Cardiovascular and cerebrovascular disease information management method and system

Abstract

The invention discloses a cardiovascular and cerebrovascular disease information management method and system, and relates to the technical field of genomics, and the method comprises the steps: collecting health information and a biological sample of a patient, and generating a health file; performing genomics analysis by using the generated health archive, and calculating a genetic risk score of the patient; according to the genetic risk score of the patient, microvascular lesion detection is carried out on the high-risk patient, microvascular anomaly indexes are quantified, and an individualized health risk score is generated; metabonomics analysis is carried out based on the health risk score, and the metabolic state and metabolic capability of the patient are evaluated; combining the metabolism assessment result, the genetic risk score and the health risk score to formulate an individualized intervention scheme; the problems of data isolation and intervention generalization in the prior art are solved, and powerful technical support is provided for early prevention and accurate management of cardiovascular and cerebrovascular diseases.

Description

Technical Field

[0001] The present invention relates to the technical field of genomics, and particularly to a method and system for managing cardiovascular and cerebrovascular disease information. Background Art

[0002] With the annual increase in the incidence of cardiovascular and cerebrovascular diseases, the rapid development of precision medicine and personalized health management technologies has provided new directions for early intervention and risk prediction of diseases. Existing technologies have made significant progress in the fields of genomics, microvascular imaging analysis, and metabolomics. For example, genetic risks are analyzed through targeted region capture sequencing and genome-wide association studies (GWAS), the vascular health status is evaluated through non-invasive microcirculation imaging technology, and metabolic balance is quantified through metabolite detection.

[0003] The existing technologies mainly have the following deficiencies: First, although genomics analysis can screen high-risk genes, it fails to combine microvascular lesions and metabolomics characteristics for comprehensive health risk scoring, resulting in the difficulty of quantifying and verifying the actual impact of genetic risks. Second, most microvascular imaging technologies are limited to lesion identification and are not combined with metabolomics assessment, making it impossible to develop personalized intervention plans for the dynamic changes of microvascular lesions. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method for managing cardiovascular and cerebrovascular disease information to solve the problem that genetic risk assessment, microvascular lesion detection, and metabolomics assessment in the prior art are independent of each other and insufficiently integrated.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: In the first aspect, the present invention provides a method for managing cardiovascular and cerebrovascular disease information, which includes collecting the health information and biological samples of patients to generate a health record; performing genomics analysis using the generated health record to calculate the genetic risk score of the patient; according to the genetic risk score of the patient, detecting microvascular lesions in high-risk patients, quantifying microvascular abnormality indicators, and generating an individualized health risk score; performing metabolomics analysis based on the health risk score to evaluate the metabolic status and metabolic capacity of the patient; formulating an individualized intervention plan by combining the metabolic assessment results, genetic risk score, and health risk score.

[0007] As a preferred embodiment of the cardiovascular and cerebrovascular disease information management method of the present invention, wherein: the health information of the patient includes basic information, medical history, family history, lifestyle, and physical examination data; the biological samples include blood and urine samples.

[0008] As a preferred embodiment of the cardiovascular and cerebrovascular disease information management method of the present invention, wherein: performing genomics analysis using the generated health record, and calculating the genetic risk score of the patient includes the following steps. Extract DNA from the biological sample using the magnetic bead method, and measure the concentration and purity of the DNA by a spectrophotometer. Adopt the target region capture technology to capture the core genes related to cardiovascular and cerebrovascular diseases in the DNA, and obtain the sequencing data. Align the sequencing data with the human reference genome, and locate the mutation sites for gene variant detection. Perform functional annotation on the detected gene variants, evaluate the pathogenicity and the association with cardiovascular and cerebrovascular diseases, and screen out the high-risk genes. Based on the screening results, combined with the effect values of the genome-wide association study, calculate the genetic risk score of the patient, and the expression is: ; Wherein, represents the genetic risk score of the patient, represents the index of the gene locus, represents the number of gene loci, represents the th effect value of the gene locus, represents the genotype coding of the patient at the th gene locus, represents the th correction factor of the gene locus.

[0009] As a preferred embodiment of the cardiovascular and cerebrovascular disease information management method of the present invention, wherein: according to the genetic risk score of the patient, perform microvascular lesion detection on high-risk patients, and obtaining the comprehensive score of microvascular lesions includes the following steps. Collect the microvascular image of the patient through a non-invasive microcirculation imaging device. Perform denoising and image enhancement processing on the collected microvascular image. Segment the processed microvascular image to obtain a binary mask image of the blood vessels. Based on the binary mask image of the blood vessels, extract the microvascular characteristics of blood vessel density, abnormal capillary ratio, blood vessel sparsity index, and blood flow rate. Based on the extracted microvascular characteristics, calculate the comprehensive score of microvascular lesions, and the expression is: ; Among them, represents the microvascular complication score, represents the weight coefficient of vascular density, represents the vascular density, represents the weight coefficient of the abnormal capillary proportion, represents the abnormal capillary proportion, represents the weight coefficient of the vascular rarefaction index, represents the vascular rarefaction index, represents the weight coefficient of the blood flow rate, represents the blood flow rate, represents the smoothing parameter.

