System, method and device for evaluating immune health

Abstract

The invention belongs to the field of medical systems, and particularly relates to a system, method and device for evaluating immune health. The system comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor. When the processor executes the computer program, the processor implements the following steps: receiving immune or mitochondrial indexes of different cell types of a subject; and inputting the received mitochondrial indexes of different cell types into an immune health evaluation module to obtain the immune health condition of the subject. The system can more accurately evaluate the immune health condition of the subject.

Description

Technical Field

[0001] The present invention belongs to the field of medical systems, and in particular relates to a system, method and device for evaluating immune health. Background Art

[0002] The existing immune-related age model learning is based on PLS and multi-parameter linear regression analysis learning algorithms, mainly at the level of immune cell ratio, cell count, etc. It combines flow cytometry, immunology, computer technology, laser technology, applied fluidics, the latest high-speed digital signal processors and computer algorithms, and has the advantages of high throughput, high speed, low cost, high sensitivity, high precision, wide linear range, and easy operation. It is widely used in immunoassay, nucleic acid analysis, enzymatic analysis, receptor and ligand recognition, etc.

[0003] Among them, flow cytometry is the main detection method for many clinical testing indicators, and clinical flow cytometry is still at the cellular level. Existing flow cytometry technology performs lymphocyte immunotyping on the surface of immune cells, but ignores intracellular detection. In addition, intracellular staining-related flow cytometry items are limited and have not been applied to immune health-related applications.

[0004] The mitochondria of immune cells are the center of cell function. Almost all immune cells need the participation of mitochondria to perform their functions, and mitochondria are in a dynamic process of continuous division and fusion. Therefore, the detection of mitochondria is very challenging, especially the stability and accuracy of its detection require very high precision. On the other hand, the flow cytometry technology for mitochondrial detection started late, including the slow development of new organelle-specific probes or dyes. Pan-peptide Biotechnology began to improve the flow cytometry technology through mitochondria in 2018, and continuously optimized it according to the characteristics of peripheral immune cells, and finally achieved a detection methodology that meets the requirements of stability and accuracy.

[0005] The state of immune cells is judged from mitochondrial mass and mitochondrial low membrane potential parameters, thus reflecting cell activity and metabolism. The factors affecting immune health are complex, and traditional flow cytometry indicators are relatively limited. Mitochondria, as one of the aging indicators, are also important organelles of immune cells. They are the intersection of immune indicators and aging indicators, and are more targeted to the concept of immune health. Summary of the invention

[0006] The present invention provides a system, method, device and storage medium for evaluating immune health based on lymphocyte mitochondrial indicators. Based on this system, method, device and storage medium, the immune health of the subject can be evaluated more accurately.

[0007] In order to achieve the above object, the present invention can adopt the following technical solutions:

[0008] On the one hand, the present invention provides a system for evaluating immune health, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When executing the computer program, the processor implements the following steps: receiving immune or mitochondrial indicators of different cell types of a subject; inputting the received immune or mitochondrial indicators of different cell types into an immune health evaluation module to obtain the immune health status of the subject.

[0009] On the other hand, the present invention provides a method for evaluating immune health, comprising: receiving immune or mitochondrial indicators of different cell types of a subject; and then inputting the received immune or mitochondrial indicators of different cell types into an immune health module to obtain the immune health status of the subject.

[0010] On the other hand, the present invention provides a device for evaluating immune health, including: a receiving module for receiving immune or mitochondrial indicators of different cell types of a subject; an immune health evaluation module for inputting the received immune or mitochondrial indicators of different cell types to output the immune health status of the subject.

[0011] The beneficial effects of the present invention include at least: the system, method, device and storage medium for evaluating immune health based on lymphocyte immunity or mitochondrial indicators provided by the present invention can more accurately evaluate the immune health of the subject. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a schematic diagram of the gating strategy for leukocyte flow cytometry;

[0013] Figure 2 Schematic diagram of T cell flow cytometry gating strategy;

[0014] Figure 3 is the sample age and gender distribution;

[0015] Figure 4 To present the correlation coefficient between the detection index and age (Pearson method);

[0016] Figure 5 The correlation between the 14 major immune indicators and BMI screened out and their age is presented;

[0017] Figure 6 The correlation between 58 non-strongly correlated indicators and chronological age is presented;

[0018] Figure 7 Mean reduced precision for random forest analysis of immune health index;

[0019] Figure 8 is the cross-validation error of the random forest analysis of the immune health index;

[0020] Fig. 9 The linear correlation between immune health index and age and the grading of immune health index. DETAILED DESCRIPTION

[0021] The examples are provided to better illustrate the present invention, but the present invention is not limited to the examples. Therefore, those skilled in the art may make non-essential improvements and adjustments to the implementation scheme according to the above invention content, which still fall within the protection scope of the present invention.

[0022] The terms used herein are only used to describe specific embodiments and are not intended to limit the present disclosure. Unless the context has a significantly different meaning, expressions in the singular include expressions in the plural. As used herein, it should be understood that terms such as "include", "have", "include" are intended to indicate the presence of features, numbers, operations, components, parts, elements, materials or combinations. The terms of the present invention are disclosed in the specification, and are not intended to exclude the possibility that one or more other features, numbers, operations, components, parts, elements, materials or combinations thereof may exist or may be added. As used herein, " / " may be interpreted as "and" or "or", depending on the circumstances.

