Method and system for evaluating plasma metabolite of patient with x-adrenal leukodystrophy

Abstract

The invention relates to the technical field of metabolite evaluation, in particular to a plasma metabolite evaluation method and system for a patient with x-adrenal leukodystrophy. The method comprises the following steps: acquiring a patient plasma information set, and analyzing dynamic change information of fatty acid and cholesterol in plasma metabolites based on the patient plasma information set to obtain a dynamic track information set; acquiring a patient condition information set, and analyzing associated risk information of neurotoxic sphingolipid accumulation and oxidative stress level increase based on the patient condition information set in combination with the dynamic trajectory information set to obtain a damage associated information set; and acquiring an individual information set of the patient, analyzing the dynamic influence of age and gender data on the illness state based on the individual information set of the patient in combination with the injury associated information set, obtaining an individualized illness state information set, and outputting a plasma metabolite evaluation log. A solid foundation is laid for accurately identifying the neurotoxicity risk and formulating an individualized diagnosis and treatment scheme, and the scientificity and clinical application value of the overall evaluation method are improved.

Description

Technical Field

[0001] This application relates to the field of metabolite assessment technology, and in particular to a method and system for assessing plasma metabolites in patients with x-adrenoleukodystrophy. Background Technology

[0002] Currently, the diagnosis and monitoring of X-linked adrenoleukodystrophy (X-ALD) mainly rely on imaging examinations and static plasma concentration measurements of very long-chain fatty acids. Studies have shown that abnormal fatty acid metabolism and cholesterol metabolism disorders in plasma are closely related to neuroinflammation, oxidative stress, and elevated neurotoxic sphingolipids. These processes jointly drive nerve demyelination and neuronal damage.

[0003] However, a comprehensive assessment system that integrates dynamic changes in plasma metabolites across multiple time points and enables personalized risk prediction is currently lacking. Existing technologies struggle to achieve the following: 1. Continuous monitoring of neurotoxic sphingolipids and oxidative stress-related fatty acids and cholesterol metabolites to characterize their temporal trajectories and accelerations, thereby providing early warning of neurological injury inflection points; 2. Incorporating individual factors such as age and gender into the dynamic model to correct metabolic trajectories and establish personalized neurological injury risk evolution curves; 3. Predicting the probability of disease progression from the asymptomatic stage to cerebral ALD based on longitudinal data, thereby guiding early intervention. Therefore, a neurological injury risk assessment method based on dynamic analysis of plasma fatty acid and cholesterol metabolites, combined with individual characteristic corrections, is needed, possessing significant clinical demand and research value. Summary of the Invention

[0004] This application provides a method and system for assessing plasma metabolites in patients with x-adrenoleukodystrophy, in order to solve the above-mentioned problems.

[0005] In a first aspect, this application provides a method for assessing plasma metabolites in patients with x-adrenoleukodystrophy. The method includes: acquiring a patient plasma information set; analyzing the dynamic changes in fatty acids and cholesterol in plasma metabolites based on the patient plasma information set to obtain a dynamic trajectory information set; acquiring a patient condition information set; analyzing the associated risk information of neurotoxic sphingolipid accumulation and elevated oxidative stress levels based on the patient condition information set and the dynamic trajectory information set to obtain a damage-related information set; acquiring a patient individual information set; analyzing the dynamic impact of age and gender data on the condition based on the patient individual information set and the damage-related information set to obtain an individualized condition information set, and outputting a plasma metabolite assessment log.

[0006] By integrating multi-dimensional information on patient plasma, disease condition, and individual status, the above technical solutions can accurately capture dynamic changes in fatty acids and cholesterol, clarify their association with neurotoxic sphingolipid accumulation and increased oxidative stress, and achieve individualized assessment by combining age and gender differences. This can enable early detection of disease signals, support personalized treatment and dynamic efficacy monitoring, provide data for disease mechanism research, fill existing assessment gaps, and improve the standardization and effectiveness of diagnosis and treatment.

[0007] Optionally, the step of analyzing the dynamic changes of fatty acids and cholesterol in plasma metabolites based on the patient plasma information set to obtain a dynamic trajectory information set includes: the patient plasma information set being plasma metabolite detection data at different time points, including fatty acid information and cholesterol information; analyzing the concentration changes of each over time based on the fatty acid information and the cholesterol information to obtain fatty acid change sequences and cholesterol change sequences; analyzing the temporal correlation and synergy of the concentration changes of the two based on the fatty acid change sequences and the cholesterol change sequences to obtain a synergistic change pattern; and integrating the fatty acid change sequences, the cholesterol change sequences, and the synergistic change pattern to construct the dynamic trajectory information set.

[0008] Optionally, the process of constructing the synergistic change pattern includes: based on the fatty acid change sequence and combined with the cholesterol change sequence, analyzing the correspondence between the direction of change of fatty acid concentration and the direction of change of cholesterol concentration within the same time window to obtain change phase information; based on the change phase information, analyzing the persistence and intensity of the same-direction or opposite-direction change of cholesterol concentration when fatty acid concentration increases or decreases to obtain change trend information; integrating the change phase information and the change trend information to generate a pattern describing the strength of the correlation and synergistic relationship between changes in fatty acid and cholesterol concentration, as the synergistic change pattern.

[0009] Optionally, based on the patient's condition information set and combined with the dynamic trajectory information set, the analysis of the associated risk information of neurotoxic sphingolipid accumulation and elevated oxidative stress levels yields a damage-related information set, including: the patient's condition information set includes neurological function score information and protein carboxyl information; based on the neurological function score information and combined with the synergistic change pattern, the analysis of the correlation between the concentration change trends of fatty acids and cholesterol and the fluctuations of neurological function scores yields functional damage-related information; based on the protein carboxyl information and combined with the synergistic change pattern, the analysis of the positive correlation between the intensity of synergistic changes of fatty acids and cholesterol and the protein carboxyl content yields oxidative stress-related information; based on the functional damage-related information and combined with the oxidative stress-related information, the analysis of the induced association effects of the two on the patient's neurotoxic sphingolipids and oxidative stress levels under their combined action yields the damage-related information set.

[0010] Optionally, the process of constructing the functional impairment association information includes: based on the neurological function score information, analyzing the magnitude and rate of change of the neurological function score at different time points to obtain dynamic change information of the score; based on the dynamic change information of the score, combined with the fatty acid change sequence and the cholesterol change sequence, analyzing the abnormal fatty acid concentration and abnormal cholesterol concentration information corresponding to the period of decline in neurological function score to obtain concentration abnormality association information; based on the concentration abnormality association information, combined with the synergistic change pattern, analyzing the persistence and severity of the accelerated decline in neurological function score when fatty acids and cholesterol exhibit a synergistic disorder relationship defined by the change phase information and the change trend information to obtain the functional impairment association information.

[0011] Optionally, the process of constructing the oxidative stress correlation information includes: based on the protein carboxyl information, analyzing the rise and fall and fluctuation amplitude of protein carboxyl content at different time points to obtain oxidative stress dynamic information; based on the oxidative stress dynamic information, combined with the synergistic change pattern, analyzing the temporal correspondence between the synergistic change pattern of fatty acids and cholesterol defined by the change phase information and the change trend information, and the protein carboxyl content increase pattern, to obtain correlation time correspondence information; based on the correlation time correspondence information, analyzing the persistence and amplitude of the accelerated increase in protein carboxyl content when the synergistic change intensity of fatty acids and cholesterol reaches a preset threshold as defined by the change trend information, generating a pattern describing the strength of the causal relationship between the two, as the oxidative stress correlation information.

[0012] Optionally, the step of analyzing the induced association effects of the functional impairment association information and oxidative stress association information on the neurotoxic sphingolipids and oxidative stress levels of patients under their combined influence to obtain the impairment association information set includes: based on the functional impairment association information, analyzing the association characteristics between the persistence and severity of the decline in neurological function scores and the synergistic dysregulation of fatty acids and cholesterol to obtain neurotoxic sphingolipid accumulation risk information; based on the oxidative stress association information, analyzing the association characteristics between the persistence and magnitude of the increase in protein carboxyl content and the intensity of the synergistic change in fatty acids and cholesterol to obtain oxidative stress level risk information; based on the neurotoxic sphingolipid accumulation risk information and oxidative stress level risk information, analyzing the joint deterioration effect on neurological function and oxidative stress state when the risk levels indicated by the two risk information evolve synchronously from low to high to obtain the joint induced association effect; and integrating the neurotoxic sphingolipid accumulation risk information, the oxidative stress level risk information, and the joint induced association effect to construct the impairment association information set.

