AUTHOR=Li Qiang , Ren Weicong , Yuan Jinfeng , Guo Haiping , Shang Yuanyuan , Wang Wei , Pan Junhua , Gao Mengqiu , Pang Yu TITLE=Significant difference in Th1/Th2 paradigm induced by tuberculosis-specific antigens between IGRA-positive and IGRA-negative patients JOURNAL=Frontiers in Immunology VOLUME=13 YEAR=2022 URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2022.904308 DOI=10.3389/fimmu.2022.904308 ISSN=1664-3224 ABSTRACT=

False negative interferon-γ release assay (IGRA) results constitute the major dilemma for the diagnosis of tuberculosis (TB) infections. Herein, we conducted a cohort study to compare the host immunological response to TB-specific antigens between active TB patients with positive and negative IGRA results and control groups. A total of 274 laboratory-confirmed TB patients were included in our analysis, consisting of 221 were IGRA positive and 53 were IGRA negative. Patients with the elderly were identified as an independent risk factor for negative IGRA results. In addition, the elevated level of IL-4 and the decreased levels of IFN-γ, IL-2, IL-6, IL-1β, and IL-12 in IGRA negative TB relative to IGRA positive TB group, demonstrating a significant difference in Th1/Th2 paradigm between two groups. The IFN-γ&IL-2 based assay could correctly identify 247 out of 307 MTB-infected individuals [271 TB patients and 36 individuals with latent TB infection (LTBI)], demonstrating a sensitivity of 80.5%. Then the IFN-γ and IL-4 were applied to distinguish healthy control and IGRA-negative group. When using the stepwise algorithm, the sensitivity for detecting Mycobacterium tuberculosis (MTB) infections was significantly increased from 80.5% to 89.6%. Additionally, patients with negative IGRA results had a conversion to culture-negative status longer than those with positive IGRA results. In conclusion, a stepwise algorithm outperforms IGRA assays to accurately identify MTB infections by the combination IFN-γ, IL-2, and IL-4. Further study is needed to evaluate the accuracy of our diagnostic algorithm in the LTBI population.