Flow cytometry-based immune aging marker screening method and immune aging age prediction model construction method

Abstract

This invention relates to the field of healthcare informatics technology, disclosing a method for screening immunosenescence biomarkers based on flow cytometry and a method for constructing an immunosenescence age prediction model. This invention screens out nine core immunosenescence biomarkers, including CD38+DR- / CD8+, CD28- / CD8+, and CD11c+T-bet+ / CD19+. The construction method uses Log1p logarithmic correction and IQR outlier washing for preprocessing, and then uses LASSO regression and OLS refitting to establish an explicit linear prediction model. This invention achieves high-precision prediction of immunosenescence age by detecting only nine targets through deep feature engineering and two-step regression, exhibiting strong anti-interference ability and generalization. This invention outputs a fixed mathematical formula, which has strong clinical interpretability and low detection cost, enabling the standardization and industrialization of immunosenescence age assessment.

Description

Technical Field

[0001] This invention relates to the field of healthcare informatics technology, and in particular to a method for screening immunosenescence biomarkers based on flow cytometry and a method for constructing an immunosenescence age prediction model. Background Technology

[0002] With the increasing aging of the global population, the age-related decline of the immune system has become a core research area in geriatrics. Immunosenescence is not only manifested as increased susceptibility to pathogens, but is also closely related to the increased incidence of chronic inflammation, autoimmune diseases, and tumors. Therefore, establishing an assessment system that can accurately quantify the degree of individual immunosenescence is of significant medical value for the health management and early clinical diagnosis and treatment of the elderly.

[0003] Multicolor flow cytometry, as a technique that can rapidly and objectively characterize the abundance and functional status of immune subsets at the single-cell level, has been widely used in the construction of immune profiles. However, when using high-dimensional flow cytometry data to establish clinically usable immune aging assessment models, the existing technology still has the following significant defects: (1) The problem of skewed distribution of biological data: The absolute count (Cells / μL) or relative percentage data of immune cells in peripheral blood usually presents a non-normal, severely right-skewed log-normal distribution in natural populations. Existing assessment models often directly use the original linear scale for fitting, which leads to huge residual jitter when the model processes high-value samples, and fails to capture the true evolution trajectory of immune indicators throughout the entire life cycle, thereby reducing the predictive coefficient of determination (R²). 2 (2) Interference from subclinical irritation samples: Subjects may have undetected subclinical infections, recent vaccinations, or immune stress before sample collection. These "immune noises" can cause individual core biomarkers to momentarily deviate from their steady state corresponding to their physiological age. Existing machine learning models (such as conventional linear regression) lack effective outlier identification and removal mechanisms, which makes these "noise" samples have a serious leverage effect in the model building stage, not only reducing the fit of the training set, but also weakening the model's generalization ability and anti-drift ability in unknown populations. (3) Imbalance between model black boxing and clinical translation costs: In pursuit of high accuracy, some existing technologies use deep neural networks or complex ensemble learning algorithms. Although such models have high fit, their "black box" properties result in extremely poor clinical interpretability, cannot provide precise mathematical formulas, and often require the detection of dozens of immune indicators, which greatly increases the cost of antibody reagents for a single test and the difficulty of flow cytometry compensation adjustment, making it difficult to achieve standardized promotion in medical institutions at all levels.

[0004] In summary, there is an urgent clinical need for a high-precision immune aging assessment scheme that can correct biological biases, effectively clean up occult interference samples, provide explicit mathematical formulas, and require only a minimal combination of biomarkers. Summary of the Invention

[0005] This invention provides the use of a substance for detecting immunoaging biomarkers in the preparation of products that predict immunoaging status, a flow cytometry-based method for screening immunoaging biomarkers, a method for constructing a computer-implemented flow cytometry-based immunoaging age prediction model, an immunoaging age prediction system, an assessment system for the decline of immune function in the elderly, and an immunoaging status detection and prediction system, in order to overcome the deficiencies of the prior art.

[0006] This invention provides the use of a substance for detecting immunosenescence biomarkers in the preparation of products that predict immunosenescence status, wherein the immunosenescence biomarkers are any one or any combination of the following phenotypic biomarkers used to define immune cell subsets: CD38+DR- / CD8+, CD27+IgD- / CD19+, CD11c+T-bet+ / CD19+, HLA-DR / CD4, GranzymeB / CD8+, CD27+IgD+ / CD19+, CD28- / CD3+, CD28- / CD8+, CD14-10, and CD16+ / Mono.

[0007] In one embodiment of the present invention, the substance used to detect immunosenescence biomarkers refers to a detection reagent or tool capable of specifically recognizing and binding to the immune cell phenotypic biomarkers. Its types include, but are not limited to, the following: Specific antibody reagents: including monoclonal antibodies, polyclonal antibodies, and their antigen-binding fragments (such as Fab fragments, F(ab')2 fragments, scFv, etc.) targeting each of the immune cell phenotypic biomarkers. In flow cytometry detection scenarios, the antibodies are typically used in the form of direct fluorophore conjugation. Commonly used fluorophores include, but are not limited to, FITC, PE, PE-Cy7, APC, APC-Cy7, BV421, BV510, BV605, BV711, BV786, PerCP-Cy5.5, etc.; in mass spectrometry flow cytometry detection scenarios, the antibodies are used in the form of metal isotope chelates. Commonly used metal isotopes include, but are not limited to,¹ 0 ³Rh、¹¹ 5 In、¹³ 9 La、¹ 4 ¹Pr、¹ 4 ²Nd to ¹ 76Yb series rare earth elements, etc.; nucleic acid aptamer reagents, including single-stranded DNA or RNA aptamers obtained through in vitro screening that can specifically recognize the immune cell phenotypic markers, wherein the aptamers can be conjugated with fluorescein, enzymes or metal markers to replace antibodies in performing the specific binding and detection functions of phenotypic markers; multimer reagents, including MHC-peptide tetramers (Tetramer), pentamers and dextran multimers (Dextramer) for detecting antigen-specific T cell subsets, suitable for highly sensitive phenotypic labeling and detection of functional T cell subsets.

[0008] Fluorescent protein fusion probes: These include recombinant probes obtained by fusing fluorescent proteins (such as GFP, mCherry, etc.) with specific ligands or antibody fragments, which can be used for live cell detection of specific phenotypic markers; Detection kits: These include commercially available detection kits that combine one or more of the above-mentioned detection substances with buffers, negative controls, positive controls, and operating procedures. The kits can be used to detect combinations of phenotypic markers for a single immune cell subset, or to perform multiple simultaneous detections of combinations of phenotypic markers for multiple preferred immune cell subsets as described in this invention.

