Risk prediction method and system for cardiovascular and metabolic diseases

Abstract

The invention provides a risk prediction method and system for cardiovascular and metabolic diseases. The method comprises the following steps: collecting continuous cardiovascular-metabolism time sequence data, and extracting phase delay parameters to construct a lipid transport rhythm out-of-synchronization characteristic spectrum; quantifying the topological connection strength of the gene cluster based on the three-dimensional genome interaction strength matrix, and generating a genetic prediction model; a vascular endothelial metabolism buffer attenuation trajectory is calculated through simulation, and a critical environment exposure parameter is determined by matching a genetic instability threshold value; and fusing multi-source data to construct a dynamic toughness model, calculating a phase space shrinkage rate of a vascular metabolism steady-state boundary, and triggering graded early warning. According to the technical scheme provided by the invention, ultra-early warning of cardiovascular metabolism risks is realized, and the individual intervention target spot recognition precision is improved.

Description

Technical Field

[0001] This application relates to the technical field of disease risk prediction, and particularly to a method and system for predicting the risk of cardiovascular and metabolic diseases. Background Art

[0002] With the increasing demand for analyzing the dynamic coupling mechanism of cardiovascular-metabolic diseases in precision medicine, there is an urgent need in clinical practice to construct a multi-modal evaluation system that integrates lipoprotein oscillation rhythm monitoring, analysis of the topological connection strength of metabolic sensing pathways, and simulation of endothelial metabolic buffering capacity, so as to break through the spatio-temporal limitations of traditional static models.

[0003] In existing solutions, mainstream risk assessments rely on static indicators such as blood pressure and cholesterol, and cannot capture the rhythm desynchronization caused by the phase delay of metabolic substrate supply; although new artificial intelligence models introduce machine learning algorithms, they lack a dynamic matching mechanism for the vascular shear stress gradient field and the genetic instability threshold, resulting in significant cross-scenario adaptation errors.

[0004] Existing solutions have the following defects: the monitoring of lipid transport rhythm desynchronization is missing, and it is impossible to warn of endothelial metabolic buffer collapse events; single-dimensional genetic analysis does not quantify the regulatory weight of gene cluster topological connections on metabolic signal coupling efficiency, resulting in insufficient accuracy in identifying individualized intervention targets; the determination of environmental exposure critical values lacks real-time simulation of the compensatory ability of vascular wall remodeling, delaying the early warning of high-risk parameter combinations. Summary of the Invention

[0005] Embodiments of this application provide a method and system for predicting the risk of cardiovascular and metabolic diseases, aiming to solve the problem of insufficient accuracy in identifying individualized intervention targets in the prior art.

[0006] In a first aspect, embodiments of this application provide a method for predicting the risk of cardiovascular and metabolic diseases, including:

[0007] Extract the phase delay parameter between the cardiovascular elastic chamber and the metabolic substrate supply from the cardiovascular-metabolic coupling data in the continuous time series of the obtained target object, and construct a multi-modal oscillation feature spectrum including lipid transport rhythm desynchronization indicators;

[0008] Based on genomic data, identify the topological connection strength between the vascular tone regulation gene cluster and the metabolic sensing pathway gene cluster, and combine the phase delay parameter in the multi-modal oscillation feature spectrum to quantify the influence weight of genetic variation on the cardiovascular-metabolic coupling efficiency, and generate a genetic prediction model;

[0009] According to the local shear stress distribution of the blood vessels of the target object and the environmental oxidative load parameters, simulate and calculate the attenuation trajectory function of the vascular endothelial metabolic buffering capacity, analyze the dynamic matching degree matrix between the attenuation trajectory function and the instability threshold in the genetic prediction model, and determine the critical environmental exposure parameter combination;

[0010] Integrate the lipid transport rhythm desynchronization index, the instability threshold, and the critical environmental exposure parameter combination to construct a dynamic resilience assessment model, calculate the phase space contraction rate of the vascular metabolic homeostasis boundary, and generate a hierarchical warning signal when the phase space contraction rate exceeds the vascular wall remodeling compensation ability.

[0011] Optionally, according to the local shear force distribution of the blood vessels of the target object and the environmental oxidation load parameters, simulate and calculate the attenuation trajectory function of the vascular endothelial metabolic buffer capacity, analyze the dynamic matching degree matrix between the attenuation trajectory function and the instability threshold in the genetic prediction model, and determine the critical environmental exposure parameter combination, including:

[0012] Based on the three-dimensional stress distribution map of the vascular wall, extract the local shear force distribution of the target vascular bifurcation segment, combine the monitoring data of endothelial cell metabolic flux to construct a blood flow-metabolism dynamic response model, calculate the phase lag amount of lipid transport efficiency to shear force fluctuations through frequency domain coupling analysis, and generate a metabolic delay characteristic map at the vascular bifurcation;

[0013] According to the spatio-temporal distribution of the environmental oxidation load parameters, establish an oxidative stress wave propagation equation, and solve the concentration standing wave formation threshold of the metabolic buffer medium at the vascular bifurcation. The concentration standing wave formation threshold is used to calibrate the dynamic response boundary of the endothelial metabolic buffer capacity;

[0014] Input the metabolic delay characteristic map at the vascular bifurcation into the blood flow-metabolism dynamic response model, combine the concentration standing wave formation threshold, and solve the attenuation trajectory function of the vascular endothelial metabolic buffer capacity through spatio-temporal convolution operation, and simultaneously obtain the evolution curve of the instability threshold corresponding to the vascular tension regulation gene cluster in the genetic prediction model;

[0015] Calculate the buffer capacity attenuation rate based on the time derivative of the attenuation trajectory function, extract the curvature extreme points of the instability threshold evolution curve, and establish a dynamic matching degree matrix between the buffer capacity attenuation rate and the curvature extreme points;

[0016] When the matching degree of the buffer capacity attenuation rate at the coordinate point of the vascular bifurcation exceeds the curvature tolerance range of the corresponding gene cluster, it is marked as a high-risk area of metabolic reserve depletion, and the spatio-temporal distribution parameters of the environmental oxidation load at the coordinate point of the vascular bifurcation are associated;

[0017] Simulate the standing wave amplification effect of the coordinate points in the high-risk area of metabolic reserve depletion through the oxidative stress wave propagation equation, and calculate the critical environmental exposure parameter combinations that lead to a cliff-like decline in the metabolic buffer capacity. The critical environmental exposure parameter combinations include the oxidative load intensity threshold, exposure duration, and spatial action radius. Optionally, calculate the buffer capacity decay rate based on the time derivative of the attenuation trajectory function, extract the curvature extreme points of the instability threshold evolution curve, and establish a dynamic matching degree matrix between the buffer capacity decay rate and the curvature extreme points, including:

[0018] Perform time-domain differential processing on the attenuation trajectory function, and use the central difference method to calculate the metabolic buffer capacity decay rate at each spatial coordinate point within a preset time window. The width of the preset time window is one-fourth of the characteristic period parameter of the vascular wall metabolic oscillation;

[0019] For the instability threshold evolution curve output by the genetic prediction model, calculate the curvature parameter of the instability threshold evolution curve, and use the sliding window extreme value detection algorithm to identify the set of curvature extreme points. The set of curvature extreme points satisfies the condition that the first derivative of the curvature parameter is zero and the second derivative is negative;

[0020] Construct a spatio-temporal matching tensor, couple the metabolic buffer capacity decay rate with the nearest neighbor curvature extreme points according to the time alignment criterion, and generate a dynamic matching degree matrix. The dimensions of the dynamic matching degree matrix include the number of spatial grids, the number of time nodes, and the number of gene clusters;

[0021] The method further includes:

[0022] Perform normalization processing on the dynamic matching degree matrix, calculate the historical matching degree mean and standard deviation of each spatial point, and form a standardized dynamic matching degree matrix distribution map. The standardized dynamic matching degree matrix distribution map contains normalized matching degrees;

[0023] Calculate the instability risk weight factor through the curvature tolerance range specific to the gene cluster. The instability risk weight factor is used to reflect the instability threshold critical values of different gene clusters;

[0024] Perform convolution operation on the normalized matching degree and the instability risk weight factor to generate a comprehensive matching degree index. The comprehensive matching degree index is used to correlate environmental exposure parameters;

[0025] Optionally, fuse the lipid transport rhythm desynchronization index, the instability threshold, and the critical environmental exposure parameter combinations to construct a dynamic resilience assessment model, calculate the phase space contraction rate of the vascular metabolic steady-state boundary, and generate a hierarchical warning signal when the phase space contraction rate exceeds the vascular wall remodeling compensation ability, including:

[0026] Map the lipid transport rhythm desynchronization index to the energy metabolism state space of vascular wall cells to generate a multi-dimensional gradient field distribution of lipid transport rhythm instability;

[0027] Construct a dynamic constraint boundary of vascular tension-metabolism coupling degree based on the instability threshold in the genetic prediction model, and the curvature characteristics of the dynamic constraint boundary are adjusted in real time according to the critical environmental exposure parameter combination;

[0028] Establish a coupling equation between the critical environmental exposure parameter combination and the multi-dimensional gradient field distribution, and calculate the non-linear dissipation rate of metabolic oscillation energy at the vascular bifurcation;

[0029] Construct a cardiovascular-metabolic system dynamic resilience assessment model, which is determined according to the lipid transport rhythm instability degree, the curvature of the dynamic constraint boundary, and the non-linear dissipation rate of metabolic oscillation energy, and is used to evaluate the vascular wall remodeling compensation ability in different regions, and quantify the absorption threshold of the vascular wall remodeling compensation ability to gradient field perturbations through Lyapunov exponent analysis;

[0030] When the non-linear dissipation rate breaks through the curvature of the dynamic constraint boundary and the phase space contraction rate exceeds the absorption threshold, generate a graded warning signal corresponding to the plaque instability tendency according to the boundary breakthrough azimuth angle and the spatial coordinates of the dissipation hot spot.

[0031] Optionally, the constructing of the cardiovascular-metabolic system dynamic resilience assessment model, which is determined according to the lipid transport rhythm instability degree, the curvature of the dynamic constraint boundary, and the non-linear dissipation rate of metabolic oscillation energy, and is used to evaluate the vascular wall remodeling compensation ability in different regions, and quantify the absorption threshold of the vascular wall remodeling compensation ability to gradient field perturbations through Lyapunov exponent analysis, includes:

[0032] Perform modal decomposition on the multi-dimensional gradient field of the lipid transport rhythm instability degree, extract the main oscillation mode of vascular wall energy metabolism, and generate a dynamic characteristic spectrum including spatio-temporal coupling coefficients;

[0033] Construct an evolution equation of vascular tension-metabolism coupling degree based on the curvature of the dynamic constraint boundary, solve the non-linear dissipation rate of metabolic oscillation energy through the dynamic modal decomposition algorithm, and generate a constraint boundary evolution matrix that is adjusted in real time according to environmental exposure parameters;

[0034] Use the Lyapunov spectrum analysis method to quantify the phase space contraction rate of the main oscillation mode and the curvature of the dynamic constraint boundary, and combine the historical data of the metabolic buffer capacity attenuation rate to calculate the absorption threshold of the vascular wall remodeling compensation ability to gradient field perturbations.

[0035] Optionally, the method of using Lyapunov spectrum analysis is adopted to quantify the phase space contraction rate of the main oscillation mode and the curvature of the dynamic constraint boundary, and in combination with the historical data of the decay rate of the metabolic buffer capacity, the absorption threshold of the vascular wall remodeling compensation ability for the gradient field perturbation is calculated, including:

[0036] Perform Lyapunov spectrum analysis on the main oscillation mode, extract the phase space contraction rate parameter, and in combination with the spatio-temporal evolution characteristics of the curvature of the dynamic constraint boundary, generate the stability decay spectrum of vascular wall energy metabolism;

[0037] Based on the historical data of the decay rate of the metabolic buffer capacity, construct the decay mode feature vector of the time series, and quantify the dynamic stability decay index of different vascular bifurcation regions in the stability decay spectrum through the spectral clustering algorithm;

[0038] Fuse the stability decay spectrum and the dynamic stability decay index, establish the weight matrix of vascular wall stress-metabolism coupling, and use the adaptive optimization algorithm to solve the absorption threshold under the gradient field perturbation.

