Alpha1-antitrypsin glycated peptide fragment marker for early prediction of cardiovascular complications of peritoneal dialysis patient and application thereof
By using α1-antitrypsin glycated peptide markers and targeted absolute quantitative methods, combined with machine learning models, the problem of insufficient sensitivity and specificity in predicting cardiovascular complications in patients with peritoneal dialysis in the prior art is solved, and early diagnosis and accurate screening are achieved.
Patent Information
- Application Number
- CN202510529891.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-25
- Publication Date
- 2025-08-01
AI Technical Summary
Existing biomarkers are insufficient in predicting cardiovascular complications in peritoneal dialysis patients, and are susceptible to renal function and dialysis status, so they cannot predict the occurrence of diseases in a timely and early stage.
The α1-antitrypsin glycated peptide marker was used to detect the concentration of α1-antitrypsin glycated peptide in serum by targeted absolute quantitative methods. Diagnostic glycated peptide marker was screened out in combination with the glmnet machine learning model to construct a kit that predicts cardiovascular complications in patients with peritoneal dialysis in the early stage.
Early diagnosis of cardiovascular complications in patients with peritoneal dialysis was achieved, the stability and accuracy of marker screening were improved, and the absolute quantification of α1-antitrypsin glycated peptide markers was achieved, reducing the false positive rate.
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Abstract
Description
Technical Field
[0001] The present invention relates to the field of biomedical technology, and in particular to an α1-antitrypsin glycosylated peptide marker for early prediction of cardiovascular complications in peritoneal dialysis patients and applications thereof. Background Art
[0002] For a long time, cardiovascular disease (CVD) has been the main complication and the most common cause of death in dialysis patients. The 2024 annual report of the U.S. Renal Data System shows that 78.1% of hemodialysis patients and 68.9% of peritoneal dialysis patients suffer from CVD, and more than half of dialysis patients' deaths are related to CVD, with arrhythmia and cardiac arrest (or sudden cardiac death) being the main causes of death. Further studies have shown that compared with hemodialysis patients, peritoneal dialysis patients have a higher cardiovascular mortality rate. The high incidence of cardiovascular complications has become a major problem that needs to be urgently addressed in the field of peritoneal dialysis treatment.
[0003] Currently, multiple biomarkers have been found to be closely associated with CVD complications in peritoneal dialysis patients, including cardiac troponin, amino-terminal pro-B-type natriuretic peptide, soluble human matrix protein 2, and angiopoietin 2. Many of these markers have been widely used in clinical practice. However, existing biomarkers still suffer from insufficient sensitivity and specificity and are easily affected by renal function and dialysis status.
[0004] Proteomics has opened up new avenues for biomarker screening research for various diseases by systematically analyzing the composition and changes of proteins in organisms. Among them, glycoproteomics has become an important branch of proteomics research. Glycation modification of proteins refers to the spontaneous covalent binding reaction of reducing monosaccharides with proteins, and the reaction process can usually be divided into two stages: early and late. Studies have shown that glycated proteins are closely related to CVD complications in dialysis patients. Studies have shown that serum advanced glycation end-product (AGE) receptor levels are independent predictors of CVD complications; the level of serum glycated albumin in hemodialysis patients is linearly correlated with their cardiovascular mortality.
[0005] These glycated proteins still have many limitations as biomarkers for CVD complications in dialysis patients. For example, AGEs are late products of protein glycation and therefore cannot predict the onset of disease early. Furthermore, the information provided by individual proteins regarding glycated modification sequences and sites is limited. Therefore, it is necessary to utilize glycoproteomics technology to conduct comprehensive and systematic analyses, screen for markers that can predict CVD complications early in peritoneal dialysis patients, and develop robust quantitative methods for these markers to further enhance their clinical application value. Summary of the Invention
[0006] The object of the present invention is to provide a glycated peptide marker capable of early predicting cardiovascular complications in peritoneal dialysis patients, construct a quantitative method thereof and apply it to the early prediction of cardiovascular complications in clinical peritoneal dialysis patients.
