Metabolic marker combination and use thereof in evaluation of arteriosclerosis grade

Abstract

A metabolic marker combination and a detection kit thereof and the use thereof, a method for establishing a prediction model for an arteriosclerosis grade, and a method for evaluating an arteriosclerosis grade. The metabolic marker combination comprises: 2-hydroxybutyric acid, hydrogenated cinnamic acid, 2-phenylpropionic acid, methyl 5β-androstan-3β-ol-17β-carboxylate, C18:1 ceramide, vanilmandelic acid, menadione, piperine, hippuric acid, carnitine, propionyl glycine, myristic acid, urocanic acid, α-linolenic acid, and methionine and 5-hydroxymethyl-2-furoic acid. The efficient and accurate evaluation of the arteriosclerosis grade can be achieved.

Description

Metabolic marker combination and its application in the assessment of arteriosclerosis grade Technical Field

[0001] The present application relates to the field of intelligent medical care. Specifically, the present application relates to a combination of metabolic markers and their application in the assessment of arteriosclerosis level. Background Art

[0002] Increased arterial stiffness is a clinical sign of physiological vascular aging and has recently become a key indicator of interest in cardiovascular disease risk assessment. Large population studies across multiple regions have strongly confirmed that arterial stiffness plays an emerging role in cardiovascular health, independent of traditional risk factors, prompting many hospitals to introduce arterial stiffness testing programs. Currently, many hospitals use precision medical instruments to assess arterial stiffness, including brachial-ankle pulse wave velocity (baPWV). A higher PWV indicates faster blood vessel transmission of pulse waves, stiffer blood vessel walls, and lower elasticity. Compared to the physiological elasticity of arteries, stiffened arteries transmit pulse waves more rapidly. This is because pulse waves propagate more slowly in elastic vessels, while they propagate more rapidly in stiffened vessels. The relationship between PWV and arterial stiffness can be expressed by the following formula: PWV = Transit Time / Distance. Distance is the distance the pulse wave travels, and Transit Time is the time it takes for the pulse wave to traverse that distance. An increase in PWV is often associated with increased arterial stiffness. While arterial stiffness testing can help better assess cardiovascular health, it is limited in hospital testing due to factors such as space and testing time (5-10 minutes per person), making it difficult to widely adopt.

[0003] In some studies, PWV can also be calculated using physiological parameters such as age, systolic blood pressure, and diastolic blood pressure. However, the method of calculating PWV using the above three indicators has a low correlation with baPWV, poor detection accuracy, and cannot yet be used in clinical practice.

[0004] Therefore, there is an urgent need to develop a highly accurate method for detecting arteriosclerosis for clinical application.

[0005] Summary of the Invention

[0006] The present application aims to solve at least one of the above problems. To this end, one object of the present application is to provide a method for detecting arteriosclerosis efficiently and accurately.

[0007] Specifically, this application provides the following technical solutions:

[0008] In the first aspect of the present application, the present application proposes a metabolite marker combination. According to an embodiment of the present application, the metabolite marker combination includes at least one selected from the following: 2-hydroxybutyric acid, hydrocinnamic acid, 2-phenylpropionic acid, 5β-androstane-3β-ol-17β-carboxylic acid methyl ester, C18:1 ceramide, vanillylmandelic acid, menadione, piperine, hippuric acid, carnitine, propionylglycine, myristic acid, urocanic acid, α-linolenic acid, methionine and 5-hydroxymethyl-2-furancarboxylic acid. In some examples of the present application, the aforementioned markers can be used to efficiently and accurately predict or diagnose the level of arteriosclerosis in a sample.

[0009] In a second aspect, this application provides a kit. According to embodiments of this application, the kit includes reagents for detecting at least one of the metabolite marker combinations described in the first aspect of this application. In some examples of this application, this kit offers advantages such as high efficiency, convenience, and high accuracy in predicting or diagnosing the degree of arteriosclerosis.

[0010] In a third aspect of the present application, the present application proposes a use of the kit described in the second aspect in detecting cardiovascular diseases in different populations. In some examples of the present application, the kit can be used to efficiently and accurately detect cardiovascular disease status in people of different age groups.

[0011] In the fourth aspect of the present application, the present application proposes a method for establishing a prediction model, wherein the prediction model is used to predict the level of arteriosclerosis. According to an embodiment of the present application, the method includes: obtaining feature information of a training sample, wherein the feature information includes at least metabolite features and age features; analyzing the association between the metabolite features and the level of arteriosclerosis to determine a first prediction metabolite group; wherein each prediction metabolite in the first prediction metabolite group can independently predict the level of arteriosclerosis; analyzing the association between the age group features and the first prediction metabolite group to determine a sub-prediction metabolite group related to the age group; and constructing the prediction model based on the sub-prediction metabolite group using a regression method. In some examples of the present application, the method provides an efficient and accurate means for predicting the level of arteriosclerosis by integrating metabolite features and age group features to construct a prediction model.

[0012] In the fifth aspect of the present application, a method for assessing the level of arteriosclerosis is proposed. According to an embodiment of the present application, the method includes: inputting the age information and metabolic marker detection information of the sample to be tested into a trained prediction model to obtain the brachial-ankle pulse wave velocity; assessing the level of arteriosclerosis of the sample to be tested based on the brachial-ankle pulse wave velocity; wherein the age information of the sample to be tested is correlated with the metabolic marker; and the trained prediction model is established by the method described in the fourth aspect of the present application. In some examples of the present application, this method can accurately assess the level of arteriosclerosis of a batch of samples in a short time.

[0013] In the sixth aspect of the present application, the present application proposes a prediction model establishment device, which is used to predict the level of arteriosclerosis. According to an embodiment of the present application, the device includes: a feature information acquisition unit, which is used to acquire feature information of a training sample, wherein the feature information includes at least metabolite features and age group features; a first prediction metabolite group acquisition unit, which is used to analyze the association between the metabolite features and the level of arteriosclerosis to determine a first prediction metabolite group; wherein each prediction metabolite in the first prediction metabolite group can independently predict the level of arteriosclerosis; a sub-metabolite group acquisition unit, which is used to analyze the association between the age group features and the first prediction metabolite group to determine a sub-prediction metabolite group related to the age group; and a model construction unit, which is used to construct the prediction model based on the sub-prediction metabolite group using a regression method. In some examples of the present application, the device can be used to build an accurate prediction model for the level of arteriosclerosis in a short time.

