Abstract
Background Screening for tuberculosis (TB)-infection often includes QuantiFERON (QFT) testing. Previous studies showed that two-third 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 results 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+ Mycobacterium tuberculosis (Mtb)-infected 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 CXCL10/IP-10, CXCL9/MIG and IL-1ra in the antigen-stimulated tubes were each significantly higher in patients with a positive QFT result compared to QFT low-negative 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/specificity: 1.00 [95% CI: 0.79–1.00]/0.95 [95% CI: 0.74–1.00]). This same model predicted TB-infection in 68% of 87 patients with a borderline QFT result.
Conclusion 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.
Footnotes
This manuscript has recently been accepted for publication in the European Respiratory Journal. It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article.
Conflict of interest: Jonathan W. Uzorka has nothing to disclose.
Conflict of interest: Jaap A. Bakker has nothing to disclose.
Conflict of interest: Krista E. van Meijgaarden has nothing to disclose.
Conflict of interest: Eliane M.S. Leyten has nothing to disclose.
Conflict of interest: Nathalie M. Delfos has nothing to disclose.
Conflict of interest: David J. Hetem has nothing to disclose.
Conflict of interest: Jos Kerremans has nothing to disclose.
Conflict of interest: Mieke Zwarts has nothing to disclose.
Conflict of interest: Sandra Cozijn has nothing to disclose.
Conflict of interest: Tom Ottenhoff reports grants from NWO-TTW (PI: THMO), Dutch Government, Technical Sciences; ZonMW (PI: THMO), Dutch Government (ZonMW); IMI2 HOR2020 VSV EBOPLUS (PI: CA Siegrist), European Commission HOR2020 IMI2 Program; NWO-TTW (PI: prof J Bouwstra); Dutch Government, Technical Sciences; NWO-TTW (PI: THMO); Dutch Government, Technical Sciences, NACTAR Program; NWO-Chemical Sciences (PI: Prof Adriaan Minnaard); Dutch Government, Technical Sciences; EC HOR2020 TRANSVAC2 (PI: EVI); European Commission, HOR2020 Program; IMI2 EC HOR2020 Respiri-TB (PI: M Lamers); European Commission HOR2020 IMI2 Program; IMI2 EC HOR2020 Respiri-NTM (PI: M Lamers); European Commission HOR2020 IMI2 Program; NIH (PI: THMO); NIH, NIAID, grant #: 1RO1AI141315-01A1; EC HOR2020 SMA-TB (PI: Cris Vilaplana); European Commission, HOR2020 Program; leadership at TBVI (www.tbvi.eu); outside the submitted work.
Conflict of interest: Simone Joosten reports grants from NIH (PI: THMO, co_PI:SAJ); NIH, NIAID, grant #: 1RO1AI141315-01A1; outside the submitted work.
Conflict of interest: Sandra Arend reports travel support from Oxford Immunotec, outside the submitted work.
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- Received October 8, 2021.
- Accepted December 18, 2021.
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