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Biomarkers to identify Mycobacterium tuberculosis infection among borderline QuantiFERON results

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...

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Autores principales: Uzorka, Jonathan W., Bakker, Jaap A., van Meijgaarden, Krista E., Leyten, Eliane M.S., Delfos, Nathalie M., Hetem, David J., Kerremans, Jos, Zwarts, Mieke, Cozijn, Sandra, Ottenhoff, Tom H.M., Joosten, Simone A., Arend, Sandra M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: European Respiratory Society 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9363845/
https://www.ncbi.nlm.nih.gov/pubmed/35058249
http://dx.doi.org/10.1183/13993003.02665-2021
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author Uzorka, Jonathan W.
Bakker, Jaap A.
van Meijgaarden, Krista E.
Leyten, Eliane M.S.
Delfos, Nathalie M.
Hetem, David J.
Kerremans, Jos
Zwarts, Mieke
Cozijn, Sandra
Ottenhoff, Tom H.M.
Joosten, Simone A.
Arend, Sandra M.
author_facet Uzorka, Jonathan W.
Bakker, Jaap A.
van Meijgaarden, Krista E.
Leyten, Eliane M.S.
Delfos, Nathalie M.
Hetem, David J.
Kerremans, Jos
Zwarts, Mieke
Cozijn, Sandra
Ottenhoff, Tom H.M.
Joosten, Simone A.
Arend, Sandra M.
author_sort Uzorka, Jonathan W.
collection PubMed
description 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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spelling pubmed-93638452022-08-11 Biomarkers to identify Mycobacterium tuberculosis infection among borderline QuantiFERON results Uzorka, Jonathan W. Bakker, Jaap A. van Meijgaarden, Krista E. Leyten, Eliane M.S. Delfos, Nathalie M. Hetem, David J. Kerremans, Jos Zwarts, Mieke Cozijn, Sandra Ottenhoff, Tom H.M. Joosten, Simone A. Arend, Sandra M. Eur Respir J Original Research Articles 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. European Respiratory Society 2022-08-11 /pmc/articles/PMC9363845/ /pubmed/35058249 http://dx.doi.org/10.1183/13993003.02665-2021 Text en Copyright ©The authors 2022. https://creativecommons.org/licenses/by-nc/4.0/This version is distributed under the terms of the Creative Commons Attribution Non-Commercial Licence 4.0. For commercial reproduction rights and permissions contact permissions@ersnet.org (mailto:permissions@ersnet.org)
spellingShingle Original Research Articles
Uzorka, Jonathan W.
Bakker, Jaap A.
van Meijgaarden, Krista E.
Leyten, Eliane M.S.
Delfos, Nathalie M.
Hetem, David J.
Kerremans, Jos
Zwarts, Mieke
Cozijn, Sandra
Ottenhoff, Tom H.M.
Joosten, Simone A.
Arend, Sandra M.
Biomarkers to identify Mycobacterium tuberculosis infection among borderline QuantiFERON results
title Biomarkers to identify Mycobacterium tuberculosis infection among borderline QuantiFERON results
title_full Biomarkers to identify Mycobacterium tuberculosis infection among borderline QuantiFERON results
title_fullStr Biomarkers to identify Mycobacterium tuberculosis infection among borderline QuantiFERON results
title_full_unstemmed Biomarkers to identify Mycobacterium tuberculosis infection among borderline QuantiFERON results
title_short Biomarkers to identify Mycobacterium tuberculosis infection among borderline QuantiFERON results
title_sort biomarkers to identify mycobacterium tuberculosis infection among borderline quantiferon results
topic Original Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9363845/
https://www.ncbi.nlm.nih.gov/pubmed/35058249
http://dx.doi.org/10.1183/13993003.02665-2021
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