-stage renal disease, a condition that is usually irreversible and progressive and often requires dialysis or a kidney transplant. On the other hand, acute kidney injury is a condition in which kidney function deteriorates rapidly within days or weeks. Common causes include dehydration or low blood pressure, nephrotoxic substances or drugs, and urinary tract obstruction. In most cases, original renal function is restored through conservative treatment that improves dehydration through fluid supplementation or removes the cause of kidney strain, but in some cases, depending on the severity of the causative disease, it may progress to chronic renal failure.

Despite the advancement of modern medicine, many patients admitted to hospitals suffer from a decline in kidney function. In particular, patients with high disease severity often require renal replacement therapy due to a decline in 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 this prevalence rate is steadily increasing despite the development of new treatments (Lameire et al., Lancet, 2005; Devarajan, Contrib Nephrol, 2007).

There may be several reasons for the high mortality rate of acute kidney injury, but the main cause may be missing the timing of appropriate treatment due to the lack of a method to diagnose acute kidney injury early. The traditional way to evaluate kidney function is to indirectly reflect the degree of kidney function through measuring serum creatinine. However, serum creatinine has the disadvantage of not reflecting changes in kidney function in real time because it is influenced by an individual's weight, age, gender, muscle mass, protein intake level, and medications. In other words, since serum creatinine rises only when kidney function declines by more than 50%, the diagnostic method using serum creatinine has limitations in diagnosing acute kidney injury early (Belcher et al., Am J Kidney Dis, 2011; Endre and Westhuyzen, Nephrology, 2008). Fortunately, with the recent development and application of innovative cutting-edge technologies such as functional genomics and proteomics, the possibility of various proteins and gene products as biomarkers has been proposed, but there is a limitation that clinical efficacy has not yet been verified.

NGAL (Neutrophil gelatinase-associated lipocalin) is a 25 kDa glycoprotein bound to the epithelium of neutrophils or renal tubules, and is a biological marker that increases rapidly when acute kidney injury occurs for various reasons. Currently, it is mainly used as a substance to diagnose kidney function abnormalities and determine the prognosis of kidney transplant patients.

In addition, 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 ischemia and reperfusion renal injury, nephrotoxic drugs, or renal disease, starting several hours after kidney injury. KIM-1 is composed of a cytoplasmic domain and an extracellular domain, of which the ectodomain is excreted in urine, and is being studied as a diagnostic biomarker for kidney disease.

However, conventional clinicopathological kidney disease diagnosis techniques using not only NGAL and/or KIM-1, but also serum creatinine and SDMA (Symmetric Dimethylarginine), were unable to distinguish between a normal group and a risk group for kidney disease.

Accordingly, the present inventors made research efforts to develop an optimal index that can diagnose the kidney disease group at an early stage and, in particular, distinguish the normal group from the risk group of kidney disease, and NGAL and KIM in the body fluid sample of the individual. -1 After measuring each concentration, the degree to which each variable, including the concentration of each of the above NGAL and KIM-1, influences the presence of disease is confirmed through crude logistic regression analysis (univariate logistic regression analysis), and the significance level is determined. It is a value estimated by identifying the functional relationship between variables through multiple logistic regression analysis by combining only the variables that are significant at 0.05, and has the highest pseudo R2 (explanatory power, strength of relationship between dependent and independent variables). An optimal model (function) was derived, and the present invention was completed after confirming that the accuracy, sensitivity, and specificity of early diagnosis of kidney disease were significantly high when using the derived optimal model.

KR 10-2013-0089474 A KR 10-1657881 B1

One aspect is a method for diagnosing kidney disease, including an agent capable of measuring the expression level of NGAL (Neutrophil gelatinase-associated lipocalin) protein, KIM-1 (Kidney injury molecule-1) protein, or a combination thereof, or a gene encoding the same. A composition is provided.

Another aspect is to provide a kit for diagnosing kidney disease comprising the composition.

Another aspect is the detection of NGAL (Neutrophil gelatinase-associated lipocalin) protein, KIM-1 (Kidney injury molecule-1) protein, or a combination thereof, or a gene encoding the same in a biological sample obtained from an individual. measuring expression level; and comparing the measured expression level with the expression level of a protein or a combination thereof, or a gene encoding the same, in a normal group.

Another aspect is the detection of NGAL (Neutrophil gelatinase-associated lipocalin) protein, KIM-1 (Kidney injury molecule-1) protein, or a combination thereof, or a gene encoding the same in a biological sample obtained from an individual. To provide a method of treating kidney disease comprising measuring the expression level.

Another aspect is the detection of NGAL (Neutrophil gelatinase-associated lipocalin) protein, KIM-1 (Kidney injury molecule-1) protein, or a combination thereof, or a gene encoding the same in a biological sample obtained from an individual. measuring expression level; and comparing the measured expression level with the expression level of a protein or a combination thereof, or a gene encoding the same, in a normal group.

Another aspect is 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 together with the concentration of the marker. An input unit for inputting the concentration of one or more markers selected from SDMA, creatinine, phosphorus inorganic, amylase, and BUN; The concentration of the input marker is set as a single or multiple independent variable, respectively, and chronic kidney disease is determined according to the guidelines for staging chronic kidney disease (CKD) of the International Renal Interest Society (IRIS). A variable setting unit that sets the 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) as a dependent variable; an inference engine unit for inferring a model equation by modeling the relationship between the plurality of independent variables and the dependent variable through logistic regression analysis; And a value is derived by substituting the data on the markers input from the input unit into the independent variables of the inferred model equation, and when the derived value is greater than or equal to a predetermined cutoff value, the entity is Kidney disease, including a diagnostic section that determines a risk group for kidney disease (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) to provide a diagnostic system.

Another aspect is to provide a computer-readable recording medium recording a computer program for executing the method on a computer.

One aspect is a method for diagnosing kidney disease, including an agent capable of measuring the expression level of NGAL (Neutrophil gelatinase-associated lipocalin) protein, KIM-1 (Kidney injury molecule-1) protein, or a combination thereof, or a gene encoding the same. A composition is provided.

The NGAL (Neutrophil gelatinase-associated lipocalin; lipocalin-2) is a 25 kDa glycoprotein bound to the epithelium of neutrophils or renal tubules, and is known to be a protein that plays an important role in evaluating kidney health or kidney damage. The NGAL may be expressed when the proximal tubule of the kidney is damaged or the nephron is damaged.

The 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 ischemia and reperfusion renal injury, nephrotoxic drugs, or renal disease from a few hours after kidney injury. It is one of the proteins that indicates kidney damage and is mainly used as a biomarker for diagnosis or monitoring of acute kidney injury. The KIM-1 is composed of a cytoplasmic domain and an extracellular domain, of which the ectodomain is known to be excreted in urine. The expression of KIM-1 may increase when proximal tubules are damaged.

As used herein, the term "marker" or "biomarker" refers to a substance that can be used for diagnosis by distinguishing normal subjects from diseased subjects, and is found to be increased in subjects with kidney-related diseases of the present invention. It may include all organic biomolecules such as polypeptides, proteins or nucleic acids, genes, lipids, glycolipids, glycoproteins, or sugars.

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

The composition is an agent that measures the expression level of one or more proteins selected from the group consisting of SDMA, BUN, creatinine, Phosphorus inorganic, Amylase, inulin, and cystatin C, or the gene encoding the same. It may additionally include.

The SDMA, BUN, creatinine, inulin, and cystatin C may be commercially available and used as biomarkers for evaluating glomerular filtration rate.

As used herein, the term "Glomerular Filtration Rate (GFR)" is an indicator of kidney function and refers to the speed at which the kidneys filter specific substances in the blood. The glomerular filtration rate refers to the rate at which the kidneys filter waste products contained in the blood. This may indicate the ability to filter and excrete substances through urine.

Agents capable of measuring the expression level of the protein include monoclonal antibodies, polyclonal antibodies, chimeric antibodies, ligands, and PNA ( It may be selected from the group consisting of peptide nucleic acid, aptamer, and nanoparticle, but is not limited thereto.

Methods for measuring the level of protein expression include protein chip analysis, immunoassay, ligand binding assay, MALDI-TOF (Matrix Desorption/Ionization Time of Flight Mass Spectrometry) analysis, and SELDI-TOF (Sulface Enhanced Laser Desorption/Ionization Time of Flight). Mass Spectrometry analysis, radioimmunoassay, radioimmunodiffusion method, Ouchteroni immunodiffusion method, rocket immunoelectrophoresis, tissue immunostaining, complement fixation assay, two-dimensional electrophoresis analysis, liquid chromatography-mass spectrometry. Spectrometry, LC-MS), LC-MS/MS (liquid chromatography-Mass Spectrometry/Mass Spectrometry), Western blot, and ELISA (enzyme linked immunosorbent assay), etc., but are not limited thereto. Accordingly, the agent for measuring the protein level may include an antibody that specifically binds to the NGAL protein or KIM-1 protein.

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

An agent capable of measuring the expression level of the gene encoding the protein may be selected from the group consisting of a primer pair, probe, and antisense nucleotide that specifically binds to the gene. , but is not limited to this.

As used herein, the term “primer pair” includes all combinations of primer pairs consisting of forward and reverse primers that recognize the target gene sequence, and more specifically, a primer pair that provides analysis results with specificity and sensitivity. High specificity can be granted when the nucleic acid sequence of the primer is a sequence that is inconsistent with the non-target sequence present in the sample, so that the primer amplifies only the target gene sequence containing the complementary primer binding site and does not cause non-specific amplification. .

The term "probe" used herein refers to a substance that can specifically bind to a target substance to be detected in a sample, and refers to a substance that can specifically confirm the presence of the target substance in the sample through said binding. do. The type of probe molecule is not limited as it is a material commonly used in the art, but preferably may be PNA (peptide nucleic acid), LNA (locked nucleic acid), peptide, polypeptide, protein, RNA or DNA. More specifically, the probe is a biomaterial that is derived from or similar to living organisms or includes those produced in vitro, such as 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 RNA, mRNA, and oligonucleotides, and examples of proteins may include antibodies, antigens, enzymes, peptides, etc.

As used herein, the term "antisense oligonucleotide" is DNA or RNA or a derivative thereof containing a nucleic acid sequence complementary to the sequence of a specific mRNA, and binds to the complementary sequence in the mRNA and inhibits the translation of the mRNA into a protein. It works. Antisense oligonucleotide sequence refers to a DNA or RNA sequence that is complementary to the mRNA of the genes and is capable of binding to the mRNA. This may inhibit translation, translocation into the cytoplasm, maturation, or any other essential activity for overall biological functions of the gene mRNA. The length of the antisense oligonucleotide may be 6 to 100 bases, preferably 8 to 60 bases, and more preferably 10 to 40 bases. The antisense oligonucleotide can be synthesized in vitro using a conventional method and administered in vivo, or the antisense oligonucleotide can be synthesized in vivo. One example of synthesizing antisense oligonucleotides in vitro is using RNA polymerase I. One example of allowing antisense RNA to be synthesized in vivo is to transcribe the antisense RNA using a vector with the origin of the multiple cloning site (MCS) in the opposite direction. It is preferable that the antisense RNA has a translation stop codon within the sequence so that it is not translated into a peptide sequence.

In one embodiment, the kidney disease may be acute kidney injury (AKI) or chronic kidney disease (CKD).

The acute kidney injury is not particularly limited, but is any one selected from the group consisting of acute renal failure, acute tubular necrosis, acute tubulointerstitial nephropathy, ischemic acute kidney injury, acute pyelonephritis, acute progressive nephritis, and toxic acute kidney injury. However, it is not limited to this.

The chronic kidney disease is not particularly limited, but may be any one selected from the group consisting of nephritis syndrome, tubular dysfunction, renal hypertension, uremia, chronic glomerulonephritis, renal failure, and chronic renal failure, but is not limited thereto.

The above kidney diseases include diabetic nephropathy, hypertensive nephropathy, glomerulonephritis, polycystic nephroma, urinary tract obstruction, renal fibrosis, nephritis, pyelonephritis, renal cancer, hydronephrosis, nephrotic hemorrhagic fever, renal tuberculosis, small glomerulosclerosis, diabetic nephropathy, membranous It may include, but is not limited to, one or more diseases selected from the group consisting of nephropathy, membranous proliferative glomerulonephritis, and nephrosis syndrome.

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

In one embodiment, the subject may be a mammal, for example, a human, dog, cat, cow, horse, pig, sheep or goat, but is not limited thereto.

The entity may be an animal other than a human.

The subject may be selected for early diagnosis of kidney disease based on the prior presence of one or more risk factors selected from prerenal, intrinsic renal, and postrenal kidney damage.

As used herein, the term “Prerenal Kidney Injury” refers to kidney damage caused by factors external to the kidney. It mainly occurs when there is a problem with blood circulation, which may mean that kidney function is reduced due to insufficient blood supply to the kidneys. The renal kidney damage may generally be caused by a drop in blood pressure, a decrease in blood volume, or a change in blood viscosity.

As used herein, the term "Intrinsic Kidney Injury" refers to kidney damage caused by a problem with the kidney itself, and may result in damage to the kidney tissue itself, resulting in a decrease in function. The renal kidney damage may be caused by cell damage, inflammation, exposure to toxic substances, infection, hematologic abnormalities, etc. that directly affect kidney tissue.

As used herein, the term "Postrenal Kidney Injury" refers to kidney damage caused by problems occurring in the renal excretion system. It can mainly occur when urine produced in the kidneys is unable to be excreted abnormally and accumulates. . The post-renal kidney damage may occur when urine is not excreted normally due to urinary tract infection, ureteral obstruction, bladder obstruction, or enlarged prostate.

These risk factors include congestive heart failure, pre-eclampsia, convulsions, diabetes mellitus, hypertension, coronary artery disease, proteinuria, renal insufficiency, glomerular filtration below the normal range, serum creatinine above the average range, sepsis, and renal dysfunction. One or more pre-existing diagnoses selected from: impairment, reduced renal function, and acute renal failure (ARF); Experience with one or more surgeries selected from the following: major vascular surgery, coronary artery bypass grafting, 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 streptozotocin; It is not limited to this.

