A Diagnostic System for Abdominal Aortic Aneurysm and Its Application

Abstract

This invention belongs to the field of biomedical technology, specifically relating to the application of metabolic markers in the preparation of products for diagnosing abdominal aortic aneurysms. The metabolic markers include 5-δ-hydroxybutylhydantoin, 1-α-amino acid, and PGF. 2α The present invention provides one or more of the following metabolites: D-pyroglutamic acid, nicotinic glucuronide, cysteine ​​glutathione disulfide, or Trp-P-1. Experiments have confirmed that the seven metabolites, alone or in combination, have good specificity and sensitivity as metabolic markers for diagnosing abdominal aortic aneurysms.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, specifically relating to the application of metabolic markers in the preparation of products for diagnosing abdominal aortic aneurysms. Background Technology

[0002] Metabolomics is an emerging research field downstream of genomics, proteomics, and transcriptomics. The metabolome is a quantitative collection of low-molecular-weight compounds produced by metabolism, such as metabolic substrates and products, lipids, small peptides, vitamins, and other protein cofactors. There are over 40,000 metabolites in the human body, and their concentrations provide a snapshot of an individual's current health status.

[0003] The metabolite 5-δ-hydroxybutylhydantoin plays an important role as an intermediate in the synthesis of the essential amino acid lysine. The literature (Patel RN. Enzymatic synthesis of chiral intermediates for Omapatrilat, an antihypertensive drug. Biomol Eng. 2001 Jun;17(6):167-82.) discloses the synthesis and enzymatic conversion of 2-keto-6-hydroxyhexanoic acid 3 to L-6-hydroxydemethylleucine 2 in the enzymatic synthesis of chiral intermediates of the antihypertensive drug Omapatrilat using the reductive amination reaction of bovine liver glutamate dehydrogenase. To avoid the lengthy chemical synthesis of keto acid 3, a method was developed to treat racemic 6-hydroxydemethylleucine (obtained from the hydrolysis of 5-δ-hydroxybutylhydantoin) with D-amino acid oxidase derived from pig kidney or variant deltoid muscle, followed by reductive amination, thereby increasing the yield of the intermediate.

[0004] The metabolite 2-amino-4-[(2-hydroxy-1-oxopropyl)amino]butanoic acid belongs to the organic compound class of I-α-amino acids. A study by Li et al. (Li T, Liang M, Luo J, Peng X. Metabolites of Clostridium leptum fermenting flaxseed polysaccharide alleviate obesity in rats. Int J Biol Macromol. 2024 Apr;264(Pt 1):129907.) revealed that analysis of the fermentation broth of Clostridium leptum fermenting flaxseed polysaccharide in vitro showed the presence of multiple metabolites, including 2-amino-4-[(2-hydroxy-1-oxopropyl)amino]butanoic acid. Intervention of obese mice with the fermentation broth rich in these metabolites significantly reduced body weight, abdominal fat ratio, and total fat ratio, indicating improved lipid metabolism. This demonstrates that the fermentation broth of Clostridium leptum fermenting flaxseed polysaccharide has a significant anti-obesity effect.

[0005] Metabolite, 19(R)-hydroxy-prostaglandin F2α (19(R)-hydroxy PGF2 Alpha), also known as PGF 2α It belongs to the prostaglandin family, and studies have shown that PGF... 2α PGF can play multiple roles in bone metabolism by regulating multiple signaling pathways. 2α Activating PKC can stimulate the Na-dependent inorganic phosphate transport system in bone cells, upregulate interleukin (IL)-6 synthesis, and increase vascular endothelial growth factor (VEGF) and pGFR. 2α The molecular mechanisms regulating bone metabolism may be a new avenue for developing novel targeted therapies for bone diseases (see: Agas D, Marchetti L, Hurley MM, Sabbieti MG. Prostaglandin F2α: a bone remodeling mediator. J Cell Physiol. 2013 Jan;228(1):25-9.).

[0006] The metabolite D-pyroglutamate ((R)-(+)-2-Pyrrolidone-5-carboxylic acid) exists in free form in various body fluids and tissues in vivo. It participates in the formation of the amino acid terminus of neuropeptides such as neurotensin and thyrotropin-releasing hormone. Pyroglutamate can interconvert with glutamate and antagonize glutamate receptors in vivo. The literature (Huang Yu, Gu Caiyun, Wu Hanzhong, et al. Metabolomics study of ischemic stroke based on ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry [J]. Journal of Chemical Research in Chinese Universities, 2017, 38(10):1742-1750.) disclosed that the content of pyroglutamate, as a cyclized derivative of L-glutamate, showed a decreasing trend in the plasma of stroke patients. This is related to abnormal glutamine metabolism and can be used as a metabolic marker for the diagnosis and treatment of clinical stroke.

[0007] Nicotine glucuronide is a metabolite produced during the metabolism of nicotine. Patent CN105891357A states that nicotine ingested into the body rapidly crosses the blood-brain barrier and enters the brain tissue. Most of the metabolism of nicotine is completed in the liver. In fact, nicotine can be converted into more than 20 metabolites in the body. Nicotine glucuronide, as one of the metabolites, can be used to trace and detect the metabolic changes of nicotine in animals. It can also be used as a biomarker to measure the degree of human exposure to smoke and to distinguish between smokers and non-smokers.

