Cytokines in the differential diagnosis of tuberculosis and their application in the risk assessment of tuberculosis infection

Abstract

The present invention provides T cell-specific cytokines for differential diagnosis of latent infection of Mycobacterium tuberculosis and bacteriologically negative active infection and IFN-γ-positive active infection of non-tuberculous lung disease. The T cell-specific cytokines include interleukin 2 (IL-2), interleukin 6 (IL-6) and tumor necrosis factor-α (TNF-α). According to the expression levels of the T cell-specific cytokines, a mathematical model is used to perform differential diagnosis or risk assessment of latent infection of Mycobacterium tuberculosis and bacteriologically negative active infection and IFN-γ-positive active infection of non-tuberculous lung disease. The present invention provides a basis for differential diagnosis of latent infection of Mycobacterium tuberculosis and bacteriologically negative active infection and the risk of tuberculosis in non-tuberculous lung disease.

Description

Technical Field

[0001] The present invention relates to cytokines and diagnostic models for differential diagnosis of latent Mycobacterium tuberculosis infection, bacteriologically negative active infection, and IFN-γ-positive non-tuberculosis lung disease, and their application in tuberculosis infection risk assessment, belonging to the technical field of tuberculosis detection. Background Art

[0002] The World Health Organization estimates that one-third of the population has latent tuberculosis infection. The transition from latent infection to active tuberculosis under specific conditions, leading to the development of clinical tuberculosis, is a major factor contributing to the current severe burden of tuberculosis. As one of the countries with a high burden of tuberculosis, reducing the incidence of tuberculosis is a key goal of current tuberculosis prevention and control in my country. Despite years of progress in the diagnosis, treatment, and scientific prevention and control of tuberculosis infection, the incidence of tuberculosis remains high due to the high prevalence of latent infection and the country's large population.

[0003] Currently, there are limited methods for clinically diagnosing tuberculosis infection. The tuberculin skin test (TST) is a commonly used immunological method for screening for tuberculosis. Although simple to perform and easy to observe, its high false-positive rate makes it of limited diagnostic value for clinical tuberculosis. Bacteriology is the gold standard for diagnosing tuberculosis, but culture requires a long time, and the positive rate depends on the number of bacteria in the collected specimen, resulting in a low positive rate, which is not conducive to early diagnosis and treatment. Imaging, as an auxiliary diagnostic method, has some value in diagnosing active pulmonary tuberculosis, but it is difficult to diagnose extrapulmonary tuberculosis and has poor specificity for tuberculosis diagnosis. Serological tests such as ELISA and gold label tests, which detect antigens or antibodies, are extremely difficult to detect active tuberculosis. Due to their high false-negative and false-positive rates, the WHO explicitly proposed to stop using blood tests for active tuberculosis in July 2011.

[0004] The tuberculosis-induced T-cell interferon-gamma release assay (IGRA) is a recently developed method for diagnosing tuberculosis (TB) and latent tuberculosis infection (LTBI). The US considers IGRA an alternative to the tuberculin skin test (TST), and UK guidelines recommend the combined use of IGRA and TST. The QuantiFERON-TBGold test (Cellestis Limited, Carnegie, Victoria, Australia) and the T-SPOT.TB test (Oxford Immunotec Limited, Abingdon, United Kingdom) have been successfully developed. These tests use the 6-kD early secretory target antigen (ESAT-6) encoded by the RD1 region and the 10-kD culture filtrate protein (CFP-10) as stimuli to detect TB-specific T lymphocytes in peripheral blood that release interferon-gamma. These tests are highly specific and sensitive, and are particularly effective in diagnosing TB infection, particularly in latent infection, such as natural infection and BCG vaccination. However, IGRA testing cannot reliably differentiate between active and latent tuberculosis infection.

[0005] Active tuberculosis and latent tuberculosis are the two main manifestations of tuberculosis infection. Active tuberculosis is highly contagious, while latent tuberculosis is generally not. Differentiating active tuberculosis from the widespread latent tuberculosis population, enabling early diagnosis, isolation, and treatment, is crucial for controlling the spread of tuberculosis. However, effective diagnostic tools for distinguishing latent from active tuberculosis infection remain unavailable. Furthermore, patients with bacteriologically negative tuberculosis infection are easily confused with those with other non-tuberculous lung diseases due to the lack of laboratory evidence. Patients with non-tuberculous lung diseases are at risk of developing active infection due to the prevalence of latent Mycobacterium tuberculosis infection and the decline in immune response during treatment. Therefore, clinically, there is a need for laboratory diagnostic reagents that can assess the risk of active tuberculosis infection in patients with bacteriologically negative tuberculosis infection and non-tuberculous lung diseases.

