Biomarkers for diagnosing diabetic nephropathy and use thereof

Abstract

The application discloses a biomarker for diagnosing diabetic nephropathy and application thereof, and relates to the technical field of biological medicine. The biomarker for diagnosing diabetic nephropathy comprises urine MBP-1 or urine MBP-1 / urine creatinine. The urine MBP-1 or urine MBP-1 / urine creatinine biomarker has high identification efficiency: when the urine MBP-1 or urine MBP-1 / urine creatinine ratio is used for identifying DKD and NDRD, the area under the ROC curve (AUC) reaches 0.899, the sensitivity and the specificity are both higher than 0.85, and the two diseases can be efficiently distinguished. The method is non-invasive and convenient: only urine samples need to be collected, the risk of complications such as bleeding and infection caused by invasive operations such as kidney biopsy is avoided, the patient has high acceptance, repeated detection can be performed to dynamically track the disease condition, and special equipment is not needed, so that the method is convenient for clinical popularization and application. Through kidney tissue immunofluorescence verification, MBP-1 is deposited in a large amount in the glomerulus of DKD and is not deposited in NDRD, and thus the kidney local pathological change can be directly reflected.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to a biomarker for diagnosing diabetic nephropathy and its application. Background Technology

[0002] Diabetic kidney disease (DKD) is the most common microvascular complication of diabetes. Although combination therapy strategies have significantly slowed the progression of DKD, it remains a leading cause of end-stage renal disease (ESRD). The National Kidney Foundation (NKF) Kidney Disease Outcome Quality Initiative (K / DOQI) working group defines DKD as: in diabetic patients, elevated urinary albumin excretion associated with a progressively declining glomerular filtration rate, elevated systolic blood pressure, and a high risk of kidney failure, after excluding non-diabetic kidney injury.

[0003] Current clinical diagnosis of diabetic kidney disease (DKD) still primarily relies on conventional indicators such as serum creatinine and the urine albumin / creatinine ratio. However, the typical disease course (glomerular hyperfiltration → appearance of urine albumin → decreased GFR → ESRD) exhibits significant individual variability and lacks specificity. A meta-analysis encompassing 48 studies (PMID: 27190327, Renal biopsy in patients with diabetes: a pooled meta-analysis of 48 studies) showed that only 50% of DKD cases diagnosed based on clinical indicators could be confirmed by pathological examination, indicating a high risk of misdiagnosis and potential misclassification of non-diabetic kidney disease (NDRD) as DKD. In terms of treatment, DKD's core pathogenesis is hyperglycemia-mediated metabolic disturbances, with treatment focusing on glycemic / blood pressure control and kidney protection; while NDRD is often associated with immune abnormalities and frequently requires intervention with hormones or immunosuppressants. This significant difference in treatment strategies means that misdiagnosis may cause NDRD patients with non-diabetic kidney disease to miss the optimal treatment window, accelerating the deterioration of kidney function.

[0004] Renal biopsy, as the gold standard for diagnosing diabetic kidney disease (DKD), provides accurate pathological staging and lesion characteristics, which are crucial for guiding treatment strategies. However, the invasive nature of this examination limits its clinical application: the incidence of complications such as postoperative bleeding and perirenal hematoma is relatively high, and obesity, which is often present in type 2 diabetes patients, increases the difficulty of puncture, significantly raising the operational risk. Furthermore, it is costly and difficult to repeat for dynamic monitoring of the condition. Therefore, clinical practice only considers performing the procedure when there is a high suspicion of non-diabetic kidney disease (NDRD) or a sudden increase in urinary protein that cannot be explained by DKD, leading most patients to rely on empirical diagnosis.

[0005] In recent years, machine learning models based on fundus imaging have achieved certain identification effects in multi-ethnic populations, but there are two limitations: First, fundus examinations rely on specialized equipment, making it difficult to popularize screening among the general population; second, although fundus and renal vascular lesions share a core commonality—both are driven by systemic factors such as diabetes and hypertension, leading to vascular damage through mechanisms such as endothelial injury, oxidative stress, and inflammatory responses, and both often occur concurrently in diabetic patients—fundus changes are not directly related to kidney disease. In addition, local factors such as glaucoma and eye infections can independently cause fundus vascular changes, which are easily interfered with by various factors such as hypertension and immune status, resulting in asynchronous progression of fundus lesions and renal microvascular pathology, thus limiting accuracy.