[0014] In the seventh aspect of the present application, the present application proposes a system for evaluating the level of arteriosclerosis. According to an embodiment of the present application, the system includes: a brachial-ankle pulse wave velocity acquisition device for inputting the age information and metabolic marker detection information of the sample to be tested into a trained prediction model to obtain the brachial-ankle pulse wave velocity; and an evaluation device for evaluating the level of arteriosclerosis of the sample to be tested based on the brachial-ankle pulse wave velocity. The age information of the sample to be tested is correlated with the metabolic marker; the trained prediction model is established by the method described in the fourth aspect of the present application or by the device described in the sixth aspect of the present application. In some examples of the present application, the system is capable of accurately evaluating the level of arteriosclerosis of a batch of samples in a short time.

[0015] In an eighth aspect of the present application, a computer program product is provided. According to an embodiment of the present application, the computer program product includes computer instructions, and when part or all of the computer instructions are executed on a computer, the method described in the fourth or fifth aspect of the present application is executed.

[0016] In a ninth aspect of the present application, a computing device is provided. According to an embodiment of the present application, the device includes: a processor and a memory; the memory is configured to store a computer program; and the processor is configured to execute the computer program to implement the method described in the fourth or fifth aspect of the present application.

[0017] In a tenth aspect of the present application, a computer-readable storage medium is provided. According to an embodiment of the present application, the storage medium includes computer instructions, which, when executed by a computer, enable the computer to implement the method described in the fourth or fifth aspect of the present application.

[0018] In some examples of the present application, the aforementioned computer program products, computing devices, and computer-readable storage media achieve efficient automation through the automatic execution of computer instructions, thereby improving operational efficiency and accuracy. Secondly, the instruction-based nature of the method makes it highly reproducible across different environments, ensuring consistency and reliability. Furthermore, it achieves real-time performance, making it suitable for applications requiring timely results, such as issuing test reports in a short period of time. By embedding the method into a computer program, computer resources are effectively utilized, reducing the time and labor costs required for manual operations.

[0019] It should be noted that the features and technical effects described in this article for different aspects can be used as reference for each other and will not be repeated here.

[0020] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0022] FIG1 is a schematic diagram of a prediction model construction process according to an embodiment of the present application;

[0023] FIG2 is a schematic diagram of a flow chart of arteriosclerosis level detection for a sample to be tested according to one embodiment of the present application;

[0024] FIG3 is a schematic diagram of a prediction model establishment device according to an embodiment of the present application;

[0025] FIG4 is a schematic diagram of an arteriosclerosis level assessment system according to one embodiment of the present application;

[0026] FIG5 is a schematic diagram of constructing a prediction model according to an embodiment of the present application;

[0027] FIG6 is a schematic diagram showing the correlation between the predicted baPWV and the actual baPWV according to one embodiment of the present application;

[0028] FIG7 is a schematic diagram of the ROC curve results of the prediction model according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0030] As used herein, unless otherwise indicated, the singular forms "a," "an," and the like include plural referents (more than one); "a set" or "a plurality" refers to two or more.

[0031] In this document, unless otherwise specified, the terms "first", "second", "third", "fourth", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated; features specified as "first", "second", etc. may explicitly or implicitly include one or more of the said features.

[0032] As used herein, unless otherwise indicated, the terms "metabolite" and "metabolic marker" have the same meaning.

[0033] Metabolic marker panel

[0034] In one aspect of the present application, a metabolic marker combination is provided. The combination includes at least one selected from the following metabolic markers: 2-hydroxybutyric acid, hydrocinnamic acid, 2-phenylpropionic acid, 5β-androstane-3β-ol-17β-carboxylic acid methyl ester, C18:1 ceramide, vanillylmandelic acid, menadione, piperine, hippuric acid, carnitine, propionylglycine, myristic acid, urocanic acid, α-linolenic acid, methionine, and 5-hydroxymethyl-2-furancarboxylic acid.

[0035] It should be noted that metabolic markers refer to molecules or compounds produced during the metabolic process in an organism, which can indicate the biological characteristics of physiological state, disease risk, disease progression or treatment effect through their presence or level changes in the organism.

[0036] In some examples of the present application, the inventors unexpectedly discovered that the above-mentioned metabolic marker combination can be used to accurately predict the degree of arteriosclerosis.

[0037] Reagent test kit

[0038] In another aspect of the present application, a kit is provided. The kit includes a reagent for detecting at least one of the above-mentioned metabolite marker combination. In some examples of the present application, the kit can be effectively used to detect arteriosclerosis status in an individual.

[0039] It should be noted that the term “individual” is not particularly limited in this application and includes normal samples and samples with arteriosclerosis.

[0040] In some examples of the present application, the sample can be selected from blood, serum, plasma or urine. In some preferred examples of the present application, the sample is selected from blood.

[0041] use

[0042] In another aspect of the present application, the present application proposes a use of the aforementioned kit in detecting cardiovascular diseases in different populations. In some examples of the present application, the kit can be used to efficiently and accurately detect cardiovascular disease status in people of different age groups.

[0043] In some examples of the present application, the cardiovascular diseases include arteriosclerosis, coronary heart disease, heart failure, arrhythmia and stroke. In some preferred examples of the present application, the metabolic marker panel is more suitable for detecting arteriosclerosis status.

[0044] In some examples of the present application, the different populations can be divided according to age groups, such as 35 years old, 40 years old, 45 years old, 50 years old, and 55 years old, including people under 35 years old, people aged 35-40 years old, people aged 40-45 years old, people aged 45-50 years old, people aged 50-55 years old, and people aged 55 and above. In some preferred examples of the present application, the aforementioned metabolic markers are divided into three age groups, including people under 35 years old, people aged 35-55 years old, and people aged 55 and above.

[0045] In some examples of the present application, the metabolite marker combination used to detect the population under 35 years old includes at least one selected from 2-hydroxybutyric acid, hydrocinnamic acid, 2-phenylpropionic acid, 5β-androstane-3β-ol-17β-carboxylic acid methyl ester, and C18:1 ceramide. In some examples of the present application, the aforementioned metabolites can be used to efficiently and accurately detect arteriosclerosis in people under 35 years old.