[0023] In the present invention, the meanings of immune or mitochondrial indicators of different cell types are as follows:

[0024]

[0025]

[0026]

[0027] In the present invention, the terms "infant-level immune status", "good immune health status", "normal immune health status", "low immune risk" and "high immune risk" are as follows:

[0028] Infant-level immune status: or optimal immune status, infants’ immune systems may be more powerful than adults’ immune systems in fighting new pathogens because their T cells react faster against new viruses and can quickly build up a strong defense;

[0029] Good immune health: also known as green immune status. Although it is lower than the optimal infant-level immune status, it responds quickly and powerfully to diseases and infections, and can quickly eliminate invading antigenic microorganisms, ensuring individual health and maintaining a youthful state;

[0030] Normal immune health status: also known as normal immune status, maintaining antiviral function, having strong antigen microbial clearance ability, occasional infection but can recover quickly, individual health, but trying hard to maintain a young state, there is aging of some immune cells;

[0031] Low immune risk: also known as mild immunodeficiency, occasional infections that take time to recover, obvious immune aging, the possibility of chronic inflammation, tumors in a very small number of individuals, and difficulty maintaining a youthful state. The immune function still has the ability to eliminate pathogenic microorganisms;

[0032] High-risk immune system: also known as immunocompromised state. The immune system of some individuals is greatly affected, and inflammatory response is more likely. Disease healing takes a long time and may be accompanied by chronic diseases. Most individuals are immunocompromised, and the incidence of tumors is high, requiring long-term attention and adjustment of the immune status.

[0033] In the present invention, the immune or mitochondrial indicators of different cell types are marked with "(T)" as the detection indicators of the leukocyte flow cytometry gating strategy, i.e., TBNK-mito panel; and the indicators marked with "(F)" are the detection indicators of the T cell flow cytometry gating strategy, i.e., TFUN-mitopanel.

[0034] An embodiment of the present invention provides a system for evaluating immune health, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0035] receiving immune or mitochondrial indicators of different cell types of the subject;

[0036] The received immune or mitochondrial indicators of different cell types are input into the immune health evaluation module to obtain the immune health status of the subject.

[0037] It should be noted that clinical flow cytometry technology is still at the cellular level. Existing flow cytometry technology performs lymphocyte immunophenotyping on the surface of immune cells, ignoring intracellular detection, and intracellular staining-related flow cytometry projects are limited and have not been applied to immune health-related applications. Intracellular immune or mitochondrial indicators can well reflect immune health in the present invention.

[0038] In some specific examples, the immune or mitochondrial indicators of the above-mentioned different cell types include: T8Tn% (F), Ts.MMP low %(F)、T4Tn%(F)、Ts.MMP low %(T),T8Tn.MMP low%(F), PCT, T8Tem%(F), T4Tem%(F), Ts.MM(T) or Ts.MM(F).

[0039] It should be noted that the above-mentioned immune or mitochondrial indicators of different cell types have a strong correlation with age, and judging from the final immune health status, the relationship with age is consistent with the actual situation, which further proves the accuracy of evaluating immune health based on the above-mentioned indicators in the present invention.

[0040] It should also be noted that the above-mentioned immune or mitochondrial indicators of different cell types can be selected to input one of them into the immune health evaluation module to obtain the immune health status of the subject, or multiple indicators can be input into the immune health evaluation module to obtain the immune health status of the subject; more preferably, multiple indicators are used, as the accuracy of evaluation is higher.

[0041] In some specific examples, the immune or mitochondrial indicators of the above-mentioned different cell types include: T8Tn% (F), Ts.MMP low %(F)、T4Tn%(F)、Ts.MMP low %(T),T8Tn.MMP low At least 6 of %(F), PCT, T8Tem%(F), T4Tem%(F), Ts.MM(T) or Ts.MM(F).

[0042] It should be noted that, as mentioned above, in the present invention, it is preferred to input multiple immune or mitochondrial indicators to evaluate immune health, and the multiple indicators are preferably at least 6, that is, 6 or more.

[0043] In some specific examples, the immune health evaluation module obtains the immune health index value (IHI) through dimensionality reduction analysis. PCA ).

[0044] It should be noted that the immune health index value (IHI PCA ) is proposed for the first time in this invention. It is different from the concepts of ImmuneInflammation Index, immune index and immunological age. The definition of this index is: an age-related health evaluation index obtained by calculation and statistical analysis of immune cells and their immune or mitochondrial indicators, which is used to evaluate the immune status level and immune health.

[0045] In some specific examples, the above-mentioned dimensionality reduction analysis includes PCA dimensionality reduction analysis, t-SNE dimensionality reduction analysis or UMAP dimensionality reduction analysis, etc.

[0046] It should be noted that the dimensionality reduction analysis in the present invention is well known in the art, such as PCA dimensionality reduction analysis, t-SNE dimensionality reduction analysis or UMAP dimensionality reduction analysis; among them, PCA dimensionality reduction analysis is preferred, and the accuracy of evaluation after analysis is higher.

[0047] In some specific examples, the immune health index value (IHI) obtained by dimensionality reduction analysis is PCA ) includes: standardizing the immune or mitochondrial indicators of different cell types through data, constructing a covariance matrix, calculating the eigenvalues ​​and eigenvectors of the covariance matrix to obtain the principal component calculation method, selecting different principal components, constructing a projection matrix, data projection, interpreting the data results, and then outputting the immune health index value.

[0048] It should be noted that different principal components (y values) are calculated according to the number of immune or mitochondrial indicators of the selected cell types above, for example:

[0049] (I) Immune or mitochondrial indicators of different cell types include: T8Tn% (F), Ts.MMP low %(F)、T4Tn%(F)、Ts.MMP low %(T),T8Tn.MMP low %(F), PCT, T8Tem%(F), T4Tem%(F), Ts.MM(T) and Ts.MM(F); the calculation method of different principal components (y) is as follows:

[0050] y 1 =0.36*x 1 +0.39*x 2 +0.31*x 3 +0.35*x 4 +0.29*x 5 +0.19*x 6 -0.28*x 7 -0.27*x 8 -0.33*x 9 -0.34*x 10 ;

[0051] y 2 =0.26*x 1 -0.21*x 2 +0.45*x 3 -0.23*x 4 -0.26*x 5 +0.14*x 6 -0.3*x 7 -0.46*x 8 +0.35*x 9 +0.34*x 10;

[0052] y 3 =-0.0004*x 1 +0.004*x 2 -0.14*x 3 +0.07*x 4 -0.26*x 5 +0.92*x 6 +0.02*x 7 +0.25*x 8 -0.03*x 9 +0.05*x 10 ;

