Metabolic marker for diagnosing abdominal aortic aneurysm and application thereof

Abstract

The invention provides a metabolic marker for diagnosing abdominal aortic aneurysm and application thereof. The invention further provides an abdominal aortic aneurysm diagnosis model and application thereof. The metabolic marker provided by the invention has better specificity and sensitivity in diagnosis of abdominal aorta.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine technology, and in particular relates to a metabolic marker for diagnosing abdominal aortic aneurysm and an application thereof. Background Art

[0002] Abdominal aortic aneurysm (AAA) is a persistent dilatation of the abdominal aorta, with a diameter exceeding 50% of the normal aorta diameter. The main complication of AAA is rupture. At present, the pathogenesis of AAA is still not fully understood. The known pathogenesis and progression of AAA are related to inflammatory infiltration of the aortic wall, oxidative stress, extracellular matrix remodeling, vascular smooth muscle cell dysfunction and intraluminal thrombosis.

[0003] Clinically, most patients with aneurysms have insidious onset, lack obvious signs and symptoms, and are difficult to diagnose. Current detection and risk assessment of AAA mainly rely on Doppler ultrasound or CT angiography (CTA), but existing diagnostic methods have disadvantages such as high cost, invasiveness, and clinical risks. Clinical treatment measures for AAA include surgical treatment and non-surgical treatment. Surgical treatment is often limited by the diameter and growth rate of AAA, and the benefit effect is unclear. Non-surgical treatment of AAA mainly reduces the risk of combined cardiovascular disease and slows down the growth rate, such as smoking cessation / exercise / blood pressure control and clinical medication beta-receptor antagonists / angiotensin converting enzyme inhibitors (ACEI) and statins. However, these methods and means cannot effectively slow down the growth rate of AAA and reduce the risk of rupture (see literature: Wemmelund H, Hogh A, Hundborg HH, et al.Statin use and rupture of abdominal aorticaneurysm [J]. Br J Surg, 2014, 101 (8): 966-975. etc.). Therefore, it is of great significance to study a key molecular marker for early diagnosis and recurrence detection as well as a target for targeted therapy.

[0004] Metabolomics is an emerging field of "omics" research. It is a science that has developed with the development of life sciences. It mainly studies small molecule metabolites that serve as substrates and products of various metabolic pathways. It examines the changes in metabolites or their changes over time after the biological system (cells, tissues or organisms) is stimulated or disturbed. There are more than 40,000 metabolites in the human body, and their concentrations can provide a snapshot of an individual's current health status. Currently, there are few reports on the use of metabolomics to analyze abdominal aortic aneurysms. Summary of the invention

[0005] This application applies metabolomics to the screening of diagnostic markers for abdominal aortic aneurysm and obtains three metabolic markers with excellent specificity and sensitivity.

[0006] In a first aspect, the present invention provides a diagnostic model for abdominal aortic aneurysm, the diagnostic model comprising:

[0007] S1: a data receiving unit, wherein the data includes the concentration level of the metabolic marker in the sample;

[0008] S2: Data analysis unit;

[0009] The metabolic markers described in S1 include one or more of 2,6-dimethylheptanoylcarnitine, 2-azahypoxanthine or stevioside E.

[0010] Preferably, the metabolic markers include a combination of 2,6-dimethylheptanoylcarnitine, 2-azahypoxanthine and steviol E.

[0011] In a specific embodiment of the present invention, the metabolic marker is one or more of 2,6-dimethylheptanoylcarnitine, 2-azahypoxanthine or stevioside E.

[0012] In a specific embodiment of the present invention, the metabolic marker is a combination of 2,6-dimethylheptanoylcarnitine, 2-azahypoxanthine and stevioside E.

[0013] The PubChem CID of the 2,6-dimethylheptanoyl carnitine is 53477823.

[0014] The PubChem CID of the 2-Azahypoxanthine is 135421876.

[0015] The PubChem CID of the stevioside E (Sterebin E or 4-[(1E,3Z)-5-hydroxy-3-methylpenta-1,3-dienyl]-3,4a,8,8-tetramethyl-2,4,5,6,7,8a-hexahydro-1H-naphthalene-1,2,3-triol) is 131751729.