[0010] As a preferred embodiment of the method for managing cardiovascular and cerebrovascular disease information according to the present invention, among them: coupling the genetic risk score and the microvascular complication score to obtain an individualized health risk score, and the expression is: ; Among them, represents the individualized health risk score, represents the non-linear exponential weight of the genetic risk score, represents the non-linear exponential weight of the microvascular complication score, represents the coupling strength parameter, represents the microvascular complication score offset value.

[0011] As a preferred embodiment of the method for managing cardiovascular and cerebrovascular disease information according to the present invention, among them: based on the health risk score, performing metabolomics analysis to evaluate the metabolic status and metabolic capacity of the patient, including the following steps, Pre-treat the blood and urine samples in the biological sample, and perform metabolite separation and detection; Extract the metabolite concentrations related to cardiovascular and cerebrovascular diseases from the detected metabolites, and calculate the metabolic balance index and the metabolic imbalance index, and the expression is: ; Among them, represents the metabolic balance index, represents the metabolite index, represents the total number of metabolites, represents the metabolite relative concentration, represents the metabolite weight; ; Among them, represents the metabolic imbalance index, Represents the average concentration of metabolites; Combined with the microvascular lesion score, metabolic balance index, and metabolic imbalance index, calculate the comprehensive metabolic capacity score of the patient, and the expression is: ; Wherein, Represents the comprehensive score of metabolic capacity.

[0012] As a preferred embodiment of the cardiovascular and cerebrovascular disease information management method of the present invention, wherein: combining the metabolic assessment results, genetic risk score, and health risk score, formulating an individualized intervention plan includes the following steps, Based on the patient's metabolic capacity score and genetic risk score, formulate a personalized nutrition intervention plan; According to the comprehensive score of microvascular lesions and the individualized health score, formulate an exercise intervention plan to improve vascular function and metabolic capacity; Combining the personalized nutrition intervention plan and the exercise intervention plan, generate an individualized intervention plan.

[0013] In a second aspect, the present invention provides a cardiovascular and cerebrovascular disease information management system, including, An information collection module that collects the patient's health information and biological samples to generate a health record; A genetic evaluation module that uses the generated health record for genomics analysis to calculate the patient's genetic risk score; A microvascular detection module that, according to the patient's genetic risk score, detects microvascular lesions in high-risk patients, quantifies microvascular abnormality indicators, and generates an individualized health risk score; A metabolic assessment module that performs metabolomics analysis based on the health risk score to evaluate the patient's metabolic status and metabolic capacity; An intervention plan module that combines the metabolic assessment results, genetic risk score, and health risk score to formulate an individualized intervention plan.

[0014] In a third aspect, the present invention provides a computer device, including a memory and a processor, wherein: the memory stores a computer program, and wherein: when the computer program is executed by the processor, it implements any step of the cardiovascular and cerebrovascular disease information management method described in the first aspect of the present invention.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and wherein: when the computer program is executed by the processor, it implements any step of the cardiovascular and cerebrovascular disease information management method described in the first aspect of the present invention.

[0016] The beneficial effects of the present invention are as follows: By calculating the genetic risk score and the microvascular lesion score, and using a non-linear coupling formula to generate an individualized health risk score, a more accurate risk quantification model is provided. Compared with single-dimensional gene or imaging analysis, the accuracy and reliability of risk assessment are significantly improved; the metabolomics analysis method based on the health risk score, combined with the metabolic balance index and the metabolic imbalance index, comprehensively evaluates the patient's metabolic status and metabolic capacity, and the correlation analysis with microvascular lesion characteristics reveals the impact of metabolic abnormalities on vascular function; by constructing a multi-dimensional data-driven personalized intervention plan, a closed-loop management from risk assessment to intervention is achieved, which can dynamically respond to changes in the patient's health status, thereby optimizing the intervention effect; the problems of data isolation and intervention generalization in the prior art are overcome, providing strong technical support for the early prevention and precise management of cardiovascular and cerebrovascular diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0018] Figure 1 It is a flowchart of the cardiovascular and cerebrovascular disease information management method in Embodiment 1.

