Biomarker combination for stage diagnosis of tuberculosis infection and detection method

Abstract

The invention discloses a biomarker combination for tuberculosis infection stage diagnosis and a detection method, the detection method specifically comprises the following steps: S1, sample collection and processing, S2, detection processing, S3, data processing, and S4, marker screening, and the invention relates to the technical field of clinical medical treatment. According to the biomarker combination for tuberculosis infection stage diagnosis and the detection method, ATB and HC and ATB and LTBI are distinguished through the core marker combination, AUC of LTBI and HC is larger than or equal to 0.875, AUC of multiple markers reaches 1.0, the diagnosis efficiency is remarkably superior to that of an existing TST and IGRAs method, diagnosis accuracy is higher, transition state metabolic characteristics of LTBI are defined, accurate division of different stages of tuberculosis infection is achieved, and the method is suitable for clinical application. According to the present invention, the problem that the core pain points of LTBI and ATB cannot be distinguished in the prior art is solved, the good staging specificity is provided, the immune correlation is good, and the basis is provided for the disease progression mechanism research and the treatment target screening by revealing the correlation between the metabolite and the CD4 < + > / CD8 < + > T cell subset.

Description

Technical Field

[0001] This invention relates to the field of clinical medical technology, specifically to a combination of biomarkers and detection methods for staging diagnosis of tuberculosis infection. Background Technology

[0002] Tuberculosis is a leading infectious disease with a high mortality rate worldwide, affecting approximately one-quarter of the global population. Latent tuberculosis infection (LTBI) accounts for about 25% of this, making it a major potential source of active tuberculosis (ATB). Currently, the diagnosis of LTBI and ATB primarily relies on the tuberculin skin test (TST) and interferon-gamma release assays (IGRAs, such as QFT-Plus), but these methods have significant limitations: 1. It is impossible to reliably distinguish between LTBI and ATB, nor can the risk of LTBI converting to ATB be predicted; 2. The testing is costly and complex, relying on specialized laboratories, making it difficult to implement in resource-constrained areas with a high tuberculosis burden; 3. It only reflects the "static" memory state of the host's immune system against pathogen antigens and cannot reveal the dynamic progression of infection; 4. TST specificity is affected by BCG vaccination and nontuberculous mycobacterial infection, and traditional IGRAs are insufficient for detecting CD8⁺T cell responses.

[0003] Therefore, there is an urgent need to develop a new diagnostic technology and biomarker combination that can dynamically reflect the infection status, differentiate disease stages, and has high clinical accessibility. Summary of the Invention

[0004] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a combination of biomarkers and a detection method for staging tuberculosis infection. Breaking through the limitations of static detection, it reveals for the first time that LTBI exists in a "transitional state" between HC and ATB in the metabolic spectrum, dynamically reflecting the continuous evolution of tuberculosis infection. This solves the problem that traditional methods cannot reflect dynamic changes in infection, demonstrating high diagnostic efficacy. The combination of 10 core biomarkers exhibits excellent diagnostic performance; for example, 5-octenylcarnitine and other markers have an AUC of 1.0 distinguishing ATB from HC, aspartate-glutamic acid dipeptide and other markers have an AUC of 1.0 distinguishing ATB from LTBI, and dodecanoic acid has an AUC of 1.0 distinguishing LTBI from HC. Superior to TST and IGRAS, with a clear mechanism, it establishes the association between metabolites and immune cell subsets, elucidates the role of lipid metabolism, bile acid metabolism and other pathways in the progression of tuberculosis infection, and enables the diagnostic marker to have both diagnostic value and immunopathological indication function. It has high clinical accessibility, is a minimally invasive test based on plasma samples, has a relatively simple procedure, does not require complex immune stimulation experiments, is easy to promote in primary healthcare institutions, and solves the problems of traditional methods relying on professional laboratories and high costs. It provides multi-dimensional staging and simultaneously realizes the three-level differentiation of healthy status, latent infection and active infection. It can also provide a basis for risk assessment of the conversion of LTBI to ATB, filling the functional gap of existing technologies.

