Diabetic nephropathy diagnostic marker combination and model construction and application

Abstract

The application discloses a diabetes nephropathy diagnosis marker combination and a model construction and application, and belongs to the technical field of disease diagnosis. A technical problem to be solved is that a marker combination in the prior art cannot specifically distinguish simple diabetes from diabetes nephropathy and cannot accurately determine a disease state of a subject. A technical solution is that a diabetes nephropathy diagnosis marker combination is composed of P02763 ORM1 and P00738 HP. The application has excellent effects in distinguishing type 2 diabetes from early diabetes nephropathy, has high AUC values, good sensitivity and specificity, can accurately distinguish disease types, and provides reliable support for early diabetes nephropathy intervention.

Description

Technical Field

[0001] This invention belongs to the field of disease diagnosis technology, specifically relating to the combination of diagnostic biomarkers for diabetic nephropathy, model construction, and application. Background Technology

[0002] Diabetic kidney disease (DKD) is one of the most common microvascular complications of diabetes mellitus (DM) and a leading cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD), placing a heavy burden on patients' families and the social healthcare system. Currently, clinical diagnosis of DKD mainly relies on urinary albumin excretion rate (UACR) and estimated glomerular filtration rate (eGFR), but these traditional diagnostic indicators have significant limitations and are insufficient to meet the needs of precise clinical diagnosis and treatment as well as disease prevention and control.

[0003] Urinary albumin excretion rate (eGFR) is a commonly used indicator for clinical screening of diabetic kidney disease (DKD). However, this indicator is affected by various factors, including exercise, infection, fever, abnormally high blood pressure and blood sugar, and pregnancy, all of which can lead to physiologically elevated levels, affecting diagnostic accuracy. Furthermore, albuminuria lacks specificity; many non-diabetic kidney diseases can also present with microalbuminuria, making it difficult to effectively differentiate DKD from other chronic kidney diseases. While eGFR is often estimated based on serum creatinine, reflecting renal filtration function, serum creatinine levels are influenced by factors such as age, muscle volume, and protein intake. In cases where the kidneys have compensatory function, even with early glomerular damage, eGFR may remain normal, leading to diagnostic delays.

[0004] Kidney biopsy is the gold standard for diagnosing disseminated kidney disease (DKD), clearly demonstrating pathological changes such as glomerular basement membrane thickening and mesangial widening. However, this procedure is invasive, carrying risks of bleeding, perirenal hematoma, and infection, and is not suitable for special patients with coagulation disorders or severe renal insufficiency, thus limiting its clinical application. While cystatin C has been proven superior to serum creatinine in assessing glomerular filtration function, it still cannot achieve early and accurate diagnosis of DKD or predict disease progression. The inadequacies of the current diagnostic system cause many DKD patients to miss the opportunity for early intervention. Early DKD lesions are reversible, but once it progresses to the middle or late stages, kidney damage is irreversible, resulting in extremely poor treatment outcomes. Therefore, developing a highly specific, sensitive, non-invasive, and convenient diagnostic biomarker for DKD to compensate for the deficiencies of existing diagnostic methods and achieve early screening and accurate diagnosis has become a pressing technical challenge in the field of clinical medicine.

[0005] Relevant patent documents retrieved: Publication country: United States, Publication number: US20230152333A1, Publication date: May 18, 2023. This document discloses a method for diagnosing chronic kidney disease (CKD) or glomerular disease in a subject, comprising: measuring the levels of at least 5, at least 6, or at least 7 protein biomarkers in a subject's urine sample, said biomarkers being selected from the following groups: immunoglobulin γ-2 chain constant region (IGHG2), serum albumin (ALB), ceruloplasmin (CP), thrombin (F2), haptoglobin β chain (HP), α1-antitrypsin (SERPINA1), immunoglobulin The following proteins are listed: white κ chain variable region type I HK102 subtype (IGKV1-5), myoglobin (MB), α1-acid glycoprotein 1 (ORM1), serum transferrin (TF), α1B-glycoprotein (A1BG), immunoglobulin κ chain variable region type I Daudi subtype (P04432), ganglioside GM2 activator protein (GM2A), α1-acid glycoprotein 2 (ORM2), zinc-α2-glycoprotein (AZGP1), albumin (AFM), NHL repeat sequence protein 3 (NHLC3), and inter-α-trypsin inhibitor heavy chain H2 (ITIH2).

[0006] Relevant non-patent literature retrieved: The journal title is *China Modern Medicine Journal*, and the article title is "Correlation between Serum HMGA2, RBP, and SDF-1 Levels and Tervaert Staging in Pathological Biopsy of Diabetic Kidney Disease," volume number 2025, 27(10), publication date 2025.11.21. This article discloses the use of enzyme-linked immunosorbent assay (ELISA) to detect serum HMGA2, RBP, and SDF-1 levels and analyzes their correlation with clinical indicators and renal pathological characteristics. A logistic regression prediction model was constructed. The results showed that with the progression of DKD pathological stages, serum HMGA2, RBP, and SDF-1 levels showed a monotonically increasing trend (P<0.001). The levels of the three serum biomarkers were positively correlated with disease duration, Tervaert stage, HbA1c, 24-hour urinary protein quantification, systolic blood pressure, serum creatinine, basement membrane thickness, mesangial area percentage, interstitial fibrosis and tubular atrophy (IFTA) score, and podocyte foot process width (P<0.05), and significantly negatively correlated with estimated glomerular filtration rate (eGFR) (P<0.01). Multivariate logistic regression analysis showed that HbA1c, 24-hour urinary protein quantification, serum HMGA2, RBP, and SDF-1 were all independent risk factors for DKD stage progression, while eGFR was a protective factor.