[0046] In some examples of the present application, the metabolite marker combination used to detect the 35-55 year old population includes at least one selected from vanillylmandelic acid, menadione, piperine, hippuric acid, and carnitine. In some examples of the present application, the aforementioned metabolites can be used to efficiently and accurately detect arteriosclerosis in the 35-55 year old population.

[0047] In some examples of the present application, the metabolite marker combination used to detect the population over 55 years old includes at least one selected from propionylglycine, myristic acid, urocanic acid, α-linolenic acid, methionine, and 5-hydroxymethyl-2-furancarboxylic acid. In some examples of the present application, the aforementioned metabolites can be used to efficiently and accurately detect arteriosclerosis in people over 55 years old.

[0048] Prediction model building method

[0049] In another aspect of the present application, a method for establishing a prediction model is proposed for sequencing the level of arteriosclerosis. The method comprises:

[0050] 1) obtaining characteristic information of training samples, wherein the characteristic information includes at least metabolite characteristics and age group characteristics;

[0051] In some examples of this application, the method of obtaining training samples is not particularly limited. In order to improve the accuracy of predicting or diagnosing arteriosclerosis, the training set samples should meet at least one of the following conditions:

[0052] (1) Aged over 20 years; (2) No cardiovascular disease; (3) No liver or kidney failure; (4) No history of tumor.

[0053] In some examples of the present application, the metabolite features of the training samples are selected from self-tested targeted metabolome data or reference targeted metabolome data from a public database, wherein the metabolites are all derived from blood metabolites.

[0054] In other examples of the present application, the metabolites may also be metabolites from other body fluid samples, such as urine.

[0055] In some examples of this application, metabolites obtained from metabolomic data analysis were used as metabolite features to construct prediction models. For example, a total of 420 metabolites were obtained from training sample analysis, including amino acids and peptides, fatty acids, organic acids and their derivatives, bile acids, carbohydrates, benzyl ring compounds, carnitine, indole and its derivatives, nucleosides, organic heterocycles, phenylpropanoids and polyketides, organic oxygen compounds, and others. Prediction models were constructed using these metabolites as metabolite features.

[0056] In some examples of the present application, the features for establishing the prediction model also include: age features, which are used to distinguish people of different ages to avoid the influence of metabolite differences between different populations on the accuracy of the prediction results.

[0057] In order to further improve the accuracy of prediction, in some examples of establishing prediction models, the characteristic information may further include: gender, smoking habits, drinking habits, body mass index (BMI), systolic and diastolic blood pressure.

[0058] In other examples of the present application, the prediction accuracy of the prediction model can be further improved by combining lifestyle characteristics, such as sleep conditions.

[0059] 2) based on the metabolite signature obtained above, analyzing the correlation between the metabolite signature and the arteriosclerosis grade to determine a first predicted metabolite group;

[0060] Herein, the term "arteriosclerosis level" is a physiological parameter used to assess arteriosclerosis and vascular elasticity. In some examples of this application, the inventors found that metabolic markers can accurately predict the arteriosclerosis level.

[0061] In some examples of the present application, associations are determined using multiple regression analysis.

[0062] Multiple regression analysis, as it's called, can qualitatively and quantitatively describe the linear dependency between a dependent variable (e.g., PWV) and multiple independent variables (e.g., a metabolite, gender, smoking habits, drinking habits, body mass index, systolic and diastolic blood pressure, etc.). In multiple regression analysis, some independent variables with known associations with the dependent variable can be used as correction variables (e.g., gender, smoking habits, drinking habits, body mass index, systolic and diastolic blood pressure, etc.) to qualitatively assess whether the remaining independent variables (e.g., a metabolite) are associated with the dependent variable, thereby eliminating false-positive results due to associations between independent variables.

[0063] In some examples of the present application, metabolite markers with a P value of less than 0.05 determined by a multivariate regression analysis method are considered associated metabolites. The multiple associated metabolites obtained by the aforementioned method constitute a first predicted metabolite group. Each predicted metabolite in the first predicted metabolite group can independently predict the grade of arteriosclerosis.

[0064] In other examples of the present application, the correlation may also be determined by the Spearman correlation coefficient.

[0065] The so-called Spearman correlation coefficient is a statistical method for measuring the correlation between two variables. Unlike the Pearson correlation coefficient, the Spearman correlation coefficient does not require the relationship between the variables to be linear, but is calculated based on the rank order of the variables. The Spearman correlation coefficient is mainly suitable for evaluating the monotonic relationship between two variables, that is, whether it increases or decreases, the trend of change is consistent. When there are outliers in the data set or the data does not conform to the normal distribution, the Spearman correlation coefficient is usually more applicable. In one embodiment of the present application, the Spearman correlation coefficient is used to screen metabolite features associated with the level of arteriosclerosis.

[0066] In some examples of the present application, the first predicted metabolite group obtained by screening by the above method includes at least one of 2-hydroxybutyric acid, hydrocinnamic acid, 2-phenylpropionic acid, 5β-androstane-3β-ol-17β-carboxylic acid methyl ester, C18:1 ceramide, vanillylmandelic acid, menadione, piperine, hippuric acid, carnitine, propionylglycine, myristic acid, urocanic acid, α-linolenic acid, methionine, 5-hydroxymethyl-2-furancarboxylic acid, fucose, arginine and inositol.

[0067] In a preferred example of the present application, the first predicted metabolite group includes at least one of 2-hydroxybutyric acid, hydrocinnamic acid, 2-phenylpropionic acid, 5β-androstane-3β-ol-17β-carboxylic acid methyl ester, C18:1 ceramide, vanillylmandelic acid, menadione, piperine, hippuric acid, carnitine, propionylglycine, myristic acid, urocanic acid, α-linolenic acid, methionine and 5-hydroxymethyl-2-furancarboxylic acid.

[0068] 3) analyzing the association between the age group characteristics and the first predicted metabolite group to determine a sub-predicted metabolite group associated with the age group;

[0069] In some examples of the present application, associations are determined using a backward stepwise regression approach.

[0070] Backward stepwise regression is a regression method that screens features based on the explanatory power of variables, aiming to address multivariate collinearity. This method includes all variables in the model and then gradually attempts to remove certain variables to observe their impact on the overall model. If the removal of a variable has no significant effect on the model, the variable is removed; otherwise, the variable is retained. This process is repeated until all remaining factors have a significant impact on the model.

[0071] In some examples of the present application, sub-predicted metabolites with a P value < 0.1 are considered as associated metabolites. The multiple sub-predicted metabolites obtained by the above method constitute a sub-predicted metabolite group. Each metabolite in the sub-predicted metabolite group is derived from the first predicted metabolite group.

