Biomarkers to identify Mycobacterium tuberculosis infection among borderline QuantiFERON results

结核分枝杆菌
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  • Eur Respir J
  • 21
  • 60(2):2102665.
  • Human
  • Luminex
  • 呼吸系统
  • 呼吸系统
  • 结核病
  • β-NGF,CTACK/CCL27,Eotaxin/CCL11,FGF-basic,G-CSF,GM-CSF,GRO-α (Gro-a/KC/CXCL1),HGF,IFN-α2,IFN-γ,IL-1α,IL-1Rα,IL-2Rα,IL-1β,IL-2,IL-3,IL-4,IL-5,IL-6,IL-7,IL-8/CXCL8,IL-9,IL-10,IL-12(p40),IL-12(p70),IL-13,IL-15,IL-16,IL-17A,IL-18,IP-10/CXCL10,LIF,M-CSF,MCP-1/CCL2,MCP-3/CCL7,MIG,MIP-1α/CCL3,MIP-1β,MIF,PDGF-BB,RANTES,SCF,SCGF-β,SDF-1α,TRAIL,TNF-α,TNF-β,VEGF-A
  • doi: 10.1183/13993003.02665-2021.

相关货号

LXLBH48-1

Abstract

Background: Screening for tuberculosis (TB) infection often includes QuantiFERON-TB Gold Plus (QFT) testing. Previous studies showed that two-thirds of patients with negative QFT results just below the cut-off, so-called borderline test results, nevertheless had other evidence of TB infection. This study aimed to identify a biomarker profile by which borderline QFT results due to TB infection can be distinguished from random test variation. Methods: QFT supernatants of patients with a borderline (≥0.15 and <0.35 IU·mL-1), low-negative (<0.15 IU·mL-1) or positive (≥0.35 IU·mL-1) QFT result were collected in three hospitals. Bead-based multiplex assays were used to analyse 48 different cytokines, chemokines and growth factors. A prediction model was derived using LASSO regression and applied further to discriminate QFT-positive Mycobacterium tuberculosis-infected patients from borderline QFT patients and QFT-negative patients RESULTS: QFT samples of 195 patients were collected and analysed. Global testing revealed that the levels of 10 kDa interferon (IFN)-γ-induced protein (IP-10/CXCL10), monokine induced by IFN-γ (MIG/CXCL9) and interleukin-1 receptor antagonist in the antigen-stimulated tubes were each significantly higher in patients with a positive QFT result compared with low-negative QFT individuals (p<0.001). A prediction model based on IP-10 and MIG proved highly accurate in discriminating patients with a positive QFT (TB infection) from uninfected individuals with a low-negative QFT (sensitivity 1.00 (95% CI 0.79-1.00) and specificity 0.95 (95% CI 0.74-1.00)). This same model predicted TB infection in 68% of 87 patients with a borderline QFT result. Conclusions: This study was able to classify borderline QFT results as likely infection-related or random. These findings support additional laboratory testing for either IP-10 or MIG following a borderline QFT result for individuals at increased risk of reactivation TB.
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