[0005] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: a combination of biomarkers and a detection method for staging diagnosis of tuberculosis infection, specifically including the following steps: S1. Sample collection and processing; S2. Detection and Processing: CD3 was detected using a Navios EX flow cytometer with specific monoclonal antibody combination labeling. + T cells, CD3 + CD4 + T cells, CD3 + CD8 + The absolute counts and proportions of T cells and other subsets were determined, and then ultra-high performance liquid chromatography-orbit trap mass spectrometry system was used for separation with HSS T3 column. Data were acquired in positive and negative ionization modes, and peak detection, comparison and standardization were performed using ProgenesisQI software. Metabolites were identified in combination with database. S3. Data Processing: Perform multi-source statistical analysis and pathway enrichment analysis on the data respectively; S4. Biomarker Screening: After excluding drug-related metabolites through cross-screening among the three groups, 10 core candidate biomarkers were obtained. Then, ROC curve analysis was used to screen biomarkers with AUC ≥ 0.875 for the construction of diagnostic models.

[0006] Preferably, step S1, sample collection and processing, specifically includes the following steps: T1. Study subjects: Healthy control group (HC), latent tuberculosis infection group (LTBI), and active tuberculosis group (ATB) subjects who meet the inclusion and exclusion criteria, with at least 12 subjects in each group; T2. Sample collection: Fasting blood samples were collected from the elbow vein in the morning before treatment for lymphocyte subset detection and plasma metabolomics analysis. T3. Quality Control: Prepare quality control samples (QC). Insert one QC sample for every 5-10 samples to ensure the stability and repeatability of the detection system.

[0007] Preferably, in step T2, the lymphocyte subset detection uses EDTA-K2 anticoagulated vacuum blood collection tubes, and the plasma metabolomics analysis uses liquid nitrogen flash freezing after plasma separation and storage at -80°C.

[0008] Preferably, the specific monoclonal antibody combination in step S2 is labeled as CD3-FITC, CD4-APC, and CD8-APC-Cy7.

[0009] Preferably, the database in step S2 is an HMDB database or a METLIN database.

[0010] Preferably, in step S3, the multivariate statistical analysis uses the PCA / OPLS-DA model to evaluate the separation effect of the three metabolite profiles, and uses VIP>1 and P<0.05 as the criteria to screen differential metabolites. The pathway enrichment analysis is to classify differential metabolites by HMDB and enrich them by the KEGG pathway to identify stage-specific metabolic pathways.

[0011] Preferably, the 10 core candidate biomarkers obtained in step S4 include: N-docosahexaenoic acid-γ-aminobutyric acid, dodecanoic acid, glycerophosphatidylethanolamine, 5-octenylcarnitine, glycosylated oxycholic acid-3-glucuronic acid, allochondylcholic acid, bile pigmentogen, aspartic acid-glutamic acid dipeptide, 2-(3,4-dihydroxyphenyl)acetamide, and 4-hydroxy-5-(phenyl)valerate-O-glucuronide.

[0012] Preferably, the expression levels of 10 core biomarkers in plasma are detected, combined with peripheral blood CD4. + / CD8 + T cell subset parameters utilize the correlation characteristics between metabolites and immune cells to achieve precise differentiation of HC, LTBI, and ATB.

[0013] (III) Beneficial Effects This invention provides a combination of biomarkers and a detection method for staging diagnosis of tuberculosis infection. Compared with existing technologies, it has the following advantages: (1) The combination of biomarkers and detection method for the staging diagnosis of tuberculosis infection can distinguish ATB from HC, ATB from LTBI, and LTBI from HC with an AUC of ≥0.875. Among them, the AUC of multiple biomarkers reaches 1.0. The diagnostic efficacy is significantly better than the existing TST and IGRAS methods, and the diagnostic accuracy is higher.

[0014] (2) The combination of biomarkers and detection method for the staging diagnosis of tuberculosis infection clearly defines the "transitional" metabolic characteristics of LTBI, realizes the accurate division of different stages of tuberculosis infection, solves the core pain point that existing technologies cannot distinguish between LTBI and ATB, and has good staging specificity.

[0015] (3) The combination of biomarkers and detection methods used for staging diagnosis of tuberculosis infection have better immune correlation, by revealing the relationship between metabolites and CD4. + / CD8 + The correlation of T cell subsets provides a basis for studying disease progression mechanisms and screening therapeutic targets.

[0016] (4) The combination of biomarkers and detection methods for the staging diagnosis of tuberculosis infection are simple to collect (peripheral blood) and the detection process is standardized. They are suitable for large-scale screening and grassroots application, which reduces the technical threshold and cost of staging diagnosis of tuberculosis infection and greatly improves clinical applicability.