[0008] Cysteineglutathione disulfide, a metabolite formed under glutathione oxidative stress, forms mixed disulfides with protein thiol groups, leading to reversible S-glutathionization. S-glutathionization is an important post-translational modification responsible for transducing oxidative signals. Literature (Lim JE, Huang J, Weinstein SJ, Parisi D, Mӓnnistö S, Albanes D. Serum metabolomic profile of hair dye use. Sci Rep. 2023 Mar 7;13(1):3776.) indicates that some substances in hair dyes may be related to human metabolism and cancer risk. By comparing serum metabolites of hair dye users and non-users, 11 compounds showed significant differences between the two groups. Among them, cysteglutathione disulfide showed a significant correlation with hair dye, suggesting that it can serve as a diagnostic marker for diseases caused by hair dye.

[0009] The metabolite, tryptophan P1 (Trp-P-1), also known as 3-amino-1,4-dimethyl-5H-pyridyl[4,3-b]indole, belongs to the class of γ-carboline organic compounds. Studies have shown that Trp-P-1 is one of the dietary carcinogens. Its outer ring amino group is activated by the cytochrome P450 (CYP) 1A and 1B subfamilies to a hydroxyl amino derivative. The literature (Bunsyo Shiotani, Hitoshi Ashida, 3-Amino-1,4-dimethyl-5H-pyrido[4,3-b]indole (Trp-P-1) triggers apoptosis by DNA double-strand breaks caused by inhibition of topoisomerase I, Carcinogenesis, Volume 25, Issue 7, July 2004, Pages 1149–1155) studied the types of DNA damage involved in Trp-P-1-induced apoptosis. High concentrations of Trp-P-1 treatment of rat liver cells led to apoptotic events such as loss of cell viability and nuclear condensation. The results showed that Trp-P-1-induced apoptosis was triggered by DNA double-strand breaks caused by inhibition of topoisomerase I.

[0010] However, there are few reports of metabolomics analysis of abdominal aortic aneurysms, especially of the specific metabolites mentioned above. Summary of the Invention

[0011] To fill the gaps in existing technologies, this invention provides a set of metabolic biomarkers for the diagnosis of abdominal aortic aneurysms. Specifically, it involves screening metabolic biomarkers for abdominal aortic aneurysms using a discovery cohort, and further validating their diagnostic efficacy using a validation cohort. Results show that the metabolic biomarkers provided by this invention have high specificity and sensitivity. Combining seven metabolic biomarkers further improves the diagnostic specificity and sensitivity, achieving an AUC value of 1. The specific scheme is as follows:

[0012] A first aspect of the present invention provides the application of metabolic markers in the preparation of products for diagnosing abdominal aortic aneurysms, said metabolic markers including 5-δ-hydroxybutylhydantoin, 1-α-amino acid, and PGF. 2α One or more of D-pyroglutamic acid, nicotinic glucuronide, cysteine ​​glutathione disulfide, or Trp-P-1.

[0013] Preferably, the metabolic markers are 5-δ-hydroxybutylhydantoin, 1-α-amino acid, and PGF. 2αA combination of D-pyroglutamic acid, nicotinic glucuronide, cysteine ​​glutathione disulfide and Trp-P-1.

[0014] Preferably, the I-α-amino acid is 2-amino-4-[(2-hydroxy-1-oxopropyl)amino]butyric acid.

[0015] In one specific embodiment of the present invention, the metabolic markers are 5-δ-hydroxybutylhydantoin, 2-amino-4-[(2-hydroxy-1-oxopropyl)amino]butyric acid, and PGF. 2α One or more of D-pyroglutamic acid, nicotinic glucuronide, cysteine ​​glutathione disulfide, or Trp-P-1.

[0016] In one specific embodiment of the present invention, the metabolic markers are 5-δ-hydroxybutylhydantoin, 2-amino-4-[(2-hydroxy-1-oxopropyl)amino]butyric acid, and PGF. 2α A combination of D-pyroglutamic acid, nicotinic glucuronide, cysteine ​​glutathione disulfide and Trp-P-1.

[0017] The PubChem CID of the 5-δ-hydroxybutylhydantoin is 95514.

[0018] The PubChem CID for the 2-amino-4-[(2-hydroxy-1-oxopropyl)amino]butanoic acid is 131751168.

[0019] The PGF 2α , namely 19(R)-hydroxy-prostaglandin F2α (19(R)-hydroxy PGF2 Alpha), with PubChemCID 6443315.

[0020] The PubChem CID of the D-pyroglutamic acid ((R)-(+)-2-Pyrrolidone-5-carboxylic acid) is 439685.

[0021] The PubChem CID of the nicotinic glucuronide is 3035848.

[0022] The PubChemCID of the cysteglutathione disulfide is 53477713.

[0023] The PubChem CID of the Trp-P-1 (tryptophan P1) is 5284474.

[0024] The metabolic markers mentioned are metabolic markers found in blood, plasma, serum, saliva, urine, tears, amniotic fluid, cerebrospinal fluid, lymph, or feces.

[0025] Preferably, the metabolic markers are metabolic markers in plasma.

[0026] The products mentioned include reagent kits, test strips, chips, or devices.

[0027] The products include reagents for detecting metabolic markers.

[0028] The reagents described herein are used to detect the presence or concentration level (abundance) of metabolic markers.

[0029] Preferably, the reagents for detecting metabolic markers include reagents used by one or more of the following methods: chromatography, spectroscopy, mass spectrometry, or chemical analysis.

[0030] Preferably, the product also includes reagents for sample processing and / or standards for blood metabolites.