In one embodiment, the individual may not be receiving renal replacement therapy, but is not limited thereto.

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

The composition is capable of distinguishing risk groups (stages with risk factors) and each stage according to the staging guidelines for chronic kidney disease (CKD) of the International Renal Interest Society (IRIS). It could be.

In this specification, the term "International Renal Interest Society (IRIS) Staging Guidelines for Chronic Kidney Disease (CKD)" or "IRIS CKD Stage" is provided by the International Society of Veterinary Nephrology. refers to the criteria for classifying kidney disease.

As used herein, the term “early diagnosis of kidney disease” refers to a 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). ), or early diagnosis of kidney damage or kidney disease.

As used herein, the term “kidney disease risk group” refers to a stage in which a person does not yet have kidney disease but has risk factors for kidney disease.

In this specification, the term “kidney disease stage 1” refers to a stage in which the glomerular filtration rate (GFR) is still normal or close to normal, but chronic kidney disease is diagnosed through medical history, continuous decrease in glomerular filtration rate, diagnostic imaging tests, etc. .

Another aspect provides a kit for diagnosing kidney disease comprising the composition.

The composition is as described above.

The kit is suitable for chronic kidney risk group (stage with inherent risk factors), chronic kidney disease stage 1 according to the International Renal Interest Society (IRIS) staging guideline for chronic kidney disease (CKD). It may provide information on the presence or absence of chronic kidney disease (IRIS stage 1), or chronic kidney disease stages 2 to 4 (IRIS stages 2 to 4). Additionally, the kit may be capable of distinguishing stages based on the International Society of Nephrology's staging guidelines for chronic kidney disease.

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

The kit may include tools and reagents commonly used in immunological analysis, as well as agents for measuring the expression level of the protein or gene.

Examples of the tool or reagent may include, but is not limited to, a suitable carrier, a labeling substance capable of generating a detectable signal, a chromophore, a solubilizer, a detergent, a buffer, and a stabilizer. If the labeling substance is an enzyme, it may include a substrate that can measure enzyme activity and a reaction stopper. Carriers include soluble carriers and insoluble carriers. Examples of soluble carriers include physiologically acceptable buffers known in the art, such as PBS, and examples of insoluble carriers include polystyrene, polyethylene, polypropylene, polyester, and polyester. It may be acrylonitrile, fluororesin, cross-linked dextran, polysaccharide, polymers such as magnetic fine particles plated with metal on latex, other paper, glass, metal, agarose, and combinations thereof.

The kit may include, but is limited to, a sample pad, conjugate pad, stacking pad, membrane, absorbent pad, and solid backing card. That is not the case.

The “sample pad” refers to a pad that accommodates a sample to be analyzed and is capable of diffusing flow, and is made of a material with sufficient porosity to accommodate and contain the sample to be analyzed. Such porous materials include fibrous paper, microporous membranes made of cellulose materials, cellulose, cellulose derivatives such as cellulose acetate, nitrocellulose, glass fiber, naturally occurring cotton, fabrics such as nylon, or porous gels. , but is not limited to this.

The “conjugate pad” refers to a pad that accommodates a sample that diffuses and moves from the sample pad. The conjugate pad, like the sample pad, may be made of a material capable of diffusive flow, but is not limited thereto.

The “membrane” may be formed with a test line to capture the analyte in the sample, and may be made of any material as long as the sample material can pass through it. For example, naturally occurring materials, whether natural, synthetic, or synthetically modified, such as polysaccharides (e.g., cellulosic materials, paper, cellulose derivatives such as cellulose acetate and nitrocellulose); polyether sulfone; polyethylene; nylon; polyvinylidene fluoride (PVDF); Polyester; polypropylene; silica; Inorganic materials uniformly dispersed in a porous polymer matrix together with polymers such as vinyl chloride, vinyl chloride-propylene copolymer and vinyl chloride-vinyl acetate copolymer, such as deactivated alumina, diatomaceous earth, MgSO4, or other inorganic fine materials. ; Naturally occurring (such as cotton) and synthetic (such as nylon or rayon) fabrics; porous gels such as silica gel, agarose, dextran, and gelatin; It may be formed from materials such as polymer films, such as polyacrylamide. In one embodiment, the membrane is a nitrocellulose (NC) membrane, a glass fiber membrane, a polyethersulfone (PES) membrane, a cellulose membrane, a nylon membrane, or any of these. Combinations may be used, but are not limited thereto.

A test area and a control area may be sequentially formed on the membrane in the direction from the conjugate pad to the absorption pad, but are not limited thereto.

The “absorbent pad” may be located adjacent to or near the end of the membrane. An absorbent pad typically accepts a fluid sample that moves across the entire membrane. Absorbent pads can help promote capillary action and diffusive flow of fluid through the membrane.

The solid support may be formed of any material as long as it can support and carry the sample pad, conjugate pad, membrane, stacking pad, and absorbent pad. In one embodiment, the support is liquid impermeable so that fluid from the sample diffusing through the membrane does not leak through the support. For example, glass; Polymeric materials include, but are not limited to, polystyrene, polypropylene, polyester, polybutadiene, polyvinylchloride, polyamide, polycarbonate, epoxide, methacrylate, polymelamine, etc.

The kit may include the sample pad, conjugate pad, stacking pad, membrane, and absorption pad sequentially placed on the same solid support.

Another aspect is the detection of NGAL (Neutrophil gelatinase-associated lipocalin) protein, KIM-1 (Kidney injury molecule-1) protein, or a combination thereof, or a gene encoding the same in a biological sample obtained from an individual. measuring expression level; and comparing the measured expression level with the expression level of a protein or a combination thereof, or a gene encoding the same, in a normal group.

In one embodiment, the method measures the expression level of one or more proteins selected from the group consisting of SDMA, serum creatinine (sCr), Phosphorus inorganic, Amylase, and BUN, or the gene encoding them, and sets them as an independent variable. It may include an additional step.

The method sets the expression levels of the measured proteins or genes as independent variables, and determines the staging guidelines for chronic kidney disease (CKD) of the International Renal Interest Society (IRIS). Setting 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) as a dependent variable; Inferring a model equation by modeling the relationship between the independent variable and the dependent variable through logistic regression analysis; And when the value derived from the model equation is greater than or equal to a predetermined cutoff value, the individual is classified into a risk group for kidney disease (stage with inherent risk factors), chronic kidney disease stage 1 (IRIS stage 1), or It may further include the step of determining chronic kidney disease stages 2 to 4 (IRIS stages 2 to 4).

In one embodiment, the logistic regression analysis refers to data on markers related to kidney disease, kidney disease risk group (stage with inherent risk factors), chronic kidney disease stage 1 (IRIS stage 1), or chronic kidney disease 2 to 4. It is a binary algorithm for modeling the relationship between stages (IRIS 2 to 4 stages), and the basic equation is as follows.

At this time, the dependent variable P in the basic equation is in the risk group of chronic kidney disease (stage with inherent risk factors), chronic kidney disease stage 1 (IRIS stage 1), or chronic kidney disease stages 2 to 4 (IRIS stage 2 to 4). The dependent variable 1-P is the probability value corresponding to normal, and the independent variables X1 to Xn are variables for markers related to kidney disease of the subject.

Markers related to kidney disease in the subject may be used by replacing the respective concentrations of NGAL, KIM-1, SDMA, creatinine, Phosphorus inorganic, Amylase, and BUN with corresponding units.

That is, the markers associated with kidney disease in the subject are 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). The relationship between probability values for is modeled through logistic regression analysis to derive the estimation coefficient of the basic equation and create a model equation.

In one embodiment, the model equation may be the equation with the highest explanatory power (pseudo R2) in the logistic regression method.

In one embodiment, the cutoff value may be determined by converting the point where “sensitivity × specificity” represents the maximum value according to the consensus probability method on the receiver operating characteristic (ROC) curve. there is.

In one embodiment, the model equation may be any one selected from equations 1 to 6 below:

[Calculation Formula 1]

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

[Calculation Formula 2]

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

[Calculation Formula 3]

RNKS(y) = 1.928 × pNGAL (ng/mL) + 3.948 × pKIM-1 (ng/mL) + 0.4207 × SDMA (μμg/dl) - 19.09

[Calculation Equation 4]

RNKA = 1.398 × pNGAL (ng/mL) + 3.989 × pKIM-1 (ng/mL) + 0.5979 × age (year) - 17.02

[Calculation Equation 5]

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

[Calculation Equation 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

In one embodiment, the cutoff value is a value determined such that the value derived from any one of the calculation formulas selected from formulas 1 to 6 exceeds the degree seen in normal individuals without kidney disease, and the value is greater than or equal to the cutoff value. If so, this means your kidneys are damaged and you have or are at risk for kidney disease. Specifically, those with risk factors for chronic kidney disease according to the International Renal Interest Society (IRIS) staging guidelines for chronic kidney disease (CKD) or those with chronic kidney disease stages 1 to 4 (IRIS) It means that it corresponds to steps 1-4).

In a preferred embodiment, the range of the cutoff value of 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 0. Any number selected from to 1.58, more preferably any number selected from 1.40 to 1.58, more preferably 1.58.

In a preferred embodiment, the range of the cutoff value of Equation 2 (RNKC) is any number selected from -2.02 to 1.49, preferably any number selected from -1.00 to 1.49, more preferably 0.00. It is any number selected from 1.49 to 1.49, more preferably 1.26 to 1.49, more preferably 1.49.

In a preferred embodiment, the range of the cutoff value of Calculation 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 1.93 to 1.93. Any number selected from 2.09, more preferably 2.085.

In a preferred embodiment, the range of the cutoff value of Equation 4 (RNKA) is any number selected from -3.33 to 0.54, preferably any number selected from -2.77 to 0.53, more preferably - It is any number selected from 0.36 to 0.52, more preferably any number selected from 0.00 to 0.51, more preferably 0.51.

In a preferred embodiment, the range of the [cutoff value] of the calculation formula 5 (RNKR) is any number selected from -3.49 to -0.04, preferably any number selected from -0.30 to -0.05, further Preferably it is -0.077.

In a preferred embodiment, the range of the cutoff value of Equation 6 (RNKCS) is any number selected from -4.27 to 2.50, preferably any number selected from -2.35 to 2.40, more preferably 2.03. It is any number selected from 2.30 to 2.30, more preferably any number selected from 2.10 to 2.20, and more preferably 2.11.

In one specific example, any one value derived from Equations 1 to 6 may be converted to converge to the sense of 1 and 0 using an exponential function. At this time, the prediction formula using the exponential function to classify into the functional disease risk group and IRIS stages 1-4 (representing a value of 1) and normal people (representing a value of 0) is as shown in Equation 7 below. In equation 7 below, the p value is distributed as a percentage from 0 to 1. If it is less than 0.5, there is no risk of kidney disease, and if it is more than 0.5, it can be determined that the person is at risk for kidney disease or higher.

[Calculation Equation 7]

p = exp(y) / [exp(y) + 1]

In the above equation, y is a value derived from the above calculation equations 1 to 6.

In one embodiment, the model equation may be any one selected from equations 8 to 13 below:

[Calculation Equation 8]

SNK(y) = 0.5541 × pNGAL (ng/mL) + 0.3766 × pKIM-1 (ng/mL) - 2.614

[Calculation Equation 9]

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

[Calculation Equation 10]

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

[Calculation Equation 11]

SNKA(y) = 0.447 × pNGAL (ng/mL) + 0.2079 × pKIM-1 (ng/mL) + 0.2108 × Age (year) - 3.55

[Calculation 12]

SNKP(y) = 0.4599 × pNGAL (ng/mL) + 0.3363 × pKIM-1 (ng/mL) + 1.004 × Phosphorus Inorganic (mg/dl) - 5.678

[Calculation Equation 13]

SNKCS(y) = 0.4406

In one embodiment, the cutoff value is a value determined such that the value derived from any one of the calculation formulas 8 to 13 exceeds the degree seen in normal individuals without kidney disease or individuals in the kidney disease risk group, If the value is greater than or equal to the cutoff value, this means that the glomerular filtration rate is normal or close to normal, but the kidneys are damaged and kidney disease is present. Specifically, it corresponds to chronic kidney disease stages 1 to 4 (IRIS stages 1 to 4) according to the International Renal Interest Society (IRIS) staging guideline for chronic kidney disease (CKD). means that The cutoff value for the value derived from any one of the calculation formulas 8 to 13 for determining IRIS stages 1 to 4 of the present invention is any number selected from -1.67 to 4.26.

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

In a preferred embodiment, the range of the cutoff value in Equation 9 (SNKC) is any number selected from -0.70 to 4.26, more preferably 0.83.

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

In a preferred embodiment, the range of the cutoff value of Equation 11 (SNKA) is any number selected from -1.67 to 4.10, more preferably 0.96.

In a preferred embodiment, the range of the cutoff value in Equation 12 (SNKP) is any number selected from -0.76 to 3.65, more preferably 0.76.

In a preferred embodiment, the range of the cutoff value in Equation 13 (SNKCS) is any number selected from -1.26 to 3.30, more preferably 0.48.

In one specific example, any one value derived from Equations 8 to 13 may be converted to converge to the values of 1 and 0 using an exponential function. At this time, the prediction formula using the exponential function to classify chronic kidney disease stages 1 to 4 (IRIS stages 1 to 4, indicating a value of 1) and normal people and kidney disease risk groups (representing a value of 0) is the following calculation formula 14 same. In Calculation Formula 14 below, the p value is distributed as a percentage from 0 to 1. If it is less than 0.5, it is determined that there is no kidney disease or a risk group (stage with inherent risk factors), and if it is more than 0.5, it is determined that it is chronic kidney disease stages 1 to 4. It could be.

[Calculation Equation 14]

p = exp(y) / [exp(y) + 1]

In the above equation, y is a value derived from the above calculation equations 8 to 13.