[0016] The data analysis unit includes analyzing the data in S1 using one or more of orthogonal partial least squares discriminant analysis, classification and logistic regression, k-nearest neighbor algorithm, naive Bayes, support vector machine, decision tree, random forest, regression tree, gradient boosting decision tree, xgboost (eXtreme Gradient Boosting), lightweight gradient boosting machine, gradient boosting machine, LASSO (Least Absolute Shrinkage and Selection Operator) or convolutional neural network.

[0017] The sample is selected from blood, plasma, serum, saliva, urine, tears, amniotic fluid, cerebrospinal fluid, lymph or feces.

[0018] Preferably, the sample is plasma.

[0019] The second aspect of the present invention provides a method for constructing the diagnostic model described in the first aspect.

[0020] In a specific embodiment of the present invention, the construction method comprises:

[0021] i) Collect samples from patients with abdominal aortic aneurysm and healthy controls;

[0022] ii): Detect the concentration level of metabolites in the sample and screen out metabolic markers;

[0023] iii): Analyze the metabolic marker concentration level data.

[0024] Preferably, the metabolic marker concentration level data are analyzed using one or more of orthogonal partial least squares discriminant analysis, classification and logistic regression, k-nearest neighbor algorithm, naive Bayes, support vector machine, decision tree, random forest, regression tree, gradient boosted decision tree, xgboost, lightweight gradient boosting machine, gradient boosting machine, LASSO or convolutional neural network.

[0025] In a specific embodiment of the present invention, the construction method comprises:

[0026] 1) Collect samples from subjects and detect the concentration of metabolic markers;

[0027] 2) Conduct clinical diagnosis on the subjects and divide them into abdominal aortic aneurysm patient group and healthy control group;

[0028] 3) Constructing a diagnostic model based on the detection results of step 1) and the diagnosis results of step 2).

[0029] Preferably, the clinical diagnosis method includes CT (Computed Tomography) examination, digital subtraction angiography, color Doppler ultrasound, magnetic resonance imaging, magnetic resonance angiography or histopathological examination.

[0030] The CT examination includes plain CT, enhanced CT and CT angiography (CTA).

[0031] The metabolic markers include one or more of 2,6-dimethylheptanoylcarnitine, 2-azahypoxanthine or stevioside E.

[0032] Preferably, the metabolic markers include a combination of 2,6-dimethylheptanoylcarnitine, 2-azahypoxanthine and steviol E.

[0033] The third aspect of the present invention provides an application of a metabolic marker or the diagnostic model described in the first aspect in the preparation of a product for diagnosing abdominal aortic aneurysm, wherein the metabolic marker includes one or more of 2,6-dimethylheptanoylcarnitine, 2-azahypoxanthine or stevioside E.

[0034] Preferably, the metabolic markers include a combination of 2,6-dimethylheptanoylcarnitine, 2-azahypoxanthine and steviol E.

[0035] Preferably, the product comprises a test kit, a test strip, a chip or a device.

[0036] Preferably, the product comprises a reagent for detecting a metabolic marker.

[0037] The reagent detects the presence or concentration level (abundance) of a metabolic marker.

[0038] Preferably, the reagents for detecting metabolic markers include reagents used by one or more of chromatography, spectroscopy, mass spectrometry or chemical analysis.

[0039] Preferably, the product also includes reagents for sample processing and / or standards for blood metabolites.

[0040] The diagnosis of abdominal aortic aneurysm includes detecting metabolic markers by one or more of chromatography, spectroscopy, mass spectrometry or chemical analysis.

[0041] Preferably, the spectroscopy includes one or more of ultraviolet-visible spectroscopy, infrared spectroscopy, near-infrared spectroscopy, nuclear magnetic resonance spectroscopy, ion mobility spectroscopy or Raman spectroscopy.

[0042] Preferably, the mass spectrometry includes one or more of electron spray ionization mass spectrometry, quadrupole mass spectrometer, ion trap mass spectrometer, matrix-assisted activation desorption / ionization time-of-flight mass spectrometer, matrix-assisted laser desorption ionization-time-of-flight mass spectrometry, MALDI quadrupole-time-of-flight mass spectrometry, electrospray ionization (ESI)-TOF mass spectrometry, ESI ion trap mass spectrometry, ESI triple quadrupole mass spectrometry or Fourier transform ion cyclotron resonance.

[0043] Preferably, the chemical analysis method comprises electrochemical analysis method and / or radiochemical analysis method.

[0044] In a specific embodiment of the present invention, the diagnosis of abdominal aortic aneurysm includes detecting metabolic markers by high performance liquid chromatography.