[0007] To achieve the above object, the specific technical solutions adopted by the present invention are as follows:
[0008] In the first aspect, the present invention provides an α1 - antitrypsin glycated peptide marker capable of early predicting cardiovascular complications in peritoneal dialysis patients, and its amino acid sequence (N - terminal → C - terminal) is ELDRDTVFALVNYIFFK(g)GK, where g is the glycation modification site, the molecular weight is 2447.86Da, and it is derived from α1 - antitrypsin. The glycation modification process generally can be divided into two stages: in the first stage, glycation modification mainly occurs on the side - chain amino group of lysine, and the formed product is the early glycation product; in the second stage, the early glycation product further reacts to form a late glycation product with a more complex structure. The α1 - antitrypsin glycated peptide marker of the present invention is an early glycation product with glycation occurring on lysine, so detecting it can achieve the early prediction of cardiovascular complications in peritoneal dialysis patients.
[0009] In the second aspect, the present invention provides the application of the α1 - antitrypsin glycated peptide marker in the preparation of a kit for early predicting cardiovascular complications in peritoneal dialysis patients.
[0010] Furthermore, the concentration level of the α1 - antitrypsin glycated peptide marker in the serum of peritoneal dialysis patients with cardiovascular complications is higher than that of peritoneal dialysis patients without cardiovascular complications.
[0011] Furthermore, the application method is to perform targeted absolute quantification on the concentration level of the α1 - antitrypsin glycated peptide marker in the patient's serum.
[0012] Furthermore, the targeted absolute quantification method is as follows:
[0013] S1. Pretreatment of serum samples: Add sodium cyanoborohydride solution to the serum sample to reduce the protein; add dithiothreitol and iodoacetamide solution to the reduced protein solution for reduction alkylation reaction; then add peptide N - glycosidase F to remove N - glycans; collect the protein filtrate and add trypsin for enzymatic hydrolysis;
[0014] S2. Enrichment and desalting of glycated peptides: Add the collected enzymatic hydrolysis filtrate to boric acid material for glycated peptide enrichment; after enrichment, take the supernatant and add it to a Sep - Pak C18 solid - phase extraction column for desalting, collect the eluate and perform freeze - drying;
[0015] S3. Quantification of glycated peptide segments: After reconstituting the freeze-dried powder obtained in step S2, add standard heavy-labeled glycated peptide segments and perform LC-MS / MS analysis. The glycated peptide segments in the patient's serum are endogenous light-labeled glycated peptide segments. Analyze the signal intensity ratio between them and the standard heavy-labeled glycated peptide segments, and substitute it into the linear curve of the standard heavy-labeled glycated peptide segments to obtain the concentration of the endogenous light-labeled glycated peptide segments in the serum sample.
[0016] Further, in step S3, the preparation method of the standard heavy-labeled glycated peptide segments is as follows:
[0017] (1) Synthesize peptide segments according to the α1-antitrypsin glycated peptide segment sequence. During the synthesis process, add three amino acids GGR to the original N-terminus, and then perform N-terminal selective dimethylation reaction to block the free amino group of glycine, so that the subsequent glycation reaction can only occur on the side-chain amino group of lysine. At the same time, label lysine with 13C and 15N at the C-terminus as isotope labeling to obtain the standard heavy-labeled peptide segments;
[0018] (2) Prepare D-(+)-glucose monohydrate and sodium cyanoborohydride solutions with PBS respectively, mix the two to obtain solution A, dissolve the standard heavy-labeled peptide segments and mix them with solution A for glycation reaction, and then use trypsin to excise the three amino acids GGR at the N-terminus to obtain the target glycated peptide segments.
[0019] Further, in step S3, the method for making the linear curve of the standard heavy-labeled glycated peptide segments is as follows: Dilute the standard heavy-labeled glycated peptide segments to gradient concentrations and add them to the light-labeled glycated peptide segments (i.e., α1-antitrypsin glycated peptide segments without isotope labeling treatment) respectively for LC-MS / MS analysis; Use the obtained mass spectrometry signal intensity as the ordinate and the concentration ratio of the light-labeled glycated peptide segments to the heavy-labeled glycated peptide segments as the abscissa to fit a linear curve, and the linear quantitative range of each standard heavy-labeled glycated peptide segment can be obtained.
[0020] In the third aspect, the present invention provides the application of a reagent for detecting the level of the α1-antitrypsin glycated peptide segment marker in the preparation of a kit for early prediction of cardiovascular complications in peritoneal dialysis patients.
