Marker for diagnosing thoracic aortic dissection, diagnostic model and application

Abstract

The present application relates to the technical field of clinical diagnosis model construction, in particular to a marker for diagnosing thoracic aortic dissection and construction and application of a diagnosis model, and provides a marker for diagnosing thoracic aortic dissection, and a diagnosis model for thoracic aortic dissection is constructed according to the marker, the diagnosis model has good effect on diagnosis of thoracic aortic dissection, high sensitivity, and the diagnosis model constructed in combination with other markers has higher accuracy of diagnosis results, and can accurately diagnose thoracic aortic dissection.

Description

Technical Field

[0001] This invention relates to the field of clinical prediction model construction technology, specifically to biomarkers, diagnostic models, and applications for diagnosing thoracic aortic dissection. Background Technology

[0002] Aortic dissection (TAD) is a serious cardiovascular disease that severely threatens human health. Various factors lead to a tear in the intima of the aorta, allowing blood to enter the media and completely separate the intima from the media, resulting in the formation of two separate lumens (true and false). It is typically classified into Stanford type A and Stanford type B based on the location of the tear and the affected areas. With the increasing incidence of hypertension and other diseases, as well as the accelerating aging of the population, the incidence of aortic dissection is also showing a year-on-year upward trend. Aortic dissection (TAD) has a rapid onset, progresses quickly, and has a very poor prognosis. Without timely and appropriate intervention, the mortality rate after aortic dissection rupture is >90%, ranking first among cardiovascular critical illnesses. Studies show that approximately 48.6% of AD patients die before admission, and 30% die during hospitalization. For untreated AD patients, the risk of death increases by 1% to 3% for every hour of delay in diagnosis and treatment within 48 hours of onset. Currently, there are no effective drug treatments for aortic dissection, and surgical intervention is the main treatment method. Therefore, implementing early diagnosis, early assessment, and early treatment is crucial to improving the prognosis of AD patients; however, there are many shortcomings in the above aspects of AD diagnosis and treatment.

[0003] Early detection of aortic aortic (AD) is difficult. Clinical diagnosis primarily relies on patient symptoms, laboratory tests, and imaging findings (such as maximum aortic diameter and true / false lumen area). However, these indicators are hard to obtain in the early stages, hindering early risk assessment. Furthermore, aortic CTA and MRA, the gold standard for diagnosing transcatheter aortic (TAD), are rarely routinely performed in primary care hospitals. A simple, easy-to-use, accurate, and specific diagnostic method for AAD remains lacking. Currently, there is an urgent clinical need for a method that can diagnose and provide early warning of TAD.

[0004] The occurrence of aortic dissection is closely related to metabolic disorders. In patients with hyperhomocysteinemia, high concentrations of homocysteine ​​can upregulate the expression of MMP-9 in the blood vessel wall, leading to aortic wall remodeling. Metabolicomics, by detecting changes in the expression of metabolites under physiological and pathological conditions, can systematically screen for differentially expressed metabolites reflecting the body's state. Compared to genomic, transcriptomic, and proteomic analyses, metabolite analysis can more directly and accurately reflect the pathophysiological processes of an organism. Currently, metabolomics plays an important role in exploring the pathogenesis of cardiovascular diseases such as myocardial hypertrophy, heart failure, and coronary artery disease, and in identifying biomarkers. Therefore, discovering new and more accurate biomarkers for predicting aortic dissection through metabolomics has significant practical implications for clinical applications. Summary of the Invention

[0005] In view of this, the technical problem to be solved by the present invention is to provide markers, diagnostic models and applications for diagnosing thoracic aortic dissection.

[0006] This invention provides the application of γ-aminobutyric acid (GABA) as a diagnostic marker for thoracic aortic dissection.

[0007] In this invention, γ-aminobutyric acid (GABA) can be used as a diagnostic marker for thoracic aortic dissection.

[0008] In this invention, the biomarker was established through metabolomics, and its expression showed significant differences between healthy and diseased individuals. Its application in the diagnosis of thoracic aortic dissection can lead to accurate diagnosis. Furthermore, the biomarker can be rapidly detected using ELISA, with a short detection cycle and low dosage. Compared to other biomarkers (such as NLR and lactate dehydrogenase), it exhibits differential expression in the Stanford A and Stanford B groups, demonstrating higher specificity.

