Immune dynamic trajectory modeling method and system

Abstract

The application discloses an immune dynamic trajectory modeling method and system, which solves the problems that the prior art cannot accurately locate the inflection point of the change of the immune parameter with age, cannot comprehensively evaluate the immune state of multiple parameters, and lacks visual dynamic trajectory output. The method comprises the following steps: collecting multiple lymphocyte subpopulation parameters in peripheral blood; detecting the trend of each immune parameter with age, calculating the change slope before and after each inflection point; dimension reduction and weighting are performed on multiple immune parameters to construct a comprehensive immune index (CII); and a dynamic trajectory graph containing the inflection point age of each immune parameter, the change slope and the comprehensive index trend graph is output. The application integrates flow cytometry detection data, piecewise regression model and principal component analysis model, can intuitively display the nonlinear and multi-stage characteristics of the evolution of the immune system with age, identify the key aging inflection point, and provide a quantitative tool for population immune state screening, aging mechanism research and drug or nutrition intervention effect evaluation.

Description

Technical Field

[0001] This invention belongs to the field of biomedical data analysis technology, specifically relating to a method and system for modeling immune dynamic trajectories. Background Technology

[0002] Immunoreshining is the functional decline and homeostasis imbalance of the body's immune system that occurs with age. It is characterized by a decrease in the diversity of T cell receptor repertoire, a reduction in naive T cells, an expansion of terminally differentiated memory T cells, and a chronic low-grade inflammatory state. Immunoreshining directly affects an individual's susceptibility to pathogens and the effectiveness of vaccine responses, and is closely related to an increased incidence of malignant tumors, autoimmune diseases, and various chronic diseases.

[0003] In existing technologies, immune assessments often employ single absolute counts or percentages of lymphocyte subsets, typically using reference ranges for Western populations. However, research indicates significant differences in immune aging trajectories across different sexes and age groups. Directly applying Western population reference standards may lead to misjudgments of immune status in other populations. Existing technologies for assessing immune status often rely on stratified comparisons of age groups or simple linear regression, failing to capture the non-linear, multi-stage characteristics of immune aging. While some patents (such as CN116355983A and CN115775624A) propose immune age calculation models or immune aging risk assessment methods, they lack the ability to detect inflection points in immune parameter changes with age, thus failing to accurately pinpoint key age points where the immune system accelerates its decline. 1. A single immune parameter is insufficient to fully reflect the overall state of immune aging. Existing comprehensive index construction methods (such as CN113241177A) use partial least squares classification models, which have a limited number of principal components extracted and are not combined with inflection point analysis. 2. No dynamic trajectory modeling system integrating flow cytometry data, segmented regression models, and principal component analysis is provided, making it impossible to intuitively display the multi-stage evolution of the immune status.

[0004] Therefore, there is an urgent need for an immune dynamic trajectory modeling system that can integrate multi-source data, identify key inflection points, construct comprehensive indices, and output visualized trajectories. Summary of the Invention

[0005] This invention provides a method and system for modeling the dynamic trajectory of immune status, in order to solve the problems of existing technologies that cannot accurately locate the inflection point of immune parameters changing with age, cannot comprehensively evaluate immune status with multiple parameters, and lack visual dynamic trajectory output.

[0006] In a first aspect, the present invention proposes an immune dynamic trajectory modeling method, comprising: acquiring detection data of multiple lymphocyte subset parameters in peripheral blood samples obtained by flow cytometry, wherein the detection data includes immune parameter values ​​of individuals of different ages; performing piecewise regression fitting on the sequence data of each immune parameter changing with age, identifying at least one inflection point age of the immune parameter changing with age, and calculating the slope of change before and after each inflection point; performing principal component analysis on multiple immune parameters, calculating a weighted composite index based on the contribution rate of each principal component, and obtaining a comprehensive immune index (CII); generating an immune dynamic trajectory graph, wherein the dynamic trajectory graph includes at least one or more of the following: the inflection point age of each immune parameter, the slope of change of each immune parameter before and after the inflection point, and a trend graph of the comprehensive immune index changing with age.