[0017] (5) The combination of biomarkers and detection methods for the staging diagnosis of tuberculosis infection, based on the temporal change trend of metabolites (such as the Profile 0 / 3 / 4 model), can preliminarily assess the risk of LTBI conversion to ATB, and provide a basis for precise intervention in tuberculosis prevention and control. Attached Figure Description

[0018] Figure 1 This is a flowchart of the biomarker combination and detection method for staging diagnosis of tuberculosis infection according to the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1 This invention provides two technical solutions: a combination of biomarkers and a detection method for staging diagnosis of tuberculosis infection, specifically including the following embodiments: Example 1: A combination of biomarkers and detection methods for staging diagnosis of tuberculosis infection, specifically including the following steps: S1. Sample collection and processing; S2. Detection and Processing: CD3 was detected using a Navios EX flow cytometer with specific monoclonal antibody combination labeling. + T cells, CD3 + CD4 + T cells, CD3 + CD8 + The absolute counts and proportions of T cells and other subsets were determined, and then ultra-high performance liquid chromatography-orbitrap LC-MS (UHPLC-Orbitrap LC-MS) was used for separation on an HSS T3 column. Data were acquired in both positive and negative ionization modes, and peak detection, comparison, and standardization were performed using Progenesis QI software. Metabolites were identified using a database, and specific monoclonal antibody combinations were labeled as CD3-FITC, CD4-APC, and CD8-APC-Cy7. S3. Data processing: Multi-source statistical analysis and pathway enrichment analysis were performed on the data. The multivariate statistical analysis used the PCA / OPLS-DA model to evaluate the separation effect of the three metabolite profiles. Differential metabolites were screened with VIP>1 and P<0.05 as the criteria. The pathway enrichment analysis was to classify the differential metabolites by HMDB and enrich them by the KEGG pathway to identify the stage-specific metabolic pathways. S4. Biomarker Screening: After excluding drug-related metabolites through cross-screening among the three groups, 10 core candidate biomarkers were obtained. Then, ROC curve analysis was used to screen biomarkers with AUC ≥ 0.875 for the construction of diagnostic models.

[0021] In this embodiment of the invention, step S1, sample collection and processing, specifically includes the following steps: T1. Study subjects: Healthy control group (HC), latent tuberculosis infection group (LTBI), and active tuberculosis group (ATB) subjects who meet the inclusion and exclusion criteria, with at least 12 subjects in each group; T2. Sample collection: Fasting blood was collected from the elbow vein in the morning before treatment for lymphocyte subset detection and plasma metabolomics analysis. Lymphocyte subset detection was performed using EDTA-K2 anticoagulated vacuum blood collection tubes, and plasma metabolomics analysis was performed by separating plasma and then flash-freezing it in liquid nitrogen and storing it at -80℃. T3. Quality Control: Prepare quality control samples (QC). Insert one QC sample for every 5-10 samples to ensure the stability and repeatability of the detection system.

[0022] In this embodiment of the invention, the database in step S2 is the HMDB database.

[0023] Example 2: Biomarker combination and detection method for staging diagnosis of tuberculosis infection, specifically including the following steps: S1. Sample collection and processing; S2. Detection and Processing: CD3 was detected using a Navios EX flow cytometer with specific monoclonal antibody combination labeling. + T cells, CD3 + CD4 + T cells, CD3 + CD8 + The absolute counts and proportions of T cells and other subsets were determined, and then ultra-high performance liquid chromatography-orbitrap LC-MS (UHPLC-Orbitrap LC-MS) was used for separation on an HSS T3 column. Data were acquired in both positive and negative ionization modes, and peak detection, comparison, and standardization were performed using Progenesis QI software. Metabolites were identified using a database, and specific monoclonal antibody combinations were labeled as CD3-FITC, CD4-APC, and CD8-APC-Cy7. S3. Data processing: Multi-source statistical analysis and pathway enrichment analysis were performed on the data. The multivariate statistical analysis used the PCA / OPLS-DA model to evaluate the separation effect of the three metabolite profiles. Differential metabolites were screened with VIP>1 and P<0.05 as the criteria. The pathway enrichment analysis was to classify the differential metabolites by HMDB and enrich them by the KEGG pathway to identify the stage-specific metabolic pathways. S4. Biomarker Screening: After excluding drug-related metabolites through cross-screening among the three groups, 10 core candidate biomarkers were obtained. Then, ROC curve analysis was used to screen biomarkers with AUC ≥ 0.875 for the construction of diagnostic models.