[0007] The prior art represented by the aforementioned documents has at least the following unresolved technical problems or defects: The existing biomarker combination lacks the ability to specifically distinguish between diabetes and diabetic nephropathy, and cannot accurately determine whether a subject has simple diabetes or has progressed to diabetic nephropathy. Summary of the Invention

[0008] The purpose of this invention is to provide: A combination of diagnostic biomarkers for diabetic nephropathy, model construction and application, and related technologies, to solve the technical problems in existing biomarker combinations such as the inability to specifically distinguish between simple diabetes and diabetic nephropathy and the difficulty in accurately determining the disease status of subjects, or a combination thereof.

[0009] Terminology Explanation: Unless otherwise defined, all technical terms used herein have the same meanings as commonly understood by one of ordinary skill in the art to which this subject matter pertains. Unless otherwise stated, all patents, patent inventions, and disclosures cited throughout this document are incorporated herein by reference in their entirety. Where multiple definitions exist for terms herein, the definitions provided in this chapter shall prevail.

[0010] It should be understood that the above brief description and the following detailed description are exemplary and for illustrative purposes only, and do not limit the subject matter of the invention in any way. In this invention, the singular is used in conjunction with the plural unless otherwise specifically stated. It should also be noted that, unless otherwise stated, the use of “or” or “or” means “and / or”. Furthermore, the use of the term “comprising” and other forms such as “including,” “containing,” and “contains” are not limiting.

[0011] Definitions of standard terms can be found in the reference books "Internal Medicine (10th Edition), People's Medical Publishing House, authors: Ge Junbo, Wang Chen, Wang Jian'an, 2024.06" and "Clinical Proteomics, Science Press, authors: Qiu Zongyin, Yin Yibing, 2008.04".

[0012] Unless otherwise stated, conventional methods within the scope of this art, such as ultracentrifugation, shall be used. Unless specifically defined, the use of all commercially available products used herein shall employ standard techniques. For example, they may be performed using the manufacturer's instructions for use with the kit, or in accordance with methods known in the art or the description of this invention. The techniques and methods described herein are generally performed according to conventional methods well known in the art, based on the descriptions in the various general and more specific documents cited and discussed in this specification.

[0013] The terms "optional / arbitrary" or "optionally / arbitrarily" mean that the event or situation subsequently described may or may not occur, including both the occurrence and non-occurrence of the event or situation. For example, according to the definition below: the diagnostic product includes any one or more of reagent kits, test strips, chips, devices, and detection systems. This indicates that the diagnostic product can be a reagent kit, or the product can be a test strip, or the product can be a chip, or the product can be a reagent kit, test strip, chip, device, and detection system.

[0014] The term "diabetic nephropathy" used in this article refers to Diabetic Kidney Disease (DKD), a microvascular complication of the kidneys caused by long-term hyperglycemia. It is characterized by glomerular sclerosis and tubulointerstitial damage, and clinically manifests as proteinuria and progressive decline in renal function. It is one of the main causes of end-stage renal disease.

[0015] The term "type 2 diabetes" used in this article refers to Type 2 Diabetes Mellitus (T2DM), a metabolic disease with insulin resistance and relative insulin insufficiency as its core pathogenesis. It mainly has an onset in adulthood, is characterized by persistent hyperglycemia, and can induce chronic complications in multiple systems.

[0016] The term "logistic regression" used in this article refers to Logistic Regression (LR), a generalized linear statistical model that maps linear prediction results to a probability interval of 0-1 using a log-odds function. It is mainly used for binary and multi-class classification, risk factor analysis, and predictive modeling.

[0017] The term "ROC curve" used in this article refers to the Receiver Operating Characteristic Curve (ROC Curve), an evaluation curve plotted with the true positive rate (sensitivity) of the classification model on the vertical axis and the false positive rate (1-specificity) on the horizontal axis. The area under the curve (AUC) is the core quantitative indicator of the model's discriminative efficacy.

[0018] The term "Youden Index" used in this article refers to the Youden Index (YI), a comprehensive evaluation index for diagnostic tests. It is calculated as sensitivity + specificity - 1, and its value ranges from 0 to 1. A higher value indicates higher diagnostic accuracy and is used to determine the optimal diagnostic threshold.

[0019] The term “data-independent acquisition” used in this article refers to Data Independent Acquisition (DIA), a mass spectrometry quantitative acquisition mode that divides the mass-to-charge ratio scanning range into a continuous window and performs uniform fragmentation detection on all ions within the window, achieving full coverage and reproducible quantification of peptides / proteins.

[0020] The term "parallel reaction monitoring" used in this article refers to Parallel Reaction Monitoring (PRM), a high-resolution targeted mass spectrometry quantitative technology that targets and fragments precursor ions, simultaneously detecting all fragment ions to achieve highly specific and accurate quantification of target proteins / peptides.

[0021] The term "liquid chromatography-mass spectrometry" used in this article refers to Liquid Chromatography-Mass Spectrometry (LC-MS), which combines the separation capabilities of high-performance liquid chromatography with the qualitative and quantitative capabilities of mass spectrometry for the separation and detection of proteins, peptides, and metabolites in complex biological samples.

[0022] The term “mobility peak detection threshold” used in this article refers to Mobility Peak Detection Threshold (MPDT), which is the minimum response intensity threshold used in ion mobility separation-mass spectrometry to determine whether a mobility dimension signal peak is a valid detection peak, and is used to distinguish the real signal from background noise.

[0023] The term “diabetic nephropathy diagnostic biomarker combination” used in this article refers to a combined detection index consisting of two synergistic protein biomarkers. Specifically, it refers to the combination of P02763_ORM1 and P00738_HP proteins used to differentiate between type 2 diabetes and diabetic nephropathy and to achieve early prediction of diabetic nephropathy. Diagnostic models can be constructed by detecting their expression levels, providing quantitative evidence for the prediction of diabetic nephropathy.