[0039] Optionally, the topological connection strength between the vascular tone regulation gene cluster and the metabolic sensing pathway gene cluster is identified based on the genomics data, and in combination with the phase delay parameter in the multi-modal oscillation feature spectrum, the influence weight of genetic variation on the cardiovascular-metabolism coupling efficiency is quantified to generate a genetic prediction model, including:

[0040] Extract the co-expression network of the vascular tone regulation gene cluster and the metabolic sensing pathway gene cluster based on the genomics data, and calculate the topological connection strength between the gene clusters. The topological connection strength reflects the functional synergy of the gene clusters in regulating cardiovascular-metabolism coupling;

[0041] Map the phase delay parameter in the multi-modal oscillation feature spectrum to the gene cluster co-expression network to generate a phase delay weighted gene cluster network, which is used to characterize the correlation between the functional synergy between gene clusters and the cardiovascular-metabolism coupling efficiency;

[0042] Identify the single nucleotide polymorphism sites in the vascular tone regulation gene cluster and the metabolic sensing pathway gene cluster based on the genetic variation data, and calculate the regulatory effect value of each polymorphism site on the topological connection strength between the gene clusters. The regulatory effect value is used to quantify the influence of genetic variation on the functional synergy of the gene clusters;

[0043] Integrate the regulatory effect value with the phase delay weighted gene cluster network, and calculate the influence weight of genetic variation on the cardiovascular-metabolism coupling efficiency. The influence weight reflects the contribution degree of genetic variation to the coupling efficiency by regulating the functional synergy of the gene clusters;

[0044] Construct a genetic prediction model based on the influence weight, where the genetic prediction model is used to predict the change trend of the cardiovascular-metabolic coupling efficiency of the target object under a specific genetic variation background.

[0045] In a second aspect, an embodiment of the present application provides a risk prediction system for cardiovascular and metabolic diseases, including:

[0046] An extraction module, configured to extract the phase delay parameter between the cardiovascular elastic chamber and the metabolic substrate supply from the cardiovascular-metabolic coupling data in the continuous time series of the obtained target object, and construct a multi-modal oscillation feature spectrum including the lipid transport rhythm desynchronization index;

[0047] An identification module, configured to identify the topological connection strength between the vascular tone regulation gene cluster and the metabolic sensing pathway gene cluster based on genomic data, and combine the phase delay parameter in the multi-modal oscillation feature spectrum to quantify the influence weight of genetic variation on the cardiovascular-metabolic coupling efficiency, and generate a genetic prediction model;

[0048] A calculation module, configured to simulate and calculate the attenuation trajectory function of the vascular endothelial metabolic buffer capacity according to the local shear force distribution of the blood vessels of the target object and the environmental oxidation load parameter, analyze the dynamic matching degree matrix between the attenuation trajectory function and the instability threshold in the genetic prediction model, and determine the critical environmental exposure parameter combination;

[0049] A construction module, configured to fuse the lipid transport rhythm desynchronization index, the instability threshold, and the critical environmental exposure parameter combination, construct a dynamic toughness evaluation model, calculate the phase space contraction rate of the vascular metabolic steady-state boundary, and generate a hierarchical warning signal when the phase space contraction rate exceeds the vascular wall remodeling compensation ability.

[0050] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a risk prediction method for cardiovascular and metabolic diseases as described in the first aspect above.

[0051] In a fourth aspect, an embodiment of the present application provides a computer storage medium, storing a computer program, where when the computer program is executed by a computer, it implements a risk prediction method for cardiovascular and metabolic diseases as described in the first aspect.

[0052] In the embodiments of the present application, phase delay parameters between the cardiovascular elastic chamber and the metabolic substrate supply are extracted from the cardiovascular-metabolic coupling data in the continuous time series of the obtained target object, and a multi-modal oscillation feature spectrum including lipid transport rhythm desynchronization indexes is constructed; based on genomic data, the topological connection strength between the vascular tone regulation gene cluster and the metabolic sensing pathway gene cluster is identified, and combined with the phase delay parameters in the multi-modal oscillation feature spectrum, the influence weight of genetic variation on the cardiovascular-metabolic coupling efficiency is quantified to generate a genetic prediction model; according to the local shear force distribution and environmental oxidation load parameters of the blood vessels of the target object, the attenuation trajectory function of the vascular endothelial metabolic buffer capacity is simulated and calculated, and the dynamic matching degree matrix between the attenuation trajectory function and the instability threshold in the genetic prediction model is analyzed to determine the critical environmental exposure parameter combination; the lipid transport rhythm desynchronization index, the instability threshold and the critical environmental exposure parameter combination are fused to construct a dynamic resilience evaluation model, and the phase space contraction rate of the vascular metabolic steady-state boundary is calculated, and a hierarchical warning signal is generated when the phase space contraction rate exceeds the vascular wall remodeling compensation ability.

[0053] The technical solution of the present application has the following beneficial effects:

[0054] Integrating dynamic physiological parameters and genetic topological networks, breaking through the limitations of traditional static risk assessment, and improving the cross-scale prediction accuracy; simulating through shear force gradient field parameters to quantify the attenuation trajectory of the vascular wall remodeling compensation ability; combining the metabolic oscillation energy dissipation rate and oxidation load parameters to accurately identify high-risk environmental exposure combinations; providing a quantitative basis for formulating targeted intervention strategies through a hierarchical warning mechanism, and reducing the incidence of major cardiovascular events.

[0055] Further, by integrating the local shear force distribution of the blood vessels of the target individual and the environmental oxidation load parameters, a blood flow-metabolic coupling simulation model is used to calculate the attenuation trajectory function of the vascular endothelial metabolic buffer capacity; combined with the instability threshold regulated by the KLF2-BMPER pathway in the genetic prediction model, the matching degree between the metabolic buffer attenuation rate and the genetic instability boundary is analyzed through a dynamic Bayesian network, and finally the critical environmental exposure parameter combination that triggers the failure of vascular compensation is determined. This method breaks through the limitations of traditional static risk assessment, and through shear force gradient field simulation and dynamic stability boundary analysis, realizes the real-time quantitative monitoring of the vascular metabolic compensation ability, accurately identifies the environmental exposure critical point leading to plaque instability, and provides a spatio-temporal dynamic warning window for individualized intervention.

[0056] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. Description of the Drawings

[0057] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0058] Figure 1 The flowchart of a risk prediction method for cardiovascular and metabolic diseases provided by the present application is shown;

[0059] Figure 2 The structural schematic diagram of a risk prediction system for cardiovascular and metabolic diseases provided by the present application is shown;

[0060] Figure 3 The structural schematic diagram of a computing device provided by the present application is shown. Detailed implementation manners

[0061] In order to enable those skilled in the art to better understand the solutions of the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application.

[0062] In some processes described in the specification, claims, and the above-mentioned drawings of the present application, multiple operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The operation numbers such as 101, 102, etc. are only used to distinguish different operations, and the numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0063] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0064] Figure 1 A flowchart of a risk prediction method for cardiovascular and metabolic diseases provided for the embodiments of the present application is as Figure 1 shown, and the method includes:

[0065] 101. Extract the phase delay parameter between the cardiovascular elastic chamber and the metabolic substrate supply from the cardiovascular-metabolic coupling data in the continuous time series of the obtained target object, and construct a multi-modal oscillation feature spectrum including the lipid transport rhythm desynchronization index;

[0066] In this step, the cardiovascular elastic chamber refers to the functional role of the heart and large blood vessels in the circulatory system to dynamically store and release blood, including the changes during the cardiac systolic and diastolic phases and the compliance changes of the blood vessel wall, and is used to evaluate the health status of the cardiovascular system.

[0067] The metabolic substrate supply involves substances required for cells to obtain energy, such as glucose, fatty acids, etc., and the supply of these substances is crucial for maintaining cell function.

[0068] The cardiovascular-metabolic coupling data refers to a dataset that simultaneously monitors the cardiovascular system (such as heart rate, blood pressure) and the metabolic system (such as blood glucose level, lipid metabolism), and is used to analyze the interaction between the two, including hemodynamic oscillation waveforms, blood vessel wall stress response curves, and energy metabolism fluctuation signals.

[0069] The phase delay parameter is a key indicator to measure the time difference between these two physiological processes and is used to identify the coupling relationship between cardiovascular and metabolic activities.

[0070] The multi-modal oscillation feature spectrum is an analysis tool that integrates the periodic and aperiodic components of different physiological signals to evaluate the state of cardiovascular-metabolic coupling. The multi-modal oscillation feature spectrum includes the blood vessel compliance attenuation gradient, the oxidative stress fluctuation frequency, and the lipid transport rhythm desynchronization index.

[0071] The lipid transport rhythm desynchronization index reflects the time mismatch phenomenon that occurs during the lipid transport process and is used to evaluate the coordination between the cardiovascular and metabolic systems.

[0072] In the embodiments of the present application, first, collect the cardiovascular and metabolic continuous time series data of the target object; second, use this data to identify the coupling mode between the cardiovascular system and metabolic activities; third, calculate the phase delay between the two to determine their synchronization, and extract relevant features; finally, construct a multi-modal oscillation feature spectrum including lipid transport rhythm desynchronization to comprehensively evaluate the state of cardiovascular-metabolic coupling.

[0073] Suppose there is a medical research center specializing in cardiovascular and metabolic diseases, and a long-term monitoring project for hypertensive patients is underway. First, the heart rate, blood pressure, and blood glucose levels of patients are continuously monitored through wearable devices, which automatically record data every few minutes; second, the collected data is imported into analysis software to identify the coupling patterns between the cardiovascular system and metabolic activities, and significant fluctuations in heart rate and blood glucose levels are found during certain time periods; third, the phase delay parameter between the two is calculated to determine a significant time difference between cardiovascular and metabolic activities and extract relevant features; finally, a multimodal oscillation feature spectrum is constructed based on this information to help researchers more accurately evaluate the patient's condition and provide a scientific basis for formulating personalized treatment plans.

[0074] 102. Identify the topological connection strength between the gene cluster regulating vascular tone and the gene cluster of the metabolic sensing pathway based on genomics data, and combine the phase delay parameter in the multimodal oscillation feature spectrum to quantify the influence weight of genetic variation on the cardiovascular-metabolic coupling efficiency, and generate a genetic prediction model.

[0075] In this step, the genomics data includes all the genetic information of an individual and can be used to identify the role of specific gene clusters.

[0076] The gene cluster regulating vascular tone involves genes related to controlling vascular tension, while the gene cluster of the metabolic sensing pathway is related to the mechanism by which cells sense and respond to metabolic demands.

[0077] The topological connection strength refers to the tightness of the interaction network between genes and is used to quantify the influence of genetic variation on the coupling efficiency between cardiovascular-metabolic coupling data.

[0078] The genetic prediction model is a mathematical framework that predicts an individual's risk of developing cardiovascular and metabolic diseases by combining genomic data and other physiological parameters.

[0079] In the embodiment of this application, first, gene clusters related to vascular tone regulation and metabolic sensing are screened from the genomic database; second, the topological connection strength between these gene clusters is analyzed; third, combined with the previously obtained phase delay parameter, the influence of genetic variation on the cardiovascular-metabolic coupling efficiency is quantified; finally, a genetic prediction model is generated to evaluate an individual's disease risk.

[0080] For example, continuing with the previous example, assume that the medical research center further analyzes the genomic data of the patient. First, gene clusters related to vascular tone regulation and metabolic sensing are screened out from the genomic database; second, the topological connection strength between these gene clusters is analyzed using complex mathematical models, revealing the potential impact of certain key genes on cardiovascular health; third, combined with the obtained phase delay parameters, the impact of genetic variations on the cardiovascular-metabolic coupling efficiency of this patient is quantified, and several important mutation points are discovered; finally, a genetic prediction model is generated based on this information, providing more accurate guidance for personalized treatment, which helps improve the treatment effect and reduce side effects.

[0081] 103. According to the local vascular shear stress distribution and environmental oxidative load parameters of the target object, simulate and calculate the decay trajectory function of the vascular endothelial metabolic buffer capacity, analyze the dynamic matching degree matrix between the decay trajectory function and the instability threshold in the genetic prediction model, and determine the critical environmental exposure parameter combination.