[0009] Furthermore, the present invention provides a combined biomarker comprising at least one of the ratio of neutrophils to lymphocytes (NLT) or lactate dehydrogenase and γ-aminobutyric acid.

[0010] The combined biomarker described in this invention is used for the diagnosis of thoracic aortic dissection, and the diagnostic results are accurate and reliable, with a more accurate diagnostic effect compared to other combined biomarkers.

[0011] This invention provides a method for evaluating biomarkers for diagnosing thoracic aortic dissection, comprising: performing serological tests on test samples and healthy samples to obtain level data of the biomarkers and / or combined biomarkers described in this invention, and then setting the occurrence of dissection as the dependent variable and performing statistical difference analysis.

[0012] The level data includes concentration and / or proportion, etc.

[0013] Furthermore, in the evaluation method described in this invention,

[0014] The statistical difference analysis includes univariate and / or multivariate statistical difference analysis;

[0015] The statistical difference analysis requires P < 0.05.

[0016] The concentration of the biomarker was detected using the ELISA method;

[0017] The test sample consisted of patients aged 18 years or older who had experienced chest pain, abdominal pain, back pain, syncope, and signs of aortic dissection within 14 days.

[0018] The healthy sample refers to healthy individuals whose age, gender, and test sample do not differ statistically from those of the healthy sample, and who do not have ischemic heart disease, cancer, lung disease, or infectious diseases.

[0019] This invention provides the application of γ-aminobutyric acid and / or the combined biomarker as described in claim 2 in the construction of a clinical diagnostic model for thoracic aortic dissection.

[0020] This invention provides a method for constructing a clinical diagnostic model of thoracic aortic dissection, which includes constructing a clinical diagnostic model of thoracic aortic dissection using γ-aminobutyric acid and / or the combined biomarkers described in this invention.

[0021] Furthermore, the construction method includes the following steps:

[0022] Obtain level data of γ-aminobutyric acid and / or the combined biomarker described in this invention in the sample, construct ROC analysis curves based on the level data, and obtain diagnostic thresholds and sensitivity.

[0023] The level data includes concentration and / or proportion, etc.

[0024] This invention calculates a cutoff value of 124.9 pg / mL, indicating that when the plasma GABA concentration is below 124.9 pg / mL, the risk of developing AD is 95.6% (sensitivity). The AUC for predicting Stanford type A AD with plasma GABA level is 0.8943, with a sensitivity of 94.12% and a specificity of 68% at a cutoff value of 112.4 pg / mL; the AUC for predicting Stanford type B AD is 0.6954, with a sensitivity of 89.74% at a cutoff value of 124.9 pg / mL.

[0025] Furthermore, the sample may include serum or plasma.

[0026] In this invention, the biomarker γ-aminobutyric acid (GABA) is detected by ELISA to obtain its concentration; the concentrations of other biomarkers can be obtained by serological detection.

[0027] This invention provides a clinical diagnostic model of thoracic aortic dissection obtained by the construction method described above.

[0028] This invention provides the application of any one of the following (i) to (v) in the preparation of a diagnostic product that distinguishes between aortic dissection and acute coronary syndrome:

[0029] i) γ-aminobutyric acid;

[0030] ii) The combined markers described in this invention;

[0031] iii) The clinical diagnostic model for aortic dissection described in this invention.

[0032] This invention provides diagnostic markers for thoracic aortic dissection and constructs a diagnostic model for thoracic aortic dissection based on the predictive markers. The diagnostic model has good efficacy and high sensitivity in diagnosing thoracic aortic dissection. The diagnostic model constructed in combination with other markers has even higher accuracy and can accurately diagnose thoracic aortic dissection. Attached Figure Description

[0033] Figure 1 The PCA score plots of metabolites in the disease group and the control group show that there are differences in the principal components of metabolites between the two groups.