[0053] y 4 =-0.18*x 1 -0.12*x 2 +0.26*x 3 -0.33*x 4 +0.53*x 5 +0.28*x 6 +0.56*x 7 -0.28*x 8 +0.03*x 9 -0.13*x 10 ;

[0054] y 5 =-0.22*x 1 +0.05*x 2 +0.22*x 3 +0.47*x 4 -0.45*x 5 -0.08*x 6 +0.49*x 7 -0.32*x 8 -0.29*x 9 +0.22*x 10 ;

[0055] y 6 =0.29*x 1 +0.4*x 2 -0.06*x 3 +0.33*x 4 +0.29*x 5 -0.001*x 6 +0.27*x 7 +0.14*x 8 +0.52*x 9 +0.45*x 10 ;

[0056] y7 =-0.11*x 1 -0.24*x 2 -0.04*x 3 +0.18*x 4 +0.24*x 5 +0.04*x 6 -0.16*x 7 -0.015*x 8 -0.15*x 9 +0.27*x 10 ;

[0057] y 8 =0.37*x 1 -0.17*x 2 +0.04*x 3 -0.07*x 4 -0.02*x 5 -0.05*x 6 +0.16*x 7 +0.14*x 8 -0.19*x 9 +0.06*x 10 ;

[0058] y9=-0.04*x 1 -0.11*x 2 +0.2*x 3 +0.12*x 4 +0.0004*x 5 -0.01*x 6 -0.004*x 7 +0.16*x 8 +0.1*x 9 -0.1*x 10 ;

[0059] y10=-0.08*x 1 +0.15*x 2 +0.15*x 3 -0.12*x 4 +0.01*x 5 -0.01*x 6 -0.03*x 7 +0.13*x 8 -0.11*x 9 +0.13*x 10 ;

[0060] Among them, x 1 to x 10 T8Tn%(F), Ts.MMPlow %(F)、T4Tn%(F)、Ts.MMP low %(T),T8Tn.MMP low %(F), PCT, T8Tem%(F), T4Tem%(F), Ts.MM(T) and Ts.MM(F) are the normalized values.

[0061] At this time, IHI PCA You can arbitrarily select two or more of the following principal components and obtain them using the following calculation method:

[0062] IHI [PCA10] =0.48*y 1 +0.19*y 2 +0.08*y 3 +0.07*y 4 +0.06*y 5 +0.04*y 6 +0.03*y 7 +0.02*y 8 +0.01*y 9 +0.0

[0063] 1*y 10 ;

[0064] IHI [PCA9] =0.48*y 1 +0.19*y 2 +0.08*y 3 +0.07*y 4 +0.06*y 5 +0.04*y 6 +0.03*y 7 +0.02*y 8 +0.01*y 9 ;

[0065] IHI [PCA8] =0.48*y 1 +0.19*y 2 +0.08*y 3 +0.07*y 4 +0.06*y 5 +0.04*y 6 +0.03*y 7 +0.02*y 8 ;

[0066] IHI [PCA7] =0.48*y 1 +0.19*y 2 +0.08*y 3 +0.07*y4 +0.06*y 5 +0.04*y 6 +0.03*y 7 ;

[0067] IHI [PCA6] =0.48*y 1 +0.19*y 2 +0.08*y 3 +0.07*y 4 +0.06*y 5 +0.04*y 6 ;

[0068] IHI [PCA5] =0.48*y 1 +0.19*y 2 +0.08*y 3 +0.07*y 4 +0.06*y 5 ;

[0069] IHI [PCA4] =0.48*y 1 +0.19*y 2 +0.08*y 3 +0.07*y 4 ;

[0070] IHI [PCA3] =0.48*y 1 +0.19*y 2 +0.08*y 3 ;

[0071] IHI [PCA2] =0.48*y 1 +0.19*y 2 .

[0072] (II) Immune or mitochondrial indicators of different cell types include: T8Tn% (F), Ts.MMP low %(F)、T4Tn%(F)、Ts.MMP low %(T),T8Tn.MMP low %(F) and PCT total 6 items; the calculation method of different principal components (y) is as follows:

[0073] y 1 =0.47*x 1 +0.49*x 2 +0.38*x 3 +0.44*x 4 +0.36*x 5 +0.26*x6 ;

[0074] y 2 =0.28*x 1 -0.32*x 2 +0.49*x 3 -0.34*x 4 -0.42*x 5 +0.53*x 6 ;

[0075] y 3 =-0.31*x 1 +0.12*x 2 -0.49*x 3 +0.21*x 4 -0.074*x 5 +0.76*x 6 ;

[0076] y 4 =-0.32*x 1 -0.19*x 2 +0.07*x 3 -0.49*x 4 +0.81*x 5 +0.23*x 6 ;

[0077] y 5 =-0.72*x 1 -0.12*x 2 +0.59*x 3 +0.36*x 4 +0.02*x 5 -0.01*x 6 ;

[0078] y 6 =0.28*x 1 -0.77*x 2 -0.14*x 3 +0.52*x 4 +0.19*x 5 +0.01*x 6 .

[0079] At this time, IHI PCA You can arbitrarily select two or more of the following principal components and obtain them using the following calculation method:

[0080] IHI [PCA6] =0.51*y 1 +0.18*y 2 +0.13*y 3 +0.1*y4 +0.05*y 5 +0.03*y 6 ;

[0081] IHI [PCA5] =0.51*y 1 +0.18*y 2 +0.13*y 3 +0.1*y 4 +0.05*y 5 ;

[0082] IHI [PCA4] =0.51*y 1 +0.18*y 2 +0.13*y 3 +0.1*y 4 ;

[0083] IHI [PCA3] =0.51*y 1 +0.18*y 2 +0.13*y 3 ;

[0084] IHI [PCA2] =0.51*y 1 +0.18*y 2 .

[0085] (III) Immune or mitochondrial indicators of different cell types include: T8Tn% (F), Ts.MMP low %(F), T4Tn%(F), a total of 3 items; the calculation method of different principal components (y) is as follows:

[0086] y 1 =0.71*x 1 +0.71*x 2 ;

[0087] y 2 =-0.71*x 1 +0.71*x 2 ;

[0088] At this time, IHI PCA =0.79*y 1 +0.21*y 2 .

