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• W2550360630 abstract "•Combinations of cytokine biomarkers improved the detection of Mtb-infection. •Biomarkers improved the ability to discriminate between active TB and LTBI. •Biomarkers improved the ability to discriminate between active TB and non-active TB. •VEGF is a key biomarker for reflecting active TB. Objective We aimed to determine whether combinations of multiplex cytokine responses could differentiate Mycobacterium tuberculosis (Mtb) infection states. Methods Mtb-specific antigen-induced and unstimulated cytokines were measured by Luminex assay in supernatants of QuantiFERON® Gold In-Tube assay (QFT) in 48 active pulmonary TB patients (TB), 15 latent TB infection subjects (LTBI), and 13 healthy controls (HCs). Results Among the 29 cytokines, eight Mtb antigen-specific biomarkers (GM-CSF, IFN-γ, IL-1RA, IL-2, IL-3, IL-13, IP-10, and MIP-1β) in the Mtb-infected group were significantly different from those of the HCs. Five Mtb-specific biomarkers (EGF, GM-CSF, IL-5, IL-10, and VEGF), two unstimulated biomarkers (TNF-α[Nil] and VEGF[Nil]), and one Mtb-specific biomarker ratio (IL-2/IFN-γ) showed significant differences between active TB and LTBI. Three unstimulated biomarkers (IL-8[Nil], IL-13[Nil], and VEGF[Nil]) and 5 Mtb-specific biomarkers (IFN-γ, IL-2, IL-3, IP-10, and VEGF) were significantly different between active TB and non-active TB groups. Combinations of three cytokine biomarkers resulted in the accurate prediction of 92.1–93.7% of Mtb-infected cases and 92.3–100% of HCs, respectively. Moreover, combinations of five biomarkers accurately predicted 90.9–100% of active TB cases and 80–100% of LTBI subjects, respectively. In discriminating between active TB and non-active TB regardless of QFT results, combinations of six biomarkers predicted 79.2–95.8% of active TB cases and 67.9–89.3% of non-active TB subjects. Conclusions Taken together, our data suggest that combinations of whole blood Mtb antigen-dependent cytokines could serve as biomarkers to determine TB disease states. Especially, VEGF is highlighted as a key biomarker for reflecting active TB, irrespective of stimulation. We aimed to determine whether combinations of multiplex cytokine responses could differentiate Mycobacterium tuberculosis (Mtb) infection states. Mtb-specific antigen-induced and unstimulated cytokines were measured by Luminex assay in supernatants of QuantiFERON® Gold In-Tube assay (QFT) in 48 active pulmonary TB patients (TB), 15 latent TB infection subjects (LTBI), and 13 healthy controls (HCs). Among the 29 cytokines, eight Mtb antigen-specific biomarkers (GM-CSF, IFN-γ, IL-1RA, IL-2, IL-3, IL-13, IP-10, and MIP-1β) in the Mtb-infected group were significantly different from those of the HCs. Five Mtb-specific biomarkers (EGF, GM-CSF, IL-5, IL-10, and VEGF), two unstimulated biomarkers (TNF-α[Nil] and VEGF[Nil]), and one Mtb-specific biomarker ratio (IL-2/IFN-γ) showed significant differences between active TB and LTBI. Three unstimulated biomarkers (IL-8[Nil], IL-13[Nil], and VEGF[Nil]) and 5 Mtb-specific biomarkers (IFN-γ, IL-2, IL-3, IP-10, and VEGF) were significantly different between active TB and non-active TB groups. Combinations of three cytokine biomarkers resulted in the accurate prediction of 92.1–93.7% of Mtb-infected cases and 92.3–100% of HCs, respectively. Moreover, combinations of five biomarkers accurately predicted 90.9–100% of active TB cases and 80–100% of LTBI subjects, respectively. In discriminating between active TB and non-active TB regardless of QFT results, combinations of six biomarkers predicted 79.2–95.8% of active TB cases and 67.9–89.3% of non-active TB subjects. Taken together, our data suggest that combinations of whole blood Mtb antigen-dependent cytokines could serve as biomarkers to determine TB disease states. Especially, VEGF is highlighted as a key biomarker for reflecting active TB, irrespective of stimulation." @default.
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• W2550360630 date "2017-03-01" @default.
• W2550360630 modified "2023-10-01" @default.
• W2550360630 title "Biomarkers for discrimination between latent tuberculosis infection and active tuberculosis disease" @default.
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• W2550360630 doi "https://doi.org/10.1016/j.jinf.2016.11.010" @default.
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