[0024] The term “P02763_ORM1” used in this article refers to Orosomucoid1, ORM1, which corresponds to UniProt accession number P02763. It is human α-1-acid glycoprotein 1, a plasma acute-phase reactive protein, and a candidate biomarker for diabetes and its complications.

[0025] The term “P00738_HP” used in this article refers to Haptoglobin, HP, corresponding to UniProt accession number P00738. It is human haptoglobin that can bind to and clear free hemoglobin, participate in the regulation of inflammation and oxidative stress in the body, and is a commonly used clinical plasma biomarker.

[0026] In a first aspect, the present invention provides: a combination of diagnostic biomarkers for diabetic nephropathy.

[0027] This includes: a combination of diagnostic markers for diabetic nephropathy.

[0028] The diagnostic biomarker combination for diabetic nephropathy consists of P02763_ORM1 and P00738_HP.

[0029] Based on further solutions to the technical problems of the present invention, or simultaneous solutions to multiple technical problems, the preferred solution in the technical solution provided in the first aspect of the present invention includes: The first preferred option: The diagnostic biomarker combination for diabetic nephropathy consists of P02763_ORM1 and P00738_HP. This technical solution not only solves the technical problem that "existing biomarker combinations cannot specifically distinguish between simple diabetes and diabetic nephropathy, and are difficult to accurately determine the disease status of subjects," but also further solves the technical problem of "providing a specific diagnostic biomarker combination for diabetic nephropathy."

[0030] Secondly, the present invention provides the application of the above-mentioned combination of diagnostic biomarkers for diabetic nephropathy in the preparation of diagnostic products for diabetic nephropathy.

[0031] This includes: Diagnostic products for diabetic nephropathy.

[0032] Among them, the diagnostic product for diabetic nephropathy is used to differentiate between diabetic nephropathy and type 2 diabetes.

[0033] Based on further solutions to the technical problems of the present invention, or simultaneous solutions to multiple technical problems, the preferred solution in the technical solution provided in the second aspect of the present invention includes: The first preferred option is the application of the above-mentioned combination of diagnostic biomarkers for diabetic nephropathy in the preparation of a diagnostic product for diabetic nephropathy used to differentiate between diabetic nephropathy and type 2 diabetes. This technical solution, while addressing the existing problem that "the combination of biomarkers cannot specifically distinguish between simple diabetes and diabetic nephropathy, and it is difficult to accurately determine the disease status of the subject," further addresses the technical problem of "providing a diagnostic product for diabetic nephropathy."

[0034] Thirdly, the present invention provides a diagnostic product for diabetic nephropathy.

[0035] This includes: diagnostic products.

[0036] The diagnostic products include the aforementioned combination of diagnostic biomarkers for diabetic nephropathy.

[0037] Specifically, the diagnostic products include any one or more of reagent kits, test strips, chips, devices, and detection systems.

[0038] More specifically, the diagnostic product also includes reagents for detecting the combined expression levels of diagnostic biomarkers for diabetic nephropathy.

[0039] Based on further solutions to the technical problems of the present invention, or simultaneous solutions to multiple technical problems, the preferred solution in the technical solution provided in the third aspect of the present invention includes: The first preferred embodiment is a diagnostic product for diabetic nephropathy, wherein the diagnostic product comprises the aforementioned combination of diagnostic biomarkers for diabetic nephropathy. This technical solution, while addressing the technical problem that "existing biomarker combinations cannot specifically distinguish between simple diabetes and diabetic nephropathy, and are difficult to accurately determine the subject's disease status," further addresses the technical problem of "providing a diagnostic product containing the aforementioned combination of diagnostic biomarkers for diabetic nephropathy."

[0040] Fourthly, the present invention provides a diagnostic model for diabetic nephropathy.

[0041] This includes: diagnostic models.

[0042] The diagnostic model was constructed using computer algorithms with the expression levels of a combination of diagnostic biomarkers for diabetic nephropathy as input parameters.

[0043] Specifically, the method for constructing the diagnostic model includes the following steps: S1. Collect samples from the diabetes group, diabetic nephropathy group and control group, and extract proteins; S2. Detect the expression levels of P02763_ORM1 and P00738_HP in the proteins obtained in step S1, and obtain quantitative data; S3. Using the quantitative data obtained in step S2 as the training set, a computer algorithm is used to train and obtain a diagnostic model for diabetic nephropathy.

[0044] More specifically, the samples mentioned in step S1 include any one or more of the following: urine, blood, blood spots, oral cells, semen, sperm spots, bones, hair, saliva, saliva spots, sweat, and amniotic fluid containing fetal cells.

[0045] More specifically, the computer algorithms described in step S3 include any one or more of the following: logistic regression, support vector machine, random forest, decision tree, gradient boosting tree, neural network, K-nearest neighbors, and Naive Bayes.

[0046] Based on further solutions to the technical problems of the present invention, or simultaneous solutions to multiple technical problems, the preferred solution in the technical solution provided in the fourth aspect of the present invention includes: The first preferred option is a diagnostic model for diabetic nephropathy, which is constructed using a computer algorithm with the expression levels of the aforementioned biomarker combination as input parameters. This technical solution, while addressing the existing problem that "biomarker combinations cannot specifically distinguish between simple diabetes and diabetic nephropathy, and are difficult to accurately determine the disease status of subjects," further solves the technical problem of "providing a diagnostic model for diabetic nephropathy."

[0047] Fifthly, the present invention provides the application of the above-mentioned diagnostic model in the preparation of diagnostic products for diabetic nephropathy.

[0048] In a sixth aspect, the present invention provides: a method for analyzing the expression levels of proteins associated with diabetic nephropathy.

[0049] This includes: methods.