[0007] In some examples, the lymphocyte subset parameters include: absolute CD4+ T cell count, CD4+ T cell percentage, absolute CD8+ T cell count, CD8+ T cell percentage, CD4+ / CD8+ T cell ratio, absolute NK cell count, NK cell percentage, absolute total B cell count, total B cell percentage, absolute total T cell count, and total T cell percentage.

[0008] In some examples, the Comprehensive Immune Index (CII) = (0.439 × absolute CD4+ T cell count) + (0.452 × CD4+ T cell percentage) - (0.008 × absolute CD8+ T cell count) - (0.198 × CD8+ T cell percentage) - (0.227 × absolute NK cell count) - (0.383 × NK cell percentage) + (0.358 × CD4+ / CD8+ ratio) + (0.455 × absolute total B cell count) + (0.421 × total B cell percentage) + (0.265 × absolute total T cell count) + (0.198 × total T cell percentage).

[0009] In some examples, the identified inflection point age includes at least one of the following: an inflection point for the percentage of CD8+ T cells and / or the CD4+ / CD8+ ratio around 54 years of age; an inflection point for CD4+ T cells and / or B cells around 62-63 years of age; an inflection point for total T cells around 67-68 years of age; and an inflection point for the comprehensive immune index around 63 years of age.

[0010] In some examples, when an individual's age exceeds a preset inflection point age threshold, or when the slope of an individual's immune parameter changes exceeds a preset accelerated decline threshold, it serves as a warning signal of accelerated immune aging.

[0011] In some examples, independent reference ranges for immune parameters and immune dynamic trajectories are established for males and females respectively, and the gender-specific inflection point age and slope are output.

[0012] In some examples, for individuals aged ≥63 years, the output measures of female dominance in CD4+ T cells, B cells, CD4+ / CD8+ ratio, and total T cell percentage, and male dominance in NK cell number and percentage.

[0013] In some examples, the dynamic trajectory plot may also include one or more of the following: the original data distribution in the form of a box plot, the fitted curve and its confidence interval, statistical comparison markers between different age groups, and the location markers of individual immune age in the population trajectory.

[0014] Secondly, this invention proposes an immune dynamic trajectory modeling system, comprising: a data acquisition unit configured to acquire detection data of multiple lymphocyte subset parameters in peripheral blood samples obtained by flow cytometry, wherein the detection data includes immune parameter values ​​of individuals of different ages; a piecewise regression analysis unit configured to perform piecewise regression fitting on the sequence data of each immune parameter changing with age, identify at least one inflection point age of the immune parameter changing with age, and calculate the slope of change before and after each inflection point; a principal component analysis unit configured to perform principal component analysis on multiple immune parameters, calculate a weighted composite index based on the contribution rate of each principal component, and obtain a comprehensive immune index (CII); and a trajectory generation unit configured to generate an immune dynamic trajectory graph based on the outputs of the piecewise regression analysis unit and the principal component analysis unit, wherein the dynamic trajectory graph includes at least one or more of the following: the inflection point age of each immune parameter, the slope of change of each immune parameter before and after the inflection point, and a trend graph of the comprehensive immune index changing with age.

[0015] Thirdly, the present invention proposes a computer-readable storage medium having a computer program stored thereon, characterized in that the program, when executed by a processor, implements the immune dynamic trajectory modeling method described above.

[0016] Significant advancements of this invention: The study precisely identified significant inflection points for CD8+ T cell percentage and CD4+ / CD8+ ratio at age 54, CD4+ T cell and B cell counts at age 62-63, total T cell counts at age 67-68, and comprehensive immune index at age 63, providing a clear time window for immune intervention. The CII index, constructed using principal component analysis, integrates several key immune parameters, providing a more comprehensive and stable reflection of an individual's overall immune status than a single indicator. Furthermore, this index shows a significant negative correlation with age.

[0017] By integrating flow cytometry data, segmented regression inflection points, and comprehensive index trends into a dynamic trajectory graph, the nonlinear and multi-stage evolution process of immune status can be intuitively displayed, facilitating clinical application.

