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NCT Number: NCT07638670

Effect of Mycobacterial Infection on Immune Status

This study, titled "Effect of Mycobacterial Infection on Immune Status" (EMIIS), investigates the immune-driven mechanisms of mycobacterial infections, focusing on the dynamic immune characteristics of multidrug-resistant tuberculosis (MDR-TB), nontuberculous mycobacterial (NTM) infections, and tuberculous pleurisy. Mycobacterial infections (including the Mycobacterium tuberculosis complex and nontuberculous mycobacteria) remain a major global public health threat. EMIIS is a single-center, randomized, single-blind,prospective study. The study recruited 120 participants, divided into groups of healthy individuals/community-acquired pneumonia patients, active pulmonary tuberculosis patients, latent tuberculosis infection patients, tuberculous pleurisy patients, and nontuberculous mycobacteria patients. Blood samples were collected from all groups within 3 days before treatment and 2-3 months after treatment. Pleural effusion samples were additionally collected from the tuberculous pleurisy group within 3 days before treatment and 2 months after treatment. Exhaled breath condensate (EBC) was collected from the nontuberculous mycobacteria group. Utilizing mass cytometry (CyTOF) and multi-dimensional indicators, the study aims to elucidate the immune-driven mechanisms of mycobacterial infections and provide new strategies for individualized treatment.

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Key information

Conditions

Tuberculosis Actinomycetales Infections Bacterial Infections Bacterial Infections and Mycoses Gram-Positive Bacterial Infections Infections Mycobacterium Infections

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

The First Affiliated Hospital of Ningbo University

Ningbo, China

Location status: Recruiting

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

and Exclusion Criteria:

Inclusion criteria

：

  • Age ≥ 18 years, all genders and races accepted.
  • Patients with active pulmonary tuberculosis diagnosed clinically or by bronchoscopy within less than 1 week.
  • Patients with latent tuberculosis infection (positive T-SPOT test but no evidence of active tuberculosis infection).
  • Patients with tuberculous pleurisy with onset within less than 1 week.
  • Voluntarily join this study and sign the informed consent form.
  • Patients whose drug susceptibility test or NGS results indicate resistance to at least isoniazid and rifampicin (MDR-TB).
  • Patients whose drug susceptibility test or NGS results indicate sensitivity to first-line anti-tuberculosis drugs.
  • Patients whose drug susceptibility test or NGS results indicate resistance to only one anti-tuberculosis drug.
  • Patients with newly identified nontuberculous mycobacterial infection (within less than 1 week) by sputum culture or NGS.

Exclusion criteria

：

  • Immunosuppressive conditions including HIV infection, long-term use (>1 month) of immunosuppressive agents or corticosteroids, severe malnutrition, etc.
  • Concurrent other lung diseases, severe liver or kidney dysfunction, severe endocrine diseases, hematological diseases, or malignant tumors that may affect the study outcomes.
  • Patients with diabetes mellitus.
  • Pregnant or lactating women.
  • Patients unable or unwilling to provide informed consent, or with poor compliance.

Treatment and study plan

Primary outcomes

  1. To establish a multi-immune pathway interaction network and composite biomarkers in mycobacterial infection thing

    Time frame: 3 days before treatment and 2 months after treatment

    This study utilized mass cytometry (CyTOF) and a pre-designed panel containing 41 metal-tagged antibodies for detection. After data normalization and doublet exclusion, multiple machine learning algorithms were applied for clustering analysis to quantitatively compare the proportions of various immune subsets (such as Th1 cells, Th17 cells, classical monocytes, CD4TEM cells, CD8TEM cells,etc.) among CD45+ leukocytes in the peripheral blood of healthy individuals and patients with active tuberculosis.

Secondary outcomes

  1. Immune cell subsets and mechanisms of possible effects of anti-tuberculosis drugs

    Time frame: 3 days before treatment and 2 months after treatment

    This study utilized mass cytometry (CyTOF) and a pre-designed panel containing 41 metal-tagged antibodies for detection. After data normalization and doublet exclusion, multiple machine learning algorithms were applied for clustering analysis to quantitatively compare the proportions of various immune subsets (such as Th1 cells, Th17 cells, classical monocytes, CD4TEM cells, CD8TEM cells,etc.) among CD45+ leukocytes in the peripheral blood of healthy individuals and patients with active tuberculosis.

  2. Differences in immune subsets between normal persons and patients with active pulmonary tuberculosis

    Time frame: 3 days before treatment and 2 months after treatment

    This study utilized mass cytometry (CyTOF) and a pre-designed panel containing 41 metal-tagged antibodies for detection. After data normalization and doublet exclusion, multiple machine learning algorithms were applied for clustering analysis to quantitatively compare the proportions of various immune subsets (such as Th1 cells, Th17 cells, classical monocytes, CD4TEM cells, CD8TEM cells,etc.) among CD45+ leukocytes in the peripheral blood of healthy individuals and patients with active tuberculosis.