[0082] In this step, the local vascular shear stress distribution refers to the force distribution exerted by blood flow on the blood vessel wall, which is one of the important factors affecting the function of vascular endothelial cells.

[0083] The environmental oxidative load parameters involve the exposure levels of free radicals and other oxidative stress sources in the external environment, and these factors act together on the metabolic buffer capacity of the vascular endothelium.

[0084] The spatio-temporal decay trajectory describes the trend of a certain variable changing with time and space, specifically referring to the changing trend of the vascular endothelial metabolic buffer capacity here.

[0085] The instability threshold is the key point at which the system changes from a stable state to an unstable state, and is used to predict the occurrence and development of diseases.

[0086] The critical environmental exposure parameter combination refers to the set of external conditions that can cause the system to become unstable, and they act together with internal biological factors to affect the development of diseases.

[0087] In the embodiments of the present application, first, a simulation model is established according to the local vascular shear stress distribution and environmental oxidative load parameters of the target object; second, the decay trajectory function of the vascular endothelial metabolic buffer capacity is simulated and calculated; third, the dynamic matching degree between these trajectories and the instability threshold in the genetic prediction model is analyzed; finally, the critical environmental exposure parameter combination is determined to predict the disease risk.

[0088] For example, continuing with the previous example, assume that the medical research center has established a detailed simulation model to simulate the vascular health status of patients. First, based on the local shear stress distribution of the patient's blood vessels and the environmental oxidative load parameters, a simulation model is established to simulate the functional changes of vascular endothelial cells under different environmental conditions; second, the decay trajectory function of the vascular endothelial metabolic buffer capacity is simulated and calculated, and it is observed that the metabolic buffer capacity significantly decreases in a high oxidative load environment; third, the matching degree between these trajectories and the instability threshold in the genetic prediction model is analyzed, and several critical points are determined, indicating that the patient may face a higher disease risk; finally, the critical environmental exposure parameter combination is determined, providing a scientific basis for doctors to formulate preventive strategies and helping patients better manage their health.

[0089] 104. Integrate the lipid transport rhythm desynchronization index, the instability threshold, and the critical environmental exposure parameter combination to construct a dynamic resilience assessment model, calculate the phase space contraction rate of the vascular metabolic homeostasis boundary, and generate a graded warning signal when the phase space contraction rate exceeds the vascular wall remodeling compensation ability.

[0090] In this step, the desynchronization index reflects the degree of disharmony between cardiovascular and metabolic activities and is an important indicator for evaluating the overall health status.

[0091] The instability threshold represents the critical boundary for the system to change from stable to unstable and is of great significance for understanding the disease progression.

[0092] The critical environmental exposure parameter combination refers to the set of external conditions that can cause system instability, and they interact with internal biological factors to affect the disease development.

[0093] The dynamic resilience assessment model is a comprehensive framework for evaluating an individual's ability to cope with internal and external challenges, calculating the phase space contraction rate of the vascular metabolic homeostasis boundary, and issuing a warning when the contraction rate exceeds a certain threshold.

[0094] The graded warning signal is a prompt message issued according to different risk levels and is used to guide clinical intervention measures.

[0095] In the embodiment of the present application, first, integrate the desynchronization index, the instability threshold, and the critical environmental exposure parameter combination to construct a dynamic resilience assessment model for evaluating the vascular wall remodeling compensation ability in different regions; second, calculate the phase space contraction rate of the vascular metabolic homeostasis boundary; third, evaluate whether the phase space contraction rate exceeds the vascular wall remodeling compensation ability; finally, generate a graded warning signal when the contraction rate exceeds the threshold to indicate potential health risks.

[0096] For example, continuing with the previous example, assume that the medical research center is developing a new dynamic resilience assessment model to optimize patient health management. First, integrate desynchronization metrics, instability thresholds, and critical environmental exposure parameter combinations to construct a dynamic resilience assessment model for evaluating the vascular wall remodeling compensation ability in different regions. Second, calculate the phase space contraction rate of the patient's vascular metabolic homeostasis boundary and find that its contraction rate is close to the warning line. Third, evaluate whether this contraction rate exceeds the vascular wall remodeling compensation ability and confirm that the patient needs to take emergency measures to prevent the condition from worsening. Finally, generate a graded warning signal when the contraction rate exceeds the threshold to remind the doctor to take timely intervention measures to avoid further deterioration of the condition and ensure that the patient receives timely and effective treatment.

[0097] In summary, steps 101 to 104 are aimed at providing a comprehensive and efficient cardiovascular and metabolic disease risk prediction system that meets the full process requirements from data collection to risk assessment, helping doctors with accurate diagnosis and personalized treatment.

[0098] To further improve the risk prediction accuracy of cardiovascular and metabolic diseases, in some embodiments, for the simulation calculation of the attenuation trajectory function of the vascular endothelial metabolic buffer capacity according to the local shear stress distribution of the blood vessels of the target object and the environmental oxidation load parameters in step 103, and the analysis of the dynamic matching degree matrix between the attenuation trajectory function and the instability threshold in the genetic prediction model to determine the critical environmental exposure parameter combination, including:

[0099] 1031. Based on the three-dimensional stress distribution map of the blood vessel wall, extract the local shear stress distribution of the target blood vessel bifurcation segment, combine the endothelial cell metabolic flux monitoring data to construct a blood flow-metabolism dynamic response model, calculate the phase lag amount of lipid transport efficiency to shear stress fluctuations through frequency domain coupling analysis, and generate a metabolic delay characteristic map at the blood vessel bifurcation;

[0100] In step 1031, the three-dimensional stress distribution map of the blood vessel wall is an image that describes the stress conditions of the blood vessel at different positions, including shear stress, pressure, and tension, etc. The local shear stress distribution refers to the spatial variation of the frictional force generated by blood flow on the blood vessel wall within a specific region (such as the blood vessel bifurcation). The blood flow-metabolism dynamic response model is used to simulate and predict the interaction between hemodynamics and endothelial cell metabolic activities.

[0101] In the embodiments of the present application, first, the three-dimensional stress distribution map of the blood vessel wall is usually obtained by combining medical imaging techniques (such as MRI or CT scans) with computational fluid dynamics (CFD) simulations. Specifically, first, the blood vessel structure is reconstructed using high-resolution image data, and then CFD software is applied for numerical simulation, considering factors such as blood flow characteristics, blood vessel wall elasticity, and boundary conditions, and finally, a three-dimensional stress distribution map of the blood vessel wall containing stress distribution information is generated.

[0102] Secondly, use image processing algorithms to extract data of the target vascular bifurcation segment from the above-mentioned three-dimensional stress distribution atlas of the blood vessel wall, especially focusing on the changes of shear stress over time and space. This step requires accurate identification of the maximum value, minimum value of the shear stress and its changing trend.

[0103] Next, combine the extracted local shear stress distribution of the blood vessel with the endothelial cell metabolic flux data obtained from experimental measurements (such as glucose uptake rate, ATP generation rate), and use the method of systems biology to establish a mathematical model that can reflect how shear stress affects the metabolism of endothelial cells. The modeling method can be implemented by using a conventional scheme, and this application will not expand on this.

[0104] Finally, for the study of lipid transport efficiency, first convert the shear stress fluctuation and lipid concentration signals into the frequency domain using the fast Fourier transform (FFT) method. Then, determine the phase lag amount by comparing the phase differences of the two at different frequencies, and further generate a metabolic delay characteristic atlas at the vascular bifurcation. Among them, the shear stress fluctuation refers to the change in the intensity of the shear stress acting on the vascular endothelium over time. It can be short-term fluctuations caused by factors such as periodic changes in heartbeat, respiratory movement, and body position changes, or long-term trend changes due to long-term living habits and disease progression.

[0105] For example, researchers first used MRI technology to collect vascular imaging data of a group of patients and performed CFD analysis using ANSYS Fluent to obtain a detailed blood vessel wall stress distribution atlas. Next, they used MATLAB programming to implement an image processing algorithm and successfully extracted shear stress data of the target vascular bifurcation segment from the atlas. Based on these data and the endothelial cell metabolic indicators measured in the laboratory, a preliminary blood flow-metabolism dynamic response model was established. Finally, by performing FFT transformation on the collected time series data, it was found that there was a significant phase lag phenomenon of lipid transport efficiency relative to shear stress fluctuations, providing a new perspective for understanding the development mechanism of arteriosclerosis.

[0106] 1032. Establish an oxidative stress wave propagation equation according to the spatio-temporal distribution of environmental oxidation load parameters, and solve the concentration standing wave formation threshold of the metabolic buffer medium at the vascular bifurcation. The concentration standing wave formation threshold is used to calibrate the dynamic response boundary of the endothelial metabolic buffer capacity;

[0107] Step 1032 mainly involves establishing an oxidative stress wave propagation equation according to the spatio-temporal distribution of environmental oxidation load parameters and solving the concentration standing wave formation threshold of the metabolic buffer medium at the vascular bifurcation. This standing wave formation threshold is used to calibrate the dynamic response boundary of the endothelial metabolic buffer capacity, that is, to determine under what conditions the vascular endothelial cells will lose their normal metabolic buffer function.

[0108] In the embodiments of the present application, first, it is necessary to obtain the spatio-temporal distribution data of reactive oxygen species (ROS) in the environment through experiments or sensor monitoring. These data include, but are not limited to, the concentrations of ROS at different time points and different positions.

[0109] Secondly, based on the collected data, a mathematical modeling method is used to establish an equation describing how ROS propagates in the body. This usually involves partial differential equations (PDEs), taking into account factors such as the generation rate, diffusion coefficient, and scavenging rate of ROS. The model also needs to consider the influence of blood flow on the propagation of ROS, and fluid dynamics simulation can be combined to more accurately predict the distribution of ROS in blood vessels.

[0110] Then, numerical methods (such as the finite difference method or the finite element method) are used to solve the above equations to determine under which conditions ROS concentration standing waves will form at blood vessel bifurcations. The "standing wave" here refers to a state where the ROS concentration remains relatively stable within a specific region, indicating that a balanced state has been reached in this region, but this balance is often unhealthy and marks the beginning of the loss of normal function of endothelial cells.

[0111] Finally, based on the obtained standing wave formation threshold, the relationship between it and the metabolic buffering ability of endothelial cells is further analyzed to determine at what value of the ROS concentration endothelial cell dysfunction will occur. This process can be verified by combining biological experiments and computer simulations. For example,

[0112] For example, researchers first used implantable sensors to monitor in real time the changes in ROS levels at different sites in the patient's body (such as the rate of ROS production by endothelial cells under different conditions and the ability of the antioxidant system to scavenge ROS), and used biochemical analysis techniques (such as enzyme-linked immunosorbent assay ELISA, fluorescent probes, etc.) to detect the functional changes of endothelial cells at different ROS concentrations, including but not limited to indicators such as lipid peroxidation level, antioxidant enzyme activity (such as SOD, CAT), etc. Then, based on the experimental data, partial differential equations (PDEs) were used to describe the diffusion, generation, and scavenging processes of ROS in blood vessels. Considerations included the influence of blood flow on the distribution of ROS, the physicochemical properties of local tissues, etc. Parameters were introduced in the model to represent the ability of endothelial cells to resist oxidative stress, and the corresponding critical conditions were set when cell function began to be impaired. Next, numerical methods such as the finite difference method or the finite element method were used to solve the above PDEs to determine at what level the ROS concentration at a specific location (such as a blood vessel bifurcation) would form a standing wave. This usually involved multiple iterative calculations, adjusting the parameters until the best parameter combination that could accurately reflect the experimental observations was found, and analyzing the conditions for standing wave formation in the simulation results, that is, finding the ROS concentration threshold that led to a significant decrease in the metabolic buffering capacity of endothelial cells. Finally, the standing wave formation threshold obtained from the simulation was compared and verified with the results of actual biological experiments. If there were deviations, then return to step 2 to adjust the model parameters or improve the assumptions until the theoretical prediction was consistent with the experimental observation.

[0113] This method can not only provide specific values on how ROS concentration affects the health of endothelial cells, but also help understand the underlying mechanism of action, providing a scientific basis for the prevention and treatment of related diseases. For example, in the study of atherosclerosis, understanding the ROS concentration threshold helps identify high-risk individuals and develop targeted intervention measures.