[0013] Optionally, the step of analyzing the dynamic impact of age and gender data on the disease condition based on the patient's individual information set and the injury association information set to obtain an individualized disease condition information set and outputting a plasma metabolite assessment log includes: the patient's individual information set including patient age and patient gender; based on the patient's age and the injury association information, analyzing the accelerating or delaying effect of age increase on the synergistic dysregulation relationship to obtain age-modified risk information; based on the patient's gender and the injury association information, analyzing the enhancing or weakening effect of gender differences on the intensity of synergistic changes to obtain gender-modified risk information; based on the age-modified risk information and the gender-modified risk information, analyzing the superimposed or offsetting effect of both on the jointly induced association effect to obtain individualized moderating effect; based on the individualized moderating effect, dynamically correcting the injury association information set to generate a set reflecting the disease evolution characteristics of patients with different ages and genders, as the individualized disease condition information set, and outputting the plasma metabolite assessment log containing the individualized disease condition information set.

[0014] Optionally, the specific implementation of dynamically correcting the damage-related information set includes: based on the age-modulated risk information, analyzing the degree to which different age intervals accelerate or delay the synergistic dysregulation relationship to obtain an age correction factor; based on the gender-modulated risk information, analyzing the degree to which different genders enhance or weaken the intensity of the synergistic change to obtain a gender correction factor; based on the age correction factor and combined with the gender correction factor, analyzing the combined regulatory effect of the two on the neurotoxic sphingolipid accumulation risk information and the oxidative stress level risk information to obtain individualized risk correction information; based on the individualized risk correction information, recalibrating the rate and magnitude of the synchronous evolution of risk levels in the co-induced association effect to obtain individualized co-induced association effects, continuously correcting the damage-related information set as the basis for the individualized disease information set.

[0015] Secondly, this application provides a plasma metabolite assessment system for patients with x-adrenoleukodystrophy. The system includes: a dynamic change module for acquiring a patient's plasma information set, and based on the patient's plasma information set, analyzing the dynamic changes in fatty acids and cholesterol in plasma metabolites to obtain a dynamic trajectory information set; a damage association module for acquiring a patient's condition information set, and based on the patient's condition information set, combined with the dynamic trajectory information set, analyzing the associated risk information of neurotoxic sphingolipid accumulation and increased oxidative stress levels to obtain a damage association information set; and a dynamic correction module for acquiring a patient's individual information set, and based on the patient's individual information set, combined with the damage association information set, analyzing the dynamic impact of age and gender data on the condition to obtain an individualized condition information set, and outputting a plasma metabolite assessment log. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram illustrating an application scenario provided in one embodiment of this application;

[0018] Figure 2 A flowchart of a method for assessing plasma metabolites in patients with x-adrenoleukodystrophy is provided as an embodiment of this application;

[0019] Figure 3 This is a schematic diagram of the structure of a plasma metabolite assessment system for patients with x-adrenoleukodystrophy, provided as an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0021] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0022] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0023] In the assessment of plasma metabolites in patients with x-adrenoleukodystrophy, there is currently no assessment system that integrates the dynamic changes of plasma metabolites at multiple time points and achieves individualized risk prediction. Existing technologies cannot continuously monitor relevant metabolites to characterize their temporal change trajectory and acceleration, or provide early warning of the inflection point of nerve damage. They also cannot incorporate individual factors to correct metabolic trajectories, establish personalized risk curves, or predict disease outcome risks based on longitudinal data. There is an urgent need to develop corresponding assessment methods that have significant clinical and research value.

[0024] Based on this, this application provides a method and system for assessing plasma metabolites in patients with x-adrenoleukodystrophy. It integrates multi-dimensional information on patient plasma, disease condition, and individual status to accurately track dynamic fluctuations in fatty acids and cholesterol, clarifying their association with neurotoxic sphingolipid accumulation and increased oxidative stress. Individualized assessments are conducted considering age and gender differences. This allows for early identification of disease signals, supports personalized treatment and dynamic monitoring of efficacy, and provides data for disease mechanism research, filling gaps in existing assessment methods.

[0025] Figure 1 This application provides an illustration of an application scenario. In the process of assessing plasma metabolites in patients with x-adrenoleukodystrophy, the method provided in this application integrates multi-dimensional patient information to track lipid changes, clarify related disease risks, and conduct assessments based on individual differences. This can help identify the disease early, support personalized diagnosis and treatment and efficacy monitoring, and also provide data for disease research and fill assessment gaps.

[0026] Specifically, the method provided in this application can be applied to any server, where the server interacts with patient plasma samples, clinical records, and personal information registration forms to obtain patient plasma information sets provided by patient plasma samples, patient condition information sets provided by clinical records, and patient individual information sets provided by personal information registration forms. This allows for early detection of disease signals, supports personalized treatment and dynamic efficacy monitoring, generates individualized condition information sets for patients' families, and outputs plasma metabolite assessment logs for medical personnel, laying a solid foundation for accurately identifying neurotoxicity risks and developing individualized treatment plans.

[0027] For specific implementation details, please refer to the following examples.

[0028] Figure 2 This is a flowchart illustrating a method for assessing plasma metabolites in patients with x-adrenoleukodystrophy, provided as an embodiment of this application. The method of this embodiment can be applied to servers in the above-described scenario. For example... Figure 2 As shown, the method includes:

[0029] S201. Obtain the patient's plasma information set. Based on the patient's plasma information set, analyze the dynamic changes of fatty acids and cholesterol in plasma metabolites to obtain a dynamic trajectory information set.

[0030] The patient plasma information set can be a collection of relevant information extracted from plasma samples of patients with x-adrenoleukodystrophy, using clinically collected patient plasma samples as the data source. Plasma metabolites can be various small molecule compounds present in the patient's plasma. Fatty acids can be an important category of plasma metabolites, belonging to the products of fat breakdown. Cholesterol can be a key lipid in plasma metabolites. Dynamic change information can be the fluctuation of the content and proportion of fatty acids and cholesterol in plasma metabolites at different time points and different disease stages. The dynamic trajectory information set can be obtained by continuously tracking the dynamic changes of plasma metabolites at different disease stages based on the patient plasma information set.

[0031] Specifically, in the assessment of plasma metabolites in patients with x-adrenoleukodystrophy, existing methods relying on single-point detection cannot capture the dynamic evolution of metabolic disorders, leading to assessment lag. By analyzing the patient's plasma information set, static metabolite concentrations can be transformed into dynamic change trajectories. This can fundamentally reveal the accumulation trends of key substances such as very long-chain fatty acids, providing a continuous and objective data dimension reflecting disease activity for subsequent assessments. This is the core foundation for overcoming the insufficient sensitivity of existing methods and achieving early and process-oriented monitoring.

[0032] S202. Obtain the patient's condition information set. Based on the patient's condition information set and combined with the dynamic trajectory information set, analyze the associated risk information of neurotoxic sphingolipid accumulation and increased oxidative stress levels to obtain the damage-related information set.

[0033] The patient condition information set can be a collection of information characterizing the condition status of patients with x-adrenoleukodystrophy, using clinical diagnostic and treatment records from medical institutions as the data source. Neurotoxic sphingolipid accumulation refers to the pathological phenomenon where excessive synthesis or impaired breakdown of sphingolipids in the patient's body due to metabolic abnormalities leads to abnormal deposition in nerve tissue. Elevated oxidative stress levels refer to an imbalance between the oxidative and antioxidant systems in the patient's body, where the production of reactive oxygen species exceeds the body's clearance capacity, resulting in a series of pathological reactions such as lipid peroxidation, protein oxidative damage, and nucleic acid breakage. The damage correlation information set can be a collection of information formed by integrating the dynamic trajectory information set and the patient condition information set to deeply analyze the causal relationship and risk level between abnormal fatty acid and cholesterol metabolism and neurotoxic sphingolipid accumulation and elevated oxidative stress levels.