[0006] In contrast, the kidneys, as the primary organ for urine production, directly reflect local pathological changes in urine components. Furthermore, urine samples offer the practical advantage of being non-invasive and readily available; however, specific urine biomarkers are currently lacking. Therefore, research into urine biomarkers is urgently needed to achieve accurate differentiation between DKD and NDRD. Summary of the Invention

[0007] To address the technical problems existing in the prior art, embodiments of the present invention provide a biomarker for diagnosing diabetic nephropathy and its application. The technical solution is as follows:

[0008] A biomarker for diagnosing diabetic nephropathy, the biomarker comprising: urinary MBP-1 or urinary MBP-1 / urinary creatinine.

[0009] Optionally, the biomarker is urinary MBP-1 / urinary creatinine.

[0010] Optionally, the biomarkers are used to differentiate between diabetic nephropathy and non-diabetic kidney disease.

[0011] Application of reagents for detecting urinary MBP-1 or urinary MBP-1 / urinary creatinine in the preparation of kits for the diagnosis of diabetic nephropathy.

[0012] Optionally, the reagent for detecting urinary MBP-1 or urinary MBP-1 / urinary creatinine is a reagent for detecting urinary MBP-1 / urinary creatinine.

[0013] Optionally, the kit is used to differentiate between diabetic nephropathy and non-diabetic kidney disease.

[0014] Optionally, the method of using the kit for diagnosing diabetic nephropathy includes:

[0015] The urinary MBP-1 level or urinary MBP-1 / urinary creatinine level in the sample is measured, and the obtained data are compared with the reference value to determine whether it is diabetic nephropathy.

[0016] Optionally, the sample is a urine sample from the subject of the test.

[0017] Optionally, the determination of urinary MBP-1 level or urinary MBP-1 / urinary creatinine level in the sample is to determine the urinary MBP-1 / urinary creatinine level.

[0018] A kit for diagnosing diabetic nephropathy, the kit comprising: reagents for detecting urinary MBP-1 or urinary MBP-1 / urinary creatinine.

[0019] And / or, the kit is used to differentiate between diabetic nephropathy and non-diabetic kidney disease.

[0020] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0021] To overcome the technical bottleneck in the non-invasive differential diagnosis of DKD and NDRD, this invention aims to provide a non-invasive molecular marker for the differential diagnosis of DKD and NDRD in patients with type 2 diabetes—urinary eosinophil MBP-1, or simply urinary MBP-1. Clinical studies, genetic analysis, and molecular testing have confirmed that urinary MBP-1 can accurately differentiate between the two conditions, and it possesses the advantages of being non-invasive, highly sensitive, and specific. Compared to existing methods for differentiating between DKD and NDRD, the urinary eosinophil MBP-1 marker has significant advantages:

[0022] 1. High diagnostic efficacy: With the support of clinical data, when urinary eosinophil MBP-1 or the ratio of urinary MBP-1 to urinary creatinine is used to differentiate between DKD and NDRD, the area under the ROC curve (AUC) reaches 0.899 (95% CI: 0.852-0.946), and the sensitivity and specificity both exceed 0.85, which can effectively distinguish between the two diseases.

[0023] 2. Non-invasive and convenient: It only requires the collection of urine samples, avoiding the risks of complications such as bleeding and infection caused by invasive procedures such as kidney biopsy. Patients have high acceptance, and it can be repeated to dynamically track the condition. Moreover, it does not require special equipment, which facilitates its clinical application.

[0024] 3. Direct correlation with pathology: Immunofluorescence of renal tissue shows that MBP-1 is deposited in large quantities in the glomeruli of DKD, but not in NDRD, which can directly reflect local pathological changes in the kidney and make up for the inability of fundus examination to correlate with renal pathology. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1A This is a single-factor restricted cubic spline analysis diagram provided in Embodiment 1 of the present invention to explore the nonlinear association features between eosinophils and diabetic nephropathy; Figure 1B This is a feature map of the nonlinear association between eosinophils and diabetic nephropathy, obtained through multifactorial restricted cubic spline analysis.