[0114] 1033. Input the metabolic delay characteristic map at the blood vessel bifurcation into the blood flow-metabolism dynamic response model, combine the concentration standing wave formation threshold, and solve the decay trajectory function of the metabolic buffering capacity of vascular endothelium through spatio-temporal convolution operation, and simultaneously obtain the instability threshold evolution curve corresponding to the vascular tension regulation gene cluster in the genetic prediction model;

[0115] Step 1033 aims to input the metabolic delay characteristic map at the blood vessel bifurcation into the blood flow-metabolism dynamic response model, and combine the concentration standing wave formation threshold to solve the decay trajectory function of the metabolic buffering capacity of vascular endothelium through spatio-temporal convolution operation. At the same time, this step also simultaneously obtains the instability threshold evolution curve corresponding to the vascular tension regulation gene cluster in the genetic prediction model, providing a basis for subsequent analysis.

[0116] First, it is necessary to integrate the metabolic delay feature map at the vascular bifurcation obtained from step 1031 with the concentration standing wave formation threshold determined in step 1032. This step involves standardizing data from different sources to ensure they can be compared and analyzed within the same framework.

[0117] Utilize deep learning techniques to construct a spatio-temporal convolutional network (STCN) for simulating the complex interaction between blood flow and metabolism. STCN can capture local patterns in space and dynamic changes in time, and is particularly suitable for processing multi-dimensional data such as shear stress fluctuations and ROS concentration changes.

[0118] Through the trained STCN model, input the integrated dataset to solve the variation law of vascular endothelial metabolic buffer capacity over time and space, that is, the decay trajectory function. This function describes how the endothelial cell metabolic buffer capacity gradually weakens under specific conditions, such as different shear stress distributions or ROS concentration levels.

[0119] When calculating the decay trajectory function, the influence of genetic factors also needs to be considered. Using a pre-established genetic prediction model, based on an individual's genetic information (especially gene clusters related to vascular tone regulation), synchronously obtain the evolution curve of the instability threshold caused by the change of these gene expression levels over time.

[0120] For example, the researchers first collected and sorted out the data obtained from steps 1031 and 1032, including the metabolic delay feature map at the vascular bifurcation and the ROS concentration standing wave formation threshold. Then, using the Python programming language and the TensorFlow framework, a spatio-temporal convolutional network model was constructed and optimized for training specifically on the relationship between hemodynamics and endothelial cell metabolic activities. By inputting the standardized dataset into the model, the research team successfully generated a trajectory function describing the decay process of vascular endothelial metabolic buffer capacity. In addition, based on the gene sequencing results of the participants, the researchers also calculated the evolution curve of the instability threshold of gene clusters related to vascular tone regulation using the genetic prediction model. The experimental results showed that in a high oxidative load environment, some participants showed an obvious trend of rapid decay of metabolic buffer capacity, and the corresponding gene instability threshold also showed a phenomenon of early decline, suggesting that these people may be more likely to develop severe cardiovascular diseases.

[0121] 1034. Calculate the buffer capacity decay rate based on the time derivative of the decay trajectory function, extract the curvature extreme points of the instability threshold evolution curve, and establish a dynamic matching degree matrix between the buffer capacity decay rate and the curvature extreme points;

[0122] Step 1034 involves calculating the time derivative of the vascular endothelial metabolic buffering capacity decay trajectory function obtained in the previous steps to determine the buffering capacity decay rate. Meanwhile, the curvature extreme points of the instability threshold evolution curve in the genetic prediction model are extracted, and a dynamic matching degree matrix is established between these decay rates and the curvature extreme points. This matrix is used to evaluate the changing trend of the vascular health state under different combinations of environmental exposure parameters.

[0123] In the embodiments of this application, first, it is necessary to perform a differential operation on the decay trajectory function obtained from step 1033, that is, calculate its first-order derivative with respect to time. This can be achieved through numerical differentiation methods, such as using the finite difference method to approximately calculate the instantaneous change rate at each time point.

[0124] For the instability threshold evolution curve in the genetic prediction model, the extreme points are found by analyzing its second-order derivative (i.e., curvature). These extreme points mark the positions where the curve shape changes significantly, and may indicate important turning points in gene expression levels or functional states.

[0125] Combining the above-calculated buffering capacity decay rate with the curvature extreme points forms a two-dimensional matrix. In this matrix, the rows represent different time points or spatial positions, and the columns correspond to various environmental exposure parameters (such as oxidation load intensity, duration, and scope of action). By comparing the matching degrees under each parameter combination, it is possible to identify which conditions are most likely to lead to the deterioration of vascular health.

[0126] Finally, through statistical analysis of the data in the dynamic matching degree matrix, key environmental exposure factors and their critical values are identified, providing a scientific basis for prevention and treatment.

[0127] For example, first, according to the results of step 1033, the researchers used MATLAB software to calculate the decay rate of the vascular endothelial metabolic buffering capacity over time. They adopted the central difference method to approximately calculate the first-order derivative, thus obtaining a detailed decay rate data set. Then, for the instability threshold evolution curve in the genetic prediction model, by solving its second-order derivative and finding the curvature maximum points, several potential key turning points were determined. Then, the research team integrated these data together to create a dynamic matching degree matrix, which contains the matching scores under different combinations of environmental exposure parameters. Further analysis found that in a high-oxidation load environment, especially within a specific time period, when the oxidation load intensity exceeds a certain critical value, the buffering capacity decay rate reaches the maximum and highly coincides with the curvature extreme points of the gene instability threshold evolution curve. This means that under these conditions, the individual's risk of developing cardiovascular diseases increases significantly.

[0128] 1035. When the matching degree of the buffer capacity attenuation rate at the coordinate point of the vascular bifurcation exceeds the curvature tolerance range of the corresponding gene cluster, it is marked as a high-risk area of metabolic reserve depletion, and the spatio-temporal distribution parameters of the environmental oxidative load at the coordinate point of the vascular bifurcation are associated;

[0129] The goal of step 1035 is to determine which areas are regarded as high-risk areas of metabolic reserve depletion based on the dynamic matching degree matrix of the buffer capacity attenuation rate and the instability threshold evolution curve calculated in the previous step. Specifically, when the matching degree of the buffer capacity attenuation rate at the coordinate point of the vascular bifurcation exceeds the curvature tolerance range of the corresponding gene cluster, this coordinate point will be marked as a high-risk area, and its spatio-temporal distribution parameters of the environmental oxidative load will be associated.

[0130] First, it is necessary to clarify the curvature tolerance range corresponding to each gene cluster. This is usually set based on large-scale population data or experimental studies, reflecting the acceptable fluctuation range of gene expression level changes under normal physiological conditions. Exceeding this range may mean that cell functions begin to show abnormalities.

[0131] Using the dynamic matching degree matrix constructed in step 1034, compare the matching degree of the buffer capacity attenuation rate of each coordinate point (i.e., the vascular bifurcation segment at a specific position and time) with the instability threshold of the gene cluster. If the matching degree exceeds the preset curvature tolerance range, it is considered that there is a high risk of metabolic reserve depletion at this point.

[0132] Mark all coordinate points that exceed the curvature tolerance range to form a high-risk area map. This step can be achieved with the help of geographic information system (GIS) software to visually display the risk levels of different parts.

[0133] Finally, link these high-risk areas with the specific spatio-temporal distribution parameters of the environmental oxidative load, and analyze which external factors (such as air pollution, smoking, etc.) may be the main reasons for the metabolic reserve depletion in these areas. This process helps to identify potential risk factors and formulate targeted intervention measures.

[0134] For example, the researchers first determined the curvature tolerance ranges of several major vascular tone regulation gene clusters through large-scale epidemiological surveys. Then, using the dynamic matching degree matrix obtained in step 1034, they conducted a detailed analysis of each participant and found that some individuals showed an abnormally high decay rate of buffer capacity at the vascular bifurcations during a specific period, exceeding the corresponding curvature tolerance range. Subsequently, the research team used GIS technology to map these high-risk areas and combined them with environmental monitoring data, especially focusing on the impacts of factors such as air pollutant concentrations and smoking habits. The results showed that in those populations frequently exposed to high levels of air pollution and smoking environments, the development rate of arteriosclerosis was significantly accelerated, and the high-risk areas highly overlapped with these external exposure factors. This study not only revealed the profound impact of environmental factors on cardiovascular health but also provided a scientific basis for the formulation of public health policies, emphasizing the importance of reducing environmental pollution and promoting healthy lifestyles.

[0135] 1036. Simulate the standing wave amplification effect of the coordinate points in the high-risk area of metabolic reserve depletion through the oxidative stress wave propagation equation, and calculate the critical environmental exposure parameter combinations that lead to a cliff-like decline in metabolic buffer capacity. The critical environmental exposure parameter combinations include the oxidative load intensity threshold, exposure duration, and spatial action radius. The goal of step 1036 is to simulate the standing wave amplification effect of the coordinate points in the high-risk area of metabolic reserve depletion through the oxidative stress wave propagation equation and calculate the critical environmental exposure parameter combinations that lead to a cliff-like decline in metabolic buffer capacity. These parameters include, but are not limited to, the oxidative load intensity threshold, exposure duration, and spatial action radius. This step aims to identify the key environmental factors and their action mechanisms that are most likely to cause a sharp deterioration in vascular health.

[0136] First, based on the data obtained in the previous steps (such as the high-risk areas determined in step 1035), set the initial conditions and boundary conditions of the oxidative stress wave propagation equation. This includes specifying information such as the ROS concentration distribution at the initial moment and the ROS flux on the boundary.

[0137] Use numerical methods such as the finite difference method or the finite element method to solve the oxidative stress wave propagation equation. Consider the impacts of factors such as oxidative load intensity, exposure time, and spatial action radius in the model. In particular, simulate how ROS propagates in the body and forms a standing wave amplification effect under different environmental exposure conditions.

[0138] By changing the input parameters (such as increasing the oxidative load intensity or extending the exposure time), observe the system response, especially focusing on the key turning points that cause a sudden decline in metabolic buffer capacity. These turning points usually mark the critical points for the transition from a relatively stable physiological state to an acute injury state.

[0139] Based on the simulation results, determine the specific combination of environmental exposure parameters that can trigger a cliff-like decline in metabolic buffering capacity. This step requires multiple iterative simulations, adjusting the parameters until the exact critical values are found. The final output results will include important indicators such as the threshold of oxidative load intensity, exposure duration, and spatial action radius.

[0140] For example, the researchers first used the high-risk area map obtained in step 1035 as a basis to set the initial conditions and boundary conditions of the oxidative stress wave propagation equation. Next, they used COMSOL Multiphysics software to establish a detailed numerical model to simulate the propagation of ROS in the body under different environmental exposure conditions. The research team specifically focused on which specific locations would exhibit a standing wave amplification effect and lead to a rapid decline in the metabolic buffering capacity of endothelial cells when the oxidative load intensity gradually increased. After a series of simulation experiments, it was found that when the oxidative load intensity exceeded a certain critical value (for example, the concentration of reactive oxygen species in the air per cubic meter reached a certain level), and the continuous exposure time exceeded a certain threshold (such as continuous exposure for more than several hours), a significant decline in metabolic buffering function was indeed observed in some high-risk areas. In addition, it was also found that this effect had a certain spatial limitation, that is, it was mainly concentrated in a small area directly exposed to high oxidative load. This discovery not only reveals the potential triggering mechanism of atherosclerosis but also provides a scientific basis for developing effective preventive strategies, emphasizing the importance of controlling environmental pollution and personal protective measures. Through these studies, susceptible populations can be more accurately identified, and targeted measures can be taken to reduce the risk of cardiovascular diseases.

