Arteriosclerosis disease risk prediction method, device and system and storage medium

Abstract

The invention discloses an arteriosclerosis risk prediction method, device and system and a storage medium, and the method comprises the steps: obtaining a genetic risk score, a blood glucose index and basic features of a crowd arteriosclerosis index, thereby constructing an arteriosclerosis risk prediction model; and drawing a working characteristic ROC curve of the testee and calculating an area AUC under the ROC curve to evaluate the discrimination degree of the model for predicting the arteriosclerosis risk of the testee. By adopting the technical scheme of the invention, arteriosclerosis high-risk groups of the middle-aged and elderly people can be accurately screened out, and overhigh disease burden of arteriosclerosis of the middle-aged and elderly people can be prevented and controlled. Genetic and non-genetic factors are simultaneously included to judge the possibility of suffering from arteriosclerosis of middle-aged and elderly people, so that the method has certain practical guiding significance for preventing and controlling arteriosclerosis in people with abnormal blood glucose index levels.

Description

Technical Field

[0001] The present invention belongs to the technical field of disease risk assessment, and in particular relates to a method and device, a system, and a storage medium for predicting the risk of arteriosclerosis. Background Art

[0002] Data from the seventh national census showed that in 2020, the population of China aged 65 and over reached 190 million. With the aging of the population, more and more age-related diseases such as atherosclerosis have become major public health issues. Atherosclerosis, characterized by vascular wall remodeling and loss of elasticity of the central or ductal artery walls, is an indicator of early vascular aging and an independent predictor of cardiovascular disease. Increased atherosclerosis not only potentially increases hemodynamic stress and causes damage to end organs such as the kidneys, but also increases the risk of impaired cognitive function and cerebrovascular disease in the elderly. Atherosclerosis is caused by a decrease in arterial elasticity and can be quantified by measuring different parameters, among which brachial-ankle pulse wave velocity is more commonly used in Asian populations. It has the advantages of being non-invasive, automated, reflecting the elasticity of muscular arteries (including the aorta and peripheral arteries), and easy to measure and reproduce, and can better reflect vascular elasticity and stiffness.

[0003] Studies have found that increased arterial stiffness in people with diabetes and metabolic syndrome partially explains their increased risk of cardiovascular disease, and the risk of cardiovascular events in patients with type 2 diabetes is mainly related to the severity of atherosclerosis. In addition, genetically determined type 2 diabetes and reduced insulin secretion are associated with an increased risk of arteriosclerosis, and an elevated triglyceride glucose index is causally associated with the risk of cardiovascular events. Therefore, it is urgent to better understand the potential determinants of arteriosclerosis, such as abnormal blood glucose metabolism, in order to develop innovative strategies to further and more effectively prevent and intervene in more chronic diseases of the elderly.

[0004] Atherosclerosis is a complex polygenic disease. In addition to exposure to acquired environmental factors, the contribution of genetic susceptibility to the increased risk of arteriosclerosis cannot be ignored. Research reports show that 65% of pulse wave velocity can be explained by heritability. The polygenic genetic risk score is an indicator that integrates the effects of multiple genetic variants and quantifies and summarizes the complex genetic risk level. At present, only significant loci have been reported in genome-wide association analysis of indirect indicators of arteriosclerosis such as pulse pressure difference. There is no genome-wide association analysis study on brachial-ankle pulse wave velocity, a direct indicator of arteriosclerosis, in Asian populations. Although the heritability analysis between pulse wave velocity and blood pressure components such as pulse pressure difference shows that there is no less than 50% genetic overlap between them, the differences in sensitivity and specificity of different indicators, the differences in key influencing mechanisms, the differences in the frequency of variant sites and linkage disequilibrium patterns among different populations have led to the fact that the genetic risk score constructed only by the genetic variant sites and effect values ​​of pulse pressure difference may not be the best choice as a genetic risk surrogate for arteriosclerosis. The occurrence and development of arteriosclerosis are jointly affected by genetic factors, aging, metabolic abnormalities and other chronic disease states. The coexistence of genetic susceptibility and abnormal blood glucose homeostasis may be associated with a further increased risk of atherosclerosis. The proportion of middle-aged and elderly people suffering from metabolic diseases such as abnormal glucose metabolism is high, and these chronic disease states are associated with a higher risk of atherosclerosis. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method and device, system, and storage medium for predicting the risk of arteriosclerosis in middle-aged and elderly people based on the genetic risk score of brachial-ankle pulse wave velocity, a direct indicator of arteriosclerosis, and blood glucose indicators, which can more accurately screen out high-risk groups for arteriosclerosis in middle-aged and elderly people, and prevent and control the excessive disease burden of arteriosclerosis in middle-aged and elderly people.

