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Background <p>Monitoring tuberculosis (TB) treatment response remains challenging, necessitating robust host-derived biomarkers. While standard monitoring relies on clinical assessment, adherence, radiological improvement, and microbiological culture conversion, these methods can be slow to manifest.Cytokines like IFN-γ, IL-2, and IL-6 are implicated in the immune response to <i>Mycobacterium tuberculosis</i> (<i>M. tb</i>), but their longitudinal dynamics and discriminative power during treatment are inadequately characterized. These host-derived biomarkers are intended to serve as adjuncts to, rather than replacements for, established clinical and microbiological monitoring protocols.</p> Methods <p>In a retrospective cohort of 163&#xa0;TB patients, we quantified twelve cytokines using a multiplex immunoassay in paired plasma samples collected pre-treatment and two months post-treatment. Longitudinal changes were assessed via Wilcoxon signed-rank tests, and the discriminatory ability of significant cytokines for treatment status was evaluated using receiver operating characteristic (ROC) analysis. Logistic regression identified factors associated with post-treatment biomarker levels.</p> Results <p>IFN-γ and IL-6 decreased significantly (median change: -15.4% and − 47.0%, respectively), while IL-2 increased (+ 19.0%). However, their individual power to discriminate pre- from post-treatment status was limited, with area under the curve (AUC) values of 0.555 for IFN-γ, 0.558 for IL-2, and 0.611 for IL-6. Pre-treatment cytokine levels were the strongest predictors of post-treatment concentrations.</p> Conclusion <p>IFN-γ, IL-2, and IL-6 exhibit significant dynamic changes during early TB treatment, reflecting immunomodulation, but perform poorly as standalone discriminators of treatment status. Future strategies should prioritize multi-parameter models integrating cytokine trends to improve monitoring accuracy.</p>

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Dynamics of IFN-γ, IL-2, and IL-6 during early tuberculosis treatment: significant longitudinal changes but limited value as standalone discriminators of treatment status

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月18日

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  • Zhan Qiu Mao,
  • Huilie Zheng,
  • Xiao Mei Tang,
  • Lin Ye,
  • Bing Yu Liu,
  • Zhen Qiong Liu,
  • Li Zhou,
  • Yang Hu,
  • Qi Long Zhang

摘要

Background

Monitoring tuberculosis (TB) treatment response remains challenging, necessitating robust host-derived biomarkers. While standard monitoring relies on clinical assessment, adherence, radiological improvement, and microbiological culture conversion, these methods can be slow to manifest.Cytokines like IFN-γ, IL-2, and IL-6 are implicated in the immune response to Mycobacterium tuberculosis (M. tb), but their longitudinal dynamics and discriminative power during treatment are inadequately characterized. These host-derived biomarkers are intended to serve as adjuncts to, rather than replacements for, established clinical and microbiological monitoring protocols.

Methods

In a retrospective cohort of 163 TB patients, we quantified twelve cytokines using a multiplex immunoassay in paired plasma samples collected pre-treatment and two months post-treatment. Longitudinal changes were assessed via Wilcoxon signed-rank tests, and the discriminatory ability of significant cytokines for treatment status was evaluated using receiver operating characteristic (ROC) analysis. Logistic regression identified factors associated with post-treatment biomarker levels.

Results

IFN-γ and IL-6 decreased significantly (median change: -15.4% and − 47.0%, respectively), while IL-2 increased (+ 19.0%). However, their individual power to discriminate pre- from post-treatment status was limited, with area under the curve (AUC) values of 0.555 for IFN-γ, 0.558 for IL-2, and 0.611 for IL-6. Pre-treatment cytokine levels were the strongest predictors of post-treatment concentrations.

Conclusion

IFN-γ, IL-2, and IL-6 exhibit significant dynamic changes during early TB treatment, reflecting immunomodulation, but perform poorly as standalone discriminators of treatment status. Future strategies should prioritize multi-parameter models integrating cytokine trends to improve monitoring accuracy.