[0027] Figure 2 This is a subgroup analysis diagram of multi-category logistic regression and multivariate logistic regression of eosinophils and DKD provided in Embodiment 1 of the present invention;

[0028] Figure 3A This is a graph showing the results of hierarchical linkage disequilibrium fraction analysis based on GTEx gene expression data provided in Embodiment 2 of the present invention; Figure 3B This is a graph showing the results of hierarchical linkage disequilibrium score analysis based on the Franke laboratory dataset;

[0029] Figure 4A This is an MR cluster scatter plot showing the relationship between eosinophil percentage and DKD provided in Example 2 of the present invention; Figure 4B This is an MR cluster segmentation plot showing the relationship between eosinophil percentage and DKD.

[0030] Figure 5A This is an MR cluster scatter plot showing the relationship between eosinophil percentage and CKD provided in Example 2 of the present invention; Figure 5B This is an MR cluster segmentation plot showing the relationship between eosinophil percentage and CKD.

[0031] Figures 6A to 6B This is an immunofluorescence imaging image of MBP-1 in DKD renal biopsy tissue provided in Embodiment 3 of the present invention, wherein... Figure 6A This is an image showing eosinophil infiltration after HE staining; Figure 6B This is a diagram showing no obvious eosinophil infiltration after HE staining;

[0032] Figure 7 This is an immunofluorescence imaging image of MBP-1 in NDRD renal biopsy tissue provided in Embodiment 3 of the present invention;

[0033] Figure 8 This is a Western blot analysis of the MBP-1 protein levels in the urine of DKD and NDRD patients after urine protein correction, provided in Example 3 of the present invention. The black arrows indicate the specific band locations of MBP-1 protein.

[0034] Figure 9This is a picture of the enzyme-labeled plate for urine MBP-1 ELISA detection provided in Example 3 of the present invention;

[0035] Figure 10A This is an intergroup scatter plot of urine MBP-1 / creatinine provided in Example 3 of the present invention; Figure 10B This is the ROC prediction curve of urine MBP-1 / creatinine. Detailed Implementation

[0036] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0037] To overcome the technical bottleneck in the non-invasive differential diagnosis of DKD and NDRD, this invention aims to provide a non-invasive molecular marker for the differential diagnosis of DKD and NDRD in patients with type 2 diabetes—urine MBP-1 / urine creatinine. Clinical studies, genetic analysis, and molecular testing have confirmed that urinary MBP-1 / urine creatinine can accurately differentiate between the two, and has the advantages of being non-invasive, highly sensitive, and specific.

[0038] In this study, MBP-1 (major basic protein 1), encoded by the human PRG2 gene, is a core protein in the cytoplasmic granules of eosinophils. Under physiological or pathological stimuli, activated eosinophils initiate a degranulation process, releasing MBP-1 into the extracellular environment. This released MBP-1 deposits in tissues and can persist for a long time, becoming a specific "molecular footprint" reflecting eosinophil activity.

[0039] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0040] Example 1: Case-control study and screening of diagnostic biomarkers

[0041] 1.1 Selection of Research Subjects

[0042] This retrospective study included patients with type 2 diabetes mellitus (T2DM) who underwent renal biopsy at the First Medical Center of the General Hospital of the Chinese People's Liberation Army between January 2011 and January 2021. Patients were divided into three groups: pure DKD group, NDRD group, and T2DM control group without renal complications.

[0043] Inclusion criteria: age ≥ 18 years; diagnosed with T2DM; pathologically confirmed as pure DKD or NDRD.

[0044] Exclusion criteria: incomplete medical records; pathology showing a mixed DKD / NDRD pattern; and serious complications such as active infection, allergic or autoimmune diseases, end-stage renal disease, malignant tumors, or cirrhosis.

[0045] Control group matching criteria: matched 1:4 by age (±2 years), sex, and duration of diabetes (±2 years); negative urine protein; no use of renin-angiotensin system inhibitors within 3 months; estimated glomerular filtration rate (eGFR) ≥60 mL / min / 1.73 m³ / min. 2 .