[0018] This invention provides a simple and feasible technical means for analyzing the immune status of a population, overcoming the bias caused by directly using Western population reference standards, and is applicable to the immune health assessment of the Chinese population.

[0019] It supports gender stratification and early warning functions, clearly revealing significant gender immune differences in the elderly (≥63 years old) population, and provides early warning of accelerated aging, which helps in precise health management. Attached Figure Description

[0020] Figure 1 This is a block diagram of an immune dynamic trajectory modeling system according to an embodiment of the present invention.

[0021] Figure 2 This is a gate strategy diagram for analyzing lymphocyte subsets using BD FACSCanto clinical software according to an embodiment of the present invention.

[0022] Figure 3 This is a piecewise regression fitting curve of immune parameters changing with age according to an embodiment of the present invention.

[0023] Figure 4 This is a piecewise regression fitting curve of the Comprehensive Immunity Index (CII) as a function of age according to an embodiment of the present invention.

[0024] Figure 5 This is a comparison chart of immune parameters after gender stratification according to an embodiment of the present invention. Detailed Implementation

[0025] Example 1: A method for modeling immune dynamic trajectories.

[0026] Step 1: Data Acquisition.

[0027] Data on multiple lymphocyte subset parameters in peripheral blood samples obtained by flow cytometry were acquired, including immune parameter values ​​of individuals of different ages.

[0028] Peripheral blood samples were analyzed using BD FACSCanto flow cytometry, and lymphocyte subsets were labeled using antibody combinations (specific antibody combinations and concentrations are shown in Table 1). At least 10,000 lymphocyte events were collected and gated using BD FACSCanto clinical software, outputting the following 11 immune parameters: Absolute number (cells / μL) and percentage (%) of CD4+ T cells, absolute number and percentage of CD8+ T cells, absolute number and percentage of NK cells, CD4+ / CD8+ ratio, absolute number and percentage of B cells, and absolute number and percentage of total T cells.

[0029] The data includes each individual's age, gender, and the values ​​of the 11 parameters mentioned above, totaling 6,550 cases.

[0030] Table 1. Antibody-conjugated fluorescent labels and concentration values

[0031] (Includes the concentration values ​​of the six antibodies used in the kit. The reagent column lists the antibodies and their conjugated fluorescent labels, while the concentration column shows the initial concentration of the reagents.) For flow cytometry data from different batches or different instruments, Z-score normalization based on control samples or ComBat batch correction methods are used to eliminate batch effects and ensure the model's generalization ability.

[0032] Figure 2 A sorting strategy for lymphocyte subsets in representative samples was demonstrated using flow cytometry. Lymphocyte populations were sorted based on side scattering (SSC) and CD45, while fluorescent microsphere populations were sorted based on CD4 and SSC. Microspheres possess unique CD4 fluorescence signals and light scattering characteristics, allowing for clear differentiation from cell populations. The absolute counts (cells / μL) of lymphocyte subsets were calculated by comparing the collected counts of target cells and microspheres, combined with the known total microsphere count in BD Trucount tubes. Within the lymphocyte sorting gate, T cells (CD3+) and non-T cells (CD3+) were separated. Identification was performed using CD3 and SSC. In CD3+ T cells, helper / inducing T cells (CD4+CD8+) were identified. ) and cytotoxic T cells (CD4) CD8+ is sorted based on CD4 and CD8. In CD3... Within the non-T cell phylum, B cells (CD19+) are recognized by CD19 and SSC, while NK cells (CD16+CD56+) are sorted using CD16+56 and SSC. All data were acquired using BD FACSCanto clinical software, and staining was performed using the BDMultitest IMK four-color kit.

[0033] Step 2: Piecewise regression analysis.

[0034] Piecewise regression fitting was performed on the sequence data of each immune parameter changing with age to identify at least one inflection point age for the change of the immune parameter with age, and the slope of change before and after each inflection point was calculated.

[0035] For example, using the "segmented" package in R (version 4.4.1), a piecewise regression model is performed to model the relationship between each immune parameter and age. The maximum number of inflection points is set to 1-3, and the BIC criterion is used to select the optimal model. The output for each parameter is the inflection point age and its 95% confidence interval, the slope before the inflection point, the slope after the inflection point, and the change in slope. p value.