[0021] In a fourth aspect, the present invention provides a kit for early prediction of cardiovascular complications in peritoneal dialysis patients, comprising sodium cyanoborohydride, dithiothreitol, iodoacetamide, peptide N-glycosidase F, trypsin, boric acid material (for peptide enrichment, commercially available), Sep-Pak C18 solid phase extraction column, and standard heavy-labeled glycosylated peptides; the standard heavy-labeled glycosylated peptides are heavy isotope-labeled α1-antitrypsin glycosylated peptides. The kit is used to detect the concentration level of the α1-antitrypsin glycosylated peptide marker in the patient's serum according to the above-mentioned targeted absolute quantitative method, and the occurrence of cardiovascular complications in peritoneal dialysis patients is predicted based on the concentration level of the marker.
[0022] The present invention has the following beneficial effects:
[0023] Currently, there is a lack of early predictive biomarkers for CVD complications in peritoneal dialysis patients. Although AGEs have been shown to be closely associated with the development of CVD in dialysis patients, they are late products of protein glycation and therefore cannot predict the onset of the disease in a timely and early manner. The α1-antitrypsin glycated peptide biomarker screened in this invention enables the first early diagnosis of CVD complications in peritoneal dialysis patients.
[0024] 2. Traditional screening methods typically rely on statistical analysis, which often struggles to effectively control false positive rates when processing high-throughput proteomic data, and the stability and reliability of screening results are insufficient. The present invention utilizes the GLMNet machine learning model for screening glycosylated peptide markers. This model, through self-learning, can process nonlinear relationships and multidimensional features in data, uncovering underlying patterns from high-dimensional data and significantly improving the stability and accuracy of marker screening.
[0025] 3. Currently, most methods for quantifying glycated proteins are relative. For example, clinically, glycated hemoglobin is only a ratio and cannot be quantitatively measured. This invention utilizes a synthetic standard re-labeled glycated peptide method to achieve targeted absolute quantification of the α1-antitrypsin glycated peptide marker. BRIEF DESCRIPTION OF THE DRAWINGS
[0026] Figure 1 : Flowchart of the targeted absolute quantification of α1-antitrypsin glycosylated peptide markers of the present invention.
[0027] Figure 2 : The linear quantitative range of the α1-antitrypsin glycated peptide marker standard re-standardized glycated peptide in Example 2.
[0028] Figure 3 : The concentration levels of the α1-antitrypsin glycated peptide marker in Example 3 in the serum of peritoneal dialysis patients with CVD complications and without CVD.
[0029] Figure 4 : ROC analysis chart of α1-antitrypsin glycated peptide markers in Example 3. Detailed implementation manners
[0030] Glycosylation modification can change the structure and function of proteins and play an important role in the occurrence and development of diseases. Existing studies have shown that glycated proteins are closely related to the occurrence of cardiovascular diseases. Glycoproteomics provides a powerful research tool for revealing the mechanism of disease occurrence by systematically analyzing the composition and variation rules of glycated proteins in organisms, and also opens up a new way for the early diagnosis of diseases. The present invention utilizes this technology to screen glycated peptide markers for CVD complications by machine learning by comparing the compositions of serum glycoproteomes of peritoneal dialysis patients with and without CVD, and constructs an absolute quantification method for glycated peptide markers (the method flow is as Figure 1 shown), and finally applies it to the diagnosis of CVD complications in peritoneal dialysis patients.
[0031] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.
[0032] Example 1: Screening of glycated peptide markers
[0033] (1) Inclusion of research subjects:
[0034] Forty-eight pairs of peritoneal dialysis patients were included, and serum samples and relevant clinical data before dialysis of the patients were collected. According to whether CVD had occurred within the recent 6 months when the serum samples were collected, the patients were divided into a CVD group and a non-CVD group, and the genders, ages, and dialysis durations of the two groups of patients were paired.
[0035] (2) Pretreatment of serum samples:
[0036] ① Add 1.11 μL of 75 mmol / L sodium cyanoborohydride solution to 10 μL of serum sample, and react at 37 °C and 1100 rpm for 4 hours to reduce the protein;
[0037] ② Take 500 μg of the reduced protein solution into a 10 KDa ultrafiltration tube, use ammonium bicarbonate solution to remove the excess sodium cyanoborohydride solution, then add 4.04 μL of 1 mol / L dithiothreitol, and react at 56 °C for 1 hour; subsequently add 8.08 μL of 1 mol / L iodoacetamide solution, and react at room temperature and in the dark for 45 minutes; after the above reduction alkylation reaction is completed, ultrafilter to remove the unreacted reagents;
[0038] ③ Add 0.5 μL of peptide N-glycosidase F, and react at 37 °C and 150 rpm for 16 hours to remove N-glycans. After the reaction is completed, collect the protein filtrate;
[0039] ④Add trypsin to the protein filtrate at a mass ratio of 50:1 (serum protein: trypsin), and enzymatically digest for 16 hours at 37 °C and 1100 rpm. After the enzymatic digestion is completed, collect the filtrate.