[0034] Specifically, in the process of analyzing the relationship between metabolic abnormalities and clinical damage, there is a lack of quantitative bridge between known pathological mechanisms (such as neurotoxic sphingolipid accumulation) and patients' actual symptoms. By combining metabolic trajectories with disease information, a quantitative correlation model of "specific metabolite changes → specific pathobiochemical processes (such as oxidative stress levels) → clear clinical damage manifestations" can be systematically established. This makes plasma metabolite data no longer isolated numbers, but a biological language that can explain and predict neurological functional risks. It is a key leap in assessment from "phenomenon description" to "mechanism explanation".

[0035] S203. Obtain the patient's individual information set. Based on the patient's individual information set and combined with the injury-related information set, analyze the dynamic impact of age and gender data on the condition to obtain an individualized condition information set and output the plasma metabolite assessment log.

[0036] The patient individual information set can be a collection of basic and specific information about patients with x-adrenoleukodystrophy, using the personal information registration form filled out by the patient at the time of consultation as the data source. Age and gender data can be core influencing factors in the patient individual information set. The individualized disease information set can be a collection of information that accurately reflects the differences in disease condition, metabolic characteristics, and risk of injury among different individuals. The plasma metabolite assessment log can be a standardized report document that summarizes the core data and conclusions of the entire assessment process.

[0037] Specifically, in the process of generating the final individualized assessment conclusion, patients with x-adrenoleukodystrophy exhibit significant age- and sex-related phenotypic differences, making uniform standards inapplicable. By integrating individual information, it is possible to analyze how factors such as age and sex modulate the aforementioned "metabolism-damage" association strength, thereby enabling personalized calibration of the general risk model. This allows the assessment results to accurately match the specific subgroup to which the patient belongs (such as pediatric cerebral dystrophy or adult spinal cord dystrophy), outputting predictions and judgments that truly fit the characteristics of their condition. This is the ultimate guarantee for achieving precision medicine and guiding individualized management decisions.

[0038] The method provided in this embodiment integrates multi-dimensional information on patient plasma, disease condition, and individual status to accurately capture dynamic changes in fatty acids and cholesterol, clarify their association with neurotoxic sphingolipid accumulation and increased oxidative stress, and achieve individualized assessment by combining age and gender differences. This can detect disease signals early, support personalized treatment and dynamic efficacy monitoring, provide data for disease mechanism research, fill existing assessment gaps, and improve the standardization and effectiveness of diagnosis and treatment.

[0039] In some embodiments, the patient plasma information set consists of plasma metabolite detection data at different time points, including fatty acid information and cholesterol information. Based on the fatty acid information and combined with the cholesterol information, the concentration changes of each over time are analyzed to obtain fatty acid change sequences and cholesterol change sequences. Based on the fatty acid change sequences and combined with the cholesterol change sequences, the temporal correlation and synergy of the concentration changes of the two are analyzed to obtain the synergistic change pattern. The fatty acid change sequences, cholesterol change sequences, and synergistic change patterns are integrated to construct a dynamic trajectory information set.

[0040] Fatty acid information can be data representing the specific content of various fatty acids in a patient's plasma. Cholesterol information can reflect the content of total cholesterol, free cholesterol, etc., in a patient's plasma. A fatty acid change sequence can be a continuous data sequence formed by arranging fatty acid concentration data from different time points in chronological order. A cholesterol change sequence can be a continuous data sequence formed by integrating cholesterol concentration data from different time points in chronological order. Co-variation patterns can describe the correlation direction, persistence characteristics, and interaction strength between fatty acid and cholesterol concentration changes over time.

[0041] Specifically, in the assessment of plasma metabolites in patients with x-adrenoleukodystrophy, relying solely on single-time-point data without analyzing the dynamic changes and synergistic patterns of fatty acids and cholesterol will fail to capture the fluctuating characteristics of metabolites throughout the disease course. Consequently, it will be impossible to accurately correlate neurotoxic sphingolipid accumulation with the risk of oxidative stress, resulting in a lack of reliable evidence for subsequent damage correlation analysis. To address these issues, firstly, based on a "patient plasma information set" (e.g., containing plasma sample data measured by liquid chromatography-mass spectrometry at months 0, 3, 6, and 12), time series analysis is used to perform curve fitting and smoothing on "fatty acid information" (e.g., the concentration sequence of C26:0) and "cholesterol information" (e.g., the total cholesterol concentration sequence), respectively, generating "fatty acid change sequences" and "cholesterol change sequences" that can intuitively reflect the continuous changes in concentration over time. Furthermore, to analyze the "synergistic change patterns" between the two, correlation analysis and trend coupling analysis methods are employed. For example, by calculating the correlation coefficient between the two sequence changes within a sliding time window (e.g., a 3-month window), the "phase information" is quantified to determine whether the two are moving in the same direction (e.g., cholesterol also rises when C26:0 rises) or diverging in opposite directions. Next, by assessing the persistence of a specific trend (e.g., whether cholesterol undergoes a synchronous and equally strong persistent change during a period when C26:0 continuously rises above the baseline level, such as 0.2 mg / L), the strength and robustness of the "trend information" are quantified. Finally, using structured data modeling methods, the two sequence data, phase information, and trend strength information are integrated and encapsulated to construct a multidimensional "dynamic trajectory information set." This dataset not only records the original concentration at each time point (e.g., C26:0 is 1.8 μmol / L in month 6), but more importantly, it annotates its temporal trend (e.g., "rapid rise period") and its synergistic pattern with cholesterol changes (e.g., "strong positive synergy"), thus comprehensively depicting a dynamic panorama of metabolite evolution.

[0042] By using the method provided in this embodiment, the synergistic relationship between the two is explored and a dynamic trajectory information set is constructed, providing comprehensive and continuous core data support for subsequent damage correlation analysis. This effectively makes up for the limitations of static detection, ensures the accuracy of the correlation analysis between metabolite changes and disease progression, lays a solid foundation for accurately identifying neurotoxicity risks and developing individualized treatment plans, and enhances the scientific nature and clinical application value of the overall assessment method.

[0043] In some embodiments, based on fatty acid change sequences and combined with cholesterol change sequences, the correspondence between the direction of change in fatty acid concentration and the direction of change in cholesterol concentration within the same time window is analyzed to obtain change phase information; based on the change phase information, the persistence and intensity of the same-direction or opposite-direction change in cholesterol concentration when fatty acid concentration increases or decreases are analyzed to obtain change trend information; the change phase information and change trend information are integrated to generate a pattern describing the strength and synergistic relationship between changes in fatty acid and cholesterol concentration, as a synergistic change pattern.

[0044] Phase information can be the correspondence between the directions of change in fatty acid concentration and cholesterol concentration within the same time window. Trend information can be the persistence and intensity characteristics of the corresponding (same or opposite) changes in cholesterol concentration when fatty acid concentration increases, decreases, or stabilizes. The same time window can be a unified time interval defined for analyzing the correlation between the changes of the two metabolites. The direction of change in fatty acid concentration can be the increase, decrease, or stabilization of fatty acid concentration within a specific time window. The direction of change in cholesterol concentration can also be the increase, decrease, or stabilization of cholesterol concentration within a specific time window. Persistence can be the number of consecutive time windows in which the two metabolites show a specific direction of change corresponding to each other. Intensity can be the magnitude of the change in cholesterol concentration with fatty acid concentration within a persistent correlation.