[0072] In some examples of the present application, the age group feature is selected from 35 years old, 40 years old, 40 years old, 45 years old, 50 years old, and 55 years old. That is, the population is divided into people under 35 years old, people aged 35-40 years old, people aged 40-45 years old, people aged 45-50 years old, people aged 50-55 years old, and people aged 55 and above. In some preferred examples of the present application, the aforementioned metabolic markers are divided into three age groups, including people under 35 years old, people aged 35-55 years old, and people aged 55 and above.

[0073] In a preferred example of the present application, the age group feature is selected from 35 and 55. That is, the population is divided into people under 35 years old, people aged 35 to 55, and people over 55 years old.

[0074] According to the present application, it is preferred that the age group characteristics are matched with the sub-prediction metabolite marker group related thereto, wherein the sub-prediction metabolite marker group is derived from the first prediction metabolite group.

[0075] In some examples of the present application, the predicted sub-metabolite group associated with an age group under 35 years old includes at least one selected from 2-hydroxybutyric acid, hydrocinnamic acid, 2-phenylpropionic acid, 5β-androstane-3β-ol-17β-carboxylic acid methyl ester and C18:1 ceramide.

[0076] In some examples of the present application, the predicted sub-metabolite group related to the age group of 35 to 55 years old includes at least one selected from the group consisting of vanillylmandelic acid, menadione, piperine, hippuric acid and carnitine;

[0077] In some examples of the present application, the predicted sub-metabolite group associated with an age group of 55 years and above includes at least one selected from propionylglycine, myristic acid, urocanic acid, α-linolenic acid, methionine, and 5-hydroxymethyl-2-furancarboxylic acid.

[0078] 4) Based on the aforementioned sub-prediction metabolite groups, a regression method is used to construct the prediction model.

[0079] In some examples of the present application, the regression method is selected from at least one of linear regression, ridge regression, random forest regression, support vector regression, and decision tree regression. In a preferred example of the present application, the regression method is selected from linear regression.

[0080] Based on the linear regression method, a prediction model for arteriosclerosis grade in different age groups was established.

[0081] In other examples of the present application, the level of arteriosclerosis may be predicted using other models, such as a self-supervised neural network or an adversarial neural network.

[0082] In some examples of the present application, the prediction model for the age group under 35 years old is determined by 2-hydroxybutyric acid, hydrocinnamic acid, 2-phenylpropionic acid, 5β-androstane-3β-ol-17β-carboxylic acid methyl ester and C18:1 ceramide.

[0083] In a preferred example of the present application, the prediction model for the age group under 35 years old has the following formula:

[0084] Brachial-ankle pulse wave velocity = 1229.62 + 0.7 × 2-hydroxybutyric acid - 1779.2 × hydrocinnamic acid + 5593.65 × 2-phenylpropionic acid + 0.71 × 5β-androstane-3β-ol-17β-carboxylic acid methyl ester - 1124.76 × C18:1 ceramide;

[0085] The unit of brachial-ankle pulse wave velocity is cm / s; the unit of 2-hydroxybutyric acid, hydrocinnamic acid, 2-phenylpropionic acid, 5β-androstane-3β-ol-17β-carboxylic acid methyl ester, and C18:1 ceramide is μmol / L.

[0086] In some examples of the present application, the 35-55 age range prediction model is determined by vanillylmandelic acid, menadione, piperine, hippuric acid, and carnitine.

[0087] In a preferred example of the present application, the 35-55 age group measurement model has the following formula:

[0088] Brachial-ankle pulse wave velocity = 1369.04 + 879.67 × vanillylmandelic acid - 19.48 × menadione + - 8.72 × piperine - 6.51 × hippuric acid - 8.04 × carnitine;

[0089] The unit of brachial-ankle pulse wave velocity is cm / s; the units of vanillylmandelic acid, menadione, piperine, hippuric acid, and carnitine are μmol / L.

[0090] In some examples of the present application, the prediction model for the age group over 55 years old is determined by propionylglycine, myristic acid, urocanic acid, α-linolenic acid, methionine and 5-hydroxymethyl-2-furancarboxylic acid.

[0091] In a preferred example of the present application, the prediction model for the age group over 55 years old has the following formula:

[0092] Brachial-ankle pulse wave velocity = 1604.68 - 60.38 × propionylglycine + 11.03 × myristic acid + 77.16 × urocanic acid - 78.09 × α-linolenic acid + 4.49 × methionine + 22.51 × 5-hydroxymethyl-2-furancarboxylic acid;

[0093] The unit of brachial-ankle pulse wave velocity is cm / s; the units of propionylglycine, myristic acid, urocanic acid, α-linolenic acid, methionine, and 5-hydroxymethyl-2-furancarboxylic acid are μmol / L.

[0094] In some examples of the present application, the method further includes performing a discrimination evaluation and a goodness of fit evaluation on the prediction model obtained above. In some examples of the present application, the discrimination evaluation includes: using a validation set sample with known results (independent of the training set sample to prevent overfitting) to verify the accuracy, sensitivity, specificity, and AUC value of the model in predicting the level of arteriosclerosis in people of different age groups. In some examples of the present application, the goodness of fit evaluation includes: using a validation set sample with known results (independent of the training set sample to prevent overfitting) to verify the logarithmic correlation and rank correlation between the model's predicted level of arteriosclerosis in people of different age groups and the actual results.

[0095] The model was evaluated using the above method and the constructed prediction model had good accuracy (0.831), specificity (0.88), sensitivity (0.758), AUC value (0.909), logarithmic correlation (0.729) and rank correlation (0.727), indicating that the model has good accuracy and stability.

[0096] It should be noted that the above-mentioned assessment of the level of arteriosclerosis by obtaining the brachial-ankle pulse wave velocity is only one embodiment. In other examples, the level of arteriosclerosis can also be assessed by quantifying the vasodilation response, such as observing the brachial artery blood flow-mediated vasodilation using high-resolution color Doppler ultrasound imaging technology. The judgment method is as follows: The vasodilation calculation formula is: FMD (%) = [maximum vessel diameter (mm) - vessel diameter at rest (mm)] / vessel diameter at rest (mm) × 100%; among them, the normal value of FMD is ≥6%, and a result of <6% indicates the presence of endothelial dysfunction and arterial stiffness.

