Serum cytokine biosignatures for identification of tuberculosis among HIV-positive inpatients

Background Serum cytokines correlate with tuberculosis (TB) progression and are predictors of TB recurrence in people living with HIV. We investigated whether serum cytokine biosignatures could diagnose TB among HIV-positive inpatients. Methods We recruited HIV-positive inpatients with symptoms of TB and measured serum levels of inflammation biomarkers including IL-2, IL-4, IL-6, IL-10, tumour necrosis factor-alpha (TNF-α) and interferon-gamma (IFN-γ). We then built and tested our TB prediction model. Results 236 HIV-positive inpatients were enrolled in the first cohort and all the inflammation biomarkers were significantly higher in participants with microbiologically confirmed TB than those without TB. A binary support vector machine (SVM) model was built, incorporating the data of four biomarkers (IL-6, IL-10, TNF-α and IFN-γ). Efficacy of the SVM model was assessed in training (n=189) and validation (n=47) sets with area under the curve (AUC) of 0.92 (95% CI 0.88 to 0.96) and 0.85 (95% CI 0.72 to 0.97), respectively. In an independent test set (n=110), the SVM model yielded an AUC of 0.85 (95% CI 0.76 to 0.94) with 78% (95% CI 68% to 87%) specificity and 85% (95% CI 66% to 96%) sensitivity. Moreover, the SVM model outperformed interferon-gamma release assay (IGRA) among advanced HIV-positive inpatients irrespective of CD4 + T-cell counts, which may be an alternative approach for identifying Mycobacterium tuberculosis infection among HIV-positive inpatients with negative IGRA. Conclusions The four-cytokine biosignature model successfully identified TB among HIV-positive inpatients. This diagnostic model may be an alternative approach to diagnose TB in advanced HIV-positive inpatients with low CD4 + T-cell counts.