In one embodiment, the model equation may be any one selected from equations 15 to 23 below:

[Calculation Equation 15]

TNK(y) = 0.03031 × pNGAL (ng/mL) + 1.187 × pKIM-1 (ng/mL) - 5.538

[Calculation Equation 16]

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

[Calculation Equation 17]

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

[Calculation Equation 18]

TNKA(y) = 0.02812 × pNGAL (ng/mL) + 1.078 × pKIM-1 (ng/mL) + 0.2033 × Age (year) - 7.344

[Calculation Equation 19]

TNKP(y) = -0.1125 × pNGAL (ng/mL) + 1.42 × pKIM-1 (ng/mL) + 0.7162 × Phosphorus Inorganic (mg/dl) - 8.453

[Calculation Equation 20]

TNKAm(y) = -0.05275 × pNGAL (ng/mL) + 1.001 × pKIM-1 (ng/mL) + 0.001492 × Amylase (U/L) - 5.468

[Calculation 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

[Calculation Equation 22]

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

[Calculation 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

In one embodiment, the cutoff value is a value determined such that the value derived from any one of the calculation formulas selected from calculation formulas 15 and 23 exceeds the degree seen in normal individuals without kidney disease, risk groups, or individuals corresponding to IRIS stage 1. , if the value is greater than or equal to the cutoff value, it means that the kidneys are damaged and there is kidney disease. Specifically, it corresponds to chronic kidney disease stages 2 to 4 (IRIS stages 2 to 4) according to the International Renal Interest Society (IRIS) staging guideline for chronic kidney disease (CKD). means that The cutoff value for the value derived from any one of the calculation formulas 15 to 23 for determining IRIS stages 2 to 4 of the present invention is any number selected from -5.17 to 2.23.

In a preferred embodiment, the range of the cutoff value of Equation 15 (TNK) is any number selected from -5.17 to -4.08, more preferably -4.83.

In a preferred embodiment, the range of the cutoff value of Equation 16 (TNKC) is any number selected from -3.72 to 1.31, more preferably -0.14.

In a preferred embodiment, the range of the cutoff value in Equation 17 (TNKS) is any number selected from -3.57 to -0.05, more preferably -0.08.

In a preferred embodiment, the range of the cutoff value in equation 18 (TNKA) is any number selected from -4.28 to 1.65, more preferably -0.30.

In a preferred embodiment, the range of the cutoff value in Equation 19 (TNKP) is any number selected from -1.78 to 1.31, more preferably -0.52.

In a preferred embodiment, the range of the cutoff value of Equation 20 (TNKAm) is any number selected from -2.12 to 1.37, more preferably -0.51.

In a preferred embodiment, the range of the cutoff value in Equation 21 (TNKCS) is any number selected from -0.76 to 2.23, more preferably -0.76.

In a preferred embodiment, the range of the cutoff value in Equation 22 (TNKB) is any number selected from -4.18 to 1.73, more preferably 0.13.

In a preferred embodiment, the range of the cutoff value in Equation 23 (TNKCSB) is any number selected from -1.66 to 1.51, more preferably -0.52.

In one specific example, any one value derived from Equations 15 to 23 may be converted to converge to the values of 1 and 0 using an exponential function. At this time, using the exponential function, chronic kidney disease stages 2 to 4 (IRIS stages 2 to 4, indicating a value of 1), normal people, kidney disease risk group (stage with inherent risk factors), and chronic kidney disease stage 1 (IRIS) The prediction formula classified as level 1) (indicating a value of 0) is as shown in Equation 24 below. In calculation formula 24 below, the p value shows a percentage distribution from 0 to 1. If it is less than 0.5, it can be judged as kidney disease stage 1 or lower, and if it is 0.5 or more, it can be judged as kidney disease stage 2 or higher.

[Calculation Formula 24]

p = exp(y) / [exp(y) + 1]

In the above equation, y is a value derived from the above calculation equations 15 to 23.

The information provision 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 when the dependent variable is a chronic kidney disease risk group (stage with inherent risk factors). Therefore, the present invention can detect an individual's kidney function abnormality, especially the chronic kidney disease risk group (stage with inherent risk factors) at an early stage and enable rapid treatment.

The information provision method of the present invention can distinguish IRIS stage 1 from the normal group and chronic kidney disease risk group when the dependent variable is the occurrence of IRIS stage 1 with a sensitivity of 85% or more and a specificity of 90% or more. Therefore, the present invention can detect an individual's kidney function abnormality, especially chronic kidney disease stage 1 (IRIS stage 1), at an early stage and enable rapid treatment.

In addition, the information provision method of the present invention is used when the dependent variable is the occurrence of IRIS stages 2 to 4, normal group, chronic kidney disease risk group (stage with inherent risk factors), and chronic kidney disease stage 1 (IRIS stage 1). Wow, IRIS stages 2 to 4 can be distinguished with a sensitivity of over 90% and a specificity of over 90%.

Another aspect is the detection of NGAL (Neutrophil gelatinase-associated lipocalin) protein, KIM-1 (Kidney injury molecule-1) protein, or a combination thereof, or a gene encoding the same in a biological sample obtained from an individual. measuring expression level; and comparing the measured expression level with the expression level of a protein or a combination thereof, or a gene encoding the same, in a normal group.

Another aspect is the detection of NGAL (Neutrophil gelatinase-associated lipocalin) protein, KIM-1 (Kidney injury molecule-1) protein, or a combination thereof, or a gene encoding the same in a biological sample obtained from an individual. A method of treating kidney disease is provided, comprising measuring the expression level.

Another aspect is 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 together with the concentration of the marker. An input unit for inputting the concentration of one or more markers selected from SDMA, Creatinine, Phosphorus inorganic, Amylase, and BUN; The concentration of the input marker is set as a single or multiple independent variable, respectively, and chronic kidney disease is determined according to the guidelines for staging chronic kidney disease (CKD) of the International Renal Interest Society (IRIS). A variable setting unit that sets a 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) as a dependent variable; an inference engine unit for inferring a model equation by modeling the relationship between the plurality of independent variables and the dependent variable through logistic regression analysis; And a value is derived by substituting the data on the markers input from the input unit into the independent variables of the inferred model equation, and when the derived value is greater than or equal to a predetermined cutoff value, the entity is Kidney disease, including a diagnosis section that determines 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) Provides an early diagnosis system.

Another aspect provides a computer-readable recording medium recording a computer program for executing the method on a computer.

The recording medium may be implemented as an application (or program) and readable by a terminal device (or computer). Specifically, the recording medium may include all types of recording devices or media that store data that can be read by a computing system.

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 present invention, a normal group and a chronic kidney disease risk group (those with inherent risk factors) are divided into a normal group and a chronic kidney disease risk group (with inherent risk factors) based on the guidelines for staging chronic kidney disease (CKD) of the International Renal Interest Society (IRIS). stage), chronic kidney disease stage 1 (IRIS stage 1), or chronic kidney disease stages 2 to 4 (IRIS stages 2 to 4) can be distinguished with a sensitivity of more than 85% and a specificity of more than 90%.

Figure 1 shows the results of a log scale scatter plot analysis of the correlation between the concentrations of NGAL (pNGAL) and KIM-1 (pKIM-1) in the plasma of dogs and the concentrations of existing biomarkers, creatinine (Cr) and SDMA.
Figure 2 shows the results of statistical analysis after log transformation of the scatter plot in Figure 1.
Figure 3 shows the results of ROC (receiver operating characteristic) curve analysis in the kidney disease risk group or IRIS stages 1-4 for evaluating diagnostic accuracy.
Figure 4 shows the results of ROC (receiver operating characteristic) curve analysis in IRIS stages 1-4 for evaluating diagnostic accuracy by disease state criteria.
Figure 5 shows the results of ROC (receiver operating characteristic) curve analysis at IRIS stages 2-4 for evaluating diagnostic accuracy by disease state criteria.
Figure 6a is a schematic diagram simply showing the structure of a kit for diagnosing kidney disease according to an embodiment of the present invention, and Figure 6b is a diagram briefly showing the operating principle of the kit for diagnosing kidney disease according to an embodiment of the present invention.

Below, preferred embodiments are presented to aid understanding of the present invention. However, the following examples are provided only to make the present invention easier to understand, and the content of the present invention is not limited by the following examples. The embodiments may be subject to various changes, and the embodiments are not limited to the embodiments disclosed below and may be implemented in various forms.

Example 1. Diagnosis of kidney disease

1.1. Selection of experimental subjects

Plasma samples and medical records from dogs brought to the Konkuk University Animal Hospital from July 1, 2018 to August 31, 2022 were used. To minimize the influence of dog weight and breed during the study period and to reflect the characteristics of Korea, which mainly raises small dogs, only small dogs weighing less than 10 kg were included in the study. In addition, plasma samples with insufficient volume for analysis or with hemolysis were excluded because they may affect the results of enzyme-linked immunosorbent assay. Next, we included dogs from the small dog population that were tested for plasma SDMA, BUN, and plasma creatinine (Cr) levels. Lastly, dogs that did not undergo SDMA testing, but had no major abnormalities in clinical symptoms or clinicopathological test results, were not on long-term medication or had underlying diseases, and visited for general examination or mild limping were used as normal controls.

1.2. Sample processing and biomarker analysis

Concentrations of pNGAL and pKIM-1 were analyzed using residual plasma samples after clinical examination of dogs that met the conditions for selecting test subjects in Example 1.1 above.

Specifically, venous blood collected in a lithium-heparin tube was centrifuged at 3,000 rpm for 6 minutes at room temperature to separate plasma. The levels of creatinine, BUN, SDMA, Phosphorus inorganic, and Amylase in the plasma were measured immediately after sample collection using a Catalyst One chemistry analyzer (IDEXX Laboratories). Afterwards, the remaining plasma samples were frozen within 6 hours and stored at -78°C, and the SDMA concentration of the control group was measured using the frozen plasma samples with a Catalyst One analyzer.

pNGAL and pKIM-1 concentrations were measured using dog-specific sandwich ELISA (enzyme-linked immunosorbent assay) kits (ab205084 and ab205085, respectively; Abcam, Cambridge, UK) according to the manufacturer's instructions. For this purpose, the freshly frozen plasma samples were rapidly thawed in a water bath at 37°C. Then, to measure pKIM-1 concentration, the plasma sample was diluted at a ratio of 1:10 using a diluent, and to measure pNGAL concentration, the plasma sample was diluted at a ratio of 1:50 using a diluent. Next, the absorbance of the diluted samples was measured at 450 nm using a microplate reader (Tecan, Zurich, Switzerland) according to the manufacturer's instructions. Next, the concentrations of pNGAL and pKIM-1 were calculated from a standard curve (4 parameter logistic curve) calculated from the absorbance measurements of the standard substances.

1.3. Stages and grouping of kidney disease

The study population selected under the conditions of Example 1.1 above was classified into 4 stages based on the staging guidelines for chronic kidney disease (CKD) according to the International Society of Renal Diseases (IRIS) published in 2019. For reference, the risk group consisted of individuals who had risk factors for kidney disease and did not meet the conditions of the control group, but had plasma creatinine and SDMA concentrations within the normal range. The basis for determining the risk group is Myxomatous Mitral Valve Disease (MMVD), Portosystemic shunt, Chronic heart failure, Hyperadrenocorticism (HAC), and diabetes. mellitus), babesiosis, etc. Underlying diseases, including MMVD and HAC, were confirmed using medical records such as physical examination, clinicopathological analysis, and/or radiological examination. Additionally, because the number of subjects in stage 4 was significantly small, they were combined into the stage 3-4 group. Accordingly, 15 dogs were classified into the risk group, 43 dogs were classified into IRIS stage 1, 33 dogs were classified into stage 2, and 16 dogs were classified into stage 3-4.

1.4. Statistical analysis and optimal model derivation

Statistical analysis and derivation of the optimal diagnostic model were performed using GraphPad Prism (version 9.3.1; GraphPad Software, San Diego, CA, USA).

Categorical variables such as gender and body condition score were analyzed using the chi-square test. At this time, the patient's age was converted to a decimal based on the sample collection date and considered as a continuous variable. Differences between the four groups in variables following normal distribution, including log-transformed data, were analyzed by performing one-way analysis of variance followed by Bonferroni's post hoc test.

To derive the optimal diagnostic model, the extent to which multiple independent variables influence the dependent variable was confirmed through univariate logistic regression analysis (simple logistic regression analysis, crude logistic regression analysis), and only variables significant at the significance level of 0.05 were combined. The calculation formula was deduced by modeling through multiple logistic regression analysis.

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

Experimental Example 1. Comparison of NGAL and KIM-1 with existing biomarkers

We compared and analyzed the concentrations of SDMA and creatinine in the plasma of dogs and the concentrations of NGAL and KIM-1, and also analyzed the correlation with the concentrations measured in normal dogs without kidney disease (control group).

Specifically, the equivalence and difference between the concentrations of NGAL (pNGAL) and KIM-1 (pKIM-1) in the plasma and the concentrations of creatinine (Cr) and SDMA, which are existing biomarkers, were analyzed on a log scale through scatter plot (Figures 1 and Figure 2).

Figure 1 is a graph showing the correlation between the concentrations of NGAL (pNGAL) and KIM-1 (pKIM-1) in the plasma of dogs and the concentrations of creatinine (Cr) and SDMA, which are existing biomarkers, as a scatter plot, and Figure 2 is a graph of Figure 1. This is a graph showing the difference in biomarker concentration by stage of kidney disease by converting the scatter plot to log scale.

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 the IRIS 3-4 stage, but in the risk group and IRIS 1 stage It can be confirmed that there is a significant difference. In particular, while 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.

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

Experimental Example 2. Optimal model for diagnosing kidney disease risk groups

We invented an algorithm that combines biomarkers that show statistical significance in the two groups, the normal group and the kidney disease risk group, to provide meaningful information and distinguishes the differences between the two groups using a formula. The mathematical model used in the present invention is based on binary logistic regression analysis. The present inventors were able to obtain various regression analysis models using various combinations of biomarkers, and showed high sensitivity and specificity in differentiating normal groups from kidney disease risk groups, making it possible to diagnose kidney damage or kidney disease early. I confirmed that it does.