[0019] Figure 2 It is a schematic diagram of generating a personalized intervention plan in Embodiment 1. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will provide a detailed description of the specific embodiments of the present invention with reference to the accompanying drawings of the specification.

[0021] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0022] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selectively exclusive embodiment from other embodiments.

[0023] Embodiment 1, refer to Figure 1 and Figure 2, which is the first embodiment of the present invention. This embodiment provides a method for managing cardiovascular and cerebrovascular disease information, including the following steps: S1. Collect the health information and biological samples of the patient to generate a health record.

[0024] S1.1 The health information of the patient includes basic information, medical history, family history, lifestyle, and physical examination data; the biological samples include blood and urine samples.

[0025] Specifically, the basic information refers to basic information such as name, gender, age, contact information, etc. (all of the above information has obtained the consent of the user and is used for legal purposes); the medical history refers to the past medical history related to cardiovascular and cerebrovascular diseases (such as hypertension, diabetes, angina pectoris), family history (such as relatives suffering from coronary heart disease, stroke, etc.), and medication history answered through a standardized questionnaire; the lifestyle refers to eating habits (such as daily salt intake, frequency of fruit and vegetable intake), exercise situation (such as exercise frequency, duration of each exercise), smoking and drinking situation (such as daily smoking amount, types and frequencies of drinking); the physical examination data refers to blood pressure measurement, weight and height measurement, and heart rate measurement.

[0026] For blood pressure measurement, after the patient sits still for 5 minutes, the blood pressure of the left upper arm is measured using an automatic electronic sphygmomanometer, and the systolic blood pressure (SBP) and diastolic blood pressure (DBP) are recorded. Measure 3 times repeatedly and take the average value. Determine whether the blood pressure is abnormal (for example, SBP>140 mmHg or DBP>90 mmHg indicates a risk of hypertension).

[0027] Classify the weight status of the patient according to BMI: BMI<18.5: Underweight; 18.5≤BMI<24.9: Normal; 25≤BMI<29.9: Overweight; BMI≥30: Obese.

[0028] S2. Use the generated health record for genomics analysis to calculate the genetic risk score of the patient.

[0029] S2.1. Extract DNA from the biological sample using the magnetic bead method and measure the concentration and purity of DNA by a spectrophotometer.

[0030] Furthermore, the specific operation of extracting DNA using the magnetic bead method is as follows: Mix the sample with the lysis solution to break the cell membrane and nuclear membrane in the sample and release DNA; mix the magnetic beads with the lysed sample, and the surface of the magnetic beads binds to DNA molecules through a chemical coating; use a magnetic field to separate the magnetic beads from the sample impurities, and then wash the magnetic beads with a washing solution to remove excess impurities; finally, use an elution solution to release the DNA from the surface of the magnetic beads to obtain pure DNA.

[0031] The concentration of DNA is measured using the ultraviolet absorption value (OD260) to determine the concentration of DNA. The purity is measured by evaluating the OD260 / OD280 ratio. An ideal ratio is between 1.8 - 2.0, indicating less impurities such as proteins.

[0032] S2.2. Adopt the target region capture technology to capture the core genes related to cardiovascular and cerebrovascular diseases in DNA, obtaining sequencing data.

[0033] Furthermore, the target region capture technology is used to capture the core gene regions related to specific diseases (such as cardiovascular and cerebrovascular diseases) in the whole genome, avoiding sequencing the entire genome, thereby reducing costs and improving efficiency. The specific operations are as follows: Design specific probes: Based on the known genes related to cardiovascular and cerebrovascular diseases, design probes that can be complementary to these gene regions.

[0034] Capture the target region: Mix the probes with the patient's DNA and capture the target DNA through the principle of complementary hybridization.

[0035] Enrich the target DNA: Use the magnetic bead method or other methods to separate the captured target DNA and remove the DNA in non-target regions.

[0036] S2.3. Align the sequencing data with the human reference genome, locate the mutation sites for gene variant detection.

[0037] Furthermore, the reference genome is the known standard genome sequence, used to analyze the differences between the patient's genes and the standard sequence. The specific alignment steps are to perform quality control on the sequencing data using bioinformatics tools (such as FastQC), remove low-quality sequences and adapters, align the patient's sequencing data with the reference genome, find the mutation sites and sequence differences in the patient's genome, and generate an alignment file (such as a BAM file) after alignment, which contains the position and alignment quality of each sequencing fragment in the reference genome.