[0006] The present invention will use a multifactor detection platform based on flow cytometry technology as a basis to perform multifactor detection and analysis on whole blood supernatant stimulated by tuberculosis-specific antigens, screen cytokine expression combinations, and verify the detection efficacy of multifactor detection in the differential diagnosis of tuberculosis infection through mathematical models, and detect and evaluate the risk of active tuberculosis infection in patients with bacteriologically negative tuberculosis infection and non-tuberculosis lung diseases. Summary of the Invention

[0007] In order to perform differential diagnosis between latent infection of Mycobacterium tuberculosis and bacteriologically negative active infection and IFN-γ positive active infection of non-tuberculous lung disease, the present invention provides the following technical solutions:

[0008] In one aspect, the present invention provides a method for determining the expression of interleukin-2 (IL-2), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) in a sample for use in preparing a kit for differential diagnosis of latent infection and bacteriologically negative active infection of Mycobacterium tuberculosis and IFN-γ positive active infection of non-tuberculous lung disease, wherein

[0009] Y=1 / (1+EXP(-0.097967922×A-0.001303236×B+0.003329857×C+1.918391909)), where EXP represents the index; A represents the expression level of IL-2, B represents the expression level of IL-6; and C represents the expression level of TNF-α. When Y is less than 0.7, the sample to be tested is diagnosed as bacteriologically negative active Mycobacterium tuberculosis infection; and when Y is greater than or equal to 0.7, the sample to be tested is diagnosed as latent Mycobacterium tuberculosis infection.

[0010] In some embodiments, the agent is selected from an interleukin 2 (IL-2) antibody or antigen-binding fragment, an interleukin 6 (IL-6) antibody or antigen-binding fragment, and a tumor necrosis factor-α (TNF-α) antibody or antigen-binding fragment.

[0011] In some embodiments, the kit further comprises a Mycobacterium tuberculosis-specific antigen selected from the group consisting of ESAT-6, CFP-10, Rv3873, and Rv3615c.

[0012] In some embodiments, the kit further comprises a phytohemagglutinin (PHA) solution or other non-specific positive stimulators of T cells, wherein the other non-specific positive stimulators of T cells are selected from phorbol esters, ionomycin and CD3 activating antibodies.

[0013] On the other hand, the present invention provides T cell-specific cytokines for differential diagnosis of latent infection and bacteriologically negative active infection of Mycobacterium tuberculosis and IFN-γ positive active infection of non-tuberculosis lung disease, wherein the T cell-specific cytokines include interleukin 2 (IL-2), interleukin 6 (IL-6) and tumor necrosis factor-α (TNF-α).

[0014] On the other hand, the present invention provides a composition or kit for differentially diagnosing latent infection and bacteriologically negative active infection of Mycobacterium tuberculosis and IFN-γ positive active infection of non-tuberculous lung disease, comprising an interleukin 2 (IL-2) antibody or antigen-binding fragment, an interleukin 6 (IL-6) antibody or antigen-binding fragment, and a tumor necrosis factor-α (TNF-α) antibody or antigen-binding fragment.

[0015] In some embodiments, the composition or kit for differential diagnosis of latent infection and bacteriologically negative active infection of Mycobacterium tuberculosis and IFN-γ positive active infection of non-tuberculosis lung disease further comprises a Mycobacterium tuberculosis-specific antigen, wherein the Mycobacterium tuberculosis-specific antigen is selected from ESAT-6, CFP-10, Rv3873 and Rv3615c.

[0016] In some embodiments, the composition or kit for differential diagnosis of latent infection and bacteriologically negative active infection of Mycobacterium tuberculosis and IFN-γ positive active infection of non-tuberculous lung disease further includes a phytohemagglutinin (PHA) solution or other T cell nonspecific positive stimulators. The other T cell nonspecific positive stimulators include but are not limited to phorbol myfismte acetate (PMA), ionomycin (Ion) and CD3 activating antibodies.

