Application of serum N-acetylserine in preparation of type 2 diabetic nephropathy diagnosis and prognosis evaluation product

Abstract

The invention belongs to the field of disease diagnosis and prognosis evaluation, and relates to application of serum N-acetylserine in preparation of diagnosis and prognosis evaluation of type 2 diabetic nephropathy (DKD), and through serum targeted metabonomics detection, it is found that the concentration of serum N-acetylserine is remarkably increased in a DKD patient and is independently related to the degree of renal function impairment. The invention further provides a risk grading method for evaluating the renal lesion progress of T2DM patients based on serum N-acetylserine detection, and the method has high sensitivity and specificity and can be used for screening and stage diagnosis of diabetic nephropathy and monitoring of the treatment effect. By analyzing the level difference of the serum N-acetylserine of T2DM patients of different renal function groups, the prediction value of the serum N-acetylserine on the occurrence and progress of DKD is explored, and the method has important significance in early diagnosis, disease progress evaluation and adjuvant therapy decision making.

Description

Technical Field

[0001] The present invention belongs to the field of disease diagnosis and prognosis evaluation, and specifically relates to the application of serum N-acetylserine level as a marker for the occurrence and progression of type 2 diabetic nephropathy in the preparation of type 2 diabetic nephropathy diagnosis and prognosis evaluation products. Background Art

[0002] Diabetic kidney disease (DKD) is one of the most serious microvascular complications in patients with type 2 diabetes mellitus (T2DM), with an incidence rate as high as 30% to 50%.

[0003] Currently, DKD is usually diagnosed based on persistent proteinuria (urine albumin / creatinine ratio ≥ 30 mg / g) and / or a decrease in estimated glomerular filtration rate (eGFR) (eGFR < 60 ml / min -1 ·(1.73m 2 ) -1 ) and exclude other causes of chronic kidney disease (CKD). Renal biopsy is only used to exclude other suspected causes. However, previous studies have found that a large proportion of DKD patients do not have proteinuria or eGFR less than 60 ml min -1 ·(1.73m 2 ) -1 , but showed typical DKD histopathological changes. These studies highlight the unpredictability of DKD development and the need for new strategies to better assess disease progression.

[0004] Diabetes is considered a classic metabolic disease because a large number of metabolic changes are associated with the disease. Through metabolomics methods, many metabolites have been discovered as potential biomarkers for predicting the occurrence of diabetes, including amino acids, lipids, carbohydrates, etc. DKD is one of the most common complications of diabetes. Metabolic pathway disorders and abnormal production of metabolites are crucial in the occurrence and development of the disease. Metabolic dysregulation is a key factor in the initiation of DKD. Hyperglycemia-induced metabolic changes, including changes in energy utilization and mitochondrial dysfunction, play a key role in the occurrence of the disease. Therefore, exploring the key metabolic changes associated with DKD is of great significance for understanding the occurrence process of DKD and prognosis assessment.

[0005] DKD can be improved by regulating metabolic disorders, and can also further indicate the characteristics of metabolic disorders in DKD. Studies have shown that sodium-glucose cotransporter-2 inhibitors can improve renal function, reduce oxidative stress, alleviate mitochondrial dysfunction, and delay the progression of renal damage by regulating metabolic reprogramming in DKD mice. N-acetylserine or its homologous enzyme ACY1 may be a functional participant in the pathogenesis of DKD. Summary of the invention

[0006] The present invention proposes the use of serum N-acetylserine level as a marker for the occurrence and progression of type 2 diabetic nephropathy in the preparation of type 2 diabetic nephropathy diagnosis and prognosis assessment products. The present invention explores the predictive value of serum N-acetylserine for the occurrence and progression of DKD by analyzing the differences in serum N-acetylserine levels in T2DM patients with different renal function groups, which has important significance for early diagnosis, evaluation of disease progression and auxiliary treatment decision-making.

[0007] The present invention specifically adopts the following technical solutions:

[0008] In a first aspect, the present invention provides the use of serum N-acetylserine in the preparation of a product for diagnosing and evaluating prognosis of type 2 diabetic nephropathy.

[0009] In a further embodiment, the type 2 diabetic nephropathy is late stage type 2 diabetic nephropathy.

[0010] The present invention uses ultra-high performance liquid chromatography tandem mass spectrometry to perform targeted quantitative metabolomics detection of N-acetylserine on serum samples of three groups of subjects: a simple T2DM group, an early DKD group, and a late DKD group.

