Detection item combination and detection method for evaluating age of human immune system

Abstract

The invention belongs to the field of biomedical detection, and relates to a detection item combination for evaluating the age of a human immune system, and detection items comprise: 1) immune cell mitochondrial function; 2) the content of reactive oxygen species (ROS) in immune cells; 3) the proportion of T cell surface markers PD-1, LAG3, KLRG-1 and CD57; and 4) T cell subtype. The evaluation method based on the detection item combination can accurately reflect the real age of the immune system, and is used for screening anti-aging products.

Description

[0001] This invention claims priority from Chinese application number 202311526238.3, filed on November 16, 2023. Technical Field

[0002] The present invention belongs to the field of biomedical testing and relates to a combination of test items for evaluating the age of a human immune system. The evaluation method based on the above combination of test items can accurately reflect the real age of the immune system. Background Art

[0003] The immune system changes with age. After the age of 40, the immune cells in the body begin to age significantly, which reduces the ability to fight viruses and bacteria and increases the risk of infection, chronic diseases and cancer. Guido Kroemer, a top immune scientist, first defined the aging of the immune system and clearly gave the eight characteristics of the aging of the immune system: four main signs: thymic degeneration, mitochondrial dysfunction, genetic changes, and imbalance of T cell homeostasis; and four secondary signs: reduction of the TCR library, imbalance of immature cell memory, T cell exhaustion, and reduced T cell plasticity. However, there is no more accurate immune system age assessment method in the market at this stage. Summary of the invention

[0004] The purpose of the present invention is to provide a combination of test items for evaluating the age of the human immune system, which can accurately reflect the real age of the immune system.

[0005] The specific technical solutions are as follows:

[0006] A combination of test items for evaluating the age of a human immune system, the test items comprising:

[0007] 1) Mitochondrial function of immune cells;

[0008] 2) Reactive oxygen species (ROS) content in immune cells;

[0009] 3) The proportion of T cell surface markers PD-1, LAG3, KLRG-1, and CD57;

[0010] 4) T cell subtypes.

[0011] In some specific embodiments, the mitochondrial function is mitochondrial membrane potential; and / or the proportion of the T cell surface markers PD-1, LAG3, KLRG-1, and CD57 is the ratio of the number of T cells that simultaneously bind to the CD3 antibody and the corresponding marker antibody to the number of T cells that bind to the CD3 antibody; and / or the T cell subtype is the ratio of initial T cells (Tn) to central memory T cells (Tcm).

[0012] The present invention also provides the use of the above-mentioned detection item combination and / or its detection reagent in evaluating the age of the human immune system.

[0013] The present invention also provides the use of the above-mentioned detection item combination and / or its detection reagent in preparing a kit for evaluating the age of the human immune system.

[0014] The kit for assessing the age of the human immune system provided by the present invention comprises:

[0015] 1) A detection reagent for detecting mitochondrial function of immune cells in a sample;

[0016] 2) a detection reagent for detecting the content of reactive oxygen species (ROS) in immune cells in a sample;

[0017] 3) Detection reagents used to detect the proportion of T cell surface markers PD-1, LAG3, KLRG-1, and CD57 in samples;

[0018] 4) a detection reagent for detecting T cell subtypes in a sample; and

[0019] 5) Optional instruction manual.

[0020] Among them, the mitochondrial function is mitochondrial membrane potential; and / or the proportion of the T cell surface markers PD-1, LAG3, KLRG-1, and CD57 is the ratio of the number of T cells that simultaneously bind to the CD3 antibody and the corresponding marker antibody to the number of T cells that bind to the CD3 antibody; and / or the T cell subtype is the ratio of initial T cells (Tn) to central memory T cells (Tcm).

[0021] The present invention also provides a method for assessing the age of the human immune system for non-diagnostic and therapeutic purposes, wherein the method uses the above-mentioned combination of detection items and / or the above-mentioned kit.

[0022] In some embodiments, any one of linear regression, nearest neighbor, support vector machine, ridge regression, Lasso, multi-layer perceptron, decision tree, and random forest is used to establish an evaluation model for the detection item data.

[0023] In some specific embodiments, linear regression is used to establish an evaluation model for the test item data.