[0040] (3) Enrichment and desalting of glycated peptides:
[0041] ①Add the collected filtrate to 20 mg of equilibrated and activated boric acid material (Bio-rad, Affi-Gel Boronate Media, 1536103), and react at 37 °C and 1100 rpm for 16 hours;
[0042] ②After the glycation enrichment is completed, take the supernatant and add it to a Sep-Pak C18 solid-phase extraction cartridge for desalting; after loading 3 times and washing 2 times, collect the desalted eluate and store it by lyophilization.
[0043] (4) Identification and screening of glycated peptide markers:
[0044] After reconstituting the lyophilized sample with 0.1% formic acid solution to a concentration of 0.5 μg / μL, perform high-performance liquid chromatography-tandem mass spectrometry analysis using an Orbitrap Exploris 480 high-resolution mass spectrometer. Use the PEAKS Online software to process and analyze and identify the original data files generated by the above LC-MS / MS tests. The database is the Swiss-Prot annotated human database downloaded from the Uniport official website (release time: 2023-04). Obtain the amino acid sequence of the peptide using the de novo sequencing algorithm, set the digestion method to full digestion, set the false discovery rate to less than 1%, and set the maximum allowed missed cleavage sites to 4. When retrieving the database, the fixed modification is set to carbamidomethylation (+57.021 Da); the variable modifications are set to protein N-term acetylation (+42.010 Da), methionine oxidation (+15.995 Da), asparagine deamidation (+0.984 Da), and lysine glycation (+164.068 Da). Set the precursor mass error to 10 ppm and the fragment mass error to 0.05 Da. Use the LFQ algorithm in the software to extract the relative intensity obtained based on the peptide mass peaks.
[0045] The R package mlr3verse in R software (version 4.3.0) was used to perform machine learning analysis on glycoproteomics data. The machine learning model used was the Generalized linear model network (glmnet), and the parameters of the model were optimized using the auto_tuner in mlr3tuning. Based on the results of four-fold cross-validation, the coefficient values of each glycopeptide characteristic variable were obtained and used as an important basis for screening biomarkers. The 48 pairs of peritoneal dialysis patients in the screening group were randomly divided into a training set and a test set at a ratio of 7:3. After completing the training of the optimal model in the training set, the model performance was further tested in the test set.
[0046] The glmnet model finally screened out 55 glycopeptides that had a significant contribution to distinguishing CVD patients from non-CVD patients. The 55 differential glycopeptides were further screened according to the selection criteria of the target peptides. The screening criteria were as follows: 1. The unique peptide of a specific protein; 2. The length was in line with 7-25 amino acids; 3. The mass < 6000 Da and the detectability ≥ 0.5; 4. It did not contain methionine, cysteine or other post-translational modification sites. According to the above screening criteria, important candidate glycopeptide biomarkers with a high ranking in importance and diagnostic significance were comprehensively screened.
[0047] Finally, the glycopeptide biomarker sequence for CVD complications in peritoneal dialysis patients screened in this example was ELDRDTVFALVNYIFFK(g)GK (g represents glycation, which is the glycation modification site), with a molecular weight of 2447.86 Da and derived from α1-antitrypsin.
[0048] Example 2: Targeted quantification of glycopeptide biomarkers in serum samples
[0049] (1) Synthesis of standard heavy-labeled peptides:
[0050] During the synthesis process, three amino acids GGR were added to the original N-terminus, and then the free amino group of glycine was blocked by N-terminal selective dimethylation reaction, so that the subsequent glycation reaction could only occur on the side-chain amino group of lysine. At the same time, lysine was labeled with 13C and 15N at the C-terminus as an isotope reagent, making it have a mass difference of 18 Da compared with the endogenous glycopeptide, and it could be identified by the mass number difference in the mass spectrometry data.
[0051] (2) Preparation of standard heavy-labeled glycopeptides:
[0052] Prepare solutions of D-(+)-glucose monohydrate and sodium cyanoborohydride in 1×PBS buffer at concentrations of 2 mol / L and 600 mmol / L, respectively, and mix them well to obtain Solution A. Dissolve the standard heavy-labeled peptide segments and mix them with Solution A in equal volumes, and react under the conditions of 37 °C and 1100 rpm. After the glycation reaction is completed, use trypsin to remove the three N-terminal amino acids GGR to obtain the target glycated peptide segments.