[0141] In order to further improve the risk prediction accuracy of cardiovascular and metabolic diseases, in some embodiments, calculate the buffering capacity decay rate based on the time derivative of the attenuation trajectory function in step 1034, extract the curvature extreme points of the instability threshold evolution curve, and establish a dynamic matching degree matrix between the buffering capacity decay rate and the curvature extreme points, including:

[0142] Perform time-domain differentiation on the attenuation trajectory function, and use the central difference method to calculate the attenuation rate of the metabolic buffer capacity at each spatial coordinate point within a preset time window. The width of the preset time window is one-fourth of the characteristic period parameter of the vascular wall metabolic oscillation. For the instability threshold evolution curve output by the genetic prediction model, calculate the curvature parameter of the instability threshold evolution curve, and use the sliding window extreme value detection algorithm to identify the set of curvature extreme points. The set of curvature extreme points satisfies the conditions that the first derivative of the curvature parameter is zero and the second derivative is negative. Construct a spatio-temporal matching tensor, couple the attenuation rate of the metabolic buffer capacity with the nearest neighbor curvature extreme points according to the time alignment criterion, and generate a dynamic matching degree matrix. The dimensions of the dynamic matching degree matrix include the number of spatial grids, the number of time nodes, and the number of gene clusters. The method further includes: performing normalization processing on the dynamic matching degree matrix, calculating the historical matching degree mean and standard deviation of each spatial point, and forming a standardized dynamic matching degree matrix distribution map. The standardized dynamic matching degree matrix distribution map contains normalized matching degrees. Calculate the instability risk weight factor through the curvature tolerance range specific to the gene cluster. The instability risk weight factor is used to reflect the instability threshold critical values of different gene clusters. Perform convolution operation on the normalized matching degree and the instability risk weight factor to generate a comprehensive matching degree index. The comprehensive matching degree index is used to correlate environmental exposure parameters.

[0143] In this embodiment, the attenuation trajectory function describes the trend of the vascular endothelial metabolic buffer capacity changing with time and space, and is used to evaluate the change of local metabolic function. The central difference method is a numerical differentiation method that estimates the derivative by calculating the difference between adjacent points and is used here to calculate the attenuation rate of the metabolic buffer capacity. The width of the preset time window is one-fourth of the characteristic period parameter of the vascular wall metabolic oscillation, ensuring that the calculation result has a high time resolution. The instability threshold evolution curve shows the change trend of the instability threshold corresponding to the vascular tension regulation gene cluster over time, helping to understand the impact of genetic variation on cardiovascular health. The curvature parameter describes the degree of bending of the instability threshold evolution curve, and the set of curvature extreme points can be identified through the sliding window extreme value detection algorithm. These points mark important transitions in the system state. The spatio-temporal matching tensor is a multi-dimensional data structure used to integrate the information of the attenuation rate of the metabolic buffer capacity and the curvature extreme points to generate a dynamic matching degree matrix. The standardized dynamic matching degree matrix distribution map provides a unified reference framework by normalizing the historical matching degree mean and standard deviation of each spatial point. The instability risk weight factor reflects the instability threshold critical values of different gene clusters and is used to quantify the potential risks of different gene clusters. The comprehensive matching degree index combines the normalized matching degree and the instability risk weight factor through convolution operation to generate a comprehensive evaluation index for correlating environmental exposure parameters.

[0144] In the embodiments of the present application, first, perform time-domain differential processing on the attenuation trajectory function, and use the central difference method to calculate the attenuation rate of the metabolic buffer capacity at each spatial coordinate point within a preset time window; second, for the instability threshold evolution curve output by the genetic prediction model, calculate its curvature parameter, and use the sliding window extreme value detection algorithm to identify the set of curvature extreme points; third, construct a spatio-temporal matching tensor, couple the attenuation rate of the metabolic buffer capacity with the nearest neighbor curvature extreme points according to the time alignment criterion, and generate a dynamic matching degree matrix; finally, perform normalization processing on the dynamic matching degree matrix, calculate the historical matching degree mean and standard deviation of each spatial point, form a standardized dynamic matching degree matrix distribution map, calculate the instability risk weight factor through the curvature tolerance range specific to the gene cluster, and perform a convolution operation on the normalized matching degree and the instability risk weight factor to generate a comprehensive matching degree index for correlating environmental exposure parameters.

[0145] The following is a specific example:

[0146] Suppose in a research project focusing on the early diagnosis of arteriosclerosis, researchers are developing a new dynamic resilience assessment model. First, use high-resolution imaging technology to obtain a three-dimensional stress distribution map of the patient's blood vessel wall, perform time-domain differential processing on the attenuation trajectory function, and use the central difference method to calculate the attenuation rate of the metabolic buffer capacity at each spatial coordinate point within a preset time window; second, based on the patient's genomic data, generate an instability threshold evolution curve output by the genetic prediction model, calculate its curvature parameter, and use the sliding window extreme value detection algorithm to identify the set of curvature extreme points; third, construct a spatio-temporal matching tensor, couple the attenuation rate of the metabolic buffer capacity with the nearest neighbor curvature extreme points according to the time alignment criterion, and generate a dynamic matching degree matrix; finally, perform normalization processing on the dynamic matching degree matrix, calculate the historical matching degree mean and standard deviation of each spatial point, form a standardized dynamic matching degree matrix distribution map, calculate the instability risk weight factor through the curvature tolerance range specific to the gene cluster, and perform a convolution operation on the normalized matching degree and the instability risk weight factor to generate a comprehensive matching degree index to provide accurate risk assessment and treatment recommendations for doctors.

[0147] To further improve the risk prediction accuracy of cardiovascular and metabolic diseases, in some embodiments, the combination of the desynchronization index, instability threshold, and critical environmental exposure parameters described in step 104 is fused to construct a dynamic resilience assessment model, and the phase space contraction rate of the vascular metabolic homeostasis boundary is calculated. When the phase space contraction rate exceeds the vascular wall remodeling compensation ability, a hierarchical warning signal is generated, including:

[0148] 1041. Map the lipid transport rhythm desynchronization index to the vascular wall cell energy metabolism state space to generate a multi-dimensional gradient field distribution of the lipid transport rhythm instability degree;

[0149] Step 1041 involves mapping the desynchronization index in the multimodal oscillation feature spectrum (especially the desynchronization index of lipid transport rhythm) to the energy metabolism state space of vascular wall cells. This step aims to generate a multi-dimensional gradient field distribution that describes the instability degree of lipid transport rhythm, which helps to understand the instability degree during lipid transport at different positions.

[0150] First, extract the desynchronization index data regarding lipid transport rhythm from the previous steps. These data usually come from physiological signal analysis, such as electrocardiogram, hemodynamic monitoring, etc.

[0151] Secondly, construct a mathematical model to represent the energy metabolism state space of vascular wall cells, where each dimension represents different metabolic parameters (such as ATP level, redox state, etc.).

[0152] Finally, map the desynchronization index into the energy metabolism state space defined above, and use interpolation techniques or other mathematical methods to create a multi-dimensional gradient field distribution map. This map shows the stability differences of lipid transport rhythm in different regions.

[0153] For example, researchers first collected the lipid transport rhythm data of patients and determined the desynchronization index through frequency domain analysis. Then, they constructed an energy metabolism state space model including ATP concentration, NADH / NAD+ ratio, etc. By mapping the desynchronization index into this model, a three-dimensional gradient field distribution map showing the instability degree of lipid transport rhythm was successfully generated. The results showed that in some specific regions, especially near the vascular bifurcation points, the lipid transport rhythm showed significant instability, suggesting that these regions may be high-risk areas for atherosclerosis.

[0154] 1042. Construct a dynamic constraint boundary for the vascular tension-metabolism coupling degree based on the instability threshold in the genetic prediction model, and the dynamic constraint boundary adjusts its curvature characteristics in real time according to the combination of critical environmental exposure parameters;

[0155] The goal of step 1042 is to construct a dynamic constraint boundary between vascular tension and metabolism coupling degree based on the instability threshold in the genetic prediction model, and this boundary can adjust its curvature characteristics in real time according to the change of the combination of critical environmental exposure parameters. This dynamic constraint boundary reflects the range of the ability of vascular wall cells to maintain normal physiological functions under the influence of different external conditions.

[0156] Extract the expression level changes of gene clusters related to vascular tension regulation and their corresponding instability thresholds from the genetic prediction model. These thresholds mark the key points where significant changes occur in gene expression or functional states.

[0157] Based on the instability threshold and the known vascular tension and metabolic coupling mechanisms, a mathematical model is established to describe the relationship between the two. This usually involves a system of non-linear dynamic equations, which include the effects of factors such as vascular smooth muscle contraction and nitric oxide released by endothelial cells on vascular tension, as well as the support of energy metabolism for these processes.

[0158] Combined with environmental exposure parameters (such as oxidation load intensity, duration, and spatial action radius), the curvature of the above dynamic constraint boundary is adjusted by numerical simulation methods. When environmental conditions change, such as increasing the level of oxidative stress, the boundary may become steeper or new inflection points may appear, reflecting the weakened response ability of the system to external pressure.

[0159] The model is verified using experimental data to ensure that it can accurately reflect the actual situation. If deviations are found, the model parameters are adjusted or the assumptions are improved until the model prediction results are consistent with the experimental observations.

[0160] For example, the researchers first determined the instability thresholds of several key genes (such as the gene encoding nitric oxide synthase), which are crucial for maintaining normal vascular tension. Then, they used this information to construct a mathematical model that includes the interaction between vascular tension regulation and metabolic support. The model not only considers the equilibrium situation under the basal physiological state but also simulates the response of the vascular system under different environmental exposure conditions (such as long-term exposure to high concentrations of air pollutants). Through numerical simulation, it was observed that as the level of oxidative stress increases, the curvature of the dynamic constraint boundary gradually becomes larger, indicating that the ability of the vascular wall to maintain normal tension is challenged in a high oxidative load environment. In addition, the study also found that under certain specific conditions, even if the oxidation load intensity does not reach the preset instability threshold, due to the cumulative effect over time, obvious morphological changes will occur in the boundary, suggesting that chronic low-dose exposure may also lead to health problems. Based on these findings, the research team proposed a series of preventive measures, including reducing pollution exposure and strengthening antioxidant protection, aiming to reduce the risk of cardiovascular diseases.

[0161] 1043. Establish a coupling equation between the combination of the critical environmental exposure parameters and the multi-dimensional gradient field distribution, and calculate the non-linear dissipation rate of the metabolic oscillation energy at the vascular bifurcation;

[0162] Step 1043 aims to establish a coupling equation between the combination of the critical environmental exposure parameters and the multi-dimensional gradient field distribution (describing the instability degree of lipid transport rhythm), and calculate the non-linear dissipation rate of the metabolic oscillation energy at the vascular bifurcation through this equation. This process helps to understand how external environmental factors affect the energy metabolism state of vascular endothelial cells, especially its stability in the face of adverse conditions such as oxidative stress.

[0163] First, a mathematical model needs to be constructed that can describe the relationship between critical environmental exposure parameters (such as oxidation load intensity, exposure time, and spatial action radius) and the distribution of multi-dimensional gradient fields. This model should take into account the influence mechanisms of different environmental factors on the instability degree of lipid transport rhythm.

[0164] Secondly, integrate the data obtained from the previous steps, including but not limited to: the specific values of the combination of critical environmental exposure parameters; the multi-dimensional gradient field distribution map describing the instability degree of lipid transport rhythm; the changes in the curvature of the dynamic constraint boundary.

[0165] Using the above coupling equations and the integrated dataset, calculate the non-linear dissipation rate of metabolic oscillation energy at a specific location (such as the bifurcation of blood vessels) through numerical simulation methods. This usually involves solving complex differential equations to capture the dynamic characteristics of energy dissipation over time.

[0166] Conduct a detailed analysis of the calculated non-linear dissipation rate to identify the conditions under which significant energy loss or system instability will occur. This information is of great significance for evaluating the vascular health status and predicting potential risks.

[0167] 1044. Construct a cardiovascular-metabolic system dynamic resilience assessment model, which is determined according to the instability degree of lipid transport rhythm, the curvature of the dynamic constraint boundary, and the non-linear dissipation rate of metabolic oscillation energy, and is used to evaluate the vascular wall remodeling compensation ability in different regions, and quantify the absorption threshold of the vascular wall remodeling compensation ability to gradient field perturbations through Lyapunov exponent analysis;

[0168] The objective of step 1044 is to construct a dynamic resilience assessment model of the cardiovascular-metabolic system. This model is based on the instability degree of lipid transport rhythm, the curvature of the dynamic constraint boundary, and the non-linear dissipation rate of metabolic oscillation energy, and is used to evaluate the vascular wall remodeling compensation ability in different regions, and quantify the absorption threshold of the vascular wall to gradient field perturbations through Lyapunov exponent analysis. This step is crucial for predicting and preventing the development of cardiovascular diseases.