[0097] It should be noted that the above formula can be adjusted accordingly based on the amount of training data and the adjustment of training parameters (such as increasing or decreasing the number of metabolites, etc.). In a specific example of the present application, the above formula is only a prediction model obtained under the training sample embodiment of the present application.

[0098] For ease of understanding, the prediction model building process is exemplarily described with reference to FIG1 .

[0099] The sample data are divided into training set data and validation set data. The training set data is used to construct the prediction model of arteriosclerosis level, and the validation set data is used to verify the accuracy and generalization ability of the obtained prediction model.

[0100] A prediction (multi-feature) model was constructed based on the clinical indicator information contained in the training set data. The clinical indicator information included metabolite information, age information, gender information, smoking habits, drinking habits, and body mass index (BMI).

[0101] Based on the above clinical indicator information, metabolites related to arteriosclerosis are determined independently of traditional clinical indicators, including age, gender, smoking habits, drinking habits, and body mass index (BMI).

[0102] Furthermore, we performed age-matching based on the metabolites independently associated with arteriosclerosis to improve prediction accuracy for different age groups. First, we grouped the age groups into multiple age groups. Next, we screened metabolites independently associated with age groups based on feature significance and importance. Finally, we obtained independently correlated metabolites corresponding to different age groups.

[0103] Then, a prediction model for arteriosclerosis grade was constructed based on the independent correlated metabolites corresponding to age groups obtained above.

[0104] Finally, the discrimination and fit of the prediction model constructed above are evaluated using the validation set data to verify the model's prediction accuracy and adaptability to new data.

[0105] Method for assessing the degree of arteriosclerosis

[0106] In another aspect of the present application, a method for assessing the level of arteriosclerosis is proposed. The method comprises:

[0107] The age information and metabolic marker detection information of the sample to be tested are input into a trained prediction model to obtain the brachial-ankle pulse wave velocity; wherein the trained prediction model is established by the aforementioned prediction model establishment method.

[0108] In some examples of the present application, the sample can be selected from blood, serum, plasma or urine. In some preferred examples of the present application, the sample is selected from blood.

[0109] The arteriosclerosis level of the sample to be tested is assessed based on the brachial-ankle pulse wave velocity output by the model; wherein the age information of the sample to be tested is correlated with the metabolic marker.

[0110] It should be noted that the term "correlation" refers to selecting different metabolites based on the age of the sample and inputting them into the corresponding prediction model to obtain the brachial-ankle pulse wave velocity.

[0111] In some examples of the present application, the metabolic markers for the age group under 35 years old include at least one selected from 2-hydroxybutyric acid, hydrocinnamic acid, 2-phenylpropionic acid, 5β-androstane-3β-ol-17β-carboxylic acid methyl ester, and C18:1 ceramide. In some preferred examples of the present application, the metabolic markers for the age group under 35 years old are selected from 2-hydroxybutyric acid, hydrocinnamic acid, 2-phenylpropionic acid, 5β-androstane-3β-ol-17β-carboxylic acid methyl ester, and C18:1 ceramide.

[0112] In some examples of the present application, the metabolite marker for the 35-55 age group includes at least one selected from the group consisting of vanillylmandelic acid, menadione, piperine, hippuric acid, and carnitine. In some preferred examples of the present application, the metabolite marker for the 5-55 age group includes at least one selected from the group consisting of vanillylmandelic acid, menadione, piperine, hippuric acid, and carnitine.

[0113] In some preferred examples of the present application, the metabolite marker for the age group of 55 years and above includes at least one selected from the group consisting of propionylglycine, myristic acid, urocanic acid, α-linolenic acid, methionine, and 5-hydroxymethyl-2-furancarboxylic acid. In some preferred examples of the present application, the metabolite marker for the age group of 55 years and above includes at least one selected from the group consisting of propionylglycine, myristic acid, urocanic acid, α-linolenic acid, methionine, and 5-hydroxymethyl-2-furancarboxylic acid.

[0114] The arteriosclerosis level of the sample to be tested is assessed based on metabolic markers corresponding to age. In some examples of the present application, if the sample to be tested is under 35 years old, when the brachial-ankle pulse wave velocity is less than 900 cm / s, the sample to be tested is determined to be a soft artery sample; when 900 cm / s≤brachial-ankle pulse wave velocity<1300 cm / s, the sample to be tested is determined to be a normal artery sample; when 1300 cm / s≤brachial-ankle pulse wave velocity<1600 cm / s, the sample to be tested is determined to be a stiff artery sample; when the brachial-ankle pulse wave velocity is ≥1600 cm / s, the sample to be tested is determined to be a significantly stiff artery sample.

[0115] If the age of the test sample is between 35 and 55 years old, when the brachial-ankle pulse wave velocity is less than 1100 cm / s, the test sample is determined to be a soft artery sample; when 1100 cm / s≤brachial-ankle pulse wave velocity<1400 cm / s, the test sample is determined to be a normal artery sample; when 1400 cm / s≤brachial-ankle pulse wave velocity<1900 cm / s, the test sample is determined to be a stiff artery sample; when the brachial-ankle pulse wave velocity is ≥1900 cm / s, the test sample is determined to be a significantly stiff artery sample;

[0116] If the age of the test sample is over 55 years old, when the brachial-ankle pulse wave velocity is less than 1200 cm / s, the test sample is determined to be a soft artery sample; when 1200 cm / s≤brachial-ankle pulse wave velocity<1600 cm / s, the test sample is determined to be a normal artery sample; when 1600 cm / s≤brachial-ankle pulse wave velocity<2200 cm / s, the test sample is determined to be a stiff artery sample; when the brachial-ankle pulse wave velocity is ≥2200 cm / s, the test sample is determined to be a significantly stiff artery sample;

[0117] This method accurately assesses the level of arteriosclerosis in a sample by measuring brachial-ankle pulse wave velocity. This method, consistent with current clinical examination practices, can improve the efficiency of clinical arteriosclerosis testing, facilitate mass testing, and help individuals undergoing physical examinations better understand their cardiovascular health.

[0118] For ease of understanding, referring to FIG2 , an exemplary description of the process of detecting the arteriosclerosis level of a sample to be tested is given.