[0050] Specifically, the method includes the following steps: detecting the expression levels of P02763_ORM1 and P00738_HP in the above-mentioned diabetic nephropathy diagnostic biomarker combination in the sample to be tested, inputting the expression levels into the above-mentioned diagnostic model, and obtaining the expression level association evaluation results of the diabetic nephropathy diagnostic biomarker combination.

[0051] Based on further solutions to the technical problems of the present invention, or simultaneous solutions to multiple technical problems, the preferred solution in the technical solution provided in the sixth aspect of the present invention includes: The first preferred embodiment is a method for analyzing the expression levels of proteins related to diabetic nephropathy. The method includes the following steps: detecting the expression levels of P02763_ORM1 and P00738_HP in the aforementioned combination of diagnostic biomarkers for diabetic nephropathy in the sample to be tested; inputting the expression levels into the aforementioned diagnostic model to obtain the correlation assessment results of the expression levels of the diagnostic biomarker combination for diabetic nephropathy. This technical solution, while addressing the technical problem that "existing biomarker combinations cannot specifically distinguish between simple diabetes and diabetic nephropathy, and are difficult to accurately determine the disease status of subjects," further addresses the technical problem of "providing a method for analyzing the expression levels of proteins related to diabetic nephropathy."

[0052] Examples 1-4 of this invention at least support the protection scope of "diabetic nephropathy diagnostic biomarker combination".

[0053] The term "diabetic nephropathy diagnostic biomarker combination" is derived from the foregoing explanation and / or the corresponding terms "P02763_ORM1 and P00738_HP" in Examples 1-4. Therefore, those skilled in the art can reasonably infer that the "diabetic nephropathy diagnostic biomarker combination," its subordinate concepts, its essentially equivalent technical means, and technical means that can replace the "diabetic nephropathy diagnostic biomarker combination" based on existing technology and conventional technical means and common knowledge, should all fall within the protection scope of the "diabetic nephropathy diagnostic biomarker combination." Replacing the "diabetic nephropathy diagnostic biomarker combination" with "diabetic nephropathy diagnostic protein molecule combination," etc., still falls within the protection scope of this invention.

[0054] Examples 1-4 of this invention at least support the protection scope of "diagnostic products for diabetic nephropathy".

[0055] The term "diagnostic product for diabetic nephropathy" is derived from the foregoing explanation and / or the corresponding terms "reagent kit," "chip," and "detection system" in Examples 1-4. Therefore, those skilled in the art can reasonably infer that "diagnostic product for diabetic nephropathy," its subordinate concepts, its essentially equivalent technical means, and technical means that can replace "diagnostic product for diabetic nephropathy" based on existing technology and conventional technical means and common knowledge, should all fall within the protection scope of "diagnostic product for diabetic nephropathy." Replacing "diagnostic product for diabetic nephropathy" with "diagnostic reagent kit for diabetic nephropathy," "diagnostic test strip for diabetic nephropathy," etc., still falls within the protection scope of this invention.

[0056] Examples 1-4 of this invention at least support the protection scope of "diabetic nephropathy diagnostic model".

[0057] The term "diagnostic model for diabetic nephropathy" is derived from the foregoing explanation and / or the corresponding statements in Examples 1-4, such as "the diagnostic model constructed based on the above-mentioned biomarker combination and logistic regression algorithm has high diagnostic efficacy." Therefore, those skilled in the art can reasonably infer that the "diagnostic model for diabetic nephropathy," its subordinate concepts, its essentially equivalent technical means, and technical means that can replace the "diagnostic model for diabetic nephropathy" within the scope of conventional technical means and common knowledge based on the existing level of technology should all fall within the protection scope of the "diagnostic model for diabetic nephropathy." Replacing the "diagnostic model for diabetic nephropathy" with the "diagnostic model for DKD," etc., still falls within the protection scope of this invention.

[0058] Examples 1-4 of this invention at least support the protection scope of "method for analyzing the expression level of proteins related to diabetic nephropathy".

[0059] The term "method for analyzing the expression level of proteins related to diabetic nephropathy" is summarized in the foregoing explanation and / or the corresponding phrase in Examples 1-4, such as "screening 40 key candidate proteins based on the quantitative data of the DIA proteome obtained in Example 1." Therefore, those skilled in the art can reasonably infer that "method for analyzing the expression level of proteins related to diabetic nephropathy," its subordinate concepts, its essentially equivalent technical means, and technical means that can replace "method for analyzing the expression level of proteins related to diabetic nephropathy" within the scope of conventional technical means and common knowledge based on the existing technical level, should all fall within the protection scope of "method for analyzing the expression level of proteins related to diabetic nephropathy." Replacing "method for analyzing the expression level of proteins related to diabetic nephropathy" with "method for analyzing the expression level of DKD-related proteins" still falls within the protection scope of this invention.

[0060] The present invention has at least the following beneficial effects: Compared with existing technologies, this invention is more effective in distinguishing between type 2 diabetes (DM) and early diabetic nephropathy (DKD). Experimental verification shows that it has a high AUC value in distinguishing between the two, with good sensitivity and specificity, and can accurately identify the disease type, providing reliable support for early DKD intervention and making up for the shortcomings of existing technologies.

[0061] Furthermore, based on the present invention: Based on the comparison of Example 4 and Comparative Examples 1-4, the present invention uses technical means to construct a combination of diagnostic biomarkers for diabetic nephropathy by combining P02763_ORM1 and P00738_HP, achieving new technical effects: better differentiation between type 2 diabetes mellitus (DM) and diabetic nephropathy (DKD).

[0062] Considering the possibility of this invention entering other countries, this invention also provides the following technical solutions: A method for diagnosing diabetic nephropathy, the method comprising using a combination of diagnostic biomarkers, diagnostic products, or diagnostic models for diabetic nephropathy of the present invention.