[0031] The diagnosis of abdominal aortic aneurysm includes detecting metabolic markers by one or more of the following methods: chromatography, spectroscopy, mass spectrometry, or chemical analysis.

[0032] Preferably, the chromatographic method includes one or more of gas chromatography, liquid chromatography, capillary electrophoresis, high performance liquid chromatography, or ultra-high performance liquid chromatography.

[0033] Preferably, the spectroscopic method includes one or more of ultraviolet-visible spectroscopy, infrared spectroscopy, near-infrared spectroscopy, nuclear magnetic resonance spectroscopy, ion mobility spectroscopy, or Raman spectroscopy.

[0034] Preferably, the mass spectrometry method includes one or more of the following: electron spray ionization mass spectrometry, quadrupole mass spectrometry, ion trap mass spectrometry, matrix-assisted activation desorption / ionization time-of-flight mass spectrometry, matrix-assisted laser desorption / ionization-time-of-flight mass spectrometry, MALDI quadrupole-time-of-flight mass spectrometry, electro-jet ionization (ESI)-TOF mass spectrometry, ESI ion trap mass spectrometry, ESI triple quadrupole mass spectrometry, or Fourier transform ion cyclotron resonance.

[0035] Preferably, the chemical analysis method includes electrochemical analysis and / or radiochemical analysis.

[0036] In one specific embodiment of the present invention, the diagnosis of abdominal aortic aneurysm includes detecting metabolic markers by high performance liquid chromatography.

[0037] The products described include analysis of concentration level data of metabolic biomarkers using one or more of the following methods: 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 (eXtremeGradient Boosting), lightweight gradient boosting machine, gradient boosting machine, LASSO (Least Absolute Shrinkage and Selection Operator), or convolutional neural network.

[0038] In one specific embodiment of the present invention, the product includes the analysis of concentration level data of metabolic biomarkers using the k-nearest neighbor algorithm and orthogonal partial least squares discriminant analysis.

[0039] In one specific embodiment of the present invention, the product includes concentration level data of metabolic biomarkers analyzed using one or more of orthogonal partial least squares discriminant analysis, support vector machine, or random forest.

[0040] The abdominal aortic aneurysm (AAA) refers to a persistent dilation of the abdominal aorta, exceeding 1.5 times its normal diameter, located within the abdominal aorta. CT scan is the preferred method for diagnosing abdominal aortic aneurysms.

[0041] A second aspect of the present invention provides a diagnostic model for abdominal aortic aneurysm, the diagnostic model comprising:

[0042] S1: Data receiving unit, wherein the data includes the concentration levels of metabolic markers in the sample;

[0043] S2: Data analysis unit, which includes analyzing the data in S1 using one or more of the following: 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.

[0044] The metabolic markers described in S1 include 5-δ-hydroxybutylhydantoin, 1-α-amino acids, and PGF. 2α One or more of D-pyroglutamic acid, nicotinic glucuronide, cysteine ​​glutathione disulfide, or Trp-P-1.

[0045] Preferably, the I-α-amino acid is 2-amino-4-[(2-hydroxy-1-oxopropyl)amino]butyric acid.

[0046] Preferably, the sample is selected from blood, plasma, serum, saliva, urine, tears, amniotic fluid, cerebrospinal fluid, lymph, or feces.

[0047] In one specific embodiment of the present invention, the data analysis unit includes analyzing the data in S1 using the k-nearest neighbor algorithm and the orthogonal partial least squares discriminant analysis method.

[0048] In one specific embodiment of the present invention, the data analysis unit includes analyzing the data in S1 using one or more of orthogonal partial least squares discriminant analysis, support vector machine, or random forest.

[0049] A third aspect of the present invention provides a method for constructing the diagnostic model described in the second aspect.

[0050] In one specific embodiment of the present invention, the construction method includes:

[0051] 1) Collect blood or plasma from the subjects and detect the concentration of metabolic markers;

[0052] 2) Clinical diagnosis was performed on the subjects, and they were divided into an abdominal aortic aneurysm patient group and a healthy control group;

[0053] 3) Construct a diagnostic model based on the detection results of step 1) and the diagnostic results of step 2).

[0054] Preferably, the clinical diagnostic methods include CT (Computed Tomography), digital subtraction angiography, color Doppler ultrasound, magnetic resonance imaging, magnetic resonance angiography, or histopathological examination.

[0055] The CT examinations include plain CT, enhanced CT, and CT angiography (CTA).

[0056] In one specific embodiment of the present invention, the construction method includes:

[0057] i): Collect plasma samples from patients with abdominal aortic aneurysms and healthy controls;

[0058] ii): Detect the concentration levels of metabolites in the sample and screen for metabolic biomarkers;

[0059] iii): Analyze metabolic biomarker concentration level data using one or more of the following: 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.

[0060] The metabolic markers include 5-δ-hydroxybutylhydantoin, 1-α-amino acids, and PGF. 2α One or more of D-pyroglutamic acid, nicotinic glucuronide, cysteine ​​glutathione disulfide, or Trp-P-1.

[0061] Preferably, the metabolic markers are 5-δ-hydroxybutylhydantoin, 1-α-amino acid, and PGF. 2α A combination of D-pyroglutamic acid, nicotinic glucuronide, cysteine ​​glutathione disulfide and Trp-P-1.

[0062] Preferably, the I-α-amino acid is 2-amino-4-[(2-hydroxy-1-oxopropyl)amino]butyric acid.