[0045] The product includes analyzing concentration level data of metabolic markers using one or more of orthogonal partial least squares discriminant analysis, classification and logistic regression, k-nearest neighbor algorithm, naive Bayes, support vector machine, decision tree, random forest, regression tree, gradient boosted decision tree, xgboost, lightweight gradient boosting machine, gradient boosting machine, LASSO or convolutional neural network.

[0046] In a specific embodiment of the present invention, the product includes analyzing the concentration level data of the metabolite markers using the k-nearest neighbor algorithm and the orthogonal partial least squares discriminant analysis method.

[0047] In a specific embodiment of the present invention, the product includes analyzing the concentration level data of metabolic markers using one or more of orthogonal partial least squares discriminant analysis, support vector machine or random forest.

[0048] The abdominal aortic aneurysm (AAA) is a persistent dilatation of the abdominal aorta that exceeds 1.5 times the normal diameter and is located in the abdominal aorta. CT is preferably used as a method for diagnosing abdominal aortic aneurysm.

[0049] A fourth aspect of the present invention provides a kit for diagnosing abdominal aortic aneurysm, wherein the kit comprises a reagent, a test paper or a chip for detecting a metabolic marker.

[0050] The metabolic markers include one or more of 2,6-dimethylheptanoylcarnitine, 2-azahypoxanthine or stevioside E.

[0051] Preferably, the metabolic markers include a combination of 2,6-dimethylheptanoylcarnitine, 2-azahypoxanthine and steviol E.

[0052] In a specific embodiment of the present invention, the kit comprises reagents, test paper and chips for detecting 2,6-dimethylheptanoylcarnitine, 2-azahypoxanthine and stevioside E.

[0053] The reagent, test paper or chip contains molecules that bind to metabolic markers.

[0054] A fifth aspect of the present invention provides a method for diagnosing abdominal aortic aneurysm, wherein the method comprises detecting metabolic markers in a sample from a subject.

[0055] The metabolic markers include one or more of 2,6-dimethylheptanoylcarnitine, 2-azahypoxanthine or stevioside E.

[0056] The detecting of the metabolite markers in the subject's sample includes detecting the presence or concentration level of the metabolite markers.

[0057] The method includes comparing the concentration level of the detected metabolic marker with a threshold value, wherein the threshold value is obtained in a previous experiment, that is, the threshold value is determined by experiments and data analysis based on the difference in metabolic markers between abdominal aortic aneurysm patients and healthy people.

[0058] If the metabolic markers described in this application are different or significantly different from the threshold (the difference is statistically significant, such as p<0.05, p<0.01, p<0.001, p<0.0001), it is determined to be a disease. For example:

[0059] A) 2,6-dimethylheptanoylcarnitine is below a threshold or significantly below a threshold indicating the occurrence of abdominal aortic aneurysm; and / or,

[0060] B) 2-azahypoxanthine is below the threshold or significantly below the threshold indicating the occurrence of abdominal aortic aneurysm; and / or,

[0061] C) Stevioside E below the threshold or significantly below the threshold indicates the occurrence of abdominal aortic aneurysm.

[0062] The difference in metabolite markers between abdominal aortic aneurysm patients and healthy people was evaluated by the change fold, wherein the change fold was obtained by dividing the concentration of the metabolite marker in patients by the concentration in healthy people. A change fold greater than 1 indicated that the expression of the metabolite marker was increased in patients with abdominal aortic aneurysm, and a change fold less than 1 indicated that the expression of the metabolite marker was decreased in patients with abdominal aortic aneurysm.

[0063] Preferably, the change multiple is greater than 1.2, and further preferably, the change multiple is greater than 1.5, for example, greater than 1.21, 1.3, 1.4, 1.5, 1.51, 2, 2.3, 2.366, 2.4, 2.5, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45 or 50, etc.

[0064] Preferably, the change factor is less than 0.8, and further preferably, the change factor is less than 0.67, for example, less than 0.001, 0.01, 0.05, 0.1, 0.15, 0.17, 0.19, 0.191, 0.2, 0.211, 0.22, 0.25, 0.3, 0.4, 0.5, 0.58, 0.587, 0.6, 0.7, 0.79 or 0.799, etc.

[0065] The change factor of the 2,6-dimethylheptanoylcarnitine is less than 0.8, preferably less than 0.67, and more preferably 0.211.