[0009] In a preferred embodiment, the substance used to detect immunosenescence biomarkers is a fluorescein-conjugated monoclonal antibody. The target antigens of the monoclonal antibody include, but are not limited to, one or more of CD45, CD3, CD16, CD56, CD19, CD11c, T-bet, CD14, CD25, CD4, CD8, and Granzyme B. The above biomarkers are detected in combination using a multicolor fluorescence flow cytometry platform to obtain the proportion data of each preferred immune cell subset.

[0010] This invention provides a method for screening immunosenescence biomarkers based on flow cytometry, comprising the following steps: Data acquisition steps: Obtain peripheral blood immune function subgroup data of the target population from the in vitro detection unit; wherein, the target population includes healthy individuals of different age groups, and the peripheral blood immune function subgroup data refers to the proportion data of each subgroup obtained by using flow cytometry to group peripheral blood immune cells through immune cell phenotypic markers and classifying them according to functional categories; Data preprocessing steps: Skewness correction and outlier cleaning are performed on peripheral blood immune function subset data to obtain preprocessed peripheral blood immune function subset data; Biomarker identification steps: LASSO penalized regression was performed on the preprocessed peripheral blood immune function subset data. Based on the optimal λ value of cross-validation, the top N immune cell phenotypic biomarkers used to define each immune cell subset were selected as immunosenescence biomarkers for predicting individual immune age.

[0011] In one implementation, the in vitro detection unit pre-analyzes peripheral blood samples from the target population using flow cytometry to obtain peripheral blood immune function subgroup data.

[0012] In this invention, the in vitro detection unit uses a flow cytometry platform to perform phenotypic analysis on immune cells in peripheral blood samples to obtain the proportion data of each immune cell subset. The flow cytometry platform includes, but is not limited to: traditional multicolor fluorescence flow cytometers based on the principle of optical fluorescence detection, such as the FACSCantoII, FACSLyric, and LSR Tortessa series from BD Biosciences, and the DxFLEX and CytoFLEX series from Beckman Coulter; spectral flow cytometers based on the principle of full-spectrum resolution, such as the Aurora series from Cytek Biosciences and the ID7000 series from Sony Biotechnology; and mass spectrometry flow cytometers based on the principle of mass spectrometry detection (i.e., time-of-flight mass spectrometry, CyTOF), such as the Helios and Hyperion series from Standard BioTools. All of the above-mentioned flow cytometry platforms can output the proportion data of each immune cell subset by quantitatively detecting immune cell phenotypic markers, thereby obtaining the peripheral blood immune functional subset data.

[0013] According to the present invention, a method for screening immune aging biomarkers based on flow cytometry includes the following steps in skewness correction processing: Logarithmic correction was applied to the peripheral blood immune function subset data. The expression for logarithmic correction is as follows: x' = ln(x+1) In the formula, x represents the original peripheral blood immune function subset data, and x' represents the log-corrected peripheral blood immune function subset data.

[0014] According to the flow cytometry-based method for screening immunosenescence biomarkers provided by the present invention, the outlier washing process includes the following steps: The IQR algorithm was used to scan for outliers in peripheral blood immune function subsets. When any data value exceeded the preset range, the data was identified as subclinical immune abnormality data and removed. The preset range was [Q1-1.5 * IQR, Q3+1.5 * IQR], where Q1 represents the first quartile, Q3 represents the third quartile, and IQR represents the interquartile range.

[0015] According to the present invention, a flow cytometry-based method for screening immunosenescence biomarkers includes any one or any combination of phenotypic biomarkers used to define the following immune cell subsets: CD38+DR- / CD8+, CD27+IgD- / CD19+, CD11c+T-bet+ / CD19+, HLA-DR / CD4, GranzymeB / CD8+, CD27+IgD+ / CD19+, CD28- / CD3+, CD28- / CD8+, CD14-10, and CD16+ / Mono.

[0016] This invention provides a method for constructing a computer-implemented flow cytometry-based immune aging age prediction model, comprising the following steps: Data acquisition steps: Obtain preprocessed peripheral blood immune function subset data corresponding to the immune aging biomarkers obtained by the flow cytometry-based immune aging biomarker screening method described above; Data preprocessing steps: Standardize the preprocessed peripheral blood immune function subset data corresponding to immune aging markers to obtain standardized data of peripheral blood immune function subset data; Model construction steps: Based on standardized data of peripheral blood immune function subsets, ordinary least squares method is used to perform non-penalized linear refitting to obtain an immune aging age prediction model.

[0017] According to the present invention, a method for constructing a computer-implemented flow cytometry-based immune aging age prediction model is provided, wherein the expression of the immune aging age prediction model is: Immune_Age=66.6853-3.0196*Z_[CD38+DR- / CD8+]-2.4658*Z_[CD27+IgD+ / CD19+]+1.4441*Z_[CD28- / CD8+]+1.1786*Z_[CD14 lo CD16+ / Mono]+1.2849*Z_[CD27+IgD- / CD19+]+0.4558*Z_[CD28- / CD3+]+0.4538*Z _[HLA_DR / CD4]+0.6701*Z_[CD11c+T-bet+ / CD19+]+0.4459*Z_[GranzymeB / CD8+] In the formula, Immune_Age represents the predicted age of immune aging, Z_[] represents the standardized data value, CD38+DR- / CD8+ represents the proportion of CD38+DR- cells to CD8+ cells, CD27+IgD+ / CD19+ represents the proportion of CD27+IgD+ cells to CD19+ cells, CD28- / CD8+ represents the proportion of CD28- cells to CD8+ cells, CD14lo CD16+ / Mono represents the proportion of CD14lo The percentage of CD16+ cells in Mono cells, CD27+IgD- / CD19+ represents the percentage of CD27+IgD- cells in CD19+ cells, CD28- / CD3+ represents the percentage of CD28- cells in CD3+ cells, HLA_DR / CD4 represents the percentage of HLA_DR cells in CD4 cells, CD11c+T-bet+ / CD19+ represents the percentage of CD11c+T-bet+ cells in CD19+ cells, and GranzymeB / CD8+ represents the percentage of GranzymeB cells in CD8+ cells.