  3. To explore whether the peripheral blood before treatment contains a certain marker can predict the short-term efficacy

    Time frame: 3 days before treatment and 2 months after treatment

    This study utilized mass cytometry (CyTOF) and a pre-designed panel containing 41 metal-tagged antibodies for detection. After data normalization and doublet exclusion, multiple machine learning algorithms were applied for clustering analysis to quantitatively compare the proportions of various immune subsets (such as Th1 cells, Th17 cells, classical monocytes, CD4TEM cells, CD8TEM cells,etc.) among CD45+ leukocytes in the peripheral blood of healthy individuals and patients with active tuberculosis.

  4. Comparison of the dynamic changes of immune subsets in peripheral blood and pleural effusion of TP patients before and after treatment

    Time frame: 3 days before treatment and 2 months after treatment

    Using CyTOF with a 41-metal-labeled antibody panel, peripheral blood samples from healthy controls and untreated patients with tuberculous pleurisy were analyzed. After data normalization and debarcoding, clustering was applied to determine the percentages of CD45+ leukocyte subsets (Th1, Th17, classical monocytes, CD4+/CD8+ effector memory T cells, and NK cells). Patients were divided into high- and low-symptom groups based on symptom severity. Immune subset proportions were compared between each patient group and healthy controls, as well as between the two patient groups.

  5. To explore the differences of peripheral blood immune subsets between TP patients and healthy people before treatment

    Time frame: 3 days before treatment and 2 months after treatment

    Using CyTOF with a 41-metal-labeled antibody panel, peripheral blood samples from healthy controls and untreated patients with tuberculous pleurisy were analyzed. After data normalization and debarcoding, clustering was applied to determine the percentages of CD45+ leukocyte subsets (Th1, Th17, classical monocytes, CD4+/CD8+ effector memory T cells, and NK cells). Patients were divided into high- and low-symptom groups based on symptom severity. Immune subset proportions were compared between each patient group and healthy controls, as well as between the two patient groups.

  6. To explore the metabolic differences of three major nutrients between TP patients and healthy people before treatment

    Time frame: 3 days before treatment and 2 months after treatment

    Using CyTOF with an antibody panel including metabolic markers such as GLUT1 and CPT1A, the expression levels of these markers were measured in peripheral blood immune subsets (CD4+ T cells, CD8+ T cells, monocytes, etc.) from healthy controls and untreated patients with tuberculous pleurisy. The median fluorescence intensity (MdFI) of GLUT1 and CPT1A on each subset was used as the primary metric to quantify differences in glucose metabolism and fatty acid oxidation capacity.

  7. Differences in metabolic function between multidrug-resistant tuberculosis group and drug-sensitive tuberculosis group

    Time frame: 3 days before treatment and 2 months after treatment

    In this study, CyTOF and a preconfigured panel consisting of 41 metal-conjugated antibodies were used for specimen detection. After data normalization and doublet removal, multiple machine learning algorithms were utilized for cell clustering analysis. We quantitatively compared the proportional differences of various immune subsets in peripheral blood CD45⁺ leukocytes among drug-resistant tuberculosis (DR-TB), drug-susceptible tuberculosis (DS-TB) and healthy control groups, including Th1 cells, Th17 cells, classical monocytes, CD4⁺ effector memory T cells and CD8⁺ effector memory T cells. This study aims to characterize treatment-induced quantitative changes in immune subsets and provide evidence for screening novel biomarkers.

  8. Influence of immune status on the efficacy of NTM

    Time frame: 3 days before treatment and 2 months after treatment

    This study utilized mass cytometry (CyTOF) and a pre-designed panel containing 41 metal-tagged antibodies for detection. After data normalization and doublet exclusion, multiple machine learning algorithms were applied for clustering analysis to quantitatively compare the proportions of various immune subsets (such as Th1 cells, Th17 cells, classical monocytes, CD4TEM cells, CD8TEM cells,etc.) among CD45+ leukocytes in the peripheral blood of healthy individuals and patients with active tuberculosis.

Study contacts

Contact information is provided by the study sponsor or research team.

Chao Cao

CONTACT

[email protected]

+86-0574-87089878

Shiyi He

CONTACT

[email protected]

+86-0574-87089878

Sponsors and collaborators

Lead sponsor

First Affiliated Hospital of Ningbo University

Network

Registry information

Acronym: EMIIS

Important dates

Study start
2025
Primary completion
2026
Study completion
2026
First posted
Jun 10, 2026
Registry last updated
Jun 10, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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