[0045] Specifically, in assessing the association between plasma metabolites and disease status in patients with x-adrenoleukodystrophy, if a synergistic change pattern is not established and changes in fatty acids or cholesterol are analyzed in isolation, the interaction pattern between the two cannot be revealed. This will lead to a lack of core evidence for subsequent risk analysis of neurotoxic sphingolipid accumulation and increased oxidative stress levels, causing damage association information to deviate from the actual disease status, thereby affecting the accuracy of individualized assessment and delaying the timing of clinical intervention. To address the aforementioned issues: First, a time window alignment and change direction encoding technique is employed. The concentration time series of fatty acids and cholesterol (e.g., sampling by week) are divided into fixed analysis windows (e.g., every 4 weeks). Within each window, the slope or difference of concentration changes for each metabolite is calculated. Based on preset thresholds (e.g., a change rate exceeding 5% is considered a significant increase, below -5% is a significant decrease, and in between is considered stable), the change direction is encoded as a discrete state of "increasing," "decreasing," or "stable." Then, the state combinations of the two within the same window (e.g., "fatty acid increase + cholesterol increase") are compared to generate phase information representing the corresponding relationship. Subsequently, pattern persistence tracking and intensity quantification analysis methods are used to track specific phase patterns (e.g., "simultaneous increase") in continuous... The sequences appearing within a time window are recorded for their duration (e.g., three consecutive windows). The average magnitude of concentration changes within that duration (e.g., an average weekly increase of 0.2 mg / L for fatty acids and 0.05 mg / L for cholesterol) is calculated as an intensity indicator to obtain trend information. Finally, through rule induction and correlation mapping, all phase patterns and their corresponding duration and intensity data are integrated. For example, instances of "same-direction increase" patterns that last for more than two windows and whose average increase is higher than the population median are defined as "strong positive synergy" patterns; and "opposite changes (one increase and one decrease)" patterns that appear continuously are defined as "antagonistic" patterns. This systematically constructs a set of synergistic change patterns describing different synergistic patterns.

[0046] The method provided in this embodiment constructs a synergistic change pattern, which can accurately characterize the correlation and synergistic relationship between changes in fatty acid and cholesterol concentrations, breaking the limitations of single metabolite analysis and providing reliable support for subsequent exploration of the intrinsic relationship between metabolite changes and pathological damage by combining disease information.

[0047] In some embodiments, the patient condition information set includes neurological function score information and protein carboxyl information; based on the neurological function score information, combined with the synergistic change pattern, the correlation between the concentration change trends of fatty acids and cholesterol and the fluctuation of neurological function scores is analyzed to obtain functional impairment correlation information; based on the protein carboxyl information, combined with the synergistic change pattern, the positive correlation between the intensity of synergistic changes of fatty acids and cholesterol and the protein carboxyl content is analyzed to obtain oxidative stress correlation information; based on the functional impairment correlation information, combined with the oxidative stress correlation information, the induction correlation effect of the two on the patient's neurotoxic sphingolipids and oxidative stress levels under the combined action is analyzed to obtain the impairment correlation information set.

[0048] Neurological function scoring information can be obtained by scoring patients' motor, sensory, and cognitive functions using a standardized neurological function assessment scale. Protein carboxyl information can be obtained through laboratory biochemical testing of plasma protein carboxyl content. Oxidative stress correlation information can be obtained by analyzing the correlation between the intensity of synergistic changes in plasma metabolites and protein carboxyl content.

[0049] Specifically, in the assessment of plasma metabolites in patients with x-adrenoleukodystrophy, analyzing only the dynamics of plasma metabolites or a single disease indicator in isolation cannot establish the correlation between metabolic changes and neurotoxic sphingolipid accumulation and increased oxidative stress. This leads to an inability to trace the core causes of disease damage, resulting in misjudgment of disease progression risk, delayed clinical intervention, and exacerbation of irreversible neurological damage. To address these issues: First, time series comparison and sliding window analysis techniques are used to pair neurological function score sequences (such as the monthly Loess score) with fatty acid change sequences (such as C26:0 concentration) and cholesterol change sequences within time windows. This identifies periods when neurological function scores show a specific decrease (such as a decrease of more than 2 points within 3 months), and extracts the corresponding abnormal metabolite concentration characteristics within these periods (such as C26:0 consistently above 30 μmol / L and cholesterol esters decreasing by more than 15%). Subsequently, the "phase information" (indicating the similarity or difference in the direction of change) and "trend information" (indicating the intensity of change) from the aforementioned synergistic change patterns are integrated. Through correlation analysis and regression models, the specific synergistic dysregulation of metabolites is quantified. When a pattern (e.g., a "negative phase" of rising fatty acids and falling cholesterol with a trend strength > 0.7) is observed, the correlation strength between the persistence and severity of the accelerated decline in neurological function scores is solidified as functional impairment-related information. Simultaneously, a similar process is applied to protein carboxyl content sequences, using time-lag cross-correlation analysis to explore the temporal correspondence between their content increase patterns (e.g., increases exceeding 20% ​​at two consecutive time points) and the aforementioned synergistic metabolite change patterns. Furthermore, by setting a threshold for synergistic change intensity (e.g., reaching a "high-intensity synergy" interval) and applying Granger causality tests or thresholded piecewise linear regression, the probability and magnitude of a significant accelerated increase in protein carboxyl content when the synergistic intensity exceeds this threshold are determined, generating oxidative stress-related information. Finally, using a risk matrix integration method, the above two types of information are mapped to a unified risk level, and a synergistic effect model (e.g., additive or multiplicative interaction model) is applied to analyze the co-deterioration effect on neurological damage and oxidative stress when both risk levels increase synchronously (e.g., both change from "moderate" to "high"), thereby integrating and outputting a structured set of damage-related information.

[0050] The method provided in this embodiment precisely identifies the synergistic effects of changes in fatty acids and cholesterol on nerve damage and oxidative stress, clarifies the key mechanisms of disease progression, and lays a solid foundation for subsequent individualized assessment. This approach avoids the limitations of single-indicator analysis, improves the accuracy of risk prediction, and helps clinicians develop targeted intervention plans in advance, effectively delaying disease progression and protecting patients' neurological function.

[0051] In some embodiments, based on neurological function score information, the magnitude and rate of change of neurological function scores at different time points are analyzed to obtain dynamic change information of scores; based on the dynamic change information of scores, combined with fatty acid change sequences and cholesterol change sequences, the abnormal information of fatty acid concentration and abnormal information of cholesterol concentration corresponding to the period of decline in neurological function score are analyzed to obtain concentration abnormality correlation information; based on the concentration abnormality correlation information, combined with the synergistic change pattern, the persistence and severity of accelerated decline in neurological function score when fatty acids and cholesterol show a synergistic disorder relationship defined by change phase information and change trend information are analyzed to obtain functional impairment correlation information.

[0052] Dynamic changes in scores can be a dataset reflecting the fluctuation characteristics of patients' neurological function scores at different time points. Synergistic dysregulation can be defined by both phase and trend information, indicating an abnormal state of synergistic changes in fatty acid and cholesterol concentrations.

[0053] Specifically, in the assessment of plasma metabolites in patients with x-adrenoleukodystrophy, relying solely on isolated protein carboxyl data or metabolite change data cannot clearly establish the causal relationship between fatty acid and cholesterol changes and oxidative stress. This can lead to misjudgment of oxidative stress risk, thereby masking key signals of disease progression, delaying clinical intervention, and causing a lack of core evidence for subsequent damage association information sets. To address these issues: First, time series analysis is used to calculate the neurological function scores sorted by follow-up time, obtaining the score change amount (e.g., from 5 points to 3 points, the change amount is -2) and its rate of change (e.g., a decrease of 1 point per month) between each adjacent time point. This constructs dynamic score change information, accurately identifying periods of rapid deterioration in neurological function (e.g., identifying the period from 4 to 6 months as the period of accelerated score decline). Subsequently, through time window alignment and mapping technology, the aforementioned deterioration period is correlated with the fatty acid and cholesterol change sequences of the same period. Specifically, it is analyzed whether the concentrations of both exceed the preset pathological threshold or exhibit drastic fluctuations during this period (e.g., finding that C26:0... (VLCFA concentration consistently above 40 μmol / L and HDL-C concentration below 1.0 mmol / L) to generate concentration abnormality correlation information. Finally, based on this correlation information, a predefined synergistic change pattern is invoked for deep pattern matching and impact analysis: it examines whether the changes in fatty acids and cholesterol during the period of neurological function deterioration exhibit a specific synergistic dysregulation pattern defined by the pattern (e.g., the phase information of the change indicates that the two are continuously changing in opposite directions, and the trend information of the change indicates that their negative correlation strength is as high as -0.8). By quantitatively analyzing the intensity and duration of this synergistic dysregulation pattern, its specific contribution to the accelerated decline in neurological function scores can be assessed. For example, it is determined that this strong negative correlation synergistic dysregulation pattern increases the rate of score decline by about 50% and causes the onset of severe neurological dysfunction (score > 6 points) to be advanced by several months, thus ultimately generating functional impairment correlation information that combines quantitative and qualitative analysis.