[0089] In some specific examples, the above normalization includes min-max normalization.

[0090] It should be noted that the standardization in the present invention is well known in the art, such as min-max standardization, etc.; min-max standardization is also preferred in the present invention, and its evaluation accuracy is better than other standardizations.

[0091] In some specific examples, the above-mentioned system for evaluating immune health also includes a flow cytometer or a mitochondrial detection reagent for detecting and counting different cell types for immune or mitochondrial indicators respectively.

[0092] It should be noted that many current clinical detection indicators are mostly relative counts, especially the flow indicators of peripheral lymphocyte subset detection are mostly output as relative data of cell ratio (percentage), and the percentage data are increasingly unable to meet the judgment of many precise immune systems. If accurate counting is required, matching absolute counting microspheres are required for cell number quantification. Mitochondrial detection requires accurate cell counts to ensure the accuracy and precision of detection, so the flow cytometer in the present invention requires matching absolute counting microspheres. In addition, the operation links involving absolute counting microspheres in each step require accurate sample addition, mixing to obtain a uniformly distributed microsphere state, and the steps of centrifugation or washing are required in the subsequent operation process, and the whole process needs to be protected from light.

[0093] An embodiment of the present invention also provides a method for evaluating immune health, comprising: receiving immune or mitochondrial indicators of different cell types of a subject; and then inputting the received immune or mitochondrial indicators of different cell types into an immune health module to obtain the immune health status of the subject.

[0094] In some specific examples, the immune or mitochondrial indicators of the above-mentioned different cell types are obtained by mitochondrial fluorescence staining based on flow cytometry technology; the gating strategy of flow cytometry detection includes: screening the live cell population to remove dead cells; removing adherent cells from the live cell population and selecting a single cell population; selecting a lymphocyte population from the single cell population; selecting T cell populations, B cell populations and NK cell populations from the lymphocyte population; distinguishing Th cell subpopulations, Ts cell subpopulations or Tc cell subpopulations from the T cell population; T cell differentiation subpopulations include T4Tef, T4Tn, T4Tem, T4Tcm, T8Tef, T8Tn, T8Tem, T8Tcm cell populations and mitochondrial MMP low The characteristics of grouping.

[0095] In some specific examples, the immune health status of the subject is graded according to the immune health index value, including:

[0096] If the immune health index value is > 0.65, the output is infant-level immune status;

[0097] The immune health index value is 0.5<IHI PCA ≤0.65, the output is good immune health status;

[0098] The immune health index value is 0.33<IHI PCA ≤0.5, the output is normal immune health status;

[0099] The immune health index value is 0≤IHI PCA ≤0.33, the output is low risk of immunity;

[0100] The immune health index value is <0, and the output is high immune risk.

[0101] The embodiment of the present invention also provides a device for evaluating immune health, comprising:

[0102] A receiving module, used for receiving immune or mitochondrial indicators of different cell types of the subject;

[0103] The immune health evaluation module is used to input the received immune or mitochondrial indicators of different cell types to output the immune health status of the subject.

[0104] An embodiment of the present invention further provides a storage medium comprising computer executable instructions, which are used to implement the method for evaluating immune health in the present invention when executed by a computer processor.

[0105] In order to better understand the present invention, the content of the present invention is further explained below in conjunction with specific examples, but the content of the present invention is not limited to the following examples.

[0106] In the following examples, the detection reagents, instruments, supporting quality control products and software information used are shown in Table 1.

[0107] Table 1 Information on test reagents, instruments, supporting quality control products and software

[0108]

[0109] 1. Research subjects, groups and sample requirements

[0110] 1. Research subjects and groups

[0111] A total of 637 data were collected from the research subjects, including 607 data from healthy people, divided into young groups (infants / children / adolescents), middle-aged groups, elderly groups, and 30 cancer patients (accelerated aging group). Immune-ageingmito1.0 was used as the collection identifier for immune cell and mitochondrial data, and routine blood data, immune test results and clinical manifestations were used as auxiliary evidence. The number of participants was combined with actual clinical data to meet the sample size requirements for statistical analysis as much as possible.

[0112] (II) Sample requirements

[0113] The collected samples are EDTA (ethylenediaminetetraacetic acid) anticoagulated peripheral blood, and the sample volume is not less than 2mL. The samples must be sent for testing within 12 hours after collection. The specimens must be collected in accordance with clinical sample collection requirements and detailed medical records must be recorded.

[0114] 2. Sample preparation

[0115] (1) Preparation of monoclonal antibody premix reagents;

[0116] (2) Antibodies were mixed with peripheral blood samples, vortexed, and incubated in the dark;

[0117] (3) adding hemolysin to lyse red blood cells;

[0118] (4) After mixing, flow cytometry was performed using a DiagCyto machine;

[0119] (5) The flow cytometer data file (.fcs) is output after correction by the human lymphocyte mitochondrial function analysis system (software).

[0120] Flow cytometry data files (.fcs) are examined as follows:

[0121] (1) Take out the mitochondrial staining reagent in the 96-well plate from -20℃, wrap it with aluminum foil to avoid light, and place it at room temperature for 3-5 minutes. Place the 96-well reagent plate that has returned to room temperature in a centrifuge and centrifuge at 250g for 1 minute or hold the 96-well plate with one hand (the well opening faces the palm of the hand) and shake the plate clockwise 1-2 times. After completion, check the bottom of the well plate to see if there are any colored droplets. If not, mark it and skip the well for subsequent sample addition;

[0122] (2) Mark the 96-well plate according to the sample information. To avoid wrong addition / omission, refer to the 96-well plate information correspondence table;

[0123] (3) preparing premixed reagents by taking antibodies CD3, CD4, CD8, and CD45 respectively;

[0124] (4) Take 20 μL of the “premixed reagent” and place it in the corresponding 96-well plate;

[0125] (5) Take 100 μL of anticoagulated human peripheral blood sample that has been inverted at least 7 times and placed in a 96-well plate, wrap it in aluminum foil to protect it from light, place it on a 96-well plate mixer, adjust the speed to medium, and incubate it at room temperature in the dark for 15 min;

[0126] (6) Add 400 μL of NHLysis Solution (10×) working solution (prepared as follows: 3 parts of 10× NHLysis Solution, 7 parts of pure water, mix, and place at room temperature for later use) into a 96-well plate tube, place on a 96-well plate mixer, adjust the speed to medium, and incubate at room temperature in the dark for 15 min;

[0127] (7) For testing, if the sample cannot be tested immediately, store it at 2-8°C in the dark (completed within 24 hours of the cell collection / staining / lysis procedure) and vortex mix before testing.