[0038] S2.4. Perform functional annotation on the detected gene variants, evaluate the pathogenicity and the association with cardiovascular and cerebrovascular diseases, and screen out high-risk genes; based on the screening results, combined with the effect values of the genome-wide association study, calculate the individual's genetic risk score, with the expression: ; where, represents the patient's genetic risk score, represents the index of the gene locus, represents the number of gene loci, represents the th effect value of the gene locus, represents the patient at the Genotype encoding of a gene locus indicating the correction factor of the th gene locus

[0039] S3. Detect microvascular lesions in high-risk patients according to the genetic risk score of the patient, quantify the microvascular abnormality index and generate an individualized health risk score

[0040] S3.1. Collect microvascular images of the patient through a non-invasive microcirculation imaging device Denoise and image enhancement processing are performed on the collected microvascular images; the processed microvascular images are segmented to obtain a binary mask image of the blood vessels; based on the binary mask image of the blood vessels, microvascular features such as blood vessel density, abnormal capillary ratio, blood vessel sparsity index, and blood flow rate are extracted

[0041] Specifically, the expression formula of blood vessel density is ; wherein represents the binary mask image of blood vessels of the pixel point in the image, represents the total area (total number of pixels) of the image, represents the pixel point

[0042] The expression formula of the abnormal capillary ratio is ; wherein represents the number of abnormal capillaries (interrupted or isolated parts of blood vessels), represents the total number of capillaries (complexity of the blood vessel network)

[0043] The expression formula of the blood vessel sparsity index is ; wherein represents the number of blood vessel endpoints, represents the number of blood vessel branch points

[0044] The expression formula of the blood flow rate is ; wherein represents the pixel displacement in the horizontal direction, represents the pixel displacement in the vertical direction, represents the frame rate of the video

[0045] S3.2. Based on the extracted microvascular features, calculate the comprehensive score of microvascular lesions, and the expression formula is ; Among them, represents the microvascular complication score, represents the weight coefficient of vascular density, represents the vascular density, represents the weight coefficient of the abnormal capillary proportion, represents the abnormal capillary proportion, represents the weight coefficient of the vascular rarefaction index, represents the vascular rarefaction index, represents the weight coefficient of the blood flow rate, represents the blood flow rate, represents the smoothing parameter.

[0046] S3.3. Couple the genetic risk score and the microvascular complication score to obtain an individualized health risk score, and the expression is: ; Among them, represents the individualized health risk score, represents the non-linear exponential weight of the genetic risk score, represents the non-linear exponential weight of the microvascular complication score, represents the coupling strength parameter, represents the microvascular complication score offset value.

[0047] S4. Perform metabolomics analysis based on the health risk score to evaluate the patient's metabolic status and metabolic capacity.

[0048] S4.1. Pretreat the blood and urine samples in the biological samples, and perform metabolite separation and detection.

[0049] Specifically, the sample pretreatment includes removing proteins, concentrating the samples, and removing impurities.

[0050] Removing proteins: Blood and urine contain a large amount of proteins, and these proteins will interfere with the detection of metabolites. Common methods include organic solvent precipitation (such as methanol or acetonitrile) or ultrafiltration.

[0051] Concentrating the samples: Concentrate the metabolites by freeze-drying or rotary evaporation to increase the detection sensitivity.

[0052] Removing impurities: Use centrifugation and filtration methods to remove particulate matter and other impurities to provide a pure sample for subsequent metabolite separation and detection.

[0053] For metabolite separation, liquid chromatography (LC) or gas chromatography (GC) is used to separate the metabolites.

[0054] Liquid chromatography is suitable for detecting water-soluble metabolites with strong polarity (such as amino acids and sugars); gas chromatography is suitable for detecting volatile or non-polar metabolites (such as fatty acids and cholesterol). The principle is that metabolites are separated in the chromatographic column according to their chemical properties (such as polarity and molecular weight) and their interaction with the stationary phase.

[0055] Metabolite detection uses a mass spectrometer (MS) to detect the separated metabolites, obtain the molecular weight and structural information of the metabolites, and uses nuclear magnetic resonance technology to detect the metabolites and extract the chemical structure.

[0056] S4.2. Extract the concentrations of metabolites related to cardiovascular and cerebrovascular diseases from the detected metabolites, and calculate the metabolic balance index and the metabolic imbalance index. The expressions are: ; Among them, represents the metabolic balance index, represents the metabolite index, represents the total number of metabolites, represents metabolite 's relative concentration, represents metabolite 's weight; ; Among them, represents the metabolic imbalance index, represents the average concentration of metabolites.

[0057] Extract metabolites related to cardiovascular and cerebrovascular diseases, such as: Metabolites related to lipid metabolism: cholesterol, triglyceride, free fatty acid.

[0058] Metabolites related to carbohydrate metabolism: glucose, lactate, glyceraldehyde.