[0054] The method provided in this embodiment accurately captures the dynamic correspondence between synergistic changes in metabolites and increases in protein carboxyl content, clarifies the strength of the causal relationship between the two, provides a precise basis for oxidative stress risk assessment, effectively eliminates irrelevant interfering factors, improves the accuracy of oxidative stress-related information, lays a solid foundation for the subsequent construction of damage-related information, and helps in the analysis of disease progression mechanisms and precise clinical intervention.

[0055] In some embodiments, based on protein carboxyl information, the changes and fluctuations in protein carboxyl content at different time points are analyzed to obtain dynamic information on oxidative stress. Based on the dynamic information on oxidative stress, combined with the synergistic change pattern, the temporal correspondence between the synergistic change pattern of fatty acids and cholesterol defined by the change phase information and change trend information and the protein carboxyl content increase pattern is analyzed to obtain correlation time correspondence information. Based on the correlation time correspondence information, the persistence and magnitude of the accelerated increase in protein carboxyl content when the intensity of the synergistic change of fatty acids and cholesterol reaches a preset threshold as defined by the change trend information are analyzed to generate a pattern describing the strength of the causal relationship between the two, as oxidative stress correlation information.

[0056] Oxidative stress dynamics can be generated based on the temporal changes in protein carboxyl content, reflecting the evolution of a patient's oxidative stress level over time. Correlation information can characterize the temporal matching relationship between the synergistic changes in fatty acids and cholesterol and the increasing pattern of protein carboxyl content. A preset threshold can be used to determine whether the intensity of the synergistic changes in fatty acids and cholesterol reaches a critical value that induces an accelerated increase in oxidative stress.

[0057] Specifically, in analyzing the risk associated with neurotoxic sphingolipid accumulation and oxidative stress levels, the lack of a functional impairment association construction step makes it impossible to clearly define the link between fatty acid and cholesterol synergistic dysregulation and neurological function deterioration. This leads to an inability to accurately identify key triggers of neurological damage, resulting in misjudgments of the disease progression rate and severity, creating hidden dangers for subsequent treatment planning, and seriously affecting the accuracy of assessment and the effectiveness of clinical intervention. To address the above problems, a series of biochemical tests are performed on continuously collected plasma samples. For example, a high-performance liquid chromatography (HPLC) method based on 2,4-dinitrophenylhydrazine (DNPH) derivatization is used to quantitatively determine the protein carbonyl content at different follow-up time points (such as baseline, month 3, and month 6), thereby obtaining a set of protein carboxyl information sequences that evolve over time, i.e., dynamic information on oxidative stress. Subsequently, using time-series data correlation analysis, this dynamic information was integrated with a pre-constructed synergistic change pattern that includes phase change information (describing the correspondence between changes in fatty acid and cholesterol concentrations) and trend change information (describing the persistence and intensity of changes). Through sliding time window comparison and statistical correlation analysis (such as cross-correlation analysis), the correlation between specific metabolic synergistic patterns and subsequent oxidative stress exacerbation events on the time axis was systematically explored. For example, the analysis found that when the phase change information indicated that fatty acid C26:0 and cholesterol exhibited a "high-intensity co-increase" pattern, and the intensity of this pattern (quantified by the trend change information) consistently exceeded a preset "highly synergistic" threshold (this threshold was determined by analyzing historical cohort data), the correlation was further strengthened. When the subject operating characteristic curve analysis determines that, in a relatively fixed time window (such as the next 1-3 months), the probability of a sustained accelerated increase in protein carboxyl content is significantly increased. This strong temporal correspondence is captured and quantified as associated time-correspondence information. Finally, based on this information, a regression model or causal inference method is used, with "metabolic synergy intensity reaching or exceeding the threshold" as the independent variable and "the magnitude and rate of increase in protein carboxyl content" as the dependent variable, to model and quantify the dose-response relationship between the two. This generates a clear quantitative law describing the strength of the causal chain of "specific pattern of metabolic synergy dysregulation driving accelerated oxidative stress," which is the final output of oxidative stress association information.

[0058] The method provided in this embodiment clearly reveals the pattern of nerve damage induced by dyssynergism. It not only provides core support for the damage-related information set, but also helps medical staff identify the risk of deterioration of nerve function in the early stage, buy time for timely adjustment of treatment strategies and block the progression of damage, and significantly improve the pertinence and clinical guidance value of the assessment of x-adrenoleukodystrophy.

[0059] In some embodiments, based on functional impairment association information, the association characteristics between the persistence and severity of the decline in neurological function scores and the synergistic dysregulation of fatty acids and cholesterol are analyzed to obtain neurotoxic sphingolipid accumulation risk information; based on oxidative stress association information, the association characteristics between the persistence and magnitude of the increase in protein carboxyl content and the intensity of synergistic changes in fatty acids and cholesterol are analyzed to obtain oxidative stress level risk information; based on neurotoxic sphingolipid accumulation risk information, combined with oxidative stress level risk information, the joint deterioration effect on neurological function and oxidative stress state when the risk levels indicated by the two risk information evolve synchronously from low to high is analyzed to obtain the joint inducing association effect; integrating neurotoxic sphingolipid accumulation risk information, oxidative stress level risk information, and joint inducing association effect, an impairment association information set is constructed.

[0060] Information on the risk of neurotoxic sphingolipid accumulation can reflect the impact of the association between decreased neurological function scores and dysregulation of fatty acids and cholesterol on the likelihood of neurotoxic sphingolipid accumulation. Information on the risk of oxidative stress levels can reflect the impact of the association between increased protein carboxyl content and the strength of synergistic changes in fatty acids and cholesterol on the risk of abnormally elevated oxidative stress levels. Co-inducing association effects can be information on the combined deteriorating effects on neurological function and oxidative stress state when the risks of neurotoxic sphingolipid accumulation and oxidative stress levels escalate simultaneously.

[0061] Specifically, in the process of assessing plasma metabolites in x-adrenoleukodystrophy, analyzing only the single risk of neurological function impairment or oxidative stress may overlook the danger of their synergistic deterioration—the accumulation of neurotoxic sphingolipids and the increase in oxidative stress mutually promote each other, accelerating nerve cell damage and disease progression. This leads to a one-sided assessment, which cannot provide a comprehensive risk basis for subsequent individualized analysis and is likely to mislead clinical judgment. To address the aforementioned issues: First, regarding "functional impairment-related information," we employ a multi-factor association strength calculation model to quantify the statistical association strength between specific metabolite synergistic dysregulation patterns (e.g., a phase combination of sustained increases in long-chain fatty acids accompanied by abnormal decreases in cholesterol) and the trajectory of accelerated decline in neurological function scores (e.g., a rate of decline exceeding a certain monthly score). This is quantified into a dynamic risk score, thereby generating structured "neurotoxic sphingolipid accumulation risk information." Simultaneously, regarding "oxidative stress-related information," a time-series causal inference algorithm is used to focus on analyzing the lead-lag relationship and regression causal strength between the synergistic changes in fatty acids and cholesterol (e.g., the consistency index of their trends) exceeding a preset threshold and the increase in protein carboxyl content (e.g., an increase in concentration greater than a specific percentage from baseline) within a time window. Based on this, a quantitative... The "oxidative stress level risk information" is then analyzed. Subsequently, the two risk information items are compared over time using risk evolution trend synchronization analysis technology. The core is to identify and define the pattern of "synchronous evolution of risk level". For example, when the neurotoxicity risk level rises from medium risk to high risk, the oxidative stress risk level also jumps from low risk to medium-high risk. Then, using the effect superposition and synergistic analysis model, the joint aggravating effect of this synchronous evolution on the rate of neurological function decline and the accumulation magnitude of oxidative damage markers is assessed (its effect is often greater than the sum of the two independent effects). Finally, the "co-induced associated effects" are extracted. Finally, these three pieces of information - the risk quantification results pointing to different pathological pathways and their interaction patterns - are systematically integrated to construct a multi-level "damage association information set", thereby providing a panoramic picture of the comprehensive risk pattern of the patient's individual condition.

[0062] The method provided in this embodiment accurately captures the disease exacerbation effect jointly induced by both factors, filling the gap in single-dimensional analysis, making the damage-related information set more systematic and comprehensive, providing reliable data support for subsequent correction of the disease by combining individual information such as age and gender, improving the pertinence and scientific nature of the assessment, and laying a solid foundation for the formulation of precision treatment plans.