[0034] Figure 2 The orthogonal distance regression analysis plot shows that the data in disease 5 are outliers, and its sample is an outlier sample.

[0035] Figure 3 The diagram shows the OPLS-DA model, where R2X and R2Y represent the explanatory power of the model for the metabolite X matrix and the sample type Y matrix, respectively; Q2 represents the predictive power of the model. A good model fit is indicated by R2Y close to 1, Q2 greater than 0.5, and the difference between R2Y and Q2 less than 0.3.

[0036] Figure 4 The OPLS-DA score plot shows that there are significant differences in metabolites in the endometrial tissue between the disease group and the control group. The horizontal axis is the principal component score plot, representing the differences between groups; the vertical axis is the orthogonal component score plot, representing the differences within groups.

[0037] Figure 5 The scatter plot shows that there were differences in 304 metabolites in the endometrial tissue of the disease group, of which 124 metabolites were upregulated and 180 metabolites were downregulated.

[0038] Figure 6A bubble chart showing the enrichment of differentially expressed metabolites in the KEGG pathway is presented. The horizontal axis represents the ratio of differentially expressed metabolites to metabolites in the metabolic pathway, and the vertical axis represents the metabolic pathway enriched by differentially expressed metabolites.

[0039] Figure 7 This indicates that GABA is a common metabolite in the nicotinic acid-nicotinamide, arginine-proline, and alanine-aspartic acid-glutamate metabolic pathways and participates in inflammatory responses.

[0040] Figure 8 The LC-MS / MS results showed that GABA expression was significantly downregulated in the intima tissue of the disease group (P < 0.05).

[0041] Figure 9 The expression of GABA in the vascular intima and blood of the disease group was detected by ELISA. * indicates P < 0.05.

[0042] Figure 10 The plasma GABA concentration in the TAD group and the control group was detected by ELISA. The results showed that the expression in the TAD group was lower than that in the control group.

[0043] Figure 11 The plasma GABA concentration in the Stanford A and Stanford B groups was detected by ELISA. The results showed that the expression in the Stanford A group was lower than that in the Stanford B group and the control group.

[0044] Figure 12 The diagnostic efficacy of GABA and its combined markers is demonstrated.

[0045] Figure 13 The diagnostic model shown in the DCA curve exhibits a significantly higher net benefit;

[0046] Figure 14 The Calibration plot curve visualizes the goodness of fit. The Calibration plot curve is near the 45° reference line, indicating that the predicted probability of the plotted nomogram prediction model has good consistency with the actual probability. Detailed Implementation

[0047] This invention provides markers, diagnostic models, and applications for diagnosing thoracic aortic dissection. Those skilled in the art can refer to the content of this document and appropriately modify the process parameters to achieve the desired results. It is particularly important to note that all similar substitutions and modifications are obvious to those skilled in the art and are considered to be included in this invention. The methods and applications of this invention have been described through preferred embodiments. Those skilled in the art can clearly modify or appropriately change and combine the methods and applications described herein without departing from the content, spirit, and scope of this invention to implement and apply the technology of this invention.

[0048] The test materials used in this invention are all common commercial products and can be purchased on the market.

[0049] The present invention will be further illustrated below with reference to the embodiments:

[0050] Example 1: Construction and Application of Markers

[0051] In this invention, SPSS 20.0 software was used for statistical analysis of the data. For continuous data, a normality test was first performed. Variables conforming to a normal distribution were expressed as mean ± standard deviation (X ± S), and differences between groups were analyzed using a t-test. For categorical data, a chi-square test was used. Variables not conforming to a normal distribution were expressed as median [interquartile range] (IQR), and a Wilcoxon signed-rank test was performed. Categorical variables were expressed as numbers and percentages (%), and comparisons between two groups were performed using Fisher's exact test or a chi-square test. Based on general clinical, laboratory, and pathological data, univariate logistic regression was first used to screen variables, followed by multivariate logistic regression, lasso regression, and stepwise regression to construct a GABA predictive model. Statistical analysis of continuous data was performed using mean ± standard deviation, and experimental results were expressed as mean ± standard deviation. P < 0.05 indicated a statistically significant difference.