[0011] The results showed that there were significant differences in serum N-acetylserine levels among the three groups (P<0.001). The serum N-acetylserine level in the advanced DKD group was higher than that in the simple T2DM group and the early DKD group, and the difference was statistically significant (P<0.001). There was no significant statistical difference between the simple T2DM group and the early DKD group (P>0.05). The serum level of N-acetylserine was significantly negatively correlated with eGFR, with a correlation coefficient of r=-0.74 (P<0.001), and was significantly negatively correlated with creatinine level. There was a significant positive correlation, with a correlation coefficient of r=0.64 (P<0.001), indicating that increased serum N-acetylserine levels in T2DM patients were significantly correlated with worsening renal function; the results of multivariate logistic regression analysis showed that the risk of DKD in the high N-acetylserine group was 10.63 (4.21-26.87) times that of the low N-acetylserine group; the risk of advanced DKD in the high N-acetylserine group was 24.21 (6.12-95.77) times that of the low N-acetylserine group.

[0012] The above results indicate that serum N-acetylserine level has a good diagnostic and predictive effect for DKD, especially advanced DKD.

[0013] In a second aspect, the present invention provides a risk stratification method for assessing the progression of renal lesions in patients with type 2 diabetes based on serum N-acetylserine detection, comprising:

[0014] The ROC curve of serum N-acetylserine level in the diagnosis of DKD was constructed. The results showed that the area under the ROC curve (AUC) was 0.738. When the cutoff value was 2.758 μmol / L, the sensitivity was 93.9%, the specificity was 53.7%, and the Youden index was 0.476 (P<0.001). The ROC curve of serum N-acetylserine level in the diagnosis of advanced DKD was constructed. The results showed that the area under the ROC curve was 0.910. When the cutoff value was 2.870 μmol / L, the sensitivity was 82.0%, the specificity was 86.7%, and the Youden index was 0.687 (P<0.001).

[0015] The above results show that when the serum N-acetylserine level is ≥2.758μmol / L, T2DM patients have a higher risk of developing DKD; when the serum N-acetylserine level is ≥2.870μmol / L, T2DM patients have a higher risk of developing advanced DKD.

[0016] Based on the above, the present invention provides a risk grading system for assessing the progression of renal lesions in patients with type 2 diabetes based on serum N-acetylserine levels, the system comprising:

[0017] Serum N-acetylserine level detection module, used to detect the level of N-acetylserine in the serum of patients with type 2 diabetes;

[0018] The risk stratification module evaluates the risk of renal disease progression in patients with type 2 diabetes based on the serum N-acetylserine level. The risk of DKD in patients with type 2 diabetes whose serum N-acetylserine level is ≥2.758μmol / L is higher than that in patients with type 2 diabetes whose serum N-acetylserine level is less than 2.758μmol / L. The risk of advanced DKD in patients with type 2 diabetes whose serum N-acetylserine level is ≥2.870μmol / L is higher than that in patients with type 2 diabetes whose serum N-acetylserine level is less than 2.870μmol / L.

[0019] The beneficial effects of the present invention are:

[0020] The present invention uses serum targeted metabolomics detection to find that serum N-acetylserine concentration is significantly increased in DKD patients and is independently correlated with the degree of renal function damage. Therefore, serum N-acetylserine level can be used as a marker for the occurrence and progression of type 2 diabetic nephropathy for the preparation of type 2 diabetic nephropathy diagnosis and prognosis assessment products.

[0021] The present invention provides a risk grading system for evaluating the progression of renal lesions in patients with type 2 diabetes based on serum N-acetylserine detection. The system has high sensitivity and specificity and can be used for screening, staging diagnosis and monitoring of treatment effects of diabetic nephropathy. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 .Differences in serum N-acetylserine levels among the three groups. DKD_E represents the early DKD group, and DKD_A represents the late DKD group; *P<0.05, **P<0.01, ***P<0.001.

[0023] Figure 2 .Spearman correlation analysis showed the correlation between serum N-acetylserine level and (A) estimated glomerular filtration rate eGFR and (B) serum creatinine.