[0017] On the other hand, the present invention provides a diagnostic device, comprising a processor, which uses the expression levels of T cell-specific cytokines for differential diagnosis of latent infection of Mycobacterium tuberculosis and bacteriologically negative active infection and IFN-γ positive active infection of non-tuberculous lung diseases according to claim 1 to establish a diagnostic model for diagnosing and differentiating latent infection of Mycobacterium tuberculosis and bacteriologically negative active infection and IFN-γ positive active infection of non-tuberculous lung diseases.

[0018] On the other hand, the present invention provides the use of the above-mentioned T cell-specific cytokine for differential diagnosis of latent infection of Mycobacterium tuberculosis and bacteriologically negative active infection and IFN-γ positive active infection of non-tuberculous lung disease in the preparation of a composition or kit for differential diagnosis of latent infection of Mycobacterium tuberculosis and bacteriologically negative active infection and IFN-γ positive active infection of non-tuberculous lung disease.

[0019] On the other hand, the present invention provides the use of the above-mentioned T cell-specific cytokines for differential diagnosis of latent infection and bacteriologically negative active infection of Mycobacterium tuberculosis and IFN-γ positive active infection of non-tuberculous lung disease in the preparation of a composition or kit for predicting the risk of latent infection or active infection of Mycobacterium tuberculosis in patients with IFN-γ positive non-tuberculous lung disease.

[0020] On the other hand, the present invention provides the use of the above-mentioned T cell-specific cytokine for differential diagnosis of latent infection and bacteriologically negative active infection of Mycobacterium tuberculosis and IFN-γ positive active infection of non-tuberculous lung disease in the preparation of a composition or kit for in vitro detection of specific T cell immune response caused by tuberculosis infection.

[0021] On the other hand, the present invention provides a method for detecting the expression level of the above-mentioned T cell-specific cytokines for differential diagnosis of latent infection and bacteriologically negative active infection of Mycobacterium tuberculosis and IFN-γ positive active infection of non-tuberculosis lung disease, comprising detecting the expression level of T cell-specific cytokines using flow cytometry technology.

[0022] In some embodiments, a method for detecting the expression level of T cell-specific cytokines for differential diagnosis of latent infection and bacteriologically negative active infection of Mycobacterium tuberculosis and IFN-γ positive active infection of non-tuberculous lung disease comprises:

[0023] 1) Collect anticoagulated peripheral blood from individuals, aliquot and add negative control, tuberculosis-specific antigen and nonspecific stimulant for in vitro culture;

[0024] 2) After incubation, centrifuge each tube at room temperature and carefully aspirate the supernatant plasma.

[0025] 3) Take the microspheres pre-coated with anti-T cell-specific cytokines from the multi-factor detection kit and incubate them with T cell-specific cytokine standards or plasma samples. After incubation, add room temperature PBST to each well and wash by centrifugation;

[0026] 4) Remove the wash solution, add diluted detection antibody to each well, and incubate at room temperature;

[0027] 5) Remove the detection antibody solution, wash with PBST, then add diluted streptavidin-PE conjugate to each well and incubate at room temperature;

[0028] 6) Wash with PBST, then with PBS, perform detection and analysis using a flow cytometer, record the fluorescence value, and present the detection results. Calculate the standard curve using the detection values of each cytokine standard, and calculate the concentration of each cytokine in the detected plasma supernatant to present the final reaction results.

[0029] In another aspect, the present invention provides a composition or kit for differential diagnosis of latent infection and bacteriologically negative active infection of Mycobacterium tuberculosis and IFN-γ positive active infection of non-tuberculous lung disease, comprising:

[0030] a. Mycobacterium tuberculosis-specific antigens, including but not limited to ESAT-6, CFP-10, and Rv3615c;

[0031] b. Phytohemagglutinin (PHA) solution or other non-specific positive stimulators of T cells, including but not limited to phorbol myfismte acetate (PMA), ionomycin (Ion) and CD3 activating antibodies;

[0032] c. Sterile EP tubes, including positive control EP tubes, negative control EP tubes, and T cell nonspecific antigen stimulation EP tubes;

[0033] d. a multi-factor detection kit, wherein the factors include at least IFN-γ, IL-2, IL-6 and TNF-α;

[0034] e. Reagents and consumables required for other flow cytometry and multifactor detection.