[0046] Ultimately, 366 patients with DKD (matched with 1464 control patients) and 212 patients with NDRD (matched with 848 control patients) were included.

[0047] 1.2 Data Collection and Detection

[0048] Demographic data (age, sex, body mass index), clinical information (duration of diabetes, hypertension status, medication history), and laboratory data (albumin, creatinine, glycated hemoglobin, blood lipids, transaminases, complete blood count, and platelet count, etc.) were collected. The diagnosis of type 2 diabetes mellitus (T2DM) conformed to the American Diabetes Association criteria; hypertension was defined according to the "2024 Chinese Guidelines for the Prevention and Treatment of Hypertension"; DKD pathological staging adopted the 2010 Chinese Society of Nephrology criteria; and eGFR was calculated using the CKD-EPI 2009 formula.

[0049] 1.3 Statistical Analysis

[0050] Categorical variables are expressed as frequency (%), while continuous variables are expressed as mean ± standard deviation or median [interquartile range]. Comparisons between groups are performed using t-tests, Mann-Whitney U tests (for continuous variables), or chi-square tests (for categorical variables).

[0051] Univariate logistic regression and restricted cubic spline analysis were used to identify the association between leukocyte subsets and the outcome, and potential associated subsets were screened. For positively associated subsets, multivariate logistic regression and restricted cubic spline analysis were further performed to adjust covariates (body mass index, duration of diabetes, hypertension, medication history, eGFR, glycated hemoglobin, albumin, blood lipids, transaminase, and platelets).

[0052] Sensitivity analysis: Multivariate logistic regression was performed in stratified groups according to the pathological stage of DKD; subgroup analysis was performed according to gender, age, body mass index, and duration of diabetes.

[0053] All analyses were performed using R software (version 4.3.1), and P < 0.05 was considered statistically significant.

[0054] 1.4 Experimental Results

[0055] Baseline characteristics: There were no significant differences in age and sex between the DKD and NDRD groups, but there were several statistically significant differences between the two groups in clinical characteristics and laboratory indicators, as shown in Table 1.

[0056] Table 1. Baseline characteristics of patients in the diabetic kidney disease (DKD) group and the non-diabetic kidney disease (NDRD) group

[0057]

[0058] Specifically, the DKD group had a longer duration of diabetes, a lower body mass index, and a higher proportion of hypertension. Among laboratory indicators, the DKD group had higher serum creatinine, lower eGFR, higher glycated hemoglobin, lower serum albumin, and lower ALT and total cholesterol. In terms of leukocyte subsets, the DKD group had a significantly higher percentage of eosinophils in peripheral blood than the NDRD group (2.30 [1.60,3.70] vs 1.95 [1.10,3.00], P<0.001), a higher percentage of neutrophils, and a lower percentage of lymphocytes. In addition, the percentage of eosinophils was significantly higher in the DKD group compared with the control group (2.30 [1.60,3.70] vs 2.00 [1.20,3.10], P<0.001), while there was no difference between the NDRD group and the control group (1.95 [1.10,3.00] vs 1.90 [1.10,3.10], P=0.945).

[0059] Correlation analysis:

[0060] The results of the univariate logistic regression analysis showed that:

[0061] The percentage of eosinophils was positively correlated with the risk of DKD, with an association strength of 1.11 (95% confidence interval: 1.06, 1.17), which was statistically significant (P<0.001).

[0062] The percentage of neutrophils was positively correlated with the risk of DKD, with an association strength of 1.06 (95% confidence interval: 1.05, 1.07), and the difference was statistically significant (P<0.001).

[0063] The percentage of lymphocytes was negatively correlated with the risk of DKD, with an association strength of 0.92 (95% confidence interval: 0.91, 0.94), and the difference was statistically significant (P<0.001).

[0064] Basophil count and monocyte percentage were not associated with the risk of DKD.