[0066] The change factor of 2-azahypoxanthine is less than 0.8, preferably less than 0.67, and more preferably 0.587.

[0067] The change factor of stevioside E is less than 0.8, preferably less than 0.67, and more preferably 0.191.

[0068] The diagnosing of abdominal aortic aneurysm includes using a cut-off value to assess the risk of abdominal aortic aneurysm.

[0069] The cutoff value of the 2,6-dimethylheptanoylcarnitine is ≤6500, preferably ≤6000, for example ≤6500, 6400, 6300, 6200, 6100, 6000, 5900, 5870, 5869.3224, 5860, 5850, 5800, 5500 or 5000, etc.

[0070] The cutoff value of 2-azahypoxanthine is ≤150, preferably ≤100, for example ≤150, 130, 110, 100, 90, 85, 83, 80.3283, 75, 70, 65, 60, 55, 50, 45, 40, 35 or 30, etc.

[0071] The cutoff value of stevioside E is ≤1000, preferably ≤850, for example ≤1000, 950, 900, 850, 800, 790, 770, 760, 755, 752.3225, 750, 700, 650, 600, 550, 500, 450 or 400, etc.

[0072] Preferably, the detection method can be selected from chromatography, spectroscopy, mass spectrometry or chemical analysis.

[0073] The method comprises analyzing the metabolic marker concentration level data using one or more of orthogonal partial least squares discriminant analysis, classification and logistic regression, k-nearest neighbor algorithm, naive Bayes, support vector machine, decision tree, random forest, regression tree, gradient boosting decision tree, xgboost, lightweight gradient boosting machine, gradient boosting machine, LASSO or convolutional neural network.

[0074] Preferably, the method comprises using the diagnostic model described in the first aspect or the kit described in the fourth aspect to diagnose abdominal aortic aneurysm.

[0075] A sixth aspect of the present invention provides a system for diagnosing abdominal aortic aneurysm, the system comprising a device, unit or module for determining whether a subject suffers from abdominal aortic aneurysm based on the concentration of a metabolic marker.

[0076] Preferably, the diagnostic system comprises:

[0077] A data detection device, unit or module for detecting the concentration level of a metabolic marker in a sample;

[0078] a data input device, unit or module for inputting concentration level data of metabolic markers;

[0079] A data analysis device, unit or module for analyzing the risk of abdominal aortic aneurysm based on the concentration level data of the metabolic markers;

[0080] A data output device, unit or module is used to output the analysis result of whether an individual suffers from abdominal aortic aneurysm.

[0081] The analysis can be one or more of orthogonal partial least squares discriminant analysis, classification and logistic regression, k-nearest neighbor algorithm, naive Bayes, support vector machine, decision tree, random forest, regression tree, gradient boosted decision tree, xgboost, lightweight gradient boosting machine, gradient boosting machine, LASSO or convolutional neural network.

[0082] The metabolic markers include one or more of 2,6-dimethylheptanoylcarnitine, 2-azahypoxanthine or stevioside E.

[0083] Implementation of the diagnostic system may include manually and / or automatically performing or completing selected tasks.

[0084] The diagnostic system may perform tasks via software and / or hardware.

[0085] The present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0086] According to a seventh aspect of the present invention, a device is provided, wherein the device comprises the diagnostic system according to the sixth aspect.

[0087] The eighth aspect of the present invention provides a reagent for detecting metabolic markers or the use of the diagnostic system described in the seventh aspect in the preparation of a product for diagnosing abdominal aortic aneurysm.

[0088] The metabolic markers include one or more of 2,6-dimethylheptanoylcarnitine, 2-azahypoxanthine or stevioside E.

[0089] Preferably, the metabolic marker is a combination of 2,6-dimethylheptanoylcarnitine, 2-azahypoxanthine and steviol E.

[0090] The reagent detects the presence or concentration level (abundance) of a metabolic marker.

[0091] Preferably, the reagents for detecting metabolic markers include reagents used by one or more of chromatography, spectroscopy, mass spectrometry or chemical analysis.

[0092] The term "diagnosis" as used herein refers to finding out whether a patient has had a disease or condition in the past, at the time of diagnosis, or in the future, or to finding out the progression of a disease or its possible future progression.