[0024] In this embodiment of the invention, step S1, sample collection and processing, specifically includes the following steps: T1. Study subjects: Healthy control group (HC), latent tuberculosis infection group (LTBI), and active tuberculosis group (ATB) subjects who meet the inclusion and exclusion criteria, with at least 12 subjects in each group; T2. Sample collection: Fasting blood was collected from the elbow vein in the morning before treatment for lymphocyte subset detection and plasma metabolomics analysis. Lymphocyte subset detection was performed using EDTA-K2 anticoagulated vacuum blood collection tubes, and plasma metabolomics analysis was performed by separating plasma and then flash-freezing it in liquid nitrogen and storing it at -80℃. T3. Quality Control: Prepare quality control samples (QC). Insert one QC sample for every 5-10 samples to ensure the stability and repeatability of the detection system.

[0025] In this embodiment of the invention, the database in step S2 is the METLIN database.

[0026] In this embodiment of the invention, the 10 core candidate biomarkers obtained in step S4 include: N-docosahexaenoic acid-γ-aminobutyric acid, dodecanoic acid, glycerophosphatidylethanolamine (16:0 / 18:2), 5-octenylcarnitine, glycated oxycholic acid 3-glucuronic acid (GCDCA-3G), allochondylcholic acid, bile pigmentogen, aspartate-glutamic acid dipeptide, 2-(3,4-dihydroxyphenyl)acetamide, and 4-hydroxy-5-(phenyl)valerate-O-glucuronide. The expression levels of these 10 core biomarkers in plasma are detected, combined with peripheral blood CD4+. + / CD8 + T cell subset parameters, utilizing the correlation characteristics between metabolites and immune cells (such as dodecanoic acid, 5-octenylcarnitine, and CD4). + / CD8 + T cells are positively correlated with GCDCA-3G and CD4. + / CD8 + (Negative correlation with T cells) to achieve accurate differentiation of HC, LTBI, and ATB.

[0027] In summary, this invention overcomes the limitations of static detection and, for the first time, reveals that LTBI exists in a "transitional state" between HC and ATB in the metabolic spectrum, dynamically reflecting the continuous evolution of tuberculosis infection. This overcomes the shortcomings of traditional methods in failing to reflect dynamic changes in infection, demonstrating high diagnostic efficacy. The combination of 10 core biomarkers exhibits excellent diagnostic performance; for example, 5-octenylcarnitine and other markers show an AUC of 1.0 distinguishing ATB from HC, aspartate-glutamic acid dipeptide and other markers show an AUC of 1.0 distinguishing ATB from LTBI, and dodecanoic acid shows an AUC of 1.0 distinguishing LTBI from HC, significantly superior to TST and IGRAS, with a clearly defined mechanism. This study established the association between metabolites and immune cell subsets, elucidated the roles of lipid metabolism, bile acid metabolism and other pathways in the progression of tuberculosis infection, and enabled diagnostic markers to have both diagnostic value and immunopathological indication function. It has high clinical accessibility, is a minimally invasive test based on plasma samples, has a relatively simple procedure, does not require complex immune stimulation experiments, and is easy to promote in primary healthcare institutions. It solves the problems of traditional methods relying on professional laboratories and high costs. It provides multi-dimensional staging and simultaneously realizes the three-level differentiation of healthy status, latent infection and active infection. It can also provide a basis for risk assessment of the conversion of LTBI to ATB, filling the functional gap of existing technologies.

[0028] The AUCs for differentiating ATB from HC, ATB from LTBI, and LTBI from HC using a combination of core biomarkers were all ≥0.875, with multiple biomarkers achieving an AUC of 1.0. This diagnostic efficacy is significantly superior to existing TST and IGRAS methods, demonstrating higher diagnostic accuracy. It clarifies the "transitional" metabolic characteristics of LTBI, enabling precise segmentation of different stages of tuberculosis infection. This addresses the core challenge of existing technologies failing to differentiate LTBI from ATB, exhibiting excellent staging specificity and better immune correlation. Furthermore, it reveals the relationship between metabolites and CD4+. + / CD8 + The correlation of T cell subsets provides a basis for the study of disease progression mechanisms and the screening of therapeutic targets. The sample collection is simple (peripheral blood) and the detection process is standardized, making it suitable for large-scale screening and grassroots applications. It reduces the technical threshold and cost of tuberculosis infection staging diagnosis, greatly improving clinical applicability. Based on the temporal change trend of metabolites (such as the Profile 0 / 3 / 4 model), the risk of LTBI conversion to ATB can be preliminarily assessed, providing a basis for precise intervention in tuberculosis prevention and control.