[0006] To achieve the above object, the present invention adopts the following technical solution:

[0007] A method for predicting the risk of arteriosclerosis comprises the following steps:

[0008] Step S1, obtaining arteriosclerosis index of target population;

[0009] Step S2, extracting genome-wide association single nucleotide polymorphism (SNP) sites of brachial-ankle pulse wave velocity according to the arteriosclerosis index of the target population;

[0010] Step S3, obtaining the genetic risk score of the target population's arteriosclerosis index according to the SNP site;

[0011] Step S4, constructing an arteriosclerosis risk prediction model based on the genetic risk score of the arteriosclerosis index, the blood sugar index of the target population and the basic characteristics of the individuals;

[0012] Step S5: confirming the risk of arteriosclerosis in the middle-aged and elderly population according to the prediction model.

[0013] Preferably, in step S4, a prediction model for the risk of arteriosclerosis is constructed using statistical methods based on the genetic risk score of arteriosclerosis indicators, combined with the levels of fasting blood glucose, glycosylated hemoglobin or triglyceride glucose index and the age and gender of the individual, and the ROC curve is drawn and the area under the ROC curve AUC is calculated to evaluate the discrimination of the prediction model; wherein, the risk of arteriosclerosis is a brachial-ankle pulse wave velocity ≥1400cm / s and <1800cm / s or a brachial-ankle pulse wave velocity ≥1800cm / s.

[0014] Preferably, in step S1, participants with kinship in the target population are removed, and a genome-wide association analysis of the arteriosclerosis index, i.e., brachial-ankle pulse wave velocity, is performed, and the minor allele frequency is > 0.05, Hardy-Weinberg equilibrium P> 1×10 -6 The screening criteria for selecting SNP loci significantly associated with brachial-ankle pulse wave velocity, an indicator of arteriosclerosis, were used to select the SNP loci, and the r 2 The linkage disequilibrium sites of SNPs were removed based on the standard of <0.2, and the SNP sites that met the standard were rs1475113, rs13099435, rs2317167, rs113278154, rs7121592, rs982979, rs75275903 and rs62229351.

[0015] Preferably, in step S3, PRS=8×[(21.18×the number of effect alleles G of rs1475113)+(39.00×the number of effect alleles C of rs13099435)+(35.82×the number of effect alleles A of rs2317167)+(22.11×the number of effect alleles C of rs113278154)+(22.24×the number of effect alleles C of rs7121592)] PRS is the genetic risk score.

[0016] The present invention also provides a device for predicting the risk of arteriosclerosis, comprising:

[0017] An acquisition module is used to obtain arteriosclerosis indicators of the target population;

[0018] An extraction module is used to extract genome-wide association single nucleotide polymorphism (SNP) sites of brachial-ankle pulse wave velocity according to the arteriosclerosis index of the target population;

[0019] A processing module is used to obtain a genetic risk score of arteriosclerosis indicators of a target population based on the SNP sites;

[0020] A construction module is used to construct an arteriosclerosis risk prediction model based on the genetic risk score of arteriosclerosis indicators, blood sugar indicators of the target population and basic individual characteristics;

[0021] The prediction module is used to confirm the risk of arteriosclerosis in the middle-aged and elderly population based on the prediction model.

[0022] Preferably, the construction module constructs a prediction model for the risk of arteriosclerosis based on the genetic risk score of arteriosclerosis indicators, combined with the levels of fasting blood glucose, glycosylated hemoglobin or triglyceride glucose index and the age and gender of the individual, using statistical methods, and draws the ROC curve and calculates the area under the ROC curve AUC to evaluate the discrimination of the prediction model; wherein, the risk of arteriosclerosis is a brachial-ankle pulse wave velocity ≥1400cm / s and <1800cm / s or a brachial-ankle pulse wave velocity ≥1800cm / s.