[0169] First, it is necessary to integrate the data obtained from the previous steps, including information such as the multi-dimensional gradient field distribution of the instability degree of lipid transport rhythm, the curvature characteristics of the dynamic constraint boundary, and the non-linear dissipation rate of metabolic oscillation energy. Ensure that all data are compared and analyzed on the same spatio-temporal scale.

[0170] Based on the above data, use the methods of systems biology to establish a mathematical model to describe the dynamic behavior of the cardiovascular-metabolic system. This model should include:

[0171] The instability degree of lipid transport rhythm as an input variable;

[0172] The dynamic constraint boundary curvature reflects the stability and adaptability of the system;

[0173] The non - linear dissipation rate of metabolic oscillation energy represents the response of the system to external perturbations.

[0174] The Lyapunov Exponent is used to quantify the stability of the system. The Lyapunov Exponent can be used to evaluate whether the system can return to its original state when faced with small perturbations, or whether it will deviate from the original trajectory and enter an unstable state. Calculate the Lyapunov Exponents in different regions to determine its ability to absorb perturbations.

[0175] Verify and optimize the model to ensure that it accurately reflects the actual situation. If deviations are found, adjust the parameters or improve the assumptions until the model prediction results match the experimental observations.

[0176] 1045. When the non - linear dissipation rate breaks through the dynamic constraint boundary curvature and the phase - space contraction rate exceeds the absorption threshold, generate a graded early - warning signal corresponding to the instability tendency of the patch according to the boundary - breakthrough azimuth angle and the spatial coordinates of the dissipation hot - spot.

[0177] The goal of step 1045 is to generate a graded early - warning signal when the non - linear dissipation rate breaks through the dynamic constraint boundary curvature and the phase - space contraction rate exceeds the absorption threshold. Specifically, according to the spatial coordinates of the boundary - breakthrough azimuth angle and the dissipation hot - spot, generate a graded early - warning signal corresponding to the instability tendency of the patch. This step aims to provide a mechanism to identify and warn potential cardiovascular risk areas for timely intervention.

[0178] First, monitor the non - linear dissipation rate of metabolic oscillation energy in real - time and compare it with the curvature of the dynamic constraint boundary. When the non - linear dissipation rate increases significantly and breaks through the dynamic constraint boundary curvature, it indicates that the system has entered an unstable state.

[0179] Second, calculate the phase - space contraction rate through Lyapunov exponent analysis to quantify the system's ability to absorb perturbations. If the phase - space contraction rate exceeds a pre - set absorption threshold, it means that the system cannot effectively cope with the current external or internal pressure and there is a high risk of instability.

[0180] Then, determine the specific position (i.e., azimuth angle) where the non - linear dissipation rate breaks through the dynamic constraint boundary and the spatial coordinates of the dissipation hot - spot in this region. This information helps to accurately locate high - risk areas for subsequent medical evaluation and treatment planning.

[0181] According to the above analysis results, generate hierarchical warning signals according to preset standards. The grading standards can be divided into three levels: low, medium, and high according to the risk level, and each level corresponds to different intervention measure suggestions. For example, low risk may only require lifestyle adjustments, while high risk requires immediate medical intervention or even surgical treatment.

[0182] Finally, feedback the warning signal to the medical team, and continuously optimize the model parameters and warning standards according to the actual effect to ensure the accuracy and effectiveness of the system.

[0183] In a simulation experiment, the research team found that there was a significant increase in the non-linear dissipation rate at the bifurcation of a certain patient's blood vessels, and the phase space contraction rate exceeded the set absorption threshold. Further analysis showed that this area was located at the bifurcation of the right carotid artery and had obvious dissipation hotspots. Based on this, the system automatically generated a warning signal of the "high risk" level and recommended that the patient immediately undergo further examinations and possible treatments.

[0184] This example demonstrates how to use advanced computational models and technical means to achieve early warning and precise management of cardiovascular diseases. By integrating multi-source data and applying complex mathematical analysis methods, the accuracy of disease prediction can be greatly improved, providing strong support for personalized medicine.

[0185] In order to further improve the risk prediction accuracy of cardiovascular and metabolic diseases, in some embodiments, the cardiovascular-metabolic system dynamic resilience assessment model described in step 1043 is constructed. The cardiovascular-metabolic system dynamic resilience assessment model is determined according to the lipid transport rhythm instability degree, the dynamic constraint boundary curvature, and the non-linear dissipation rate of metabolic oscillation energy, and is used to evaluate the vascular wall remodeling compensation ability of different regions, and quantify the absorption threshold of the vascular wall remodeling compensation ability to the gradient field perturbation through Lyapunov exponent analysis, including:

[0186] Perform modal decomposition on the multi-dimensional gradient field of the lipid transport rhythm instability degree, extract the main oscillation mode of vascular wall energy metabolism, and generate a dynamic characteristic spectrum including spatio-temporal coupling coefficients; construct an evolution equation of vascular tension-metabolism coupling degree based on the dynamic constraint boundary curvature, and solve the non-linear dissipation rate of metabolic oscillation energy through the dynamic modal decomposition algorithm to generate a constraint boundary evolution matrix that is adjusted in real time according to environmental exposure parameters; use the Lyapunov spectrum analysis method to quantify the phase space contraction rate of the main oscillation mode and the dynamic constraint boundary curvature, and combine the historical data of the metabolic buffer capacity decay rate to calculate the absorption threshold of the vascular wall remodeling compensation ability to the gradient field perturbation.

[0187] In this embodiment, the lipid transport rhythm instability degree describes the temporal and spatial instability that occurs during the transport of lipids within the blood vessel wall, and is used to evaluate the changes in local metabolic function. The multi-dimensional gradient field is a data set generated by mapping the desynchronization index to the cell energy metabolism state space, and is used to identify the unstable regions in energy metabolism. Modal decomposition is a signal processing technique used to extract the main oscillation modes from complex multi-dimensional data, and here it is used to extract the main oscillation modes of the blood vessel wall energy metabolism. The dynamic feature spectrum is a data structure that contains spatio-temporal coupling coefficients and is used to describe the characteristics of metabolic activities at different time and space points. The dynamic constrained boundary curvature is a key parameter determined based on a genetic prediction model, used to define the coupling relationship between blood vessel tension and metabolism, and adjusts its boundary curvature characteristics according to environmental changes. The evolution equation describes the trend of the metabolic oscillation energy changing over time, and solves the non-linear dissipation rate through the dynamic modal decomposition algorithm. Lyapunov spectrum analysis is a mathematical tool used to quantify the stability of a system to perturbations, and here it is used to evaluate the absorption threshold of the blood vessel wall remodeling compensation ability to gradient field perturbations.

[0188] In the embodiment of the present application, first, perform modal decomposition on the multi-dimensional gradient field of the lipid transport rhythm instability degree, extract the main oscillation modes of the blood vessel wall energy metabolism, and generate a dynamic feature spectrum containing spatio-temporal coupling coefficients; second, construct an evolution equation for the blood vessel tension-metabolism coupling degree based on the dynamic constrained boundary curvature, and solve the non-linear dissipation rate of the metabolic oscillation energy through the dynamic modal decomposition algorithm to generate a constrained boundary evolution matrix that is adjusted in real time according to the environmental exposure parameters; third, use the Lyapunov spectrum analysis method to quantify the phase space contraction rate of the main oscillation mode and the dynamic constrained boundary curvature, and combine the historical data of the metabolic buffer capacity decay rate to calculate the absorption threshold of the blood vessel wall remodeling compensation ability to gradient field perturbations; finally, based on the spatio-temporal distribution characteristics of the absorption threshold and the non-linear dissipation rate, construct a dynamic probability model for the blood vessel wall instability risk, and quantify the failure probability of the blood vessel wall remodeling compensation ability under different environmental exposure scenarios by coupling the spatio-temporal coupling coefficients of the dynamic feature spectrum and the curvature parameters of the constrained boundary evolution matrix.

[0189] The following is a specific example:

[0190] Suppose in a research project focused on the cardiovascular health of the elderly. First, daily activity data (such as steps and heart rate) and eating habits (such as fat intake) of patients are collected through high-resolution imaging technology and wearable devices, and combined with the energy metabolism state of vascular wall cells to generate a multi-dimensional gradient field of lipid transport rhythm instability. Second, modal decomposition technology is used to extract the main oscillation mode of vascular wall energy metabolism from these data and generate a dynamic characteristic spectrum containing spatio-temporal coupling coefficients. Third, based on the instability threshold in the patient's genomic data and combined with environmental exposure parameters, the curvature characteristics of the dynamic constraint boundary are adjusted to ensure that the model can adapt to individual differences, and the non-linear dissipation rate of metabolic oscillation energy is solved through the dynamic modal decomposition algorithm. Then, the Lyapunov spectrum analysis method is used to quantify the phase space contraction rate of the main oscillation mode and the curvature of the dynamic constraint boundary, and combined with the historical data of the decay rate of metabolic buffer capacity, the absorption threshold of the vascular wall remodeling compensation ability to gradient field perturbations is calculated. Finally, based on the spatio-temporal distribution characteristics of the absorption threshold and the non-linear dissipation rate, a dynamic probability model of vascular wall instability risk is constructed. By coupling the spatio-temporal coupling coefficients of the dynamic characteristic spectrum and the curvature parameters of the constraint boundary evolution matrix, the failure probability of the vascular wall remodeling compensation ability under different environmental exposure scenarios is quantified.

[0191] In order to further improve the risk prediction accuracy of cardiovascular and metabolic diseases, in some embodiments, the method of using the Lyapunov spectrum analysis method to quantify the phase space contraction rate of the main oscillation mode and the curvature of the dynamic constraint boundary, and combined with the historical data of the decay rate of metabolic buffer capacity, to calculate the absorption threshold of the vascular wall remodeling compensation ability to gradient field perturbations includes:

[0192] Perform Lyapunov spectrum analysis on the main oscillation mode, extract the phase space contraction rate parameter, and combine it with the spatio-temporal evolution characteristics of the curvature of the dynamic constraint boundary to generate a stability decay spectrum of vascular wall energy metabolism; based on the historical data of the decay rate of the metabolic buffer capacity, construct a decay mode feature vector of the time series, and quantify the dynamic stability decay index of different vascular bifurcation regions in the stability decay spectrum through the spectral clustering algorithm; fuse the stability decay spectrum and the dynamic stability decay index, establish a weight matrix of vascular wall stress-metabolism coupling, and use an adaptive optimization algorithm to solve the absorption threshold under gradient field perturbations; align the absorption threshold and the phase space contraction rate in the dynamic toughness assessment model in space and time to generate a failure probability distribution map of the vascular wall remodeling compensation ability, and the failure probability distribution map is used to dynamically adjust the warning threshold of the critical environmental exposure parameter combination.

[0193] In this embodiment, the main oscillation mode describes the main fluctuation mode of the energy metabolism of the blood vessel wall and is used to identify changes in local metabolic function. Lyapunov spectrum analysis is a mathematical tool used to quantify the stability of a system to perturbations and is used here to extract the phase space contraction rate parameter to evaluate changes in the stability of the blood vessel wall energy metabolism. The dynamic constrained boundary curvature is a key parameter determined based on a genetic prediction model, used to define the coupling relationship between blood vessel tension and metabolism, and adjusts its boundary curvature characteristics according to environmental changes. The stability decay spectrum is a data structure that contains the spatio-temporal evolution characteristics of the phase space contraction rate parameter and the dynamic constrained boundary curvature and is used to describe changes in the stability of the blood vessel wall energy metabolism. The decay mode eigenvector of the time series is a feature set extracted from the historical data of the decay rate of the metabolic buffer capacity and is used to describe the metabolic change trend in different time periods. The spectral clustering algorithm is an unsupervised learning method used to group similar data points into one category and is used here to quantify the dynamic stability decay index in different blood vessel bifurcation regions of the stability decay spectrum. The weight matrix is a multi-dimensional data structure used to integrate the information of the stability decay spectrum and the dynamic stability decay index to generate the weight matrix of the blood vessel wall stress-metabolism coupling. The adaptive optimization algorithm is an optimization technique used to solve the absorption threshold under the perturbation of the gradient field to ensure that the model can adapt to different environmental exposure scenarios. The failure probability distribution map is generated by spatio-temporally aligning the absorption threshold with the phase space contraction rate in the dynamic toughness evaluation model and is used to dynamically adjust the warning threshold of the critical environmental exposure parameter combination.