[0052] I. Case Collection

[0053] (1) General Clinical Data Collection: The cases studied in this invention were approved by the Ethics Review Committee of the First Affiliated Hospital of Soochow University, and informed consent was obtained from all patients. Intima-endothelial tissue samples were obtained from patients with aortic dissection (AD) who underwent surgery at the Department of Cardiac and Vascular Surgery, First Affiliated Hospital of Soochow University from October 2019 to June 2020; blood samples were collected from patients with aortic dissection (AD) who underwent treatment at the Department of Cardiac and Vascular Surgery, First Affiliated Hospital of Soochow University from January 2021 to September 2022. Healthy individuals who underwent physical examinations at our hospital during the same period were selected as controls, and their basic information was collected. Inclusion criteria for TAD patients: ① Patients older than 18 years who experienced chest / abdominal / back pain, syncope, and signs / symptoms of hypoperfusion within 14 days; ② All patients were definitively diagnosed by aortic CTA or surgical visualization at our hospital. All diagnoses conformed to the 2017 Chinese Expert Consensus on the Diagnosis and Treatment of Aortic Dissection. The TAD patients enrolled in this study were classified as Stanford type A and Stanford type B. ③ Inclusion criteria for the healthy control group: Individuals undergoing physical examinations at our hospital who showed no statistically significant differences in age and sex compared to the TAD group (P>0.05) and had no obvious systemic diseases (ischemic heart disease, cancer, lung disease, infectious diseases). Exclusion criteria: ① Exclude confounding factors such as Marfan syndrome, LD syndrome, and other congenital connective tissue diseases, and autoimmune diseases such as Takayasu arteritis. Exclude patients with aortic trauma, pseudoaneurysms, or infectious diseases, as well as patients with hematological diseases, infectious diseases, or malignant tumors, which may cause secondary changes in white blood cells, lymphocytes, and platelets. Basic information of aortic dissection patients was collected, including clinical characteristics, vital signs, and laboratory test indicators such as complete blood count.

[0054] (2) Sample Collection: Aortic dissection tissue samples were collected from surgically treated patients (n=5) as the disease group. After surgical removal, the samples were rinsed with saline, and the intima was separated and stored at -80℃. Within 24 hours of hospitalization, 3 mL of fasting peripheral venous blood (purple blood collection tube) was collected from each subject in the morning, avoiding hemolysis due to vigorous shaking. The blood was centrifuged at 3600 rpm for 10 minutes to separate the serum, which was then frozen at -80℃ until analysis. Serum was collected from 90 patients in the AD group and 50 patients in the healthy control group.

[0055] II. LC-MS / MS Metabolomics Sequencing Analysis of Endometrial Tissue

[0056] (1) Extraction of tissue metabolites

[0057] All tissue samples were thawed and weighed, and 200 μL of H2O was added to prepare a tissue homogenate using a glass homogenizer. Then, a MeOH:ACN mixture was prepared at a 1:1 volume ratio to extract metabolites from the homogenate. The mixture was rotated for 30 s, sonicated for 10 min, and incubated at -20°C for 1 h. After centrifugation at 4°C and 20,000 g for 15 min, the supernatant was transferred to a new tube and evaporated to dryness in a vacuum concentrator. The dried extract was collected, dissolved in an aqueous solution of ACN, rotated for 30 s, sonicated for 10 min, and centrifuged at 4°C and 20,000 g for 15 min to remove insoluble impurities.

[0058] (2) LC-MS / MS detection

[0059] Extracts from the test samples were taken from -80℃, and 10 μL of supernatant from each sample was transferred to a new tube and evaporated to dryness in a vacuum concentrator. The supernatant from the QC samples and the test samples were transferred to separate HPLC vials. LC-MS / MS analysis was performed using an Agilent 1260 Infinity high-performance liquid chromatography system. Samples were separated on an amide column. Mobile phase A consisted of a mixture of water, 25 mmol / L ammonium acetate, and 25 mmol / L ammonium hydroxide; mobile phase B consisted of ACN. Mass spectrometry analysis was performed using Q-Exactive MS / MS in both positive and negative ion modes.