[0063] Specifically, the method includes obtaining the expression levels of P02763_ORM1 and P00738_HP in the sample to be tested, calculating the diagnostic probability value of diabetic nephropathy, and using it for the diagnosis of diabetic nephropathy.

[0064] Preferably, the diagnostic criteria for diabetic nephropathy are as follows: when the diagnostic probability value of the protein molecular combination marker is ≥0.5, it is determined to be diabetic nephropathy; when the diagnostic probability value of the protein molecular combination marker is <0.5, it is determined to be diabetes. Attached Figure Description

[0065] Figure 1 This is the ROC curve for Example 4.

[0066] Figure 2 This is the ROC curve for Comparative Example 1.

[0067] Figure 3 This is the ROC curve for Comparative Example 2.

[0068] Figure 4 This is the ROC curve for Comparative Example 3.

[0069] Figure 5 This is the ROC curve for Comparative Example 4. Detailed Implementation

[0070] The following non-limiting embodiments are intended to enable those skilled in the art to gain a more comprehensive understanding of the present invention, but do not limit the invention in any way. The following content is merely an exemplary description of the scope of protection claimed by the present invention, and those skilled in the art can make various changes and modifications to the present invention based on the disclosed content, and such changes should also fall within the scope of protection claimed by the present invention.

[0071] The present invention will be further described below by way of specific embodiments. Unless otherwise specified, all instruments, devices, equipment, reagents, products, etc., used in the embodiments of the present invention are obtained through conventional commercial means.

[0072] Basic Example 1: Sample Collection and Allocation 1. Inclusion criteria for the diabetes group, diabetic nephropathy group, and control group 1.1 Inclusion criteria for the diabetes group: The recommendations in the "Guidelines for the Prevention and Treatment of Type 2 Diabetes in China (2020 Edition)" are shown in Table 1: Table 1

[0073] Note: OGTT in the table refers to the oral glucose tolerance test; HbA1c refers to glycated hemoglobin. Typical symptoms of diabetes include polydipsia, polyuria, polyphagia, and unexplained weight loss; random blood glucose refers to blood glucose at any time of day, regardless of the time of the last meal, and cannot be used to diagnose impaired fasting glucose or impaired glucose tolerance; fasting status refers to at least 8 hours without caloric intake. It is recommended that glycated hemoglobin be measured in medical institutions that use standardized testing methods and have strict quality control (US National Glycated Hemoglobin Standardization Program, China Glycated Hemoglobin Consistency Study Program).

[0074] 1.2 Inclusion criteria for the diabetic nephropathy group: Clinical diagnostic criteria based on the "Guidelines for the Prevention and Treatment of Diabetic Nephropathy in China (2021 Edition)" issued by the Chinese Diabetes Society and the "Chinese Guidelines for Clinical Diagnosis and Treatment of Diabetic Nephropathy" issued by the Chinese Nephrology Society in 2021: Diabetic nephropathy can be diagnosed if at least one of the following criteria is met, provided that diabetes is clearly the cause of kidney damage and other causes of chronic kidney disease have been ruled out: (1) Excluding interfering factors, at least two out of three tests within 3-6 months have a urine albumin / creatinine ratio (UACR) ≥230mg / g or a 24h urine albumin excretion rate (UAER) ≥30mg / 24h (20μg / min).

[0075] (2) Estimated glomerular filtration rate (eGFR, using the CKD-EPI formula) < 60 ml·min -1· (1.73m) 2 ) -1 Lasting for more than 3 months.

[0076] (3) The renal biopsy is consistent with the pathological changes of diabetic nephropathy.

[0077] Case inclusion criteria: (1) Age 30-70 years, gender matched; (2) eGFR≥45ml min -1 (1.73m 2 ) -1 ; (3) Meets the clinical diagnostic criteria for diabetic nephropathy and is classified accordingly; (4) The kidney biopsy results are consistent with the diagnosis of diabetic nephropathy and are classified accordingly; (5) The patient has good compliance and agrees to and signs the informed consent form.

[0078] 1.3 Inclusion criteria for the control group: (1) Age 18-75 years old, gender not limited, no underlying diseases; physical examination, routine biochemical tests, electrocardiogram, CT, ultrasound and other auxiliary examinations showed no obvious abnormalities; (2) Blood pressure: 90-140 / 60-90 mmHg; (3) Fasting blood glucose: 3.9-6.1 mmol / L; (4) Body Mass Index: 18.5-23.9 (BMI); (5) Heart rate: 60-100 beats / minute at rest; (6) Body temperature: 36-37 degrees Celsius.

[0079] 2. Sample collection Sampling criteria: Collect the first midstream urine of the study subjects in the morning: discard the first and last urination segments, collect only the midstream urine in a sterile container, immediately label and aliquot it, 50ml per tube, for a total of 100ml per sample, aliquoted into 2 tubes, and store in a -80℃ freezer for later use, avoiding repeated freeze-thaw cycles.

[0080] 3. Sample allocation The training and validation sets included a total of 120 samples, with 50 samples in the diabetes group, 50 samples in the diabetic nephropathy group, and 20 samples in the control group.

[0081] Eighty samples were selected from the sample as the training set for proteomics experiments, including 30 samples in the diabetes group, 30 samples in the diabetic nephropathy group, and 20 samples in the control group.

[0082] Forty samples were selected from the sample as the validation set for the PRM experiment, including 20 samples from the diabetes group and 20 samples from the diabetic nephropathy group.

[0083] Example 1: Proteomics Experimental Procedure 1. Preparation of reagents used in this invention (1) Lysis buffer: containing 6M urea and 2M thiourea, prepared by dissolving urea and thiourea in 0.1M ABB.

[0084] (2) 0.2M TCEP solution: Dissolve tris(2-carboxyethyl)phosphine hydrochloride in 0.1M ABB (ammonium bicarbonate) to obtain 0.2M TCEP solution.