[0128] 3. Flow cytometry

[0129] Gating strategies for flow cytometry Figure 1 and Figure 2 As shown in the figure, the suffix (T) in the index is the TBNK-mitopanel detection index; the suffix (F) is the TFUN-mitopanel detection index; the suffix (BRE) is the blood routine detection index; .MM is the mitochondrial quality index (the red area in the figure); MMP low (or MMP low %) is the mitochondrial low membrane potential indicator (the blue area in the figure); in addition, the addition of suffixes is to distinguish the different sources of data obtained from the detection template or methodology of the same indicator.

[0130] The flow cytometry functions in the figure are to remove dead cells, remove adherent cells, remove debris and red blood cells, and select target cells (panel A is lymphocytes, panel B is T cells). The dotted arrow is the mitochondrial parameter flow cytometer corresponding to the output of the target cell subset. The red area in panel B is the mitochondrial parameter of the Th cell subset; the blue area is the mitochondrial parameter of the Ts cell subset. The gray arrow is the surface protein PD-1 expression corresponding to the target cell, which is displayed in the form of a peak graph. The details are as follows:

[0131] Figure 1 Gating strategy for TBNK-mito panel detection: a) Select the live cell population (NIR - ); b) remove adherent cells from the living cell population and select the single cell population; c) select the white blood cell population (WBC) from the single cell population; d) select the monocyte population (Mono) from the white blood cell population; e) remove the Mono population and select the lymphocyte population (CD45 + ); f) select T cell population (CD3+), B cell population (CD19+) and NK cell population from the lymphocyte population; g) distinguish Th cell subsets (T helper cells, CD4 +) and Ts cell subsets (suppressor T cells or cytotoxic T cells, CD8 + );dotted line They are: peak graphs showing mitochondrial MMP of lymphocytes, T cells, B cells, NK cells, Ts cells, and Th cells low The characteristics of the clustering;

[0132] Figure 2 Gating strategy for TFUN-mito panel detection: a) Select the live cell population (NIR - ); b) remove adherent cells from the living cell population and select the single cell population; c) select the T cell population (CD3 + d) selection of Th cells and Ts cell subsets in T cells; e) Ts cell differentiation subset gates (T8Tn, T8Tcm, T8Tem, T8Tef); f) Th cell differentiation subset gates (T4Tn, T4Tcm, T4Tem, T4Tef); g) recognition of PD-1 in T8Tef cells + Percentage; h) PD-1 recognition of T8Tem cells + Percentage; i) PD-1 recognition of T8Tcm cells + Percentage; j) PD-1 recognition of T4Tef cells + Percentage; k) PD-1 recognition of T4Tem cells + Percentage; l) PD-1 recognition of T4Tcm cells + Percentage; m) PD-1 recognition of Th cells + Percentage; n) PD-1 recognition of Ts cells + Percentage; o) PD-1 recognition of T cells + Percentage; Dashed Line (Blue area) are: peak diagram showing the mitochondrial MMP stage characteristics of T8Tef, T8Tn, T8Tem, and T8Tcm cell populations; dotted line (Red area) are: peak diagram showing the mitochondrial MMP stage characteristics of T4Tef, T4Tn, T4Tem, and T4Tcm cell populations; dotted line For: Peak graph showing the mitochondrial MMP stage characteristics of Ts cell population; dotted line For: Peak graph showing the mitochondrial MMP stage characteristics of Th cell population; dotted line For: The peak diagram shows the mitochondrial MMP stage characteristics of the T cell population.

[0133] In addition, the flow cytometry images were analyzed by NovoExpress software, and the compensation adjustment was completed and the data was corrected by the human lymphocyte mitochondrial function analysis system before the overall output. TBNK-mito and TFUN-mito detected a total of 61 indicators, and blood routine indicators 11 items, a total of 72 indicators.

[0134] 4. Output streaming parameters and data analysis

[0135] 1. Sample age and gender distribution

[0136] Statistical analysis was performed based on the age and gender distribution of the samples. The results are as follows: Figure 3 As shown, the age distribution of the sample population covers from 0 to 90 years old, concentrated in 25-30 years old and 45-55 years old. The sample sizes of male and female are relatively balanced (268 / 339), and the age distribution is relatively symmetrical. Both male and female groups are normally distributed, and the midpoint line shows the median value.

[0137] 2. Statistical analysis

[0138] Statistical analysis software SPSS25.0 was used for calculation, and GraphPad Prism 8 software was used for chart drawing. The normal distribution test was performed by single-sample Kolmogorov-Smirnov. P ≥ 0.05 indicated that the data obeyed the normal distribution, otherwise it was non-normally distributed. When both groups of data obeyed the normal distribution, the t test was used for comparison between the two groups, and the non-parametric Mann-Whitney U test was used for non-normally distributed data. The count data were expressed as a constituent ratio, and the chi-square test was used for inter-group comparison. In the correlation analysis, Pearson correlation analysis was used for quantitative data that conformed to the normal distribution, and Spearman correlation analysis was used for quantitative data that were non-normally distributed, and the correlation coefficient and P value were calculated; P < 0.05 indicated that the difference was statistically significant. The specific indicators and detailed data statistical results are shown in Table 2 below.