[0024] In some more specific implementation schemes, the linear regression-based evaluation model formula is as follows:

[0025] y=3.31x 1 -2.24x 2 -10.6x 3 -1.16x 4 -3.52x 5 -4.9x6 -0.03x 7 +47.24

[0026] Among them, y is the age of the immune system, x 1 ~x 7 are the detection data of the detection items, respectively, where x 1 is the ROS content, x 2 is the mitochondrial membrane potential, x 3 is the ratio of initial T cells (Tn) to central memory T cells (Tcm), x 4 ~x 7 They are the ratios of the number of T cells simultaneously binding to CD3+PD-1 antibody, or CD3+LAG3 antibody, CD3+KLRG-1 antibody, or CD3+CD57 antibody to the number of T cells binding to CD3 antibody.

[0027] In some specific implementation schemes, the detection data is normalized, and the normalization formula is: X is the sample detection data, Xmin is the minimum value of all sample detection data, and Xmax is the maximum value of all sample detection data.

[0028] The above-mentioned combination of test items for evaluating the age of the human immune system, the kit for evaluating the age of the human immune system, and the method for evaluating the age of the human immune system can all be used to screen anti-aging products.

[0029] The invention also provides a human immune system age assessment system, which includes the following modules:

[0030] 1) An input module, used to input test sample data, wherein the test sample data is selected from the test item combination as described above;

[0031] 2) Analysis module, which obtains analysis results by detecting sample data.

[0032] In some embodiments, the evaluation system has one or more of the following features:

[0033] 1) The test sample is derived from a blood sample;

[0034] 2) The test sample data is normalized data;

[0035] 3) The analysis module uses a linear regression method to build a model and obtains results after analysis.

[0036] In some specific implementation schemes, the model constructed based on the linear regression method adopts the following formula:

[0037] y=3.31x 1 -2.24x2 -10.6x 3 -1.16x 4 -3.52x 5 -4.9x 6 -0.03x 7 +47.24

[0038] Among them, y is the age of the immune system, x 1 ~x 7 are the detection data of the detection items, respectively, where x 1 is the ROS content, x 2 is the mitochondrial membrane potential, x 3 is the ratio of initial T cells (Tn) to central memory T cells (Tcm), x 4 ~x 7 They are the ratios of the number of T cells simultaneously binding to CD3+PD-1 antibody, or CD3+LAG3 antibody, CD3+KLRG-1 antibody, or CD3+CD57 antibody to the number of T cells binding to CD3 antibody.

[0039] In some more specific embodiments, the detection data is normalized, and the normalization formula is: X is the sample detection data, Xmin is the minimum value of all sample detection data, and Xmax is the maximum value of all sample detection data.

[0040] The invention also provides a readable medium, which stores a program. When the program is executed by a processor, it can realize the functions of the human immune system age assessment system as described above.

[0041] The invention also provides a human immune system age assessment device, comprising:

[0042] 1) A readable medium as described above;

[0043] 2) a processor, configured to execute a program to implement the functions of the human immune system age assessment system;

[0044] 3) An output device, used for outputting the evaluation results.

[0045] The beneficial effects of the present invention are:

[0046] 1) A group of test items for assessing the age of the human immune system was screened, covering the main evaluation indicators of the immune system age, with easy sample acquisition and high detection accuracy;

[0047] 2) By verifying different algorithm models, a set of algorithm models with high fit that can accurately reflect the true age of the immune system was established. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is the result of flow cytometry cell clustering for immune cell exhaustion indicators (PD-1, LAG3 ratio);

[0049] Figure 2 It is the result of flow cytometry cell clustering for immune cell function decline indicators (KLGR-1, CD57 ratio);

[0050] Figure 3 It is an indicator of immune system plasticity, Tn, Tcm, Tem, Teff flow cytometry cell clustering results;

[0051] Figure 4 It is the data changes of each test item of the volunteers after they took the anti-aging preparations for 7 consecutive days;

[0052] Figure 5 It is the result of mitochondrial membrane potential flow cytometry;

[0053] Figure 6 The data on mitochondrial membrane potential changes after volunteers took anti-aging preparations for 7 consecutive days;