[0035] On the other hand, the present invention provides a method for differentially diagnosing latent infection of Mycobacterium tuberculosis and bacteriologically negative active infection and IFN-γ positive active infection of non-tuberculous lung disease, the method comprising detecting the expression level of the above-mentioned T cell-specific cytokines for differentially diagnosing latent infection of Mycobacterium tuberculosis and bacteriologically negative active infection and IFN-γ positive active infection of non-tuberculous lung disease.

[0036] In some embodiments, the method for differentially diagnosing latent infection and bacteriologically negative active infection of Mycobacterium tuberculosis and IFN-γ positive active infection of non-tuberculous lung disease comprises detecting the expression level of T cell-specific cytokines using flow cytometry technology.

[0037] Beneficial effects

[0038] The present invention uses multi-cytokine detection technology to quantitatively detect cytokines in plasma after tuberculosis antigen stimulation, and establishes a differential diagnosis model for latent infection and active tuberculosis infection through regression analysis. The present invention performs multi-factor detection in samples including 50 healthy controls, latent infections, and bacteriologically positive tuberculosis active infections and preliminarily establishes a differential diagnosis model. The differential diagnosis is further verified by using samples including approximately 150 cases each of the latent infection group, the bacteriologically negative tuberculosis active infection group, and the non-tuberculosis lung disease group. The established model is evaluated for its ability to detect and assess the risk of active tuberculosis infection in patients with bacteriologically negative tuberculosis infection and non-tuberculosis lung disease. It is found that the established mathematical model can be used to detect the risk of active tuberculosis infection in patients with bacteriologically negative tuberculosis infection and non-tuberculosis lung disease, providing a basis for the differential diagnosis of bacteriologically negative tuberculosis infection and the risk prediction of active tuberculosis infection in patients with non-tuberculosis lung disease. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1Comparisons of IL-2 (A), IFN-γ (B), IL-6 (C), and TNF-α (D) cytokines were shown between the healthy control group (HC), the latent tuberculosis infection group (LTBI), and the bacteriologically positive active tuberculosis group (ATB-P). Within each group, NC represents the negative control stimulation value, and TB represents the tuberculosis antigen stimulation value. Statistical differences between the two groups were analyzed using a paired T-test. Differences between different sample groups were analyzed using an independent-samples T-test. *: p < 0.05; **: p < 0.01; ***: p < 0.001; ns: p > 0.05.

[0040] Figure 2 The ROC analysis of the diagnostic efficiency of four cytokines, IFN-γ (A), IL-2 (B), IL-6 (C), and TNF-α (D), between the healthy group (HC) and the latent tuberculosis infection group (LTBI) is shown.

[0041] Figure 3 Figure 2 shows the receiver operating characteristic (ROC) analysis of the diagnostic efficiency of four cytokines, IFN-γ (A), IL-2 (B), IL-6 (C), and TNF-α (D), between the latent and active tuberculosis infection groups. A mathematical model established using regression analysis yielded the comparison of Y values between the latent and active tuberculosis infection groups (E) and the ROC analysis of diagnostic efficiency (F).

[0042] Figure 4 The figure shows the comparison of Y values obtained by the mathematical model established by regression analysis of IL-2, IL-6 and TNF-α cytokines among the latent infection group (148 cases), the bacteriologically negative active tuberculosis infection group (187 cases) and the non-tuberculous lung disease IFN-γ positive group.

[0043] Figure 5 Receiver operating characteristic (ROC) analysis of the diagnostic efficiency of the Y-values between the latent infection group (148 cases) and the bacteriologically negative active tuberculosis infection group (187 cases) (A), and between the latent infection group (148 cases) and the non-tuberculosis lung disease group (IFN-γ-positive) (B), using a mathematical model established by regression analysis of IL-2, IL-6, and TNF-α cytokines. AUC: area under the curve. DETAILED DESCRIPTION

[0044] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0045] Example 1. Detection of multiple cytokines in plasma

[0046] The samples involved in this embodiment include: 50 healthy controls, 50 latently infected persons, and 50 active tuberculosis infected persons.

[0047] The screening criteria for different sample groups are:

[0048] A. Healthy controls:

[0049] Imaging showed no lung shadows, and tuberculosis-specific IGRA testing was negative.

[0050] B. Latently infected people:

[0051] Imaging showed no lung shadows, and tuberculosis-specific IGRA test was positive.

[0052] C. Patients with bacteriologically positive (positive) active tuberculosis:

[0053] a. The attending physician confirms the volunteer's diagnosis of pulmonary tuberculosis based on the volunteer's clinical manifestations;

[0054] b. Imaging shows lung shadows;

[0055] c. Tuberculosis culture is positive.

