TW202035986A - Small molecular biomarkers for nephropathy and applications thereof - Google Patents

Abstract

The present invention relates to a biomarker, method and assay kit for identifying and screening nephropathy and monitoring nephropathy treatment.

Description

Small molecular biomarkers for nephropathy and their applications

Related applications. This application claims the rights and interests of U.S. Provisional Application No. USSN 62/688,147 filed on June 21, 2018 in accordance with Article 119 of the U.S. Patent Law (35U.S.C. §119), the entire contents of which are incorporated herein by reference.

The invention relates to biomarkers, methods, and analysis kits for identifying and screening nephropathy and monitoring the progression of nephropathy.

Kidney damage, also called nephropathy, can be caused by, for example, drug toxicity, inflammatory reactions, high blood pressure, and diabetes. Kidney disease is usually a progressive disease, which means that kidney damage is often permanent and irreversible. Therefore, it is very important to detect kidney disease as early as possible before damage occurs. If kidney disease is found at an early stage, it can be treated very effectively. The treatment of chronic kidney disease focuses on slowing the progression of kidney damage, usually by controlling the underlying cause. Chronic kidney disease can develop into end-stage renal failure, which can be fatal without artificial filtration (dialysis, dialysis) or kidney transplantation. In particular, the prevalence of diabetes (diabetes mellitus, DM) in the adult population is increasing1,2 ^, and diabetic nephropathy (DN) is still the main cause of end-stage renal ^disease3,4 . About 30-40% of dialysis patients suffering from diabetes mellitus (DM) and related cardiovascular complications ^4. The survival rate of such patients is lower than the non-dialysis patients with diabetes or diabetic dialysis patient survival ^5. The current consensus in the management of diabetes mellitus (DM) is to recognize the importance of early diabetic nephropathy (DN) detection, because it can initiate specific treatment and dietary restrictions for patients with diabetic nephropathy early to prevent the progression of renal function decline to chronic kidney disease ( chronic kidney disease, CKD) and even stage renal failure ^6.

Usually, nephropathy is diagnosed by analyzing proteinuria content (for example, urine albumin content) or by checking glomerular filtration rate (GFR). Other related parameters include, for example, systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), and hemoglobin A1c (HbA1c). However, these methods lack sufficient sensitivity and/or selectivity, especially to detect early nephropathy when no obvious symptoms occur. Although nephropathy can also be detected by renal biopsy, this invasive procedure is not an ideal method because most patients are unwilling to do so, which may lead to late diagnosis until clinical features are apparent or the disease has progressed. . Kidney biopsies may also lead to the risk of serious bleeding complications.

There is a need to develop a method for detecting nephropathy, especially for general screening, early detection, and non-invasive methods.

The present invention unexpectedly found that compared with the normal content of control (healthy) individuals without nephropathy, in patients with nephropathy, the decrease in the content of certain metabolites, including N1, can be detected in the urine of individuals. -Methylguanosine, 7-methyluric acid, xanthine nucleoside, and histidine, or valine, the amino acid content increased. Therefore, metabolites including N1-methylguanosine, 7-methyluric acid, xanthine nucleoside, histidine, and amino acid valine can be used as specific biomarkers for the diagnosis of nephropathy , Especially for early diagnosis, can also be used to monitor the progress of nephropathy in patients suffering from nephropathy.

In one aspect, the present invention provides a method for detecting nephropathy in an individual, the method comprising: (i) Provide a biological sample obtained from the individual to be tested; (ii) Performing a first test, which includes determining the content of the first biomarker in the biological sample to obtain the first detection content, and comparing the first detection content with the first reference content of the first biomarker to obtain A first comparison result, and based on the first comparison result to assess whether the individual suffers from nephropathy or is at risk of developing nephropathy, wherein the first biomarker is selected from N1-methylguanosine, 7-methyluric acid, yellow A group of purine nucleosides, histidine, and any combination thereof, and compared to the first reference level, a decrease in the first detected level indicates that the individual suffers from nephropathy or is at risk of developing nephropathy; and/or (iii) Performing a second test, which includes determining the content of a second biomarker in the biological sample to obtain a second detection content, and comparing the second detection content with a second reference content of the second biomarker to obtain A second comparison result, and based on the second comparison result to assess whether the individual suffers from nephropathy or is at risk of developing nephropathy, wherein the second biomarker is valine, and compared to the second reference content, the first 2. An increase in the detected content indicates that the individual suffers from nephropathy or is at risk of developing nephropathy.

In another aspect, the present invention provides a method for monitoring the progression of nephropathy in a patient suffering from nephropathy, the method comprising: (a) Provide early biological samples from the patient at an early time point; (b) Provide a later biological sample from the patient at a later time point, wherein the later time point is later than the early time point; (c) Performing the first test, which includes determining the content of the first biomarker in the early biological sample and the later biological sample respectively, to obtain the early detection content and the later detection content of the first biomarker, respectively, The early detection content of the first biomarker is compared with the later detection content to obtain a first comparison result, and the patient’s nephropathy progression is evaluated based on the first comparison result, wherein the first biomarker is selected from N1-A Guanosine, 7-methyluric acid, xanthine, histidine, and any combination thereof, and compared with the early detection content of the first biomarker, the first biomarker The decrease in the later detection level indicates the progression of nephropathy in the patient; and/or (d) Performing a second test, which includes determining the content of the second biomarker in the early biological sample and the later biological sample to obtain the early detection content and the later detection content of the second biomarker respectively, Comparing the early detection content of the second biomarker with the later detection content to obtain a second comparison result, and assessing the patient’s nephropathy progression based on the second comparison result, wherein the second biomarker is valine, And compared with the early detection content of the second biomarker, the increase of the later detection content of the second biomarker indicates the progression of nephropathy in the patient.

In some embodiments, the detection is performed by mass spectrometry.

In some specific embodiments, the biological sample is a urine sample.

In some embodiments, the method as described herein further includes determining at least one physiological parameter in the biological sample. Examples of the physiological parameters include, but are not limited to, age, gender, systolic blood pressure (systolic blood pressure, SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), heme A1c (hemoglobin A1c, HbA1c), duration of diabetes, creatinine, estimated glomerular filtration rate (eGFR), albuminuria, urine albumin to creatinine ratio (albumin to creatinine ratio, ACR), and Any combination.

In some embodiments, the subject to be tested is a diabetic patient.

In some embodiments of the present invention, the nephropathy is chronic nephropathy (CKD).

In some specific embodiments of the present invention, the chronic kidney disease (CKD) is early chronic kidney disease (CKD), specifically the first stage or the second stage, or the chronic kidney disease (CKD) is the third stage Or the fourth stage of chronic kidney disease (CKD).

In some specific embodiments, if it is determined that the individual suffers from nephropathy, then the individual is treated with a treatment method for nephropathy.

In another aspect, the present invention provides a kit for implementing the method as described herein, and instructions for using the kit to detect the presence or content of a biomarker as described herein.

Also provided is the use of a reagent that specifically recognizes a biomarker as described herein for diagnosing nephropathy or monitoring the progress of nephropathy, or for preparing a reagent for diagnosing nephropathy or monitoring the progress of nephropathy or a reagent composition .

The details of one or more specific embodiments of the present invention are set forth in the following description. Other features or advantages of the present invention will become apparent from the detailed description of the following specific embodiments and the scope of the attached patent application.

In order to provide a clear and quick understanding of the present invention, first define certain terms. Additional definitions are set forth throughout the detailed description. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

As used herein, the articles "a" and "a" refer to one or more than one (ie, at least one) grammatical objects of the article. For example, "an element" means one element or more than one element.

As used herein, words such as "about" or "approximately" refer to the acceptable degree of deviation as understood by those of ordinary skill in the art, which may vary depending on the environment in which it is used. Generally, "about" or "approximately" can mean a value within a ±10% range around the quoted value.

As used herein, words such as "contain (verb)" or "contain (gerund)" are usually used in the meaning of including (verb)/including (gerund), which means that one or more features, ingredients or components are allowed. Words such as "contain (verb)" or "contain (gerund)" include words such as "consisting of (verb)" or "consisting of (gerund)".

As used herein, the terms "subject", "individual" and "patient" refer to any mammalian individual that requires diagnosis, prognosis, treatment, or treatment, especially humans. Other individuals may include cows, dogs, cats, guinea pigs, rabbits, rats, mice, horses, etc.

As used herein, the term "diagnosis" as used herein generally includes determining whether an individual may be affected by the target disease, disorder, or dysfunction. Those skilled in the art usually make a diagnosis based on one or more diagnostic indicators (ie, markers), and diagnose the presence or absence of the marker, or the content indicating the presence or absence of a disease, disorder or dysfunction. Those skilled in the art will understand that diagnosis does not mean determining the presence or absence of a specific disease with 100% accuracy, but an increase in the possibility of a certain disease in an individual.

As used herein, the term "treatment" refers to the application or administration of one or more active agents to an individual affected by a disease, a symptom or disorder of the disease, or the progression of the disease, for the purpose of treating, Cure, alleviate, alleviate, change, remedy, improve, promote, or affect the disease, the symptoms or conditions of the disease, the disability caused by the disease, or the progress or tendency of the disease.

As used herein, "normal individual" can be used to refer to an individual who is basically in a healthy state without a specific disease (eg, nephropathy), and can refer to a single normal individual or a group of normal individuals.

As used herein, the term "control individual" can be used to refer to individuals who do not have the target disease (eg, nephropathy), and can refer to a single control individual or a group of control individuals. In some embodiments, a control individual may refer to a normal/healthy individual. In some embodiments, a control individual may refer to an individual (or diabetic) who does not suffer from nephropathy.

As used herein, "abnormal amount" refers to the amount of the indicator that is increased or decreased compared to an individual or a reference amount or a control amount not suffering from the target disease (eg, nephropathy). Specifically, for example, the abnormal amount can be 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% higher than the reference or control amount. More; or the abnormal amount can be 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% or more lower than the reference or control amount. A reference or control amount can refer to an amount measured in a normal individual or a control sample (e.g., tissue or cells free of the target disease or any biological sample). In the art, a range of normal values can be obtained by using conventional detection and statistical methods to analyze the detected amount of markers in samples from normal individual populations.