[0063] In some embodiments, the patient individual information set includes patient age and patient gender; based on patient age and combined with injury association information, the accelerating or delaying effect of age-related dysregulation is analyzed to obtain age-modified risk information; based on patient gender and combined with injury association information, the enhancing or weakening effect of gender differences on the intensity of dysregulation is analyzed to obtain gender-modified risk information; based on age-modified risk information and combined with gender-modified risk information, the superimposed or offsetting effects of both on the jointly induced association are analyzed to obtain individualized moderating effects; based on individualized moderating effects, the injury association information set is dynamically corrected to generate a set reflecting the disease evolution characteristics of patients with different ages and genders, as an individualized disease information set, and a plasma metabolite assessment log containing the individualized disease information set is output.

[0064] Age-modulated risk information can reflect the accelerating or delaying effect of different age ranges on the synergistic dysregulation of fatty acid and cholesterol. Gender-modulated risk information can reflect the enhancing or weakening effect of different genders on the intensity of synergistic changes in fatty acid and cholesterol. Synergistic dysregulation can be a state of temporal disorder and imbalance in the correlation between changes in fatty acid and cholesterol concentrations, defined by both phase and trend information. The intensity of synergistic change can be the strength and duration of the unidirectional or anisodirectional changes in fatty acid and cholesterol concentrations. Co-induced associated effects can be the additive deterioration effect on the patient's neurological function and oxidative stress state when the risks of neurotoxic sphingolipid accumulation and oxidative stress levels evolve synchronously. Individualized moderating effects can be the additive or offsetting effects of age-modulated and gender-modulated risk information on the co-induced associated effects of neurotoxic sphingolipids and oxidative stress. Dynamic correction can be a process of real-time adjustment and calibration of risk data in the damage-associated information set based on individualized moderating effects, combined with age and gender correction factors.

[0065] Specifically, in the assessment of plasma metabolites in x-adrenoleukodystrophy, ignoring age and gender differences can lead to assessment results that are out of touch with individual realities. Children, whose metabolic activity may accelerate the disease, may have their condition underestimated; men are more sensitive to the intensity of synergistic changes, but this is often overlooked; and the cumulative risk effect in middle-aged and elderly patients may be ignored. This can result in a lack of targeted treatment, delayed intervention, and exacerbation of neurological damage. To address these issues: First, obtain individual patient information, such as the age "45" and gender "male" for a 45-year-old male patient. Then, use statistical modeling techniques such as multiple regression analysis to correlate age with damage information using a quantified "synergistic disorder" index (e.g., a comprehensive score describing the mismatch between fatty acid and cholesterol changes). By fitting a function relating age to the slope of this index, calculate the specific risk moderating coefficient caused by age, thus generating "age-modified risk information." This may lead to the quantitative conclusion that "for every 10-year increase in age, the rate of synergistic disorder worsening increases by a certain percentage." Simultaneously, use co-hoc methods... The difference analysis model, after controlling for confounding factors such as age and disease duration, compares the differences in the "intensity of synergistic change" indicator between different gender groups, calculates gender-specific moderating weights, and generates "gender-modified risk information." For example, it determines that "the average intensity of synergistic change in male patients is higher than the female baseline by a certain margin." Then, using a generalized linear model with interaction terms, the moderating coefficients of age and gender are simultaneously introduced to analyze their combined effect on the "co-induced associated effects" (i.e., the risk of dual deterioration of neurological function and oxidative stress), determining whether it is a simple superposition, amplification, or cancellation, thereby calculating a composite "individualized moderating influence factor." Finally, based on this factor, a set of preset rules and dynamic correction algorithms are used (e.g., if the factor exceeds a preset threshold (e.g., >0.7), the risk level and expected progression rate of the "neurotoxic sphingolipid accumulation risk information" in the damage-associated information set are simultaneously increased proportionally) to recalibrate and weight the original associated risks, generating a "personalized disease information set" that ultimately reflects the unique risk spectrum of this 45-year-old male patient.

[0066] The method provided in this embodiment can accurately predict the differences in disease progression among different patients, provide personalized data support for clinical practice, help formulate appropriate intervention plans, avoid risks in advance, optimize treatment strategies, effectively delay the accumulation of neurotoxic sphingolipids and oxidative stress damage, improve patient prognosis, and significantly improve the accuracy and effectiveness of disease diagnosis and treatment.

[0067] In some embodiments, based on age-modulated risk information, the degree of acceleration or delay of synergistic dysregulation in different age ranges is analyzed to obtain an age-modified factor; based on gender-modulated risk information, the degree of enhancement or weakening of the intensity of synergistic change by different genders is analyzed to obtain a gender-modified factor; based on the age-modified factor and combined with the gender-modified factor, the combined regulatory effect of the two on the risk information of neurotoxic sphingolipid accumulation and oxidative stress level is analyzed to obtain individualized risk-modified information; based on the individualized risk-modified information, the rate and magnitude of synchronous evolution of risk levels in the co-induced association effects are recalibrated to obtain individualized co-induced association effects, continuously modifying the damage association information set as the basis for the individualized disease information set.

[0068] Age-corrected factors can be quantitative indicators of the degree to which the synergistic dysregulation of fatty acid and cholesterol relationships is accelerated or delayed across different age ranges. Gender-corrected factors can be quantitative indicators of the degree to which the intensity of synergistic changes in fatty acid and cholesterol is enhanced or weakened by different genders. Individualized risk correction information can be risk-corrected data reflecting individual patient differences, obtained by combining age-corrected and gender-corrected factors and analyzing their combined regulatory effects on neurotoxic sphingolipid accumulation risk information and oxidative stress level risk information. Individualized co-induced association effects can be association effect data tailored to individual patient characteristics, obtained by recalibrating the rate and magnitude of synchronous evolution of risk levels in the "co-induced association effects" set within the injury association information set, based on individualized risk correction information.

[0069] Specifically, in the process of assessing plasma metabolites in x-adrenoleukodystrophy, without dynamically correcting the damage-associated information set, age and gender differences can lead to assessments that are out of touch with the individual. Children may be over-estimated in terms of risk, while elderly men may be underestimated in terms of the rate of disease progression. This can result in a lack of targeted treatment plans, increase medical risks, and affect the effectiveness of diagnosis and treatment. To address the aforementioned issues: First, relying on statistical analysis methods and based on previously generated age-modified risk information, the patient cohort was stratified according to different age ranges (e.g., 0-10 years, 11-20 years, and 21 years and above). The average degree of regulation of the synergistic dysregulation of fatty acids and cholesterol in each range was quantitatively analyzed. For example, the analysis found that the 11-20 year old adolescent patient group may show an accelerated effect, and an age correction factor of 1.5 was assigned, while the 0-10 year old children group may show a delayed effect, and a correction factor of 0.7 was assigned. Second, comparative analysis was used to process the gender-modified risk information. By comparing the historical data distribution differences in the intensity of synergistic changes in fatty acids and cholesterol in male and female patient groups, a gender-specific regulation coefficient was determined. For example, a gender correction factor of 1.2 was assigned to male patients to reflect the enhancing effect, and 0.9 was assigned to female patients to reflect the weakening effect. Subsequently, through weighted fusion, the two factors mentioned above are combined (e.g., multiplication or linear combination based on clinical weights). For example, the age correction factor of 1.5 and the gender correction factor of 1.2 for a 15-year-old male patient are combined to obtain a composite adjustment coefficient of 1.8. This coefficient simultaneously affects the risk information of neurotoxic sphingolipid accumulation and the risk information of oxidative stress levels, thereby generating comprehensive individualized risk correction information. Finally, a recalibration algorithm is used to iteratively adjust the parameters of the dynamic process of "synchronous evolution of risk level" in the co-induced association effect using this individualized risk correction information: for example, the daily rate of increase of risk level in the original model is calibrated from 0.4 units to 0.72 units based on the composite coefficient 1.8; or the risk accumulation threshold is calibrated from a preset 100 units to a more sensitive 56 units. Through this series of continuous operations based on specific analytical techniques and calibration algorithms, the damage association information set can be continuously and dynamically corrected, ultimately forming a highly individualized disease information set.