[0024] Figure 3 .ROC curve shows the diagnostic predictive efficacy of N-acetylserine for the occurrence and progression of diabetic nephropathy, where DKD_E represents the early DKD group and DKD_A represents the late DKD group. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] The structural formula of N-Acetylserine is

[0027] Example 1

[0028] 1. Test materials

[0029] 1.1 Research subjects

[0030] The study population of this example was 144 patients with type 2 diabetes mellitus (T2DM) hospitalized in Henan Provincial People's Hospital from April 2022 to June 2023. Inclusion criteria: a) Meet the T2DM diagnosis and classification criteria revised by the World Health Organization (WHO) in 1999; b) Age 35 to 78 years old, regardless of gender; c) The patient's clinical data is complete. Exclusion criteria: a) Type 1 diabetes, single gene mutation diabetes, secondary diabetes caused by pancreatic damage, Cushing's syndrome, thyroid dysfunction or acromegaly; b) Patients with other renal diseases such as membranous nephropathy, lupus nephritis, IgA nephropathy, etc.; c) Patients with acute complications of diabetes, blood system diseases, malignant tumors, severe heart failure, liver dysfunction, acute infectious diseases; d) Patients who have undergone dialysis and kidney transplantation; e) Pregnant women; f) Patients whose expected survival time (due to life expectancy or related diseases) is less than 5 years as judged by the researchers; g) Patients with active sexually transmitted diseases such as viral hepatitis, AIDS and syphilis, and infectious diseases such as tuberculosis; h) Any situation that affects enrollment as judged by the researchers. All subjects have signed informed consent forms and the approval of the hospital ethics committee has been obtained.

[0031] T2DM patients were divided into three groups according to their urinary albumin-to-creatinine ratio (UACR): simple T2DM group (T2DM, UACR<30mg / g), with 49 cases in total; early-stage DKD group (early-stage DKD, DKD_E, UACR 30-300mg / g), with 50 cases in total; advanced-stage DKD group (advanced-stage DKD, DKD_A, UACR≥300mg / g), with 45 cases in total.

[0032] 1.2 Survey of Baseline Characteristics of the Population and Collection of Biological Samples

[0033] The electronic medical record system was used to collect the baseline demographic and laboratory data of the subjects. The main data included age, sex, height, weight, smoking, drinking, history of hypertension, history of diabetes, systolic blood pressure, diastolic blood pressure, UACR, 24-hour urine protein, fasting blood glucose, glycosylated hemoglobin, urea nitrogen, creatinine, uric acid, cystatin C, serum albumin, total cholesterol, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, hemoglobin and other clinical data. The estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula (Clin J Am Soc Nephrol. 2017; 12(1): 29-37).

[0034] All patients fasted for more than 8 hours, and 15 mL of fasting blood was collected the next morning. The blood sample was placed at room temperature for 2 hours and then centrifuged at 4°C (1000×g for 15 min). The serum was aliquoted and stored in a -80°C ultra-low temperature refrigerator.

[0035] 2. Experimental Methods

[0036] 2.1 Targeted quantitative detection of serum N-acetylserine

[0037] Ultra-high Performance Liquid Chromatography-tandem Mass Spectrometry (UPLC-MS / MS) was used to perform targeted quantitative detection of N-acetylserine in serum samples of the study subjects. Using mass spectrometry-based quantitative metabolomics analysis, metabolomics features were annotated as metabolites with primary confidence by comparison with standard metabolites.

[0038] The target metabolite standards were purchased from Sigma-Aldrich (St. Louis, MO, USA). The standards were accurately weighed and dissolved in water, methanol, sodium hydroxide solution or hydrochloric acid solution to prepare a stock solution with a concentration of 5.0 mg / mL. Take an appropriate amount of the stock solution of each standard to prepare a calibration solution.

[0039] The specific experimental steps are as follows:

[0040] (1) Sample preparation

[0041] The serum samples were thawed in an ice bath to reduce degradation. 20 μL of serum samples were added to a 96-well plate. 120 μL of ice-cold methanol solution (containing internal standard) was added, vortexed vigorously for 5 minutes and centrifuged, the supernatant was extracted for derivatization, and the plate was sealed for LC-MS analysis.

[0042] (2) Analysis and testing

[0043] Ultra-high performance liquid chromatography tandem mass spectrometry system UPLC-MS / MS (Waters, USA) was used to quantitatively analyze the targeted metabolites, wherein the column temperature was 40°C and the flow rate was 0.40 mL / min.