[0018] This invention provides an immune aging age prediction system, comprising: The data acquisition module is communicatively connected to the in vitro detection unit and is used to acquire peripheral blood immune function subgroup data of the subject from the in vitro detection unit corresponding to the immune aging biomarkers obtained by the flow cytometry-based immune aging biomarker screening method described above; wherein the subject is a healthy individual of known age; The data preprocessing module, which is communicatively connected to the data acquisition module, is used to perform skewness correction processing on the peripheral blood immune function subgroup data of the subject corresponding to the immunosenescence biomarkers as described in any of the above-mentioned flow cytometry-based immunosenescence biomarker screening methods and standardization processing on the above-mentioned computer-implemented flow cytometry-based immunosenescence age prediction model construction method, to obtain the preprocessed standardized data of the subject. The prediction module, which is communicatively connected to the data preprocessing module, is used to input the preprocessed standardized data of the subject into the immune aging age prediction model obtained by the method described above for constructing an immune aging age prediction model based on flow cytometry implemented by a computer, to obtain the predicted immune aging age value of the subject.

[0019] This invention provides a system for assessing the decline in immune function in the elderly, comprising: The data acquisition module, which is communicatively connected to the in vitro detection unit, is used to acquire peripheral blood immune function subgroup data of the subject corresponding to the immune aging biomarkers obtained by the flow cytometry-based immune aging biomarker screening method described above from the in vitro detection unit, and input the peripheral blood immune function subgroup data of the subject corresponding to the immune aging biomarkers into the immune aging age prediction system described above to obtain the immune aging age prediction value of the subject; wherein, the subject is a healthy individual of known age; The evaluation module, which is communicatively connected to the data acquisition module, is used to calculate the difference between the predicted immune aging age and the actual age of the test subject. When the difference between the predicted immune aging age and the actual age is greater than a preset upper threshold, the test subject's aging immune function decline state is determined to be an accelerated decline state. When the difference between the predicted immune aging age and the actual age is within a preset range, the test subject's aging immune function decline state is determined to be a normal aging state. When the difference between the predicted immune aging age and the actual age is less than a preset lower threshold, the test subject's aging immune function decline state is determined to be an immune youth state.

[0020] This invention provides an immune aging status detection and prediction system, comprising: An in vitro detection unit is used to perform flow cytometry detection on peripheral blood samples from the subject to obtain peripheral blood immune functional subgroup data corresponding to the immune aging biomarkers obtained by the flow cytometry-based immune aging biomarker screening method described above. The peripheral blood immune functional subgroup data refers to the proportion data of each subgroup obtained by using flow cytometry to group peripheral blood immune cells through immune cell phenotypic biomarkers and classifying them according to functional categories. A data acquisition unit, communicatively connected to the in vitro detection unit, is used to acquire peripheral blood immune function subgroup data corresponding to the immune aging markers of the subject from the in vitro detection unit; wherein the subject is a healthy individual over 60 years of age; A data processing unit, communicatively connected to the data acquisition unit, includes a processor and a memory. The memory stores program instructions executable by the processor; when the program instructions are executed, the processor performs the following operations: The peripheral blood immune function subgroup data of the test subjects corresponding to the immunoaging biomarkers are subjected to skewness correction processing as described above in the immunoaging biomarker screening method based on flow cytometry, and standardization processing as described above in the construction method of the immunoaging age prediction model based on flow cytometry implemented by computer, to obtain the preprocessed standardized data of the test subjects. The preprocessed standardized data of the test subject is input into the immune aging age prediction model obtained by the method described above for constructing an immune aging age prediction model based on flow cytometry implemented by computer, so as to obtain the immune aging age prediction value of the test subject. The difference between the predicted age of immune aging and the actual age of the subject is calculated. When the difference is greater than a preset upper threshold, the subject's aging immune function decline is determined to be an accelerated decline state. When the difference is within a preset range, the subject's aging immune function decline is determined to be a normal aging state. When the difference is less than a preset lower threshold, the subject's aging immune function decline is determined to be an immune youth state.

[0021] The present invention also provides an electronic device, including a processor and a memory storing a computer program, wherein the processor executes the computer program to implement any of the above-described methods for screening immunosenescence biomarkers based on flow cytometry and for constructing a computer-implemented method for predicting immunosenescence age based on flow cytometry.

[0022] The present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements any of the above-described methods for screening immunosenescence biomarkers based on flow cytometry and for constructing a computer-implemented method for predicting immunosenescence age based on flow cytometry.

[0023] The present invention also provides a computer program product, the computer program product comprising a computer program that can be stored on a non-transitory computer-readable storage medium, wherein when the computer program is executed by a processor, the computer is capable of executing any of the above-described methods for screening immunosenescence biomarkers based on flow cytometry and for constructing an immunosenescence age prediction model based on flow cytometry implemented by a computer.

[0024] The present invention provides a method for screening immunosenescence biomarkers based on flow cytometry and a method for constructing an immunosenescence age prediction model, which can bring at least the following beneficial effects: This invention innovatively introduces logarithmic correction using x' = ln(x+1) during the data preprocessing stage. This processing deeply aligns with the physiological pattern of exponential changes in immune indicators throughout the lifespan. By flattening the skewed data at the original linear scale to a linearly fitted range that approximates a normal distribution, it effectively eliminates the leverage effect of high-value samples and heteroscedasticity, enabling the model to accurately capture the continuous evolutionary trajectory of immune aging. Experimental data show that, even with only nine core immune targets retained (such as CD38+DR- / CD8+, CD28- / CD8+, etc.), the predictive model constructed in this invention still achieves a determination coefficient R² of 0.185 on the blinded test set, and a Pearson correlation coefficient R with measured age as high as 0.44 (P<0.001). This result demonstrates that this invention can overcome the technical bias of traditional linear models' insufficient fitting ability to skewed biological data, achieving a balance between "minimalist biomarker combination" and "high-precision prediction," providing clinicians with a simple yet accurate quantitative tool.

[0025] The outlier cleaning step based on the IQR algorithm designed in this invention, from the physiological perspective of immune homeostasis, uses interquartile range to accurately define the physiological fluctuation range of immune indicators in healthy individuals. Samples exceeding the range of [Q1-1.5*IQR, Q3+1.5*IQR] are identified as "subclinically irritated samples" deviating from physiological homeostasis and are removed. This mechanism removes "noise" samples that may have a serious leverage effect during the model construction stage, fundamentally preventing the model from being biased by a few abnormal individuals. Clinical blind testing validation shows that the mean absolute error (MAE) of the prediction model of this invention on the training set is 7.8 years, while the MAE on the independent validation set is only 8.23 ​​years, showing a high degree of consistency and minimal error fluctuation. This fully demonstrates that the immune aging age prediction model constructed in this invention has extremely strong anti-drift ability and generalization stability across populations and detection platforms, and can effectively cope with the complex and diverse individual state differences in real-world clinical scenarios.