[0065] Multivariate logistic regression analysis further confirmed that among the included leukocyte subsets, only the percentage of eosinophils was independently associated with the risk of DKD, with an association strength of 1.28 (95% confidence interval: 1.12, 1.45), which was statistically significant (P=0.002). Restricted cubic spline analysis showed a linear relationship between eosinophil count and the risk of DKD; results are shown in [link to results]. Figures 1A to 1B The specific analysis is as follows:

[0066] from Figure 1A As can be seen from the univariate restricted cubic spline plot analysis, the odds ratio (95% confidence interval) shows a continuous upward trend as the percentage of eosinophils increases. The overall p-value of this univariate analysis result is <0.01, indicating a significant association between the percentage of eosinophils and the risk of DKD; and the nonlinear p-value = 0.14 indicates that the association between the two has obvious linear characteristics.

[0067] from Figure 1B As can be seen from the multivariate restricted cubic spline plot analysis, the odds ratio (95% confidence interval) also increases with the increase of the percentage of eosinophils. The overall P-value of this multivariate analysis is <0.01, indicating a significant association between the percentage of eosinophils and the risk of DKD. Although the nonlinear P-value = 0.05 shows some signs of nonlinearity, the overall multivariate analysis results still support that the association between eosinophils and DKD is mainly linear, that is, as the percentage of eosinophils increases, the risk of DKD increases linearly.

[0068] Sensitivity analysis:

[0069] See results Figure 2 .from Figure 2 As can be seen from the data, in the multi-category logistic regression analysis, the odds ratio (95% confidence interval) of the DKD pathology stage II+III group relative to the normal control group was 1.26 (1.11, 1.43), which was statistically significant (P<0.001); the odds ratio (95% confidence interval) of the DKD pathology stage IV group relative to the normal control group was 1.30 (1.13, 1.49), which was also statistically significant (P<0.001).

[0070] from Figure 2 As can be seen from the subgroup analysis, after stratification according to gender, age, duration of type 2 diabetes, and body mass index, the correlation between eosinophils and DKD remained significant in each subgroup (P<0.05); in addition, the analysis results showed that there was an interaction between body mass index and eosinophils (interaction P=0.005).

[0071] The above sensitivity analysis results further confirm that the association between eosinophils and DKD is robust.

[0072] Example 2: Genetic analysis and screening of diagnostic biomarkers

[0073] 2.1 Data Source

[0074] Data from the European Genome Association Study (GWAS) were selected, including: leukocyte subsets (UK Biobank, n=349861) and T2DM-DKD (FinnGen, n=312650, ICD-10 N08.3). Chronic kidney disease (CKDGen, eGFR < 60 mL / min / 1.73 mcg) 2 ).

[0075] 2.2 Key Analysis

[0076] Hierarchical linkage disequilibrium score regression (S-LDSC): Based on gene expression data from the Genotype-Tissue Expression Project (GTEx) and the Franke Labs dataset (the above data are available from https: / / alkesgroup.broadinstitute.org / LDSCORE / ), the S-LDSC method was used to analyze the DKD summary statistic and identify the most relevant tissue (the tissue with the smallest p-value).

[0077] Heritability and genetic relevance: Linkage disequilibrium reference data from 1000 Genomes Phase 3 European populations (available at https: / / alkesgroup.broadinstitute.org / LDSCORE / ) were used. Coherence of single nucleotide polymorphisms (SNPs) was assessed using an LDSC regression model, with a Z-score >4 as the selection criterion to ensure the statistical reliability of the heritability estimates. Mendelian randomization (MR) analysis:

[0078] Instrumental variable (IV) selection: Genomically significant SNPs were screened using a threshold of P < 5E-08; grouping was based on the linkage disequilibrium structure of the 1000 Genomes Project, retaining independent SNPs (those with a lower R-value than other SNPs within 10000 kb). 2 IVs with the most significant p-value (<0.001) were excluded; IVs associated with confounding factors such as hypertension, blood lipids, body mass index, glycated hemoglobin, and creatinine were excluded; palindromic SNVs with intermediate allele frequencies and weak IVs (F value <10) were excluded; and IVs with a stronger association with DKD outcome than exposure were deleted to avoid reverse causality.

[0079] Statistical analysis: The inverse variance weighted (IVW) method was used to combine the causal estimates of independent IVs (in the absence of heterogeneity and pleiotropy); the Steiger test was used to assess the causal direction; the MR-Egger method was used to assess pleiotropy; the Cochran Q test was used to detect heterogeneity, and if heterogeneity was present and different biological mechanisms were suspected, the MR Cluster method was used to group the IVs.