[0054] Figure 7 It is the result of grouping ROS positive group and negative group;

[0055] Figure 8 It is the change of ROS data after volunteers took anti-aging preparations for 7 consecutive days;

[0056] Fig. 9 is the physiological age and immune age assessed by support vector machine;

[0057] Fig.10 is the physiological age and the immune age evaluated by the nearest neighbor algorithm;

[0058] Fig.11 is the biological age and the baseline immune system age before taking anti-aging products:

[0059] Fig.12 is the baseline immune system age and the immune system age after taking the anti-aging agent for 7 days:

[0060] Fig.13 is the baseline immune system age, the immune system age after 7 days and 30 days of taking anti-aging preparations;

[0061] Fig.14 It is the biological age, the baseline immune system age, the immune system age after 7 days and 30 days of taking anti-aging preparations, and the immune system age 45 days after stopping anti-aging preparations;

[0062] Fig.15 The immune system age and physiological age of 8 volunteers who had not taken NMN;

[0063] Fig.16 The immune system age and physiological age of 15 volunteers taking NMN. DETAILED DESCRIPTION

[0064] The present invention starts from multiple dimensions of immune system aging, sets multiple immune cell detection items, scores the immune system in different dimensions by comparing the test results, and establishes an evaluation model through the algorithm developed by the inventor, which can accurately reflect the true age of the immune system.

[0065] The present application provides a combination of test items for assessing the age of a human immune system, which includes:

[0066] 1) Mitochondrial function of immune cells;

[0067] 2) Reactive oxygen species (ROS) content in immune cells;

[0068] 3) The proportion of T cell surface markers PD-1, LAG3, KLRG-1, and CD57;

[0069] 4) T cell subtypes.

[0070] The above test items can be detected in any sample containing immune cells from the human body to assess the age of the immune system, and the preferred sample is a sample derived from peripheral blood. In some embodiments, the sample is PBMC.

[0071] The immune cells include lymphocytes, dendritic cells, monocytes / macrophages, granulocytes, mast cells, etc., preferably lymphocytes.

[0072] Mitochondrial function can be selected from mitochondrial DNA content, mitochondrial membrane potential, mitochondrial respiratory chain enzyme activity, mitochondrial ATP synthesis rate, etc. In some embodiments, mitochondrial function is mitochondrial membrane potential. Mitochondrial membrane potential can be detected by methods known in the art, such as fluorescent probe method, membrane potential sensitive electrode method and protein fluorescence resonance energy transfer method, etc. Fluorescent probe can be selected from JC-1, TMRE, Rhodamine 123, TMRM Perchlorate, MitoTracker RedCMXRos, etc.

[0073] The reactive oxygen species (ROS) content can be detected by methods known in the art, such as fluorescence staining, electron paramagnetic resonance technology (EPR), chemiluminescence, chromatography, spectrophotometry, electrochemical biosensor and fluorescent protein, etc., preferably using the DCFH-DA fluorescent probe method.

[0074] In some embodiments, mitochondrial membrane potential and reactive oxygen species (ROS) content are both expressed by MFI. MFI is an indicator for measuring the intensity of a fluorescent signal, and is the abbreviation of Mean Fluorescence Intensity, which is a unit of measurement used to describe the average fluorescence intensity of a fluorescent dye on the surface of a cell or particle.