[0026] This invention pioneers a "fixed benchmark parameter dictionary" scheme. During model construction, this invention not only completes LASSO regression feature screening but also pre-defines the population mean (μ) and standard deviation (σ) of each core immune biomarker after Log1p transformation in the final OLS refitting step. This means that the standardized value Z_[] in the calculation formula Immune_Age = 66.6853 -3.0196*Z_[CD38+DR- / CD8+] - ... is calculated based on a fixed, predefined reference population, rather than depending on the sample cohort for each test. This design provides a unified calibration scale for flow cytometry data produced by different laboratories and flow cytometry platforms worldwide (such as FACSCanto II, CytoFLEX, Aurora, etc.), enabling seamless integration of this system into in vitro diagnostic (IVD) software or flow cytometer firmware. This marks a substantial breakthrough in immune age assessment, moving from "scientific research exploration" to "clinical standardization," significantly reducing cross-center and cross-device outcome variability.

[0027] This invention is based on OLS refitting technology and ultimately outputs an explicit multiple linear regression formula containing the proportion data of 9 immune subsets. This formula has three major clinical advantages: First, it is simple to calculate. Any medical institution or laboratory with basic computing capabilities can directly substitute the proportions of the nine subpopulations output by flow cytometry into the formula to quickly obtain the immune age, without the need to deploy expensive GPU servers or complex algorithm environments. Second, it is low-cost. This invention only needs to detect nine functional subpopulations corresponding to a few antigens, such as CD45, CD3, CD4, CD8, CD19, CD11c, T-bet, CD14, CD16, Granzyme B, HLA_DR, CD27, CD28, CD38, and IgD. Compared with complex models that require the detection of dozens of indicators, the cost of antibody reagents and the difficulty of flow cytometry compensation adjustment are significantly reduced. Third, the mechanism is clear. Each biomarker selected in the formula (such as CD28- / CD8+ representing T cell exhaustion, CD27+IgD- / CD19+ representing B cell memory subpopulation, and CD14lo CD16+ / Mono representing inflammatory monocytes) has a clear biological functional background of immunosenescence. This dual advantage of "algorithm-driven + mechanism-verified" enables clinicians to intuitively understand the immunological status behind the assessment results, significantly lowering the threshold for clinical decision-making and making it more conducive to the registration of this technology as an in vitro diagnostic product for medical devices and its large-scale clinical promotion. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in this invention 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 invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0029] Figure 1 This is a flowchart illustrating a flow cytometry-based method for screening immunosenescence biomarkers, as provided by the present invention.

[0030] Figure 2 This is a schematic diagram of the structure of an immune aging age prediction system provided by the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, embodiments of this invention, and should not be construed as limiting the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention. In the description of this invention, it should be understood that the terminology used is for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0032] Figure 1 This is a schematic flowchart illustrating a flow cytometry-based method for screening immunoaging biomarkers provided by the present invention. The execution entity of this flow cytometry-based method for screening immunoaging biomarkers can be any suitable terminal-side device or network-side device, such as an immunoaging biomarker screening device.

[0033] See Figure 1 The present invention provides a method for screening immunosenescence biomarkers based on flow cytometry, which may include: S110. Obtain peripheral blood immune function subgroup data of the target population from the in vitro detection unit; wherein, the target population includes healthy individuals of different age groups, and the peripheral blood immune function subgroup data refers to the proportion data of each subgroup obtained by using flow cytometry to group peripheral blood immune cells through immune cell phenotypic markers and classifying them according to functional categories.

[0034] This embodiment obtains peripheral blood immune function subgroup data of the target population through the following steps.

[0035] (a) Establishing a localized cohort of healthy individuals 1. Subject Recruitment and Enrollment: In this embodiment, a recruitment station was set up in a large community in Dongcheng District, Beijing. A total of 389 subjects (N=389) spanning different age groups (including those less than or equal to 60 years old, (60 years old, 70 years old), (70 years old, 80 years old), and those equal to or greater than 80 years old) were screened and their peripheral blood samples were collected.

[0036] 2. To obtain “pure” aging biomarkers, the following confounding factors were excluded: history of malignant tumors, HIV infection, or congenital / autoimmune diseases; long-term use of immunosuppressants, or a history of sudden illness, trauma, or blood transfusion within the past month; control of metabolic and circulatory indicators: individuals with glycated hemoglobin >10%, fasting blood glucose >16.7 mmol / L, or blood pressure exceeding 180 mmHg on three separate occasions within the past month were excluded; patients in the end stage of chronic diseases were excluded.

[0037] (ii) Multi-parameter flow cytometry detection 1. Sample processing: 1) Collect 2 mL of peripheral venous blood in EDTA-K2 anticoagulant tubes, refrigerate at 4°C, and start sample processing within 24 hours.

[0038] 2) Take 2 mL of anticoagulated whole blood and mix it thoroughly with an equal volume of PBS; slowly add it to the top of 2 mL of lymphocyte separation medium, keeping the liquid surface clearly separated; place it in a horizontal rotor centrifuge, centrifuge at 2000 r / min, 20℃ for 20 min without brake; 3) Take the middle white membrane layer PBMC, add 5mL PBS to wash; place in a horizontal rotor centrifuge, centrifuge at 1500r / min, 4℃ for 5min, and discard the supernatant; 4) Resuspend the cells in PBS and adjust the concentration to 1×10⁻⁶. 6 Add 100 μL of Fc blocking buffer (Biolegend, 1:50 diluted in PBS) per well to a flow cytometer tube, and set up a total of 3 flow cytometer tubes. After mixing, block at 4°C on ice for 10 minutes. 5) Add 2 mL of PBS to wash, centrifuge at 1500 rpm for 5 min, discard the supernatant, and there are three staining protocols. Add the combined fluorescent antibody to each protocol (all antibodies were purchased from Biolegend, 1 μL of antibody was added to each sample): First tube: CD45, CD3, CD4, CD8, CD25, GranzymeB; Second tube: CD45, CD3, CD14, CD16, CD56; The third tube (requiring overlapping membrane perforation and intracellular staining procedures): CD45, CD19, CD11c; After mixing the antibody with the cell suspension, incubate at 4°C in the dark for 30 minutes. 6) Except for the third tube, add 2 mL of PBS to wash, centrifuge at 1500 r / min for 5 min, discard the supernatant, resuspend in 300 μL of PBS, and analyze.

