scholarly journals Influence of Seasonality and Circulating Cytokines on Serial QuantiFERON Discordances

2018 ◽  
Vol 2018 ◽  
pp. 1-5
Author(s):  
Marsha L. Griffin ◽  
Saroochi Agarwal ◽  
Melissa Zhu Murphy ◽  
Larry D. Teeter ◽  
Edward A. Graviss

Objectives. An 18-month prospective study serially tested healthcare workers (HCWs) for tuberculosis infection (TBI) and reported discordant QuantiFERON Gold In-Tube® (QFT) results in some participants. The purpose of the current study was to investigate whether the interferon-gamma (IFN-γ) measured by QFT in discordant individuals could be influenced by other circulating cytokines that vary seasonally at the time of phlebotomy. Methods. The CDC funded TBESC Task Order 18 (TO18) project to assess the use of Interferon Gamma Release Assays (IGRAs), T-SPOT.TB® and QFT, compared to the tuberculin skin test (TST) for the serial testing of TBI in HCW at 4 US sites. Unstimulated plasma from 9 discordant TO18 participants at 4 different time points from the Houston site was multiplexed to determine the association between circulating cytokines and antigen stimulated IFN-γ levels. Results. IL-12, IL-1β, IL-3, GCSF, and IL-7 were associated with the amount of IFN-γ measured in response to antigen stimulation. In addition to these cytokines, a significant relationship was found between a positive QFT result and the spring season. Conclusions. Allergens during the spring season can result in the upregulation of IL-1β and IL-3, and this upregulation was observed with the amount of IFN-γ measured in discordant results.

Author(s):  
Ping-Chin Chang ◽  
Pin-Hui Wang ◽  
Kow-Tong Chen

The value of QuantiFERON in the diagnosis of tuberculosis and in the monitoring of the response to anti-tuberculosis treatment is unclear. The aims of this study were to evaluate the accuracy of the QuantiFERON-TB Gold In-Tube (QFT-GIT) test in the diagnosis of tuberculosis and in the monitoring of the response to anti-tuberculosis treatment in patients with active pulmonary tuberculosis (PTB). Between January 2013 and December 2015, 128 cases with active PTB and 128 controls with no mycobacterial infection, matched by age (within 3 years) and by the week that they visited Tainan Chest Hospital, were enrolled in the study. Serial testing by QFT-GIT at baseline and after 2 and 6 months of treatment was performed. At these time points, a comparison of the performance of QFT-GIT with that of sputum culture status among study subjects was conducted. Compared to baseline, 116 (87.2%) cases showed a decreased response, whereas 17 (12.8%) showed persistent or stronger interferon-gamma (IFN-γ) responses at 2 months. Their IFN-γ responses declined significantly from baseline to 2 months (median, 6.32 vs. 4.12; P < 0.005). The sensitivity values of the QFT-GIT test for the detection of pulmonary tuberculosis at cut-off points of 0.35 IU/ml, 0.20 IU/ml, and 0.10 IU/ml were 74.4%, 78.2%, and 80.5%, respectively. The specificity values at cut-off points of 0.35 IU/ml, 0.20 IU/ml, and 0.10 IU/ml were 66.2%, 63.9%, and 57.1%, respectively. Our results support the QFT-GIT assay as a potential tool for diagnosing tuberculosis and for monitoring the efficacy of anti-tuberculosis treatment.


Thorax ◽  
2011 ◽  
Vol 67 (1) ◽  
pp. 62-70 ◽  
Author(s):  
Alice Zwerling ◽  
Susan van den Hof ◽  
Jerod Scholten ◽  
Frank Cobelens ◽  
Dick Menzies ◽  
...  

2021 ◽  
Vol 9 (5) ◽  
pp. AB021-AB021
Author(s):  
Rebecca L. Krain ◽  
Rebecca G. Gaffney ◽  
Emily R. Keyes ◽  
Rui Feng ◽  
Victoria P. Werth

2016 ◽  
Vol 54 (4) ◽  
pp. 845-850 ◽  
Author(s):  
Niaz Banaei ◽  
Rajiv L. Gaur ◽  
Madhukar Pai

Interferon gamma release assays (IGRAs) are blood-based tests intended for diagnosis of latent tuberculosis infection (LTBI). IGRAs offer logistical advantages and are supposed to offer improved specificity over the tuberculin skin test (TST). However, recent serial testing studies of low-risk individuals have revealed higher false conversion rates with IGRAs than with TST. Reproducibility studies have identified various sources of variability that contribute to nonreproducible results. Sources of variability can be broadly classified as preanalytical, analytical, postanalytical, manufacturing, and immunological. In this minireview, we summarize known sources of variability and their impact on IGRA results. We also provide recommendations on how to minimize sources of IGRA variability.


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