[0085] (3) 0.8M IAA solution: Dissolve iodoacetamide in 0.1M ABB to obtain 0.8M IAA solution.

[0086] (4) Trypsin enzyme solution: 100 μg of trypsin protease was dissolved in 200 μL of 0.1 mM ABB to obtain a trypsin enzyme solution with a concentration of 0.5 μg / μL.

[0087] (5) rLys-C enzyme solution: 50 μg rLys-C protease was dissolved in 200 μL 0.1 mM ABB to obtain an rLys-C enzyme solution with a concentration of 0.5 μg / μL.

[0088] 2. Sample pretreatment (1) The urine samples of the training set stored at -80℃ in the basic example 1 were ultracentrifuged at 100,000g for 70 min, and the supernatant was collected to obtain exosome samples. 200 μL of exosome sample was added to a 3KD ultrafiltration tube, 200 μL of 0.1 MABB to remove glycerol was added, and the sample was centrifuged at 12,000g for 30 min until the remaining liquid in the ultrafiltration tube was 50 μL. Then, 200 μL of lysis buffer was added to replace the liquid, and the sample was transferred to a 1.5 mL EP tube to obtain the replacement lysis buffer.

[0089] (2) In each PCT tube, add 30 μL of the above-mentioned replacement lysis buffer and mix well; add 5 μL of 0.2 MTCEP solution and 2.5 μL of 0.8 M IAA solution to each tube and mix well; add 75 μL of 0.1 M ABB, 10 μL of trypsin enzyme solution and 2.5 μL of rLys-C enzyme solution to each tube, and make up to 150 μL with 0.1 M ABB, and adjust the pH of the system to 8.0. Perform the enzymatic digestion operation at 20 kpsi, 50 seconds of high pressure, 10 seconds of normal pressure, 120 pressure cycles, and 30 °C.

[0090] (3) After enzymatic hydrolysis, the sample was transferred to a 1.5 mL EP tube and 15 μL of 10% TFA solution was added to each tube. The enzymatic hydrolysis was terminated when the final TFA concentration was 1%, and the hydrolysate was obtained.

[0091] 3. Desalination treatment The pH of the above enzymatic hydrolysate was adjusted to ensure it was between 2 and 3. Desalting was performed according to the user guide provided by the manufacturer of SOLAμ solid-phase extraction SPE plates (Thermo Fisher Scientific™, San Jose, USA). The specific steps were as follows: activation of the desalting column with 200 μL MeOH × 2 times; equilibration of the desalting column with 200 μL 80% ACN and 0.1% TFA × 2 times; washing the desalting column with 200 μL 2% ACN and 0.1% TFA × 2 times; desalting after sample loading with 200 μL 2% ACN and 0.1% TFA × 10 times; collecting the sample with 100 μL 40% ACN and 0.1% TFA × 2 times, centrifuging and concentrating at 40°C and below 10 mBar until the sample was dry, reconstituted, and peptide content was measured at A280 wavelength.

[0092] 4. Fractionation treatment Take the peptide solutions from each sample whose content has been determined in step 3, mix them in equal amounts to prepare a mixed peptide sample with a total amount of 100 μg, which will be used to construct a spectral library.

[0093] The above-mentioned mixed peptide sample was injected into a DIONEX UltiMate™ 3000 liquid chromatography system and separated using an XBridge Peptide BEH C18 column (300 Å, 5 μm × 4.6 mm × 250 mm, Waters, Milford, MA, USA) with the following parameters set: Mobile phase: Mobile phase A was a 10 mM ammonium hydroxide aqueous solution (pH=10), and mobile phase B was a mixture of 98% CAN and 10 mM ammonium hydroxide (pH=10); Flow rate: 0.5 mL / min; Gradient elution program: A gradient of 5% to 35% ACN was used over 60 minutes; Collection and pooling: Starting from the gradient elution, eluted fractions were collected every minute for a total of 60 fractions. Adjacent fractions were then pooled in elution order to obtain 30 pooled fractions. After drying, the 30 pooled fractions were resuspended in 2% ACN and 0.1% formic acid, respectively, for subsequent analysis.

[0094] 5. Mass spectrometry analysis Liquid chromatography-mass spectrometry (LC-MS) analysis was performed using a UHPLC (Bruker Daltonics, Germany) system and a timsTOF Pro mass spectrometer (Bruker Daltonics, Germany). Data acquisition was independent (DIA); library construction was performed using data-dependent acquisition (DDA). Mobile phase A: 100% water, 0.1% formic acid; Mobile phase B: 100% acetonitrile, 0.1% formic acid. All reagents were mass spectrometry grade.

[0095] During DDA collection for library construction, peptides were first loaded onto a pre-column (5 mm) at a pressure of 217.5 bar. 300 µmi.d.), then injected into the analytical column (1.9 µm, 120 Å, 150 mm) at a flow rate of 300 nL / min. Analysis was performed using a 60-minute liquid chromatography gradient (0-50 min, 5%-27% mobile phase B; 50-60 min, 27%-40% mobile phase B) at 75 µm id. Mass spectrometry parameters were as follows: PASEF MS and MS / MS mass scan range 100-1700 m / z, 1 / k0 scan range 0.6-1.6, and mobility peak detection threshold 5000; PASEF MS / MS scan number 10, charge range 0-5, and peak detection threshold 2500 cts / s.

[0096] During DIA collection of all samples, the peptides were first loaded onto a pre-column (5 mm) at a pressure of 217.5 bar. 300 µmi.d.), then injected into the analytical column (1.9 µm, 120 Å, 150 mm) at a flow rate of 300 nL / min. Analysis was performed using a 60-minute liquid chromatography gradient (0-50 min, 5%-27% mobile phase B; 50-60 min, 27%-40% mobile phase B) at 75 µm id. Mass spectrometry parameters were as follows: PASEF MS mass scan range 100-1700 m / z, 1 / k0 scan range 0.7-1.3, resolution 60000, mobility peak detection threshold 5000; PASEF MS / MS scan number 10, charge range 0-5, peak detection threshold 2,500 cts / s, window number 56.