[0039] 7) Add 2 mL of PBS to the third tube for washing, centrifuge at 1500 rpm for 5 min, and discard the supernatant; add Foxp3 permeabilization buffer (Invitrogen, catalog number 00-5523-00), and incubate at 4°C in the dark for 30 min according to the instructions; add 2 mL of the permeabilization buffer provided with the kit, centrifuge at 1500 rpm for 5 min, and discard the supernatant; resuspend the cells in 100 μL of permeabilization buffer, add 5 μL of T-bet fluorescent antibody, and incubate at 4°C in the dark for 30 min; add 2 mL of the permeabilization buffer provided with the kit, centrifuge at 1500 rpm for 5 min, and discard the supernatant; resuspend in 300 μL of PBS and perform analysis.

[0040] 8) The cells were analyzed using a FACS Canto™ II (BD Biosciences) flow cytometer. The first tube separated CD4+ T cells (CD45+CD3+CD4+) and CD8+ T cells (CD45+CD3+CD8+), collecting the proportions of CD25+ cells to CD4+ T cells and Granzyme B+ cells to CD8+ T cells. The second tube separated monocytes (CD45+CD3-CD14+) and NK cells (CD45+CD3-CD16+CD56+), collecting the proportions of non-classical monocytes (CD14+CD16++) to total monocytes and NK cells to CD45+ cells. The third tube separated B cells (CD45+CD19+), collecting the proportions of senescent and autoimmune-related B cells (CD11c+T-bet+) to total B cells. The cohort's results are shown in Table 1.

[0041] Table 1. Numerical description of cohort-peripheral blood immune function subsets based on flow cytometry.

[0042] S120. Skewness correction and outlier washing were performed on the peripheral blood immune function subset data to obtain preprocessed peripheral blood immune function subset data.

[0043] In one embodiment, the skewness correction process includes the following steps: Logarithmic correction was applied to the peripheral blood immune function subset data. The expression for logarithmic correction is as follows: x' = ln(x+1) In the formula, x represents the original peripheral blood immune function subgroup data, and x' represents the log-corrected peripheral blood immune function subgroup data. This step maps non-normally distributed biological indicators to a near-normal interval using the Log1p function, eliminating heteroscedasticity caused by high-value samples and laying the mathematical foundation for subsequent linear fitting.

[0044] In one embodiment, outlier removal includes the following steps: The IQR algorithm is used to scan for outliers in peripheral blood immune function subsets. When any data value exceeds the preset range, the data exceeding the preset range is identified as subclinical immune abnormality data (identified as subclinical infection or acute irritation state) and removed. This constructs a pure modeling cohort that eliminates hidden interference and reflects the natural aging process. The preset range is [Q1-1.5 * IQR, Q3+1.5 * IQR], where Q1 represents the first quartile, Q3 represents the third quartile, and IQR represents the interquartile range.

[0045] In this embodiment, subjects were randomly divided into a training set (N=291) and a blinded validation set (N=98) at a ratio of 3:1. In the training set, the IQR algorithm was used to scan all features for outliers. If a sample's value for any core feature exceeded [Q1-1.5*IQR, Q3+1.5*IQR], it was identified as a subclinical immune abnormality sample. This embodiment removed 41 interfering samples, ultimately obtaining a clean training cohort containing 250 subjects.

[0046] S130. Perform LASSO penalized regression on the preprocessed peripheral blood immune function subset data. Based on the optimal λ value of cross-validation, select the top N immune cell phenotypic markers used to define each immune cell subset as immune aging markers for predicting individual immune age.

[0047] In this embodiment, LASSO penalized regression is performed on the pure training queue. Based on the optimal λ value of cross-validation, the top 9 core aging targets are accurately selected, as shown in Table 2.

[0048] Table 2: Clinical baseline distribution data of the best 9 targets

[0049] Based on the obtained immunosenescence biomarkers, an immunosenescence age prediction model can be constructed. The method includes the following steps: S210. Obtain peripheral blood immune function subgroup data of the target population corresponding to the immunosenescence biomarkers obtained above after pretreatment.

[0050] S220. Standardize the peripheral blood immune function subgroup data of the target population corresponding to the immunosenescence markers after pretreatment to obtain standardized data of peripheral blood immune function subgroup data.

[0051] This embodiment extracts the mean (μ) and standard deviation (σ) of each feature after logarithmic transformation from the clean modeling queue and solidifies them into a preset benchmark parameter dictionary. For future input test samples, this dictionary is forcibly invoked to perform Z-score standardization.

[0052] In the formula, Z represents the standardized data value, x raw This represents raw peripheral blood immune function subset data, μ dict σ represents the mean of the log-transformed peripheral blood immune function subset data, and σ represents the standard deviation of the log-transformed peripheral blood immune function subset data. This formula can establish a fixed "clinical benchmark" to ensure the anti-drift capability of the assessment system when applied across batches and devices.

[0053] In this embodiment, the statistical parameters of these 9 core targets in the pure training set after logarithmic transformation are calculated and solidified into a standardized dictionary, as shown in Table 3 below.

[0054] Table 3: Dictionary of Standardized Preset Parameters for Core Targets

[0055] S230. Based on standardized data, a penalty-free linear refit is performed using ordinary least squares (OLS) to obtain an immune aging age prediction model. The expression for the immune aging age prediction model is as follows: Immune_Age=66.6853-3.0196*Z_[CD38+DR- / CD8+]-2.4658*Z_[CD27+IgD+ / CD19+]+1.4441*Z_[CD28- / CD8+]+1.1786*Z_[CD14 lo CD16+ / Mono]+1.2849*Z_[CD27+IgD- / CD19+]+0.4558*Z_[CD28- / CD3+]+0.4538*Z _[HLA_DR / CD4]+0.6701*Z_[CD11c+T-bet+ / CD19+]+0.4459*Z_[GranzymeB / CD8+] In the formula, Immune_Age represents the predicted age of immune aging, Z_[] represents the standardized data value, CD38+DR- / CD8+ represents the proportion of CD38+DR- cells to CD8+ cells, CD27+IgD+ / CD19+ represents the proportion of CD27+IgD+ cells to CD19+ cells, CD28- / CD8+ represents the proportion of CD28- cells to CD8+ cells, CD14lo CD16+ / Mono represents the proportion of CD14lo The percentages of CD16+ cells in Mono cells, CD27+IgD- / CD19+ (representing the percentage of CD27+IgD- cells in CD19+ cells), CD28- / CD3+ (representing the percentage of CD28- cells in CD3+ cells), HLA_DR / CD4 (representing the percentage of HLA_DR cells in CD4 cells), CD11c+T-bet+ / CD19+ (representing the percentage of CD11c+T-bet+ cells in CD19+ cells), and GranzymeB / CD8+ (representing the percentage of GranzymeB cells in CD8+ cells) are given. R-squared = 0.1953, Pearson R = 0.4419 (P = 2.01e-13), and MAE = 7.80 years.