Experimental Example 2.1. Derivation of diagnostic model for kidney disease risk group - Differentiation of normal group vs. risk group and IRIS stages 1-4

We sought to derive the optimal index for differentiation of kidney disease risk groups (normal group vs. risk group and IRIS stages 1-4). Specifically, markers related to kidney disease (including chemical test results or clinical indicators) were set as multiple independent variables, and the risk group or higher was set as the dependent variable, and then univariate logistic regression analysis (crude) of the Graphpad Prism program was performed. Through logistic regression analysis, the extent to which the multiple independent variables affected the dependent variable was confirmed, and only variables significant at the significance level of 0.05 were combined (Table 1). As a result of crude analysis, except for sCr and BUN, the dependent variables showed correlations with the independent variables NGAL, KIM-1, SDMA, and CRP, so the optimal combination was created using the six variables.

division Crude Logistic regression analysis n OR S.E. z p (95% CI) Pseudo-R2 NGAL 112 2.983 1.048 2.766 0.0057 1.598~7.711 0.2372 KIM-1 116 13.65 2.22 3.042 0.0024 3.337~101.9 0.2766 SDMA 115 1.475 1.506 2.649 0.0081 1.157~2.063 0.143 sCr 117 7.241 0.8588 1.881 0.06 1.423~81.73 0.04226 Age 117 1.51 0.7133 3.958 <0.0001 1.257~1.905 0.2661 BW 116 1.147 0.9324 0.6453 0.5187 0.7729~1.797 0.002732 BP 105 1.042 3.378 1.545 0.1223 0.9908~1.101 0.02565 BUN 117 1.071 0.6869 1.866 0.062 1.011~1.167 0.04213 BCR 117 1.015 0.6628 0.5672 0.5706 0.9737~1.078 0.002341 Globulin 103 7.132 2.588 2.566 0.0103 1.836~39.46 0.07069 Amylase 45 1.005 1.889 1.488 0.1366 1~1.014 0.07687 CRP 57 78.94 0.7484 2.022 0.0432 2.768~11689 0.1738 PhophorusInorganic 68 3.219 2.117 1.783 0.0746 1.129~15.85 0.0841 Crude: Univariate logistic, Simple logistic
*p<.05 **p<.01 ***p<.001

In the upper part of the table, n is the number of observations, S.E. is the standard error, 95% CI (confidence interval) is the lower and upper limits of the 95% confidence interval of the estimated coefficient, and z is the estimated coefficient divided by SE. t distribution statistic, p is the test statistic for the degree to which the null hypothesis can be rejected, and Pesudo R2 means the degree to which the dependent variable is explained by independent variables, that is, explanatory power (predictive power).

The functional relationship between the combined independent and dependent variables was modeled through multiple logistic regression analysis in the Graphpad Prism program, and the calculation formula was deduced. Here, logistic regression is a binary algorithm for modeling the relationship between data on risk factors and kidney disease risk.

Through the above modeling, RNK, RNKC, RNKS, RNKA, RNKR, and RNKCS, which are the optimal models (functions) with the highest pseudo R2 (explanatory power, strength of relationship between dependent and independent variables) were derived (Tables 2 to 2). 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 SDMA, and RNKA is a function using pNGAL, pKIM-1. 1 and age, RNKR is a function using pNGAL, pKIM-1, and CRP, and RNKCS is a function using pNGAL, pKIM-1, sCr, and SDMA.

Model 1: RNK division Crude Coef. S.E. z p (95% CI) Pseudo-R2 NGAL 1.648 0.7615 2.163 0.0305 0.5359 to 3.672 0.5937 KIM-1 3.287 1.291 2.545 0.0109 1.300 to 6.582 Constant -12.2 4.796 2.543 0.011 -24.96 to -4.964 n=111, LR chi2(2)=37.09, p<.0001

In the top item of the table above, Coef. is the regression coefficient (estimated coefficient) indicating the size of the influence of the independent variable on the dependent variable, S.E. is the standard error, and 95% CI (confidence interval) is the estimated coefficient. The lower and upper limits of the 95% confidence interval, z is the estimated coefficient divided by SE, which is the t distribution statistic, p is the test statistic for the extent to which the null hypothesis can be rejected, and Pesudo R2 is the dependent variable explained by the independent variables. It means the degree to which it is possible, that is, the explanatory power (predictive power).

The estimated function equation through logistic regression analysis of Model 1 (RNK) is as follows.

[Calculation Formula 1]

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

Model 2: RNKC division Crude Coef. S.E. z p (95% CI) Pseudo-R2 NGAL 1.71 0.7944 2.153 0.0313 0.5571 to 3.819 0.5985 KIM-1 3.306 1.32 2.505 0.0122 1.277 to 6.695 sCr 0.9716 1.815 0.5353 0.5924 -2.522 to 5.061 Constant -13.22 5.34 2.475 0.0133 -27.22 to -5.126 n=111, LR chi2(3)=37.39, p<.0001

The estimated function equation for Model 2 (RNKC) through logistic regression analysis is as follows.

[Calculation Formula 2]

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

Model 3: RNKS division Crude Coef. S.E. z p (95% CI) Pseudo-R2 NGAL 1.928 0.9222 2.09 0.0366 0.5523 to 4.312 0.6716 KIM-1 3.948 1.529 2.582 0.0098 1.635 to 7.900 SDMA 0.4207 0.2544 1.653 0.0983 0.03526 to 1.061 Constant -19.09 7.196 2.653 0.008 -37.42 to -8.207 n=111, LR chi2(3)=41.95, p<.0001

The estimated function equation for model 3 (RNKCS) through logistic regression analysis is as follows.

[Calculation Formula 3]

RNKS(y) = 1.928 × pNGAL (ng/mL) + 3.948 × pKIM-1 (ng/mL) + 0.4207 × SDMA (μμg/dl) - 19.09

Model 4: RNKA division Crude Coef. S.E. z p (95% CI) Pseudo-R2 NGAL 1.398 0.7237 1.931 0.0534 0.2698 to 3.557 0.7906 KIM-1 3.989 1.791 2.227 0.0259 1.429 to 8.754 Age 0.5979 0.2533 2.361 0.0182 0.2158 to 1.342 Constant -17.02 6.612 2.574 0.01 -34.94 to -7.432 n=111, LR chi2(3)=49.39, p<.0001

The estimated function equation for model 4 (RNKA) through logistic regression is as follows.

[Calculation Equation 4]

RNKA = 1.398 × pNGAL (ng/mL) + 3.989 × pKIM-1 (ng/mL) + 0.5979 × age (year) - 17.02

Model 5: RNKR division Crude Coef. S.E. z p (95% CI) Pseudo-R2 NGAL 2.832 1.829 1.548 0.1216 0.4175 to 7.871 0.7447 KIM-1 4.726 2.613 1.809 0.0705 1.355 to 12.84 CRP 8.756 6.283 1.394 0.1634 -0.4786 to 25.23 Constant -21.36 11.41 1.873 0.0611 -55.48 to -6.346 n=56, LR chi2(3)=31.42, p<.0001

The estimated function equation for Model 5 (RNKR) through logistic regression analysis is as follows.

[Calculation Equation 5]

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

Model 6: RNKCS division Crude Coef. S.E. z p (95% CI) Pseudo-R2 NGAL 2.15 1.027 2.095 0.0362 0.6298 to 4.836 0.6828 KIM-1 4.178 1.646 2.538 0.0111 1.701 to 8.475 SDMA 0.4377 0.2569 1.704 0.0885 0.04838 to 1.083 sCr 1.798 2.268 0.7927 0.4279 -2.354 to 7.378 Constant -21.97 8.561 2.566 0.0103 -43.63 to -9.004 n=111, LR chi2(4)=42.65, p<.0001

The estimated function equation for Model 6 (RNKS) through logistic regression analysis is as follows.

[Calculation Equation 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

Looking at Tables 2 to 7 above, Model 4 (RNKA), which consists of Age along with pNGAL and pKIM-1, showed a high explanatory power (Pseudo R2) of 79.06% and was found to be the optimal model (function).

In the above functional formulas, 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.

Afterwards, any one value derived from equations 1 to 4, which is a linear equation model, can be converted to converge to the values of 1 and 0 using an exponential function. At this time, the prediction formula for classifying people into kidney disease risk groups (IRIS stage 1, indicating a value of 1) and normal people (representing a value of 0) using an exponential function is as shown in Equation 7 below. In Calculation Formula 5 below, the p value is distributed as a percentage from 0 to 1. If it is less than 0.5, there is no risk of kidney disease, and if it is more than 0.5, it can be determined that the person is at risk for kidney disease.

[Calculation Equation 7]

p = exp(y) / [exp(y) + 1]

In the above equation, y is a value derived from the above calculation equations 1 to 6.

Experimental Example 2.2. Cutoff value range for diagnosing kidney disease risk groups

To evaluate the accuracy of the optimal model for diagnosing kidney disease risk groups derived in 2-1 above, the ROC (receiver operating characteristic) curve was analyzed using the GraphPad Prism program, and NGAL, KIM-1, and 2-1 above were analyzed. The cutoff values for kidney disease risk group diagnosis (normal group vs. risk group and IRIS stages 1 to 4) of the RNK, RNKC, RNKS, RNKA, RNKR, and RNKCS models derived from are shown in Tables 8 to 15 below.

pNGAL cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR+ > 1.451 99.02 94.65% to 99.95% 10 0.5129% to 40.42% 1.111 >3.298 81.37 72.73% to 87.74% 90 59.58% to 99.49% 6.604 > 4.026 69.61 60.10% to 77.69% 100 72.25% to 100.0% -

As shown in Table 8, the cutoff value of pNGAL concentration for differentiating kidney disease risk groups ranges from 1.451 ng/mL (sensitivity 99.02%, specificity 10%) to 4.026 ng/mL (sensitivity 69.61%, specificity 100%). ) can be. Preferably, the cutoff value may be 3.30 (sensitivity 81.37%, specificity 90%).

pKIM-1 cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR+ > 1.705 100 96.50% to 100.0% 10 0.5129% to 40.42% 1.286 > 3.308 66.04 56.60% to 74.35% 90 59.58% to 99.49% 8.294 > 3.321 66.04 56.60% to 74.35% 100 72.25% to 100.0% -

As shown in Table 9 above, the cutoff value of pKIM-1 concentration for differentiating kidney disease risk groups ranges from 1.705 ng/mL (sensitivity 100%, specificity 10%) to 3.321 ng/mL (sensitivity 66.04%, specificity 100%).

Model 1: RNK cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR > -1.941 100 96.37% to 100.0% 22.22 3.948% to 54.74% 1.286 > 1.408 92.16 85.28% to 95.97% 88.89 56.50% to 99.43% 8.294 > 1.584 92.16 85.28% to 95.97% 100 70.09% to 100.0% -

As shown in Table 10, the cutoff value of RNK for distinguishing kidney disease risk groups may range from -1.941 (sensitivity 100%, specificity 22.22%) to 1.584 (sensitivity 92.16%, specificity 100.0%).

Model 2: RNKC cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR > -2.024 100 96.37% to 100.0% 22.22 3.948% to 54.74% 1.286 > 1.257 93.14 86.51% to 96.64% 88.89 56.50% to 99.43% 8.382 > 1.490 93.14 86.51% to 96.64% 100 70.09% to 100.0% -

As shown in Table 11 above, the cutoff value of RNKC for differentiating kidney disease risk groups may range from -2.024 (sensitivity 100%, specificity 22.22%) to 1.490 (sensitivity 93.14%, specificity 100.0%).

Model 3: RNKS cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR > -3.574 100 96.37% to 100.0% 11.11 0.5699% to 43.50% 1.125 > 1.932 93.14 86.51% to 96.64% 88.89 56.50% to 99.43% 8.382 >2.085 93.14 86.51% to 96.64% 100 70.09% to 100.0% -

As shown in Table 12, the cutoff value of RNKS for distinguishing kidney disease risk groups may range from -3.574 (sensitivity 100%, specificity 11.11%) to 2.085 (sensitivity 93.14%, specificity 100.0%).

Model 4: RNKA cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR > -3.334 99.02 94.65% to 99.95% 0 0.000% to 29.91% 0.9902 > -2.773 99.02 94.65% to 99.95% 11.11 0.5699% to 43.50% 1.114 > -0.3581 99.02 94.65% to 99.95% 88.89 56.50% to 99.43% 8.912 > 0.5082 99.02 94.65% to 99.95% 100 70.09% to 100.0% -

As shown in Table 13 above, the cutoff value of RNKA for differentiating kidney disease risk groups may range from -3.334 (sensitivity 100%, specificity 0%) to 0.5082 (sensitivity 99.02%, specificity 100.0%).

Model 5: RNKR cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR > -3.490 100 92.73% to 100.0% 14.29 0.7328% to 51.31% 1.167 > -0.1544 97.96 89.31% to 99.90% 85.71 48.69% to 99.27% 6.857 > -0.07702 97.96 89.31% to 99.90% 100 64.57% to 100.0% -

As shown in Table 14 above, the cutoff value of RNKR for distinguishing kidney disease risk groups may range from -3.490 (sensitivity 100%, specificity 14.29%) to -0.07702 (sensitivity 97.96%, specificity 100.0%).

Model 6: RNKCS cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR > -4.266 100 96.37% to 100.0% 11.11 0.5699% to 43.50% 1.125 > -2.352 99.02 94.65% to 99.95% 11.11 0.5699% to 43.50% 1.114 > 2.034 93.14 86.51% to 96.64% 88.89 56.50% to 99.43% 8.382 > 2.110 93.14 86.51% to 96.64% 100 70.09% to 100.0% -

As shown in Table 15 above, the cutoff value of RNKCS for differentiating kidney disease risk groups may range from -4.266 (sensitivity 100%, specificity 11.11%) to 2.110 (sensitivity 93.14%, specificity 100.0%).