[0023] The present invention also provides an arteriosclerosis risk prediction system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes an arteriosclerosis risk prediction method when executed by the processor.

[0024] The present invention also provides a storage medium, on which a computer program is stored, and when the computer program is run, a method for predicting the risk of arteriosclerosis is executed.

[0025] The present invention is based on the screening of the risk of arteriosclerosis in the middle-aged and elderly people based on genetic risk scores and blood sugar indexes (fasting blood sugar, glycosylated hemoglobin, triglyceride glucose index). By detecting the genotypes of 8 SNP sites related to the brachial-ankle pulse wave velocity, a direct indicator of arteriosclerosis, and combining the individual's fasting blood sugar or glycosylated hemoglobin level or triglyceride glucose index and the individual's age and gender, the risk of arteriosclerosis is predicted. It has the characteristics of high detection accuracy, convenience, speed, safety and non-invasiveness, and has important public health significance for the prediction and early prevention and control of the risk of arteriosclerosis in the middle-aged and elderly people. This prediction tool includes the genetic risk of the brachial-ankle pulse wave velocity, an arteriosclerosis indicator of the target population, fasting blood sugar or glycosylated hemoglobin or triglyceride glucose index, and the basic characteristics of the individual. The simultaneous inclusion of such genetic and non-genetic factors has certain practical guiding significance for judging the possibility of arteriosclerosis in the middle-aged and elderly people and preventing and controlling arteriosclerosis in the middle-aged and elderly people with abnormal levels of these blood sugar indicators. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0027] Figure 1 is a flow chart of the method for predicting the risk of arteriosclerosis of the present invention;

[0028] Figure 2 It is a nomogram of the arteriosclerosis risk prediction model based on basic characteristics, genetic risk score and fasting blood glucose of the present invention;

[0029] Figure 3 It is a nomogram of the arteriosclerosis risk prediction model based on basic characteristics, genetic risk score and glycosylated hemoglobin of the present invention;

[0030] Figure 4 It is a nomogram of the arteriosclerosis risk prediction model based on basic characteristics, genetic risk score and triglyceride glucose index of the present invention;

[0031] Figure 5 The present invention is a ROC curve for predicting the risk of marginal arteriosclerosis based on basic characteristics, genetic risk score and blood glucose index (fasting blood glucose or glycosylated hemoglobin or triglyceride glucose index);

[0032] Figure 6The present invention is a ROC curve for predicting the increased risk of arteriosclerosis based on basic characteristics, genetic risk scores and blood glucose indicators (fasting blood glucose or glycosylated hemoglobin or triglyceride glucose index). DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0035] Embodiment 1:

[0036] like Figure 1 As shown, an embodiment of the present invention provides a method for predicting the risk of arteriosclerosis, which is based on genetic risk scores, combined with blood sugar indicators (fasting blood sugar, glycosylated hemoglobin, triglyceride glucose index), and basic characteristics (age and gender) to construct an arteriosclerosis risk prediction model for screening the risk of arteriosclerosis in middle-aged and elderly people, including the following steps:

[0037] The target population of the present invention was determined, and the population source was participants with complete arteriosclerosis indicator brachial-ankle pulse wave velocity in the 2018 survey of the Dongfeng Tongji cohort. 11,438 participants with genotyping data but no kinship were determined as the target population of the present invention.

[0038] In the above target population, the brachial-ankle pulse wave velocity data was obtained by using an arteriosclerosis detector (BP-203RPE III; Omron Healthcare Co., Japan). The brachial-ankle pulse wave velocity was calculated by dividing the difference between the path length from the suprasternal notch to the ankle joint and the path length from the suprasternal notch to the humerus by the conduction time of the pulse wave between the brachial artery wavefront and the ankle artery wavefront. After the measurement, the brachial-ankle pulse wave velocity readings on both sides were obtained at the same time. In the present invention, the mean of the brachial-ankle pulse wave velocity readings on both sides was calculated as the final brachial-ankle pulse wave velocity value, and the brachial-ankle pulse wave velocity was divided into three groups, namely, a normal arteriosclerosis group (<1400cm / s, reference group), a marginal arteriosclerosis group (≥1400~<1800cm / s), and an elevated arteriosclerosis group (≥1800cm / s).