[0060] (3) Data processing and analysis

[0061] For mass spectrometry analysis data, peak identification was performed using mass spectrometry deconvolution software, obtaining approximately 6680 substance peak signals. Qualitative analysis of the compounds was then performed using the mzCloud (ddMS2) and ChemSpider mass spectrometry databases. Lipid MAPS was used to screen the compounds, and Compound Discover 2.1 software was used for data quantification and annotation, retaining 2750 valid peak signals for further analysis, ultimately yielding 788 metabolites.

[0062] (4) Multivariate analysis:

[0063] In metabolomics analysis, due to the close relationships between metabolites, univariate analysis is often insufficient to identify candidate molecules. Multivariate analysis, on the other hand, can handle thousands of variables simultaneously and focuses more on the relationships between metabolites and their related biological processes. To determine the relationship between metabolites in the disease group and the control group, principal component analysis was performed on the data. The results showed differences in the first and second principal components between the disease group and the control group, and it was found that the "disease 5" specimen was dispersed among other samples within the group. Figure 1 Since outliers can affect the OPLS model, orthogonal distance regression analysis is used to detect outliers. Figure 2The data from the "disease 5" sample were found to be outliers, so the data from the "disease 5" sample were removed in subsequent data analysis. To obtain more reliable differentially expressed metabolites from the disease and control groups, OPLS-DA analysis was performed to filter out orthogonal signals unrelated to the categorical variables. In the OPLS model, R²X and R²Y represent the explanatory power of the model for the metabolite X matrix and sample type Y matrix; Q² represents the predictive power of the model. The closer R²X, R²Y, and Q² are to 1, the better the fit. The results showed that R²Y was close to 1, Q² was greater than 0.5, and the difference between R²Y and Q² was less than 0.3, indicating that the OPLS model had a good fit and high prediction accuracy for differentially expressed metabolites in the disease and control groups. Figure 3 The metabolites expressed in the disease group and control group were divided into principal components and orthogonal components using the OPLS model. OPLS-DA score plots were then plotted. The metabolite expression profiles in the intima of the disease group and control group showed clear clustering, indicating that their expression profiles were different. Figure 4 ).

[0064] III. Screening and Validation of Differential Metabolites

[0065] Based on the OPLS-DA model and analysis of covariance, differentially expressed metabolites were analyzed and annotated. Differentially expressed metabolites were then screened using correlation coefficients and variable projection importance, resulting in an S-scatter plot. Figure 5 Differentially expressed metabolites were screened based on FDR < 0.05, |Log2FC| > 1, and Co-variation (Cov) and Correlation (Corr) in OPLS-DA analysis (|Cov| > 10.14, |Corr| > 0.8). The results showed 304 differentially expressed metabolites in the intima of the disease group. Cluster analysis revealed a significant differential metabolic expression profile between the disease group and the control group. To further elucidate the relationship between differentially expressed metabolites and pathological changes in aortic aneurysm / dissection, a comprehensive enrichment analysis of differentially expressed metabolites in the intima of the disease group was performed using the KEGG database. The analysis revealed that the differentially expressed metabolites were enriched in pathways such as the nicotinic acid-nicotinamide metabolic pathway and the arginine-proline metabolic pathway. Figure 6 GABA, as a common metabolite of the nicotinic acid-nicotinamide metabolic pathway, the arginine-proline metabolic pathway, and the alanine-aspartate-glutamate metabolic pathway, participates in inflammatory responses. Figure 7 ).

[0066] IV. ELISA to verify the content of GABA in aortic intima tissue

[0067] (1) Protein extraction from aortic intima tissue

[0068] Eight samples each from aortic aneurysms / dissections and control groups were collected, and divided into a disease group (TAD) and a control group (Control). The intima was dissected, rinsed with ice-cold PBS (0.01 mol / L, pH 7.0–7.2), weighed, and minced with tissue scissors before being placed in a glass homogenizer. Tissue lysis: RIPA lysis buffer containing PMSF (W:V = 1:20–1:50, i.e., 1 mL of lysis buffer per 20–50 mg tissue sample) was added to the glass homogenizer, and the homogenizer was ground on ice. The resulting suspension was sonicated using an ultrasonic cell disruptor until the solution became clear. The solution was centrifuged at 10000g for 5 min at 4°C, and the supernatant was collected in a 1.5 mL EP tube for the determination of GABA content in the tissue.