2.3. Evaluation of the diagnostic accuracy of the derived optimal model for kidney disease risk groups

Diagnostic accuracy was derived by analyzing the ROC (receiver operating characteristic) curve using the GraphPad Prism program, and the cutoff values of pNGAL, pKIM-1, indicators RNK, RNKC, RNKS, RNKA, RNKR, and RNKCS derived in 2-2 above. Diagnostic value was evaluated by deriving the optimal cutoff value with the highest sensitivity × specificity value from the range using the concordance probability method (Figure 3). In addition, the diagnostic sensitivity, specificity, accuracy, and range of reference standards for discriminating between normal control group and risk group and IRIS stages 1-4 are shown in Table 16 below. According to IRIS 2019, 18 < SDMA (μg/dl) < 35 or 1.4 < sCr (mg/dl) < 2.8 is CKD stage 2, 36 < SDMA < 54 or 2.9 < sCr < 5.0 is CKD stage 3, and 54 < SDMA or 5.0 < sCr indicates CKD stage 4 and increased risk of systemic manifestations and uremic crisis.

division Cut-off
(unit) AUC
(95% CI) Sensitivity %
(95% CI) Specificity %
(95% CI) positive likelihood ratio Negative likelihood ratio sCr 0.95(mg/dL) 0.71* (0.59-0.82) 50.94 (41.56-60.26) 90.91 (62.26-99.53) 5.60 0.54 SDMA 10.50(㎍/dL) 0.86*** (0.76-0.95) 83.02 (74.75-88.98) 77.78 (45.26-96.05) 3.74 0.22 pNGAL 3.30(ng/mL) 0.90*** (0.83-0.97) 81.37
(72.73-87.74) 90 (59.58-99.49) 8.14 0.21 pKIM-1 3.32(ng/mL) 0.90*** (0.82-0.99) 66.04 (56.6-74.35) 100 (72.25-100) - 0.34 RNK > 1.584 0.97*** (0.94-1) 92.16 (85.28-95.97) 100 (70.09-100) - 0.08 RNKS >2.085 0.98*** (0.96-1) 93.14 (86.51-96.64) 100 (70.09-100) - 0.07 RNKC > 1.490 0.97*** (0.94-1) 93.14 (86.51-96.64) 100 (70.09-100) - 0.07 RNKA > 0.5082 0.99*** (0.97-1) 99.02 (94.65-99.95) 100 (70.09-100) - 0.01 RNKR > -0.07702 0.98*** (0.95-1) 97.96 (89.31-99.9) 100 (64.57-100) - 0.02 RNKCS > 2.110 0.98*** (0.96-1) 93.14 (86.51-96.64) 100 (70.09-100) - 0.07

Looking at Figure 3 and Table 16, the diagnostic accuracy (AUC, area under curve) for the risk group or IRIS stages 1-4 is in the order of sCr < SMDA < pNGAL = pKIM-1 < RNKC = RNK < RNKS = RNKR = RNKCS < RNKA appeared high.

Specifically, the AUC (area under the curve) of RNK of the present invention was 0.97, and the cutoff value below that value indicating no kidney disease was 1.584. At the above cutoff value, diagnostic sensitivity was 92.16% and diagnostic specificity was 100.0%. LR+ (Positive Likelihood Ratio) is incalculable (infinity), and LR- (Negative Likelihood Ratio) is 0.08.

On the other hand, the AUC of pNGAL was 0.90, and the cutoff value was 3.30 ng/mL. At the above cutoff value, diagnostic sensitivity was 81.37% and diagnostic specificity was 90%. LR+ (positive likelihood ratio) is 8.14, and LR- (negative likelihood ratio) is 0.21.

Additionally, the AUC of pKIM-1 was 0.90, and the cutoff value was 3.32 ng/mL. At the above cutoff value, diagnostic sensitivity was 66.04% and diagnostic specificity was 100%. LR+ (Positive Likelihood Ratio) is incalculable (infinity), and LR- (Negative Likelihood Ratio) is 0.34.

The above results show that pNGAL and pKIM-1 of the present invention can distinguish kidney damage or kidney disease risk groups with higher accuracy than the existing indicators sCr and SDMA, and also that RNK has significantly higher sensitivity and higher sensitivity than pNGAL and pKIM-1. Specificity means that it is possible to differentiate risk groups for kidney disease.

Additionally, an experiment was conducted to compare the clinical effectiveness of sCr, SMDA, pNGAL, and pKIM-1 by baseline stage of disease status. When the diagnostic accuracy was confirmed with the disease state including the risk group and IRIS stages 1-4 as described above (Figure 3), overall, the AUC, sensitivity, and specificity between pKIM-1 and pNGAL were similar, and were higher than those of SDMA and sCr. It was confirmed that it was high.

When the disease state is IRIS stage 1 or higher, the diagnostic sensitivity, specificity, accuracy, and range of reference standards are shown in Table 17 below.

division Cut-off
(unit) AUC
(95% CI) Sensitivity %
(95% CI) Specificity %
(95% CI) positive likelihood ratio Negative likelihood ratio sCr 0.95(mg/dL) 0.65* (0.55-0.75) 53.26 (43.14-63.12) 76 (56.57-88.5) 2.22 0.62 SDMA 13.50(㎍/dL) 0.84*** (0.77-0.92) 70.65 (60.67-78.98) 91.3 (73.2-98.45) 8.12 0.32 pNGAL 4.19(ng/mL) 0.88*** (0.8-0.95) 76.14 (66.26-83.83) 87.5 (69-95.66) 6.09 0.27 pKIM-1 3.70(ng/mL) 0.72*** (0.62-0.82) 57.61 (47.41-67.2) 79.17 (59.53-90.76) 2.77 0.54

As shown in Figure 4, the AUC of pNGAL was higher than that of SDMA, and the AUC of pKIM-1 was confirmed to be higher than that of sCr.

In addition, when the disease state is IRIS stage 2 or higher, the diagnostic sensitivity, specificity, accuracy, and range of reference standards are shown in Table 18 below.

division Cut-off
(unit) AUC
(95% CI) Sensitivity %
(95% CI) Specificity %
(95% CI) positive likelihood ratio Negative likelihood ratio sCr 1.25(mg/dL) 0.89*** (0.83-0.96) 69.39 (55.47-80.48) 95.59 (87.81-98.8) 15.73 0.32 SDMA 16.50(㎍/dL) 0.95*** (0.9-0.99) 85.71 (73.33-92.9) 98.48 (91.9-99.92) 56.39 0.15 pNGAL 4.89(ng/mL) 0.75*** (0.66-0.84) 82.98 (69.86-91.11) 60 (47.86-71.03) 2.07 0.28 pKIM-1 4.14
(ng/mL) 0.88*** (0.81-0.95) 79.59 (66.36-88.52) 86.57 (76.4-92.77) 5.93 0.24

As shown in Figure 5, since sCr and SDMA are used as the standard for the IRIS stage, it was confirmed that the accuracy of SDMA and sCr is higher than that of other biomarkers.

Experimental Example 3. Optimal model for diagnosis of stage 1 kidney disease

Experimental Example 3.1. Derivation of kidney disease stage 1 diagnosis model - Differentiation between normal and risk groups vs. IRIS stages 1-4

We sought to derive the optimal index for differentiation of stage 1 kidney disease groups (normal group and risk group vs. IRIS stages 1-4). Specifically, risk factors related to kidney disease (including chemical test results or clinical indicators) are set as multiple independent variables, and the prevalence (probability of onset) of IRIS stages 1-4 is set as the dependent variable, and then Graphpad Through univariate logistic regression analysis of the Prism program, the degree to which the plurality of independent variables influenced the dependent variable was confirmed, and only variables significant at the significance level of 0.05 were combined (Table 19).

division Crude Logistic regression analysis n OR S.E. z p (95% CI) Pseudo-R2 NGAL 112 1.85 0.6343 3.766 0.0002 1.396~2.66 0.3166 KIM-1 116 1.902 0.7839 2.846 0.0044 1.297~3.145 0.1071 SDMA 115 1.407 1.065 3.712 0.0002 1.201~1.724 0.2534 sCr 117 3.907 0.5456 2.378 0.0174 1.53~14.45 0.06252 Age 117 1.299 0.6021 4.087 <0.0001 1.154~1.487 0.1815 BW 116 0.7859 0.7002 1.719 0.0856 0.5937~1.034 0.0235 BP 105 1.014 2.147 0.879 0.3794 0.9832~1.047 0.00673 BUN 117 1.073 0.5053 2.8 0.0051 1.028~1.135 0.0911 BCR 117 1.03 0.5015 1.421 0.1553 0.9938~1.077 0.01569 Globulin 103 2.4 1.666 1.898 0.0577 1.021~6.328 0.03462 Amylase 45 1.006 1.619 2.118 0.0342 1.002~1.013 0.1832 CRP 57 6.987 0.5504 1.867 0.0619 1.489~86.59 0.1697 Phosphorus In organic 68 3.674 1.501 3.014 0.0026 1.738~9.623 0.2339 Crude: Univariate logistic

The functional relationship between the combined independent and dependent variables was modeled through multiple logistic regression analysis in the Graphpad Prism program, and the calculation formula was deduced. Here, logistic regression is a binary algorithm for modeling the relationship between data on risk factors and the likelihood of developing kidney disease.

Through the above modeling, SNK, SNKC, SNKS, SNKA, SNKP, and SNKCS, which are the optimal models (functions) with the highest pseudo R2 (explanatory power, strength of relationship between dependent and independent variables) were derived (Tables 20 to 20) 25). 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, and SNKA is a function using pNGAL, pKIM-1. 1 and age, SNKP is a function using pNGAL, pKIM-1, and Phosphorus Inorganic, and SNKCS is a function using pNGAL, pKIM-1, sCr, and SDMA.

Model 7: SNK division Crude Coef. S.E. z p (95% CI) Pseudo-R2 NGAL (ng/mL) 0.5541 0.165 3.357 0.0008 0.2690 to 0.9201 0.3213 KIM-1 (ng/mL) 0.3766 0.2574 1.463 0.1435 -0.05742 to 0.9412 Constant -2.614 1.046 2.5 0.0124 -4.866 to -0.7600 n=111, LR chi2(2)=36.40, p<.0001

In the top item of the table above, Coef. is the regression coefficient (estimated coefficient) indicating the size of the influence of the independent variable on the dependent variable, S.E. is the standard error, and 95% CI (confidence interval) is the estimated coefficient. The lower and upper limits of the 95% confidence interval, z is the estimated coefficient divided by SE, which is the t distribution statistic, p is the test statistic for the degree to which the null hypothesis can be rejected, and Pesudo R2 is the dependent variable explained by the independent variables. It means the degree to which it is possible, that is, the explanatory power (predictive power).

The estimated function equation for Model 7 (SNK) through logistic regression analysis is as follows.

[Calculation Equation 8]

SNK(y) = 0.5541 × pNGAL (ng/mL) + 0.3766 × pKIM-1 (ng/mL) - 2.614

Model 8: SNKC division Crude Coef. S.E. z p (95% CI) Pseudo-R2 NGAL (ng/mL) 0.5522 0.1657 3.333 0.0009 0.2659 to 0.9198 0.3244 KIM-1 (ng/mL) 0.3146 0.2764 1.138 0.255 -0.1517 to 0.9128 Creatinine 0.4417 0.7777 0.5679 0.5701 -0.9532 to 2.160 Constant -2.792 1.118 2.497 0.0125 -5.265 to -0.8363 n=111, LR chi2(3)=36.74, p<.0001

The estimated function equation for Model 8 (SNKC) through logistic regression is as follows.

[Calculation Equation 9]

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

Model 9: SNKS division Crude Coef. S.E. z p (95% CI) Pseudo-R2 NGAL (ng/mL) 0.442 0.1601 2.761 0.0058 0.1677 to 0.7995 0.4048 KIM-1 (ng/mL) 0.001992 0.3291 0.006052 0.9952 -0.6138 to 0.6873 SDMA 0.2562 0.09982 2.567 0.0103 0.08261 to 0.4765 Constant -4.079 1.365 2.988 0.0028 -7.127 to -1.722 n=111, LR chi2(3)=45.85, p<.0001

The estimated function equation for model 9 (SNKS) through logistic regression analysis is as follows.

[Calculation Equation 10]

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

Model 10: SNKA division Crude Coef. S.E. z p (95% CI) Pseudo-R2 NGAL (ng/mL) 0.447 0.1699 2.632 0.0085 0.1621 to 0.8222 0.3807 KIM-1 (ng/mL) 0.2079 0.2395 0.8679 0.3854 -0.1550 to 0.7786 Age 0.2108 0.08657 2.436 0.0149 0.04981 to 0.3949 Constant -3.55 1.136 3.125 0.0018 -6.092 to -1.557 n=111, LR chi2(3)=43.12, p<.0001

The estimated function equation through logistic regression analysis of Model 10 (SNKA) is as follows.

[Calculation Equation 11]

SNKA(y) = 0.447 × pNGAL (ng/mL) + 0.2079 × pKIM-1 (ng/mL) + 0.2108 × Age (year) - 3.55

Model 11: SNKP division Crude Coef. S.E. z p (95% CI) Pseudo-R2 NGAL (ng/mL) 0.4599 0.2185 2.104 0.0353 0.09859 to 0.9728 0.4172 KIM-1 (ng/mL) 0.3363 0.3761 0.8942 0.3712 -0.3390 to 1.153 Phosphorus Inorganic 1.004 0.4893 2.052 0.0402 0.1271 to 2.095 Constant -5.678 2.263 2.509 0.0121 -10.93 to -1.792 n=67, LR chi2(3)=2628, p<.0001

The estimated function equation for Model 11 (SNKP) through logistic regression analysis is as follows.