[0029] Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0030] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0031] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A combination of biomarkers and a detection method for staging diagnosis of tuberculosis infection, characterized in that: Specifically, the following steps are included: S1. Sample collection and processing; S2. Detection and Processing: CD3 was detected using a Navios EX flow cytometer with specific monoclonal antibody combination labeling. + T cells, CD3 + CD4 + T cells, CD3 + CD8 + The absolute counts and proportions of T cells and other subsets were determined, and then ultra-high performance liquid chromatography-orbit trap mass spectrometry system was used for separation with HSS T3 column. Data were acquired in positive and negative ionization modes, and peak detection, comparison and standardization were performed using Progenesis QI software. Metabolites were identified in combination with database. S3. Data Processing: Perform multi-source statistical analysis and pathway enrichment analysis on the data respectively; S4. Biomarker Screening: After excluding drug-related metabolites through cross-screening among the three groups, 10 core candidate biomarkers were obtained. Then, ROC curve analysis was used to screen biomarkers with AUC ≥ 0.875 for the construction of diagnostic models.

2. The biomarker combination and detection method for staging diagnosis of tuberculosis infection according to claim 1, characterized in that: Step S1, sample collection and processing, specifically includes the following steps: T1. Study subjects: Healthy control group (HC), latent tuberculosis infection group (LTBI), and active tuberculosis group (ATB) subjects who meet the inclusion and exclusion criteria, with at least 12 subjects in each group; T2. Sample collection: Fasting blood samples were collected from the elbow vein in the morning before treatment for lymphocyte subset detection and plasma metabolomics analysis. T3. Quality Control: Prepare quality control samples (QC). Insert one QC sample for every 5-10 samples to ensure the stability and repeatability of the detection system.

3. The biomarker combination and detection method for staging diagnosis of tuberculosis infection according to claim 2, characterized in that: In step T2, lymphocyte subset detection was performed using EDTA-K2 anticoagulated vacuum blood collection tubes, and plasma metabolomics analysis was performed by quick-freezing plasma in liquid nitrogen after separation and storing it at -80°C.

4. The biomarker combination and detection method for staging diagnosis of tuberculosis infection according to claim 1, characterized in that: In step S2, the specific monoclonal antibody combination is labeled as CD3-FITC, CD4-APC, and CD8-APC-Cy7.

5. The biomarker combination and detection method for staging diagnosis of tuberculosis infection according to claim 1, characterized in that: In step S2, the database is either an HMDB database or a METLIN database.

6. The biomarker combination and detection method for staging diagnosis of tuberculosis infection according to claim 1, characterized in that: In step S3, the multivariate statistical analysis uses the PCA / OPLS-DA model to evaluate the separation effect of the three metabolite profiles. Differential metabolites are screened with VIP>1 and P<0.05 as the criteria. The pathway enrichment analysis is to classify the differential metabolites by HMDB and enrich them by the KEGG pathway to identify the stage-specific metabolic pathways.

7. The biomarker combination and detection method for staging diagnosis of tuberculosis infection according to claim 1, characterized in that: The 10 core candidate biomarkers obtained in step S4 include: N-docosahexaenoic acid-γ-aminobutyric acid, dodecanoic acid, glycerophosphatidylethanolamine, 5-octenylcarnitine, glycosylated oxycholic acid-3-glucuronic acid, allochondylcholic acid, bile pigmentogen, aspartic acid-glutamic acid dipeptide, 2-(3,4-dihydroxyphenyl)acetamide, and 4-hydroxy-5-(phenyl)valerate-O-glucuronide.

8. The biomarker combination and detection method for staging diagnosis of tuberculosis infection according to claim 7, characterized in that: By detecting the expression levels of 10 core biomarkers in plasma, combined with peripheral blood CD4... + / CD8 + T cell subset parameters utilize the correlation characteristics between metabolites and immune cells to achieve precise differentiation of HC, LTBI, and ATB.

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