[0056] The selected tuberculosis cases and healthy volunteers were aged between 18 and 60 years, and their gender was randomly selected.

[0057] The samples used in this invention are obtained from individual venous peripheral blood. After the screened individuals have passed a physical examination by their attending physician, the experimenter will inform them of the specific project procedures and the amount of blood required. After the volunteers agree and sign an informed consent form, a clinician will perform blood sampling on the volunteers. Blood is collected using disposable vacuum blood collection tubes containing heparin (Greiner, Austria), with approximately 5 ml of blood collected from each volunteer.

[0058] 1. Preparation of tuberculosis-specific antigen-stimulated plasma:

[0059] Anticoagulant blood: 3-5ml, heparin anticoagulant 6ml blood collection tube.

[0060] Post-blood collection processing:

[0061] a) Add 1 ml of anticoagulated blood to the negative control tube (Nil), tuberculosis-specific antigen stimulation tube (TB), and positive control tube (Mitogen) provided in the QuantiFERON-gold-tb kit (Qiagen, Germany). Mix by inverting the tubes to thoroughly mix the antigen and whole blood. Incubate the three stimulation tubes with anticoagulated blood at 37°C for 18-20 hours, keeping them upright.

[0062] b) After incubation, centrifuge each tube at 2000 rpm / min for 10 minutes at room temperature, and carefully aspirate the supernatant plasma.

[0063] 2. Detection of multiple cytokines in plasma:

[0064] a. Incubate the microspheres pre-coated with anti-IFN-γ, IL-2, IL-6, and TNF-α from the multifactor detection kit with IFN-γ, IL-2, IL-6, and TNF-α standards or plasma samples. After incubation, add 200 μl of room-temperature PBST to each well and wash twice by centrifugation.

[0065] b. Remove the wash solution, add 100 μl of diluted detection antibody to each well, and incubate at room temperature for 2 h.

[0066] c. Remove the detection antibody solution, wash three times with PBST, then add 100 μl of diluted streptavidin-PE conjugate to each well and incubate at room temperature for 1 hour.

[0067] d. Wash three times with PBST, then twice with PBS. Analyze using flow cytometry, record PE fluorescence values, and present the test results.

[0068] 3. Result analysis:

[0069] Through the quantitative detection of different cytokine standards, standard curves for the quantitative detection of different cytokines were established, and the content of each cytokine in each test plasma supernatant was calculated based on the PE fluorescence value of different cytokines detected by flow cytometry. Through comparative analysis of the content of each cytokine in the samples of the healthy group, latent infection group and bacteriologically positive active tuberculosis group, it was found that IFN-γ, IL-2 and IL-6 in the latent infection group and the active infection group increased significantly after tuberculosis antigen stimulation. There was no significant difference in IFN-γ and TNF-α between the latent infection group and the active infection group, while there was a significant difference in IL-2 and IL-6 between the latent infection group and the active infection group ( Figure 1 A- Figure 1 D).

[0070] Example 2. Establishment of diagnostic models for latent and active tuberculosis infection

[0071] Based on the detection of different cytokines in Example 1, the inventors further conducted a receiver operating characteristic (ROC) analysis to evaluate the diagnostic value of different cytokines in different groups and derived the AUC value for diagnostic efficiency. The AUC value is a key indicator for evaluating overall diagnostic efficiency. Its value between 0.5 and 1 represents the level of diagnostic efficiency, where a value closer to 1 indicates a higher diagnostic efficiency, and vice versa.

[0072] The IGRA release assay, which targets the detection of tuberculosis antigen-specific IFN-γ, is an effective method currently known to be able to identify tuberculosis infection. It includes assays based on enzyme-linked immunosorbent assay (ELISA) and enzyme-linked immunospot (ELISPOT) techniques. In Example 1, IGRA assay was also used as one of the criteria for determining latently infected people. By comparing the diagnostic efficiency of the four cytokines obtained by flow cytometry in Example 1 in the healthy group and the latently infected group, it was found that IFN-γ had the highest diagnostic efficiency for the healthy group and the latently infected group (AUC = 0.949), while the diagnostic efficiency of IL-2 was also high (AUC = 0.885). The diagnostic efficiency of IL-6 and TNF-α was low in the healthy group and the latently infected group (AUC = 0.949). Figure 2 A- Figure 2 D). Therefore, the IFN-γ value obtained by the multifactor detection technology is consistent with the IGRA test results and can be used as a diagnostic basis for tuberculosis infection.