[0036] Taking the absolute number of CD4+ T cells as an example: the inflection point age was 63.12 years (95% CI: 59.46-66.78), the slope before the inflection point was -0.857 units / year, and the slope after the inflection point was -9.081 units / year. The slope change... p =0.033, indicating that the rate of decline accelerated significantly after the inflection point.

[0037] Similarly, the inflection point for CD8+ T cell percentage is around 54 years old, for B cell inflection point around 62 years old, for total T cell inflection point around 67.5 years old, and for CII inflection point around 63 years old.

[0038] Step 3: Principal component analysis.

[0039] Principal component analysis was performed on multiple immune parameters, and a weighted composite index was calculated based on the contribution rate of each principal component to obtain the comprehensive immune index (CII).

[0040] For example, principal component analysis was performed on 11 immune parameters from all 6,550 samples. First, the KMO test (KMO = 0.622) and Bartlett's test for sphericity were performed. p The eigenvalue <0.001 indicates that the data is suitable for factor analysis. Principal components with eigenvalues ​​>1 were extracted, resulting in four principal components with a cumulative contribution rate of 97.252%.

[0041] The total variance explained by principal component analysis is shown in Table 2, and the score coefficient matrix of each principal component is shown in Table 3. The weighted composite index was calculated based on the variance contribution rates of each principal component (28.486%, 26.075%, 25.329%, and 17.36%). CII = (PC1 score × 28.486 + PC2 score × 26.075 + PC3 score × 25.329 + PC4 score × 17.36) / 97.252.

[0042] The final simplified formula for calculating CII is as follows: CII = (0.439 × absolute CD4+ T cell count) + (0.452 × CD4+ T cell percentage) - (0.008 × absolute CD8+ T cell count) - (0.198 × CD8+ T cell percentage) - (0.227 × absolute NK cell count) - (0.383 × NK cell percentage) + (0.358 × CD4+ / CD8+ ratio) + (0.455 × absolute total B cell count) + (0.421 × total B cell percentage) + (0.265 × absolute total T cell count) + (0.198 × total T cell percentage).

[0043] According to Spearman correlation test, the correlation coefficient between CII and age is p = -0.208. p <0.001, verifying its effectiveness as a biomarker of immune comprehensive status.

[0044] Table 2 Total Variance Explained by Principal Component Analysis

[0045] (This displays the variance explained by the first four principal components extracted through principal component analysis. The initial eigenvalue column shows the eigenvalues, variance percentage, and cumulative percentage of each principal component before rotation. The extracted sum of squared loadings column only represents the variance explained by the four principal components; therefore, the data for the 5th to 11th principal components are blank. The rotated sum of squared loadings column shows the eigenvalues, variance percentage, and cumulative percentage of each principal component after applying the maximum variance rotation method. Except for the first four principal components, the loadings of the remaining components approach zero after rotation and are therefore not displayed. The first four principal components cumulatively explain 97.252% of the total variance.) Table 3 Component Rating Coefficient Matrix

[0046] (This shows the coefficient matrix used to calculate the scores of each principal component. Each column corresponds to a principal component (components 1-4), and each row corresponds to an immune parameter. The principal component scores are obtained by multiplying the standardized raw variable values ​​by the corresponding coefficients in the table and summing the results.) Step 4: Generate an immune dynamic trajectory map.

[0047] Based on the results of piecewise regression analysis and principal component analysis, the following graphs were generated: box plots of each immune parameter (age on the x-axis, immune parameter values ​​on the y-axis), overlaid with the piecewise regression fitted curves, and inflection points marked with vertical dashed lines (see...). Figure 3 ); Box plots and piecewise regression fitted curves of CII, with the inflection point at age 63 marked (see Figure 4 ); optionally, generate a comparative radar chart with gender stratification (see Figure 5 ).