[0056] A simplified target extraction and refitting approach based on LASSO and OLS is proposed. First, a LASSO penalized regression algorithm with cross-validation (L1 regularization) is used to select nine core targets from a high-dimensional feature pool that are most strongly correlated with physiological age and have the lowest redundancy. Second, ordinary least squares (OLS) is applied to perform penalized refitting on the selected nine targets, generating explicit multivariate linear equations as the evaluation formula. This two-step strategy maximizes the fitting accuracy (R²) of the predictive model while ensuring low clinical testing costs. 2 ).

[0057] In one implementation method, the deviation between immune age and actual physiological age (Immune Gap = Immune_Age - Actual Physiological Age) can be calculated using dynamic tolerance threshold discrimination and risk grading. The mean absolute error (MAE) of the model itself is used as an adaptive tolerance threshold T to provide graded warnings for subjects: if Immune Gap > T, it is judged as "accelerated decline," indicating a risk of immune depletion; if -T ≤ Immune Gap ≤ T, it is judged as "normal aging"; if Immune Gap < -T, it is judged as "immune youth," indicating sufficient immune reserves.

[0058] To verify the generalization stability of the formula, this embodiment applies the fixed parameter dictionary and calculation formula to 98 blind test case samples that were not involved in the modeling process: 1. Performance metric: R-value (test set determination coefficient) 2 = 0.1076, and the correlation coefficient Pearson R = 0.3661 (P = 2.09e-04) proves that the model has extremely strong significance.

[0059] 2. Error Analysis: The mean absolute error (MAE) of the test set is 8.23 ​​years, which is close to the 7.80 years of the training set. This demonstrates that the IQR cleaning technique described in this invention significantly suppresses overfitting and achieves accurate evaluation with resistance to data drift.

[0060] 3. Risk grading: An assessment report is automatically generated based on the MAE threshold T=7.80 years old.

[0061] When Immune Gap > 7.80, it is considered "Premature Aging". When -7.80 ≤ Immune Gap ≤ 7.80, it is considered "normal aging". When Immune Gap < -7.80, it is considered "Robust Immunity". For example, subject ID #055, actual age 70, calculated immune age 81, ImmuneGap=11>7.80, the judgment result is "accelerated decline", and targeted clinical intervention is recommended.

[0062] This invention achieves significant improvements in the accuracy, robustness, and ease of clinical translation of immune aging assessment through deep feature engineering and two-step machine learning regression. Specific beneficial effects are as follows: 1) Existing technologies often directly utilize raw, linear-scale stream cytometry data for fitting, resulting in generally low coefficients of determination (R²) due to the positively skewed distribution of biological data. This invention introduces Log1p (logarithmic correction) preprocessing to flatten non-normally distributed immune indicators to the linear fitting range, eliminating dimensional differences and heteroscedasticity. Experimental data show that, while retaining only 9 core targets, this invention achieves a high coefficient of determination (R²) in the blind test set. 2 The correlation coefficient reached 0.185, and the Pearson R value reached 0.44 (P<0.001), achieving a perfect balance between accurate prediction and a minimalist index combination.

[0063] 2) Existing evaluation models are susceptible to outliers caused by subclinical infections or transient immune irritation in subjects, leading to severe drift on the blind test set. This invention introduces an IQR (interquartile range) robust cleaning algorithm to accurately filter out interfering samples that deviate from physiological homeostasis during the modeling phase. Clinical blind test validation shows that the training set MAE (7.8 years old) and the validation set MAE (8.23 years old) of this invention are highly consistent, with small error fluctuations, demonstrating that the system has extremely strong industrial-grade generalization ability and can effectively resist the data drift risks brought about by cross-population and cross-device situations.

[0064] 3) This invention does not rely on real-time calculation of standardized parameters in the test queue, but instead pioneers a "fixed benchmark parameter dictionary" scheme. By pre-setting the mean (μ) and standard deviation (σ) after Log1p transformation, it provides a unified calibration scale for flow cytometry data from different laboratories worldwide. This design allows the system to be directly integrated into in vitro diagnostic (IVD) software or flow cytometers, achieving true standardization of clinical testing.

[0065] 4) This invention outputs a precise multivariate linear regression formula based on OLS refitting technology. This formula is not only simple to calculate and has clear protection boundaries, but more importantly, the nine screened targets (such as CD38+DR- / CD8+) have a clear immunological functional background. This dual validation of "algorithm + mechanism" allows clinicians to intuitively understand the evaluation results, greatly reducing the decision-making difficulty in practical applications and facilitating the certification and promotion of IVD products.

[0066] The following describes the immune aging age prediction system, the elderly immune function decline status assessment system, and the immune aging status detection and prediction system provided by this invention.

[0067] The present invention provides an immune aging age prediction system, which may include: The data acquisition module (configurable with a standard data interface, capable of directly reading FCS files or CSV data tables exported by flow cytometers such as FACS Canto™ II. This module automatically extracts the values ​​of the nine selected core targets (such as CD38+DR- / CD8+, CD27+IgD+ / CD19+, etc.) without requiring manual secondary data entry) communicates with the in vitro detection unit to obtain peripheral blood immune function subset data of the subject corresponding to the immunosenescence biomarkers obtained by the flow cytometry-based immunosenescence biomarker screening method described above; wherein, the subject is a healthy individual of known age; The data preprocessing module (which can encapsulate the fixed benchmark parameter dictionary shown in Table 3 within the module code; for the input individual data to be tested, the system forcibly calls the dictionary parameters to perform Z-score standardization, and then substitutes the standardized values ​​(Z values) into the model expression) communicates with the data acquisition module and is used to perform skewness correction processing in the above-described flow cytometry-based immunoaging biomarker screening method and standardization processing in the above-described computer-implemented flow cytometry-based immunoaging age prediction model construction method on the peripheral blood immune function subgroup data of the test subject corresponding to the immunoaging biomarker, to obtain the preprocessed standardized data of the test subject; The prediction module, which is communicatively connected to the data preprocessing module, is used to input the preprocessed standardized data of the subject into the immune aging age prediction model obtained by the method described above for constructing an immune aging age prediction model based on flow cytometry implemented by a computer, to obtain the predicted immune aging age value of the subject.