[0063] A fourth aspect of the present invention provides the application of the diagnostic model described in the second aspect in the preparation of products for diagnosing abdominal aortic aneurysms.

[0064] In a fifth aspect, the present invention provides a kit for diagnosing abdominal aortic aneurysms, the kit comprising reagents, test strips, or chips for detecting metabolic markers, said metabolic markers including 5-δ-hydroxybutylhydantoin, 1-α-amino acids, and PGF. 2α One or more of D-pyroglutamic acid, nicotinic glucuronide, cysteine ​​glutathione disulfide, or Trp-P-1.

[0065] Preferably, the metabolic markers are 5-δ-hydroxybutylhydantoin, 1-α-amino acid, and PGF. 2α A combination of D-pyroglutamic acid, nicotinic glucuronide, cysteine ​​glutathione disulfide and Trp-P-1.

[0066] Preferably, the I-α-amino acid is 2-amino-4-[(2-hydroxy-1-oxopropyl)amino]butyric acid.

[0067] In one specific embodiment of the present invention, the kit contains reagents for detecting 5-δ-hydroxybutylhydantoin, 2-amino-4-[(2-hydroxy-1-oxopropyl)amino]butyric acid, and PGF. 2αReagents, test strips, or chips for D-pyroglutamic acid, nicotinic glucuronide, cysteine ​​glutathione disulfide, and Trp-P-1.

[0068] The test strip or chip contains molecules that bind metabolic markers.

[0069] A sixth aspect of the present invention provides a method for diagnosing abdominal aortic aneurysm, the method comprising detecting metabolic markers in a subject sample.

[0070] The metabolic markers include 5-δ-hydroxybutylhydantoin, 1-α-amino acids, and PGF. 2α One or more of D-pyroglutamic acid, nicotinic glucuronide, cysteine ​​glutathione disulfide, or Trp-P-1.

[0071] The detection of metabolic biomarkers in the subject samples includes detecting the presence or concentration level of metabolic biomarkers.

[0072] The method involves comparing the concentration levels of detected metabolic markers with a threshold obtained from previous experiments, i.e., determining the threshold by analyzing the differences in metabolic markers between patients with abdominal aortic aneurysms and healthy individuals through experiments and data analysis.

[0073] A disease is identified when the metabolic markers described in this application differ from or significantly differ from the thresholds (the difference is statistically significant, e.g., p<0.05, p<0.01, p<0.001, p<0.0001). For example:

[0074] A) A 5-δ-hydroxybutylhydantoin level below or significantly below the threshold indicates the occurrence of an abdominal aortic aneurysm; and / or,

[0075] B) I-α-amino acid levels below the threshold or significantly below the threshold indicate the occurrence of abdominal aortic aneurysm; and / or,

[0076] C) PGF 2α A value below or significantly below the threshold indicates the occurrence of an abdominal aortic aneurysm; and / or,

[0077] D) D-pyroglutamate levels below the threshold or significantly below the threshold indicate the occurrence of abdominal aortic aneurysm; and / or,

[0078] E) Nicotinic glucuronide levels below or significantly below the threshold indicate the occurrence of abdominal aortic aneurysm; and / or,

[0079] F) Cysteine-glutathione disulfide levels below the threshold or significantly below the threshold indicate the occurrence of abdominal aortic aneurysm; and / or,

[0080] G) A Trp-P-1 level above or significantly above the threshold indicates the occurrence of an abdominal aortic aneurysm.

[0081] The method includes diagnosing abdominal aortic aneurysms by fold changes.

[0082] The fold change is calculated as the concentration (expression abundance) of the metabolic marker in patients / the concentration (expression abundance) in healthy individuals.

[0083] The fold change greater than 1 indicates that the metabolic marker is expressed at an elevated level in patients with abdominal aortic aneurysms, while the fold change less than 1 indicates that the metabolic marker is expressed at a decreased level in patients with abdominal aortic aneurysms.

[0084] Preferably, the change factor is greater than 1.2, and more preferably, the change factor is greater than 1.5, such as greater than 1.2, 1.3, 1.4, 1.5, 1.5, 1.5, 2, 3, 4, 5, 10, 15, 20, 21, 22, 23, 24, 25, 25.2, 25.4, 25.4, 25.5, 26, 30, 35, 40, 50, 60, 80, etc.

[0085] Preferably, the change factor is less than 0.8, and more preferably, the change factor is less than 0.67, for example, less than 0.00001, 0.0001, 0.001, 0.01, 0.02, 0.05, 0.055, 0.06, 0.1, 0.15, 0.17, 0.19, 0.191, 0.2, 0.22, 0.227, 0.25, 0.254, 0.3, 0.352, 0.398, 0.4, 0.5, 0.6, 0.66, 0.67, 0.7, 0.79, 0.799, etc.

[0086] The fold change of the 5-δ-hydroxybutylhydantoin is less than 0.8, preferably less than 0.67, and more preferably 0.055.

[0087] The variation factor of the I-α-amino acid is less than 0.8, preferably less than 0.67, and more preferably 0.191.

[0088] The PGF mentioned 2α The change factor is less than 0.8, preferably less than 0.67, and even more preferably 0.254.

[0089] The variation factor of the D-pyroglutamic acid is less than 0.8, preferably less than 0.67, and more preferably 0.398.

[0090] The nicotinic acid glucoside ratio is less than 0.8, preferably less than 0.67, and more preferably 0.227.