[0053] (3) Determination of the levels of glycated peptide segment markers in serum samples:
[0054] ① Dilute the standard heavy-labeled glycated peptide segments into a concentration gradient and add them to the light-labeled glycated peptide segments respectively for LC-MS / MS analysis. Take the obtained mass spectrometry signal intensity as the ordinate and the concentration ratio of the light-labeled glycated peptide segments (i.e., the target glycated peptide segments in the patient's serum samples) to the heavy-labeled glycated peptide segments as the abscissa, and fit a linear curve to obtain the linear quantitative range of each standard heavy-labeled glycated peptide segment.
[0055] For example, take the mixed peptide segment sample obtained by enzymolysis of the serum mixture of 20 randomly selected patients from 48 pairs of peritoneal dialysis patients in the screening group as the light-labeled glycated peptide segments; dilute the heavy-labeled peptide segments with known concentrations into multiple groups of concentrations and add them to the mixed peptide samples respectively for LC-MS / MS analysis to find the heavy-labeled concentration corresponding to the signal intensity ratio of light:heavy of 1:1; then, according to this heavy-labeled concentration, establish concentration gradients of 6 other ratios by gradient dilution of the heavy-labeled peptide segments at this concentration and adding them to the mixed peptide samples respectively (the mass spectrometry signal intensity ratio can be equivalent to the concentration ratio); obtain 7 gradient samples with the concentration ratios of the light-labeled glycated peptide segments to the standard heavy-labeled glycated peptide segments reaching 1:8, 1:4, 1:2, 1:1, 2:1, 4:1, and 8:1, and perform LC-MS / MS analysis on them to obtain the Figure 2 linear curve as shown.
[0056] ② After the patient's serum samples are treated as described above (including pretreatment and enrichment and desalting), add standard heavy-labeled glycated peptide segments with a known concentration (such as 50 pg / μL) for LC-MS / MS analysis. The patient's serum glycated peptide segments are endogenous light-labeled glycated peptide segments. Substitute the mass spectrometry signal intensity obtained from the LC-MS / MS analysis into the linear curve of the standard heavy-labeled glycated peptide segments to obtain the concentration ratio of the light-labeled glycated peptide segments to the heavy-labeled glycated peptide segments. According to the known concentration of the heavy-labeled glycated peptide segments, the concentration of the endogenous light-labeled glycated peptide segments in the serum samples of the research subjects can be absolutely quantified.
[0057] Example 3: Verification of the effect of glycated peptide segment markers
[0058] Another 16 pairs of peritoneal dialysis patients were included. According to whether CVD had occurred in the past 6 months when serum samples were collected, the patients were divided into a CVD group and a non-CVD group, and the gender, age, and dialysis duration of the two groups of patients were paired. Serum samples were collected before dialysis, and the content of the target glycated peptide segment was measured according to the method of Example 2 and Figure 2 the linear curve, and t-test and receiver operating characteristic curve analysis were used to verify the diagnostic efficacy of the target glycated peptide segment marker for CVD.
[0059] The results showed that the concentration level of the α1-antitrypsin glycated peptide segment marker was significantly increased in the serum of peritoneal dialysis patients with CVD complications, 12.43±3.01 pg / μL in the CVD group and 1.97±0.54 pg / μL in the non-CVD group ( Figure 3 ), and the area under the receiver operating characteristic curve was as high as 0.926 ( Figure 4 ), indicating that the α1-antitrypsin glycated peptide segment marker had a very strong ability to distinguish between CVD and non-CVD patients and had a good diagnostic effect on CVD complications in peritoneal dialysis patients. When the marker concentration threshold was set at 3.225 pg / μL (when the marker concentration ≥ the threshold, the occurrence of CVD complications was predicted, and when the marker concentration < the threshold, the non-occurrence of CVD complications was predicted), the sensitivity of the α1-antitrypsin glycated peptide segment marker in diagnosing CVD reached 93.3%, and the specificity was 86.7%.
[0060] This specific implementation manner is only an interpretation of the present invention and not a limitation thereof. Any changes made by those skilled in the art after reading the specification of the present invention will be protected by the patent law as long as they are within the scope of the claims of the present invention.
