Proportions of interferon-γ-producing ascites lymphocytes in response to mycobacterial antigens: A help for early diagnosis of peritoneal tuberculosis in a low TB incidence country

2019 
BACKGROUND: Peritoneal tuberculosis (TB) remains difficult to diagnose because of its non-specific clinical features and the lack of efficient microbiological tests. As delayed diagnosis is associated with high mortality rates, new diagnostic tools are needed. METHODS AND FINDINGS: We investigated for 24 patients prospectively enrolled with a possible diagnosis of peritoneal TB, the diagnostic value of the analysis of IFN-γ production by peritoneal fluid lymphocytes in response to a short in vitro stimulation with mycobacterial antigens. The patients were classified in two groups: non-TB and confirmed or highly probable TB. Diagnosis of TB was based on microbiological and histopathological criteria and/or a favorable response to anti-TB treatment. The IFN-γ production by peritoneal CD4+ T lymphocytes was analyzed by flow cytometry after an overnight in vitro stimulation with three different mycobacterial antigens, purified protein derivative (PPD), heparin-binding haemagglutinin (HBHA) or early-secreted-antigen-target-6 (ESAT-6). The percentages of PPD-, HBHA- or ESAT-6-induced IFN-γ-producing peritoneal fluid CD4+ T lymphocytes were higher in the TB group than in the non-TB group (p = 0.0007, p = 0.0004, and p = 0.0002 respectively). Based on cut-off values determined by ROC curve analysis of the results from TB and highly probable TB compared to those of non-TB patients, the sensitivity of these three tests was 100% with a specificity of 92%. CONCLUSIONS: The analysis of mycobacterial-induced IFN-γ production by peritoneal lymphocytes is a promising tool to reliably and rapidly diagnose peritoneal TB. Further studies should be performed on larger cohorts of patients in high-TB-incidence countries to confirm the clinical value of this new diagnostic approach for peritoneal TB.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    23
    References
    3
    Citations
    NaN
    KQI
    []