As used herein, "low performance" and "high performance" of a biomarker as used herein refer to relative terms relative to the amount of biomarker found in a sample. In some specific embodiments, the biomarker performance content in the control group and the non-diseased samples can be compared to determine the low performance and the high performance. The low performance can refer to the comparison of the performance content of the control and non-diseased samples. Low performance content, and high performance can refer to the higher performance content relative to the performance content in the control, unaffected samples.

As used herein, biological markers (biomarkers) are features that are objectively measured and evaluated (for example, proteins, amino acids, metabolites, genes, or genetic manifestations), as normal or abnormal biological processes, diseases, pathogenic processes, or An indicator of response to treatment or therapeutic intervention. Biomarkers can include the presence or absence of features or patterns or collections of features that indicate a particular biological process. Biomarker measurements can be increased or decreased to indicate a certain biological event or process. Markers are mainly used for diagnostic and prognostic purposes. However, it can be used for the treatment, monitoring, drug screening, and other purposes described herein, including evaluating the effectiveness of therapeutic agents.

As used herein, the biological sample to be analyzed by any of the methods described herein can be any type of sample obtained from the individual to be diagnosed. In some embodiments, the biological sample may be a body fluid sample, such as a blood sample, a urine sample, or an ascites sample. Generally, the biological sample is a urine sample. In other specific embodiments, the blood sample may be whole blood or a part thereof, such as serum or plasma, which is subjected to heparinization or EDTA treatment to avoid blood clotting. Alternatively, the biological sample may be a tissue sample or a biopsy sample obtained from the kidney.

As used herein, the term "physiological parameter" as used herein generally refers to any parameter that can be monitored to determine one or more quantitative physiological content and/or activity related to the patient. Examples of physiological parameters include, but are not limited to, age, gender, systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), heme A1c (HbA1c), duration of diabetes, creatinine, estimated renal Estimated glomerular filtration rate (eGFR), albuminuria, urine albumin to creatinine ratio (ACR), and any combination thereof. In some specific embodiments, the physiological parameter includes fasting blood glucose (FBG) and/or diastolic blood pressure (DBP).

As used herein, the term "nephropathy" refers to the physiological condition in which kidney damage occurs, which specifically destroys its ability to properly regulate the concentration of solutes in blood and urine. Nephropathy can be characterized by one or more pathological changes: glomerular size, clustered fibrosis, Bowman’s bursa fibrosis, dilation, narrowing of microvessels, thickening of basement membrane, increased cells (interglomerular membrane (Or endothelium), leukocyte infiltration, microvascular thrombosis, renal tubular atrophy, necrosis, vacuoles and hyaline droplet changes, basement membrane thickening, dilation, inflammatory cells and casts in the lumen, interstitial fibrosis, edema, acute and chronic Leukocyte infiltration, arteriolar fibrosis, thrombosis, transparent changes and narrowing. Generally speaking, in the early stages of nephropathy, the kidneys can still filter out waste products in the blood well; in the intermediate stages, the kidneys may need to work harder to get rid of waste products; in the late stages, the kidneys may stop working. Usually and routinely, renal disease can be assessed by urine protein concentration. The early clinical features of nephropathy are low but abnormal urine albumin concentration (albumin excretion rate (AER): 30-300 mg/24 hours; or albumin to creatinine ratio (ACR): 30- 300 mg/g), called microalbuminuria, the patient has initial nephropathy (initial nephropathy). Without proper treatment, such patients will develop persistent microalbuminuria and turn into severe nephropathy (obvious nephropathy), also known as macroalbuminuria (albumin excretion rate (AER)> 300 mg/24 hours or albumin to creatinine ratio (ACR)> 300 mg/g), and finally progressed to end stage renal disease (ERSD). The estimated glomerular filtration rate (eGFR) is also an indicator of nephropathy. Chronic kidney disease (CKD) is defined as having an estimated glomerular filtration rate (eGFR) of less than 60 ml/min for more than 3 months in patients with or without proteinuria. Regardless of whether the patient has a low or high estimated glomerular filtration rate (eGFR) level, as long as the patient has proteinuria for more than 3 months, he will still be considered a chronic kidney disease (CKD) patient. Nephropathy can also be assessed based on, for example, serum creatinine concentration, urine protein concentration, urine protein to creatinine ratio, or by using tracer compounds such as phthalates.

In some embodiments, Chronic Kidney Disease (CKD) can be considered to include five (5) stages of kidney damage, from very slight damage in stage 1 to complete renal failure in stage 5. See Table A.

Table A. Different stages of chronic kidney disease (CKD). Stages of chronic kidney disease (CKD) feature Stage 1 The estimated glomerular filtration rate (eGFR) is greater than (and equal to) 90 ml/min. The function of the kidneys is still good. Usually, no symptoms are found. Other signs of kidney damage are observed (eg, proteinuria). Stage 2 The estimated glomerular filtration rate (eGFR) is between 60 and 89 ml/min. The function of the kidneys is still good. Usually, no symptoms are found. Other signs of kidney damage are observed (eg, proteinuria). Stage 3 The estimated glomerular filtration rate (eGFR) is between 30 and 59 ml/min. The kidney is moderately damaged and does not function as expected. Most patients still have no symptoms, but sometimes common symptoms are found, such as swelling of hands and feet, back pain, and more or less than normal urination. Stage 4 The estimated glomerular filtration rate (eGFR) is between 15 and 29 ml/min. The kidneys are moderately or severely damaged and fail to function as they should. More patients have symptoms, such as swelling of hands and feet, back pain, and more or less than normal urination. Stage 5 The estimated glomerular filtration rate (eGFR) is less than 15 ml/min. The kidney is severely damaged, very close to failure or complete failure. Patients have more severe symptoms, such as itching, nausea, vomiting, and difficulty breathing, caused by kidney failure and the accumulation of toxins and waste products in the blood.

In particular, the early stages of chronic kidney disease (CKD) as described herein may include stage 1 and stage 2 as shown above, and these patients may have a relatively high (normal) estimated glomerular filtration rate ( eGFR) but with at least one sign of kidney damage, such as microalbumin.

As used herein, the term "diabetic nephropathy" refers to nephropathy caused by diabetes. In some embodiments, the diabetes is type 2 diabetes.

The present disclosure is based (at least in part) on the identification of one or more novel and reliable biomarkers of nephropathy, that is, the metabolites include N1-methylguanosine, 7-methyluric acid, xanthine, and histamine Acid, and the amino acid valine. As demonstrated in the following examples, the urine samples of individuals suffering from nephropathy found reduced levels of certain metabolites, including N1-methylguanosine, 7-methyluric acid, xanthine, and group Amino acid, or valine, the increased content of this amino acid. Therefore, the nephropathy detection method described herein can identify whether an individual has, is suspected of having, or is at risk of developing nephropathy. The detection method described herein can be applied to any individual, especially as an initial, routine, and frequent screening method to identify those patients who suffer from nephropathy or are at risk of progressive nephropathy.

7-甲基尿酸 7,9-二氫-7-甲基-1H-嘌呤-2,6,8(3H)-三酮 C₆ H₆ N₄ O₃ 分子量182.14 g/mol

黃嘌呤核苷 9-[(2R ,3R ,4S ,5R )-3,4-二羥基-5-(羥甲基)氧戊環-2-基]-3H -嘌呤-2,6-二酮 C₁₀ H₁₂ N₄ O₆ 分子量284.228 g/mol  

組胺酸 2-胺基-3-(1H -咪唑-4-基)丙酸 C₆ H₉ N₃ O₂ 分子量155.1546 g/mol

纈胺酸 2-胺基-3-甲基丁酸 C₅ H₁₁ NO₂ 分子量117.148 g·mol⁻¹

The biomarkers of nephropathy as described herein are as follows: Table B name structure N1-methylguanosine 2-amino-9-[(2R,3R,4S,5R)-3,4-dihydroxy-5-(hydroxymethyl)oxolane-2-yl]-1-methan Purin-6-one C ₁₁ H ₁₅ N ₅ O ₅ Molecular weight 297.27 g/mol

7-Methyluric acid 7,9-dihydro-7-methyl-1H-purine-2,6,8(3H)-trione C ₆ H ₆ N ₄ O ₃ Molecular weight 182.14 g/mol

Xanthine nucleoside 9-[(2 R ,3 R ,4 S ,5 R )-3,4-dihydroxy-5-(hydroxymethyl)oxolane-2-yl]-3 H -purine-2 ,6-Diketone C ₁₀ H ₁₂ N ₄ O ₆ Molecular weight 284.228 g/mol

Histidine 2-amino-3-(1 H -imidazol-4-yl) propionic acid C ₆ H ₉ N ₃ O ₂ Molecular weight 155.1546 g/mol

Valine 2-amino-3-methylbutyric acid C ₅ H ₁₁ NO ₂ Molecular weight 117.148 g·mol ⁻¹

The presence and content of metabolic or amino acid biomarkers as described herein in biological samples can be determined by conventional techniques. In some embodiments, the presence and/or content of biomarkers as described herein can be determined by mass spectrometry, which allows direct measurement of analytes with high sensitivity and reproducibility. There are many mass spectrometry methods to choose from. Examples of mass spectrometry include, but are not limited to, matrix-assisted laser desorption ionization/time of flight (MALDI-TOF), surface enhanced laser desorption ionization/time of flight mass spectrometer (surface-enhanced laser desorption ionisation/time of flight, SELDI-TOF), liquid chromatography-mass spectrometry (LC-MS), liquid chromatography tandem mass spectrometer (liquid chromatography tandem mass spectrometry) spectrometry, LC-MS-MS), and electrospray ionization mass spectrometry (ESI-MS). A specific example of this method is tandem mass spectrometry (MS/MS), which involves multiple steps of mass selection or analysis, usually separated by some form of fragmentation.

In other specific embodiments, the presence and/or content of a biomarker can be determined by immunoassay. Examples of immunoassays include, but are not limited to, Western blot analysis, enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA), radioimmunoprecipitation assay (RIPA) , Immunofluorescence assay (IFA), enzyme-linked fluorescent immunoassay (ELFA), electrochemiluminescence (ECL), and capillary gel electrophoresis (CGE). In some embodiments, a reagent that specifically recognizes the biomarker can be used to determine the presence and/or content of the biomarker, such as an antibody that specifically binds to the biomarker.

As described in this article, it was found that the content of certain metabolites (including N1-methylguanosine, 7-methyluric acid, xanthine nucleoside, and histidine) was reduced, or the content of the amino acid valine The increase is related to the appearance and progression of nephropathy. Therefore, the methods described herein for screening patients with nephropathy and monitoring the progression of nephropathy can be performed using these molecules as reliable biomarkers. As mentioned above, the detection method described herein can be used as an early screening method for any individual to detect whether it may suffer from nephropathy (especially early nephropathy).