[0070] The method provided in this embodiment fully incorporates individualized factors such as age and gender into the dynamic correction step, making the individualized disease information set fit the patient's actual situation, significantly improving the accuracy of the assessment, providing personalized disease reference for clinical practice, helping to formulate appropriate treatment plans, reducing the risk of diagnosis and treatment, and improving the assessment system to expand its clinical application value.

[0071] Figure 3 A schematic diagram of the structure of a plasma metabolite assessment system for patients with x-adrenoleukodystrophy, provided in an embodiment of this application, is shown below. Figure 3As shown, the plasma metabolite assessment system 300 for patients with x-adrenoleukodystrophy in this embodiment includes: a dynamic change module 301, a damage association module 302, and a dynamic correction module 303.

[0072] The dynamic change module 301 is used to acquire the patient's plasma information set, and based on the patient's plasma information set, analyze the dynamic changes of fatty acids and cholesterol in plasma metabolites to obtain a dynamic trajectory information set; the damage association module 302 is used to acquire the patient's condition information set, and based on the patient's condition information set, combined with the dynamic trajectory information set, analyze the associated risk information of neurotoxic sphingolipid accumulation and increased oxidative stress levels to obtain a damage association information set; the dynamic correction module 303 is used to acquire the patient's individual information set, and based on the patient's individual information set, combined with the damage association information set, analyze the dynamic impact of age and gender data on the condition to obtain an individualized condition information set, and output a plasma metabolite assessment log.

[0073] Optionally, when the dynamic change module 301 analyzes the dynamic change information of fatty acids and cholesterol in plasma metabolites based on the patient plasma information set to obtain a dynamic trajectory information set, it is specifically used for: the patient plasma information set being plasma metabolite detection data at different time points, including fatty acid information and cholesterol information; based on the fatty acid information and combined with the cholesterol information, analyzing the concentration change information of each over time to obtain fatty acid change sequences and cholesterol change sequences; based on the fatty acid change sequences and combined with the cholesterol change sequences, analyzing the temporal correlation and synergy of their concentration changes to obtain a synergistic change pattern; and integrating the fatty acid change sequences, the cholesterol change sequences, and the synergistic change pattern to construct the dynamic trajectory information set.

[0074] Optionally, the dynamic change module 301, during the construction of the synergistic change law, is specifically used for: analyzing the correspondence between the direction of change of fatty acid concentration and the direction of change of cholesterol concentration within the same time window based on the fatty acid change sequence and the cholesterol change sequence, to obtain change phase information; analyzing the persistence and intensity of the same-direction or opposite-direction change of cholesterol concentration when fatty acid concentration increases or decreases based on the change phase information, to obtain change trend information; and integrating the change phase information and the change trend information to generate a law describing the strength of the correlation and synergistic relationship between changes in fatty acid and cholesterol concentration, as the synergistic change law.

[0075] Optionally, when the damage association module 302 analyzes the associated risk information of neurotoxic sphingolipid accumulation and elevated oxidative stress levels based on the patient's condition information set and the dynamic trajectory information set to obtain a damage association information set, it is specifically used for: the patient's condition information set including neurological function score information and protein carboxyl information; based on the neurological function score information and the synergistic change pattern, analyzing the correlation between the concentration change trends of fatty acids and cholesterol and the fluctuations of the neurological function score to obtain functional damage association information; based on the protein carboxyl information and the synergistic change pattern, analyzing the positive correlation between the intensity of synergistic changes of fatty acids and cholesterol and the protein carboxyl content to obtain oxidative stress association information; based on the functional damage association information and the oxidative stress association information, analyzing the induced association effects of the two on the patient's neurotoxic sphingolipids and oxidative stress levels under their combined action to obtain the damage association information set.

[0076] Optionally, the damage association module 302, during the construction of the functional damage association information, is specifically used for: analyzing the rise and fall amplitude and rate of change of the neurological function score at different time points based on the neurological function score information to obtain dynamic change information of the score; analyzing the abnormal information of fatty acid concentration and abnormal information of cholesterol concentration corresponding to the period of decline in neurological function score based on the dynamic change information of the score, combined with the fatty acid change sequence and the cholesterol change sequence, to obtain concentration abnormality association information; and analyzing the persistence and severity of the accelerated decline in neurological function score when fatty acids and cholesterol exhibit a synergistic disorder relationship defined by the change phase information and the change trend information, based on the concentration abnormality association information and the synergistic change law, to obtain the functional damage association information.

[0077] Optionally, the damage correlation module 302, during the construction of the oxidative stress correlation information, is specifically used for: analyzing the rise and fall and fluctuation amplitude of protein carboxyl content at different time points based on the protein carboxyl information to obtain dynamic oxidative stress information; based on the dynamic oxidative stress information, combined with the synergistic change pattern, analyzing the temporal correspondence between the synergistic change pattern of fatty acids and cholesterol defined by the change phase information and the change trend information, and the protein carboxyl content increase pattern to obtain correlation time correspondence information; based on the correlation time correspondence information, analyzing the persistence and amplitude of the accelerated increase in protein carboxyl content when the synergistic change intensity of fatty acids and cholesterol reaches a preset threshold as defined by the change trend information, generating a pattern describing the strength of the causal relationship between the two, as the oxidative stress correlation information.

[0078] Optionally, when the damage association module 302 analyzes the induced association effects of the functional impairment association information and the oxidative stress association information on the patient's neurotoxic sphingolipids and oxidative stress levels under their combined effects to obtain the damage association information set, it is specifically used for: analyzing the association characteristics between the persistence and severity of the decline in neurological function scores and the synergistic dysregulation of fatty acids and cholesterol based on the functional impairment association information to obtain neurotoxic sphingolipid accumulation risk information; analyzing the association characteristics between the persistence and magnitude of the increase in protein carboxyl content and the intensity of the synergistic change in fatty acids and cholesterol based on the oxidative stress association information to obtain oxidative stress level risk information; analyzing the joint deterioration effect on neurological function and oxidative stress state when the risk levels indicated by the two risk information evolve synchronously from low to high based on the neurotoxic sphingolipid accumulation risk information and the oxidative stress level risk information to obtain a joint induced association effect; and integrating the neurotoxic sphingolipid accumulation risk information, the oxidative stress level risk information, and the joint induced association effect to construct the damage association information set.

[0079] Optionally, when the dynamic correction module 303 analyzes the dynamic impact of age and gender data on the disease condition based on the patient's individual information set and the damage association information set to obtain an individualized disease information set and output a plasma metabolite assessment log, it is specifically used for: the patient's individual information set including the patient's age and gender; based on the patient's age and the damage association information, analyzing the accelerating or delaying effect of age increase on the synergistic dysregulation relationship to obtain age-modified risk information; based on the patient's gender and the damage association information, analyzing the enhancing or weakening effect of gender differences on the intensity of synergistic changes to obtain gender-modified risk information; based on the age-modified risk information and the gender-modified risk information, analyzing the superposition or offsetting effect of both on the jointly induced association effect to obtain individualized moderating effect; based on the individualized moderating effect, dynamically correcting the damage association information set to generate a set reflecting the disease evolution characteristics of patients with different ages and genders, as the individualized disease information set, and outputting the plasma metabolite assessment log containing the individualized disease information set.

[0080] Optionally, the dynamic correction module 303, in the specific implementation of dynamically correcting the damage-related information set, is specifically used for: analyzing the degree of acceleration or delay of the synergistic dysregulation relationship by different age intervals based on the age-modified risk information, and obtaining an age correction factor; analyzing the degree of enhancement or weakening of the synergistic change intensity by different genders based on the gender-modified risk information, and obtaining a gender correction factor; analyzing the combined regulatory effect of the age correction factor and the gender correction factor on the neurotoxic sphingolipid accumulation risk information and the oxidative stress level risk information based on the age correction factor and the gender correction factor, and obtaining individualized risk correction information; and recalibrating the rate and magnitude of the synchronous evolution of risk levels in the co-induced association effect based on the individualized co-induced association effect, continuously correcting the damage-related information set as the basis for the individualized disease information set.

