Abstract
Background Biomarkers of unfavourable tuberculosis (TB) treatment outcomes are needed to accelerate new drug and regimen development. Whether plasma cytokine levels can predict unfavourable TB treatment outcomes is unclear.
Methods We identified and internally validated the association between 20 a priori selected plasma inflammatory markers and unfavourable treatment outcomes of failure, recurrence and all-cause mortality among adults with drug-sensitive pulmonary TB in India. We externally validated these findings in two independent cohorts of predominantly diabetic and HIV co-infected TB patients in India and South Africa, respectively.
Results Pre-treatment interferon-γ, interleukin (IL)-13 and IL-6 were associated with treatment failure in the discovery analysis. Internal validation confirmed higher pre-treatment IL-6 concentrations among failure cases compared with controls. External validation among predominantly diabetic TB patients found an association between pre-treatment IL-6 concentrations and subsequent recurrence and death. Similarly, external validation among predominantly HIV co-infected TB patients found an association between pre-treatment IL-6 concentrations and subsequent treatment failure and death. In a pooled analysis of 363 TB cases from the Indian and South African validation cohorts, high pre-treatment IL-6 concentrations were associated with higher risk of failure (adjusted OR (aOR) 2.16, 95% CI 1.08–4.33; p=0.02), recurrence (aOR 5.36, 95% CI 2.48–11.57; p<0.001) and death (aOR 4.62, 95% CI 1.95–10.95; p<0.001). Adding baseline IL-6 to a risk prediction model comprised of low body mass index, high smear grade and cavitation improved model performance by 15% (C-statistic 0.66 versus 0.76; p=0.02).
Conclusions Pre-treatment IL-6 is a biomarker for unfavourable TB treatment outcomes. Future studies should identify optimal IL-6 concentrations for point-of-care risk prediction.
Abstract
Pre-treatment IL-6 is a biomarker for unfavourable tuberculosis treatment outcomes independent of disease severity and improves the performance of risk prediction models comprised of established clinical predictors https://bit.ly/38394xE
Footnotes
Conflict of interest: A.N. Gupte has nothing to disclose.
Conflict of interest: P. Kumar has nothing to disclose.
Conflict of interest: M. Araújo-Pereira has nothing to disclose.
Conflict of interest: V. Kulkarni has nothing to disclose.
Conflict of interest: M. Paradkar has nothing to disclose.
Conflict of interest: N. Pradhan has nothing to disclose.
Conflict of interest: P. Menon has nothing to disclose.
Conflict of interest: C. Padmapriyadarsini reports funding from the Dept of Biotechnology, Government of India, within the scope of the present manuscript.
Conflict of interest: L-E. Hanna has nothing to disclose.
Conflict of interest: S.V.B. Yogendra Shivakumar has nothing to disclose.
Conflict of interest: N. Rockwood has nothing to disclose.
Conflict of interest: E. Du Bruyn has nothing to disclose.
Conflict of interest: R. Karyakarte has nothing to disclose.
Conflict of interest: S. Gaikwad has nothing to disclose.
Conflict of interest: R.C. Bollinger reports research support from the NIH and Ujala Foundation, within the scope of the present manuscript.
Conflict of interest: J. Golub reports grants to their institution from the NIH, within the scope of the present manuscript.
Conflict of interest: N. Gupte reports grants to their institution from the NIH, within the scope of the present manuscript.
Conflict of interest: V. Viswanathan has nothing to disclose.
Conflict of interest: R.J. Wilkinson has nothing to disclose.
Conflict of interest: V. Mave has nothing to disclose.
Conflict of interest: S. Babu has nothing to disclose.
Conflict of interest: H. Kornfeld has nothing to disclose.
Conflict of interest: B.B. Andrade has nothing to disclose.
Conflict of interest: A. Gupta reports grants to their institution from the National Institutes of Health within the scope of the present manuscript; grants to their institution from CRDF outside the scope of the present manuscript; and membership of an NIH/NIAID Advisory Council and the Indo-US Science Technology Forum Board.
Support statement: Data were collected as part of the Regional Prospective Observational Research for Tuberculosis (RePORT) India Consortium. This project has been funded in whole or in part with Federal funds from the Government of India's Dept of Biotechnology (DBT), Indian Council of Medical Research (ICMR), NIH, NIAID, Office of AIDS Research (OAR), and distributed in part by CRDF Global. Research reported in this publication was also supported by the NIAID (R01AI097494 and UM1AI069465), the Wyncote Foundation, the Gilead Foundation and the Ujala Foundation. This research was also funded in part by a 2019 developmental grant from the Johns Hopkins University Center for AIDS Research, an NIH-funded program (1P30AI094189), which is supported by the following NIH Co-Funding and Participating Institutes and Centers: NIAID, NCI, NICHD, NHLBI, NIDA, NIA, NIGMS, NIDDK and NIMHD. A.N. Gupte was supported by the NIAID (K99AI151094). M. Araújo-Pereira received a fellowship from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (finance code 001). The work of B.B. Andrade and R.J. Wilkinson was supported by a grant from the NIAID (U01AI115940), by the Intramural Research Program of the Oswaldo Cruz Foundation (FIOCRUZ) and by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brazil. R.J. Wilkinson is supported by The Francis Crick Institute which receives support from the Medical Research Council (FC001218), Cancer Research UK (FC001218) and Wellcome (FC001218). Additional support was provided by the Wellcome Trust (203135 and 104803), the European and Developing Countries Clinical Trials Partnership (SRIA 2015-1065), and the Foundation for the NIH (WILKI16PTB). The funders had no role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication. The contents of this publication are solely the responsibility of the authors and do not represent the official views of the DBT, ICMR, MRC, NIH or CRDF Global. Any mention of trade names, commercial projects or organisations does not imply endorsement by any of the sponsoring organisations. The authors also acknowledge support from Persistent Systems in kind. Funding information for this article has been deposited with the Crossref Funder Registry.
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- Received April 7, 2021.
- Accepted August 18, 2021.
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