Discrimination between active and latent tuberculosis based on ratio of antigen-specific to mitogen-induced IP-10 production

基于抗原特异性IP-10与有丝分裂原诱导的IP-10产生比例来区分活动性结核病和潜伏性结核病。

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Abstract

Mycobacterium tuberculosis is the major causative agent of tuberculosis (TB). The gamma interferon (IFN-γ) release assay (IGRA) has been widely used to diagnose TB by testing cell-mediated immune responses but has no capacity for distinguishing between active TB and latent TB infection (LTBI). This study aims to identify a parameter that will help to discriminate active TB and LTBI. Whole-blood samples from 33 active TB patients, 20 individuals with LTBI, and 26 non-TB controls were applied to the commercial IFN-γ release assay, QuantiFERON-TB Gold In-Tube, and plasma samples were analyzed for interleukin-2 (IL-2), IL-6, IL-8, IL-10, IL-13, tumor necrosis factor-alpha (TNF-α), IFN-γ, monokine induced by IFN-γ (MIG), interferon gamma inducible protein 10 (IP-10), interferon-inducible T cell alpha chemoattractant (I-TAC), and monocyte chemoattractant protein 1 (MCP-1) by using a commercial cytometric bead array. The Mycobacterium tuberculosis antigen-specific production of most of the assayed cytokines and chemokines was higher in the active TB than in the LTBI group. The mitogen-induced responses were lower in the active TB than in the LTBI group. When the ratio of TB-specific to mitogen-induced responses was calculated, IL-2, IL-6, IL-10, IL-13, TNF-α, IFN-γ, MIG, and IP-10 were more useful in discriminating active TB from LTBI. In particular, most patients showed higher IP-10 production to Mycobacterium tuberculosis antigens than to mitogen at the individual level, and the ratio for IP-10 was the strongest indicator of active infection versus LTBI with 93.9% sensitivity and 90% specificity. In conclusion, the ratio of the TB-specific to the mitogen-induced IP-10 responses showed the most promising accuracy for discriminating active TB versus LTBI and should be further studied to determine whether it can serve as a biomarker that might help clinicians administer appropriate treatments.

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