[0080] 2.3 Experimental Results

[0081] The S-LDSC analysis results can be found in [link to relevant documentation]. Figures 3A to 3B The details are as follows:

[0082] Based on the GTEx dataset Figure 3A with -log 10 As an indicator of association strength, the P-value was the highest among the five tissues with the strongest association (fallopian tube, ovary, thyroid, uterus and whole blood), indicating that whole blood had the lowest P-value and the strongest association with DKD.

[0083] In similar analyses based on the Franke Labs dataset, Figure 3B Display the -log corresponding to whole blood. 10 The (P-value) bars still retain their prominent features.

[0084] The results from the two different datasets show that the genetic association between whole blood and DKD consistently exhibits a more significant characteristic.

[0085] Heritability and genetic association: all phenotypes had a Z-score >4 for heritability; only the percentage of eosinophils showed a significant genetic association with DKD (rg=0.13, P=0.004).

[0086] MR analysis: MR clustering identified a cluster of 12 independent SNPs that mediated a positive causal association between eosinophil percentage and DKD. Figure 4A In the data, the effect line fitted by cluster 1 shows a positive trend, which intuitively reflects the positive relationship between the percentage of eosinophils and DKD. Figure 4B In the results, the estimated value of cluster 1 is greater than 0, which numerically verifies the existence of a positive association. For the causal association analysis results between eosinophils and general CKD, please refer to [link to relevant data]. Figure 5A and Figure 5B All SNPs were clustered into null value groups or meaningless groups, which does not support the existence of a causal relationship between the two.

[0087] The above results indicate that the percentage of eosinophils is positively causally associated with DKD through a specific SNP cluster, but not with general CKD, demonstrating the disease specificity of the effect on DKD.

[0088] Example 3: Validation of MBP-1 as a diagnostic biomarker

[0089] 3.1 Study population

[0090] This prospective study included biopsy-confirmed DKD / NDRD patients at the First Medical Center of the General Hospital of the Chinese People's Liberation Army between January 2021 and January 2025.

[0091] Inclusion criteria: age ≥ 18 years; diagnosed with T2DM; pathologically confirmed as pure DKD or NDRD.

[0092] Exclusion criteria: Pathological findings of mixed DKD / NDRD; coexisting serious complications such as active infection, allergic or autoimmune diseases, end-stage renal disease, malignant tumors, and cirrhosis.

[0093] Ultimately, 77 patients with DKD and 81 patients with NDRD were included.

[0094] 3.2 MBP-1 detection in kidney tissue

[0095] HE staining: Paraffin sections of kidney tissue were dewaxed and immersed in water according to routine procedures: soaked in xylene I for 10 min → xylene II for 15 min → 100% ethanol for 5 min → 95% ethanol for 5 min; then the cell nuclei were stained with hematoxylin for 3-5 min, and rinsed with running water; the sections were then immersed in 1% hydrochloric acid ethanol solution for differentiation for a few seconds, and rinsed with running water again; after ammonia blueing for a few seconds, the sections were rinsed thoroughly with running water; after staining with eosin for a few seconds, the sections were rinsed with running water, dehydrated by gradient alcohol (95%, 100%), cleared with xylene, and finally mounted with neutral resin.

[0096] Immunofluorescence staining: Paraffin sections of kidney tissue were dewaxed and immersed in water according to the standard HE staining procedure; 3% H2O2 solution was added to the sections, and the sections were blocked at room temperature for 10-15 min to eliminate endogenous peroxidase activity, followed by washing three times with PBS buffer; antigen retrieval was performed using citrate buffer (pH 6.0) (incubated in a boiling water bath for 10-15 min, then naturally cooled to room temperature), followed by washing three times with PBS buffer; after blocking with 5% BSA solution at room temperature, anti-MBP-1 antibody (purchased from Santa Cruz Biotechnology, diluted 1:50) or anti-EPX antibody (purchased from Beijing Bio-Sens Biotechnology Co., Ltd., diluted 1:50) was added, and the sections were incubated overnight at 4°C; after washing three times with PBS buffer, Cy3-labeled secondary antibody of the corresponding species (purchased from Beyotime Biotechnology Co., Ltd., diluted 1:200) was added, and the sections were incubated at room temperature for 2 hours. h; After washing with PBS buffer, the slides were mounted with DAPI containing mounting medium, and finally observed and imaged using an FV3000 confocal microscope (400× magnification). The staining of glomeruli, renal tubules and renal interstitium was evaluated in detail (sample size: DKD group n=3; NDRD group n=8).