[0091] The variation factor of the cysteine ​​glutathione disulfide is less than 0.8, preferably less than 0.67, and more preferably 0.352.

[0092] The variation factor of Trp-P-1 is greater than 1.2, preferably greater than 1.5, and even more preferably 25.491.

[0093] The diagnosis of abdominal aortic aneurysm includes using a cut-off value to assess the risk of developing an abdominal aortic aneurysm.

[0094] The cutoff value of the 5-δ-hydroxybutylhydantoin is ≤800, preferably ≤600, for example ≤800, 750, 700, 650, 600, 550, 530, 510, 509, 508.5, 508.4871, 508, 500, 450, 400, etc.

[0095] The cutoff value of the I-α-amino acid is ≤1800, preferably ≤1500, for example ≤1800, 1700, 1600, 1500, 1480, 1475, 1473, 1471, 1470.5, 1470.3784, 1470, 1400, 1200, 1000, 800, etc.

[0096] The PGF mentioned 2α The cutoff value is ≤500, preferably ≤300, for example ≤500, 450, 400, 350, 300, 250, 230, 220, 218, 216, 215.8, 215.6347, 215, 210, 200, 180, 160, 140, etc.

[0097] The cutoff value of the D-pyroglutamic acid is ≤6000, preferably ≤5800, for example ≤6000, 5900, 5800, 5700, 5600, 5590, 5560, 5550, 5540, 5535, 5530.901, 5530, 5500, 5200, 5000, etc.

[0098] The cutoff value of the nicotinic glucoside is ≤800, preferably ≤500, for example ≤800, 750, 700, 650, 600, 550, 500, 450, 430, 410, 409, 408, 3321, 408, 405, 400, 350, 300, etc.

[0099] The cutoff value of the cysteine ​​glutathione disulfide is ≤4000, preferably ≤3800, for example ≤4000, 3900, 3800, 3700, 3600, 3500, 3490, 3480, 3475, 3470.707, 3470, 3450, 3400, etc.

[0100] The cutoff value of Trp-P-1 is >300, preferably >400, for example >301, 310, 350, 400, 420, 440, 460, 480, 484, 486, 486.8928, 488, 490, 500, 550, 600, 650, 700, etc.

[0101] Preferably, the detection method can be selected from chromatography, spectroscopy, mass spectrometry or chemical analysis.

[0102] The method involves analyzing metabolic biomarker concentration level data using one or more of the following: 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.

[0103] Preferably, the method includes diagnosing abdominal aortic aneurysms using the diagnostic model described in the second aspect or the kit described in the fifth aspect.

[0104] A seventh aspect of the present invention provides a diagnostic system for abdominal aortic aneurysm, the diagnostic system comprising a device, unit or module for determining whether a subject has an abdominal aortic aneurysm based on the concentration of metabolic markers.

[0105] Preferably, the diagnostic system includes:

[0106] Data detection device, unit or module used to detect the concentration level of metabolic markers in a sample;

[0107] Data input device, unit, or module for inputting concentration level data of metabolic markers;

[0108] Data analysis devices, units, or modules that calculate the risk of abdominal aortic aneurysm based on concentration level data of metabolic markers;

[0109] A data output device, unit, or module used to output the analysis results of whether an individual has an abdominal aortic aneurysm.

[0110] The metabolic markers include 5-δ-hydroxybutylhydantoin, 1-α-amino acids, and PGF. 2α One or more of D-pyroglutamic acid, nicotinic glucuronide, cysteine ​​glutathione disulfide, or Trp-P-1.

[0111] The implementation of the diagnostic system includes the manual and / or automatic execution or completion of selected tasks.

[0112] The diagnostic system can perform tasks via software and / or hardware.

[0113] This 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 code.

[0114] An eighth aspect of the present invention provides an apparatus comprising the diagnostic system described in the seventh aspect.

[0115] In a ninth aspect, the invention provides the use of a reagent for detecting metabolic markers or the diagnostic system described in the seventh aspect in the preparation of products for diagnosing abdominal aortic aneurysms.

[0116] The metabolic markers include 5-δ-hydroxybutylhydantoin, 1-α-amino acids, and PGF. 2α One or more of D-pyroglutamic acid, nicotinic glucuronide, cysteine ​​glutathione disulfide, or Trp-P-1.

[0117] Preferably, the metabolic markers are 5-δ-hydroxybutylhydantoin, 1-α-amino acid, and PGF. 2α A combination of D-pyroglutamic acid, nicotinic glucuronide, cysteine ​​glutathione disulfide and Trp-P-1.

[0118] Preferably, the I-α-amino acid is 2-amino-4-[(2-hydroxy-1-oxopropyl)amino]butyric acid.

[0119] The reagents described herein are used to detect the presence or concentration level (abundance) of metabolic markers.

[0120] Preferably, the reagents for detecting metabolic markers include reagents used by one or more of the following methods: chromatography, spectroscopy, mass spectrometry, or chemical analysis.

[0121] The term "diagnosis" as used in this invention refers to determining whether a patient has had a disease or condition in the past, at the time of diagnosis, or in the future, or determining the progression of a disease or its possible future progression.

[0122] The term "subject" can refer to humans or non-human animals, including "patients," "suspected patients," and "healthy individuals," etc. This term does not indicate a specific age or sex and covers adult or newborn subjects as well as fetuses. Non-human animals can be wild animals, zoo animals, commercially traded animals, pets, laboratory animals, etc. Preferably, non-human animals include, but are 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.