In order to carry out the method described herein, the content of the biomarkers described herein is detected or measured in a biological sample taken from an individual in need (for example, a human patient without any symptoms of nephropathy, or suffering from, Human patients suspected of suffering from nephropathy or at risk of suffering from nephropathy) are performed by any method known in the art, such as those described herein, such as a mass spectrometer. Usually, the biological sample is a urine sample.

In some embodiments, the content of the biomarker in the sample derived from the candidate individual can be compared with the standard value to determine whether the candidate individual has or is at risk of suffering from nephropathy. The standard value represents the content of the biomarker as described herein in the control sample. The control sample can be taken from individuals who do not suffer from nephropathy. In addition, the control sample can be taken from a mixture of samples from a group of such individuals. Alternatively, the control individual is matched with the candidate individual in, for example, age, gender, and/or ethnic background. Preferably, the control sample and the biological sample of the candidate individual are samples of the same species.

In some embodiments, if the first set of biomarkers, namely N1-methylguanosine, 7-methyluric acid, xanthine nucleoside, and histidine are measured to have an amount lower than the standard value (for example, (About 10% or less below the standard value), the candidate individual can be diagnosed as suffering from, suspected of suffering from, or at risk of suffering from nephropathy. In some embodiments, if a second set of biomarkers, that is, valine acid is measured to have an amount higher than the standard value (for example, about 10% or more higher than the standard value), the candidate individual can be Diagnosed as suffering from, suspected of suffering from, or at risk of suffering from nephropathy.

In some embodiments, the content of the biomarkers as described herein in multiple samples derived from the candidate individual (eg, patients with renal disease) can be measured to determine the progression of the disease. For example, at least two biological samples, for example, urine samples can be obtained from the candidate individual at different time points. In some embodiments, if the first set of biomarkers is observed, namely N1-methylguanosine, 7-methyluric acid, xanthine nucleoside, and histidine, the trend decreases over time (for example, N1 -The amount of methylguanosine, 7-methyluric acid, xanthine nucleoside, and histidine in the sample obtained later is lower than the amount in the sample obtained earlier), the individual is diagnosed as suffering from, Suspected of suffering from or risk of suffering from nephropathy. If the individual is a patient with nephropathy, the decreasing trend of the amount of N1-methylguanosine, 7-methyluric acid, xanthine, and histidine indicates the progression (worsening) of the nephropathy. In certain embodiments, if the trend of amino acid biomarker valine is observed to increase over time (for example, the amount of valine in a sample obtained later is higher than the amount in a sample obtained earlier ), the individual is diagnosed as suffering from, suspected of suffering from, or at risk of suffering from nephropathy. If the individual is a patient with nephropathy, the increasing trend of the amount of valine indicates the progression (worsening) of nephropathy.

When an individual, such as a human patient, is diagnosed with, suspected of having, or at risk of developing nephropathy, the individual may undergo further tests (e.g., routine physical tests, including surgical biopsies or imaging methods, such as X- Radiography, magnetic resonance imaging (MRI) or ultrasound) to confirm the occurrence of the disease and/or determine the stage and type of nephropathy.

In some embodiments, the methods described herein may further include treating the patient with nephropathy to at least relieve symptoms related to the disease. It can be treated with conventional drugs for nephropathy. Examples of such drugs include, but are not limited to, (i) drugs for reducing albuminuria, such as phosphodiesterase inhibitors, for example, dipyridamole and pentoxifylline; (ii) antihypertensive drugs , Such as angiotensin converting enzyme (ACE) inhibitors, for example, imidapril, and angiotensin receptor blockers (angiotension receptor blocker, ARB), for example, losartan; (iii) Phosphate binders, such as sevelamer carbonate, lanthanum carbonate, and Al(OH) ₃ hexitol complex; (iv) calcium supplements, such as calcium carbonate, calcium citrate, and vitamin D; (v ) Anti-anemia drugs, such as erythropoietin (EPO) and iron supplements; (vi) drugs used to lower blood lipids, such as statins, such as simvastatin, pravastatin, and atorvastatin ; (Vii) drugs that lower uric acid, such as isopurinol, febuxostat, and benzbromarone; (viii) other drugs, such as corticosteroids, such as prednisolone, non-steroidal anti-inflammatory drugs ( non-steriodal anti-inflammatory drugs (NSAIDs), and N-acetylcysteine (used to prevent contrast-induced nephropathy (CIN)). These drugs can be administered to an individual in need in an effective amount. Treatment of nephropathy may also include food therapy with a low protein and/or low salt diet.

As used herein, "effective amount" refers to the amount of each active substance that can be administered to the individual alone or in combination with one or more other active substances to impart a therapeutic effect to the individual. The effective amount can be varied and must be determined by those skilled in the art, which depends on the specific conditions at the time of administration, the severity of the disease, and various parameters of the patient, including age, sex, weight, height, physical condition, treatment plan, parallel treatment The nature of the drug (if any), the specific route of administration, and other possible factors based on the knowledge and professional judgment of the medical staff. These factors are well known to those of ordinary skill in the art and can be introduced without further routine experimentation.

The present invention also provides a kit or composition for implementing the method, which includes a reagent (for example, an antibody or a labeling reagent) that specifically recognizes a biomarker as described herein. The kit may further include instructions for using the kit to detect the presence or content of the biomarkers described herein, thereby detecting nephropathy, and can also be used to monitor the progress of the nephropathy. Also provided are such reagents for use in a method for detecting nephropathy in an individual in need or a method for monitoring the progression of nephropathy in a patient with nephropathy, or for preparing a kit or combination for implementing the method The method of things. Such reagents include a first reagent that specifically recognizes the first biomarker and/or a second reagent that specifically recognizes the second biomarker. In some embodiments, such reagents include a first reagent selected from the group consisting of (i) molecules that specifically recognize N1-methylguanosine, (ii) specifically recognize 7-methyluric acid (Iii) a molecule that specifically recognizes xanthine, (iv) a molecule that specifically recognizes histidine, and (v) any combination of (i) to (iv). In some specific embodiments, such reagents include a second reagent that specifically recognizes valine. An example of the reagent may be an antibody or a labeling reagent containing a detectable label (such as a fluorescent label), which can specifically recognize a biomarker. The reagent can be mixed with a carrier, such as a pharmaceutically acceptable carrier, to form a composition for testing or diagnostic purposes. Examples of such carriers include injectable saline, injectable distilled water, injectable buffer solutions and the like.

Without further elaboration, it is believed that those skilled in the art will be able to apply the present invention to the greatest extent based on the above description. Therefore, the purpose of the following specific embodiments is to illustrate, not to limit the scope of application of the present invention in any way. All documents cited in this article are incorporated into this article by reference.

Example

Metabolomics is a systems biology method used to identify and quantify total metabolites in biological samples. Since the source of the urine sample of a non-invasive, containing most of the body metabolic end products, so urine metabonomics be used to find a disease marker metabolites ^14,15.

Metabolomics methods based on gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) have been applied to urine research to find early detection of diabetes ( DM) ^{16, 17} and diabetic nephropathy (DN) ^18-21 markers. However, most of these studies used gas chromatography-mass spectrometry (GC-MS) for non-target/target metabolomics analysis, and liquid chromatography-mass spectrometry (LC-MS) for Target metabolite detection. Since liquid chromatography-mass spectrometry (LC-MS) can detect more diverse metabolites than gas chromatography-mass spectrometry (GC-MS), it is a promising non-target metabolome The study method may reveal the new metabolic markers of diabetic nephropathy (DN). However, as far as we know, there is no report using liquid chromatography-mass spectrometry (LC-MS) non-targeted metabolomics to find type 2 diabetic nephropathy (T2DN) in urine. mark.

In this article, in order to reveal the novel type 2 diabetic nephropathy (T2DN) metabolic markers, we used UPLC-MS to analyze a large number of healthy individuals from multiple medical centers and individuals with large amounts of albuminuria (macro group), without trace white Urine samples of type 2 diabetes patients with proteinuria (T2DM group) and type 2 diabetes patients with microalbuminuria (T2DM+micro group) were subjected to non-target metabonomics studies. Identify and quantify metabolites with significant fold changes between different subgroups, and evaluate their efficacy for early detection of type 2 diabetic nephropathy (T2DN).

1. Materials and Methods

1.1 Chemicals

All chemicals and solvents were purchased from Sigma-Aldrich Company (St. Louis, Missouri, USA). All chemicals are of analytical grade. Water containing 0.1% formic acid, acetonitrile, and water containing 0.1% formic acid are Chromasolv grades.

1.2 Research population and samples

The research protocol describing sample collection, preparation, and analysis was approved by the local ethics committee of the School of Medicine Affiliated to China Medical University (Taichung, Taiwan). All individuals had given informed consent before the study. All urine samples came from Taiwan Biobank (National Sample Recruitment Program led by Academia Sinica, Taiwan), the School of Medicine Affiliated to China Medical University, and Taichung Veterans General Hospital (Taichung, Taiwan), from January 2012 to 2017 Ended in December. The following four groups are defined according to the clinical course and urinary albumin excretion content: Type 2 diabetes patients with microalbuminuria (T2DM +micro, 30> albumin to creatinine ratio (ACR)> 300 mg/g), without Patients with type 2 diabetes with minimal or large albuminuria (T2DM; albumin to creatinine ratio (ACR)> 30 mg/g), patients with micro or large albuminuria (macro; albumin and muscle Acid inine ratio (ACR)> 30 mg/g), and healthy controls (albumin to creatinine ratio (ACR)> 30 mg/g).

1.3 Sample Preparation

And urine samples were collected through centrifugation (5,000 g, 30 min at 4 ^o C) to remove cell debris. Then keep the sample at -80 ^o C for long-term storage. Before the mass spectrometer (MS) analysis, samples were thawed at 4 ^o C 24 h. Exclude individuals with tumors, fever, heart failure, urinary tract infections, hematuria, or blood pressure disorders.