[0093] The "subject" may be a human or non-human animal, including "patients", "suspected patients" and "healthy individuals", etc. This term does not indicate a specific age or gender, and covers adult or newborn subjects and fetuses. The non-human animal may be a wild animal, a zoo animal, an economic animal, a pet, an experimental animal, etc. Preferably, the non-human animal includes but is not limited to pigs, cattle, sheep, horses, donkeys, foxes, raccoon dogs, minks, camels, dogs, cats, rabbits, mice (e.g., rats, mice, guinea pigs, hamsters, gerbils, chinchillas, squirrels) or monkeys, etc.

[0094] The "healthy controls" refer to subjects or a group of subjects diagnosed by doctors as not having abdominal aortic aneurysm based on qualitative or quantitative test results. The method includes the clinical diagnosis method of the present invention.

[0095] The "cut-off value" is the boundary value used to determine the positive and negative results of a test, that is, to determine the normal value of a certain indicator to distinguish between normal and abnormal. The most commonly used method for determining the cut-off value is the receiver operating characteristic curve (ROC curve). For example, in risk assessment, the cut-off value is used to determine the range of high risk. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] Figure 1:CT angiography results in patients with abdominal aortic aneurysm.

[0097] Figure 2 : Abundance of 2,6-dimethylheptanoylcarnitine in abdominal aortic aneurysms and healthy subjects, **** represents p<0.0001.

[0098] Figure 3 : Abundance of 2-azahypoxanthine in abdominal aortic aneurysms and healthy subjects, **** represents p<0.0001.

[0099] Figure 4 : Abundance of stevioside E in abdominal aortic aneurysm and healthy subjects, **** represents p<0.0001.

[0100] Figure 5 : ROC curve of 2,6-dimethylheptanoylcarnitine in the diagnosis of abdominal aortic aneurysm, where standard represents the cut-off value.

[0101] Figure 6 : ROC curve of 2-azahypoxanthine in the diagnosis of abdominal aortic aneurysm, where standard represents the cut-off value.

[0102] Figure 7 : ROC curve of stevioside E in the diagnosis of abdominal aortic aneurysm, where standard represents the cut-off value.

[0103] Figure 8 : ROC curve of the combination of 2,6-dimethylheptanoylcarnitine, 2-azahypoxanthine and stevioside E in the diagnosis of abdominal aortic aneurysm, where CI represents confidence interval. DETAILED DESCRIPTION

[0104] The present invention is further described below in conjunction with the embodiments. The following description is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any technician familiar with the profession may use the above disclosed technical content to change it into an equivalent embodiment with equivalent changes. Any simple modification or equivalent change made to the following embodiments based on the technical essence of the present invention without departing from the content of the present invention falls within the protection scope of the present invention.

[0105] Unless otherwise specified, the experimental methods used in the following examples are conventional methods.

[0106] The reagents, materials, etc. used in the following examples, unless otherwise specified, can be obtained from commercial sources. The subjects, instruments or equipment involved in the examples are as follows:

[0107] 1. Diagnosis of subjects: All subjects underwent a preoperative abdominal aortic CT examination to determine whether they had abdominal aortic aneurysm. That is, CT examination was performed on patients with abdominal aortic aneurysm ( Figure 1 ), the results showed that the diameter of the abdominal aorta in patients with abdominal aortic aneurysm was 1.5 times greater than that of the normal aorta.

[0108] 2. Collection and analysis of plasma from subjects: Obtain plasma samples from patients with abdominal aortic aneurysm and compare the metabolite expression with that of control plasma, where the control is healthy subjects without abdominal aortic aneurysm.

[0109] The specific materials, reagents, and collection methods for plasma collection and analysis of metabolite expression are as follows:

[0110] (1) Instruments:

[0111] LTQ Orbitrap velos mass spectrometer was purchased from Thermo Fisher Scientific; ultra-high performance liquid chromatography ACQUITY H-class LC system was purchased from Waters.

[0112] (2) Reagents:

[0113] Chromatographic grade acetonitrile and methanol were produced by Waters; ammonium acetate was purchased from Sigma; and ammonia was purchased from Fisher Chemical.

[0114] (3) Sample collection process: Plasma samples from patients with abdominal aortic aneurysm and controls were collected using EDTA-anticoagulated blood collection tubes, centrifuged at 2000 g for 10 min at 4°C, and the supernatant was aspirated and stored in a -80°C refrigerator.

[0115] Example 1: Screening for metabolic markers of abdominal aortic aneurysm

[0116] 1. The discovery cohort included 30 patients with confirmed abdominal aortic aneurysm (n=30, 25 males and 5 females, with an average age of 67.8 years) and 30 healthy controls without abdominal aortic aneurysm (n=30, 24 males and 6 females, with an average age of 66.2 years).