[0059] Metabolites related to amino acid metabolism: valine, leucine.

[0060] Metabolites related to inflammation: lipoxin, prostaglandin.

[0061] S4.3. Combine the microvascular lesion score, the metabolic balance index and the metabolic imbalance index, and calculate the comprehensive metabolic ability score of the patient. The expression is: ; Among them, represents the comprehensive metabolic ability score.

[0062] S5. Combine the metabolic assessment results, the genetic risk score and the health risk score to develop an individualized intervention plan.

[0063] Based on the patient's metabolic capacity score and genetic risk score, develop a personalized nutrition intervention plan; according to the comprehensive score of microvascular lesions and the individualized health score, develop an exercise intervention plan to improve vascular function and metabolic capacity; combine the personalized nutrition intervention plan and exercise intervention plan to generate an individualized intervention plan.

[0064] Specifically, based on the comprehensive metabolic capacity score, genetic risk score, and microvascular lesion score, establish a hierarchical system for the patient's health risks: Metabolic capacity score D: D > 0.8: Metabolically healthy; 0.5 ≤ D ≤ 0.8: Mild metabolic abnormality; D < 0.5: Severe metabolic abnormality.

[0065] Genetic risk score R: R > 2.0: High genetic risk; 1.0 ≤ R ≤ 2.0: Moderate genetic risk; R < 1.0: Low genetic risk.

[0066] Microvascular lesion score M: M > 0.7: Microvascular healthy; 0.4 ≤ M ≤ 0.7: Mild microvascular abnormality; M < 0.4: Severe microvascular abnormality.

[0067] The individualized intervention plan includes: Weekly diet plan: Specify the specific ingredients and proportions for each of the three daily meals (e.g., breakfast mainly consists of coarse grain porridge and low-fat milk, lunch mainly consists of deep-sea fish and green vegetables, and dinner controls carbohydrate intake).

[0068] Exercise plan: Formulate the daily exercise time, type, and intensity, such as brisk walking 5 times a week (30 minutes each day), and resistance training 2 times a week (30 minutes each time).

[0069] Nutritional supplement recommendations: Specify the types, dosages, and intake times of the required supplements (e.g., take Omega-3 and coenzyme Q10 after breakfast, and take vitamin C and E after dinner).

[0070] Regular follow-up: Re-evaluate the metabolic capacity score, genetic risk score, and microvascular health score every 3 months, and dynamically adjust the intervention plan.

[0071] This embodiment also provides a cardiovascular and cerebrovascular disease information management system, including: an information collection module that collects the health information and biological samples of patients and generates a health record; a genetic evaluation module that uses the generated health record for genomics analysis and calculates the genetic risk score of patients; a microvascular detection module that, according to the genetic risk score of patients, detects microvascular lesions in high-risk patients, quantifies microvascular abnormality indicators and generates an individualized health risk score; a metabolic evaluation module that performs metabolomics analysis based on the health risk score to evaluate the metabolic status and metabolic capacity of patients; and an intervention plan module that formulates an individualized intervention plan by combining the metabolic evaluation results, genetic risk score, and health risk score.

[0072] This embodiment also provides a computer device applicable to the case of the cardiovascular and cerebrovascular disease information management method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the cardiovascular and cerebrovascular disease information management method as proposed in the above embodiment.

[0073] This computer device can be a terminal, and this computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of this computer device is used to provide computing and control capabilities. The memory of this computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The communication interface of this computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of this computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of this computer device can be a touch layer covering the display screen, or a button, trackball, or touchpad provided on the outer shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0074] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method for managing cardiovascular and cerebrovascular disease information proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, abbreviated as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), read-only memory (Read-Only Memory, abbreviated as ROM), magnetic memory, flash memory, magnetic disk or optical disc.

[0075] In summary, the present invention calculates the genetic risk score and the microvascular lesion score, and uses a non-linear coupling formula to generate an individualized health risk score, providing a more accurate risk quantification model. Compared with single-dimensional gene or imaging analysis, it significantly improves the accuracy and reliability of risk assessment; the metabolomics analysis method based on the health risk score, combined with the metabolic balance index and the metabolic imbalance index, comprehensively evaluates the patient's metabolic status and metabolic capacity, and is associated with the microvascular lesion characteristics analysis to reveal the impact of metabolic abnormalities on vascular function; by constructing a multi-dimensional data-driven personalized intervention plan, it realizes the closed-loop management from risk assessment to intervention, can dynamically respond to the changes in the patient's health status, thereby optimizing the intervention effect; overcomes the problems of data isolation and intervention generalization in the prior art, and provides strong technical support for the early prevention and precise management of cardiovascular and cerebrovascular diseases.