1.4 Urine metabolomics analysis

From healthy individuals (n = 14) and non-diabetic patients with micro or large albuminuria (macro group) (n = 22), type 2 diabetes patients without micro or large albuminuria (T2DM group) (n = 22) or individuals with type 2 diabetes with microalbuminuria (T2DM+micro group) (n = 14), randomized and age-matched urine samples used to analyze quadrupole flight by ultra performance liquid chromatography Ultra-performance liquid chromatography quadrupole time of flight mass spectrometry (UPLC-qTOF-MS) performs metabolomics analysis to discover potential metabolic markers. Measure creatinine in all individuals, and obtain a certain volume of urine from each individual to obtain 50 µg of creatinine. The urine sample was vacuum dried and re-dissolved with 500 µL of 0.1% formic acid, and 5 µL of the urine adjusted with creatinine was injected into a liquid chromatography-mass spectrometry (LC-MS) device. Equipped with a C18 column (2.1 x 150 mm, 3 µm, T3; Waters, Milford, Massachusetts, USA) UHPLC system (Ultimate 3000; Dionex, Gemmoling, Germany) combined with a hybrid Q-TOF mass spectrometer (affected by maXis, Bruker Daltonics, Bremen, Germany), with orthogonal electrospray ionization (ESI) source. For metabolomics analysis, the LC flow rate is 0.25 mL/min, using solvent A (5% acetonitrile and 0.1% formic acid) and solvent B (acetonitrile and 0.1% formic acid). After injecting 5 µL of sample volume, keep solvent B at 1% for 4 minutes, then increase to 45% in 18 minutes, and finally increase to 99% in 2 minutes. After holding for 2 minutes, reduce solvent B to 1% and maintain the concentration for 2.5 minutes. The full scan mass range is m/z 50-1000, operating in positive or negative ion mode at 1 Hz. The capillary voltage of the ion source is set to +3,600 V (positive mode) and -3,000 V (negative mode), and the end plate offset is 500 V. The sprayer air flow is 1 bar, and the drying gas flow is 8 L/min. The drying temperature is set to 200 ^o C. Before analysis, 10 continuous quality control (QC) samples (combined samples of 40 urine samples randomly selected from 4 groups) were injected to adjust the LC column. After every 10 analysis of urine samples, QC samples were injected to check the stability of the system throughout the analysis.

For further data exploration, use ProfileAnalysis's bioinformatics software (version 2.1) to calculate the molecular characteristics of the LC flow wash time from 0.5 to 24 minutes, and the mass range from 50 m/z to 1000 m/z. The compound detection parameters are set as follows: S/N> 3, correlation coefficient threshold: 0.7, minimum compound length: 7 spectra, smooth width: 1. In the storage area generation, the advanced storage area is used, with a retention time tolerance of 0.5 minutes and a mass tolerance of 30 ppm. No signal normalization is used. In the storage area filter, the value count of the storage area and the value count of the group attribute in the storage area must be greater than 6. Allow empty group attributes, and select "None" for missing value replacement. For principal component analysis (PCA) (no scaling algorithm at 95% confidence level) and volcano map analysis, a total of 1714 and 4261 molecular features are presented in positive and negative modes, respectively.

1.5 Through LC-ESI-Q-TOF-MS Quantitative metabolite labeling

In order to quantify valine, 7-methyluric acid, N1-methylguanosine, and xanthine nucleosides, the LC-ESI-Q-TOF system with multiple reaction monitoring functions is used. Analysis of 52 healthy individuals, 53 non-diabetic patients with micro or macroalbuminuria (macro group), 86 patients with type 2 diabetes who did not have micro or macroalbuminuria (T2DM group), and 76 patients with Urine samples of type 2 diabetes patients (T2DM+micro group) with microalbuminuria. Dilute an aliquot of 4 µL of urine with 16 µL of 0.1% formic acid containing an internal standard.

For liquid chromatography-mass spectrometry (LC-MS) analysis, use a C18 column (1.0 x 150 mm, 3 µm, T3; Waters, Milford, Massachusetts, USA) with a flow rate of 0.08 mL/min, solvent A (0.1% formic acid) and solvent B (acetonitrile with 0.1% formic acid). After injecting 5 µL sample volume, solvent B was maintained at 0.5% for 3 minutes, then increased to 95% in 2 minutes, and finally increased to 95% in 2 minutes. After holding for 2 minutes, reduce solvent B to 0.5% and maintain the concentration for 2 minutes. The mass spectrometer was operated in positive mode and scanned the 50-300 m/z range at 3 Hz; operated in negative mode and scanned the 50-500 m/z range at 2 Hz. Other mass spectrometer (MS) settings are the same as described in the metabolomics analysis method.

In order to measure histidine and valine acid, the analyte and the internal standard (IS) (d ³ -histidine) are compared with the nominal analyte concentration prepared in a control urine sample by using the following calibrators. The peak area ratio, the calibration curve was constructed in orthogonal electrospray ionization (ESI) mode: 0.125, 0.25, 0.5, 1, 2, 4, and 8 ppm.

To measure 7-methyluric acid, N1-methylguanosine, and xanthosine, the concentration calibration curve was constructed in negative alternating electrospray ionization (ESI) mode, and 1-naphthalenesulfonic acid was added as an internal standard (IS) . To calculate each metabolite, subtract the endogenous peak area divided by the ratio of the internal standard (IS) peak area of the blank urine sample from the ratio of the analyte area/internal standard (IS) area of the urine sample . The precursor ion/fragment ion (collision energy) values of valine, 7-methyluric acid, N1-methylguanosine, and xanthine are 118.07/72.08 (10eV), 181.03/123.00 (30eV), 296.06/164.05 (20eV), and 283.07/151.03 (20 eV).

1.6 Statistical Analysis

The numerical variables of clinical characteristics are shown as the median value of the quartile values (25%, 75%) in parentheses. The parentless Mann-Whitney method (SigmaPlot, version 11.0) was used to detect the significant difference in the concentration of the label quantified by liquid chromatography-mass spectrometry (LC-MS) between the two groups. Use logistic regression to evaluate the biomarkers and the association between each result separately. If it is significant through the backward selection method, then potential confounding factors (including demographic and clinical variables) will be included in the multivariate model. Use the natural logarithm (LN) transformation or metabolite biomarker intermediate value in the model. A receiver operating characteristic (ROC) curve is generated to quantify the prediction accuracy of the model, and the area under the receiver operating characteristic curve (AUC) is used to evaluate the discriminative ability of the model. Calculate the area under the curve (AUC) values of metabolite biomarkers and metabolite biomarkers with clinical variables. All statistical analyses were performed using SPSS software, version 21.0 for Microsoft Windows (IBM Corporation, Armonk, New York, USA).

2. result

2.1 Patient characteristics

Compared with type 2 diabetes patients (T2DM group) with microalbuminuria (T2DM group) from healthy individuals, and type 2 diabetes patients with microalbuminuria (T2DM+micro group), non-diabetic-induced micro Or patients with large albuminuria (macro group) individuals have higher serum creatinine, lower estimated glomerular filtration rate (eGFR), higher albuminuria, and higher urine albumin and muscle Anhydride ratio (ACR). In addition, individuals from type 2 diabetes patients (T2DM group) without microalbuminuria (T2DM group) and type 2 diabetes patients with microalbuminuria (T2DM+micro group) were more likely than healthy individuals and non-diabetic patients. Individuals with micro or large albuminuria (macro group) have higher fasting blood glucose and glycosylated hemoglobin. Type 2 diabetes patients with microalbuminuria (T2DM+micro group) have a higher albumin to creatinine ratio (ACR) value than type 2 diabetes patients who do not have microalbuminuria (T2DM group) and Individuals in the healthy individual group (Table 1). Table 1. Healthy individuals, patients with microalbuminuria or macroalbuminuria not caused by diabetes (macro group), type 2 diabetic patients without microalbuminuria or macroalbuminuria (T2DM group), and second diabetic patients with microalbuminuria Clinical and biochemical parameters of type 2 diabetes patients (T2DM+micro group). The data is expressed as the median (25%/75% quartile). Healthy individuals (n=52) Large amount (n=53) T2DM (n=86) T2DM+micro (n=76) P ¹ P ² P ³ Sex (M/F) 26/26 20/33 46/40 40/36 - - - age) 61.00 (53.00/66.00) 58.00 (52.00/77.00) 60.50 (56.00/66.00) 65.00 (53.50/74.00) 0.974 0.928 0.111 SBP (mmHg) 118.00 (110.00/130.00) 130.00 (118.50/142.00) 126.00 (117.50/133.00) 124.00 (103.00/132.00) 0.001 0.002 0.021 DBP (mmHg) 72.00 (60.00/79.50) 80.00 (72.00/85.50) 76.50 (71.75/82.00) 80.00 (73.00/91.00) >0.01 0.001 0.004 FBG (mg/dL) 84.00 (75.50/91.00) 100.00 (94.25/104.00) 128.00 (110.00/142.00) 133.00 (115.00/158.75) >0.001 >0.001 0.037 Heme A1c (%) 5.40 (5.23/5.60) 5.80 (5.50/6.15) 6.70 (6.30/6.90) 6.90 (7.35/7.55) >0.001 >0.001 0.005 Heme A1c (mmol/mol) 35.30 (33.59/37.69) 39.88 (36.60/43.70) 49.72 (45.34/51.90) 51.90 (45.89/59.01) >0.001 >0.001 0.005 Time to suffer from diabetes (years) - - 6.00 (3.75/11.00) 8.00 (2.00/14.00) - - 0.452 Creatinine (mg/dL) 0.80 (0.70/1.00) 1.07 (0.80/1.25) 0.80 (0.61/1.00) 0.90 (0.70/1.04) 0.001 0.214 0.082 Estimated glomerular filtration rate (ml/min) 82.47 (71.51/95.42) 64.00 (50.75/85.50) 90.29 (74.75/107.00) 81.93 (64.25/103.50) >0.001 0.160 0.035 Albuminuria (mg/L) 1.30 (0.43/7.55) 49.70 (21.80/124.20) 0.60 (0.30/1.20) 8.55 (5.63/16.15) >0.001 0.004 >0.001 Urine ACR (mg/g) 4.81 (3.21/7.21) 292.71 (89.60/1639.52) 5.24 (3.25/8.69) 91.95 (55.37/130.79) >0.001 0.613 >0.001 M: male; F: female; SBP: systolic blood pressure; DBP: diastolic blood pressure; FBG: fasting blood glucose; eGFR: estimated glomerular filtration rate; ACR: albumin to creatinine ratio. Macro: Patients with micro or large albuminuria not caused by diabetes. T2DM: Patients with type 2 diabetes who do not have trace or large albuminuria. T2DM+micro: Type 2 diabetes patients with microalbuminuria. ^{1 The} p- value is the result of the Mann-Whitney U test when comparing healthy individuals with the macro group. ^{2 The} p value is the result of Mann-Whitney U test when comparing healthy individuals with T2DM group. ^{3 The} p value is the result of Mann-Whitney U test when the T2DM group is compared with the T2DM+micro group.