[0097] 3.3 Urine MBP-1 Detection

[0098] Urine samples were collected and diluted 1:20 for protein quantification using the BCA method. Equal amounts of protein from each sample were separated by SDS-PAGE electrophoresis and then transferred to an NC membrane. The membrane was blocked with 5% BSA and incubated at 4°C for 24 hours with anti-MBP-1 antibody (purchased from Santa Cruz Biotechnology, diluted 1:1000). The next day, the primary antibody was discarded, and anti-mouse HRP-labeled secondary antibody (purchased from Beyotime Biotechnology, diluted 1:1000) was added and incubated at room temperature for 2 hours. After development, the gray values ​​of the target bands were analyzed using ImageJ software (sample size: n=3 for the DKD group; n=8 for the NDRD group).

[0099] Urine creatinine detection: Creatinine detection reagents provided by Chongqing Boshitai Biotechnology Co., Ltd. were used. Based on the principle of immunoturbidimetry, the detection was completed on the BioSystems BA400 fully automated biochemical analyzer in Spain. The specific operation was performed in accordance with the reagent instructions and the instrument standard procedure.

[0100] Urinary MBP-1: Take the standard (0-2500 pg / mL, purchased from Linko Biotechnology Co., Ltd.), and dilute it with the standard diluent according to the gradient dilution ratio specified in the instructions to prepare standard solutions with concentrations of 2500 pg / mL, 1250 pg / mL, 625 pg / mL, 312.5 pg / mL, 156.25 pg / mL, 78.13 pg / mL, 39.06 pg / mL, and 0 pg / mL. Set up 2 replicates for each concentration. In a 96-well plate pre-coated with antibodies, 100 μL of standard solution and 100 μL of urine sample to be tested were added to the corresponding wells. 100 μL of standard diluent was added to the blank wells. 50 μL of diluted detection antibody working solution (1:100 dilution, purchased from Linko Biotechnology Co., Ltd.) was added to each well, and the plate was incubated at room temperature for 2 h. After washing, 100 μL of streptavidin working solution (1:100 dilution, purchased from Linko Biotechnology Co., Ltd.) was added, and the plate was incubated at room temperature for 45 min. After washing, chromogenic solution was added, and the reaction was stopped after 20 min by adding stop solution. The absorbance values ​​at 450 nm and 570 nm were measured. The concentration of MBP-1 in the sample was calculated according to the standard curve, and the results were normalized to the urine creatinine level measured at the same time (sample size: DKD group n=77; NDRD group n=81).

[0101] 3.4 Experimental Results

[0102] Clinical data from the enrolled cases showed that the serum eosinophil counts in both groups were within the normal reference range, but the numerical distribution exhibited significant heterogeneity. Nonparametric tests revealed no statistically significant difference between the two groups (3.50 [3.05, 3.95] vs. 1.00 [0.60, 2.25], P = 0.081). Note: The serum eosinophil data used in this study were derived from routine clinical laboratory results at patient enrollment and were not additional indicators used in this experiment.

[0103] 3.4.1 Pathological manifestations of renal tissue:

[0104] In tissue samples from 3 patients with DKD: in 2 patients, HE staining revealed scattered eosinophils in the renal interstitium and periglomerular region (marked with white arrows). See the results below. Figure 6A In another patient, no eosinophils were found by HE staining; see the results below. Figure 6B However, in all DKD patients, complete eosinophil outlines could be clearly identified after EPX staining, a specific marker for eosinophils; at the same time, diffuse granular deposits of MBP-1 were visible in the glomeruli of all DKD patients.

[0105] In tissue samples from NDRD patients: no eosinophilic infiltration was observed by HE staining; immunofluorescence assay showed no EPX and MBP-1 specific fluorescence signals in the glomeruli or renal interstitium. (See attached results). Figure 7 .