[0194] In the embodiment of the present application, first, perform Lyapunov spectrum analysis on the main oscillation mode, extract the phase space contraction rate parameter, and combine the spatio-temporal evolution characteristics of the dynamic constrained boundary curvature to generate the stability decay spectrum of the blood vessel wall energy metabolism; second, based on the historical data of the decay rate of the metabolic buffer capacity, construct the decay mode eigenvector of the time series, and quantify the dynamic stability decay index in different blood vessel bifurcation regions of the stability decay spectrum through the spectral clustering algorithm; third, fuse the stability decay spectrum and the dynamic stability decay index, establish the weight matrix of the blood vessel wall stress-metabolism coupling, and use the adaptive optimization algorithm to solve the absorption threshold under the perturbation of the gradient field; finally, spatio-temporally align the absorption threshold with the phase space contraction rate in the dynamic toughness evaluation model to generate the failure probability distribution map of the blood vessel wall reconstruction compensation ability, which is used to dynamically adjust the warning threshold of the critical environmental exposure parameter combination.

[0195] The following is a specific example:

[0196] For example, assume in a community medical project focusing on the cardiovascular health of the elderly. First, collect the daily activity data (such as walking distance, social activity frequency) and living environment data (such as noise level in the living environment) of the elderly through community health monitoring stations and wearable devices, and combine them with the energy metabolism status of vascular wall cells to generate the main oscillation mode. Second, perform Lyapunov spectrum analysis on these main oscillation modes, extract the phase space contraction rate parameter, and combine the spatio-temporal evolution characteristics of the dynamic constraint boundary curvature to generate the stability decay spectrum of vascular wall energy metabolism. Third, based on the historical data of the metabolic buffer capacity decay rate, construct the decay mode feature vector of the time series, and quantify the dynamic stability decay index of different vascular bifurcation regions in the stability decay spectrum through the spectral clustering algorithm. Fourth, fuse the stability decay spectrum and the dynamic stability decay index, establish the weight matrix of vascular wall stress-metabolism coupling, and use the adaptive optimization algorithm to solve the absorption threshold under the gradient field perturbation. Finally, perform spatio-temporal alignment of the absorption threshold and the phase space contraction rate in the dynamic toughness evaluation model to generate the failure probability distribution map of the vascular wall reconstruction compensation ability, which is used to dynamically adjust the warning threshold of the critical environmental exposure parameter combination. This method not only improves the accuracy of risk prediction but also provides strong support for community medical services, helping the elderly better maintain cardiovascular health.

[0197] To further improve the risk prediction accuracy of cardiovascular and metabolic diseases, in some embodiments, the step 102 of identifying the topological connection strength between the vascular tone regulation gene cluster and the metabolic sensing pathway gene cluster based on genomics data, and quantifying the influence weight of genetic variation on the cardiovascular-metabolic coupling efficiency by combining the phase delay parameter to generate a genetic prediction model includes:

[0198] 1021. Extract the co-expression network of the vascular tone regulation gene cluster and the metabolic sensing pathway gene cluster based on genomics data, and calculate the topological connection strength between the gene clusters. The topological connection strength reflects the functional synergy of the gene clusters in regulating cardiovascular-metabolic coupling.

[0199] In this step, obtain the relevant genomics data from public databases (such as GEO, TCGA) or the laboratory's own data. According to the known functional annotations, determine which genes belong to the vascular tone regulation gene cluster and the metabolic sensing pathway gene cluster. Use the Pearson correlation coefficient or other similarity measurement methods to identify the co-expression relationship between genes and construct the co-expression network between the gene clusters. Use graph theory algorithms (such as PageRank or Betweenness Centrality) to calculate the importance and connection strength of each gene cluster in the network, reflecting its functional synergy.

[0200] 1022. Map the phase delay parameter in the multi-modal oscillation feature spectrum to the gene cluster co-expression network to generate a phase delay weighted gene cluster network, which is used to characterize the association between the functional synergy among gene clusters and the cardiovascular-metabolic coupling efficiency;

[0201] In this step, extract the phase delay parameter from physiological signals (such as electrocardiogram, hemodynamic monitoring). Add the extracted phase delay parameter as a weight to the corresponding edges in the co-expression network to form a phase delay weighted gene cluster network. Explore the structural characteristics of the weighted network and its impact on the cardiovascular-metabolic coupling efficiency through network analysis tools (such as Cytoscape).

[0202] 1023. Identify single nucleotide polymorphism sites in the gene cluster regulating vascular tone and the gene cluster of the metabolic sensing pathway based on genetic variation data, and calculate the regulatory effect value of each polymorphism site on the topological connection strength between gene clusters, where the regulatory effect value is used to quantify the impact of genetic variation on the functional synergy of gene clusters;

[0203] In this step, identify single nucleotide polymorphism (SNP) sites in the gene cluster regulating vascular tone and the gene cluster of the metabolic sensing pathway based on genetic variation data, and calculate the regulatory effect value of each polymorphism site on the topological connection strength between gene clusters to quantify the impact of genetic variation on the functional synergy of gene clusters. Specifically, use genome-wide association study (GWAS) data or exome sequencing data to identify SNP sites within the target gene cluster. Use statistical models (such as linear regression or logistic regression) to evaluate the impact of each SNP site on the connection strength between gene clusters. Sort according to the regulatory effect value to identify the most influential SNP sites.

[0204] 1024. Integrate the regulatory effect value with the phase delay weighted gene cluster network, and calculate the influence weight of genetic variation on the cardiovascular-metabolic coupling efficiency, where the influence weight reflects the contribution degree of genetic variation to the coupling efficiency by regulating the functional synergy of gene clusters;

[0205] Step 1024 integrates the regulatory effect value with the phase delay weighted gene cluster network, and calculates the influence weight of genetic variation on the cardiovascular-metabolic coupling efficiency, reflecting the contribution degree of genetic variation to the coupling efficiency by regulating the functional synergy of gene clusters. Specifically, combine the regulatory effect value obtained in step 1023 with the phase delay weighted gene cluster network constructed in step 1022. Use machine learning methods (such as random forest or support vector machine) to train a model to predict the influence weight of genetic variation on the cardiovascular-metabolic coupling efficiency. Analyze the change trend of the coupling efficiency under different genetic variation combinations.

[0206] 1025. Construct a genetic prediction model based on the influence weights, where the genetic prediction model is used to predict the change trend of cardiovascular-metabolic coupling efficiency of a target object under a specific genetic variation background.

[0207] Step 1025 constructs a genetic prediction model based on the influence weights obtained from the above steps, which is used to predict the change trend of cardiovascular-metabolic coupling efficiency of a target object under a specific genetic variation background. Specifically, a prediction model is established using a supervised learning algorithm (such as decision tree, neural network), with the input being genetic variation data and the output being the change trend of cardiovascular-metabolic coupling efficiency. An independent dataset is used to validate the model, and the model parameters are adjusted according to the performance. The optimized model is applied to clinical practice to guide the selection of personalized treatment plans.

[0208] For example, the researchers first obtained genomic data of hundreds of healthy volunteers from a public database (such as GEO) and determined gene clusters related to vascular tone regulation and gene clusters related to metabolic sensing pathways using known functional annotations. They constructed a co-expression network between the gene clusters using the Pearson correlation coefficient and calculated the topological connection strength of each gene cluster through the PageRank algorithm (step 1021).

[0209] Next, the research team extracted phase delay parameters from the physiological signal records of the participants and added them as weights to the co-expression network to form a phase delay weighted gene cluster network. The structural characteristics of this network and its impact on cardiovascular-metabolic coupling efficiency were analyzed using Cytoscape software (step 1022).

[0210] To quantify the influence of genetic variation on the functional synergy of gene clusters, the researchers identified SNP sites within the target gene clusters using GWAS data and calculated the regulatory effect value of each polymorphism site on the topological connection strength between gene clusters through a linear regression model (step 1023).

[0211] Subsequently, the research team integrated the regulatory effect values with the phase delay weighted gene cluster network and calculated the influence weights of genetic variation on cardiovascular-metabolic coupling efficiency using a random forest model. By analyzing the change trend of coupling efficiency under different genetic variation combinations, the importance of certain key SNP sites was revealed (step 1024).

[0212] Finally, based on the above analysis results, the researchers constructed a genetic prediction model that can predict the changing trend of cardiovascular-metabolic coupling efficiency in individuals under specific genetic variation backgrounds. After validation with an independent dataset and model optimization, this model was successfully applied to clinical practice, helping doctors formulate more personalized treatment plans and significantly improving the treatment effects of patients (Step 1025). This series of studies not only deepened our understanding of the pathogenesis of cardiovascular diseases but also provided strong support for precision medicine.

[0213] Figure 2 The present application provides a schematic structural diagram of a risk prediction system for cardiovascular and metabolic diseases, as Figure 2 shown. The device includes:

[0214] An extraction module 21, configured to extract phase delay parameters between a cardiovascular elastic chamber and a metabolic substrate supply from the cardiovascular-metabolic coupling data within the continuous time series of the obtained target object, and construct a multi-modal oscillation feature spectrum including lipid transport rhythm desynchronization indicators;

[0215] An identification module 22, configured to identify the topological connection strength between a vascular tone regulation gene cluster and a metabolic sensing pathway gene cluster based on genomics data, and combine the phase delay parameters in the multi-modal oscillation feature spectrum to quantify the influence weight of genetic variation on cardiovascular-metabolic coupling efficiency, and generate a genetic prediction model;

[0216] A calculation module 23, configured to simulate and calculate the decay trajectory function of the vascular endothelial metabolic buffer capacity according to the local shear force distribution of the blood vessels of the target object and the environmental oxidation load parameters, analyze the dynamic matching degree matrix between the decay trajectory function and the instability threshold in the genetic prediction model, and determine the critical environmental exposure parameter combination;

[0217] A construction module 24, configured to fuse the lipid transport rhythm desynchronization indicator, the instability threshold, and the critical environmental exposure parameter combination, construct a dynamic toughness assessment model, calculate the phase space shrinkage rate of the vascular metabolic steady-state boundary, and generate a hierarchical warning signal when the phase space shrinkage rate exceeds the vascular wall remodeling compensation ability.

[0218] Figure 2 The described risk prediction system for cardiovascular and metabolic diseases can execute Figure 1 the described risk prediction method for cardiovascular and metabolic diseases in the embodiments shown. Its implementation principle and technical effects will not be elaborated further. For the risk prediction system for cardiovascular and metabolic diseases in the above embodiments, the specific manners in which each module and unit perform operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0219] In a possible design,Figure 2 A risk prediction system for cardiovascular and metabolic diseases in the illustrated embodiment can be implemented as a computing device, such as Figure 3 shown, the computing device may include a storage component 31 and a processing component 32;

[0220] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32.

[0221] The processing component 32 is used for the above Figure 1 A risk prediction method for cardiovascular and metabolic diseases in the illustrated embodiment.

[0222] Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above method.

[0223] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0224] Of course, the computing device may also necessarily include other components, such as an input / output interface, a display component, a communication component, etc.

[0225] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above peripheral interface module may be an output device, an input device, etc.

[0226] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0227] Among them, the computing device may be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device may refer to a cloud server, and the above processing component, storage component, etc. may be basic server resources leased or purchased from the cloud computing platform.

[0228] The embodiment of the present application also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the aboveFigure 1 A risk prediction method for cardiovascular and metabolic diseases of the illustrated embodiment.