[0076] Example 2, referring to Table 1, is the second embodiment of the present invention. To further verify the technical solution of the present invention, experimental simulation data of the method for managing cardiovascular and cerebrovascular disease information are given.

[0077] Collect the health information of the subjects and generate a health record, including the following data: Medical history: Whether there is hypertension (0 - no, 1 - yes); whether there is diabetes (0 - no, 1 - yes); whether there is hyperlipidemia (0 - no, 1 - yes); whether there is a history of cardiovascular and cerebrovascular diseases (0 - no, 1 - yes).

[0078] Family history: Whether there is a family history of cardiovascular and cerebrovascular diseases (0 - no, 1 - yes).

[0079] Lifestyle: Smoking (0 - non - smoker, 1 - smoker); Alcohol consumption (0 - non - drinker, 1 - drinker); Exercise frequency (0 - rarely exercise, 1 - 1 - 2 times a week, 2 - ≥3 times a week).

[0080] Physical examination data: Systolic blood pressure (SBP, normal range 90 - 140 mmHg, hypertension ≥140 mmHg); Diastolic blood pressure (DBP, normal range 60 - 90 mmHg, hypertension ≥90 mmHg); Blood lipid (LDL - C, low - density lipoprotein cholesterol, normal range <3.4 mmol / L); Blood lipid (HDL - C, high - density lipoprotein cholesterol, normal range >1.0 mmol / L); BMI (body mass index, normal range 18.5 - 24.9; overweight ≥25; obesity ≥30).

[0081] Biological samples: Blood samples (blood volume, unit: mL); Urine samples (urine volume, unit: mL), as shown in Table 1 below: Table 1 Data collection form Subject ID Gender Age Hypertension Diabetes Hyperlipidemia History of cardio-cerebrovascular diseases Family history Smoking Alcohol consumption Exercise frequency SBP DBP LDL-C HDL-C BMI Blood volume (mL) Urine volume (mL) 001 Male 55 1 0 1 0 1 1 1 1 145 95 4.2 0.9 27.3 8.5 50 002 Female 62 1 1 1 1 1 0 0 0 160 100 4.8 0.8 31.5 8.0 45 003 Male 48 0 0 0 0 0 1 1 2 135 85 3.2 1.2 24.2 9.0 55 004 Female 35 0 0 0 0 0 0 0 2 120 80 2.8 1.5 22.5 9.5 60 005 Male 67 1 1 1 1 1 1 0 0 150 95 5.0 0.8 29.8 7.5 40 006 Female 45 0 0 1 0 1 0 1 1 130 85 3.6 1.1 25.0 8.5 50 007 Male 70 1 1 1 1 1 1 1 0 165 105 5.5 0.7 33.2 7.0 35 008 Female 52 0 0 1 0 1 0 0 2 125 80 3.7 1.3 23.4 9.0 55 009 Male 60 1 1 1 1 1 1 1 1 158 95 4.9 0.9 28.7 8.0 45 010 Female 40 0 0 0 0 0 0 0 2 118 78 2.9 1.4 21.8 9.5 60 DNA was extracted from blood samples using the magnetic bead method, and its concentration and purity were measured by a spectrophotometer. The target region capture technology was used to capture and sequence the core genes related to cardiovascular and cerebrovascular diseases (such as APOE, PCSK9, LDLR, etc.). The sequencing data was aligned with the reference genome to screen for high - risk gene mutation sites, and the genetic risk score R of each subject was obtained. According to the scoring results, individuals with R higher than 2.0 were marked as the high - risk group, as shown in Table 2 below: Table 2 Genetic risk score table

[0082] The expression formula for the genetic risk score is: ; Taking subject 001 as an example: The number of gene loci n = 3, =[0.3, 0.4, 0.2], =[2, 1, 1], =[1.0, 0.9, 1.1]; ; R > 2.0, so it is marked as the high - risk group, and other data is calculated in the above manner.

[0083] For the high - risk group of subjects, microvascular images were collected using a non - invasive microcirculation imaging device. The collected images were denoised, and based on the segmentation algorithm, characteristic parameters such as vascular density, abnormal capillary ratio, vascular rarefaction index, and blood flow rate were extracted to calculate the microvascular lesion score M, as shown in Table 3 below: Table 3 Microvascular Lesion Scoring Table

[0084] The expression for microvascular lesion score is: ; Taking subject 001 as an example: Vascular density: = 0.75, = 0.12, = 0.35, = 85.2, = 0.3, = 0.5, = 0.4, = 0.2, = 0.01; ; Other data are calculated in the above manner.