[0044] In order to ensure the high quality of experimental data, strict quality control measures are adopted. The reliability and stability of the analysis process are ensured by adding isotope internal standards, mixed biological (quality control, QC) samples and blank samples. The QC sample is prepared by mixing a certain amount of all research samples, representing the biological average value of the entire sample set. In this embodiment, the QC sample is injected after every 14 samples are analyzed to correct the analytical deviation. The reagent blank sample is a mixture of solvents used for sample preparation, and is usually processed in the same way as the sample to be analyzed. The reagent blank can be used as an effective system contamination alarm and is also used to clean the chromatographic column and remove the accumulated matrix effect. All serum samples must be analyzed within 48 hours after extraction and derivatization.

[0045] 2.2 Statistical and bioinformatics analysis

[0046] For the baseline characteristics of the subjects, the median (interquartile range) was used to describe the characteristics of continuous variables, and the frequency (percentage) was used to describe the characteristics of categorical variables.

[0047] The raw data files generated by UPLC-MS / MS were processed using MassLynx software (v4.1, Waters, Milford, MA, USA) to perform peak integration, calibration, and quantification of metabolites. Absolute quantification of metabolites is achieved by comparing the concentration of a substance in an unknown sample with a set of standard samples of known concentration (i.e., calibration curve). In most analyses, the instrument response signal is linearly related to the concentration, which results in a model presented by the equation y = ax + b, where y represents the instrument response signal (e.g., peak height or peak area), a is the slope of the curve, representing the sensitivity of the analysis, and b is a constant describing the background. Using the above formula, the concentration of the analyte in the unknown sample can be calculated from the known calibration curve.

[0048] One-tenth of the minimum detection value of the metabolite was used to fill missing values. Univariate and multivariate statistical analyses were used to evaluate the association between serum N-acetylserine concentration and the development of DKD. The Mann-Whitney U test was used to evaluate the difference in N-acetylserine concentration among the three groups. The Spearman correlation test was used to analyze the correlation between N-acetylserine and eGFR and serum creatinine. The metabolite concentration data were logarithmically transformed, and the association between metabolites and eGFR was analyzed by multivariate linear regression. All subjects were divided into two groups according to the median of N-acetylserine levels, and the association between N-acetylserine and eGFR and the development and progression of DKD was analyzed by multivariate linear regression model and logistic regression model, respectively. The receiver operating characteristic curve (ROC) was used to evaluate the diagnostic efficacy of serum N-acetylserine for the development and progression of DKD.

[0049] The above statistical analyses were completed in R.4.2.1 (https: / / www.r-project.org). All tests were two-sided, and P < 0.05 indicated statistical significance.

[0050] 3. Experimental results

[0051] 3.1 Comparison of general information of subjects in each group

[0052] As shown in Table 1, this example included 49 cases in the simple T2DM group, 50 cases in the early DKD group, and 45 cases in the late DKD group. The median ages of the three groups were 58.0, 54.5, and 58.0 years old, respectively, and the median eGFR was 102.6, 99.7, and 36.2 ml·min -1 ·(1.73m 2 ) -1 , the median UACR were 5.6, 104.3 and 1492.0 mg / g, respectively. As the disease progresses, serum urea nitrogen, creatinine and uric acid levels gradually increase.

[0053] Table 1. Characteristics of the study subjects

[0054]

[0055] Note: BMI: body mass index; eGFR: estimated glomerular filtration rate; UACR: urinary albumin-to-creatinine ratio.

[0056] 3.2 Serum N-acetylserine levels in three groups of T2DM patients

[0057] There were significant differences in serum N-acetylserine levels among the three groups (P<0.001). The serum N-acetylserine level in the advanced DKD group was higher than that in the simple T2DM group and the early DKD group (P<0.001). There was no significant difference between the simple T2DM group and the early DKD group (P>0.05). Figure 1 ).

[0058] 3.3 Correlation between serum N-acetylserine, eGFR and creatinine in patients with T2DM

[0059] The serum level of N-acetylserine was significantly negatively correlated with eGFR, with a correlation coefficient of r = -0.74 (P < 0.001), and was significantly positively correlated with creatinine level, with a correlation coefficient of r = 0.64 (P < 0.001), indicating that the increase in serum N-acetylserine level in T2DM patients was significantly correlated with the deterioration of renal function ( Figure 2 ).