[0075] The proportion of T cell surface markers PD-1, LAG3, KLRG-1, and CD57 can be detected using binding molecules that bind to the above proteins. In some embodiments, the proportion is the ratio of the number of T cells that simultaneously bind to CD3 antibodies and corresponding marker antibodies to the number of T cells that bind to CD3 antibodies, for example, the proportion of T cell surface marker PD-1 is the ratio of the number of T cells that can simultaneously bind to CD3 antibodies and PD1 antibodies to the number of T cells that can bind to CD3 antibodies; the proportion of T cell surface marker LAG3 is the ratio of the number of T cells that can simultaneously bind to CD3 antibodies and LAG3 antibodies to the number of T cells that can bind to CD3 antibodies; the proportion of T cell surface marker KLRG-1 is the ratio of the number of T cells that can simultaneously bind to CD3 antibodies and KLRG-1 antibodies to the number of T cells that can bind to CD3 antibodies; the proportion of T cell surface marker CD57 is the ratio of the number of T cells that can simultaneously bind to CD3 antibodies and CD57 antibodies to the number of T cells that can bind to CD3 antibodies. Commonly used binding molecules are binding molecules that produce antigen-antibody reactions with corresponding substances, such as antibodies or antibody-containing conjugates. The antibodies described herein include, but are not limited to: conventional whole antibodies, single-chain antibodies, single-domain antibodies, or antigen-binding fragments thereof, such as Fab, Fab', F(ab')2, Fv, VHH, and mini antibodies, having two heavy chains and two light chains. Antibodies can be monoclonal antibodies (including whole antibodies having immunoglobulin Fc regions), antibody compositions having multi-epitope specificity, multispecific antibodies (e.g., bispecific antibodies), double antibodies, or single-chain molecules. It will be appreciated by those skilled in the art that binding molecules are not limited to antibodies, and any molecule that can bind to the corresponding protein and be detected by an instrument can be used in the present invention, such as affibodies, antibody conjugates, target binding regions of receptors, cell adhesion molecules, ligands, enzymes, cytokines, and chemokines.

[0076] T cell subtypes can be the content of Tn (initial T cells), Tscm (memory stem T cells), Tcm (central memory T cells), Tem (effector memory T cells), Teff (effector T cells), etc., or the ratio of their contents. These T cell subtypes can be detected by surface markers known in the art, such as CD45RA, CD197, etc. As described above, these surface markers can be detected using binding molecules that bind to these proteins.

[0077] After obtaining the test data of the above-mentioned test items, an evaluation model can be established for the test data to evaluate the immune system age. The evaluation model can use a machine learning algorithm known in the art, such as testing linear regression, nearest neighbor, support vector machine, ridge regression, Lasso, multi-layer perceptron, decision tree, random forest, etc., preferably a linear regression model is used for immune age prediction.

[0078] The goal of linear regression is to estimate the value of the output variable (representing immune age in the present invention) based on the input features (detection items in the present invention) and establish a linear relationship between the two. By minimizing the sum of squares of errors, linear regression can derive a calculation formula. The standard form of the linear regression model is: y =

[0079] a1*x1+a2*x2+...+an*xn+b, wherein x1, x2...xn are the detection items in the present invention, b is the intercept item, and y is the predicted immune age.

[0080] In some embodiments, the evaluation model formula based on linear regression is as follows:

[0081] y=3.31x 1 -2.24x 2 -10.6x 3 -1.16x 4 -3.52x 5 -4.9x 6 -0.03x 7 +47.24

[0082] Among them, y is the age of the immune system, x 1 ~x 7 are the test data of the above test items, where x 1 is the ROS content, x 2 is the mitochondrial membrane potential, x 3 is the ratio of initial T cells (Tn) to central memory T cells (Tcm), x 4 ~x 7 They are the ratios of the number of T cells simultaneously binding to CD3+PD-1 antibody, or CD3+LAG3 antibody, CD3+KLRG-1 antibody, or CD3+CD57 antibody to the number of T cells binding to CD3 antibody.

[0083] Each test data may be data obtained by normalizing the test data of multiple parallel samples. In some embodiments, the normalization formula is: X is the sample detection data, Xmin is the minimum value of all sample detection data, and Xmax is the maximum value of all sample detection data.

[0084] The detection reagents used to detect the above-mentioned detection items can be made into a kit for evaluating the age of the immune system, that is, the kit includes: the detection reagents used to detect the above-mentioned detection items, and optional instructions for use. The instructions for use can record the detection method of each detection item and the method for calculating the age of the immune system, such as the above-mentioned evaluation model formula based on linear regression. In addition, the kit may also contain reagents required for the detection method used, which are well known in the art.

[0085] The present application also provides a human immune system age assessment system, which includes the following modules: 1) an input module for inputting the test data of the above-mentioned test items; 2) an analysis module, wherein the analysis module obtains an analysis result based on the test data by an analysis method. The analysis method can be to assess the immune system age by the above-mentioned assessment model.

