ArticleEBioMedicine2020
A three-marker protein biosignature distinguishes tuberculosis from other respiratory diseases in Gambian children.
Article in EBioMedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 34 citations in OpenAlex.
- Development and validation of a machine learning-driven framework for differentiating pediatric bronchopneumonia from lobar pneumonia: a multicenter investigation.European journal of medical research · 2025Trial
- Interleukin-15 correlates with cytotoxic immune networks in cervical tuberculous lymphadenitis.Frontiers in immunology · 2026Article
- Exploring non-sputum biosignatures for tuberculosis triage and diagnosis in children living with HIV.Expert review of molecular diagnostics · 2025Review
- Development of a machine learning model for early pulmonary tuberculosis diagnosis using blood test biomarkers.BMC infectious diseases · 2025Article
- Plasma proteomics for biomarker discovery in childhood tuberculosis.Nature communications · 2025Article
- Sputum host cytokine signatures for diagnosis of TB in children and adults.Frontiers in immunology · 2025Article
- A plasma proteomic signature of the actin-coagulation axis accurately predicts progression to active tuberculosis.Frontiers in microbiology · 2025Article
- A machine learning-based model for assessing community-acquired pneumonia severity using routine blood tests.Frontiers in cellular and infection microbiology · 2025Article
- Accuracy of CAD4TB (Computer-Aided Detection for Tuberculosis) on paediatric chest radiographs.The European respiratory journal · 2024Article
- Host biomarker-based quantitative rapid tests for detection and treatment monitoring of tuberculosis and COVID-19.iScience · 2023Article
- Development of diagnostic algorithm using machine learning for distinguishing between active tuberculosis and latent tuberculosis infection.BMC infectious diseases · 2022Article
- Plasma host protein signatures correlating with Mycobacterium tuberculosis activity prior to and during antituberculosis treatment.Scientific reports · 2022Article
- Pediatric Tuberculosis Diagnostics: Present and Future.Journal of the Pediatric Infectious Diseases Society · 2022Article
- Host blood-based biosignatures for subclinical TB and incipient TB: A prospective study of adult TB household contacts in Southern India.Frontiers in immunology · 2022Article
- Plasma chemokines as immune biomarkers for diagnosis of pediatric tuberculosis.BMC infectious diseases · 2021Article
- Perspectives for systems biology in the management of tuberculosis.European respiratory review : an official journal of the European Respiratory Society · 2021Review
- Host-Based Biomarkers in Saliva for the Diagnosis of Pulmonary Tuberculosis in Children: A Mini-Review.Frontiers in pediatrics · 2021Review
- Activation Phenotype ofFrontiers in immunology · 2021Article
- Lymphocyte-Related Immunological Indicators for StratifyingFrontiers in immunology · 2021Article
- Host Blood RNA Transcript and Protein Signatures for Sputum-Independent Diagnostics of Tuberculosis in Adults.Frontiers in immunology · 2020Article
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Authors and funding
10 authors at 6 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOur study aimed to identify a host cytokine biosignature that could distinguish childhood tuberculosis (TB) from other respiratory diseases (OD).
methodsCytokine responses in prospectively recruited children with symptoms suggestive of TB were measured in whole blood assay supernatants, harvested after overnight incubation, using a Luminex platform. We used logistic regression models with Least Absolute Shrinkage and Selection Operator (LASSO) penalty to identify the optimal biosignature associated with confirmed TB disease in the training set. We subsequently assessed its performance in the test set.
findingsOf the 431 children included in the study, 44 had bacteriologically confirmed TB, 60 had clinically diagnosed TB while 327 had OD. All children were HIV-negative. Application of LASSO regression models to the training set (n = 260) resulted in the combination of IL-1ra, IL-7 and IP-10 from unstimulated samples as the optimally discriminant cytokine biosignature associated with bacteriologically confirmed TB. In the test set (n = 171), this biosignature distinguished children diagnosed with TB disease, irrespective of microbiological confirmation, from OD with area under the receiver operator characteristic curve (AUC) of 0•74 (95% CI: 0•67, 0•81), and demonstrated sensitivity and specificity of 72•2% (95% CI: 60•4, 82•1%) and 75•0% (95% CI: 64•9, 83•4%) respectively, with its performance independent of their age group and their age- and sex-adjusted nutritional status.
interpretationThis novel biosignature of childhood TB derived from unstimulated supernatants is promising. Independent validation with further optimisation will improve its performance and translational potential.
fundingSteinberg Fellowship (McGill University); Grand Challenges Canada; MRC Program Grant.
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