[0069] (2) ELISA detection of GABA content in tissues and plasma

[0070] GABA was detected using a commercially available ELSIA kit.

[0071] i. Standard dilution: The original concentration of the standard solution was 2000 pg / mL. After dilution, its concentrations were 2000 pg / mL, 666.67 pg / mL, 222.22 pg / mL, 74.07 pg / mL, 24.69 pg / mL and 0 pg / mL, respectively.

[0072] ii. Preparation of working solutions for test reagents A and B: Dilute Detection Reagent A and Detection Reagent B with Assay Diluent A and Assay Diluent B respectively at a ratio of 1:100 to prepare working solutions for test reagents A and B.

[0073] iii. Add 50 μL of each of the different concentration standards to the test wells in sequence. Add equal volumes of extracted aortic intima tissue homogenate and plasma samples from the disease group and the control group to the test wells.

[0074] iv. Add working solution of test reagent A: Add 50 μL of working solution of test reagent A to each well of the standard and sample, shake gently, cover with sealing film, and incubate at 37°C in the dark for 1 hour.

[0075] v. Aspirate the solution from the test wells, add 350 μL of washing buffer (1×) to each well, and let stand for 1 min. Invert the plate onto absorbent paper to completely absorb any remaining liquid in the test wells, and repeat 3 times.

[0076] vi. Add 100 μL of working solution of test reagent B to each test well, gently shake the plate, cover with sealing film, and incubate at 37°C in the dark for 30 min. Wash with washing buffer (1×), repeat 5 times.

[0077] Vii. Add 90 μL of TMB colorimetric solution to each well, cover with a new sealing film, and incubate at 37°C in the dark for 15 min. The liquid in the well will turn blue.

[0078] viii. Add 50 μL of stop solution to each well, gently tap the side of the plate to mix the liquid, and check that the liquid in the well changes from blue to yellow. Remove any water droplets and fingerprints from the bottom of the plate, and ensure that there are no air bubbles on the liquid surface. Use a microplate reader to measure the absorbance of each well at a wavelength of 450 nm.

[0079] LC-MS / MS analysis revealed a significant decrease in GABA expression in the intima tissue of the disease group. Figure 8 The levels of GABA in the intima and blood of the disease group and the control group were detected by ELISA. It was found that the GABA content in the aortic intima of patients with aortic dissection was significantly reduced (1156±401.6 vs. 754.2±145.1 pg / mg, P<0.05). Figure 9 ).

[0080] V. Construction of Clinical Prediction Model

[0081] (1) Comparison of general data between patients with thoracic aortic lesions and healthy controls

[0082] A total of 90 patients with aortic dissection (AD) and 50 healthy controls were included. The mean age of the AD group was (54.90±13.55) years, with 77 males and 13 females. The mean age of the healthy controls was (53.30±15.23) years, with 33 males and 17 females. Baseline data were comparable. Univariate analysis of the aortic dissection patients and healthy controls revealed statistically significant differences in general characteristics (white blood cell count, platelet count, lymphocyte count, neutrophil-to-lymphocyte ratio (NLR), creatinine, blood glucose, and lactate dehydrogenase) compared with the control group (all P<0.05), suggesting that these factors may be influencing the risk of aortic dissection (Table 1).

[0083] Table 1. Comparison of general information between TAD patients and healthy controls

[0084]

[0085]

[0086] In the table, NLR is the ratio of neutrophil count to lymphocyte count.