2.2 Stability and accuracy of quality signal

Evaluate the signal stability of liquid chromatography-mass spectrometer (LC-MS) through QC samples. A positive peak signal with S/N> 3 with a mass range of 50-1000 m/z was selected to generate 1706 features in the QC sample. Among them, about 24% of the relative standard deviation (RSD) value is less than 10%, of which 13% of the relative standard deviation (RSD) is between 10-20%, and 21% of the relative standard deviation (RSD) is 20- 40% means that MS signal stability is good after long-term continuous liquid chromatography-mass spectrometry (LC-MS) operation.

2.3 Through liquid chromatography - Mass spectrometer (LC-MS) Analyze for metabonomic analysis

For healthy individuals (n = 14), non-diabetic patients with micro or large albuminuria (macro group) (n = 22), type 2 diabetes patients without micro or large albuminuria (T2DM group) (n = 22), and age-matched urine samples of type 2 diabetes patients with microalbuminuria (T2DM+micro group) for analysis by using non-target liquid chromatography-mass spectrometry (LC-MS) ) Metabolomics analysis methods discover potential metabolite markers.

Obtain the typical base peak chromatogram of urine in positive mode and negative mode. ~17,000 molecular features were detected in positive and negative modes. The analysis results of liquid chromatography-mass spectrometer (LC-MS) were analyzed by principal component analysis (PCA) and shown in Figure 1A (positive mode) and Figure 1B (negative mode). In the orthogonal electrospray ionization (ESI) mode, it is difficult to distinguish between type 2 diabetes patients without trace or large albuminuria (T2DM group) and type 2 diabetes patients with microalbuminuria (T2DM+micro group) The group of) can be completely separated from the group of healthy individuals, which is difficult to distinguish, and individuals with micro or large albuminuria (macro group) caused by non-diabetics. However, we found that metabolomics profile separation is mainly applied in type 2 diabetes patients (T2DM group) without microalbuminuria (T2DM group) and type 2 diabetes patients with microalbuminuria (T2DM+micro group) The anti-diabetic drug metformin was determined (Figure 1C). After excluding metformin, these four groups were indistinguishable in the principal component analysis (PCA) score scatter plot (Figure 1D). Because Principal Component Analysis (PCA) is designed to find the largest variance in a group and is suitable for finding the most influential variable (strong liquid chromatography-mass spectrometer (LC-MS) signal) in different groups, it lacks principal Component analysis (PCA) identification indicated that the major urinary metabolite signals in the four groups were similar.

In order to find possible peak markers that may be less but may lead to group differentiation, the volcano map is made based on the t -test results and the fold change value. Figures 2A-2B show six volcano graphs of different groups, which are constructed by plotting the logarithm of the P -value on the y-axis (baseline 10) and the logarithm of the multiple change value between the two conditions. When using the logarithm of the multiple change, the changes in the two directions (upward and downward) are equidistant from the center. In healthy individuals and non-diabetic patients with micro or large albuminuria (macro group), healthy individuals and patients with type 2 diabetes who do not have micro or large albuminuria (T2DM group), and without micro or large albumin Comparing urine type 2 diabetes patients (T2DM group) and type 2 diabetes patients with microalbuminuria (T2DM+ trace group), 85/232 (positive/negative ion number), 30/95, and 44/ 98 peak candidates. After manual inspection of these potential candidates, 43 molecular characteristic ions were selected based on their available fragment ions and stronger signals. For the comparison between healthy and non-diabetic patients with micro or large albuminuria (macro group), the difference between 3 positive ions and 6 negative ions was more than twice ( P > 0.05). Comparing type 2 diabetes patients without microalbuminuria (T2DM group) with microalbuminuria (T2DM group) and type 2 diabetes patients with microalbuminuria (T2DM+microgroup), the difference of the three positive ions is more than two times ( P > 0.05), the difference between the two negative ions is more than two times ( P > 0.05). Comparing healthy patients with type 2 diabetes (T2DM group) who do not have trace or large amounts of albuminuria, the difference between 11 positive ions ( p >0.01) and 18 negative ions ( p >0.001) is more than double. After searching the HMDB and MassBank databases, five of the 43 candidates can be identified, metformin, valine, xanthine, N1-methylguanosine, and 7-methyluric acid. These metabolites were further identified by comparing them with the characteristics of the LC flow wash time, mass spectrometry (MS), and MS/MS spectra of chemical standards.

2.4 Potential mark verification

The found metabolites are labeled valine, xanthine, N1-methylguanosine, 7-methyluric acid, and are found in a larger number of healthy individuals (n = 52), and micro or large albuminuria caused by non-diabetics Patients (macro group) (n = 53), type 2 diabetic patients without microalbuminuria (T2DM group) (n = 86), and type 2 diabetic patients with microalbuminuria (T2DM + microalbuminuria) Group) (n=76) samples were further quantified. (Table 1)

The results of these analyses are shown in Table 2 and Figures 3A-3D. Table 2. Healthy individuals, non-diabetic patients with microalbuminuria or macroalbuminuria (macro group), type 2 diabetic patients without microalbuminuria or macroalbuminuria (T2DM group), and second diabetic patients with microalbuminuria Quantification of four metabolites in urine samples of patients with type 2 diabetes (T2DM+micro group). Data are expressed as median (µg/mg creatinine) and interquartile range (25th to 75th percentile). Healthy individuals (n=52) macro (n=53) T2DM (n=86) T2DM+micro (n=76) P ¹ P ² P ³ Valine 2.37 (1.84/3.81) 3.64 (2.59/6.07) 6.79 (4.25/9.40) 6.05 (3.32/8.77) 0.002 >0.001 0.093 N1-methylguanosine 1.74 (0.67/2.74) 0.13 (0.00/0.69) 1.22 (0.26/2.47) 0.35 (0.00/1.27) >0.001 0.130 0.001 7-methyluric acid 1.27 (0.79/2.30) 0.57 (0.00/1.34) 0.91 (0.37/1.83) 0.62 (0.12/1.37) >0.01 0.008 0.062 Xanthosine 1.59 (0.49/2.24) 0.58 (0.02/1.16) 1.39 (0.58/2.66) 0.75 (0.10/1.76) 0.001 0.583 0.004 ^{1 The} p value is the result of the Mann-Whitney U test when comparing healthy individuals with WDM-NP. ^{2 The} p value is the result of the Mann-Whitney U test when comparing healthy individuals with DM-WNP. ^{3 The} p value is the result of Mann-Whitney U test when DM-WNP is compared with DM-NP.

The content of valine acid in non-diabetic patients with micro or large albuminuria (macro group) and type 2 diabetic patients without micro or large albuminuria (T2DM group) was significantly higher than that in healthy individuals ( p value, respectively) 0.002 and> 0.001). Compared with the content of healthy individuals and type 2 diabetic patients (T2DM group) without trace or large albuminuria, patients with trace or large albuminuria (macro group) not caused by diabetes and those with microalbuminuria Patients with type 2 diabetes (T2DM+micro group) had lower levels of N1-methylguanosine ( p values> 0.001 and 0.001, respectively). 7-Methyluric acid is used in patients with microalbuminuria (macro group) caused by non-diabetics, type 2 diabetic patients with no microalbuminuria (T2DM group), and second diabetic patients with microalbuminuria Type diabetes patients (T2DM+micro group) were lower than those in healthy individuals. Compared with the xanthine content in the healthy individual group, the xanthine content was significantly lower in patients with non-diabetic-induced micro or large albuminuria (macro group) ( p value = 0.001). Compared with type 2 diabetes patients (T2DM group) without microalbuminuria (T2DM group), the content of xanthine nucleoside in type 2 diabetes patients with microalbuminuria (T2DM+micro group) also significantly decreased ( p value = 0.004).

2.5 Amino acid analysis

Because amino acids are considered as potential diabetic nephropathy (DN) markers, the 16 amino acid content of the sample group was measured in this study (Table 1). Most of the amino acids (leucine, isoleucine, tryptophan, tyrosine, etc.) were found in the urine samples of individuals with type 2 diabetes (T2DM group) who did not have trace or large albuminuria. The excretion content of proline, aspartic acid, arginine, aspartame, threonine) was significantly higher than that of urine samples from healthy individuals. The excretion of methionine, tyrosine, proline, and threonine in non-diabetic patients with micro or large albuminuria (macro group) was higher than that of healthy individuals.

Compared with the histidine content of the healthy individual group, the histidine content in patients with micro or macroalbuminuria caused by non-diabetics (macro group) was significantly lower, but they did not have the second type of micro or macroalbuminuria Diabetic patients (T2DM group) had significantly higher histidine levels ( p values were 0.036 and> 0.001). Histidine in type 2 diabetes patients with microalbuminuria (T2DM+micro group) was also significantly lower than that in type 2 diabetes patients without microalbuminuria (T2DM group) ( p value = 0.021) . (Figure 3E) (Table 3) Aspartic acid was significantly higher in type 2 diabetic patients (T2DM group) without trace or large albuminuria than healthy individuals ( p value = 0.003), but in patients with microalbuminuria Urinary type 2 diabetes patients (T2DM+micro group) were lower than type 2 diabetes patients who did not have micro or large albuminuria (T2DM group) ( p value = 0.012) (Figure 3F) (Table 3). Table 3. Healthy individuals, patients with microalbuminuria or macroalbuminuria not caused by diabetes (macro group), type 2 diabetic patients with no microalbuminuria or macroalbuminuria (T2DM group), and second type diabetic patients with microalbuminuria Quantification of aspartic acid and histidine in urine samples of patients with type 2 diabetes (T2DM+micro group). Healthy individuals Patients with micro or large albuminuria not caused by diabetes (macro group) P value (comparison between healthy individuals and non-diabetic patients with micro or large albuminuria (macro group)) Patients with type 2 diabetes who do not have minimal or large albuminuria (T2DM group) P value (comparison between healthy individuals and patients with type 2 diabetes (T2DM group) who do not have trace or large albuminuria) Type 2 diabetes patients with microalbuminuria (T2DM+micro group) P value (comparison between type 2 diabetes patients without microalbuminuria (T2DM group) and type 2 diabetes patients with microalbuminuria (T2DM+micro group)) Aspartic acid 2.05 (1.35/3.13) 2.09 (0.94/3.36) 0.822 4.15 (1.9/7.6) 0.004 2.57 (1.209/4.01) 0.015 Histidine 29.8 (21.7/50.0) 25.0 (13.6/39.0) 0.037 55.4 (31.2/105.5) >0.001 43.0 (20.1/76.1) 0.021

2.6 Purine Catabolism

Since N1-methylguanosine, 7-methyluric acid, and xanthosine are involved in purine catabolism, the five main metabolites in the sample group (Table 1)-hypoxanthine, xanthine, and guanine nucleus Glycosides, guanine, and uric acid were also quantified. The results showed that the levels of guanine in patients with non-diabetic-induced micro or large albuminuria (macro group) were significantly lower than those in healthy individuals. The levels of hypoxanthine and guanosine nucleoside in type 2 diabetic patients (T2DM group) without trace or large albuminuria were significantly higher than those in healthy individuals. However, the content of these 5 metabolites was not significant between type 2 diabetes patients who do not have trace or large albuminuria (T2DM group) and type 2 diabetes patients with microalbuminuria (T2DM+micro group) difference.