[0119] First, the patient's age is obtained, and blood-based metabolite analysis is performed for the corresponding age group. The metabolite analysis results are then fed into the previously developed prediction model to calculate brachial-ankle pulse wave velocity. Finally, based on the calculated brachial-ankle pulse wave velocity results, the degree of arteriosclerosis is assessed according to the previously described criteria.

[0120] For example, if the sample is 30 years old and the brachial-ankle pulse wave velocity is 800 cm / s, the arteriosclerosis level of the sample is soft.

[0121] Device

[0122] In another aspect of the present application, a prediction model building device is proposed. Referring to FIG3 , the device includes: a feature information acquisition unit 100, a first predicted metabolite group acquisition unit 200, a sub-metabolite group acquisition unit 300, and a model building unit 400.

[0123] Unit 100 is used to obtain characteristic information of the training sample, wherein the characteristic information includes at least metabolite characteristics and age group characteristics;

[0124] Unit 200, connected to unit 100, is used to analyze the association between the metabolite characteristics and the arteriosclerosis grade to determine a first predicted metabolite group; each predicted metabolite in the first predicted metabolite group can independently predict the arteriosclerosis grade;

[0125] Unit 300, connected to unit 200, is used to analyze the association between the age group feature and the first predicted metabolite group to determine a sub-predicted metabolite group associated with the age group;

[0126] Unit 400 is connected to unit 300 and is used to construct the prediction model based on the sub-prediction metabolite group using a regression method.

[0127] By combining the above units, an accurate prediction model for the level of arteriosclerosis can be constructed in a short time.

[0128] system

[0129] In another aspect of the present application, a system for evaluating the level of arteriosclerosis is proposed. Referring to FIG4 , the system comprises: a brachial-ankle pulse wave velocity acquisition device 001 and an evaluation device 002.

[0130] Apparatus 001 (comprising a feature information acquisition unit 100, a first predicted metabolite group acquisition unit 200, a sub-metabolite group acquisition unit 300, and a model construction unit 400) is configured to input age information of a sample to be tested and metabolite marker detection information into a trained prediction model to obtain brachial-ankle pulse wave velocity; wherein the age information of the sample to be tested is correlated with the metabolite marker; and the trained prediction model is established by the method described in the fourth aspect of the present application or by the apparatus described in the sixth aspect of the present application;

[0131] Device 002 is used to evaluate the arteriosclerosis level of the sample to be tested based on the brachial-ankle pulse wave velocity.

[0132] In some examples of the present application, the system is capable of accurately assessing the level of arterial stiffness in a batch of samples in a short time.

[0133] Computer program product, computing device, computer storage medium

[0134] In another aspect of the present application, a computer program product is provided, which includes computer instructions, and when part or all of the computer instructions are run on a computer, the method described in the fourth aspect or the fifth aspect of the present application is executed.

[0135] In another aspect of the present application, a computing device is provided, comprising: a processor and a memory; the memory is configured to store a computer program; and the processor is configured to execute the computer program to implement the method described in the fourth or fifth aspect of the present application.

[0136] In another aspect of the present application, a computer-readable storage medium is provided, wherein the storage medium includes computer instructions, and when the instructions are executed by a computer, the computer implements the method according to the fourth aspect or the fifth aspect of the present application.

[0137] In some examples of this application, the computer program product and related equipment effectively improve the operational efficiency and accuracy of arterial prediction and sclerosis assessment methods through the characteristics of automation, high reproducibility, real-time performance, and resource conservation, and promote the promotion and application of the product.

[0138] It should be noted that the various embodiments of the computer program product, computing device, and computer-readable storage medium described above may be implemented in a digital electronic circuit system, an integrated circuit system, an FPGA (Field Programmable Gate Array), an ASIC (Application-Specific Integrated Circuit), an ASSP (Application Specific Standard Product), a SOC (System on Chip), a CPLD (Complex Programmable Logic Device), computer hardware, firmware, software, and / or a combination thereof. These various embodiments may include: being implemented in one or more computer programs, the one or more computer programs being executable and / or interpreted on a programmable system including at least one programmable processor, the programmable processor being a special-purpose or general-purpose programmable processor, being capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0139] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0140] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network) to enable efficient building of predictive models.

[0141] The following describes embodiments of the present invention in more detail, with examples of the embodiments illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are illustrative and intended to explain the present invention, and are not to be construed as limiting the present invention. Where specific techniques or conditions are not specified in the examples, the techniques or conditions described in the literature in the art or in the product specifications shall prevail.

[0142] Example 1: Construction and evaluation of a prediction model for arteriosclerosis grade

[0143] This example builds a model for predicting arteriosclerosis levels based on some of the details of the specific implementation methods of this application. Referring to Figure 5 , 762 volunteers who met the study inclusion criteria were selected as training and validation samples. There were 608 training samples and 154 validation samples, and the training and validation samples were independent of each other.

[0144] Brachial-ankle pulse wave velocity was obtained by participating in an arteriosclerosis testing program at the hospital;

[0145] Metabolite data were obtained through targeted metabolomics testing, with a total of 420 metabolites divided into 13 categories, including amino acids and peptides, fatty acids, organic acids and their derivatives, bile acids, carbohydrates, benzyl ring compounds, carnitine, indole and its derivatives, nucleoside compounds, organic heterocyclic compounds, phenylpropanoid and polyketide compounds, organic oxygen compounds and others.

[0146] Three age groups are set, including those under 35 years old (not included), those between 35 and 55 years old (inclusive), and those over 55 years old (inclusive).

[0147] 1) Prediction model construction

[0148] The method of the specific embodiment of the present application is used to construct a prediction model for arteriosclerosis level. Through feature screening and model iteration, the key metabolite combination and the optimal model are finally obtained, as shown below:

[0149] The key metabolite combination corresponding to the age group below 35 years old (excluding) is: 2-Hydroxybutyric acid, Hydrocinnamic acid, 2-Phenylpropionic acid, 5β-ANDROSTAN-3β-OL-17β-CARBOXYLIC ACID METHYL ESTER, C18:1 Ceramide (d18:1 / 18:1(9Z));

[0150] The corresponding prediction model is:

[0151] Brachial-ankle pulse wave velocity = 1229.62 + 0.7 × 2-hydroxybutyric acid - 1779.2 × hydrocinnamic acid + 5593.65 × 2-phenylpropionic acid + 0.71 × 5β-androstane-3β-ol-17β-carboxylic acid methyl ester - 1124.76 × C18:1 ceramide;

[0152] The unit of brachial-ankle pulse wave velocity is cm / s; the unit of 2-hydroxybutyric acid, hydrocinnamic acid, 2-phenylpropionic acid, 5β-androstane-3β-ol-17β-carboxylic acid methyl ester, and C18:1 ceramide is μmol / L.