[0087] (2) The detection of GABA levels in plasma was the same as that of the ELISA detection in Part IV. The results showed that the GABA level in plasma of the TAD group was significantly lower than that of the healthy control group [101.09 (82.25-111.78) vs. 130.72 (104.59-155.14), P<0.001], which was statistically significant. Moreover, the expression level of GABA in TAD patients was significantly lower in the Stanford type A aortic dissection group than in the Stanford type B aortic dissection group [88.72 (71.35-101.89) vs. 111.54 (103.78-121.36), P<0.05]. However, there was no significant difference in NLR and lactate dehydrogenase between Stanford type A and Stanford type B (Tables 2 and 3). This suggests that serum GABA level may also be an influencing factor on the risk of different types of dissection. Figure 10 , Figure 11 Using the occurrence of aortic dissection as the dependent variable, factors with statistically significant differences after univariate analysis were included: plasma GABA level, gender, platelet count, NLR, and alanine aminotransferase. A multivariate logistic regression equation was constructed to explore the influencing factors of aortic dissection in AD patients. The results showed (Table 4): plasma GABA level had a statistically significant effect on patients with aortic dissection (OR = 0.966 < 1, 95% CI 0.934–0.999, P = 0.046), suggesting that the likelihood of aortic dissection decreases after plasma GABA level increases in patients with aortic dissection; NLR and lactate dehydrogenase had statistically significant effects on aortic dissection (P < 0.05). The above data suggest that plasma GABA level may be a protective factor for patients with aortic dissection (OR < 1), while NLR and lactate dehydrogenase may be risk factors for patients with aortic dissection (OR > 1). See Table 3 for univariate analysis of clinical and laboratory indicators in AD patients. Based on the regression analysis results, the predictive equation for patients with aortic dissection was calculated as follows: y = -14.877 + 0.242 × NLR + 0.029 × lactate dehydrogenase - 0.034 × GABA.

[0088] Table 2. Univariate analysis of clinical and laboratory indicators in patients with Stanford type A and type B aortic dissections

[0089]

[0090]

[0091] Table 3. Comparison of plasma GABA levels in patients with different types of AD

[0092] 1.08(1.05～1.12) -5.464 <0.001 Stanford Type A 88.72(71.35～101.89) Stanford Type B 111.54(103.78～121.36)

[0093] Table 4. Univariate analysis of clinical and laboratory indicators in AD patients

[0094] HR (95% CI) P value age 1.008(0.984～1.033) 0.52 gender 0.328(0.143～0.751) 0.008 Platelet count 0.980(0.972～0.987) <0.001 NLR 1.657(1.385～1.983) <0.001 Red blood cell distribution width 3.655(1.843～7.248) <0.001 Platelet distribution width 1.000(0.980～1.019) 0.973 alanine aminotransferase 1.027(1.001～1.053) 0.039 Aspartate aminotransferase 1.051(1.008～1.096) 0.02 Creatinine 1.027(1.010～1.045) 0.002 uric acid 1.005(1.002～1.009) 0.003 blood sugar 4.487(2.502～8.047) <0.001 Total cholesterol 0.586(0.388～0.886) 0.011 Triglycerides 1.587(0.925～2.720) 0.093 High-density lipoprotein 0.069(0.020～0.238) <0.001 Low-density lipoprotein 0.707(0.445～1.124) 0.143 lactate dehydrogenase 1.033(1.019～1.048) <0.001 GABA 0.944(0.924～0.965) <0.001

[0095] To determine whether GABA concentration could serve as a potential biomarker for aortic dissection, we constructed a diagnostic model of GABA concentration and clinical parameters in TAD patients. The predictive ability of GABA was assessed using the area under the receiver operating characteristic (ROC) curve and its 95% confidence interval (AUROC). ROC analysis showed that the AUC for plasma GABA level predicting aortic dissection was 0.8081, with a cutoff value of 124.9 pg / mL and a sensitivity of 95.6%. Figure 12 Table 5 shows that when plasma GABA concentration is below 124.9 pg / ml, the risk of AD is 95.6%. Combined with other risk factors defined as a baseline (including age, sex, and blood lipids), the diagnostic model shows a significantly higher net benefit, such as... Figure 12 The DCA curves are shown in the figure. Compared with baseline, GABA alone can slightly increase the net benefit; and it is difficult to further improve the net benefit after it exceeds 90%. However, the combination of two or more GABAs shows a significantly higher net benefit, indicating that plasma GABA detection has potential clinical application value. Figure 13 (Table 6). To assess model calibration and consistency, calibration curves were used to evaluate the consistency between the model's predicted risk and the actual incidence rate, and the goodness of fit was visualized using a causation plot. A causation plot curve near the 45° reference line indicates good consistency between the predicted probability and the actual probability. The X-axis represents the predicted probability of TAD patients by the plotted ROC curve, and the Y-axis represents the actual probability of TAD in TAD patients. A higher degree of overlap between the solid black line (predictive model) and the reference line (45° dashed line) indicates a more reliable prediction from the ROC curve. Figure 14 The fact that the Calibration plot curve is near the 45° reference line indicates that the predicted probability of the nomogram prediction model has good consistency with the actual probability.