[0039] In the early stage, a genome-wide association analysis of brachial-ankle pulse wave velocity, an indicator of arteriosclerosis, was conducted in the above target population. The minor allele frequency was > 0.05 and Hardy-Weinberg equilibrium P > 1 × 10 -6 The screening criteria for selecting SNP loci significantly associated with brachial-ankle pulse wave velocity, an indicator of arteriosclerosis, were used to select the SNP loci, and the r 2 The linkage disequilibrium sites of SNPs were removed according to the standard of p<0.2. The SNP sites that met the standard were rs1475113, rs13099435, rs2317167, rs113278154, rs7121592, rs982979, rs75275903 and rs62229351. The effect alleles, reference alleles, effect allele frequencies, effect values ​​and P values ​​of the above 8 SNPs were extracted from the results of the whole genome association study. The genotyping information of the 8 target SNPs in the Dongfeng Tongji cohort population was obtained. The effect value of the effect allele of each SNP locus in the above genome-wide association study was assigned as its weight coefficient, and the genetic risk score of the arteriosclerosis indicator brachial-ankle pulse wave velocity was calculated as follows: PRS = 8 × [(21.18 × the number of effect alleles G of rs1475113) + (39.00 × the number of effect alleles C of rs13099435) + (35.82 × the number of effect alleles A of rs2317167) + (22.11 × the number of effect alleles C of rs113278154) + (22.24 × the number of effect alleles C of rs7121592)+(21.39×the number of effect alleles G of rs982979)+(26.47×the number of effect alleles G of rs75275903)+(24.16×the number of effect alleles C of rs62229351)] / (21.18+39.00+35.82+22.11+22.24+21.39+26.47+24.16), the higher the genetic risk score, the greater the individual's brachial-ankle pulse wave velocity value, and the higher the risk of arteriosclerosis.

[0040] Obtain the blood glucose indexes (fasting blood glucose (mmol / L), glycosylated hemoglobin (%), triglyceride glucose index) and basic characteristics (age and gender) of the target population. The blood glucose indexes of the target population are directly obtained from the laboratory test data of the Dongfeng Tongji cohort library or calculated, where triglyceride glucose index = ln [fasting triglycerides (mg / dL) × fasting blood glucose (mg / dL) / 2]. The basic characteristics of the target population are collected through a semi-structured questionnaire in the field survey. Among them, the average age (standard deviation) of the target population is 68.7 (7.7) years old, with males accounting for 43.4% and females accounting for 56.6%.

[0041] For continuous variables, mean ± standard deviation was used for description, and variance analysis was used to compare the distribution differences among groups; for discrete variables, the number of people (percentage) was used for description, and χ 2 The distribution differences between the groups were compared by test.Multivariate linear regression models were fitted to estimate the relationship between genetic risk scores and blood glucose indicators and participants' brachial-ankle pulse wave velocity values.Multivariate logistic regression models were fitted to estimate the hazard ratio between genetic risk scores and blood glucose indicators and arteriosclerosis risk, and further predict the probability of arteriosclerosis.All statistical analyses were performed using R software (version 3.6.1, RDevelopment Core Team), with a two-sided test level of α = 0.05.

[0042] The target population's genetic risk score is combined with blood sugar index and basic characteristics, and a multivariate logistic regression model is used to construct an arteriosclerosis risk prediction model, and the ROC curve is drawn and the area under the ROC curve AUC is calculated to evaluate the discrimination of the model. The risk of arteriosclerosis in middle-aged and elderly people is screened according to the prediction model. In practical applications, the brachial-ankle pulse wave velocity value and the risk of arteriosclerosis in middle-aged and elderly people can be calculated by the model formula.

[0043] Among the 11,438 target participants included in this study, the mean age (standard deviation) was 68.7 (7.7) years, 43.4% were male and 56.6% were female.

[0044] Table 1 Characteristics of normal, borderline, and elevated arteriosclerosis

[0045]

[0046] The present invention fits a linear regression model to predict the brachial-ankle pulse wave velocity of the participants based on the genetic risk score, fasting blood glucose (or glycosylated hemoglobin or triglyceride glucose index), age (continuous variable, unit increment) and gender (with male as the reference, the model coefficient is 0). The specific model coefficients are shown in Table 2.