[0229] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0230] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0231] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0232] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or equivalently replace some of the technical features. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. A method for predicting the risk of cardiovascular and metabolic diseases, characterized in that Including the following steps: Extract the phase delay parameter between the cardiovascular elastic chamber and the metabolic substrate supply from the cardiovascular-metabolic coupling data within the continuous time series of the obtained target object, and construct a multi-modal oscillation feature spectrum including the lipid transport rhythm desynchronization index; Based on the genomics data, identify the topological connection strength between the vascular tone regulation gene cluster and the metabolic sensing pathway gene cluster, combine the phase delay parameter in the multi-modal oscillation feature spectrum to quantify the influence weight of genetic variation on the cardiovascular-metabolic coupling efficiency, and generate a genetic prediction model; According to the local vascular shear force distribution and environmental oxidation load parameters of the target object, simulate and calculate the attenuation trajectory function of the vascular endothelial metabolic buffer capacity, analyze the dynamic matching degree matrix between the attenuation trajectory function and the instability threshold in the genetic prediction model, and determine the critical environmental exposure parameter combination; Fuse the lipid transport rhythm desynchronization index, the instability threshold, and the critical environmental exposure parameter combination to construct a dynamic resilience assessment model, calculate the phase space contraction rate of the vascular metabolic homeostasis boundary, and generate a hierarchical warning signal when the phase space contraction rate exceeds the vascular wall remodeling compensation ability.

2. The method according to claim 1, characterized in that, The step of simulating and calculating the attenuation trajectory function of the vascular endothelial metabolic buffer capacity according to the local vascular shear force distribution and environmental oxidation load parameters of the target object, analyzing the dynamic matching degree matrix between the attenuation trajectory function and the instability threshold in the genetic prediction model, and determining the critical environmental exposure parameter combination includes: Based on the three-dimensional stress distribution map of the vascular wall, extract the local vascular shear force distribution of the target vascular bifurcation segment, combine the endothelial cell metabolic flux monitoring data to construct a blood flow-metabolism dynamic response model, calculate the phase lag amount of lipid transport efficiency to shear force fluctuation through frequency domain coupling analysis, and generate a metabolic delay characteristic map at the vascular bifurcation; According to the spatio-temporal distribution of the environmental oxidation load parameters, establish an oxidative stress wave propagation equation, and solve the concentration standing wave formation threshold of the metabolic buffer medium at the vascular bifurcation, where the concentration standing wave formation threshold is used to calibrate the dynamic response boundary of the endothelial metabolic buffer capacity; Input the metabolic delay characteristic map at the vascular bifurcation into the blood flow-metabolism dynamic response model, combine the concentration standing wave formation threshold, and solve the attenuation trajectory function of the vascular endothelial metabolic buffer capacity through spatio-temporal convolution operation, and simultaneously obtain the evolution curve of the instability threshold corresponding to the vascular tone regulation gene cluster in the genetic prediction model; Calculate the buffer capacity attenuation rate based on the time derivative of the attenuation trajectory function, extract the curvature extreme points of the instability threshold evolution curve, and establish a dynamic matching degree matrix between the buffer capacity attenuation rate and the curvature extreme points; When the matching degree of the buffer capacity attenuation rate at the coordinate point of the vascular bifurcation exceeds the curvature tolerance range of the corresponding gene cluster, mark it as a high-risk area of metabolic reserve exhaustion, and associate the spatio-temporal distribution parameters of the environmental oxidation load at the coordinate point of the vascular bifurcation. Simulate the standing wave amplification effect of the coordinate points in the high-risk area of metabolic reserve depletion through the oxidative stress wave propagation equation, and calculate the critical environmental exposure parameter combinations that lead to a cliff-like decline in the metabolic buffer capacity. The critical environmental exposure parameter combinations include the oxidative load intensity threshold, exposure duration, and spatial action radius.

3. The method according to claim 2, wherein Calculate the buffer capacity decay rate based on the time derivative of the attenuation trajectory function, and extract the curvature extreme points of the instability threshold evolution curve to establish a dynamic matching degree matrix between the buffer capacity decay rate and the curvature extreme points, including: Perform time-domain differential processing on the attenuation trajectory function, and use the central difference method to calculate the metabolic buffer capacity decay rate of each spatial coordinate point within a preset time window. The width of the preset time window is one-fourth of the characteristic period parameter of the vascular wall metabolic oscillation. For the instability threshold evolution curve output by the genetic prediction model, calculate the curvature parameter of the instability threshold evolution curve, and use the sliding window extreme value detection algorithm to identify the set of curvature extreme points. The set of curvature extreme points satisfies the condition that the first derivative of the curvature parameter is zero and the second derivative is negative. Construct a spatio-temporal matching tensor, couple the metabolic buffer capacity decay rate with the nearest neighbor curvature extreme points according to the time alignment criterion, and generate a dynamic matching degree matrix. The dimensions of the dynamic matching degree matrix include the number of spatial grids, the number of time nodes, and the number of gene clusters. The method further includes: Perform normalization processing on the dynamic matching degree matrix, calculate the historical matching degree mean and standard deviation of each spatial point, and form a standardized dynamic matching degree matrix distribution map. The standardized dynamic matching degree matrix distribution map contains the normalized matching degree. Calculate the instability risk weight factor through the curvature tolerance range specific to the gene cluster. The instability risk weight factor is used to reflect the instability threshold critical values of different gene clusters. Perform convolution operation on the normalized matching degree and the instability risk weight factor to generate a comprehensive matching degree index. The comprehensive matching degree index is used to associate environmental exposure parameters.

4. The method according to claim 1, wherein Fuse the lipid transport rhythm desynchronization index, the instability threshold, and the critical environmental exposure parameter combination to construct a dynamic resilience assessment model, calculate the phase space contraction rate of the vascular metabolic homeostasis boundary, and generate a hierarchical warning signal when the phase space contraction rate exceeds the vascular wall remodeling compensation ability, including: Map the lipid transport rhythm desynchronization index to the vascular wall cell energy metabolism state space to generate a multi-dimensional gradient field distribution of the lipid transport rhythm instability degree. Based on the instability threshold in the genetic prediction model, construct a dynamic constraint boundary of the vascular tension-metabolism coupling degree. The dynamic constraint boundary adjusts its curvature characteristics in real time with the critical environmental exposure parameter combination. Establish a coupling equation between the critical environmental exposure parameter combination and the multi-dimensional gradient field distribution, and calculate the non-linear dissipation rate of the metabolic oscillation energy at the vascular bifurcation. Construct a cardiovascular-metabolic system dynamic resilience assessment model, which is determined according to the lipid transport rhythm instability degree, the dynamic constraint boundary curvature, and the nonlinear dissipation rate of metabolic oscillation energy, and is used to evaluate the vascular wall remodeling compensation ability of different regions, and quantify the absorption threshold of the vascular wall remodeling compensation ability to gradient field perturbations through Lyapunov exponent analysis; When the nonlinear dissipation rate breaks through the dynamic constraint boundary curvature and the phase space contraction rate exceeds the absorption threshold, generate a graded warning signal corresponding to the plaque instability tendency according to the boundary breakthrough azimuth angle and the dissipation hot spot spatial coordinates.

5. The method according to claim 4, wherein The construction of the cardiovascular-metabolic system dynamic resilience assessment model, which is determined according to the lipid transport rhythm instability degree, the dynamic constraint boundary curvature, and the nonlinear dissipation rate of metabolic oscillation energy, and is used to evaluate the vascular wall remodeling compensation ability of different regions, and quantify the absorption threshold of the vascular wall remodeling compensation ability to gradient field perturbations through Lyapunov exponent analysis, includes: Perform modal decomposition on the multi-dimensional gradient field of the lipid transport rhythm instability degree, extract the main oscillation mode of vascular wall energy metabolism, and generate a dynamic characteristic spectrum containing spatio-temporal coupling coefficients; Based on the dynamic constraint boundary curvature, construct an evolution equation of vascular tension-metabolism coupling degree, and solve the nonlinear dissipation rate of metabolic oscillation energy through a dynamic modal decomposition algorithm to generate a constraint boundary evolution matrix that is adjusted in real time according to environmental exposure parameters; Use the Lyapunov spectrum analysis method to quantify the phase space contraction rate of the main oscillation mode and the dynamic constraint boundary curvature, and combine the historical data of the metabolic buffer capacity attenuation rate to calculate the absorption threshold of the vascular wall remodeling compensation ability to gradient field perturbations.

6. The method according to claim 5, wherein The use of the Lyapunov spectrum analysis method to quantify the phase space contraction rate of the main oscillation mode and the dynamic constraint boundary curvature, and combine the historical data of the metabolic buffer capacity attenuation rate to calculate the absorption threshold of the vascular wall remodeling compensation ability to gradient field perturbations, includes: Perform Lyapunov spectrum analysis on the main oscillation mode, extract the phase space contraction rate parameter, and combine the spatio-temporal evolution characteristics of the dynamic constraint boundary curvature to generate a stability attenuation spectrum of vascular wall energy metabolism; Based on the historical data of the metabolic buffer capacity attenuation rate, construct a feature vector of the attenuation mode of the time series, and quantify the dynamic stability attenuation indexes of different vascular bifurcation regions in the stability attenuation spectrum through a spectral clustering algorithm; Fuse the stability attenuation spectrum and the dynamic stability attenuation indexes, establish a weight matrix of vascular wall stress-metabolism coupling, and use an adaptive optimization algorithm to solve the absorption threshold under gradient field perturbations.

7. The method according to claim 1, characterized in that The identification of the topological connection strength between the vascular tension regulation gene cluster and the metabolic sensing pathway gene cluster based on genomics data, and the quantification of the influence weight of genetic variation on the cardiovascular-metabolic coupling efficiency by combining the phase delay parameter in the multi-modal oscillation characteristic spectrum to generate a genetic prediction model, includes: Extract the co-expression network of the vasotonic regulatory gene cluster and the metabolic sensing pathway gene cluster from genomics data, and calculate the topological connection strength between the gene clusters. The topological connection strength reflects the functional synergy of the gene clusters in regulating cardio-metabolic coupling; Map the phase delay parameter in the multi-modal oscillation feature spectrum to the gene cluster co-expression network to generate a phase delay weighted gene cluster network, which is used to characterize the association between the functional synergy between gene clusters and the cardio-metabolic coupling efficiency; Identify single nucleotide polymorphism sites in the vasotonic regulatory gene cluster and the metabolic sensing pathway gene cluster based on genetic variation data, and calculate the regulatory effect value of each polymorphism site on the topological connection strength between the gene clusters. The regulatory effect value is used to quantify the impact of genetic variation on the functional synergy of the gene clusters; Integrate the regulatory effect value with the phase delay weighted gene cluster network, and calculate the influence weight of genetic variation on the cardio-metabolic coupling efficiency. The influence weight reflects the contribution degree of genetic variation to the coupling efficiency by regulating the functional synergy of the gene clusters; Construct a genetic prediction model based on the influence weight, which is used to predict the change trend of the cardio-metabolic coupling efficiency of the target object under a specific genetic variation background.

8. A risk prediction system for cardiovascular and metabolic diseases, characterized in that, Comprising: An extraction module for extracting the phase delay parameter between the cardiovascular elastic chamber and the metabolic substrate supply from the cardio-metabolic coupling data in the continuous time series of the acquired target object, and constructing a multi-modal oscillation feature spectrum including the lipid transport rhythm desynchronization index; An identification module for identifying the topological connection strength of the vasotonic regulatory gene cluster and the metabolic sensing pathway gene cluster based on genomics data, and combining the phase delay parameter in the multi-modal oscillation feature spectrum to quantify the influence weight of genetic variation on the cardio-metabolic coupling efficiency, and generating a genetic prediction model; A calculation module for simulating and calculating the attenuation trajectory function of the vascular endothelial metabolic buffer capacity according to the local shear stress distribution of the blood vessels of the target object and the environmental oxidative load parameter, analyzing the dynamic matching degree matrix between the attenuation trajectory function and the instability threshold in the genetic prediction model, and determining the critical environmental exposure parameter combination; A construction module for fusing the lipid transport rhythm desynchronization index, the instability threshold and the critical environmental exposure parameter combination, constructing a dynamic resilience assessment model, calculating the phase space contraction rate of the vascular metabolic steady-state boundary, and generating a hierarchical warning signal when the phase space contraction rate exceeds the vascular wall remodeling compensation ability.

9. A computing device, characterized in that, Comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a risk prediction method for cardiovascular and metabolic diseases according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that, Stored with a computer program, when the computer program is executed by the computer, it implements a risk prediction method for cardiovascular and metabolic diseases according to any one of claims 1 to 7.

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