[0086] The present application also provides a medium for determining the age of the immune system of a subject, which may be a printed matter, such as a card or instruction manual, on which the immune system age assessment method described in any embodiment of the present invention is recorded. The medium may also include a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the program, the immune system age assessment method described in any embodiment of the present invention is implemented.

[0087] The present invention can be used to screen anti-aging substances or evaluate the anti-aging effects of anti-aging substances. Specifically, the immune system age before and after the administration of the anti-aging substance can be evaluated and compared, and the anti-aging substance can be screened or the anti-aging effect of the anti-aging substance can be evaluated based on the change in the immune system age.

[0088] The present invention is further described below by way of examples, but the present invention is not limited to the scope of the examples. The experimental methods in the following examples without specifying specific conditions are carried out according to conventional methods and conditions, or selected according to the product specifications.

[0089] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0090] 1. Test samples

[0091] The 11 volunteers were in good health, had no chronic diseases or inflammation, had not been ill within a month, and did not take other medicines or health products. The male to female ratio was 8:3, including 4 people aged 30-40 years old, 5 people aged 41-50 years old, and 2 people aged 50-60 years old.

[0092] All volunteers took the anti-aging preparation Vital NAD (purchased from Shenghuang Technology, the active ingredient is NMN, β-nicotinamide mononucleotide) orally at a dose of 600 mg / day for 30 consecutive days. Whole blood samples were collected from the volunteers before taking the medicine, on the 7th day, on the 30th day, and on the 45th day after stopping taking the medicine.

[0093] Example 1 Detection of immune system exhaustion index, functional decline index and plasticity index

[0094] 1.1 Experimental method: Add 100 μL of whole blood to each experimental flow tube. Then add 2ul, 2ul, 2ul, 2ul, 2ul, and 2.5ul of CD3, PD-1, LAG3, KLGR-1, and CD57 antibodies, respectively, and 5μL, 2.5μL, 10μL, and 5μL of CD3, CD45, CD45RA, and CD197 antibodies, mix well, and incubate at room temperature and away from light for 30 minutes. Add 500μL of optical lysis buffer (Beckman Coulter), mix well, and incubate at room temperature and away from light for 10 minutes until complete hemolysis to clear and transparent. After hemolysis is completed, put it into a centrifuge, 300g, 5 minutes, and discard the supernatant after centrifugation. Add 1ml PBS, mix well, put it into a centrifuge, 300g, 5 minutes, and discard the supernatant after centrifugation. Add 300μL PBS, mix well, resuspend, and detect on the CytoFLEX S flow instrument (Beckman).

[0095] 1.2 Detection indicators: immune system exhaustion indicators - PD-1, LAG3 proportion; immune system function decline indicators - KLRG-1, CD57 proportion; immune system plasticity indicators - T cell subtype (Tn / Tcm).

[0096] 1.3 Experimental results: Figure 1-4 As shown, Figure 1 It is an indicator of immune cell exhaustion (PD-

[0097] 1. Flow cytometry cell clustering results of LAG3 proportion; Figure 2 It is the result of flow cytometry cell clustering for immune cell function decline indicators (KLGR-1, CD57); Figure 3 It is the result of flow cytometry cell clustering of indicators of immune system plasticity, including Tn (initial T cells), Tcm (central memory T cells), Tem (effector memory T cells), and Teff (effector T cells).

[0098] After taking anti-aging drugs for 7 consecutive days, the proportion of Lag-3, an indicator of immune system exhaustion, increased, but the proportion of PD-1 decreased in most cases ( Figure 4 -A and Figure 4-B); the proportion of KLRG-1 and CD57, indicators of immune system aging, also showed a downward trend ( Figure 4 -C and Figure 4 -D); the immune system plasticity index, namely Tn / Tcm, showed an increasing trend in the ratio in most volunteers ( Figure 4 -E).