[0153] The key metabolite combination corresponding to the age group of 35 to 55 years old (inclusive) is: Vanillymandelic acid, Menadione, Piperine, Hippuric acid, and Carnitine.

[0154] The corresponding prediction model is:

[0155] Brachial-ankle pulse wave velocity = 1369.04 + 879.67 × vanillylmandelic acid - 19.48 × menadione + - 8.72 × piperine - 6.51 × hippuric acid - 8.04 × carnitine;

[0156] The unit of brachial-ankle pulse wave velocity is cm / s; the units of vanillylmandelic acid, menadione, piperine, hippuric acid, and carnitine are μmol / L.

[0157] The key metabolite combination corresponding to the age group of 55 years and above is: Propionylglycine, Myristic acid, Urocanic acid, Alpha Linolenic acid, Methionine, and 5-Hydroxymethyl-2-furancarboxylic acid.

[0158] The corresponding prediction model is:

[0159] Brachial-ankle pulse wave velocity = 1604.68 - 60.38 × propionylglycine + 11.03 × myristic acid + 77.16 × urocanic acid

[0160] -78.09×α-linolenic acid + 4.49×methionine + 22.51×5-hydroxymethyl-2-furancarboxylic acid;

[0161] The unit of brachial-ankle pulse wave velocity is cm / s; the units of propionylglycine, myristic acid, urocanic acid, α-linolenic acid, methionine, and 5-hydroxymethyl-2-furancarboxylic acid are μmol / L.

[0162] 2) Prediction model evaluation

[0163] The prediction model obtained above was evaluated based on 154 validation set samples. The results are as follows:

[0164] ① Goodness of fit between predicted baPWV and true baPWV

[0165] The baPWV of the validation set data was predicted using the prediction model constructed above. The log-Pearson correlation was 0.729 (Figure 6) and the Spearman rank correlation was 0.727, indicating an excellent fit.

[0166] ② Whether the prediction of baPWV can effectively distinguish individuals with stiffer arteries

[0167] The ability of the predicted baPWV from the validation data to discriminate between individuals with normal and stiff arterial stiffness was evaluated using clinical criteria for arterial stiffness (1400 cm / s) (Table 1). All scores performed well, with the area under the receiver operating characteristic curve (AUC) of 0.909 (Figure 7). These results indicate that the model can effectively discriminate between individuals with normal and stiff arterial stiffness.

[0168] Table 1

[0169] The above results show that the prediction model constructed by the method of the present application has high prediction accuracy, reliable prediction results, and high adaptability to new data.

[0170] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0171] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0172] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A metabolite marker combination, characterized in that: Including at least one selected from the following: 2-Hydroxybutyric acid, hydrocinnamic acid, 2-phenylpropionic acid, methyl 5β-androstane-3β-ol-17β-carboxylate, C18:1 ceramide, vanillylmandelic acid, menadione, piperine, hippuric acid, carnitine, propionylglycine, myristic acid, urocanic acid, α-linolenic acid, methionine, and 5-hydroxymethyl-2-furoic acid.

2. A kit, characterized in that include: A reagent for detecting at least one of the metabolite marker combination according to claim 1.

3. Use of the kit according to claim 2 in detecting cardiovascular diseases in different populations.

4. The use according to claim 3, characterized in that The cardiovascular diseases include arteriosclerosis, coronary heart disease, heart failure, arrhythmia and stroke.

5. The use according to claim 3, characterized in that The different groups of people are selected from people under 35 years old, people aged 35 to 55 years old, and people over 55 years old; Optionally, the metabolite marker combination for detecting the population under 35 years old includes at least one selected from 2-hydroxybutyric acid, hydrocinnamic acid, 2-phenylpropionic acid, 5β-androstane-3β-ol-17β-carboxylic acid methyl ester and C18:1 ceramide; Optionally, the metabolite marker combination for detecting the 35-55 year old population includes at least one selected from the group consisting of vanillylmandelic acid, menadione, piperine, hippuric acid and carnitine; Optionally, the metabolite marker combination for detecting the population aged 55 and above comprises at least one selected from propionylglycine, myristic acid, urocanic acid, α-linolenic acid, methionine and 5-hydroxymethyl-2-furancarboxylic acid.

6. A method for establishing a prediction model for predicting the level of arteriosclerosis, characterized in that: include: Acquiring characteristic information of a training sample, wherein the characteristic information includes at least a metabolite characteristic and an age characteristic; Analyzing the association between the metabolite signature and the arteriosclerosis grade to determine a first predicted metabolite group; wherein each predicted metabolite in the first predicted metabolite group can independently predict the arteriosclerosis grade; analyzing the association between the age group characteristics and the first predicted metabolite group to determine a sub-predicted metabolite group associated with the age group; Based on the sub-predicted metabolite group, the prediction model was constructed using a regression method.

7. The method according to claim 6, characterized in that The regression method is selected from at least one of linear regression, ridge regression, random forest regression, support vector regression and decision tree regression; Preferably, the regression method is selected from linear regression.

8. The method according to claim 7, characterized in that The characteristic information further includes: at least one of gender, smoking habits, drinking habits, body mass index, systolic and diastolic blood pressure.

9. The method according to claim 7, characterized in that The first predicted metabolite group includes at least one of 2-hydroxybutyric acid, hydrocinnamic acid, 2-phenylpropionic acid, 5β-androstane-3β-ol-17β-carboxylic acid methyl ester, C18:1 ceramide, vanillylmandelic acid, menadione, piperine, hippuric acid, carnitine, propionylglycine, myristic acid, urocanic acid, α-linolenic acid, methionine, 5-hydroxymethyl-2-furancarboxylic acid, fucose, arginine and inositol.