[0097] 6. Data Analysis Mass spectrometry data were searched using DIA-NN software (version 1.8.1) with the "match-between-run" (MBR) function enabled. The DDA spectral library constructed from the uniprot fasta file (2023-07-25-reviewed-contam-UP000005640_human_pd.fasta) was used for the search analysis. Cysteine ​​carbamidomethylation was set as a fixed modification, and methionine oxidation was set as a variable modification. The analysis results provide qualitative and quantitative data, and were screened using a strict false discovery rate (FDR) criterion of less than 0.01.

[0098] Example 2 PRM Experiment 1. Sample pretreatment, desalting, and fractionation The pretreatment, desalting, and fractionation of the PRM experimental samples were performed in accordance with the methods described in Example 1, with the steps being completely identical. The only difference was that the samples used were urine samples from the validation set in Example 1.

[0099] 2. LC-MS / MS detection LC-MS / MS analysis was performed using an UltiMate 3000 RSLCnano liquid chromatography system coupled with an OrbitrapExploris™ 480 mass spectrometer (Thermo Scientific™, San Jose, USA), equipped with FAIMS Pro™ (Thermo Scientific™, San Jose, USA).

[0100] FAIMS compensation voltage (CV) was set to -65V and -45V. Buffer A: 2% acetonitrile, 98% water, containing 0.1% fatty acid; Buffer B: 80% acetonitrile aqueous solution (containing 0.1% fatty acid). All reagents were mass spectrometry grade.

[0101] At each acquisition, the peptide was loaded into a pre-column (3µm, 100Å, 20mm × 75µm inner diameter) at a flow rate of 6μL / min, followed by injection at a flow rate of 300nL / min using a 90-minute liquid chromatography gradient (buffer B from 8% to 35%) (analytical column, 1.9µm, 120Å, 150mm × 75µm inner diameter). MS1 had an m / z range of 350–1010, a resolution of 60,000, a normalized AGC target of 300%, and a maximum ion implantation time (maximum IT) of 100 ms. MS / MS experiments had a resolution of 30,000, a normalized AGC target of 1000%, and a maximum IT of 100 ms.

[0102] 3. Data Analysis Import the PRM mass spectrometry data acquired by LC-MS / MS detection in step 2 into Skyline software (version 22.2.0.527), export the peptide matrix, and extract key information columns: protein name, peptide sequence, and total area fragmentation, for subsequent biomarker validation.

[0103] Example 3: Diagnostic biomarker combination and its model establishment and validation Based on the DIA proteome quantitative data obtained in Example 1, 40 key candidate proteins were screened, as shown in Table 2. PRM validation was performed in Example 2, and 33 of these proteins were successfully identified. Seven proteins—O15127_SCAMP2, 5A3E0_POTEF, P18754_RCC1, P32926_DSG3, A0A075B6H7_IGKV3-7, O95568_METTL18, and Q92900_UPF1—were not identified by the PRM method and were excluded from subsequent model construction.

[0104] Table 2

[0105] A threshold of 0.5 was uniformly selected: when the diagnostic probability value of the combination of diagnostic markers is ≥0.5, it is determined to be DKD; when the diagnostic probability value of the combination of diagnostic markers is <0.5, it is determined to be DM.

[0106] Example 4: A combination of diagnostic biomarkers for diabetic nephropathy This invention provides a combination of diagnostic biomarkers for diabetic nephropathy constructed from the above-mentioned candidate proteins, consisting of P02763_ORM1 and P00738_HP.

[0107] The DIA proteomics quantitative data (30 cases in the diabetes group and 30 cases in the diabetic nephropathy group) from Example 1 were used as the training set, and the Logistic Regression algorithm was used for model training. The software version was Python 3.9.13 and sklearn 1.1.2. The model parameters were set as follows: 'C': 0.1, 'max_iter': 100, 'penalty': 'l1', 'solver': 'liblinear'.

[0108] The trained model was validated using its own training set data and the PRM validation cohort data (20 cases in the diabetes group and 20 cases in the diabetic nephropathy group) from Example 2. The results showed that the diagnostic model constructed based on the above biomarker combination and logistic regression algorithm has high diagnostic efficacy and exhibits good diagnostic stability in the PRM validation cohort. The ROC curve is shown in Figure 1. Figure 1As shown.

[0109] Comparative Example 1 A diagnostic biomarker combination for diabetic nephropathy, consisting of P02763_ORM1 and P00747_PLG. The DIA proteomics quantitative data (30 cases in the diabetic group and 30 cases in the diabetic nephropathy group) from Example 1 were used as the training set, and the model was trained using the Logistic Regression algorithm. Software versions: Python 3.9.13, sklearn 1.1.2; model parameters were set as follows: 'C': 0.2, 'max_iter': 100, 'penalty': 'l1', 'solver': 'liblinear'.

[0110] The trained model was validated using its own training set data and the PRM validation cohort data (20 cases in the diabetes group and 20 cases in the diabetic nephropathy group) from Example 2. The results showed that the diagnostic model constructed based on the above biomarker combination and logistic regression algorithm has high diagnostic efficacy and exhibits good diagnostic stability in the PRM validation cohort. The ROC curve is shown in Figure 1. Figure 2 As shown.

[0111] Comparative Example 2 A diagnostic biomarker combination for diabetic nephropathy, consisting of P00738_HP and P00747_PLG. The DIA proteomics quantitative data from Example 1 (30 cases in the diabetic group and 30 cases in the diabetic nephropathy group) were used as the training set, and the model was trained using the Logistic Regression algorithm. Software versions: Python 3.9.13, sklearn 1.1.2; model parameters were set as follows: 'C': 0.1, 'max_iter': 100, 'penalty': 'l1', 'solver': 'liblinear'.