[0099] Example 2 Mitochondrial Function Detection

[0100] 2.1 Experimental methods: ① Prepare cells: according to the amount of PBMC cells obtained by separating the whole blood sample, use an appropriate amount of RPMI1640 basal culture medium (serum-free) to resuspend the cells and count them to make the final cell concentration 1E6 / ml; ② Prepare the probe working solution concentration: mitochondrial membrane potential probe MitoTracker Red CMXRos (100nM) (Invitrogen); ③ Probe staining: take 1ml of the cell suspension prepared in step ① and place it in a tube, add 1ul of the probe working solution prepared in step ②, and incubate it in a 37°C incubator for 30min; ④ After the incubation is completed, centrifuge at 400g for 5min and discard the supernatant, resuspend and centrifuge again with preheated DPBS for washing; ⑤ After the centrifugation is completed, discard the supernatant, add 200ul DPBS to mix, and detect on the CytoFLEX S flow cytometer (Beckman).

[0101] 2.2 Detection indicators: immune cell mitochondrial function-mitochondrial membrane potential.

[0102] 2.3 Experimental results: The results of mitochondrial membrane potential flow cytometry showed a clear division between positive and negative groups ( Figure 5 ); After 11 volunteers took anti-aging preparations for 7 consecutive days, the mitochondrial membrane potential data basically showed an upward trend ( Figure 6 ).

[0103] Example 3 ROS (Reactive Oxygen Species) Detection

[0104] 3.1 Experimental methods: ① Cell counting: PBMC cells isolated from whole blood samples were resuspended in RPMI 1640 serum-free medium, counted with AOPI or trypan blue, and dispensed into 1.5 ml centrifuge tubes, with 1E6 cells per tube and a volume of 1 ml; ② 1 ul probe DCFH-DA (2,7-dichlorofluorescein diacetate) (final concentration of 10 μmol / L) was added to the tube, mixed, and incubated in a 37°C incubator for 20 min, inverted and mixed every 3-5 min to allow the probe to fully contact the cells; ③ Washed twice with DPBS 500g, 5 min to fully remove DCFH-DA that did not enter the cells, and resuspended with 200 ul DPBS; ④ Detect the fluorescence intensity of the cells using the FITC channel.

[0105] 3.2 Detection indicators: ROS (reactive oxygen species)

[0106] 3.3 Experimental results: ROS positive group and negative group are clearly divided into groups ( Figure 7 ), after taking the anti-aging preparation for 7 consecutive days, the ROS in the volunteers' cells decreased ( Figure 8 ).

[0107] Example 4 Establishment of immune system age assessment model

[0108] An immune system age calculation model is constructed based on a combination of test items for assessing the age of the human immune system: 1) mitochondrial function of immune cells; 2) reactive oxygen species (ROS) content of immune cells; 3) the proportion of T cell surface markers PD-1, LAG3, KLRG-1, and CD57; and 4) data on T cell subtypes.

[0109] The mitochondrial function is the mitochondrial membrane potential; the proportion of the T cell surface markers PD-1, LAG3, KLRG-1, and CD57 is the ratio of the number of T cells that simultaneously bind to the CD3 antibody and the corresponding marker antibody to the number of T cells that bind to the CD3 antibody; the T cell subtype is the ratio of initial T cells (Tn) to central memory T cells (Tcm).

[0110] 4.1 Model selection

[0111] The fitting effects of 14 machine learning algorithms, including linear regression, nearest neighbor, support vector machine, ridge regression, Lasso, multi-layer perceptron, decision tree, and random forest, were tested on baseline samples, and the models were used to assess the immune system age of volunteers before and after taking anti-aging preparations.

[0112] The linear regression model formula is as follows:

[0113] y=3.31x 1 -2.24x 2 -10.6x 3 -1.16x 4 -3.52x 5 -4.9x 6 -0.03x 7 +47.24

[0114] Among them, y is the age of the immune system, x 1 ~x 7 are the detection data of the detection items, respectively, where x 1 is the ROS content, x 2 is the mitochondrial membrane potential, x 3 is the ratio of initial T cells (Tn) to central memory T cells (Tcm), x 4 ~x 7They are the ratios of the number of T cells simultaneously binding to CD3+PD-1 antibody, or CD3+LAG3 antibody, CD3+KLRG-1 antibody, or CD3+CD57 antibody to the number of T cells binding to CD3 antibody.

[0115] The detection data is normalized, and the normalization formula is: X is the sample test data, Xmin is the minimum value of all sample test data, and Xmax is the maximum value of all sample test data. There are 11 volunteer samples in total.