[0139] Table 2 Quartile data of relevant test indicators in healthy people of different age groups and tumor patients

[0140]

[0141]

[0142]

[0143] Continued from the table:

[0144]

[0145]

[0146]

[0147] Continued from the table:

[0148]

[0149]

[0150]

[0151] Note: Data display format is: median (25% quartile, 75% quartile), (F) is data from TFUN-mito detection template; (T) is data from TBNK-mito detection template; (BRE) is data from blood routine test indicators; MM is mitochondrial mass; MMP low % is the percentage of low membrane potential.

[0152] 3. Analysis of age-related indicators

[0153] The correlation analysis of each pair of parameters was performed using the Pearson method. The P value for significant correlation was between 0 and 1, with 1 being irrelevant and 0 being completely correlated. Clustering was performed using the ward.D2 method and sorting was performed using the hclust method. After integration, the correlation parameter was between -1 and +1, with 0 being irrelevant, -1 being completely negatively correlated, and +1 being completely positively correlated. Subsequently, the correlation parameters for the same age were sorted from small to large. The largest negative correlation was the T8Tn% indicator, with a correlation parameter of around -0.7, and the largest positive correlation was T8Tem%, with a correlation parameter of 0.4 to 0.5. In addition, a correlation analysis was performed for different genders, and the overall correlation difference was not significant. Specifically, Figure 4 As shown in the figure, the correlation coefficient between age and immune index in women (red), the correlation coefficient between immune index in men (blue) and the total correlation coefficient (yellow) are shown respectively. The sorting is from small to large according to the correlation index, from the strongest negative correlation to the strongest positive correlation.

[0154] There is no significant difference between the integration of gender and the sorting of male or female alone. From the correlation analysis, it can be found that MMP low % were mostly negatively correlated, while MM indicators were mostly positively correlated with age, BMI was weakly positively correlated, and in T cell subsets, T cell MMPs low % is negatively correlated, and effector memory T (Tem) cells are positively correlated. Subsequently, the 72 age-related indicators and the BMI-age correlation graph are presented based on the correlation between the indicators; Figure 5 and Figure 6As shown in the figure, 14 main indicators among 73 immune indicators and BMI are presented respectively with age-related linear regression to show correlation (Pearson method), where p<0.05 is considered to be correlated, n=607.

[0155] (IV) Calculation process of immune health index

[0156] First, the 607 data were sorted from young to old according to age, and then divided into 8 groups according to age: (0,10], (11,20], (21,30], (31,40], (41,50], (51,60], (61,70] and >70. According to the grouping, the 72 indicators were subjected to random forest analysis, and the correlation strength was screened by the mean decrease accuracy (MDA) of the random forest analysis. The range of MDA can be from negative infinity to positive infinity, where negative values ​​indicate that the feature has a negative impact on the model accuracy, positive values ​​indicate a positive impact, and values ​​close to zero indicate that the feature has little impact on the model accuracy. The optimal indicator is screened from high to low by this value (combined with the mean decrease Gini coefficient MeanDecreaseGini), such as Figure 7 As shown, 14 indicators with the strongest correlation were selected, namely: T8Tn%(F), Ts.MMP low %(F)、T4Tn%(F)、Ts.MMP low %(T),T8Tn.MMP low %(F), PCT, T8Tem%(F), T4Tem%(F), Ts.MM(T), Ts.MM(F), T8Tn.MM(F), MCH, MCV, T8Tcm%(F); The curve graph was obtained by taking the cross-validation error as the ordinate and the number of included indicators as the abscissa. The number of decision trees = 3000, the number of cross-validation folds = 10, and the variable reduction amplitude = 1.5. As the number of included indicators increases, the error shows a trend of fluctuation or reduction ( Figure 8 ), the minimum error is obtained by the minimum number of indicators. It can be found that when n = 14, the error tends to be flat (the slope difference is small), and the benefits of increasing indicators are significantly reduced. When n = 10, it is in a critical state and the benefits begin to decrease. Since all 10 indicators are included in the 14 indicators, the 14 indicators with the best characteristics are selected for further screening. The characteristics of the 14 age-related indicators selected in the random forest analysis are presented in Table 3.

[0157] Table 3 Characteristics of 14 age-related indicators selected in random forest analysis

[0158] Serial number index Relevance Average reduction in precision Average reduction in Gini coefficient 1 T8Tn%(F) -0.69 37.45 19.97 2 <![CDATA[Ts.MMP low %(F)]]> -0.58 30.68 15.36 3 T4Tn%(F) -0.57 26.69 13.28 4 <![CDATA[Ts.MMP low %(T)]]> -0.52 21.15 10.98 5 <![CDATA[T8Tn.MMP low %(F)]]> -0.46 20.67 11.42 6 PCT -0.46 20.22 10.95 7 T8Tem%(F) 0.46 16.90 9.22 8 T4Tem%(F) 0.43 16.69 8.43 9 Ts.MM(T) 0.42 15.66 9.17 10 Ts.MM(F) 0.42 15.30 8.85 11 MCH 0.37 15.21 8.07 12 MCV 0.37 15.09 7.06 13 T8Tn.MM(F) 0.30 15.08 9.48 14 T8Tcm%(F) 0.23 14.73 5.76

[0159] Note: The positive or negative correlation value indicates positive / negative correlation, and the sorting is based on the correlation from large to small.