[Calculation Equation 12]

SNKP(y) = 0.4599 × pNGAL (ng/mL) + 0.3363 × pKIM-1 (ng/mL) + 1.004 × Phosphorus Inorganic (mg/dl) - 5.678

Model 12: SNKCS division Crude Coef. S.E. z p (95% CI) Pseudo-R2 NGAL (ng/mL) 0.4406 0.1608 2.74 0.0062 0.1656 to 0.8000 0.4048 KIM-1 (ng/mL) 0.007931 0.3372 0.02352 0.9812 -0.6201 to 0.7089 SDMA 0.258 0.1022 2.525 0.0116 0.08021 to 0.4826 Creatinine -0.07766 0.9396 0.08265 0.9341 -1.865 to 1.861 Constant -4.048 1.41 2.87 0.0041 -7.246 to -1.644 n=111, LR chi2(4)=45.86, p<.0001

The estimated function equation through logistic regression analysis of Model 12 (SNKCS) is as follows.

[Calculation Equation 13]

SNKCS(y) = 0.4406

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

Afterwards, any one value derived from Equations 8 to 13, which is a linear equation model, can be converted to converge to the values of 1 and 0 using an exponential function. At this time, the prediction formula using the exponential function to classify kidney disease stage 1 (IRIS stages 1 to 4, indicating a value of 1) and normal people and kidney disease risk groups (representing a value of 0) is as shown in Equation 14 below. In Calculation Equation 14 below, the p value is distributed as a percentage from 0 to 1. If it is less than 0.5, there is no kidney disease, and if it is more than 0.5, it is judged to be stage 1 kidney disease.

[Calculation Equation 14]

p = exp(y) / [exp(y) + 1]

In the above equation, y is a value derived from the above calculation equations 1 to 6.

Experimental Example 3.2. Cutoff value range for diagnosis of stage 1 kidney disease

To evaluate the accuracy of the optimal model for diagnosing stage 1 kidney disease derived in Experimental Example 3.1, the ROC (receiver operating characteristic) curve was analyzed using the GraphPad Prism program, and NGAL, KIM-1, and 3- The cutoff values for kidney disease stage 1 diagnosis (normal group, risk group vs IRIS stages 1 to 4) of the SNK, SNKC, SNKS, SNKA, SNKP and SNKCS models derived in 1 are shown in Tables 26 to 33 below.

pNGAL cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR+ > 1.932 100 95.82% to 100.0% 41.67 24.47% to 61.17% 1.714 > 4.192 76.14 66.26% to 83.83% 87.5 69.00% to 95.66% 6.091 >9.722 34.09 25.04% to 44.47% 100 86.20% to 100.0% -

As shown in Table 26 above, the cutoff value of pNGAL concentration for differentiating stage 1 kidney disease ranges from 1.932 ng/mL (sensitivity 100%, specificity 41.67%) to 9.722 ng/mL (sensitivity 34.09%, specificity 100 %).

pKIM-1 cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR+ > 1.705 100 95.99% to 100.0% 4.167 0.2137% to 20.24% 1.043 >3.701 57.61 47.41% to 67.20% 79.17 59.53% to 90.76% 2.765 > 5.138 33.7 24.86% to 43.83% 100 86.20% to 100.0% -

As shown in Table 27 above, the cutoff value of pKIM-1 concentration for differentiating stage 1 kidney disease ranges from 1.705 ng/mL (sensitivity 100%, specificity 4.167%) to 5.138 ng/mL (sensitivity 33.7%, specificity It may also be 100.0%).

Model 7: SNK cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR > -0.7360 100 95.82% to 100.0% 8.696 1.545% to 26.80% 1.095 > 0.9713 80.68 71.22% to 87.57% 86.96 67.87% to 95.46% 6.186 > 4.229 36.36 27.08% to 46.79% 100 85.69% to 100.0% -

As shown in Table 28, the cutoff value of SNK for distinguishing stage 1 kidney disease may range from -0.7360 (sensitivity 100%, specificity 8.696%) to 4.229 (sensitivity 36.36%, specificity 100.0%).

Model 8: SNKC cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR > -0.6980 100 95.82% to 100.0% 8.696 1.545% to 26.80% 1.095 >0.8345 81.82 72.49% to 88.49% 86.96 67.87% to 95.46% 6.273 > 4.256 35.23 26.06% to 45.63% 100 85.69% to 100.0% -

As shown in Table 29 above, the cutoff value of SNKC for differentiating stage 1 kidney disease ranges from -0.6980 (sensitivity 100%, specificity 8.696%) to 4.256 (sensitivity 35.23%, specificity 100.0%).

Model 9: SNKS cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR > -1.246 100 95.82% to 100.0% 21.74 9.664% to 41.90% 1.278 > 0.4947 88.64 80.33% to 93.71% 82.61 62.86% to 93.02% 5.097 > 3.307 53.41 43.06% to 63.47% 100 85.69% to 100.0% -

As shown in Table 30, the cutoff value of SNKS for distinguishing stage 1 kidney disease may range from -1.246 (sensitivity 100%, specificity 21.74%) to 3.307 (sensitivity 53.41%, specificity 100.0%).

Model 10: SNKA cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR > -1.667 100 95.82% to 100.0% 13.04 4.538% to 32.13% 1.15 >0.9648 81.82 72.49% to 88.49% 82.61 62.86% to 93.02% 4.705 > 4.106 39.77 30.18% to 50.22% 100 85.69% to 100.0% -

As shown in Table 31, the cutoff value of SNKA for distinguishing stage 1 kidney disease may range from -1.667 (sensitivity 100%, specificity 13.04%) to 4.106 (sensitivity 39.77%, specificity 100.0%).

Model 11: SNKP cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR > -0.7626 100 93.47% to 100.0% 25 8.894% to 53.23% 1.333 > 0.7632 85.45 73.84% to 92.44% 91.67 64.61% to 99.57% 10.25 >3.650 56.36 43.27% to 68.63% 100 75.75% to 100.0% -

As shown in Table 30, the cutoff value of SNKP for distinguishing stage 1 kidney disease may range from -0.7626 (sensitivity 100%, specificity 25%) to 3.650 (sensitivity 56.36%, specificity 100.0%).

Model 12: SNKCS cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR > -1.258 100 95.82% to 100.0% 21.74 9.664% to 41.90% 1.278 >0.4832 88.64 80.33% to 93.71% 82.61 62.86% to 93.02% 5.097 >3.298 53.41 43.06% to 63.47% 100 85.69% to 100.0% -

As shown in Table 33, the cutoff value of SNKCS for distinguishing stage 1 kidney disease may range from -1.258 (sensitivity 100%, specificity 21.74%) to 3.298 (sensitivity 53.41%, specificity 100.0%).

Experimental Example 3.3. Evaluation of the diagnostic accuracy of stage 1 kidney disease of the derived optimal model

The ROC (receiver operating characteristic) curve was analyzed using the GraphPad Prism program, and the diagnostic accuracy was found to be the highest from the cutoff value ranges of NGAL, KIM-1, SNK, SNKC, SNKS, SNKA, SNKP, and SNKCS derived in Experimental Example 3.2. A high optimal cutoff value was derived (Figure 4). In addition, the diagnostic sensitivity, specificity, accuracy, and range of reference standards for distinguishing chronic kidney disease stage 1 (IRIS stages 1-4) from normal control groups and risk groups are shown in Table 34 below.

division Cut-off
(unit) AUC
(95% CI) Sensitivity %
(95% CI) Specificity %
(95% CI) positive likelihood ratio Negative likelihood ratio sCr 0.95
(mg/dL) 0.65* (0.55-0.75) 53.26 (43.14-63.12) 76 (56.57-88.5) 2.22 0.62 SDMA 13.50(㎍/dL) 0.84*** (0.77-0.92) 70.65 (60.67-78.98) 91.3 (73.2-98.45) 8.12 0.32 pNGAL 4.19(ng/mL) 0.88*** (0.8-0.95) 76.14 (66.26-83.83) 87.5 (69-95.66) 6.09 0.27 pKIM-1 3.70(ng/mL) 0.72*** (0.62-0.82) 57.61 (47.41-67.2) 79.17 (59.53-90.76) 2.77 0.54 SNK > 0.8887 0.87*** (0.8-0.95) 81.82 (72.49-88.49) 86.96 (67.87-95.46) 6.27 0.21 SNKS > 0.4947 0.91*** (0.85-0.97) 88.64 (80.33-93.71) 82.61 (62.86-93.02) 5.10 0.14 SNKC >0.8345 0.87*** (0.8-0.95) 81.82 (72.49-88.49) 86.96 (67.87-95.46) 6.27 0.21 SNKA >0.9648 0.89*** (0.83-0.96) 81.82 (72.49-88.49) 82.61 (62.86-93.02) 4.71 0.22 SNKP > 0.7632 0.9*** (0.82-0.99) 85.45 (73.84-92.44) 91.67 (64.61-99.57) 10.26 0.16 SNKCS >0.4832 0.91*** (0.85-0.97) 88.64 (80.33-93.71) 82.61 (62.86-93.02) 5.10 0.14

Looking at Figure 4 and Table 34, the diagnostic accuracy (AUC, area under curve) for IRIS stages 1-4 is high in the following order: sCr < pKIM-1 < SDMA < SNK = SNKC < pNGAL < SNKA < SNKP < SNKS = SNKCS appear.

Specifically, the AUC (area under the curve) of SNK of the present invention was 0.87, and the cutoff value below that value indicating that the kidney disease was not stage 1 was 0.8887. At the above cutoff value, diagnostic sensitivity was 81.82% and diagnostic specificity was 86.96%. LR+ (positive likelihood ratio) is 6.27, and LR- (negative likelihood ratio) is 0.21.

In addition, the AUC (area under the curve) of the SNKS of the present invention was 0.91, and the cutoff value below that value indicating that the kidney disease was not stage 1 was 0.4947. At the above cutoff value, diagnostic sensitivity was 88.64% and diagnostic specificity was 82.61%. LR+ (positive likelihood ratio) is 5.10, and LR- (negative likelihood ratio) is 0.14.

In addition, the AUC (area under the curve) of the SNKCS of the present invention was 0.91, and the cutoff value below that value indicating that the kidney disease was not stage 1 was 0.4832. At the above cutoff value, diagnostic sensitivity was 88.64% and diagnostic specificity was 82.61%. LR+ (positive likelihood ratio) is 5.10, and LR- (negative likelihood ratio) is 0.14.

On the other hand, the AUC of pNGAL was 0.88, and the cutoff value was 4.19 ng/mL. At the above cutoff value, diagnostic sensitivity was 76.14% and diagnostic specificity was 87.5%. LR+ (positive likelihood ratio) is 6.09, and LR- (negative likelihood ratio) is 0.27.

Additionally, the AUC of pKIM-1 was 0.72, and the cutoff value was 3.70 ng/mL. At the above cutoff value, diagnostic sensitivity was 57.61% and diagnostic specificity was 79.17%. LR+ (positive likelihood ratio) is 2.77, and LR- (negative likelihood ratio) is 0.54.

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

Experimental Example 4. Optimal model for diagnosis of stage 2 kidney disease

Experimental Example 4.1. Derivation of diagnostic model for kidney disease group - Differentiation between normal group, risk group, and IRIS stage 1 vs. IRIS stage 2-4

We attempted to derive the optimal index for differentiation of kidney disease groups (normal group and IRIS stage 1 vs. IRIS stage 2-4). Specifically, risk factors related to kidney disease (including chemical test results or clinical indicators) are set as multiple independent variables, and the prevalence (probability of onset) of kidney disease (IRIS stages 2-4) is set as the dependent variable. Afterwards, the extent to which the plurality of independent variables influence the dependent variable was confirmed through simple logistic regression analysis using the Graphpad Prism program, and only variables significant at the significance level of 0.05 were combined ( Table 35). As a result of crude analysis, the dependent variable showed a correlation with the independent variables NGAL, KIM-1, SDMA, SDMA, sCr, Age, Amylase, Phosphorus Inorganic, and BUN, so the optimal combination was created using the above nine variables.

division Crude Logistic regression analysis n OR S.E. z p (95% CI) Pseudo-R2 NGAL 112 1.111 0.325 3.162 0.0016 1.048~1.195 0.1389 KIM-1 116 3.526 1.024 5.087 <0.0001 2.283~6.058 0.4864 SDMA 115 1.768 1.893 4.709 <0.0001 1.447~2.347 0.7042 sCr 117 101.1 0.9679 4.872 <0.0001 19.65~832.9 0.5204 Age 117 1.246 0.6729 3.877 0.0001 1.123~1.405 0.164 BW 116 One 0.5547 1.024E-05 >0.9999 0.7899~1.263 9.34E-13 BP 105 1.005 1.703 0.3873 0.6986 0.9805~1.03 0.001423 BUN 117 1.127 0.6066 5.027 <0.0001 1.081~1.188 0.4261 BCR 117 1.01 0.3652 0.8103 0.4178 0.9856~1.037 0.005945 Globulin 103 2.067 1.375 2.012 0.0442 1.042~4.346 0.04186 Amylase 45 1.003 0.9546 2.63 0.0085 1.001~1.006 0.306 CRP 57 1.786 0.3827 2.196 0.0281 1.224~3.495 0.2335 Phosphorus In organic 68 2.035 1.043 3.018 0.0025 1.37~3.437 0.2272 Crude: Univariate logistic

The functional relationship between the combined independent and dependent variables was modeled through multiple logistic regression analysis in the Graphpad Prism program, and the calculation formula was deduced. Here, logistic regression is a binary algorithm for modeling the relationship between data on risk factors and the likelihood of developing kidney disease.

Through the above modeling, TNK, TNKC, TNKS, TNKA, TNKP, TNKAm, TNKCS, TNKB, and TNKCSB, which are the optimal models (functions) with the highest pseudo R2 (explanatory power, strength of relationship between dependent and independent variables), were derived. (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, and TNKA is a function using pNGAL, pKIM-1. 1 and age, TNKP is a function using pNGAL, pKIM-1, and Phosphorus Inorganic, TNKAm is a function using pNGAL, pKIM-1, and Amylase, and TNKCS is a function using pNGAL, pKIM-1, sCr, and SDMA. It is a function, TNKB is a function using pNGAL, pKIM-1, and BUN, and TNKCSB is a function using pNGAL, pKIM-1, sCr, SDMA, and BUN.

