ArticleThorax2024
Serum cytokine biosignatures for identification of tuberculosis among HIV-positive inpatients.
Article in Thorax, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 6 citations in OpenAlex.
- Serum tuberculosis biomarkers and their association with clinical characteristics among hospitalized adults living with HIV in Uganda.Journal of clinical tuberculosis and other mycobacterial diseases · 2026Article
- Liposomal Glutathione Exhibits Direct and Intracellular Antimycobacterial Activity AgainstBioengineering (Basel, Switzerland) · 2026Article
- IFN-γ and IL-6 as Key Predicting Biomarkers for Active TB Among PLWH: Results from Four Machine Learning Methods.International journal of general medicine · 2026Article
- Interpretable machine learning model based on multimodal ultrasound for bedside diagnosis of acute exacerbations in COPD.Respiratory research · 2025Article
- A diagnostic model for NTM disease in HIV-positive patients: a machine learning-based analysis with novel inflammatory markers.Frontiers in immunology · 2025Article
- Integrating pathogen- and host-derived blood biomarkers for enhanced tuberculosis diagnosis: a comprehensive review.Frontiers in immunology · 2024Review
Corrections and comments
- Erratum issued
Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSerum 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.
methodsWe 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.
results236 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
conclusionsThe 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
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