[0160] Subsequently, the 14 indicators were normalized by min-max and then PCA was performed. The n=14 indicators and n=10 indicators were PCA-reduced and then linearly regressed with the time age. By comparing the R 2 The value (0.48 vs. 0.56) and log(P) value (110 vs. 88) were finally selected for PCA, so 10 principal component expressions (y 1 ~y 10 are principal component values ​​respectively):

[0161] y 1 =0.36*x 1 +0.39*x 2 +0.31*x 3 +0.35*x 4 +0.29*x 5 +0.19*x 6 -0.28*x 7 -0.27*x 8 -0.33*x 9 -0.34*x 10 ;

[0162] y 2 =0.26*x 1 -0.21*x 2 +0.45*x 3 -0.23*x 4 -0.26*x 5 +0.14*x 6 -0.3*x 7 -0.46*x 8 +0.35*x 9 +0.34*x 10 ;

[0163] y 3 =-0.0004*x 1 +0.004*x 2 -0.14*x 3 +0.07*x 4 -0.26*x 5 +0.92*x 6 +0.02*x 7 +0.25*x 8 -0.03*x 9 +0.05*x 10 ;

[0164] y 4 =-0.18*x 1 -0.12*x 2 +0.26*x 3 -0.33*x 4 +0.53*x 5 +0.28*x 6 +0.56*x 7 -0.28*x 8 +0.03*x 9 -0.13*x 10 ;

[0165] y 5 =-0.22*x 1 +0.05*x 2 +0.22*x 3 +0.47*x 4 -0.45*x 5 -0.08*x 6 +0.49*x 7 -0.32*x 8 -0.29*x 9 +0.22*x 10 ;

[0166] y 6 =0.29*x 1 +0.4*x 2 -0.06*x 3 +0.33*x 4 +0.29*x 5 -0.001*x 6 +0.27*x 7 +0.14*x 8 +0.52*x 9 +0.45*x 10 ;

[0167] y 7 =-0.11*x 1 -0.24*x 2 -0.04*x 3 +0.18*x 4 +0.24*x 5 +0.04*x 6 -0.16*x 7 -0.015*x 8 -0.15*x 9 +0.27*x 10 ;

[0168] y 8 =0.37*x1 -0.17*x 2 +0.04*x 3 -0.07*x 4 -0.02*x 5 -0.05*x 6 +0.16*x 7 +0.14*x 8 -0.19*x 9 +0.06*x 10 ;

[0169] y9=-0.04*x 1 -0.11*x 2 +0.2*x 3 +0.12*x 4 +0.0004*x 5 -0.01*x 6 -0.004*x 7 +0.16*x 8 +0.1*x 9 -0.1*x 10 ;

[0170] y10=-0.08*x 1 +0.15*x 2 +0.15*x 3 -0.12*x 4 +0.01*x 5 -0.01*x 6 -0.03*x 7 +0.13*x 8 -0.11*x 9 +0.13*x 10 .

[0171] In the above formula, x 1 ~x 10 T8Tn%(F), Ts.MMP low %(F)、T4Tn%(F)、Ts.MMP low %(T),T8Tn.MMP low %(F), PCT, T8Tem%(F), T4Tem%(F), Ts.MM(T) and Ts.MM(F) are the values ​​after min-max standardization; then the principal component y can be obtained by calculating the variables according to the above expression. 1 ~y 10 The final immune health index value (Immune-HealthIndex, IHI) was then calculated using the following principal component scores: PCA ).

[0172] IHI PCA =0.48*y 1 +0.19*y 2 +0.08*y 3 +0.07*y 4 +0.06*y 5 +0.04*y 6 +0.03*y 7 +0.02*y 8 +0.01*y 9 +0.01*y 10 .

[0173] (V) Reference range of immune health index

[0174] Linear regression analysis was performed on immune characteristics and age, and a linear correlation fitting was performed between the calculated value of immune health index and age to show the heterogeneity of immune health index and the identification of each partition. The results are as follows Fig. 9 As shown (the x-axis is the age, and the y-axis is the immune health index (IHI PCA ), the immune health index gradually decreases with age, showing a negative correlation, with 0 value data appearing around 40 years old, and then showing negative values. The middle trend line is fitted based on the existing scatter plot, and the red data are cancer patient data, all of which are at a low level of immune health index (<0.33), while newborns are at a higher level of immune health index (>0.65). The immune health index regional division shows a trend from high to low in the immune health index from green to yellow), and it was found that the immune characteristics of younger people are more concentrated, while the immune characteristics of older people are more dispersed and have greater heterogeneity (blue scatter points). Then 30 cases of cancer patient data were added to identify high-risk immune risk areas (red scatter points) to assist in the division of risk areas;

[0175] The reference range is set by the age ≤ 2 years old (n is 4 / 32, accounting for 5% of the total sample) to cover the optimal range (infant-level immune status, Figure 4 The green area in the upper middle part), followed by 71% of the data from 15 to 18 years old (27 / 38) is in good immune status (light green area). The immune normal group is the middle yellow-green area. The low immune risk is the tumor coverage area (light yellow area), and the high immune risk area is the yellow area where the proportion of tumor patients reaches about 50%. The specific cut-off value and reference range can be found in Table 4. Through two-dimensional data analysis, the binomial formula can be roughly obtained to calculate the immune health index (blue trend line).

[0176] Table 4 Immune zone division and critical value

[0177]

[0178] In addition, from Fig. 9 Judging from the immune status and age distribution, the relationship between the immune health index value and age in the present invention is consistent with the existing actual situation, which further proves the accuracy of the immune health evaluation method in the present invention.

[0179] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be covered by the scope of the claims of the present invention.

Claims

1. A system for evaluating immune health, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the following steps are implemented: receiving immune or mitochondrial indicators of different cell types of the subject; The received immune or mitochondrial indicators of different cell types are input into the immune health evaluation module to obtain the immune health status of the subject.

2. The system for evaluating immune health according to claim 1, characterized in that: Immune or mitochondrial indicators of different cell types include: T8Tn% (F), Ts.MMP low %(F)、T4Tn%(F)、Ts.MMP low %(T),T8Tn.MMP low %(F), PCT, T8Tem%(F), T4Tem%(F), Ts.MM(T) or Ts.MM(F).

3. The system for evaluating immune health according to claim 1 or 2, characterized in that: The immune health evaluation module obtains the immune health index value (IHI) through dimensionality reduction analysis. PCA ); Dimensionality reduction analysis includes PCA dimensionality reduction analysis, t-SNE dimensionality reduction analysis or UMAP dimensionality reduction analysis.

4. The system for evaluating immune health according to claim 3, characterized in that: The immune health index value (IHI) was obtained by dimensionality reduction analysis. PCA ) includes: standardizing the immune or mitochondrial indicators of different cell types through data, constructing a covariance matrix, calculating the eigenvalues ​​and eigenvectors of the covariance matrix to obtain the principal component calculation method, selecting different principal components, constructing a projection matrix, data projection, interpreting the data results, and then outputting the immune health index value.