Model 13: TNK division Crude Coef. S.E. z p (95% CI) Pseudo-R2 NGAL (ng/mL) 0.03031 0.04578 0.6621 0.5079 -0.06524 to 0.1148 0.4078 KIM-1 (ng/mL) 1.187 0.2489 4.769 <0.0001 0.7496 to 1.731 Constant -5.538 1.037 5.338 <0.0001 -7.805 to -3.708 n=111, LR chi2(2)=61.68, p<.0001

In the top item of the table above, Coef. is the regression coefficient (estimated coefficient) indicating the size of the influence of the independent variable on the dependent variable, S.E. is the standard error, and 95% CI (confidence interval) is the estimated coefficient. The lower and upper limits of the 95% confidence interval, z is the estimated coefficient divided by SE, which is the t distribution statistic, p is the test statistic for the extent to which the null hypothesis can be rejected, and Pesudo R2 is the dependent variable explained by the independent variables. It means the degree to which it is possible, that is, the explanatory power (predictive power).)

The estimated function equation for Model 13 (TNK) through logistic regression analysis is as follows.

[Calculation Equation 15]

TNK(y) = 0.03031 × pNGAL (ng/mL) + 1.187 × pKIM-1 (ng/mL) - 5.538

Model 14: TNKC division Crude Coef. S.E. z p (95% CI) Pseudo-R2 NGAL (ng/mL) -0.01621 0.06481 0.2501 0.8025 -0.1380 to 0.1067 0.5789 KIM-1 (ng/mL) 0.9737 0.2869 3.393 0.0007 0.4760 to 1.608 Creatinine 3.773 1.03 3.662 0.0003 2.014 to 6.096 Constant -8.309 1.596 5.207 <0.0001 -11.97 to -5.607 n=111, LR chi2(3)=87.56, p<.0001

The estimated function equation for Model 14 (TNKC) through logistic regression analysis is as follows.

[Calculation Equation 16]

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

Model 15: TNKS division Crude Coef. S.E. z p (95% CI) Pseudo-R2 NGAL (ng/mL) -0.01627 0.0648 0.2511 0.8017 -0.1550 to 0.09814 0.6532 KIM-1 (ng/mL) 0.631 0.3246 1.944 0.0519 0.04888 to 1.327 SDMA 0.4914 0.1284 3.827 0.0001 0.2780 to 0.7913 Constant -10.55 2.253 4.685 <0.0001 -15.91 to -6.873 n=111, LR chi2(3)=98.80, p<.0001

The estimated function equation for Model 15 (TNKS) through logistic regression analysis is as follows.

[Calculation Equation 17]

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

Model 16: TNKA division Crude Coef. S.E. z p (95% CI) Pseudo-R2 NGAL (ng/mL) 0.02812 0.04892 0.5749 0.5654 -0.07033 to 0.1187 0.4538 KIM-1 (ng/mL) 1.078 0.2612 4.126 <0.0001 0.6224 to 1.651 Age 0.2033 0.08362 2.431 0.015 0.04983 to 0.3815 Constant -7.344 1.432 5.127 <0.0001 -10.54 to -4.864 n=111, LR chi2(3)=68.65, p<.0001

The estimated function equation for model 16 (TNKA) through logistic regression is as follows.

[Calculation Equation 18]

TNKA(y) = 0.02812 × pNGAL (ng/mL) + 1.078 × pKIM-1 (ng/mL) + 0.2033 × Age (year) - 7.344

Model 17: TNKP division Crude Coef. S.E. z p (95% CI) Pseudo-R2 NGAL (ng/mL) -0.1125 0.09665 1.164 0.2446 -0.3175 to 0.05715 0.5221 KIM-1 (ng/mL) 1.42 0.3875 3.664 0.0002 0.7649 to 2.302 Phosphorus Inorganic 0.7162 0.419 1.709 0.0874 -0.03583 to 1.642 Constant -8.453 2.342 3.609 0.0003 -14.03 to -4.623 n=67, LR chi2(3)=48.42, p<.0001

The estimated function equation for model 17 (TNKP) through logistic regression analysis is as follows.

[Calculation Equation 19]

TNKP(y) = -0.1125 × pNGAL (ng/mL) + 1.42 × pKIM-1 (ng/mL) + 0.7162 × Phosphorus Inorganic (mg/dl) - 8.453

Model 18: TNKAM division Crude Coef. S.E. z p (95% CI) Pseudo-R2 NGAL (ng/mL) -0.05275 0.08639 0.6105 0.5415 -0.2389 to 0.08974 0.4107 KIM-1 (ng/mL) 1.001 0.4017 2.491 0.0127 0.3142 to 1.914 Amylase 0.001492 0.001407 1.061 0.2889 -0.001088 to 0.004564 Constant -5.468 1.622 3.371 0.0007 -9.301 to -2.798 n=45, LR chi2(3)=24.51, p<.0001

The estimated function equation for model 18 (TNKAm) through logistic regression analysis is as follows.

[Calculation Equation 20]

TNKAm(y) = -0.05275 × pNGAL (ng/mL) + 1.001 × pKIM-1 (ng/mL) + 0.001492 × Amylase (U/L) - 5.468

Model 19: TNKCS division Crude Coef. S.E. z p (95% CI) Pseudo-R2 NGAL (ng/mL) -0.05199 0.1035 0.5023 0.6155 -0.2740 to 0.1515 0.8344 KIM-1 (ng/mL) 0.2173 0.3619 0.6005 0.5482 -0.4621 to 1.074 SDMA 0.9584 0.3288 2.915 0.0036 0.4763 to 1.828 Creatinine 9.823 3.506 2.802 0.0051 4.663 to 19.17 Constant -26.57 9.008 2.95 0.0032 -51.09 to -13.96 n=111, LR chi2(4)=126.2, p<.0001

The estimated function equation for model 19 (TNKCS) through logistic regression analysis is as follows.

[Calculation 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

Model 20: TNKB division Crude Coef. S.E. z p (95% CI) Pseudo-R2 NGAL (ng/mL) -0.0266 0.05992 0.4439 0.6571 -0.1428 to 0.08654 0.5609 KIM-1 (ng/mL) One 0.2599 3.848 0.0001 0.5480 to 1.577 BUN 0.11 0.03181 3.459 0.0005 0.05685 to 0.1829 Constant -7.155 1.287 5.559 <0.0001 -10.05 to -4.927 n=111, LR chi2(3)=84.85, p<.0001

The estimated function equation for Model 20 (TNKCB) through logistic regression analysis is as follows.

[Calculation Equation 22]

TNKCB(y) = -0.0266 × pNGAL (ng/mL) + 1 × pKIM-1 (ng/mL) + 0.11 × BUN (mg/dl) - 7.155

Model 21: TNKCSB division Crude Coef. S.E. z p (95% CI) Pseudo-R2 NGAL (ng/mL) -0.08838 0.1169 0.7561 0.4496 -0.3505 to 0.1301 0.8433 KIM-1 (ng/mL) 0.2262 0.3563 0.6349 0.5255 -0.4688 to 1.054 SDMA 0.961 0.3357 2.863 0.0042 0.4645 to 1.838 Creatinine 8.739 3.503 2.495 0.0126 3.468 to 18.02 BUN 0.04618 0.0436 1.059 0.2896 -0.02961 to 0.1558 Constant -26.28 8.895 2.955 0.0031 -50.50 to -13.79 n=111, LR chi2(5)=127.6, p<.0001

The estimated function equation for Model 21 (TNKCSB) through logistic regression is as follows.

[Calculation 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

Looking at Tables 36 to 44 above, Model 21 (TNKCSB), which combines Creatinine, SDMA, and BUN along with pNGAL and pKIM-1, showed the highest explanatory power (Pseudo R2) of 84.33%, showing that it was the optimal model (function). .

Afterwards, any one value derived from equations 15 to 23, which is a linear equation model, can be converted to converge to the values of 1 and 0 using an exponential function. At this time, the prediction formula for classifying into the kidney disease group (IRIS stages 2 to 4, indicating a value of 1) and the normal group (normal, risk group and kidney disease stage 1, indicating a value of 0) using an exponential function is the following calculation formula. Same as 24. In Calculation Formula 24 below, the p value is distributed as a percentage from 0 to 1. If it is less than 0.5, there is no kidney disease, and if it is more than 0.5, it is determined that the kidney disease group is present.

[Calculation Formula 24]

p = exp(y) / [exp(y) + 1]

In the above equation, y is a value derived from the above calculation equations 15 to 23.

Experimental Example 4.2. Cutoff value range for diagnosis of kidney disease stage 2

To evaluate the accuracy of the optimal model for diagnosing the kidney disease group derived in Experimental Example 4.1, the ROC (receiver operating characteristic) curve was analyzed using the GraphPad Prism program, and derived from NGAL and KIM-1 and 3-1 above. The cutoff values for kidney disease diagnosis (normal group, risk group, IRIS stage 1 vs IRIS stage 2 to 4) of one TNK, TNKC, TNKS, TNKA, TNKP, TNKAm, TNKCS, TNKB and TNKCSB model are shown in Tables 45 to 45 below. Shown in 55.

pNGAL cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR+ > 2.086 100 92.44% to 100.0% 21.54 13.29% to 32.97% 1.275 > 4.888 82.98 69.86% to 91.11% 60 47.86% to 71.03% 2.074 > 40.64 6.383 2.195% to 17.16% 100 94.42% to 100.0% -

As shown in Table 45 above, the cutoff value of pNGAL concentration for differentiating the kidney disease group ranges from 2.086 ng/mL (sensitivity 100%, specificity 21.54%) to 40.64 ng/mL (sensitivity 6.383%, specificity 100%) ) can be.

pKIM-1 cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR+ > 2.360 100 92.73% to 100.0% 16.42 9.423% to 27.06% 1.196 > 4.137 79.59 66.36% to 88.52% 86.57 76.40% to 92.77% 5.925 > 5.323 55.1 41.32% to 68.15% 100 94.58% to 100.0% -

As shown in Table 46 above, the cutoff value of pKIM-1 concentration for differentiating the kidney disease group ranges from 2.360 ng/mL (sensitivity 100%, specificity 16.42%) to 5.323 ng/mL (sensitivity 55.1%, specificity It may be 100.0%).

Model 13: TNK cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR > -5.169 100 92.44% to 100.0% 12.5 6.472% to 22.77% 1.143 > -4.833 78.72 65.10% to 88.01% 85.94 75.38% to 92.42% 5.598 > -4.081 23.4 13.60% to 37.22% 100 94.34% to 100.0% -

As shown in Table 47 above, the cutoff value of TNK for distinguishing kidney disease groups may range from -5.169 (sensitivity 100%, specificity 12.5%) to -4.081 (sensitivity 23.4%, specificity 100.0%).

Model 14: TNKC cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR > -3.718 100 92.44% to 100.0% 15.63 8.715% to 26.43% 1.185 > -0.1381 85.11 72.31% to 92.59% 92.19 82.98% to 96.62% 10.89 > 1.308 70.21 56.02% to 81.35% 100 94.34% to 100.0% -

As shown in Table 48 above, the cutoff value of TNKC for differentiating kidney disease groups may range from -3.718 (sensitivity 100%, specificity 15.63%) to 1.308 (sensitivity 70.21%, specificity 100.0%).

Model 15: TNKS cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR > -3.567 100 92.44% to 100.0% 39.06 28.06% to 51.31% 1.641 > -0.1465 87.23 74.83% to 94.02% 98.44 91.67% to 99.92% 55.83 > -0.08390 87.23 74.83% to 94.02% 100 94.34% to 100.0% -

As shown in Table 49 above, the cutoff value of TNKS for differentiating kidney disease groups may range from -3.567 (sensitivity 100%, specificity 39.06%) to -0.08390 (sensitivity 87.23%, specificity 100.0%).

Model 16: TNKA cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR > -4.284 100 92.44% to 100.0% 3.125 0.5553% to 10.70% 1.032 > -0.2974 80.85 67.46% to 89.58% 87.5 77.23% to 93.53% 6.468 > 1.647 51.06 37.24% to 64.72% 100 94.34% to 100.0% -

As shown in Table 50, the cutoff value of TNKA for distinguishing kidney disease groups may range from -4.284 (sensitivity 100%, specificity 3.125%) to 1.647 (sensitivity 51.06%, specificity 100.0%).

Model 17: TNKP cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR > -1.775 100 89.28% to 100.0% 54.29 38.19% to 69.53% 2.188 > -0.5240 87.5 71.93% to 95.03% 85.71 70.62% to 93.74% 6.125 > 1.308 70.21 56.02% to 81.35% 100 94.34% to 100.0% -

As shown in Table 51, the cutoff value of TNKP for distinguishing kidney disease groups may range from -1.775 (sensitivity 100%, specificity 54.29%) to 1.308 (sensitivity 70.21%, specificity 100.0%).

Model 18: TNKAm cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR > -2.121 100 81.57% to 100.0% 50 32.63% to 67.37% 2 > -0.5133 82.35 58.97% to 93.81% 85.71 68.51% to 94.30% 5.765 > 1.374 47.06 26.17% to 69.04% 100 87.94% to 100.0% -

As shown in Table 52, the cutoff value of TNKAm for differentiating kidney disease groups may range from -2.121 (sensitivity 100%, specificity 50%) to 1.374 (sensitivity 47.06%, specificity 100.0%).

Model 19: TNKCS cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR > -0.7587 100 92.44% to 100.0% 92.19 82.98% to 96.62% 12.8 > -0.6824 97.87 88.89% to 99.89% 92.19 82.98% to 96.62% 12.53 > 1.575 80.85 67.46% to 89.58% 98.44 91.67% to 99.92% 51.74 > 2.234 80.85 67.46% to 89.58% 100 94.34% to 100.0% -

As shown in Table 53 above, the cutoff value of TNKCS for differentiating kidney disease groups may range from -0.7587 (sensitivity 100%, specificity 92.19%) to 2.234 (sensitivity 80.85%, specificity 100.0%).