[0048] When an individual's age is ≥63 years and their CII decline rate exceeds -10 units / year (i.e., the slope after the inflection point), or the individual's absolute CD4+ T cell count is lower than the reference lower limit for the same age group, it is a "red alert: the immune system has entered the accelerated evolution phase"; when an individual's age is between 55 and 62 years and the CD4+ / CD8+ ratio has decreased by >10% for two consecutive years, it is a "yellow alert: the immune system is about to enter the accelerated evolution phase".

[0049] Statistical analysis was conducted on 926 individuals aged ≥63 years (308 females and 618 males). The median and interquartile range of each immune parameter were calculated, and the Kruskal-Wallis test was used to compare gender differences. Results showed that females had significantly higher CD4+ T cell counts (701 vs 630 cells / μL). p <0.001), B cells (206 vs 163.5 cells / μL), p <0.001), CD4+ / CD8+ ratio (1.55 vs 1.40, p =0.003) and total T cell percentage (66.17% vs 62.50%). p The percentage of NK cells was significantly higher in males than in females (<0.001); while males had significantly higher absolute NK cell counts (536.5 vs 398.5 cells / μL). p <0.001) and percentages (27.84% vs 21.56%). p The difference between males and females is significantly higher than that between females (<0.001). Based on this, a radar chart showing the difference between males and females is output.

[0050] Figure 3 This is a piecewise regression fit curve plot showing the changes in immune parameters with age. Each subplot corresponds to one immune parameter. The horizontal axis represents age (years), and the vertical axis displays the raw values ​​or percentages of the immune parameters. The scatter plots represent the box plot distribution of each parameter at different age stages. The solid red line represents the fitted line of the piecewise regression model, and the purple vertical dashed line indicates the inflection point of the model estimate. This chart visually illustrates the slope trend of each parameter before and after the inflection point.

[0051] Figure 4 This is a piecewise regression curve showing the change of the Comprehensive Immune Index (CII) with age. The horizontal axis represents age (years), and the vertical axis represents the CII index value. The solid red line represents the fitted line of the piecewise regression model, and the purple vertical dashed line marks the inflection point predicted by the model (63 years old, 95% confidence interval: 59.47–66.53). The graph shows the slope changes before the inflection point (-2.64 units / year) and after the inflection point (-10.66 units / year), and there is a statistically significant difference in the slope before and after the inflection point.p <0.001).

[0052] Figure 5 This is a comparison chart of immune parameters after sex stratification (female vs. male, age ≥63 years). The chart shows sex differences in several immune parameters among participants aged ≥63 years (female n=308, male n=618). The radar chart contains 11 axes representing the following immune parameters: CD4+ T cell count, CD4+ T cell percentage, CD8+ T cell count, CD8+ T cell percentage, NK cell count, NK cell percentage, CD4+ / CD8+ T cell ratio, total B cell count, total B cell percentage, total T cell count, and total T cell percentage. The raw data for each parameter were standardized and converted to relative values ​​within the 0-1 range for comparison on the same scale. The red line connects the standardized means for all parameters for females, and the blue line connects the data for males. The grid lines radiating outwards from the center indicate an increasing trend in standardized values. This chart visually presents the differences in the distribution characteristics of the 11 immune parameters between the two sexes. Example 2: An immune dynamic trajectory modeling system. For example... Figure 1 As shown, the system includes: a data acquisition unit, a piecewise regression analysis unit, a principal component analysis unit, a trajectory generation unit, an early warning unit, and a gender stratification analysis unit.

[0053] The data acquisition unit is configured to acquire detection data of multiple lymphocyte subset parameters in peripheral blood samples obtained by flow cytometry, and the detection data includes immune parameter values ​​of individuals of different ages.

[0054] The segmented regression analysis unit is configured to perform segmented regression fitting on the sequence data of each immune parameter as it changes with age, identify at least one inflection point age for the change of the immune parameter with age, and calculate the slope of change before and after each inflection point.

[0055] The principal component analysis unit is configured to perform principal component analysis on multiple immune parameters, and calculate a weighted composite index based on the contribution rate of each principal component to obtain the comprehensive immune index (CII). The calculation method of the CII is as described in Example 1.