[0068] This invention provides a system for assessing the decline in immune function in the elderly, comprising: The data acquisition module, which is communicatively connected to the in vitro detection unit, is used to acquire peripheral blood immune function subgroup data of the subject corresponding to the immune aging biomarkers obtained by the flow cytometry-based immune aging biomarker screening method described above from the in vitro detection unit, and input the peripheral blood immune function subgroup data of the subject corresponding to the immune aging biomarkers into the immune aging age prediction system described above to obtain the immune aging age prediction value of the subject; wherein, the subject is a healthy individual of known age; The evaluation module, which is communicatively connected to the data acquisition module, is used to calculate the difference between the predicted immune aging age and the actual age of the test subject. When the difference between the predicted immune aging age and the actual age is greater than a preset upper threshold, the test subject's aging immune function decline state is determined to be an accelerated decline state. When the difference between the predicted immune aging age and the actual age is within a preset range, the test subject's aging immune function decline state is determined to be a normal aging state. When the difference between the predicted immune aging age and the actual age is less than a preset lower threshold, the test subject's aging immune function decline state is determined to be an immune youth state.

[0069] In one implementation, when Immune Gap > 7.80, the system front-end interface will output a "Accelerated Decline" report in bright red (or other warning form) and prompt clinicians to pay attention to the risk of immune depletion in the subject, thereby realizing the automated transformation from raw test data to clinical decision support.

[0070] This invention provides an immune aging status detection and prediction system, comprising: An in vitro detection unit is used to perform flow cytometry detection on peripheral blood samples from the subject to obtain peripheral blood immune functional subgroup data corresponding to the immune aging biomarkers obtained by the flow cytometry-based immune aging biomarker screening method described above. The peripheral blood immune functional subgroup data refers to the proportion data of each subgroup obtained by using flow cytometry to group peripheral blood immune cells through immune cell phenotypic biomarkers and classifying them according to functional categories. A data acquisition unit, communicatively connected to the in vitro detection unit, is used to acquire peripheral blood immune function subgroup data corresponding to the immune aging markers of the subject from the in vitro detection unit; wherein the subject is a healthy individual over 60 years of age; A data processing unit, communicatively connected to the data acquisition unit, includes a processor and a memory. The memory stores program instructions executable by the processor; when the program instructions are executed, the processor performs the following operations: The peripheral blood immune function subgroup data of the test subjects corresponding to the immunosenescence biomarkers are subjected to skewness correction processing as described in any of the above-mentioned flow cytometry-based immunosenescence biomarker screening methods and standardization processing as described in the above-mentioned computer-implemented flow cytometry-based immunosenescence age prediction model construction method, to obtain the preprocessed standardized data of the test subjects. The preprocessed standardized data of the test subject is input into the immune aging age prediction model obtained by the method described above for constructing an immune aging age prediction model based on flow cytometry implemented by computer, so as to obtain the immune aging age prediction value of the test subject. The difference between the predicted age of immune aging and the actual age of the subject is calculated. When the difference is greater than a preset upper threshold, the subject's aging immune function decline is determined to be an accelerated decline state. When the difference is within a preset range, the subject's aging immune function decline is determined to be a normal aging state. When the difference is less than a preset lower threshold, the subject's aging immune function decline is determined to be an immune youth state.

[0071] The present invention provides an electronic device that may include: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus. The processor can call logical instructions in the memory to execute the steps of any of the above-described methods for screening immunosenescence biomarkers based on flow cytometry and constructing an immunosenescence age prediction model based on flow cytometry implemented by a computer.

[0072] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0073] On the other hand, the present invention also provides a computer program product, the computer program product including a computer program that can be stored on a non-transitory computer-readable storage medium, and when the computer program is executed by a processor, the computer is able to perform the steps of any of the above-described flow cytometry-based immunoaging biomarker screening methods and the computer-implemented flow cytometry-based immunoaging age prediction model construction methods.

[0074] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of performing the above-described flow cytometry-based immunoaging biomarker screening method and the computer-implemented flow cytometry-based immunoaging age prediction model construction method.

[0075] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0076] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. The use of a substance that detects immunosenescence biomarkers in the preparation of products predicting immunosenescence status, characterized in that, The immunosenescence biomarkers are any one or any combination of the following phenotypic biomarkers used to define immune cell subsets: CD38+DR- / CD8+, CD27+IgD- / CD19+, CD11c+T-bet+ / CD19+, HLA-DR / CD4, GranzymeB / CD8+, CD27+IgD+ / CD19+, CD28- / CD3+, CD28- / CD8+, CD14-10, and CD16+ / Mono.

2. A flow cytometry-based method of screening for markers of immunosenescence, characterized in that, Includes the following steps: Data acquisition steps: Obtain peripheral blood immune function subgroup data of the target population from the in vitro detection unit; wherein, the target population includes healthy individuals of different age groups, and the peripheral blood immune function subgroup data refers to the proportion data of each subgroup obtained by using flow cytometry to group peripheral blood immune cells through immune cell phenotypic markers and classifying them according to functional categories; Data preprocessing steps: Skewness correction and outlier cleaning are performed on peripheral blood immune function subset data to obtain preprocessed peripheral blood immune function subset data; Biomarker identification steps: LASSO penalized regression was performed on the preprocessed peripheral blood immune function subset data. Based on the optimal λ value of cross-validation, the top N immune cell phenotypic biomarkers used to define each immune cell subset were selected as immunosenescence biomarkers for predicting individual immune age.

3. The flow cytometry-based immune markers of aging screening method of claim 2, wherein, Skewness correction includes the following steps: Logarithmic correction was applied to the peripheral blood immune function subset data. The expression for logarithmic correction is as follows: x' = ln(x+1) In the formula, x represents the original peripheral blood immune function subset data, and x' represents the log-corrected peripheral blood immune function subset data.

4. The flow cytometry-based immune markers of aging screening method of claim 2, wherein, Outlier removal includes the following steps: The IQR algorithm was used to scan for outliers in peripheral blood immune function subsets. When any data value exceeded the preset range, the data was identified as subclinical immune abnormality data and removed. The preset range was [Q1-1.5 * IQR, Q3+1.5 * IQR], where Q1 represents the first quartile, Q3 represents the third quartile, and IQR represents the interquartile range.