[0085] Couple the genetic risk score R and microvascular lesion score M of each subject, and calculate the individualized health risk score H, as shown in Table 4 below: Table 4 Individualized Health Risk Scoring Table

[0086] The expression for individualized health risk score is: ; Taking subject 001 as an example: R = 2.37, M = 1.68, ΔM = 0.27, , , ; ; Other data are calculated in the above manner.

[0087] Extract metabolites from blood and urine samples, detect metabolite concentrations using liquid chromatography - mass spectrometry (LC - MS), calculate the metabolic balance index (MBI) and metabolic imbalance index (MII), and calculate the comprehensive metabolic capacity score, as shown in Table 5 below: Table 5 Comprehensive Metabolic Capacity Scoring Table Subject ID MBI (Metabolic Balance Index) MII (Metabolic Imbalance Index) Microvascular lesion score Subject ID 001 0.71 0.17 1.68 1.03 003 0.76 0.14 1.82 1.12 005 0.65 0.16 1.53 0.91 007 0.80 0.13 1.95 1.23 008 0.72 0.15 1.67 0.99 010 0.75 0.14 1.84 1.15 The calculation process is as follows: The expression for the metabolic balance index is: ; The expression for the metabolic imbalance index is: ; The expression for the comprehensive metabolic capacity score is: ; Taking subject 001 as an example: Input data: =[0.8, 0.6, 0.4, 0.7, 0.9]; =[0.3, 0.4, 0.2, 0.3, 0.4], ; ; ; ; Other data are calculated in the above manner.

[0088] According to the comprehensive metabolic ability score D, genetic risk score R, and health risk score H of the subject, a personalized intervention plan is formulated as shown in Table 6 below: Table 6 Personalized Intervention Plan Table Subject ID Comprehensive metabolic capacity score Genetic risk score Health risk score Personalized intervention plan 001 1.03 2.37 1.83 Dietary intervention: Control high-fat diet and increase dietary fiber intake; Exercise intervention: Moderate-intensity aerobic exercise (30 minutes / day); Regular monitoring: Physical examination every year. 003 1.12 2.76 2.04 Medical monitoring: Strengthen blood pressure and blood glucose monitoring; Dietary intervention: Low-salt and low-fat diet; Exercise intervention: High-intensity interval training (20 minutes / day). 005 0.91 2.60 1.77 Metabolic intervention: Supplement trace elements related to metabolism; Exercise intervention: Light to moderate-intensity exercise (brisk walking for 40 minutes / day); Personalized diet: Increase the proportion of protein intake. 007 1.23 3.78 2.36 Medical guidance: Genetic risk monitoring (recommended to be checked once every six months); Dietary intervention: Strictly control high-sugar and high-fat diet; Exercise intervention: Strength training combined with aerobic exercise. 008 0.99 2.06 1.74 Metabolic and dietary adjustment: Reduce the intake of refined carbohydrates and increase vegetables and low-fat proteins; Exercise intervention: Yoga or light to moderate-intensity exercise (30 minutes / day). 010 1.15 2.85 2.10 Medical monitoring: Focus on cardiovascular health (recommended to detect blood lipids and cardiac function every year); Exercise intervention: High-intensity interval training; Dietary intervention: Low-salt diet. Metabolic ability : The metabolic ability of most subjects is moderate, and the metabolic ability of some subjects is low, requiring key intervention.

[0089] Genetic risk: All subjects have medium or high genetic risk and need regular medical monitoring.

[0090] Health risk: 50% of the subjects have a high health risk and need comprehensive intensive intervention.

[0091] Intervention strategy: Common intervention for the whole population: high-quality diet, regular exercise, and regular monitoring.

[0092] Personalized intervention: According to , and H classification, adjust the intervention intensity, and focus on high-risk groups (such as 007 and 010).

[0093] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A method for managing cardiovascular and cerebrovascular disease information, characterized in that: include, Collect patients’ health information and biological samples to generate health records; Use the generated health records to perform genomic analysis and calculate the patient's genetic risk score; Based on the patient's genetic risk score, high-risk patients are tested for microvascular lesions, microvascular abnormalities are quantified, and a personalized health risk score is generated; Perform metabolomics analysis based on health risk scores to assess the patient's metabolic status and metabolic capacity; Combining metabolic assessment results, genetic risk scores, and health risk scores, an individualized intervention plan is developed.

2. The cardiovascular and cerebrovascular disease information management method according to claim 1, characterized in that: The patient's health information includes basic information, medical history, family history, lifestyle and physical examination data; the biological samples include blood and urine samples.