[0056] The trajectory generation unit is configured to generate an immune dynamic trajectory map based on the output of the piecewise regression analysis unit and the principal component analysis unit. The dynamic trajectory map includes at least one or more of the following: the inflection point age of each immune parameter, the slope of the change of each immune parameter before and after the inflection point, the trend of the comprehensive immune index with age, the distribution of the original data in the form of a box plot, the fitted curve and its confidence interval, the statistical comparison markers between different age groups, and the location markers of the individual's immune age in the population trajectory.

[0057] The sex-stratified analysis unit is configured to establish independent reference intervals for immune parameters and immune dynamic trajectories for men and women respectively, and output sex-specific inflection point age and slope. For individuals aged ≥ 63 years, the output for women is based on CD4... + Indicators showing superiority over men in absolute T cell count, absolute B cell count, CD4 / CD8 ratio, and total T cell percentage, as well as indicators showing superiority of men over women in absolute NK cell count and percentage.

[0058] The warning unit is configured to issue a warning signal for accelerated immune aging when an individual's age exceeds a preset inflection point age threshold, or when the slope of an individual's immune parameter change exceeds a preset accelerated decline threshold. When an individual's age is ≥ 63 years and the rate of decline in the Comprehensive Immune Index (CII) exceeds -10 units / year, a "Red Warning: Entering the Accelerated Immune Evolution Phase" is output; when an individual's age is between 55 years and 63 years, a "Red Warning: Entering the Accelerated Immune Evolution Phase" is output. When an individual is 62 years old and their CD4 / CD8 ratio has decreased by more than 10% for two consecutive years, a "Yellow Warning: Imminent Entry into the Accelerated Immune Evolution Phase" is generated. The inflection point age threshold and the accelerated decline threshold can also be adjusted based on the sex-specific inflection point age output by the sex stratification analysis unit.

[0059] Example 3: A computer system.

[0060] A computer system includes a processor and memory. The memory and processor are interconnected via a bus system and / or other forms of connection. The processor may include any one or more processors such as a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP). The memory may include volatile memory, such as random access memory (RAM). The memory may also include non-volatile memory. volatile memory), such as read-only memory (read-only memory). The memory can be a ROM (ROM-only memory), flash memory, hard disk drive (HDD), or solid-state drive (SSD). The memory stores executable program code, which the processor executes to implement the immune dynamic trajectory modeling method described in Embodiment 1. That is, the memory stores instructions for executing the immune dynamic trajectory modeling method.

[0061] Example 4: A computer-readable storage medium.

[0062] A computer-readable storage medium, for example, a non-transitory computer-readable storage medium, such as a read-only memory (ROM). Read-only memory (ROM), flash memory, and compact disc (CD-ROM) Only memory, CD ROM, magnetic tape, floppy disk, and optical data storage devices, etc. This computer-readable storage medium is used to store non-transitory computer-readable instructions, which, when executed by a computer, can implement one or more steps of the immune dynamic trajectory modeling method described in Example 1.

[0063] Example 5: Used for evaluating the effectiveness of drug intervention.

[0064] In a hypothetical nutritional supplement intervention trial, 100 healthy elderly individuals aged 60-65 years underwent a 12-month intervention. Peripheral blood samples were collected at baseline, 6 months, and 12 months for flow cytometry analysis. The data were input into the system of this invention, which outputs the trajectory of CII (citric acid index) for each individual over time and compares it with the trajectory of a historical control group without intervention. If the rate of decline in CII in the intervention group slows from -10 units / year to -5 units / year, the system automatically identifies the intervention as "effective." This application can verify the practicality of this invention in evaluating the effects of nutritional or pharmacological interventions.

[0065] Example 6: Used for screening population immune status.

[0066] A health checkup center used this system to screen the immune status of 1,000 individuals aged 45-75. The system outputs each individual's CII value and inflection point stage classification ("stable phase" or "accelerated decline phase"), and automatically generates health management recommendations (such as influenza vaccination, vitamin D supplementation, and regular exercise) for individuals in the "accelerated decline phase." This system can improve the identification rate of high-risk elderly individuals.