[0123] The term "healthy control group" refers to a subject or subject group diagnosed by a physician as not having an abdominal aortic aneurysm based on qualitative or quantitative test results, and the method includes the clinical diagnostic method described in this invention.

[0124] The "cut-off value" refers to the threshold used to determine whether a test is positive or negative; that is, to determine the normal value of a certain indicator to distinguish between normal and abnormal. The most common method for determining the cut-off value is the receiver operating characteristic (ROC) curve. For example, in risk assessment, the cut-off value is used to determine the range of high risk.

[0125] The beneficial effects of this invention are:

[0126] (1) This invention uses metabolomics to analyze metabolic markers of abdominal aortic aneurysm. The metabolomics is downstream of the transcriptomics and proteomics. Any changes that occur from the normal state will be amplified, and the values ​​are easier to process, more sensitive to detection, and the detection results are more scientific.

[0127] (2) Based on the discovery cohort, this invention screened 85 metabolites that showed significant differences between patients with abdominal aortic aneurysms and healthy individuals. MetaboAnalyst and MedCalc software were used for ROC analysis of these metabolites, and seven metabolites with good diagnostic efficacy were selected as metabolic markers for diagnosing abdominal aortic aneurysms. These metabolites were 5-δ-hydroxybutylhydantoin, I-α-amino acid, and PGF. 2α D-pyroglutamic acid, nicotinic glucuronide, cysteine ​​glutathione disulfide and Trp-P-1.

[0128] (3) To verify the effectiveness of metabolic markers in diagnosing abdominal aortic aneurysms, non-targeted metabolomics analysis was performed on the seven metabolic markers selected in the validation cohort. The diagnostic performance of the metabolic markers was analyzed by combining ROC curves. The results showed that the AUC of the seven metabolic markers was greater than 0.88, and they all had good specificity and sensitivity.

[0129] (4) The present invention further uses the combination of 7 metabolites as a marker for diagnosing abdominal aortic aneurysm. The combination was analyzed with MetaboAnalyst and the results showed that the combination had a good diagnostic effect with an AUC of 1. Attached Figure Description

[0130] Figure 1 Image showing the results of CT angiography in a patient with an abdominal aortic aneurysm.

[0131] Figure 2 Abundance plot of 5-δ-hydroxybutylhydantoin in abdominal aortic aneurysm and healthy individuals, **** represents p<0.0001.

[0132] Figure 3 Abundance plot of 2-amino-4-[(2-hydroxy-1-oxopropyl)amino]butyric acid in patients with abdominal aortic aneurysms and healthy individuals, **** represents p<0.0001.

[0133] Figure 4 PGF 2α Abundance plots in patients with abdominal aortic aneurysms and healthy individuals, **** represents p<0.0001.

[0134] Figure 5 Abundance plot of D-pyroglutamate in patients with abdominal aortic aneurysm and healthy individuals, **** represents p<0.0001.

[0135] Figure 6 Abundance plot of nicotinic glucoside in patients with abdominal aortic aneurysms and healthy individuals, **** represents p<0.0001.

[0136] Figure 7 Abundance plot of cysteine-glutathione disulfide in patients with abdominal aortic aneurysms and healthy individuals, **** represents p<0.0001.

[0137] Figure 8 Abundance plot of Trp-P-1 in patients with abdominal aortic aneurysms and healthy individuals. *** represents p<0.001, actual P value is 0.0004.

[0138] Figure 9 ROC curve of 5-δ-hydroxybutylhydantoin in the diagnosis of abdominal aortic aneurysm, where the standard represents the cut-off value.

[0139] Figure 10 ROC curve of 2-amino-4-[(2-hydroxy-1-oxopropyl)amino]butyric acid in the diagnosis of abdominal aortic aneurysm, where the standard represents the cut-off value.

[0140] Figure 11 PGF 2α ROC curve in the diagnosis of abdominal aortic aneurysm, where the standard represents the cut-off value.

[0141] Figure 12 ROC curve of D-pyroglutamate in the diagnosis of abdominal aortic aneurysm, where the standard represents the cut-off value.

[0142] Figure 13 ROC curve of nicotinic glucuronide in the diagnosis of abdominal aortic aneurysm, where the standard represents the cut-off value.

[0143] Figure 14 ROC curve of cysteine-glutathione disulfide in the diagnosis of abdominal aortic aneurysm, where the standard represents the cut-off value.

[0144] Figure 15 ROC curve of Trp-P-1 in the diagnosis of abdominal aortic aneurysm, where the standard represents the cut-off value.

[0145] Figure 16 5-δ-hydroxybutylhydantoin, 1-α-amino acid, PGF 2α ROC curve of the combination of D-pyroglutamate, nicotinic glucuronide, cysteine ​​glutathione disulfide and Trp-P-1 in the diagnosis of abdominal aortic aneurysm, where CI represents the confidence interval. Detailed Implementation

[0146] The present invention will be further described below with reference to embodiments. The following description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make equivalent modifications to the disclosed technical content to create equivalent embodiments. Any simple modifications or equivalent changes made to the following embodiments based on the technical essence of the present invention without departing from the scope of the invention are all within the protection scope of the present invention.

[0147] Unless otherwise specified, the experimental methods used in the following examples are conventional methods.

[0148] Unless otherwise specified, all reagents and materials used in the following examples are commercially available.