2.7 Logistic regression and receiver operating characteristics (ROC) analysis

Logistic regression models are used to further examine the association between these markers and disease outcomes. It was found that higher levels of valine and lower levels of xanthine, N1-methylguanosine, 7-methyluric acid, and histidine and non-diabetic-induced micro or large albuminuria (macro group ) Is significantly correlated with odds ratios (OR) (95% confidence interval) of 3.04 (1.41-6.52), 4.00 (1.78- 9.01), 9.23 (3.81-22.40), 4.77 (2.09-10.87), and 0.42 (0.21- 0.83). Receiver operating characteristics (ROC) analysis was also performed to investigate whether the content of these metabolites can distinguish between healthy individuals and patients with minimal or large albuminuria (macro group) caused by non-diabetics, or those without micro or large albuminuria Of type 2 diabetes patients (T2DM group) and type 2 diabetes patients with microalbuminuria (T2DM+micro group). In order to distinguish between healthy individuals and non-diabetic patients with micro or large albuminuria (macro group), the area under the curve with valine (0.678), xanthine (0.667) and 7-methyluric acid (0.686) ( Compared with the AUC value, the area under the curve (AUC) value of N1-methylguanosine is the highest 0.752. After combining the content of N1-methylguanosine with 7-methyluric acid or xanthine, the combined area under the curve (AUC) values were 0.668 and 0.667, respectively (Table 4 and Figure 5). Compared with patients with type 2 diabetes (T2DM group) who do not have microalbuminuria or large amounts of albuminuria, lower levels of N1-methylguanosine are still comparable to patients with type 2 diabetes who have microalbuminuria (T2DM+microgroup) The risk is significantly correlated (odds ratio (OR) (95% confidence interval) = 2.75 (1.46-5.21), area under the curve (AUC) value = 0.624). After combining the content of N1-methylguanosine with 7-methyluric acid or xanthine, the combined area under the curve (AUC) values were 0.640 and 0.625, respectively (Table 4 and Figure 5). Table 4. The 95% confidence interval and the odds ratio (OR) of the area under the curve (AUC) value for predicting nephropathy with different metabolite biomarkers in a univariate logistic regression model. Healthy individuals than macro group T2DM group is better than T2DM+micro group Odds ratio 95% confidence interval P value Area under the curve Odds ratio 95% confidence interval P value Area under the curve Valine Natural logarithm_valine 3.04 1.41-6.52 0.004 0.678 0.65 0.40-1.06 0.087 0.578 Xanthosine 0.667 0.612 Median ⁺ reference reference reference reference >Median 4.00 1.78-9.01 0.001 2.48 1.32-4.67 0.005 N1 -methylguanosine 0.752 0.624 Median ⁺ reference reference reference reference >Median 9.23 3.81-22.40 8.88x10 ^-7 2.75 1.46-5.21 0.02 7-- methyl uric acid 0.686 0.578 Median ⁺ reference reference reference reference >Median 4.77 2.09-10.87 2.05x10 ^-4 1.88 1.00-3.52 0.049 Histidine Natural logarithm_histidine 0.42 0.21-0.83 0.013 0.627 0.66 0.45-0.96 0.031 0.612 Aspartic acid 0.518 0.604 Median ⁺ reference reference reference reference >Median 0.87 0.37-2.03 0.740 2.33 1.03-5.29 0.043 Combine N1 -methylguanosine and 7 -methyluric acid 0.668 0.640 Group 2 reference reference reference reference Group 1 5.30 2.10-13.37 4.18x10 ^-4 3.83 1.89-7.77 2.01x10 ^-4 Combines N1 -methylguanosine and xanthine 0.667 0.625 Group 2 reference reference reference reference Group 1 4.23 1.84-9.72 0.001 2.85 1.50-5.43 0.001 Healthy individuals group for patients with minimal or large albuminuria caused by non-diabetics (macro group ) : the median of xanthosine is 0.8024; the median of N1-methylguanosine is 0.6778; 7-methyluric acid The median of aspartic acid is 1.0107; the median of aspartic acid is 2.0788. Type 2 diabetes patients without microalbuminuria (T2DM group ) with microalbuminuria (T2DM group ) versus type 2 diabetes patients with microalbuminuria (T2DM+micro group ) : The median of xanthine is 1.0102; N1 -The median of methylguanosine is 0.7360; the median of 7-methyluric acid is 0.7993; the median of aspartic acid is 3.2213. Combining N1 -methylguanosine and 7 -methyluric acid Group 1: N1-methylguanosine> median and 7-methyluric acid> median, Group 2: N1-methylguanosine> medium Digits and 7-methyluric acid = median ⁺ , N1-methylguanosine = median ⁺ and 7-methyluric acid> median, N1-methylguanosine = median ⁺ and 7- Methyluric acid = median ⁺ combined N1 -methylguanosine and xanthosine Group 1: N1-methylguanosine> median and xanthosine> median, Group 2: N1- Methylguanosine> median and xanthosine = median ⁺ , N1-methylguanosine = median ⁺ and xanthosine> median, N1-methylguanosine = median ⁺ And xanthine nucleotide = median ⁺

2.8 Model comparison with metabolite biomarkers and metabolite biomarkers and clinical variables

By adjusting the clinical variables (systolic blood pressure and diastolic blood pressure) in the multivariate model to distinguish between healthy individuals and non-diabetic patients with micro or large albuminuria (macro group), targeting xanthine, N1-methylguanosine, The area under the curve (AUC) values of 7-methyluric acid and the combination of xanthine and N1-methylguanosine are 0.983 (95% confidence interval = 0.96-1.00) and 0.987 (95% confidence interval = 0.97) -1.00), 0.940 (95% confidence interval = 0.89-0.99), and 0.983 (95% confidence interval = 0.97-1.00) (Figure 4). For the discrimination between type 2 diabetic patients without microalbuminuria (T2DM group) and microalbuminuria (T2DM+micro group), for N1-methylguanosine The adjusted area under the curve (AUC) values of 7-methyluric acid, xanthine, and the combination of xanthine and N1-methylguanosine were 0.723 (95% confidence interval = 0.64-0.81), 0.719 (95% confidence interval = 0.64-0.80), 0.716 (95% confidence interval = 0.64-0.80), and 0.734 (95% confidence interval = 0.65-0.81) (Figure 5).

3. Discussion and conclusion

In clinical practice, estimated glomerular filtration rate (eGFR) and proteinuria are still common markers of nephropathy and chronic kidney disease (CKD) progression. Microalbuminuria is considered a predictor of progression to proteinuria ^22. In this study, in order to discover sensitive metabolite biomarkers, our individuals were divided into healthy individuals, patients with minimal or large albuminuria (macro group) caused by non-diabetics, and those with no micro or large albuminuria. Type 2 diabetes patients (T2DM group) and type 2 diabetes patients with microalbuminuria (T2DM+micro group) for metabolite analysis and comparison. As far as we know, our current study uses quantitative, non-targeted liquid chromatography-mass spectrometry (LC-MS) for the first time to examine the urine of patients with type 2 diabetes (T2DM group) and diabetic nephropathy (DN) Liquid Metabolism Group. The contents of N1-methylguanosine, 7-methyluric acid, and xanthosine have higher area under the curve (AUC) values of 0.752, 0.686, and 0.667, respectively, to distinguish between healthy individuals and traces from non-diabetics Or patients with macroalbuminuria (macro group) (with macroalbuminuria/microalbuminuria). For distinguishing type 2 diabetes patients (T2DM group) without microalbuminuria (T2DM group) and type 2 diabetes patients with microalbuminuria (T2DM+micro group), N1-methylguanosine has the highest area under the curve The (AUC) value is 0.624, which is the most suitable among all the markers. Our cross-sectional study shows that urine metabolomics may be a clinically useful platform that can provide metabolic characteristics and new biochemical insights for patients with diabetes and diabetic nephropathy. Specifically, valine, aspartic acid, and histidine can be metabolic markers predicting diabetes (DM) because they are used in type 2 diabetes patients (T2DM group) who do not have trace or large amounts of albuminuria and The content in type 2 diabetes patients with microalbuminuria (T2DM+micro group) was significantly higher. Xanthosine and N1-methylguanosine may be potential markers to predict the development of nephropathy caused by type 2 diabetic patients (T2DM group) who do not have micro or large albuminuria.

Our results show that the urine levels of tyrosine, proline, and threonine in non-diabetic patients with micro or large albuminuria (macro group) are higher than healthy individuals. The level of methionine in non-diabetic patients with micro or large albuminuria (macro group) was lower than that of healthy individuals. Compared with type 2 diabetic patients (T2DM group) without microalbuminuria (T2DM group), aspartic acid was found to be significantly lower in type 2 diabetic patients with microalbuminuria (T2DM+micro group). According to reports, during the period from microalbuminuria to macroalbuminuria, patients with type 2 diabetes without microalbuminuria (T2DM group) have lower plasma histidine levels and urine glutamine/tyrosine levels ²¹ . In our study, and compared with healthy individuals, type 2 diabetes patients (T2DM group) who do not have trace or large albuminuria have higher urine histidine levels, and compared with healthy individuals and Type 2 diabetes patients without microalbuminuria (T2DM group), non-diabetic patients with microalbuminuria (macro group), and type 2 diabetes patients with microalbuminuria (T2DM+micro The urine histidine content in group) was lower ( P >0.05). The down-regulation of plasma histidine is associated with protein-energy expenditure, inflammation, oxidative stress, and mortality in patients with chronic kidney disease (CKD) ²³ . Alternation of amino acids may indicate chronic inflammation, oxidative stress, and progressive renal function ²¹ .