Claims
1. Use of α1-antitrypsin glycated peptide segment markers in the preparation of a kit for early prediction of cardiovascular complications in peritoneal dialysis patients, characterized in that, The sequence of the α1-antitrypsin glycated peptide marker is ELDRDTVFALVNYIFFK(g)GK, where g is the glycation modification site.
2. The application according to claim 1, characterized in that The concentration level of the α1-antitrypsin glycated peptide marker in the serum of peritoneal dialysis patients with cardiovascular complications is higher than that of peritoneal dialysis patients without cardiovascular complications.
3. The application according to claim 1 or 2, characterized in that, The application method is to perform targeted absolute quantification on the concentration level of the α1-antitrypsin glycated peptide marker in the patient's serum.
4. The application according to claim 3, characterized in that, The targeted absolute quantification method is as follows: S1. Pretreatment of serum samples: Sodium cyanoborohydride solution is added to the serum samples to reduce proteins; dithiothreitol and iodoacetamide solution are added to the reduced protein solution for reduction alkylation reaction; then peptide N-glycosidase F is added to remove N-glycans; The protein filtrate is collected and added with trypsin for enzymatic hydrolysis; S2. Enrichment and desalting of glycated peptides: The collected enzymatic hydrolysis filtrate is added to boric acid material for glycated peptide enrichment; After the enrichment is completed, the supernatant is taken and added to a Sep-Pak C18 solid-phase extraction column for desalting, and the eluate is collected and freeze-dried; S3. Quantification of glycated peptides: The freeze-dried powder obtained in step S2 is re-dissolved and added with standard heavy-labeled glycated peptides, and LC-MS / MS analysis is performed. The patient's serum glycated peptides are endogenous light-labeled glycated peptides. The signal intensity ratio between them and the standard heavy-labeled glycated peptides is analyzed and substituted into the linear curve of the standard heavy-labeled glycated peptides to obtain the concentration of the endogenous light-labeled glycated peptides in the serum sample.
5. The application according to claim 4, wherein In step S3, the preparation method of the standard heavy-labeled glycated peptides is as follows: (1) Peptide synthesis is carried out according to the α1-antitrypsin glycated peptide sequence. During the synthesis process, three amino acids GGR are added to the original N-terminus, and then N-terminal selective dimethylation reaction is carried out to block the free amino group of glycine, so that subsequent glycation reactions can only occur on the side-chain amino group of lysine. At the same time, lysine is labeled with 13C and 15N at the C-terminus as an isotope label to obtain the standard heavy-labeled peptide; (2) D-(+)-glucose monohydrate and sodium cyanoborohydride solution are respectively prepared with PBS, and the two are mixed to obtain solution A. The standard heavy-labeled peptide is dissolved and mixed with solution A for glycation reaction, and then trypsin is used to excise the three amino acids GGR at the N-terminus to obtain the target glycated peptide.
6. The application according to claim 5, characterized in that, In step S3, the method for making the linear curve of the standard heavy-labeled glycated peptides is as follows: The standard heavy-labeled glycated peptides are diluted to gradient concentrations and added to the light-labeled glycated peptides respectively for LC-MS / MS analysis; The obtained mass spectrometry signal intensity is used as the vertical axis, and the concentration ratio of the light-labeled glycated peptides to the heavy-labeled glycated peptides is used as the horizontal axis to fit the linear curve, and the linear quantitative range of each standard heavy-labeled glycated peptide can be obtained.
7. Use of a reagent for detecting the level of α1-antitrypsin glycated peptide markers in the preparation of a kit for early prediction of cardiovascular complications in peritoneal dialysis patients, characterized in that, The sequence of the α1-antitrypsin glycated peptide marker is ELDRDTVFALVNYIFFK(g)GK, where g is the glycation modification site.
8. A kit for early prediction of cardiovascular complications in peritoneal dialysis patients, characterized in that, Comprising sodium cyanoborohydride, dithiothreitol, iodoacetamide, peptide N-glycosidase F, trypsin, boric acid material, Sep-Pak C18 solid phase extraction column, standard heavy isotope-labeled glycated peptide segments; the standard heavy isotope-labeled glycated peptide segments are heavy isotope-labeled α1-antitrypsin glycated peptide segments, and the sequence of the α1-antitrypsin glycated peptide segments is ELDRDTVFALVNYIFFK(g)GK, where g is the glycation modification site.