[0116] 4.2 Model Validation

[0117] The evaluation results of the support vector machine are as follows: Fig. 9 As shown, the correlation is 0.75. Although the value is high, it can only prove that the predicted value has the same change trend as the physiological age. The predicted values ​​of all samples are basically the same, which has a large deviation from the physiological age.

[0118] The evaluation results of the nearest neighbor algorithm are as follows Fig.10 As shown, the correlation is 0.44, which is a poor fit.

[0119] The evaluation results of the linear regression model are as follows Figure 11-13 shown. Fig.11 The results show the physiological age VS the baseline immune system age before taking anti-aging preparations. The immune system age fluctuates above and below the physiological age (6 cases were higher than the physiological age, 4 cases were lower than the physiological age, and 1 case was the same), with a correlation of 0.72, indicating that the model fit is good. Fig.12 The baseline immune system age VS the immune system age after taking anti-aging preparations for 7 consecutive days is shown. After taking the anti-aging preparations, the immune system age of all samples decreased to varying degrees. Fig.13 The baseline data VS the immune system age after taking the anti-aging preparation for 7 and 30 days continuously are shown. After taking it for 30 days, only a small number of samples have a further decline in immune system age, but 90% of the samples are still below the baseline immune age. This is mainly because NMN is the main energy supply molecule for cells, which theoretically helps to maintain the youthfulness of immune cells. After stopping taking it for 45 days, the "immune age" of most volunteers returned to the level before taking it ( Fig.14 ).

[0120] Therefore, considering the fitting effect and the changes in the age of the immune system before and after taking anti-aging preparations, the linear regression model was finally selected for the assessment of the age of the immune system.

[0121] 4.3 Model Detection

[0122] (1) Immune age test of volunteers who did not take NMN

[0123] Eight volunteers who had never taken NMN were selected as the validation set. All volunteers were in good health, had no chronic diseases or inflammation, had not been sick within a month, and had not taken other medicines or health products. The results showed that ( Fig.15 ), the correlation between the immune system age predicted by the model of the invention and the physiological age is 0.786, which is a high correlation.

[0124] (2) Immune age testing of volunteers taking anti-aging preparations

[0125] 15 volunteers who took anti-aging preparations (Vital NAD, purchased from Chenghuang Technology, with the active ingredient NMN, β-nicotinamide mononucleotide) were selected as the validation set. All volunteers were in good health, had no chronic diseases or inflammation, had not been sick within a month, and had not taken other medicines or health products. The taking time of each patient is shown in Table 1, and the dosage is 600 mg / day.

[0126] The results show that Fig.15 ), the correlation between the immune age and the physiological age predicted by the model of the invention is 0.9486, which is highly correlated.

[0127] Table 1

[0128] volunteer Days of use Biological age Immunity age 1 30 43.42 41.74 2 30 39.46 33.97 3 12 45.86 40.36 4 7 43.11 44.53 5 30 43.62 38.81 6 5 43.56 41.07 7 5 33.62 34.80 8 10 35.07 37.93 9 40 51.08 47.49 10 90 57.53 51.58 11 150 66.73 60.15 12 60 49.77 47.56 13 30 43.76 41.65 14 90 43.19 42.78 15 90 40.70 37.51

Claims

1. A combination of test items for evaluating the age of the human immune system, characterized in that: The detection items include: 1) Mitochondrial function of immune cells; 2) Reactive oxygen content in immune cells; 3) The proportion of T cell surface markers PD-1, LAG3, KLRG-1, and CD57; 4) T cell subtypes; Preferably, the mitochondrial function is mitochondrial membrane potential; and / or the proportion of the T cell surface markers PD-1, LAG3, KLRG-1, and CD57 is the ratio of the number of T cells that simultaneously bind to the CD3 antibody and the corresponding marker antibody to the number of T cells that bind to the CD3 antibody; and / or the T cell subtype is the ratio of initial T cells (Tn) to central memory T cells (Tcm).

2. Use of the detection item combination and / or its detection reagent according to claim 1 in assessing the age of the human immune system or in preparing a kit for assessing the age of the human immune system.