Purine metabolism is known to enhance mitochondrial defense mechanisms under oxidative stress conditions, including diabetes, activation. Recently, it was reported that inosine elevated in the plasma of ²⁴ individuals in diabetes. In our findings, the increased levels of hypoxanthine, xanthine, guanosine, and xanthine nucleoside in the urine of diabetic patients may be related to the mitochondrial antioxidant defense process.

Patients with micro or macro albuminuria (macro group) caused by non-diabetes decreased in urine guanine content, or patients with micro or macro albuminuria (macro group) caused by non-diabetics, and second patients with microalbuminuria The decrease of xanthine nucleosides in urine of type-type diabetes patients (T2DM+micro group) may be due to the inhibition of mitochondrial function, which has been reported by Sharma, K. et al. ²⁰

Compared with the N1-methylguanosine content in healthy individuals and patients with type 2 diabetes who do not have trace or large albuminuria (T2DM group), in non-diabetic patients with trace or large albuminuria (macro group) ) ( P >0.001) and type 2 diabetes patients with microalbuminuria (T2DM+micro group) ( P >0.001), the concentration of N1-methylguanosine was significantly reduced. However, we found that there was no significant difference in guanosine content in type 2 diabetes patients with microalbuminuria (T2DM+micro group) and type 2 diabetes patients without micro or large albuminuria (T2DM group) .

Because these modified nucleosides are the end products of RNA, they are not re-incorporated into newly synthesized RNA molecules, they are usually excreted in urine. Because methylated nucleosides are reported to be elevated in serum by ²⁵ , the patients with micro or large albuminuria (macro group) caused by non-diabetics and type 2 diabetes patients with microalbuminuria (T2DM+micro group) The lower amount of N1-methylguanosine in urine may be due to reduced renal clearance.

Xanthine oxidase catalyzes methylxanthine to form methyluric acid, and there are four isoforms of methyluric acid: 1-, 3-, 7-, and 9-methyluric acid. In this study, we found that the concentration of 7-methyluric acid in urine was significantly lower in patients with non-diabetic-induced micro or macro albuminuria (macro group) than healthy individuals. The decrease of 7-methyluric acid in urine of non-diabetic patients with micro or large albuminuria (macro group) may also be due to decreased renal clearance.

We found that the content of most amino acids was significantly changed in the urine of type 2 diabetic patients (T2DM group) who did not have trace or large albuminuria. A set of amino acid markers in urine can be used to assess the risk of developing diabetes (DM). Because their high area under the curve (AUC) value can distinguish type 2 diabetes patients with microalbuminuria (T2DM+micro group) and type 2 diabetes patients without microalbuminuria (T2DM group), N1- Methylguanosine and xanthine nucleosides can be used as markers to predict the development of nephropathy in type 2 diabetic patients (T2DM group) without microalbuminuria.

In short, we found that at least N1-methylguanosine, 7-methyluric acid, xanthine nucleoside, and metabolites such as histidine, valine and other amino acids can be used as specific biomarkers for the detection of nephropathy. . Therefore, the present invention provides a nephropathy screening technology that can specifically detect patients suffering from nephropathy, suspected of suffering from nephropathy, or at risk of nephropathy, and can be used at an early stage (that is, before any symptoms occur) Indicator. The technology of the present invention can be used to develop kits or technology platforms for detecting nephropathy, using amines including N1-methylguanosine, 7-methyluric acid, xanthine nucleosides, and histidine, and valine The metabolites of base acids are used as biomarkers for screening nephropathy. The technology of the present invention can further combine the measurement of physiological parameters known in the art to improve the detection rate and accuracy of nephropathy. The technology of the present invention can identify different stages of nephropathy including early stage (microalbuminuria, no obvious symptoms). It can also be used as an indicator to monitor the progress of nephropathy. The patient's condition can be tracked regularly and continuously, so that the treatment plan can be adjusted in time so that the patient can avoid or delay the deterioration. Reference 1. Lipscombe, LL; Hux, JE, Trends in diabetes prevalence, incidence, and mortality in Ontario, Canada 1995–2005: a population-based study. The Lancet 2007, 369, (9563), 750-756. 2 . Wild, S.; Roglic, G.; Green, A.; Sicree, R.; King, H., Global prevalence of diabetes estimates for the year 2000 and projections for 2030. Diabetes care 2004, 27, (5), 1047-1053. 3. Hallan, SI; Coresh, J.; Astor, BC; Åsberg, A.; Powe, NR; Romundstad, S.; Hallan, HA; Lydersen, S.; Holmen, J., International comparison of the relationship of chronic kidney disease prevalence and ESRD risk. Journal of the American Society of Nephrology 2006, 17, (8), 2275-2284. 4. Collins, AJ; Foley, RN; Chavers, B.; Gilbertson, D.; Herzog, C.; Johansen, K.; Kasiske, B.; Kutner, N.; Liu, J.; St Peter, W.,'United States Renal Data System 2011 Annual Data Report: Atlas of chronic kidney disease & end- stage renal disease in the United States. American journal of kidney diseases: the official journal of the National Kidney Foundation 2012, 59, (1 Suppl 1), A7, e1. 5. Hoffmann, F.; Haastert, B.; Koch, M.; Giani, G.; Glaeske, G.; Icks, A., The effect of diabetes on incidence and mortality in end-stage renal disease in Germany. Nephrology Dialysis Transplantation 2011, 26, (5), 1634-1640. 6. Association, AD, Standards of medical care in diabetes—2010. Diabetes care 2010, 33, (Supplement 1), S11-S61. 7. Anderson, J.; Glynn, LG, Definition of chronic kidney disease and measurement of kidney function in original research papers: a review of the literature. Nephrol Dial Transplant 2011, 26, (9 ), 2793-8. 8. Gross, JL; de Azevedo, MJ; Silveiro, SP; Canani, LH; Caramori, ML; Zelmanovitz, T., Diabetic nephropathy: diagnosis, prevention, and treatment. Diabetes Care 2005, 28, (1), 164-76. 9. Dronavalli, S.; Duka, I.; Bakris, GL, The pathogenesis of diabetic nephropathy. Nature clinical practice Endocrinology & metabolism 2008, 4, (8), 444-452. 10. Yamada, T.; Komatsu, M.; Komiya, I.; Miyahara, Y.; Shima, Y.; Ma tsuzaki, M.; Ishikawa, Y.; Mita, R.; Fujiwara, M.; Furusato, N.; Nishi, K.; Aizawa, T., Development, progression, and regression of microalbuminuria in Japanese patients with type 2 diabetes under tight glycemic and blood pressure control: the Kashiwa study. Diabetes Care 2005, 28, (11), 2733-8. 11. Bhensdadia, NM; Hunt, KJ; Lopes-Virella, MF; Michael Tucker, J.; Mataria, MR; Alge, JL; Neely, BA; Janech, MG; Arthur, JM; Veterans Affairs Diabetes Trial study, g., Urine haptoglobin levels predict early renal functional decline in patients with type 2 diabetes. Kidney Int 2013, 83, (6 ), 1136-43. 12. MacIsaac, RJ; Tsalamandris, C.; Panagiotopoulos, S.; Smith, TJ; McNeil, KJ; Jerums, G., Nonalbuminuric renal insufficiency in type 2 diabetes. Diabetes Care 2004, 27, ( 1), 195-200. 13. Afghahi, H.; Cederholm, J.; Eliasson, B.; Zethelius, B.; Gudbjornsdottir, S.; Hadimeri, H.; Svensson, MK, Risk factors for the development of albuminuria and renal impairment in type 2 diabetes--the Swedish Nati onal Diabetes Register (NDR). Nephrol Dial Transplant 2011, 26, (4), 1236-43. 14. Bouatra, S.; Aziat, F.; Mandal, R.; Guo, AC; Wilson, MR; Knox, C .; Bjorndahl, TC; Krishnamurthy, R.; Saleem, F.; Liu, P.; Dame, ZT; Poelzer, J.; Huynh, J.; Yallou, FS; Psychogios, N.; Dong, E.; Bogumil , R.; Roehring, C.; Wishart, DS, The human urine metabolome. PLoS One 2013, 8, (9), e73076. 15. Luan, H.; Liu, LF; Tang, Z.; Zhang, M. ; Chua, KK; Song, JX; Mok, VC; Li, M.; Cai, Z., Comprehensive urinary metabolomic profiling and identification of potential noninvasive marker for idiopathic Parkinson's disease. Sci Rep 2015, 5, 13888. 16. Wang, TJ; Larson, MG; Vasan, RS; Cheng, S.; Rhee, EP; McCabe, E.; Lewis, GD; Fox, CS; Jacques, PF; Fernandez, C.; O'Donnell, CJ; Carr, SA ; Mootha, VK; Florez, JC; Souza, A.; Melander, O.; Clish, CB; Gerszten, RE, Metabolite profiles and the risk of developing diabetes. Nature Medicine 2011, 17, (4), 448-U83. 17. Zhao, XJ; Fritsche, J.; Wang, JS; Chen, J.; Rittig, K.; Schmit t-Kopplin, P.; Fritsche, A.; Haring, HU; Schleicher, ED; Xu, GW; Lehmann, R., Metabonomic fingerprints of fasting plasma and spot urine reveal human pre-diabetic metabolic traits. Metabolomics 2010, 6, (3), 362-374. 18. Wei, T.; Zhao, L.; Jia, J.; Xia, H.; Du, Y.; Lin, Q.; Lin, X.; Ye, X.; Yan, Z.; Gao, H., Metabonomic analysis of potential biomarkers and drug targets involved in diabetic nephropathy mice. Sci Rep 2015, 5, 11998. 19. Ng, DP; Salim, A.; Liu, Y.; Zou, L.; Xu, FG; Huang, S.; Leong, H.; Ong, CN, A metabolomic study of low estimated GFR in non-proteinuric type 2 diabetes mellitus. Diabetologia 2012, 55, (2), 499-508. 20. Sharma, K.; Karl, B.; Mathew, AV; Gangoiti, JA; Wassel, CL; Saito, R.; Pu, M.; Sharma, S.; You, YH; Wang, L.; Diamond- Stanic, M.; Lindenmeyer, MT; Forsblom, C.; Wu, W.; Ix, JH; Ideker, T.; Kopp, JB; Nigam, SK; Cohen, CD; Groop, PH; Barshop, BA; Natarajan, L.; Nyhan, WL; Naviaux, RK, Metabolomics reveals signature of mitochondrial dysfunction in diabetic kid ney disease. J Am Soc Nephrol 2013, 24, (11), 1901-12. 21. Pena, MJ; Lambers Heerspink, HJ; Hellemons, ME; Friedrich, T.; Dallmann, G.; Lajer, M.; Bakker , SJ; Gansevoort, RT; Rossing, P.; de Zeeuw, D.; Roscioni, SS, Urine and plasma metabolites predict the development of diabetic nephropathy in individuals with Type 2 diabetes mellitus. Diabet Med 2014, 31, (9), 1138-47. 22. Hojs, R.; Ekart, R.; Bevc, S.; Hojs, N., Biomarkers of Renal Disease and Progression in Patients with Diabetes. J Clin Med 2015, 4, (5), 1010- 24. 23. Watanabe, M.; Suliman, ME; Qureshi, AR; Garcia-Lopez, E.; Barany, P.; Heimburger, O.; Stenvinkel, P.; Lindholm, B., Consequences of low plasma histidine in chronic kidney disease patients: associations with inflammation, oxidative stress, and mortality. Am J Clin Nutr 2008, 87, (6), 1860-6. 24. Kalim, S.; Clish, CB; Deferio, JJ; Ortiz, G. ; Moffet, AS; Gerszten, RE; Thadhani, R.; Rhee, EP, Cross-sectional examination of metabolites and metabolic phenotypes in uremia. B MC Nephrol 2015, 16, 98. 25. Niwa, T.; Takeda, N.; Yoshizumi, H., RNA metabolism in uremic patients: accumulation of modified ribonucleosides in uremic serum. Technical note. Kidney Int 1998, 53, (6 ), 1801-6.