[0149] The subjects, instruments, or devices involved in the embodiments are as follows:

[0150] 1. Subject Diagnosis: All subjects underwent preoperative abdominal aortic CT scan to determine if they had an abdominal aortic aneurysm. That is, CT scans were performed on patients with abdominal aortic aneurysms. Figure 1 The results showed that the diameter of the abdominal aorta in patients with abdominal aortic aneurysms was 1.5 times larger than that of the normal aorta.

[0151] 2. Plasma collection and analysis of subjects: Plasma samples were obtained from patients with abdominal aortic aneurysms; the expression of plasma metabolites was compared with that of control subjects without abdominal aortic aneurysms.

[0152] The specific materials, reagents, and collection methods for plasma collection and metabolite expression analysis are as follows:

[0153] (1) Instruments:

[0154] The LTQ Orbitrap velos mass spectrometer was purchased from Thermo Fisher Scientific; the ACQUITY H-class ultra-high performance liquid chromatograph system was purchased from Waters.

[0155] (2) Reagents:

[0156] Chromatographic grade acetonitrile and methanol were produced by Waters Corporation; ammonium acetate was purchased from Sigma Corporation; and ammonia was purchased from Fisher Chemical Corporation.

[0157] (3) Sample collection process: Plasma samples from patients with abdominal aortic aneurysm and controls were collected using EDTA anticoagulation blood collection tubes, centrifuged at 2000g for 10min at 4℃, and the supernatant was collected and stored at -80℃.

[0158] Example 1: Screening for metabolic biomarkers of abdominal aortic aneurysms

[0159] 1. The cohort included 30 patients diagnosed with abdominal aortic aneurysm (n=30, 25 males and 5 females, mean age 67.8 years) and 30 healthy controls without abdominal aortic aneurysm (n=30, 24 males and 6 females, mean age 66.2 years).

[0160] 2. Experimental Procedure: First, 150 μl of H2O was added to each plasma sample (50 μl), and the mixture was vortexed for 30 seconds. Then, 400 μl of acetonitrile was added to each sample, and the mixture was vortexed for 1 minute. After standing at -20°C for 60 minutes, the samples were centrifuged at 14000 × g for 10 minutes to remove plasma proteins. The supernatant was then dried under vacuum and reconstituted with 100 μl of 2% acetonitrile. Plasma metabolites were further separated from small protein molecules using a 10 kDa molecular weight cutoff ultracentrifuge filter (Millipore Amicon Ultra, MA). Quality control (QC) samples were prepared by mixing equal volumes of all plasma samples. QC samples were injected every ten samples throughout the analytical run to assess the stability and reproducibility of the analytical process.

[0161] Metabolomics analysis was performed using a Waters ACQUITY H-class LC system combined with an LTQ Orbitrap velos mass spectrometer (ThermoFisher Scientific, MA). Plasma metabolites were separated in 18-minute gradients at a flow rate of 500 µl / min on a Waters HSS C18 column. Mobile phase A was water with 0.1% formic acid, 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 at a resolution of 60 K, scanning from 100 to 1000 m / z. The automatic gain control (AGC) target was set to 1 × 10⁻⁶. 6 The maximum injection time is 100 milliseconds. For each target with high-energy collisional dissociation (HCD) fragments, the collision energy is optimized to 20, 40, and 60.

[0162] 3. Data Analysis: The raw data files were processed using Progenesis QI software (Waters), including peak alignment, peak picking, peak grouping, deconvolution, and normalization by total compounds. The exported feature files were imported into MetaboAnalyst for further analysis. Variables missing in more than 50% of the samples were removed from further statistical analysis, and missing values ​​were filled using the k-nearest neighbor algorithm (KNN). 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 abundant metabolites were identified according to the following criteria: P-value <0.05; fold change >1.5 or <0.67. Differentially abundant metabolites were analyzed using the enrichment and pathway analysis modules in MetaboAnalyst 6.0. Receiver operating characteristic (ROC) curve analysis was performed using the "BiomarkerAnalysis" module on the MetaAnalyst website and MedCalc software.

[0163] 4. Screening Results

[0164] Untargeted metabolite detection was performed on plasma samples from patients with abdominal aortic aneurysms and healthy controls in the discovery cohort. Differentially expressed metabolites with a fold change greater than 1.5-fold or less than 0.67 and a p-value <0.05 were screened, totaling 85 metabolites. MetaboAnalyst and MedCalc software (Statistical Software version 23.0.2 (MedCalc Software Ltd, Ostend, Belgium)) were used for metabolite ROC analysis, identifying 7 metabolites with high AUC values. These 7 metabolites are considered potential candidate metabolic biomarkers for AAA diagnosis, and their AUC values ​​are shown in Table 1.

[0165] Table 1: Candidate metabolic biomarkers for patients with abdominal aortic aneurysm

[0166]

[0167] Example 2: Validation of plasma metabolic markers in the diagnosis of AAA

[0168] To further validate the value of the screened metabolic biomarkers in the diagnosis of abdominal aortic aneurysms, non-targeted metabolomics analysis was performed in a new validation cohort.

[0169] The validation cohort included 46 AAA patients (n=46, 40 males and 6 females, mean age 70.4 years) and 42 healthy controls (n=42, 36 males and 6 females, mean age 67.6 years). The abundance of the above metabolic markers in patients with abdominal aortic aneurysms and healthy controls was detected. Sample collection, processing, and experimental procedures were the same as in Example 1.