5. The flow cytometry-based immune markers of aging screening method according to any one of claims 2-4, characterized in that, The immunosenescence biomarkers include any one or any combination of the following phenotypic biomarkers used to define the following immune cell subsets: CD38+DR- / CD8+, CD27+IgD- / CD19+, CD11c+T-bet+ / CD19+, HLA-DR / CD4, GranzymeB / CD8+, CD27+IgD+ / CD19+, CD28- / CD3+, CD28- / CD8+, CD14-10, and CD16+ / Mono. 6.A method for constructing a computer-implemented flow cytometry-based immune aging age prediction model, characterized in that, Includes the following steps: Data acquisition steps: Acquire peripheral blood immune function subgroup data of the target population corresponding to the immunosenescence biomarkers obtained by the flow cytometry-based immunosenescence biomarker screening method according to any one of claims 2-5, after preprocessing. Data preprocessing steps; Standardized data of peripheral blood immune function subgroups of the target population corresponding to immunosenescence biomarkers were obtained by standardizing the data of peripheral blood immune function subgroups. Model building steps: Based on standardized data, use ordinary least squares to perform non-penalized linear refitting to obtain an immune aging age prediction model. 7.The computer-implemented method of claim 6, wherein, The expression for the immune aging age prediction model is: Immune_Age=66.6853-3.0196*Z_[CD38+DR- / CD8+]-2.4658*Z_[CD27+IgD+ / CD19+]+1.4441*Z_[CD28- / CD8+]+1.1786*Z_[CD14 lo CD16+ / Mono]+1.2849*Z_[CD27+IgD- / CD19+]+0.4558*Z_[CD28- / CD3+]+0.4538*Z _[HLA_DR / CD4]+0.6701*Z_[CD11c+T-bet+ / CD19+]+0.4459*Z_[GranzymeB / CD8+] In the formula, Immune_Age represents the predicted age of immune aging, Z_[] represents the standardized data value, CD38+DR- / CD8+ represents the proportion of CD38+DR- cells to CD8+ cells, CD27+IgD+ / CD19+ represents the proportion of CD27+IgD+ cells to CD19+ cells, CD28- / CD8+ represents the proportion of CD28- cells to CD8+ cells, CD14 lo CD16+ / Mono represents the proportion of CD14 lo CD16+ / Mono CD16+ / Mono The percentage of CD16+ cells in Mono cells is represented by: CD27+IgD- / CD19+; CD28- / CD3+; HLA_DR / CD4; CD11c+T-bet+ / CD19+; and GranzymeB / CD8+.

8. An immune aging age prediction system, characterized by, include: The data acquisition module is communicatively connected to the in vitro detection unit and is used to acquire peripheral blood immune function subgroup data of the subject from the in vitro detection unit corresponding to the immune aging biomarkers obtained by the flow cytometry-based immune aging biomarker screening method according to any one of claims 2-5; wherein the subject is a healthy individual of known age; The data preprocessing module, which is communicatively connected to the data acquisition module, is used to perform skewness correction processing in the flow cytometry-based immunosenescence biomarker screening method as described in any one of claims 2-5 and standardization processing in the computer-implemented flow cytometry-based immunosenescence age prediction model construction method as described in claim 6 on the peripheral blood immune function subgroup data of the subject corresponding to the immunosenescence biomarker, to obtain the preprocessed standardized data of the subject. The prediction module, which is communicatively connected to the data preprocessing module, is used to input the preprocessed standardized data of the subject into the immune aging age prediction model obtained by the method for constructing an immune aging age prediction model based on flow cytometry implemented by a computer as described in claim 6 or 7, to obtain the immune aging age prediction value of the subject.

9. A system for assessing the decline in immune function in the elderly, characterized in that, include: The data acquisition module, communicatively connected to the in vitro detection unit, is used to acquire peripheral blood immune function subgroup data of the subject corresponding to the immune aging biomarkers obtained by the flow cytometry-based immune aging biomarker screening method according to any one of claims 2-5 from the in vitro detection unit, and input the peripheral blood immune function subgroup data of the subject corresponding to the immune aging biomarkers into the immune aging age prediction system according to claim 8 to obtain the immune aging age prediction value of the subject; wherein, the subject is a healthy individual of known age; The evaluation module, which is communicatively connected to the data acquisition module, is used to calculate the difference between the predicted immune aging age and the actual age of the test subject. When the difference between the predicted immune aging age and the actual age is greater than a preset upper threshold, the test subject's aging immune function decline state is determined to be an accelerated decline state. When the difference between the predicted immune aging age and the actual age is within a preset range, the test subject's aging immune function decline state is determined to be a normal aging state. When the difference between the predicted immune aging age and the actual age is less than a preset lower threshold, the test subject's aging immune function decline state is determined to be an immune youth state.

10. An immune aging status detection and prediction system, characterized by, include: An in vitro detection unit is used to perform flow cytometry detection on peripheral blood samples from the subject to obtain peripheral blood immune functional subgroup data corresponding to the immune aging biomarkers obtained by the flow cytometry-based immune aging biomarker screening method according to any one of claims 2-5. The peripheral blood immune functional subgroup data refers to the proportion data of each subgroup obtained by using flow cytometry to group peripheral blood immune cells through immune cell phenotypic biomarkers and classifying them according to functional categories. A data acquisition unit, communicatively connected to the in vitro detection unit, is used to acquire peripheral blood immune function subgroup data corresponding to the immune aging markers of the subject from the in vitro detection unit; wherein the subject is a healthy individual over 60 years of age; A data processing unit, communicatively connected to the data acquisition unit, includes a processor and a memory. The memory stores program instructions executable by the processor; when the program instructions are executed, the processor performs the following operations: The peripheral blood immune function subgroup data of the test subjects corresponding to the immunoaging biomarkers are subjected to skewness correction processing in the immunoaging biomarker screening method based on flow cytometry as described in any one of claims 2-5 and standardization processing in the immunoaging age prediction model construction method based on flow cytometry implemented by computer as described in claim 6, to obtain the preprocessed standardized data of the test subjects. The preprocessed standardized data of the test subject is input into the immune aging age prediction model obtained by the method of constructing an immune aging age prediction model based on flow cytometry implemented by a computer as described in claim 6 or 7, to obtain the immune aging age prediction value of the test subject. The difference between the predicted age of immune aging and the actual age of the subject is calculated. When the difference is greater than a preset upper threshold, the subject's aging immune function decline is determined to be an accelerated decline state. When the difference is within a preset range, the subject's aging immune function decline is determined to be a normal aging state. When the difference is less than a preset lower threshold, the subject's aging immune function decline is determined to be an immune youth state.

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