3. The cardiovascular and cerebrovascular disease information management method according to claim 2, characterized in that: Using the generated health records for genomic analysis, calculating the patient's genetic risk score includes the following steps: Extract DNA from biological samples using the magnetic bead method and measure the concentration and purity of the DNA using a spectrophotometer; The target region capture technology is used to capture the core genes related to cardiovascular and cerebrovascular diseases in DNA and obtain sequencing data; Compare the sequencing data with the human reference genome and locate the mutation sites for gene variation detection; Functionally annotate the detected gene variants, evaluate pathogenicity and association with cardiovascular and cerebrovascular diseases, and screen out high-risk genes; Based on the screening results and combined with the effect size of the whole genome association study, the genetic risk score of the patient was calculated as follows: ； in, represents the genetic risk score of the patient, represents the index of the gene locus, represents the number of gene loci, Indicates The effect size of a gene locus, Indicates that the patient The genotype code of each gene locus, Indicates Correction factor for each gene locus.

4. The cardiovascular and cerebrovascular disease information management method according to claim 3, characterized in that: Based on the patient's genetic risk score, high-risk patients are tested for microvascular disease. The comprehensive score of microvascular disease is obtained by the following steps: The patient's microvascular images are acquired through non-invasive microcirculatory imaging equipment; De-noising and image enhancement processing are performed on the collected microvascular images; Segment the processed microvascular image to obtain a binary mask image of the blood vessels; Based on the binary mask image of blood vessels, microvascular features such as blood vessel density, abnormal capillary ratio, blood vessel rarefaction index and blood flow rate are extracted; Based on the extracted microvascular features, the comprehensive score of microvascular lesions is calculated, and the expression is: ； in, represents the microangiopathy score, represents the weight coefficient of vascular density, represents the vascular density, The weight coefficient representing the proportion of abnormal capillaries, Indicates the proportion of abnormal capillaries, represents the weight coefficient of the vascular rarefaction index, represents the vascular rarefaction index, represents the weight coefficient of blood flow rate, represents the blood flow rate, represents the smoothing parameter.

5. The cardiovascular and cerebrovascular disease information management method according to claim 4, characterized in that: The genetic risk score and the microvascular disease score are coupled to obtain an individualized health risk score, which is expressed as: ； in, represents the individualized health risk score, represents the nonlinear exponential weight of the genetic risk score, represents the nonlinear exponential weight of the microvascular disease score, represents the coupling strength parameter, represents the deviation value of microangiopathy score.

6. The cardiovascular and cerebrovascular disease information management method according to claim 5, characterized in that: Metabolomics analysis based on health risk scores to assess the patient's metabolic status and metabolic capacity includes the following steps: Pre-process blood and urine samples in biological samples and perform metabolite separation and detection; The concentrations of metabolites related to cardiovascular and cerebrovascular diseases were extracted from the detected metabolites, and the metabolic balance index and metabolic imbalance index were calculated. The expressions are as follows: ； in, represents the metabolic balance index, represents the metabolite index, represents the total number of metabolites, Metabolites The relative concentration of Metabolites The weight of ； in, represents the metabolic imbalance index, represents the average concentration of metabolites; The comprehensive metabolic capacity score of the patient was calculated by combining the microvascular disease score, metabolic balance index and metabolic imbalance index. The expression is: ； in, Represents the comprehensive score of metabolic capacity.

7. The cardiovascular and cerebrovascular disease information management method according to claim 6, characterized in that: Combining metabolic assessment results, genetic risk scores, and health risk scores, developing an individualized intervention plan includes the following steps: Develop a personalized nutritional intervention plan based on the patient's metabolic capacity score and genetic risk score; According to the comprehensive score of microvascular lesions and individualized health scores, an exercise intervention plan is developed to improve vascular function and metabolic capacity; Combine personalized nutrition intervention plan and exercise intervention plan to generate an individualized intervention plan.

8. A cardiovascular and cerebrovascular disease information management system, based on the cardiovascular and cerebrovascular disease information management method according to any one of claims 1 to 7, characterized in that: include, Information collection module, which collects patients’ health information and biological samples and generates health records; The genetic assessment module uses the generated health records to perform genomic analysis and calculate the patient's genetic risk score; The microvascular detection module detects microvascular lesions in high-risk patients based on their genetic risk scores, quantifies microvascular abnormalities and generates individualized health risk scores; The metabolic assessment module conducts metabolomics analysis based on health risk scores to assess the patient's metabolic status and metabolic capacity; The intervention plan module combines metabolic assessment results, genetic risk scores and health risk scores to develop individualized intervention plans.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the cardiovascular and cerebrovascular disease information management method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the cardiovascular and cerebrovascular disease information management method according to any one of claims 1 to 7 are implemented.

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