Claims

1. A method for modeling immune dynamic trajectories, characterized in that, include: Data on multiple lymphocyte subset parameters in peripheral blood samples obtained by flow cytometry were acquired, including immune parameter values ​​of individuals of different ages. Piecewise regression fitting was performed on the sequence data of each immune parameter changing with age to identify at least one inflection point age of the immune parameter changing with age, and the slope of change before and after each inflection point was calculated. Principal component analysis was performed on multiple immune parameters, and a weighted composite index was calculated based on the contribution rate of each principal component to obtain the comprehensive immune index (CII). Generate an immune dynamic trajectory graph, which includes at least one or more of the following: the inflection point age of each immune parameter, the slope of change of each immune parameter before and after the inflection point, and the trend graph of the comprehensive immune index with age.

2. The method according to claim 1, characterized in that, The lymphocyte subset parameters include: absolute CD4+ T cell count, CD4+ T cell percentage, absolute CD8+ T cell count, CD8+ T cell percentage, CD4+ / CD8+ T cell ratio, absolute NK cell count, NK cell percentage, absolute total B cell count, total B cell percentage, absolute total T cell count, and total T cell percentage.

3. The method according to claim 1, characterized in that, The Comprehensive Immune Index (CII) is calculated as follows: (0.439 × Absolute CD4+ T cell count) + (0.452 × CD4+ T cell percentage) - (0.008 × Absolute CD8+ T cell count) - (0.198 × CD8+ T cell percentage) - (0.227 × Absolute NK cell count) - (0.383 × NK cell percentage) + (0.358 × CD4+ / CD8+ ratio) + (0.455 × Absolute total B cell count) + (0.421 × Total B cell percentage) + (0.265 × Absolute total T cell count) + (0.198 × Total T cell percentage).

4. The method according to claim 1, characterized in that, The identified inflection point age includes at least one of the following: an inflection point for the percentage of CD8+ T cells and / or the CD4+ / CD8+ ratio around 54 years of age; an inflection point for CD4+ T cells and / or B cells around 62-63 years of age; an inflection point for total T cells around 67-68 years of age; or an inflection point for the comprehensive immune index around 63 years of age.

5. The method according to claim 1, characterized in that, When an individual's age exceeds a preset inflection point age threshold, or when the slope of an individual's immune parameter changes exceeds a preset accelerated decline threshold, it is a warning signal of accelerated immune aging.

6. The method according to claim 1, characterized in that, Independent reference ranges for immune parameters and immune dynamic trajectories were established for males and females respectively, and the gender-specific inflection point age and slope were output.

7. The method according to claim 6, characterized in that, For individuals aged ≥63 years, the study outputs indicators of female dominance in CD4+ T cells, B cells, CD4+ / CD8+ ratio, and total T cell percentage, as well as indicators of male dominance in NK cell number and percentage.

8. The method according to claim 1, characterized in that, The dynamic trajectory map also includes one or more of the following: the original data distribution in the form of a box plot, the fitted curve and its confidence interval, the statistical comparison markers between different age groups, and the location markers of individual immune age in the population trajectory.

9. An immune dynamic trajectory modeling system, characterized in that, include: The data acquisition unit is configured to acquire detection data of multiple lymphocyte subset parameters in a peripheral blood sample obtained by flow cytometry, wherein the detection data includes immune parameter values ​​of individuals of different ages. The piecewise regression analysis unit is configured to perform piecewise regression fitting on the sequence data of each immune parameter changing with age, identify at least one inflection point age of the immune parameter changing with age, and calculate the slope of change before and after each inflection point. The principal component analysis unit is configured to perform principal component analysis on multiple immune parameters, calculate the weighted composite index based on the contribution rate of each principal component, and obtain the comprehensive immune index (CII). The trajectory generation unit is configured to generate an immune dynamic trajectory diagram based on the outputs of the segmented regression analysis unit and the principal component analysis unit. The dynamic trajectory diagram includes at least one or more of the following: the inflection point age of each immune parameter, the slope of the change of each immune parameter before and after the inflection point, and the trend diagram of the change of the comprehensive immune index with age.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the immune dynamic trajectory modeling method as described in any one of claims 1 to 8.

Images