[0073] The results of the analysis of the value of the four factors in the differential diagnosis of latent infection and active infection showed that the area under the curve (AUC) of the four cytokines IFN-γ, IL-2, IL-6 and TNF-α used alone in the differential diagnosis of latent infection and active infection was between 0.622 and 0.712, and the diagnostic efficiency was poor ( Figure 3 A- Figure 3 D).

[0074] Because IFN-γ, as previously reported in the literature (Alexandre Harari et al., Nature Medicine 17, 372–376 (2011)) and the results of Example 1, suggests that it is ineffective in differentiating latent from active infection, and TNF-α, although no significant difference was found between the latent and active infection groups in Example 1, showed an elevated trend in active tuberculosis infection, and other studies have suggested the importance of TNF-α in both latent and active infection, the inventors further performed regression analysis on IL-2, IL-6, and TNF-α cytokines and evaluated their value in differentiating latent from active infection groups.

[0075] Through regression analysis, IL-2, IL-6, and TNF-α cytokines were integrated into a single analysis indicator through the following mathematical model, defined as the Y value. The calculation equation of the model is:

[0076] Y=1 / (1+EXP(-0.097967922×A-0.001303236×B+0.003329857×C+1.918391909)), where EXP represents index; A represents the expression level of IL-2, B represents the expression level of IL-6; and C represents the expression level of TNF-α.

[0077] By comparing the Y values calculated by the model between the latent infection group and the active infection group, it was found that there was a significant statistical difference. Further ROC analysis showed that the area under the curve (AUC) was 0.887 (p < 0.001) ( Figure 3 E), the diagnostic efficiency is greatly improved compared with that of a single cytokine ( Figure 3 F).

[0078] The inventors further analyzed the diagnostic cutoff value of this model and the corresponding detection rate (sensitivity) and specificity in a group of 50 samples with latent infection and 50 samples with bacteriologically positive active tuberculosis infection. The results are shown in Table 1. When the detection value is less than the cutoff value, the sample is diagnosed as having active Mycobacterium tuberculosis infection. When the detection value is greater than or equal to the cutoff value, the sample is diagnosed as having latent Mycobacterium tuberculosis infection. When Y is 0.6, the detection rate (sensitivity) and specificity of latent infection are 72% and 82%, respectively; when Y is 0.7, the detection rate (sensitivity) and specificity of latent infection are 64% and 84%, respectively; and when LTB-ATB-PRED is 0.8, the detection rate (sensitivity) and specificity of latent infection are 52% and 92%, respectively.

[0079] Table 1. Diagnostic model establishment and detection rates of latent infection at different diagnostic thresholds

[0080] Diagnostic threshold Detection rate Specificity 0.6 72％ 82％ 0.7 64％ 84％ 0.8 52％ 92％

[0081] Example 3: Diagnostic application of a multifactor detection model in patients with bacteriologically negative tuberculosis and non-tuberculous lung diseases

[0082] Due to the lack of a gold standard for bacteriological diagnosis, bacteriologically negative tuberculosis can be easily misdiagnosed with non-tuberculous lung diseases. Due to the disordered immune status of the body, latent infections in non-tuberculous lung diseases are at risk of developing tuberculosis. This example further validates the differential diagnosis effect and tuberculosis risk of the differential diagnosis model established in Example 2 above in a larger sample of patients with bacteriologically negative tuberculosis and non-tuberculous lung diseases.

[0083] The samples involved in this example include: 148 latently infected people, 187 bacteriologically negative tuberculosis infected people, and 198 non-tuberculosis lung disease group. The sample inclusion criteria, sample collection and processing, and cytokine detection are as described in Example 1.

[0084] Among them, patients with bacteriologically negative (negative) active tuberculosis:

[0085] a. The attending physician confirms the volunteer's diagnosis of pulmonary tuberculosis based on the volunteer's clinical manifestations;

[0086] b. Imaging shows lung shadows;

[0087] c. Tuberculosis culture negative.