5. The system for evaluating immune health according to claim 4, characterized in that: The method for calculating the principal components by calculating the eigenvalues ​​and eigenvectors of the covariance matrix is ​​selected from any of the following methods: (i) Immune or mitochondrial indicators of different cell types include: T8Tn% (F), Ts.MMP low %(F)、T4Tn%(F)、Ts.MMP low %(T),T8Tn.MMP low %(F), PCT, T8Tem%(F), T4Tem%(F), Ts.MM(T) and Ts.MM(F); the calculation methods of different principal components (y) are as follows: y1=0.36*x1+0.39*x2+0.31*x3+0.35*x4+0.29*x5+0.19*x6-0.28*x7-0.27*x8-0.33*x9-0.34*x 10 ; y2=0.26*x1-0.21*x2+0.45*x3-0.23*x4-0.26*x5+0.14*x6-0.3*x7-0.46*x8+0.35*x9+0.34*x 10 ; y3=-0.0004*x1+0.004*x2-0.14*x3+0.07*x4-0.26*x5+0.92*x6+0.02*x7+0.25*x8-0.03*x9+0.05*x 10 ; y4=-0.18*x1-0.12*x2+0.26*x3-0.33*x4+0.53*x5+0.28*x6+0.56*x7-0.28*x8+0.03*x9-0.13*x 10 ; y5=-0.22*x1+0.05*x2+0.22*x3+0.47*x4-0.45*x5-0.08*x6+0.49*x7-0.32*x8-0.29*x9+0.22*x 10 ; y6=0.29*x1+0.4*x2-0.06*x3+0.33*x4+0.29*x5-0.001*x6+0.27*x7+0.14*x8+0.52*x9+0.45*x 10 ; y7=-0.11*x1-0.24*x2-0.04*x3+0.18*x4+0.24*x5+0.04*x6-0.16*x7-0.015*x8-0.15*x9+0.27*x 10 ; y8=0.37*x1-0.17*x2+0.04*x3-0.07*x4-0.02*x5-0.05*x6+0.16*x7+0.14*x8-0.19*x9+0.06*x 10 ; y9=-0.04*x1-0.11*x2+0.2*x3+0.12*x4+0.0004*x5-0.01*x6-0.004*x7+0.16*x8+0.1*x9-0.1*x 10 ; y 10 =-0.08*x1+0.15*x2+0.15*x3-0.12*x4+0.01*x5-0.01*x6-0.03*x7+0.13*x8-0.11*x9+0.13*x 10 ; (ii) Immune or mitochondrial indicators of different cell types include: T8Tn% (F), Ts.MMP low %(F)、T4Tn%(F)、Ts.MMP low %(T),T8Tn.MMP low %(F) and PCT; the calculation method of different principal components (y) is as follows: y1=0.47*x1+0.49*x2+0.38*x3+0.44*x4+0.36*x5+0.26*x6; y2=0.28*x1-0.32*x2+0.49*x3-0.34*x4-0.42*x5+0.53*x6; y3=-0.31*x1+0.12*x2-0.49*x3+0.21*x4-0.074*x5+0.76*x6; y4=-0.32*x1-0.19*x2+0.07*x3-0.49*x4+0.81*x5+0.23*x6; y5=-0.72*x1-0.12*x2+0.59*x3+0.36*x4+0.02*x5-0.01*x6; y6=0.28*x1-0.77*x2-0.14*x3+0.52*x4+0.19*x5+0.01*x6; (iii) Immune or mitochondrial indicators of different cell types including: T8Tn% (F) and Ts.MMP low %(F); The calculation method of different principal components (y) is as follows: y1=0.71*x1+0.71*x2; y2=-0.71*x1+0.71*x2; Among them, x1 to x 10 T8Tn%(F), Ts.MMP low %(F)、T4Tn%(F)、Ts.MMP low %(T),T8Tn.MMP low %(F), PCT, T8Tem%(F), T4Tem%(F), Ts.MM(T) and Ts.MM(F) are the normalized values.

6. The system for evaluating immune health according to claim 1, 2, 4 or 5, characterized in that: Also included are flow cytometry or mitochondrial assay reagents for detecting and counting immune or mitochondrial markers in different cell types.

7. A method for evaluating immune health, characterized in that: include: receiving immune or mitochondrial indicators of different cell types of the subject; The received immune or mitochondrial indicators of different cell types are then input into the immune health module to obtain the immune health status of the subject.

8. The method for evaluating immune health according to claim 7, characterized in that: Immune or mitochondrial indicators of different cell types are obtained through mitochondrial fluorescence staining based on flow cytometry technology; gating strategies for flow cytometry detection include: screening live cell populations to remove dead cells; removing adherent cells from live cell populations and selecting single cell populations; selecting lymphocyte populations from single cell populations; selecting T cell populations, B cell populations and NK cell populations from lymphocyte populations; distinguishing Th cell subsets, Ts cell subsets or Tc cell subsets from T cell populations; T cell differentiation subsets include T4Tef, T4Tn, T4Tem, T4Tcm, T8Tef, T8Tn, T8Tem, T8Tcm cell populations and mitochondrial MMP low The characteristics of grouping.

9. The method for evaluating immune health according to claim 7 or 8, characterized in that: The immune health status of the subjects is graded according to the immune health index value, including: If the immune health index value is > 0.65, the output is infant-level immune status; The immune health index value is 0.5<IHI PCA ≤0.65, the output is good immune health status; The immune health index value is 0.33<IHI PCA ≤0.5, the output is normal immune health status; The immune health index value is 0≤IHI PCA ≤0.33, the output is low risk of immunity; The immune health index value is <0, and the output is high immune risk.

10. A device for evaluating immune health, characterized in that: include: A receiving module, used for receiving immune or mitochondrial indicators of different cell types of the subject; The immune health evaluation module is used to input the received immune or mitochondrial indicators of different cell types to output the immune health status of the subject.

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