[0117] 2. Experimental process: First, H2O (150 μl) was added to each plasma sample (50 μl) and vortexed for 30 seconds. Then, acetonitrile (400 μl) was added to each sample and vortexed for 1 minute. After standing at -20 ° C for 60 minutes, the sample was centrifuged at 14000 × g for 10 minutes to remove plasma proteins. The supernatant was then dried under vacuum and reconstituted with 100 μl 2% acetonitrile. Plasma metabolites were further separated from small protein molecules using a 10 kDa molecular weight cutoff ultracentrifugal filter (Millipore Amicon Ultra, MA). Quality control (QC) samples were prepared by mixing equal aliquots of all plasma samples. During the entire analytical run, QC samples were injected once every ten samples to evaluate the stability and repeatability of the analytical process.

[0118] Metabolomics analysis was performed using a Waters ACQUITY H-class LC system coupled to a LTQ Orbitrap velos mass spectrometer (ThermoFisher Scientific, MA). Plasma metabolites were separated on a Waters HSS C18 column with an 18-min gradient at a flow rate of 500 μl / min. Mobile phase A was 0.1% formic acid in water and mobile phase B was acetonitrile. The gradient elution program was as follows: 0–1 min, 2% solvent B; 1–3 min, 2%–55% solvent B; 3–8 min, 55%–100% solvent B; 8–13 min, 100% solvent B; 13–13.1 min, 100%–2% solvent B; 13.1–18 min, 2% solvent B. Full MS acquisition was performed by scanning from 100 to 1000 m / z at a resolution of 60 K. The automatic gain control (AGC) target was set to 1 × 10 6 The maximum injection time was 100 ms. For each target with high-energy collisional dissociation (HCD) fragmentation, the collision energy was optimized to 20, 40, and 60.

[0119] 3. Data analysis: Progenesis QI software (Waters) was used to process the raw data files, including peak alignment, peak picking, peak grouping, deconvolution, and normalization by total compounds. The exported feature files were imported into MetaboAnalyst (http: / / www.metaboanalyst.ca) for further analysis. Variables missing in more than 50% of the samples were removed from further statistical analysis, and the missing values ​​were filled by the k-nearest neighbor algorithm (KNN algorithm). Orthogonal partial least squares discriminant analysis (OPLS-DA) was performed using SIMCA14.1 (Umetrics) software. Permutation tests were performed to validate the OPLS-DA model to avoid overfitting. Differentially enriched metabolites were identified according to the following criteria: P value <0.05; fold change >1.5 or <0.67. Differentially enriched metabolites were analyzed using the enrichment and pathway analysis modules in MetaboAnalyst 6.0. Receiver operating characteristic curve (ROC) analysis was performed using the “Biomarker Analysis” module on the MetaAnalyst website and MedCalc software.

[0120] 4. By performing non-targeted metabolite detection on plasma samples of patients with abdominal aortic aneurysm and healthy controls in the discovery cohort, differentially expressed metabolites with a change fold greater than 1.5 or less than 0.67 and a p-value < 0.05 were screened out, a total of 85 metabolites. MetaboAnalyst (http: / / www.metaboanalyst.ca) and MedCalc software (Statistical Software version 23.0.2 (MedCalc Software Ltd, Ostend, Belgium; https: / / www.medcalc.org; 2024)) were used for metabolite ROC analysis, and 3 metabolites with higher AUC values ​​were screened out. The AUC values ​​of the 3 candidate diagnostic markers for AAA are shown in Table 1.

[0121] Table 1. Candidate markers for patients with abdominal aortic aneurysm

[0122]

[0123] Example 2: Validation of plasma metabolic markers in AAA diagnosis

[0124] To further validate the value of the screened metabolite markers in the diagnosis of abdominal aortic aneurysm, non-targeted metabolomics analysis was performed in a new validation cohort.

[0125] The validation cohort included 46 AAA patients (n=46, 40 males and 6 females, with an average age of 70.4 years) and 42 healthy controls (n=42, 36 males and 6 females, with an average age of 67.6 years). The abundance of the above-mentioned metabolite markers in patients with abdominal aortic aneurysm and healthy controls was detected. The sample collection, processing and experimental procedures were the same as those in Example 1.