[0088] Non-tuberculous lung disease group:

[0089] a. The attending physician confirms that the volunteer is non-tuberculous based on the clinical manifestations;

[0090] b. Imaging shows no lung shadows;

[0091] First, the non-TB lung disease group was stratified by TB antigen-specific IFN-γ levels. Those with negative IFN-γ levels had the lowest risk of TB, thus eliminating the risk of progression from latent infection to TB. For those with positive TB antigen-specific IFN-γ levels, the risk of latent / active infection was further analyzed.

[0092] The inventors used the model of Example 2 to calculate the diagnostic marker Y value for the latent infection group, the bacteriologically negative active tuberculosis infection group, and the non-tuberculosis lung disease IFN-γ positive group, and performed ROC analysis on its discrimination efficiency between the latent infection and bacteriologically negative active tuberculosis infection group, and between the latent infection and non-tuberculosis lung disease IFN-γ positive group ( Figure 4 The results showed that the diagnostic model obtained in Example 2 had a high efficiency in distinguishing latent infection from bacteriologically negative active tuberculosis infection (AUC=0.838), which was close to the effect of distinguishing bacteriologically positive tuberculosis. However, the diagnostic model obtained in Example 2 had a low efficiency in distinguishing latent infection from non-tuberculosis lung disease IFN-γ positive group (AUC=0.681). Figure 5 This may be related to the fact that most of the IFN-γ positive patients with non-tuberculosis lung diseases are latently infected.

[0093] The inventors analyzed the diagnostic cutoff values and corresponding detection rates (sensitivity) and specificities of the model in Example 2 for these three sample groups. The results are shown in Table 2. When the detection value is less than the cutoff value, the sample is diagnosed as having active Mycobacterium tuberculosis infection. When the detection value is greater than or equal to the cutoff value, the sample is diagnosed as having latent Mycobacterium tuberculosis infection. When the critical value of Y was 0.6, the detection rates (sensitivities) of active infection in the latent infection group, bacteriologically negative active tuberculosis infection group, and non-tuberculosis lung disease group were 17.17%, 67.38%, and 50.00%, respectively; when the critical value of Y was 0.7, the detection rates (sensitivities) of active infection in the latent infection group, bacteriologically negative active tuberculosis infection group, and non-tuberculosis lung disease group were 22.22%, 71.12%, and 53.49%, respectively; and when the critical value of Y was 0.8, the detection rates (sensitivities) of active infection in the latent infection group, bacteriologically negative active tuberculosis infection group, and non-tuberculosis lung disease group were 28.28%, 76.47%, and 55.81%, respectively. Therefore, it can be concluded from the test results of this model that even if the diagnostic critical value of 0.8, which has the highest specificity for differential diagnosis of latent infection, is used, the latent infection detection rate of the latent infection group is 71.72%, while only 44.19% of the non-tuberculosis lung disease group are latently infected. Therefore, the non-tuberculosis lung disease group has a higher risk of active tuberculosis disease than healthy latently infected people.

[0094] Table 2. Validation of the diagnostic model and the detection rate of latent infection at different diagnostic cutoffs

[0095]

[0096] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. Use of a reagent for determining the expression levels of interleukin-2 (IL-2), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) in a test sample in the preparation of a kit for differential diagnosis of latent infection and bacteriologically negative active infection of Mycobacterium tuberculosis and IFN-γ positive active infection of non-tuberculous lung disease, wherein Y=1 / (1+EXP(-0.097967922×A-0.001303236×B+ 0.003329857×C+1.918391909)), where EXP represents the index; A represents the expression level of IL-2, B represents the expression level of IL-6; and C represents the expression level of TNF-α. When Y is less than 0.7, the sample is diagnosed as active Mycobacterium tuberculosis infection with negative bacteriology; and when Y is greater than or equal to 0.7, the sample is diagnosed as latent Mycobacterium tuberculosis infection.

2. The use according to claim 1, characterized in that The agent is selected from interleukin 2 (IL-2) antibody or antigen-binding fragment, interleukin 6 (IL-6) antibody or antigen-binding fragment and tumor necrosis factor-α (TNF-α) antibody or antigen-binding fragment.

3. The use according to claim 1 or 2, characterized in that The kit further comprises a Mycobacterium tuberculosis-specific antigen, which is selected from ESAT-6, CFP-10, Rv3873 and Rv3615c.

4. The use according to claim 1 or 2, characterized in that The kit further comprises a phytohemagglutinin (PHA) solution or other T cell non-specific positive stimulators, wherein the other T cell non-specific positive stimulators are selected from phorbol esters, ionomycin and CD3 activating antibodies.

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