[0106] In summary, DKD patients show molecular traces of eosinophilic infiltration, while NDRD patients do not.

[0107] 3.4.2 Urinary MBP-1 level:

[0108] Western blot analysis showed that the grayscale value of urinary MBP-1 after total protein correction was higher in the DKD group (n=3) than in the NDRD group (n=8). (See attached results.) Figure 8 The above results indicate that the level of MBP-1 protein in the urine of DKD patients is significantly higher than that of NDRD patients, suggesting that urinary MBP-1 may have potential value in differentiating between DKD and NDRD; however, this result has not been corrected for urinary creatinine and may be affected by fluctuations in urine volume, requiring further verification in accordance with the commonly used clinical correction logic for the urinary albumin / creatinine ratio.

[0109] For a visual representation of the ELISA test results for urinary MBP-1, please refer to [link / reference needed]. Figure 9 .

[0110] Test data showed that, after correction for urinary creatinine, the urinary MBP-1 level in the DKD group (n=77) was significantly higher than that in the NDRD group (n=81). Statistical analysis confirmed that the difference between the two groups remained statistically significant (P<0.001). See the results below. Figure 10A .

[0111] The ROC analysis results can be found in [link to ROC analysis]. Figure 10B .from Figure 10B As can be seen, the area under the curve (AUC) of the urinary MBP-1 / urinary creatinine ratio in differentiating DKD from NDRD is 0.899, and the sensitivity and specificity corresponding to this ratio are both >0.85, indicating that it has good diagnostic efficacy.

[0112] The results of comprehensive urinary MBP-1 level testing show that the urinary MBP-1 / urinary creatinine ratio is significantly elevated in DKD patients and has high diagnostic value for DKD and NDRD. It can be used as a potential urinary biomarker reflecting the pathological characteristics of DKD.

[0113] Example 4: Detection example of the present invention

[0114] Diagnostic efficacy verification of the urine MBP-1 / urine creatinine ratio

[0115] Experimental objective: To evaluate the efficacy of the urinary MBP-1 / urinary creatinine ratio in differentiating between DKD and NDRD.

[0116] Subjects: 3 patients with DKD and 3 patients with NDRD.

[0117] Experimental methods:

[0118] 1. Collect a morning urine sample, centrifuge at 3000 rpm for 10 minutes and collect the supernatant.

[0119] 2. The concentration of urinary MBP-1 was detected using an ELISA kit: 100 μL of standard and sample were added to the ELISA plate and incubated at 37°C for 1 hour; after washing, the detection antibody was added and incubated at 37°C for 1 hour; after washing, streptavidin-HRP was added and incubated at 37°C for 30 minutes; TMB color development was performed, and after termination, the absorbance at 450 nm / 570 nm was measured to calculate the concentration of urinary MBP-1.

[0120] 3. The urinary creatinine concentration was detected by the sarcosine oxidase method, and the urinary MBP-1 / urinary creatinine ratio was calculated.

[0121] 4. Plot the ROC curve and calculate the AUC, sensitivity, and specificity.

[0122] Experimental results:

[0123] The urinary MBP-1 / urinary creatinine ratios in the DKD group were 158 pg / mg, 266 pg / mg, and 600 pg / mg, respectively, which were significantly higher than those in the NDRD group (0.18 pg / mg, 0 pg / mg, 0 pg / mg) (P<0.001). These results demonstrate that the urinary MBP-1 / urinary creatinine ratio can effectively distinguish between the DKD and NDRD groups.

[0124] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. Application of reagents for detecting the urinary MBP-1 / urinary creatinine ratio in the preparation of kits for diagnosing diabetic nephropathy, where MBP-1 is the major basic protein 1.

2. The application according to claim 1, characterized in that, The kit is used to differentiate between diabetic nephropathy and non-diabetic kidney disease.

3. The application according to claim 1, characterized in that, The method of using the kit for diagnosing diabetic nephropathy includes: The level of the urinary MBP-1 / urinary creatinine ratio in the sample was measured, and the obtained data were compared with the reference value to determine whether it was diabetic nephropathy.

4. The application according to claim 3, characterized in that, The sample was obtained from the urine sample of the subject being tested.

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