Model 20: TNKB cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR > -4.175 100 92.44% to 100.0% 4.688 1.278% to 12.90% 1.049 > 0.1259 82.98 69.86% to 91.11% 98.44 91.67% to 99.92% 53.11 > 1.734 70.21 56.02% to 81.35% 100 94.34% to 100.0% -

As shown in Table 54 above, the cutoff value of TNKB for differentiating kidney disease groups may range from -4.175 (sensitivity 100%, specificity 4.688%) to 1.734 (sensitivity 70.21%, specificity 100.0%).

Model 21: TNKCSB cutoff value responsiveness(%) 95% CI Specificity (%) 95% CI LR > -1.657 100 92.44% to 100.0% 90.63 81.02% to 95.63% 10.67 > -0.5159 97.87 88.89% to 99.89% 93.75 85.00% to 97.54% 15.66 > 1.506 85.11 72.31% to 92.59% 100 94.34% to 100.0% -

As shown in Table 55 above, the cutoff value of TNKCSB for differentiating kidney disease groups may range from -1.657 (sensitivity 100%, specificity 90.63%) to 1.506 (sensitivity 85.11%, specificity 100.0%).

Experimental Example 4.3. Evaluation of the diagnostic accuracy of the derived optimal model for stage 2 kidney disease

Analyze the ROC (receiver operating characteristic) curve using the GraphPad Prism program, and cutoff the pNGAL, pKIM-1, indicators TNK, TNKC, TNKS, TNKA, TNKP, TNKAm, TNKCS, TNKB, and TNKCSB derived in 4-2 above. From the value range, the optimal cutoff value with the highest diagnostic accuracy was derived (Figure 5). In addition, the diagnostic sensitivity, specificity, accuracy, and range of reference standards for distinguishing normal control group, risk group, IRIS stage 1 and IRIS stage 2-4 are shown in Table 56 below.

division Cut-off
(unit) AUC
(95% CI) Sensitivity %
(95% CI) Specificity %
(95% CI) positive likelihood ratio Negative likelihood ratio sCr 1.25
(mg/dL) 0.89*** (0.83-0.96) 69.39 (55.47-80.48) 95.59 (87.81-98.8) 15.73 0.32 SDMA 16.50(㎍/dL) 0.95*** (0.9-0.99) 85.71 (73.33-92.9) 98.48 (91.9-99.92) 56.39 0.15 pNGAL 4.89(ng/mL) 0.75*** (0.66-0.84) 82.98 (69.86-91.11) 60 (47.86-71.03) 2.07 0.28 pKIM-1 4.14
(ng/mL) 0.88*** (0.81-0.95) 79.59 (66.36-88.52) 86.57 (76.4-92.77) 5.93 0.24 TNK > -4.833 0.86*** (0.79-0.93) 78.72 (65.1-88.01) 85.94 (75.38-92.42) 5.60 0.25 TNKS > -0.08390 0.95*** (0.91-1) 87.23 (74.83-94.02) 100 (94.34-100) - 0.13 TNKC > -0.1381 0.94*** (0.89-0.99) 85.11 (72.31-92.59) 92.19 (82.98-96.62) 10.90 0.16 TNKA > -0.2974 0.90*** (0.84-0.96) 80.85 (67.46-89.58) 87.5 (77.23-93.53) 6.47 0.22 TNKP > -0.5240 0.92*** (0.86-0.98) 87.5 (71.93-95.03) 85.71 (70.62-93.74) 6.12 0.15 TNKAm > -0.5133 0.89*** (0.8-0.99) 82.35 (58.97-93.81) 85.71 (68.51-94.3) 5.76 0.21 TNKCS > -0.7587 0.99*** (0.98-1) 100 (92.44-100) 92.19 (82.98-96.62) 12.80 0.00 TNKB > 0.1259 0.92*** (0.86-0.98) 82.98 (69.86-91.11) 98.44 (91.67-99.92) 53.19 0.17 TNKCSB > -0.5159 0.99*** (0.98-1) 97.87 (88.89-99.89) 93.75 (85-97.54) 15.66 0.02

Looking at Figure 5 and Table 56, the diagnostic accuracy (AUC, area under curve) for IRIS stages 2-4 is pNGAL < TNK < pKIM-1 < sCr < TNKAm < TNKA < TNKP = TNKB < TNKC < SDMA = TNKS < TNKCS = TNKCSB was highest in that order.

Specifically, the AUC (area under the curve) of TNK of the present invention was 0.86, and the cutoff value below that value for not having kidney disease was -4.833. At the above cutoff value, diagnostic sensitivity was 78.72% and diagnostic specificity was 85.94%. LR+ (positive likelihood ratio) is 5.60, and LR- (negative likelihood ratio) is 0.25.

Additionally, the AUC (area under the curve) of TNKCS of the present invention was 0.99, and the cutoff value below that value indicating no kidney disease was 0.7587. At the above cutoff value, diagnostic sensitivity was 100% and diagnostic specificity was 92.19%. LR+ (Positive Likelihood Ratio) is 12.80, and LR- (Negative Likelihood Ratio) is 0.

Additionally, the AUC (area under the curve) of TNKCSB of the present invention was 0.99, and the cutoff value below that value indicating no kidney disease was -0.5159. At the above cutoff value, diagnostic sensitivity was 97.87% and diagnostic specificity was 93.75%. LR+ (positive likelihood ratio) is 15.66, and LR- (negative likelihood ratio) is 0.02.

On the other hand, the AUC of pNGAL was 0.75, and the cutoff value was 4.14 ng/mL. At the above cutoff value, diagnostic sensitivity was 82.98% and diagnostic specificity was 60%. LR+ (positive likelihood ratio) is 2.07, and LR- (negative likelihood ratio) is 0.28.

Additionally, the AUC of pKIM-1 was 0.88, and the cutoff value was 4.14 ng/mL. At the above cutoff value, diagnostic sensitivity was 78.72% and diagnostic specificity was 85.94%. LR+ (positive likelihood ratio) is 5.93, and LR- (negative likelihood ratio) is 0.24.

The above results mean that TNKS, TNKCS and TNKCSB of the present invention can differentiate kidney disease groups with higher accuracy, sensitivity and specificity than sCr and SDMA. In other words, this means that the indicator of the present invention can diagnose kidney disease at a level equivalent to or higher than that of existing indicators.

From the above results, a kit for diagnosing kidney disease was manufactured. The kit is manufactured as a structure of strips as shown in Figure 6a, comprising a sample pad, conjugate pad, stacking pad, NC membrane, absorbent pad and solid support. Includes (backing card).

As shown in Figure 6b, depending on the combination of the control line and test line (KIM-1 and NGAL), it is possible to determine the presence or absence of chronic kidney disease according to the International Society of Nephrology's chronic kidney disease staging guideline standards. . When determining the presence of a control line as the standard, if KIM-1 and NGAL are not present, the group can be judged as normal, and depending on the presence or absence of KIM-1 and/or NGAL, it can be judged as a kidney disease group. .

Although the present invention has been described in terms of the above-mentioned preferred embodiments, various modifications and variations can be made without departing from the gist and scope of the invention. Furthermore, the appended claims cover such modifications or variations as fall within the subject matter of the present invention.

Claims (17)

A composition for diagnosing kidney disease, comprising an agent capable of measuring the expression level of NGAL (Neutrophil gelatinase-associated lipocalin) protein, KIM-1 (Kidney injury molecule-1) protein, or a combination thereof, or a gene encoding the same. In claim 1,
The composition is capable of distinguishing risk groups and each stage according to the staging guidelines for chronic kidney disease (CKD) of the International Renal Interest Society (IRIS). Composition for diagnosing diseases. In claim 1,
It further comprises an agent capable of measuring the expression level of one or more proteins selected from the group consisting of SDMA, creatinine, phosphorus inorganic, amylase, and BUN, or the gene encoding the same. , Composition for diagnosis of kidney disease. In claim 1,
Agents that can measure the expression level of the protein or the gene encoding the protein include antibodies, ligands, PNA (peptide nucleic acid), aptamers, and nanoparticles that specifically bind to the protein. ), or a composition for diagnosing kidney disease, which is selected from the group consisting of a primer pair, a probe, and an antisense nucleotide that specifically binds to the gene. In claim 1,
A composition for diagnosing kidney disease, wherein the kidney disease is acute kidney injury (AKI) or chronic kidney disease (CKD). In claim 1,
A composition for diagnosing kidney disease, wherein the expression level of the protein or the gene encoding it is measured in a body fluid sample of an individual. A kit for diagnosing kidney disease comprising the composition of claim 1. Measuring the expression level of NGAL (Neutrophil gelatinase-associated lipocalin) protein, KIM-1 (Kidney injury molecule-1) protein, or a combination thereof, or the gene encoding them, in a biological sample obtained from an individual steps; and
A method of providing information for the diagnosis of kidney disease, comprising: comparing the measured expression level with the expression level of a protein or a combination thereof, or a gene encoding the same, in a normal group. In claim 8,
The method further includes the step of measuring the expression level of one or more proteins selected from the group consisting of SDMA, creatinine, phosphorus inorganic, amylase, and BUN, or the gene encoding the same. In,method. In claim 8,
The method sets the expression levels of the measured proteins or genes as independent variables, and determines the staging guidelines for chronic kidney disease (CKD) of the International Renal Interest Society (IRIS). Setting as a dependent variable the occurrence of a risk group for kidney disease (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);
Inferring a model equation by modeling the relationship between the independent variable and the dependent variable through logistic regression analysis; and
When the value derived from the model equation is greater than or equal to a predetermined cutoff value, determining the individual as a kidney disease risk group, IRIS stage 1, or kidney disease group (IRIS stages 2 to 4); A method comprising: In claim 10,
The method wherein the model equation is any one selected from calculation equations 1 to 6 below:
[Calculation Formula 1]
RNK(y) = 1.648 × pNGAL (ng/mL) + 3.287 × pKIM-1 (ng/mL) - 12.2
[Calculation Formula 2]
RNKC(y) = 1.71 × pNGAL (ng/mL) + 3.306 × pKIM-1 (ng/mL) + 0.9716 × sCr (mg/dl) - 13.22
[Calculation Formula 3]
RNKS(y) = 1.928 × pNGAL (ng/mL) + 3.948 × pKIM-1 (ng/mL) + 0.4207 × SDMA (μg/dl) - 19.09
[Calculation Equation 4]
RNKA = 1.398 × pNGAL (ng/mL) + 3.989 × pKIM-1 (ng/mL) + 0.5979 × age (year) - 17.02
[Calculation Equation 5]
RNKR = 2.832 × pNGAL (ng/mL) + 4.726 × pKIM-1 (ng/mL) + 8.756 × CRP (mg/dl) - 21.36
[Calculation Equation 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. In claim 11,
The method wherein the cutoff value of the model equation is any number selected from -4.27 to 2.50. In claim 10,
The method wherein the model equation is any one selected from equations 8 to 13 below:
[Calculation Equation 8]
SNK(y) = 0.5541 × pNGAL (ng/mL) + 0.3766 × pKIM-1 (ng/mL) - 2.614
[Calculation Equation 9]
SNKC(y) = 0.5522 × pNGAL (ng/mL) + 0.3146 × pKIM-1 (ng/mL) + 0.4417 × sCr (mg/dl) - 2.792
[Calculation Equation 10]
SNKS(y) = 0.442 × pNGAL (ng/mL) + 0.001992 × pKIM-1 (ng/mL) + 0.2562 × SDMA (μg/dl) - 4.079
[Calculation Equation 11]
SNKA(y) = 0.447 × pNGAL (ng/mL) + 0.2079 × pKIM-1 (ng/mL) + 0.2108 × Age (year) - 3.55
[Calculation 12]
SNKP(y) = 0.4599 × pNGAL (ng/mL) + 0.3363 × pKIM-1 (ng/mL) + 1.004 × Phosphorus Inorganic (mg/dl) - 5.678
[Calculation Equation 13]
SNKCS(y) = 0.4406 In claim 13,
The method wherein the cutoff value of the model equation is any number selected from -1.67 to 4.26. In claim 10,
The method wherein the model equation is any one selected from equations 15 to 23 below:
[Calculation Equation 15]
TNK(y) = 0.03031 × pNGAL (ng/mL) + 1.187 × pKIM-1 (ng/mL) - 5.538
[Calculation Equation 16]
TNKC(y) = -0.01621 × pNGAL (ng/mL) + 0.9737 × pKIM-1 (ng/mL) + 3.773 × sCr (mg/dl) - 8.309
[Calculation 17]
TNKS(y) = -0.01627 × pNGAL (ng/mL) + 0.631 × pKIM-1 (ng/mL) + 0.4914 × sCr (mg/dl) - 10.55
[Calculation Equation 18]
TNKA(y) = 0.02812 × pNGAL (ng/mL) + 1.078 × pKIM-1 (ng/mL) + 0.2033 × Age (year) - 7.344
[Calculation Equation 19]
TNKP(y) = -0.1125 × pNGAL (ng/mL) + 1.42 × pKIM-1 (ng/mL) + 0.7162 × Phosphorus Inorganic (mg/dl) - 8.453
[Calculation Equation 20]
TNKAm(y) = -0.05275 × pNGAL (ng/mL) + 1.001 × pKIM-1 (ng/mL) + 0.001492 × Amylase (U/L) - 5.468
[Calculation 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
[Calculation Equation 22]
TNKCB(y) = -0.0266 × pNGAL (ng/mL) + 1 × pKIM-1 (ng/mL) + 0.11 × BUN (mg/dl) - 7.155
[Calculation 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. In claim 15,
The method wherein the cutoff value of the model equation is any number selected from -5.17 to 2.23. In claim 8,
The method is capable of distinguishing between a normal group and a risk group or suspect group prior to IRIS stage 1 with a sensitivity of more than 90% and a specificity of more than 95%.