[0060] 3.4 Multivariate regression analysis of serum N-acetylserine, eGFR, and DKD in patients with T2DM

[0061] The subjects were divided into two groups according to the median N-acetylserine level (2.47 μmol / L). After adjusting for covariates such as body mass index (BMI), smoking, drinking, hypertension, glycated hemoglobin, and total cholesterol, multivariate linear regression analysis showed that the eGFR in the high N-acetylserine group was 39.62 ml·min lower than that in the low N-acetylserine group. -1 (1.73m2) -1 (P<0.001). Whether T2DM patients developed DKD was set as the dependent variable (assignment: simple T2DM group = 0, early DKD group = 1, late DKD group = 1). The results of multivariate logistic regression analysis showed that the risk of DKD in the high N-acetylserine group was 10.63 (4.21-26.87) times that of the low N-acetylserine group. Subsequently, the analysis was limited to DKD patients, with whether late DKD occurred as the dependent variable (assignment: early DKD group = 0, late DKD group = 1). The results of multivariate logistic regression analysis showed that the risk of late DKD in the high N-acetylserine group was 24.21 (6.12-95.77) times that of the low N-acetylserine group.

[0062] Table 2. Relationship between N-acetylserine level, glomerular filtration rate and the development of DKD

[0063] Outcome variables Effect size P-value <![CDATA[eGFR * ]]> —— —— Model 1 -45.84(-58.82,-32.87) <0.001 Model 2 -39.62(-52.52,-26.71) <0.001 <![CDATA[DKD vs.T2DM # ]]> —— —— Model 1 10.28(4.16-25.43) <0.001 Model 2 10.63(4.21-26.87) <0.001 <![CDATA[DKD_A vs.DKD_E # ]]> —— —— Model 1 35.50(9.95-126.70) <0.001 Model 2 24.21(6.12-95.77) <0.001

[0064] Note: * Representatives used multivariate linear regression models; # The multivariate logistic regression model was used.

[0065] Model 1 was adjusted for age and sex; Model 2 was based on Model 1 and additionally adjusted for BMI, smoking, drinking, hypertension, glycated hemoglobin, and total cholesterol.

[0066] 3.5 Diagnostic efficacy of serum N-acetylserine levels for the development and progression of DKD

[0067] Taking UACR ≥ 30 mg / g as the standard for diagnosing DKD, the ROC curve of serum N-acetylserine level in the diagnosis of DKD was constructed. The results showed that the area under the ROC curve (AUC) was 0.738. When the cutoff value was 2.758 μmol / L, the sensitivity was 93.9%, the specificity was 53.7%, and the Youden index was 0.476 (P<0.001); taking UACR ≥ 300 mg / g as the standard for diagnosing advanced DKD, the ROC curve of serum N-acetylserine level in the diagnosis of advanced DKD was constructed. The results showed that the area under the ROC curve was 0.910. When the cutoff value was 2.870 μmol / L, the sensitivity was 82.0%, the specificity was 86.7%, and the Youden index was 0.687 (P<0.001). The above results show that serum N-acetylserine level has a good diagnostic and predictive effect for DKD, especially advanced DKD ( Figure 3 ), serum N-acetylserine levels can be used to assess the risk stratification of renal disease progression in patients with type 2 diabetes.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. Application of serum N-acetylserine in the preparation of products for diagnosis and prognosis assessment of type 2 diabetic nephropathy.

2. The use according to claim 1, characterized in that: The type 2 diabetic nephropathy is late stage type 2 diabetic nephropathy.

3. A risk stratification system for assessing the progression of renal disease in patients with type 2 diabetes based on serum N-acetylserine levels, characterized in that: The system includes: Serum N-acetylserine level detection module, used to detect the level of N-acetylserine in the serum of patients with type 2 diabetes; A risk stratification module was developed to assess the risk of renal disease progression in patients with type 2 diabetes based on serum N-acetylserine levels.

4. The system according to claim 3, characterized in that In the risk stratification module, the risk of type 2 diabetic patients with serum N-acetylserine levels ≥2.758μmol / L developing type 2 diabetic nephropathy was higher than that of type 2 diabetic patients with serum N-acetylserine levels <2.758μmol / L; the risk of type 2 diabetic patients with serum N-acetylserine levels ≥2.870μmol / L developing advanced type 2 diabetic nephropathy was higher than that of type 2 diabetic patients with serum N-acetylserine levels <2.870μmol / L.

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