[0047] Table 2 Brachial-ankle pulse wave velocity model coefficients

[0048]

[0049] Brachial-ankle pulse wave velocity model formula:

[0050] (1-1) Fasting blood glucose: Brachial-ankle pulse wave velocity = 6.0 + 24.8 × genetic risk score + 32.0 × fasting blood glucose + 20.8 × age + 0 × male + 14.4 × female

[0051] (1-2) Glycated hemoglobin: brachial-ankle pulse wave velocity = -54.7 + 24.6 × genetic risk score + 43.9 × Glycated hemoglobin + 20.7 × age + 0 × male + 11.7 × female

[0052] (1-3) Triglyceride-glucose index: Brachial-ankle pulse wave velocity = -679.4 + 24.7 × genetic risk score + 99.2 × triglyceride-glucose index + 21.1 × age + 0 × male + 1.3 × female

[0053] In formulas (1-1), (1-2) and (1-3) and the subsequent arteriosclerosis risk model formula: when the individual is male, the model coefficient is 0, and when the individual is female, the model coefficient takes the corresponding value; fasting blood glucose, glycosylated hemoglobin and triglyceride glucose index are the individual measured values, and the genetic risk score is the PRS calculated previously.

[0054] The present invention fits a logistic regression model to predict the risk of arteriosclerosis in participants based on the genetic risk score, fasting blood glucose (or glycosylated hemoglobin or triglyceride glucose index), age (continuous variable, unit increment) and gender (with male as the reference, the model coefficient is 0). The specific model coefficients are shown in Tables 3, 4 and 5.

[0055] Table 3 Coefficient table of arteriosclerosis risk model (fasting blood sugar)

[0056]

[0057] Atherosclerosis risk model formula (fasting blood sugar):

[0058] (2-1) Marginal arteriosclerosis: Total score = 0.09 × genetic risk score + 0.26 × fasting blood glucose + 0.10 × age + 0 × male + (-0.04) × female

[0059] Prediction probability = exp(-7.56+total score) / (1+exp(-7.56+total score))

[0060] (2-2) Increased arteriosclerosis: Total score = 0.21 × genetic risk score + 0.40 × fasting blood glucose + 0.19 × age + 0 × male + 0.07 × female

[0061] Prediction probability = exp(-15.47+total score) / (1+exp(-15.47+total score))

[0062] In formulas (2-1) and (2-2), fasting blood glucose is used as the blood glucose prediction variable, and the total scores of arteriosclerosis in the marginal arteriosclerosis group and the increased arteriosclerosis group can be calculated respectively compared with the normal arteriosclerosis group as the fasting blood glucose increases. Combined with the intercept in the arteriosclerosis risk model, the formula for comparison prediction probability: predicted probability = exp(intercept + total score) / (1+exp(intercept + total score)), the corresponding predicted probability of arteriosclerosis risk can be calculated.

[0063] Table 4 Coefficient table of risk model for arteriosclerosis (glycosylated hemoglobin)

[0064]

[0065] Atherosclerosis risk model formula (glycosylated hemoglobin):

[0066] (3-1) Marginal arteriosclerosis: Total score = 0.09 × genetic risk score + 0.34 × glycated hemoglobin + 0.10 × age + 0 × male + (-0.08) × female

[0067] Prediction probability = exp(-7.94+total score) / (1+exp(-7.94+total score))

[0068] (3-2) Increased arteriosclerosis: Total score = 0.20 × genetic risk score + 0.54 × glycated hemoglobin + 0.19 × age + 0 × male + 0.02 × female

[0069] Prediction probability = exp(-16.08+total score) / (1+exp(-16.08+total score))

[0070] In formulas (3-1) and (3-2), glycated hemoglobin is used as the blood sugar prediction variable, and the total scores of arteriosclerosis in the marginal arteriosclerosis group and the increased arteriosclerosis group can be calculated respectively as compared with the normal arteriosclerosis group. Combined with the intercept in the arteriosclerosis risk model, the formula for predicted probability is used: predicted probability = exp(intercept + total score) / (1+exp(intercept + total score)), and the corresponding predicted probability of arteriosclerosis risk can be calculated.