[0170] The results are as follows Figure 2-8 As shown, 5-δ-hydroxybutylhydantoin, 2-amino-4-[(2-hydroxy-1-oxopropyl)amino]butyric acid, PGF 2α The abundance of D-pyroglutamic acid, nicotinic glucuronide, and cysteine ​​glutathione disulfide in patients with abdominal aortic aneurysms was significantly lower than that in healthy controls, while the abundance of Trp-P-1 in patients with abdominal aortic aneurysms was significantly higher than that in healthy controls. Statistical analysis was performed using GraphPad Prism version 10.3.0 for Windows, GraphPad Software, Boston, Massachusetts USA.

[0171] ROC curves of the above metabolic markers were plotted to distinguish between patients with abdominal aortic aneurysms and healthy controls (experimental procedure and data analysis are the same as in Example 1).

[0172] ROC curve analysis showed that the AUC value of 5-δ-hydroxybutylhydantoin was 0.996, with a sensitivity of 93.48% and a specificity of 100.00%. Figure 9 The AUC of 2-amino-4-[(2-hydroxy-1-oxopropyl)amino]butyric acid was 0.987, with a sensitivity of 97.83% and a specificity of 92.86%. Figure 10 ); PGF 2α The AUC value was 0.993, the sensitivity was 95.65%, and the specificity was 100.00%. Figure 11 The AUC value of D-pyroglutamic acid was 0.971, with a sensitivity of 86.96% and a specificity of 95.24%. Figure 12 The AUC value of nicotinic acid glucoside was 0.887, with a sensitivity of 93.48% and a specificity of 80.95%. Figure 13 The AUC of cysteine-glutathione disulfide was 0.905, with a sensitivity of 76.09% and a specificity of 95.24%. Figure 14 The AUC of Trp-P-1 was 0.921, with a sensitivity of 89.13% and a specificity of 90.48%. Figure 15 The diagnostic performance of each metabolic marker is shown in Table 2.

[0173] Table 2: Diagnostic performance of metabolic markers

[0174]

[0175] Using the "Biomarker Analysis" module in MetaboAnalyst, 5-δ-hydroxybutylhydantoin, 2-amino-4-[(2-hydroxy-1-oxopropyl)amino]butyric acid, and PGF were analyzed. 2α The combination of seven metabolic markers—D-pyroglutamate, nicotinic glucuronide, cysteine ​​glutathione disulfide, and Trp-P-1—showed good diagnostic efficacy for abdominal aortic aneurysms, with an area under the ROC curve (AUC) of 1.000. Figure 16 ).

Claims

1. The application of metabolic markers in the preparation of products for diagnosing abdominal aortic aneurysms, characterized in that, The metabolic marker is PGF 2α .

2. The application according to claim 1, characterized in that, The metabolic markers also include one or more of I-α-amino acids, D-pyroglutamic acid, nicotinic glucuronide, cysteine ​​glutathione disulfide, or Trp-P-1.

3. The application according to claim 2, characterized in that, The I-α-amino acid mentioned is 2-amino-4-[(2-hydroxy-1-oxopropyl)amino]butyric acid.

4. The application according to any one of claims 1-3, characterized in that, The metabolic markers mentioned are metabolic markers found in blood, plasma, serum, saliva, urine, tears, amniotic fluid, cerebrospinal fluid, lymph, or feces.

5. The application according to claim 1, characterized in that, The products mentioned include reagent kits, test strips, chips, or devices.

6. The application according to claim 1, characterized in that, The product includes reagents for detecting metabolic markers, which detect the presence or concentration level of metabolic markers.

7. The application according to claim 1, characterized in that, The products described include the analysis of concentration level data of metabolic biomarkers using one or more of the following methods: 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.

8. The application according to claim 1, characterized in that, The diagnosis of abdominal aortic aneurysm includes detecting metabolic markers by one or more of the following methods: chromatography, spectroscopy, mass spectrometry, or chemical analysis.

9. The application of a diagnostic system or diagnostic model in the preparation of products for diagnosing abdominal aortic aneurysms; The diagnostic system includes a device, unit, or module for determining whether a subject has an abdominal aortic aneurysm based on the concentration of metabolic markers; The diagnostic model includes: S1: Data receiving unit, wherein the data includes the concentration levels of metabolic markers in the sample; S2: Data analysis unit, which includes analyzing the data in S1 using one or more of the following: 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. The metabolic marker is PGF 2α .

10. The application according to claim 9, characterized in that, The metabolic markers also include one or more of 5-δ-hydroxybutylhydantoin, I-α-amino acid, D-pyroglutamic acid, nicotinic acid glucoside, cysteine ​​glutathione disulfide, or Trp-P-1.

11. The application according to claim 9, characterized in that, The metabolic markers mentioned are 5-δ-hydroxybutylhydantoin, 1-α-amino acid, and PGF. 2α D-pyroglutamic acid, nicotinic glucuronide, cysteine ​​glutathione disulfide and Trp-P-1.

12. The application according to claim 10 or 11, characterized in that, The I-α-amino acid mentioned is 2-amino-4-[(2-hydroxy-1-oxopropyl)amino]butyric acid.

13. The application according to claim 9, characterized in that, The diagnostic system includes: Data detection device, unit or module used to detect the concentration level of metabolic markers in a sample; A data input device, unit, or module for inputting concentration level data of metabolic markers; Data analysis devices, units, or modules that calculate the risk of abdominal aortic aneurysm based on concentration level data of metabolic markers; A data output device, unit, or module used to output analysis results on whether an individual has an abdominal aortic aneurysm.

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