no

To illustrate the present invention, specific examples are described below. However, it should be understood that the present invention is not limited to the preferred embodiments shown.

In the schema:

Figures 1A-1D show the principal component analysis (PCA) of the urine metabolome. Figure 1A, positive mode. Figure 1B, negative mode. Figure 1C, the loading diagram of the positive mode. Figure 1D, positive mode principal component analysis (PCA) after removing metformin ion.

Figures 2A-2B show statistically significant volcano graphs of peak changes between the two groups. Figure 2A, positive pattern: comparison between healthy individuals and non-diabetic patients with microalbuminuria or macroalbuminuria (macro group) ( P >0.05; fold change>2); healthy individuals and type 2 diabetes without microalbuminuria Comparison of patients (T2DM group) ( P >0.01; fold change>2); Type 2 diabetes patients without microalbuminuria (T2DM group) and type 2 diabetes patients with microalbuminuria (T2DM+micro group) The comparison ( P >0.05; fold change>2). Figure 2B, negative pattern: comparison between healthy individuals and non-diabetic patients with microalbuminuria (macro group) ( P >0.05; fold change>2); healthy individuals and type 2 diabetes without microalbuminuria Comparison of patients (T2DM group) ( P >0.001; fold change>2); Type 2 diabetes patients without microalbuminuria (T2DM group) and type 2 diabetes patients with microalbuminuria (T2DM+micro group) ) Comparison ( P >0.05; fold change> 2).

Figures 3A-3F show box plots of the six biomarkers found in urine samples. Figure 3A, xanthine nucleosides; Figure 3B, valine; Figure 3C, 7-methyluric acid; Figure 3D, N1-methylguanine; Figure 3E, histidine; and Figure 3F, aspartic acid (* P >0.05; ** P >0.01; *** P >0.005; **** P > 0.001).

Figures 4A-4D show the ability to distinguish metabolic biomarkers between healthy individuals and non-diabetic patients with minimal or large albuminuria (macro group), including metabolic biomarkers, fasting blood glucose, and diastolic blood pressure (DBP) The complete model. Metabolic biomarkers are included in each model, Figure 4A, N1-methylguanosine; Figure 4B, 7-methyluric acid; Figure 4C, xanthine nucleosides; and Figure 4D, N1-methylguanosine and xanthine A combination of nucleosides.

Figures 5A-5D show the ability to distinguish metabolic biomarkers between type 2 diabetes patients without microalbuminuria (T2DM group) and type 2 diabetes patients with microalbuminuria (T2DM+micro group) and Complete models including metabolic biomarkers, fasting blood glucose, and diastolic blood pressure (DBP). Metabolic biomarkers are included in each model, Figure 5A, N1-methylguanosine; Figure 5B, 7-methyluric acid; Figure 5C, Xanthine nucleosides; and Figure 5D, N1-methylguanosine and xanthine A combination of nucleosides.

no

Claims (18)

A method for detecting individual nephropathy, the method includes: (i) Provide a biological sample obtained from the individual to be tested; (ii) Performing a first test, which includes determining the content of the first biomarker in the biological sample to obtain the first detection content, and comparing the first detection content with the first reference content of the first biomarker to obtain A first comparison result, and based on the first comparison result to assess whether the individual suffers from nephropathy or is at risk of developing nephropathy, wherein the first biomarker is selected from N1-methylguanosine, 7-methyluric acid, yellow A group of purine nucleosides, histidine, and any combination thereof, and compared to the first reference level, a decrease in the first detected level indicates that the individual suffers from nephropathy or is at risk of developing nephropathy; and/or (iii) Performing a second test, which includes determining the content of a second biomarker in the biological sample to obtain a second detection content, and comparing the second detection content with a second reference content of the second biomarker to obtain A second comparison result, and based on the second comparison result to assess whether the individual suffers from nephropathy or is at risk of developing nephropathy, wherein the second biomarker is valine, and compared to the second reference content, the first 2. An increase in the detected content indicates that the individual suffers from nephropathy or is at risk of developing nephropathy. The method of claim 1, wherein the detection is performed by mass spectrometry. The method of claim 1, wherein the biological sample is a urine sample. The method of claim 1, further comprising determining at least one physiological parameter in the biological sample. Such as the method of claim 4, wherein the physiological parameter is selected from the group consisting of age, sex, systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (fasting blood glucose) , FBG), hemoglobin A1c (hemoglobin A1c, HbA1c), duration of diabetes, creatinine, estimated glomerular filtration rate (eGFR), albuminuria, urine albumin to creatinine ratio ( albumin to creatinine ratio, ACR), other amino acids, and any combination thereof. The method of claim 1, wherein the individual is a diabetic patient. The method of claim 1, further comprising a treatment method for treating nephropathy. A method for monitoring the progression of nephropathy in patients suffering from nephropathy, the method comprising: (a) Provide early biological samples from the patient at an early time point; (b) Provide a later biological sample from the patient at a later time point, wherein the later time point is later than the early time point; (c) Performing the first test, which includes determining the content of the first biomarker in the early biological sample and the later biological sample respectively, to obtain the early detection content and the later detection content of the first biomarker, respectively, The early detection content of the first biomarker is compared with the later detection content to obtain a first comparison result, and the patient’s nephropathy progression is evaluated based on the first comparison result, wherein the first biomarker is selected from N1-A Guanosine, 7-methyluric acid, xanthine, histidine, and any combination thereof, and compared with the early detection content of the first biomarker, the first biomarker The decrease in the later detection level indicates the progression of nephropathy in the patient; and/or (d) Performing a second test, which includes determining the content of the second biomarker in the early biological sample and the later biological sample to obtain the early detection content and the later detection content of the second biomarker respectively, Comparing the early detection content of the second biomarker with the later detection content to obtain a second comparison result, and assessing the patient’s nephropathy progression based on the second comparison result, wherein the second biomarker is valine, And compared with the early detection content of the second biomarker, the increase of the later detection content of the second biomarker indicates the progression of nephropathy in the patient. The method of claim 8, wherein the detection is performed by mass spectrometry. The method of claim 8, wherein the biological sample is a urine sample. The method of claim 8, further comprising determining at least one physiological parameter in the biological sample. The method of claim 11, wherein the physiological parameter is selected from the group consisting of age, gender, systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), heme A1c (HbA1c), diabetes Duration, creatinine, estimated glomerular filtration rate (eGFR), albuminuria, urine albumin to creatinine ratio (ACR), other amino acids, and any combination thereof. The method of claim 8, wherein the individual is a diabetic patient. The method of claim 8, further comprising a treatment method for treating nephropathy. A kit for implementing the method of any one of claims 1 to 13, comprising a first reagent that specifically recognizes the first biomarker, and/or a second reagent that specifically recognizes the second biomarker , And instructions for using the kit to detect the presence or content of the first biomarker and/or the second biomarker. Such as the kit of claim 15, wherein the reagent is connected to a detectable label. A use of a first reagent, which is selected from the group consisting of (i) molecules that specifically recognize N1-methylguanosine, (ii) molecules that specifically recognize 7-methyluric acid, (iii) Molecules that specifically recognize xanthine nucleosides, (iv) Molecules that specifically recognize histidine, and (v) any combination of (i) to (iv), used in one method to perform as in claim 1 A method for detecting nephropathy in an individual in need as defined in any one of to 7 or for monitoring nephropathy in a patient suffering from nephropathy as defined in any one of claims 8 to 14 Progressive methods, or for the preparation of kits or compositions for use in the method for detecting nephropathy in individuals in need as defined in any one of claims 1 to 7, or as claimed The method defined in any one of 8 to 14 for monitoring the progression of nephropathy in a patient suffering from nephropathy. Use of a second reagent, which specifically recognizes valine, and is used in a method for detecting nephropathy in an individual in need as defined in any one of claims 1 to 7 Method, or a method for monitoring the progression of nephropathy in patients suffering from nephropathy as defined in any one of claims 8 to 14, or for preparing a kit or composition for performing as claimed in claim 1. A method for detecting nephropathy in an individual in need as defined in any one of to 7 or for monitoring nephropathy in a patient suffering from nephropathy as defined in any one of claims 8 to 14 The method of progress.

Images