[0081] The system in this embodiment can be used to execute the methods of any of the above embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

Claims

1. A method for assessing plasma metabolites in patients with x-adrenoleukodystrophy, characterized in that, include: Obtain a patient plasma information set, and based on the patient plasma information set, analyze the dynamic changes of fatty acids and cholesterol in plasma metabolites to obtain a dynamic trajectory information set; Obtain a set of patient condition information; based on the set of patient condition information and the set of dynamic trajectory information, analyze the associated risk information of neurotoxic sphingolipid accumulation and elevated oxidative stress levels to obtain a set of injury-related information. A patient individual information set is obtained. Based on the patient individual information set and the injury association information set, the dynamic impact of age and gender data on the condition is analyzed to obtain an individualized condition information set, and a plasma metabolite assessment log is output.

2. The method according to claim 1, characterized in that, Based on the patient's plasma information set, the dynamic changes in fatty acids and cholesterol in plasma metabolites are analyzed to obtain a dynamic trajectory information set, including: The patient plasma information set consists of plasma metabolite detection data at different time points, including fatty acid information and cholesterol information; Based on the fatty acid information and the cholesterol information, the concentration change information of each over time is analyzed to obtain the fatty acid change sequence and the cholesterol change sequence. Based on the fatty acid change sequence and the cholesterol change sequence, the temporal correlation and synergy of their concentration changes were analyzed to obtain the synergistic change pattern. By integrating the fatty acid variation sequence, the cholesterol variation sequence, and the synergistic variation pattern, the dynamic trajectory information set is constructed.

3. The method according to claim 2, characterized in that, The process of constructing the aforementioned coordinated change law includes: Based on the fatty acid change sequence and the cholesterol change sequence, the correspondence between the direction of change of fatty acid concentration and the direction of change of cholesterol concentration within the same time window is analyzed to obtain the change phase information. Based on the phase change information, the persistence and intensity of the cholesterol concentration changing in the same or opposite direction when the fatty acid concentration increases or decreases are analyzed to obtain the trend information. By integrating the change phase information and the change trend information, a pattern describing the strength of the correlation and synergistic relationship between changes in fatty acid and cholesterol concentration is generated, which serves as the synergistic change pattern.

4. The method according to claim 3, characterized in that, Based on the patient's condition information set and combined with the dynamic trajectory information set, the association risk information of neurotoxic sphingolipid accumulation and elevated oxidative stress levels is analyzed to obtain an injury association information set, including: The patient's condition information set includes neurological function score information and protein carboxyl information; Based on the neurological function score information and combined with the synergistic change pattern, the correlation between the concentration change trends of fatty acids and cholesterol and the fluctuations of neurological function scores was analyzed to obtain functional impairment correlation information. Based on the protein carboxyl information and the synergistic change pattern, the positive correlation between the intensity of synergistic changes in fatty acids and cholesterol and the protein carboxyl content was analyzed to obtain oxidative stress correlation information. Based on the functional impairment association information and combined with the oxidative stress association information, the combined effects of the two on the induction of neurotoxic sphingolipids and the level of oxidative stress in patients are analyzed to obtain the damage association information set.

5. The method according to claim 4, characterized in that, The process of constructing the functional impairment association information includes: Based on the neurological function score information, the magnitude and rate of change of the neurological function score at different time points are analyzed to obtain dynamic change information of the score. Based on the dynamic change information of the score, combined with the fatty acid change sequence and the cholesterol change sequence, the abnormal information of fatty acid concentration and abnormal information of cholesterol concentration corresponding to the period of decline in neurological function score are analyzed to obtain concentration abnormality correlation information. Based on the concentration abnormality correlation information and combined with the synergistic change pattern, the persistence and severity of the accelerated decline in neurological function scores are analyzed when fatty acids and cholesterol exhibit a synergistic disorder relationship defined by the change phase information and the change trend information, thereby obtaining the functional impairment correlation information.

6. The method according to claim 5, characterized in that, The process of constructing the oxidative stress correlation information includes: Based on the protein carboxyl information, the changes and fluctuations in protein carboxyl content at different time points were analyzed to obtain dynamic information on oxidative stress. Based on the dynamic information of oxidative stress and combined with the synergistic change pattern, the synergistic change pattern of fatty acids and cholesterol defined by the change phase information and the change trend information is analyzed, and the temporal correspondence between the pattern of protein carboxyl content increase is obtained, thus obtaining the associated time correspondence information. Based on the correlation time information, the persistence and magnitude of the accelerated increase in protein carboxyl content when the intensity of the synergistic change in fatty acids and cholesterol reaches a preset threshold as defined by the trend information are analyzed, and a pattern describing the strength of the causal relationship between the two is generated as the oxidative stress correlation information.

7. The method according to claim 6, characterized in that, Based on the functional impairment association information and combined with the oxidative stress association information, the induced association effects of the two on the neurotoxic sphingolipids and oxidative stress levels in patients are analyzed to obtain the impairment association information set, including: Based on the aforementioned functional impairment association information, the association characteristics between the persistence and severity of the decline in neurological function scores and the synergistic dysregulation of fatty acids and cholesterol were analyzed to obtain information on the risk of neurotoxic sphingolipid accumulation. Based on the oxidative stress correlation information, the correlation characteristics between the persistence and magnitude of the increase in protein carboxyl content and the intensity of the synergistic changes in fatty acids and cholesterol were analyzed to obtain oxidative stress level risk information. Based on the neurotoxic sphingolipid accumulation risk information and the oxidative stress level risk information, the common deterioration effect on neurological function and oxidative stress state when the risk levels indicated by the two risk information evolve synchronously from low to high is analyzed, and the common inducing correlation effect is obtained. By integrating the neurotoxic sphingolipid accumulation risk information, the oxidative stress level risk information, and the jointly induced associated effects, the damage association information set is constructed.

8. The method according to claim 7, characterized in that, Based on the patient's individual information set and the injury-related information set, the dynamic impact of age and gender data on the condition is analyzed to obtain an individualized condition information set, and a plasma metabolite assessment log is output, including: The patient individual information set includes the patient's age and gender; Based on the patient's age and the injury association information, the effects of aging on the acceleration or delay of the dysregulation relationship are analyzed to obtain age-modified risk information. Based on the patient's gender and the injury association information, the enhancing or weakening effect of gender differences on the intensity of the synergistic change is analyzed to obtain gender-modified risk information. Based on the age-modified risk information and the gender-modified risk information, the superposition or offsetting effects of the two on the jointly induced associated effects are analyzed to obtain the individualized moderating effects. Based on the individualized regulatory effects, the damage-related information set is dynamically modified to generate a set reflecting the disease evolution characteristics of patients of different ages and genders, which serves as the individualized disease information set. The plasma metabolite assessment log containing the individualized disease information set is then output.

9. The method according to claim 8, characterized in that, The specific implementation of dynamically correcting the damage association information set includes: Based on the age-modified risk information, the degree to which different age ranges accelerate or delay the synergistic imbalance relationship is analyzed to obtain the age correction factor. Based on the aforementioned gender-modified risk information, the degree to which different genders enhance or weaken the intensity of the synergistic change is analyzed to obtain a gender correction factor. Based on the age correction factor and the gender correction factor, the combined regulatory effect of the two factors on the risk information of neurotoxic sphingolipid accumulation and the risk information of oxidative stress level is analyzed to obtain individualized risk correction information. Based on the individualized risk correction information, the rate and magnitude of the synchronous evolution of risk levels in the co-induced associated effects are recalibrated to obtain individualized co-induced associated effects, and the damage association information set is continuously corrected as the basis for the individualized disease information set.

10. A plasma metabolite assessment system for patients with x-adrenoleukodystrophy, characterized in that, The method applied to any one of claims 1-9 includes: The dynamic change module is used to acquire the patient's plasma information set, and based on the patient's plasma information set, analyze the dynamic change information of fatty acids and cholesterol in plasma metabolites to obtain a dynamic trajectory information set. The injury association module is used to acquire a set of patient condition information, and based on the set of patient condition information and the set of dynamic trajectory information, analyze the associated risk information of neurotoxic sphingolipid accumulation and elevated oxidative stress levels to obtain an injury association information set. The dynamic correction module is used to acquire a set of individual patient information, and based on the set of individual patient information and the set of injury-related information, analyze the dynamic impact of age and gender data on the condition to obtain an individualized condition information set and output a plasma metabolite assessment log.

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