10. The method according to claim 9, characterized in that The age group characteristics are selected from 35 years old, 40 years old, 40 years old, 45 years old, 50 years old and 55 years old; Preferably, the age group characteristic is selected from 35 years old and 55 years old; Optionally, the predicted sub-metabolite group associated with the age group under 35 years old includes at least one selected from 2-hydroxybutyric acid, hydrocinnamic acid, 2-phenylpropionic acid, 5β-androstane-3β-ol-17β-carboxylic acid methyl ester and C18:1 ceramide; Optionally, the predicted sub-metabolite group associated with the age group of 35 to 55 years old includes at least one selected from the group consisting of vanillylmandelic acid, menadione, piperine, hippuric acid and carnitine; Optionally, the predicted sub-metabolite group associated with the age group of 55 years and above includes at least one selected from propionylglycine, myristic acid, urocanic acid, α-linolenic acid, methionine and 5-hydroxymethyl-2-furancarboxylic acid.

11. The method according to claim 10, characterized in that The prediction model for the age group under 35 years old is determined by 2-hydroxybutyric acid, hydrocinnamic acid, 2-phenylpropionic acid, 5β-androstane-3β-ol-17β-carboxylic acid methyl ester and C18:1 ceramide; Preferably, the prediction model for the age group under 35 years old has the following formula: Brachial-ankle pulse wave velocity = 1229.62 + 0.7 × 2-hydroxybutyric acid - 1779.2 × hydrocinnamic acid + 5593.65 × 2- Phenylpropionic acid + 0.71 × 5β-androstane-3β-ol-17β-carboxylic acid methyl ester - 1124.76 × C18:1 ceramide; The unit of brachial-ankle pulse wave velocity is cm / s; the unit of 2-hydroxybutyric acid, hydrocinnamic acid, 2-phenylpropionic acid, 5β-androstane-3β-ol-17β-carboxylic acid methyl ester, and C18:1 ceramide is μmol / L.

12. The method according to claim 10, characterized in that The prediction model for the age group of 35 to 55 years old is determined by vanillylmandelic acid, menadione, piperine, hippuric acid and carnitine; Preferably, the 35-55 age group measurement model has the following formula: Brachial-ankle pulse wave velocity = 1369.04 + 879.67 × vanillylmandelic acid - 19.48 × menadione + - 8.72 × piperine -6.51× hippuric acid -8.04× carnitine; The unit of brachial-ankle pulse wave velocity is cm / s; the units of vanillylmandelic acid, menadione, piperine, hippuric acid, and carnitine are μmol / L.

13. The method according to claim 10, characterized in that The prediction model for the age group over 55 years old is determined by propionylglycine, myristic acid, urocanic acid, α-linolenic acid, methionine and 5-hydroxymethyl-2-furancarboxylic acid; Preferably, the prediction model for the age group over 55 years old has the following formula: Brachial-ankle pulse wave velocity = 1604.68 - 60.38 × propionylglycine + 11.03 × myristic acid + 77.16 × urocanic acid -78.09×α-linolenic acid + 4.49×methionine + 22.51×5-hydroxymethyl-2-furancarboxylic acid; The unit of brachial-ankle pulse wave velocity is cm / s; the units of propionylglycine, myristic acid, urocanic acid, α-linolenic acid, methionine, and 5-hydroxymethyl-2-furancarboxylic acid are μmol / L.

14. A method for evaluating the level of arteriosclerosis, characterized in that: include: Input the age information and metabolic marker detection information of the sample to be tested into the trained prediction model to obtain the brachial-ankle pulse wave velocity; assessing the arteriosclerosis grade of the sample to be tested based on brachial-ankle pulse wave velocity; Wherein, the age information of the sample to be tested is correlated with the metabolic marker; The trained prediction model is established and obtained by the method according to any one of claims 6 to 13.

15. The method according to claim 14, characterized in that The dependencies include: The metabolic markers for the age group under 35 years old include at least one selected from 2-hydroxybutyric acid, hydrocinnamic acid, 2-phenylpropionic acid, 5β-androstane-3β-ol-17β-carboxylic acid methyl ester and C18:1 ceramide; The metabolite markers for the 35-55 age group include at least one selected from the group consisting of vanillylmandelic acid, menadione, piperine, hippuric acid, and carnitine; The metabolite markers for the age group of 55 years and above include at least one selected from propionylglycine, myristic acid, urocanic acid, α-linolenic acid, methionine and 5-hydroxymethyl-2-furancarboxylic acid.

16. The method according to claim 15, characterized in that The assessment includes: If the age of the sample to be tested is under 35 years old, When the brachial-ankle pulse wave velocity is less than 900 cm / s, the sample to be tested is determined to be a soft artery sample; When 900 cm / s≤brachial-ankle pulse wave velocity<1300 cm / s, the sample to be tested is determined to be a normal artery sample; When 1300 cm / s≤brachial-ankle pulse wave velocity<1600 cm / s, the sample to be tested is determined to be an arterial stiffness sample; When the brachial-ankle pulse wave velocity is ≥1600 cm / s, the sample to be tested is determined to be a sample with significant arterial stiffness; If the age of the sample to be tested is between 35 and 55 years old, When the brachial-ankle pulse wave velocity is less than 1100 cm / s, the sample to be tested is determined to be a soft artery sample; When 1100 cm / s≤brachial-ankle pulse wave velocity<1400 cm / s, the sample to be tested is determined to be a normal artery sample; When 1400 cm / s≤brachial-ankle pulse wave velocity<1900 cm / s, the sample to be tested is determined to be an arterial stiffness sample; When the brachial-ankle pulse wave velocity is ≥1900 cm / s, the sample to be tested is determined to be a sample with significant arterial stiffness; If the age of the sample to be tested is over 55 years old, When the brachial-ankle pulse wave velocity is less than 1200 cm / s, the sample to be tested is determined to be a soft artery sample; When 1200 cm / s≤brachial-ankle pulse wave velocity<1600 cm / s, the sample to be tested is determined to be a normal artery sample; When 1600 cm / s≤brachial-ankle pulse wave velocity<2200 cm / s, the sample to be tested is determined to be an arterial stiffness sample; When the brachial-ankle pulse wave velocity is ≥2200 cm / s, the sample to be tested is determined to be a sample with significant arterial stiffness.

17. A computer program product, characterized in that The computer program product includes computer instructions, and when part or all of the computer instructions are run on a computer, the method according to any one of claims 6 to 16 is executed.

18. A computing device, characterized in that include: processor and memory; The memory is used to store computer programs; The processor is configured to execute the computer program to implement the method according to any one of claims 6 to 16.

19. A computer-readable storage medium, characterized in that The storage medium includes computer instructions, and when the instructions are executed by a computer, the computer is enabled to implement the method according to any one of claims 6 to 16.

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