[0071] Table 5 Coefficient table of arteriosclerosis risk model (triglyceride glucose index)

[0072]

[0073] Atherosclerosis risk model formula (triglyceride glucose index):

[0074] (4-1) Marginal arteriosclerosis: Total score = 0.10 × genetic risk score + 0.54 × triglyceride glucose index + 0.11 × age + 0 × male + (-0.12) × female

[0075] Prediction probability = exp(-10.91+total score) / (1+exp(-10.91+total score))

[0076] (4-2) Increased arteriosclerosis: Total score = 0.21 × genetic risk score + 0.99 × triglyceride glucose index + 0.20 × age + 0 × male + (-0.07) × female

[0077] Prediction probability = exp(-21.90+total score) / (1+exp(-21.90+total score))

[0078] In formulas (3-1) and (3-2), the triglyceride glucose index is used as the blood glucose prediction variable, and the total score of arteriosclerosis in the marginal arteriosclerosis group and the increased arteriosclerosis group can be calculated respectively compared with the normal arteriosclerosis group as the triglyceride glucose index increases. Combined with the intercept in the arteriosclerosis risk model, the formula for predicted probability is used: predicted probability = exp(intercept + total score) / (1+exp(intercept + total score)), and the corresponding predicted probability of arteriosclerosis risk can be calculated.

[0079] The discrimination of a prediction model refers to its ability to correctly distinguish between people with high and low risk of the disease, and the area under the ROC curve (AUC) is the most commonly used indicator for evaluating the discrimination of a prediction model. The ROC curve is a curve with 1-specificity on the horizontal axis and sensitivity on the vertical axis. The area enclosed by the ROC curve and the coordinate axis is the AUC. The larger the AUC value, the better the ability to distinguish between people with high and low risk, and the better the discrimination. The present invention uses a multivariate logistic regression model to construct an arteriosclerosis risk prediction model, draws the ROC curve, and calculates the area under the ROC curve (AUC) to evaluate the discrimination of the model. Figure 5 As shown in the figure, the AUC of the prediction model for marginal arteriosclerosis risk based on genetic risk score, fasting blood glucose or glycosylated hemoglobin or triglyceride glucose index and basic characteristics is above 0.7, indicating that this prediction model has a strong ability to distinguish marginal arteriosclerosis from normal arterial stiffness. Figure 6 As shown, the AUCs of the elevated arteriosclerosis risk prediction models constructed based on the genetic risk score, fasting blood glucose or glycosylated hemoglobin or triglyceride glucose index, and basic characteristics were all above 0.85, indicating that this prediction model has a strong ability to distinguish between people with arteriosclerosis and those with normal arterial stiffness.

[0080] Embodiment 2:

[0081] The embodiment of the present invention further provides a device for predicting the risk of arteriosclerosis, comprising:

[0082] An acquisition module is used to obtain arteriosclerosis indicators of the target population;

[0083] An extraction module is used to extract genome-wide association single nucleotide polymorphism (SNP) sites of brachial-ankle pulse wave velocity according to the arteriosclerosis index of the target population;

[0084] A processing module is used to obtain a genetic risk score of arteriosclerosis indicators of a target population based on the SNP sites;

[0085] A construction module for constructing an arteriosclerosis risk prediction model based on the genetic risk score of arteriosclerosis indicators;

[0086] The prediction module is used to confirm the risk of arteriosclerosis in the middle-aged and elderly population based on the prediction model.

[0087] As an implementation mode of an embodiment of the present invention, a construction module constructs a prediction model for the risk of arteriosclerosis based on the genetic risk score of arteriosclerosis indicators, combined with the levels of fasting blood glucose, glycosylated hemoglobin or triglyceride glucose index and the age and gender of the individual, using statistical methods, and draws an ROC curve and calculates the area under the ROC curve AUC to evaluate the discrimination of the prediction model; wherein, the risk of arteriosclerosis is a brachial-ankle pulse wave velocity ≥1400cm / s and <1800cm / s or a brachial-ankle pulse wave velocity ≥1800cm / s.

[0088] Embodiment 3:

[0089] An embodiment of the present invention further provides an arteriosclerosis risk prediction system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes an arteriosclerosis risk prediction method when executed by the processor.

[0090] Embodiment 4:

[0091] An embodiment of the present invention further provides a storage medium, on which a computer program is stored, and when the computer program is run, a method for predicting the risk of arteriosclerosis is executed.