[0096] Table 5. Predictive efficacy of plasma GABA levels in patients with aortic dissection

[0097] GABA 0.8081 0.7312～0.8850 <0.001 124.9 0.56 0.9556 0.5156

[0098] Sensitivity: (Number of true positives / (Number of true positives + Number of false negatives)) × 100%; Specificity: (Number of true negatives / (Number of true negatives + Number of false positives)) × 100%.

[0099] Table 7. Predictive efficacy of plasma GABA levels combined with NLR and LDH in patients with aortic dissection.

[0100] GABA 0.8081 0.7312～0.8850 <0.001 124.9 0.56 0.9556 0.5156 LDH 0.8201 0.7501～0.8901 <0.001 193.3 0.92 0.6778 0.5978 NLR 0.9598 0.9172～1.000 <0.001 3.824 0.96 0.9333 0.8933 The three 0.9642 0.9281～1.000 <0.001 0.5908 0.96 0.9333 0.8933

[0101] LDH is lactate dehydrogenase; NLR is the ratio of neutrophil count to lymphocyte count.

[0102] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. Application of γ-aminobutyric acid (GABA) detection reagents in the preparation of diagnostic kits for differentiating thoracic aortic dissection in healthy and diseased individuals.

2. The application of a combined biomarker detection reagent in the preparation of a diagnostic kit for differentiating thoracic aortic dissection in healthy and diseased individuals, characterized in that, The combined biomarkers are NLR, lactate dehydrogenase, and γ-aminobutyric acid; The NLR refers to the ratio of neutrophils to lymphocytes.

3. An assessment method for differentiating markers of thoracic aortic dissection between healthy and diseased individuals, characterized in that, include: Serological tests were performed on the test samples and healthy samples to obtain the level data of γ-aminobutyric acid or the combined marker in the application of claim 2. Then, the occurrence of interlayer was set as the dependent variable, and statistical difference analysis was performed. The assessment method is applied for non-disease diagnostic and / or treatment purposes.

4. The evaluation method according to claim 3, characterized in that, The statistical difference analysis includes univariate and / or multivariate statistical difference analysis; The statistical difference analysis requires P < 0.

05.

5. The evaluation method according to claim 3 or 4, characterized in that, The test sample consisted of patients aged 18 years or older who had experienced chest pain, abdominal pain, back pain, syncope, and signs of aortic dissection within 14 days. The healthy sample refers to healthy individuals whose age and gender are not statistically different from those of the test sample, and who do not have ischemic heart disease, cancer, lung disease, or infectious diseases.

6. The use of γ-aminobutyric acid and / or the combined biomarkers as described in claim 2 in the preparation of a clinical diagnostic model for thoracic aortic dissection that distinguishes between healthy and diseased individuals.

7. A method for constructing a clinical diagnostic model of thoracic aortic dissection, characterized in that, This includes the construction of a clinical diagnostic model for thoracic aortic dissection in healthy and diseased populations using γ-aminobutyric acid and / or the combined biomarkers described in claim 2; The construction method is applied to non-disease diagnostic and / or therapeutic purposes.

8. The construction method according to claim 7, characterized in that, Includes the following steps: Obtain level data of γ-aminobutyric acid and / or the combined biomarker in the application described in claim 2 in the sample, construct ROC analysis curves based on the level data, and obtain diagnostic threshold values.

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