[0126] The results are as follows Figure 2-4 As shown in the figure, the abundance of 2,6-dimethylheptanoylcarnitine, 2-azahypoxanthine and stevioside E in patients with abdominal aortic aneurysm was significantly lower than that in healthy controls. Statistical analysis was performed using GraphPad Prism version 10.3.0 for Windows, GraphPad Software, Boston, Massachusetts USA, www.graphpad.com.

[0127] The ROC curve of the above markers in distinguishing abdominal aortic aneurysm patients from healthy controls was drawn (the experimental process and data analysis were the same as in Example 1).

[0128] According to ROC curve analysis, the AUC value of 2,6-dimethylheptanoylcarnitine was 0.899, the sensitivity was 91.30%, and the specificity was 80.95% ( Figure 5 ); the AUC value of 2-azahypoxanthine was 0.875, the sensitivity was 86.96%, and the specificity was 85.71% ( Figure 6 ); the AUC value of stevioside E was 0.875, the sensitivity was 73.91%, and the specificity was 88.10% ( Figure 7 ), the diagnostic performance of each metabolic marker is shown in Table 2.

[0129] Table 2: Diagnostic performance of metabolites

[0130]

[0131] Using the "BiomarkerAnalysis" module in MetaboAnalyst (http: / / www.metaboanalyst.ca), the combination of 2,6-dimethylheptanoylcarnitine, 2-azahypoxanthine and stevioside E showed good diagnostic effect for abdominal aortic aneurysm, with an area under the ROC curve (AUC) value of 0.964 ( Figure 8 ).

Claims

1. A diagnostic model for abdominal aortic aneurysm, characterized in that: The diagnostic model includes: S1: a data receiving unit, wherein the data includes the concentration level of the metabolic marker in the sample; S2: Data analysis unit; The metabolic markers described in S1 include one or more of 2,6-dimethylheptanoylcarnitine, 2-azahypoxanthine or stevioside E.

2. The diagnostic model according to claim 1, characterized in that The data analysis unit includes analyzing the data in S1 using one or more of orthogonal partial least squares discriminant analysis, classification and logistic regression, k-nearest neighbor algorithm, naive Bayes, support vector machine, decision tree, random forest, regression tree, gradient boosting decision tree, xgboost, lightweight gradient boosting machine, gradient boosting machine, LASSO or convolutional neural network.

3. The diagnostic model according to claim 1, characterized in that The sample is selected from blood, plasma, serum, saliva, urine, tears, amniotic fluid, cerebrospinal fluid, lymph or feces; preferably, the sample is plasma.

4. A method for constructing a diagnostic model according to any one of claims 1 to 3, characterized in that: The construction method comprises: i) Collect samples from patients with abdominal aortic aneurysm and healthy controls; ii): Detect the concentration level of metabolites in the sample and screen out metabolic markers; iii): Analyze the metabolic marker concentration level data.

5. Use of a metabolic marker or the diagnostic model according to claim 1 in the preparation of a product for diagnosing abdominal aortic aneurysm, characterized in that: The metabolic markers include one or more of 2,6-dimethylheptanoylcarnitine, 2-azahypoxanthine or stevioside E; Preferably, the metabolic markers include a combination of 2,6-dimethylheptanoylcarnitine, 2-azahypoxanthine and steviol E.

6. The use according to claim 5, characterized in that: The products include test kits, test strips, chips or devices.

7. The use according to claim 5, characterized in that: The product includes a reagent for detecting a metabolic marker, and the reagent detects the presence or concentration level of the metabolic marker.

8. The use according to claim 5, characterized in that: The diagnosis of abdominal aortic aneurysm includes detecting metabolic markers by one or more of chromatography, spectroscopy, mass spectrometry or chemical analysis.

9. The use according to claim 5, characterized in that: The product includes analyzing concentration level data of metabolic markers using one or more of orthogonal partial least squares discriminant analysis, classification and logistic regression, k-nearest neighbor algorithm, naive Bayes, support vector machine, decision tree, random forest, regression tree, gradient boosted decision tree, xgboost, lightweight gradient boosting machine, gradient boosting machine, LASSO or convolutional neural network.

10. A kit for diagnosing abdominal aortic aneurysm, characterized in that: The kit comprises a reagent, a test paper or a chip for detecting a metabolic marker, and the metabolic marker comprises one or more of 2,6-dimethylheptanoylcarnitine, 2-azahypoxanthine or stevioside E.

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