[0112] The trained model was validated using its own training set data and the PRM validation cohort data (20 cases in the diabetes group and 20 cases in the diabetic nephropathy group) from Example 2. The results showed that the diagnostic model constructed based on the above biomarker combination and logistic regression algorithm has high diagnostic efficacy and exhibits good diagnostic stability in the PRM validation cohort. The ROC curve is shown in Figure 1. Figure 3 As shown.

[0113] Comparative Example 3 A diagnostic biomarker combination for diabetic nephropathy, consisting of P00738_HP and O43687_AKAP7, was developed. The DIA proteomics quantitative data (30 cases in the diabetic group and 30 cases in the diabetic nephropathy group) from Example 1 were used as the training set. The model was trained using the Logistic Regression algorithm. Software versions used were Python 3.9.13 and sklearn 1.1.2. Model parameters were set as follows: 'C': 0.1, 'max_iter': 100, 'penalty': 'l2', 'solver': 'liblinear'.

[0114] The trained model was validated using its own training set data and the PRM validation cohort data (20 cases in the diabetes group and 20 cases in the diabetic nephropathy group) from Example 2. The results showed that the diagnostic model constructed based on the above biomarker combination and logistic regression algorithm has high diagnostic efficacy and exhibits good diagnostic stability in the PRM validation cohort. The ROC curve is shown in Figure 1. Figure 4 As shown.

[0115] Comparative Example 4 A diagnostic biomarker combination for diabetic nephropathy, consisting of P00738_HP and O75223_GGCT, was developed. The DIA proteomics quantitative data (30 cases in the diabetic group and 30 cases in the diabetic nephropathy group) from Example 1 were used as the training set. The model was trained using the Logistic Regression algorithm. Software versions used were Python 3.9.13 and sklearn 1.1.2. Model parameters were set as follows: 'C': 0.2, 'max_iter': 100, 'penalty': 'l1', 'solver': 'liblinear'.

[0116] The trained model was validated using its own training set data and the PRM validation cohort data (20 cases in the diabetes group and 20 cases in the diabetic nephropathy group) from Example 2. The results showed that the diagnostic model constructed based on the above biomarker combination and logistic regression algorithm has high diagnostic efficacy and exhibits good diagnostic stability in the PRM validation cohort. The ROC curve is shown in Figure 1. Figure 5 As shown.

[0117] Example 4, Comparative Examples 1-4: The area under the curve (AUC value) is shown in Table 3. Table 3

[0118] The closer the AUC value is to 1, the stronger the ability of the diagnostic biomarker combination to distinguish between type 2 diabetes (DM) and diabetic nephropathy (DKD). Results showed that replacing any one or more candidate biomarkers from P02763_ORM1 and P00738_HP in this invention resulted in a decrease in the AUC value of the resulting diagnostic biomarker combinations.

[0119] A validation set of 20 diabetic samples and 20 diabetic nephropathy samples was used to calculate the diagnostic probability of DKD for each clinical sample and determine the test results. The results are shown in Table 4 below: Table 4

[0120] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions made by those skilled in the art to the technical solution of the present invention do not depart from the essence and scope of the technical solution of the present invention.

Claims

1. A combination of diagnostic biomarkers for diabetic nephropathy, characterized in that, The diagnostic biomarker set for diabetic nephropathy consists of P02763_ORM1 and P00738_HP.

2. The use of the combination of diagnostic biomarkers for diabetic nephropathy as described in claim 1 in the preparation of diagnostic products for diabetic nephropathy.

3. A diagnostic product for diabetic nephropathy, characterized in that, The diagnostic product includes the combination of diagnostic biomarkers for diabetic nephropathy as described in claim 1.

4. The diagnostic product according to claim 3, characterized in that, The diagnostic products include any one or more of the following: reagent kits, test strips, chips, devices, and detection systems.

5. A diagnostic model for diabetic nephropathy, characterized in that, The diagnostic model is constructed using a computer algorithm with the expression levels of the combination of diagnostic biomarkers for diabetic nephropathy as described in claim 1 as input parameters.

6. The diagnostic model according to claim 5, characterized in that, The method for constructing the diagnostic model includes the following steps: S1. Collect samples from the diabetes group, diabetic nephropathy group and control group, and extract proteins; S2. Detect the expression levels of P02763_ORM1 and P00738_HP in the proteins obtained in step S1, and obtain quantitative data; S3. Using the quantitative data obtained in step S2 as the training set, a computer algorithm is used to train and obtain a diagnostic model for diabetic nephropathy.

7. The diagnostic model according to claim 6, characterized in that, The samples mentioned in step S1 include any one or more of the following: urine, blood, blood spots, oral cells, semen, sperm spots, bones, hair, saliva, saliva spots, sweat, and amniotic fluid containing fetal cells.

8. The diagnostic model according to claim 6, characterized in that, The computer algorithms mentioned in step S3 include any one or more of the following: logistic regression, support vector machine, random forest, decision tree, gradient boosting tree, neural network, K-nearest neighbors, and Naive Bayes.

9. The application of the diagnostic model according to any one of claims 5-8 in the preparation of diagnostic products for diabetic nephropathy.

10. A method for analyzing the expression levels of proteins associated with diabetic nephropathy, characterized in that, Includes the following steps: The expression levels of P02763_ORM1 and P00738_HP in the diabetic nephropathy diagnostic biomarker combination according to claim 1 are detected in the sample to be tested. The expression levels are then input into the diagnostic model according to any one of claims 5-8 to obtain the expression level correlation evaluation results of the diabetic nephropathy diagnostic biomarker combination.

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