3. A kit for assessing the age of a human immune system, comprising: 1) A detection reagent for detecting mitochondrial function of immune cells in a sample; 2) a detection reagent for detecting the content of reactive oxygen species in immune cells in a sample; 3) Detection reagents used to detect the proportion of T cell surface markers PD-1, LAG3, KLRG-1, and CD57 in samples; and 4) Detection reagents for detecting T cell subtypes in a sample; Preferably, the kit further comprises: 5) instructions for use; More preferably, the mitochondrial function is mitochondrial membrane potential; And / or the proportion of the T cell surface markers PD-1, LAG3, KLRG-1, and CD57 is the ratio of the number of T cells that simultaneously bind to the CD3 antibody and the corresponding marker antibody to the number of T cells that bind to the CD3 antibody; and / or the T cell subtype is the ratio of initial T cells (Tn) to central memory T cells (Tcm).

4. A method for assessing the age of the human immune system for non-diagnostic and therapeutic purposes, characterized in that: The evaluation method uses the detection item combination as described in claim 1 and / or the kit as described in claim 3.

5. The evaluation method according to claim 4, characterized in that: Use any of the following methods: linear regression, nearest neighbor, support vector machine, ridge regression, Lasso, multi-layer perceptron, decision tree, random forest to build an evaluation model for the test item data; Preferably, linear regression is used to establish an evaluation model for the detection item data; More preferably, the evaluation model formula based on linear regression is as follows: y=3.31x1-2.24x2-10.6x3-1.16x4-3.52x5-4.9x6-0.03x7+47.24 Among them, y is the age of the immune system, x1~x7 are the detection data of the detection items, wherein x1 is the active oxygen content of immune cells, x2 is the mitochondrial membrane potential, x3 is the ratio of initial T cells (Tn) to central memory T cells (Tcm), and x4~x7 are the ratios of the number of T cells simultaneously binding to CD3+PD-1 antibodies, or CD3+LAG3 antibodies, CD3+KLRG-1 antibodies, or CD3+CD57 antibodies to the number of T cells binding to CD3 antibodies.

6. The evaluation method according to claim 5, characterized in that: The detection data is normalized, and the normalization formula is: X is the sample detection data, Xmin is the minimum value of all sample detection data, and Xmax is the maximum value of all sample detection data.

7. Use of the test item combination according to claim 1, the kit according to claim 3, and the evaluation method according to any one of claims 4 to 6 in screening anti-aging products.

8. A system for assessing the age of the human immune system, characterized in that: It includes the following modules: 1) An input module, used to input test sample data, wherein the test sample data is selected from The detection item combination as claimed in claim 1; 2) Analysis module, which obtains analysis results by detecting sample data. Preferably, the evaluation system has one or more of the following features: 1) The test sample is derived from a blood sample; 2) The test sample data is normalized data; 3) The analysis module uses a linear regression method to build a model and obtains results after analysis.

9. The evaluation system according to claim 8, characterized in that The model built based on the linear regression method uses the following formula: y=3.31x1-2.24x2-10.6x3-1.16x4-3.52x5-4.9x6-0.03x7+47.24 Wherein, y is the age of the immune system, x1 to x7 are the test data of the test items, wherein x1 is the active oxygen content of immune cells, x2 is the mitochondrial membrane potential, x3 is the ratio of initial T cells (Tn) to central memory T cells (Tcm), and x4 to x7 are the ratios of the number of T cells simultaneously binding to CD3+PD-1 antibodies, or CD3+LAG3 antibodies, CD3+KLRG-1 antibodies, or CD3+CD57 antibodies to the number of T cells binding to CD3 antibodies; Preferably, the detection data is normalized, and the normalization formula is: X is the sample detection data, Xmin is the minimum value of all sample detection data, and Xmax is the maximum value of all sample detection data.

10. A readable medium, characterized in that: The readable medium stores a program, and when the program is executed by a processor, it can realize the functions of the human immune system age assessment system as described in claim 8 or 9.

11. A human immune system age assessment device, characterized in that: include: 1) The readable medium according to claim 10; 2) a processor, configured to execute a program to implement the functions of the human immune system age assessment system; 3) An output device, used for outputting the evaluation results.

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