[0092] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for predicting the risk of arteriosclerosis, characterized in that: The steps include: Step S1, obtaining arteriosclerosis index of target population; Step S2, extracting genome-wide association single nucleotide polymorphism (SNP) sites of brachial-ankle pulse wave velocity according to the arteriosclerosis index of the target population; Step S3, obtaining the genetic risk score of the target population's arteriosclerosis index according to the SNP site; Step S4, constructing an arteriosclerosis risk prediction model based on the genetic risk score of the arteriosclerosis index, the blood sugar index of the target population and the basic characteristics of the individuals; Step S5: confirming the risk of arteriosclerosis in the middle-aged and elderly population according to the prediction model.

2. The method for predicting the risk of arteriosclerosis according to claim 1, characterized in that: In step S4, according to the genetic risk score of arteriosclerosis indicators, combined with the levels of fasting blood glucose, glycosylated hemoglobin or triglyceride glucose index and the age and gender of the individual, a statistical method is used to construct a prediction model for the risk of arteriosclerosis, and the ROC curve is drawn and the area under the ROC curve AUC is calculated to evaluate the discrimination of the prediction model; wherein, the risk of arteriosclerosis is a brachial-ankle pulse wave velocity ≥1400cm / s and <1800cm / s or a brachial-ankle pulse wave velocity ≥1800cm / s.

3. The method for predicting the risk of arteriosclerosis according to claim 2, characterized in that: In step S1, participants with kinship in the target population were removed, and a genome-wide association analysis of the arteriosclerosis index, i.e., brachial-ankle pulse wave velocity, was performed. The minor allele frequency was > 0.05, Hardy-Weinberg equilibrium P > 1 × 10 -6 The screening criteria for selecting SNP loci significantly associated with brachial-ankle pulse wave velocity, an indicator of arteriosclerosis, were used to select the SNP loci, and the r 2 The linkage disequilibrium sites of SNPs were removed based on the standard of <0.2, and the SNP sites that met the standard were rs1475113, rs13099435, rs2317167, rs113278154, rs7121592, rs982979, rs75275903 and rs62229351.

4. The method for predicting the risk of arteriosclerosis according to claim 3, characterized in that: In step S3, PRS = 8 × [(21.18 × rs1475113 effect allele G number) + (39.00 × rs13099435 effect allele C number) + (35.82 × rs2317167 effect allele A number) + (22.11 × rs113278154 effect allele C number) + (22.24 × rs7121592 effect allele C number) =[(21.18+39.00+35.82+22.11+22.24+21.39+26.47+24.16]] / (21.18+39.00+35.82+22.11+22.24+21.39+26.47+24.16); where PRS is the genetic risk score.

5. A device for predicting the risk of arteriosclerosis, characterized in that: include: An acquisition module is used to obtain arteriosclerosis indicators of the target population; An extraction module is used to extract genome-wide association single nucleotide polymorphism (SNP) sites of brachial-ankle pulse wave velocity according to the arteriosclerosis index of the target population; A processing module is used to obtain a genetic risk score of arteriosclerosis indicators of a target population based on the SNP sites; A construction module is used to construct an arteriosclerosis risk prediction model based on the genetic risk score of arteriosclerosis indicators, blood sugar indicators of the target population and basic individual characteristics; The prediction module is used to confirm the risk of arteriosclerosis in the middle-aged and elderly population based on the prediction model.

6. The device for predicting the risk of arteriosclerosis according to claim 5, characterized in that: The construction module uses statistical methods to construct a prediction model for the risk of arteriosclerosis based on the genetic risk score of arteriosclerosis indicators, combined with fasting blood glucose, glycosylated hemoglobin or triglyceride glucose index levels and the individual's age and gender, and draws the ROC curve and calculates the area under the ROC curve AUC to evaluate the discrimination of the prediction model; among them, the risk of arteriosclerosis is a brachial-ankle pulse wave velocity ≥1400cm / s and <1800cm / s or a brachial-ankle pulse wave velocity ≥1800cm / s.

7. A system for predicting the risk of arteriosclerosis, characterized in that: include: A memory and a processor, wherein the memory stores a computer program executed by the processor, and when the computer program is executed by the processor, the method for predicting the risk of arteriosclerosis according to any one of claims 1 to 4 is executed.

8. A storage medium, characterized in that: The storage medium stores a computer program, which, when running, executes the method for predicting the risk of arteriosclerosis according to any one of claims 1 to 4.

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