ArticleThorax2019
Biomarkers of iron metabolism facilitate clinical diagnosis in
Article in Thorax, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.
- Revolutionizing diagnosis of pulmonaryFrontiers in microbiology · 2024Pooled it
- CD4Frontiers in cellular and infection microbiology · 2026Article
- Identification of serum tRNA-derived small RNAs biosignature for diagnosis of tuberculosis.Emerging microbes & infections · 2025Article
- Ferritin's role in infectious diseases: Exploring pathogenic mechanisms and clinical implications.New microbes and new infections · 2025Review
- Iron, Copper, and Zinc Homeostasis in the Battle between Macrophage andCurrent medicinal chemistry · 2025Review
- Association of clinical laboratory parameters with latent tuberculosis infection among healthcare workers of primary health centers-A cross-sectional observational study.PLOS global public health · 2025Article
- Mycobacterium tuberculosis modulates NUDT21-mediated alternative polyadenylation to enhance FTH1 expression in macrophages and promotes intracellular growth.Frontiers in cellular and infection microbiology · 2025Article
- Comparative Prognosis of Fungal Balls Versus Invasive Pulmonary Mucormycosis.Infection and drug resistance · 2025Article
- Granzyme A as biomarker for diagnosis in tuberculous pleural effusion.JCI insight · 2024Article
- Plasma immune profiling combined with machine learning contributes to diagnosis and prognosis of active pulmonary tuberculosis.Emerging microbes & infections · 2024Article
- Serum cytokine biosignatures for identification of tuberculosis among HIV-positive inpatients.Thorax · 2024Article
- A deep insight into ferroptosis in lung disease: facts and perspectives.Frontiers in oncology · 2024Review
- Latent Tuberculosis: Challenges in Diagnosis and Treatment, Perspectives, and the Crucial Role of Biomarkers.Current microbiology · 2023Review
- Association of Micronutrients with Tuberculosis Development in HIV Infected Patients.Indian journal of clinical biochemistry : IJCB · 2023Article
- Mycobacterium tuberculosis hijacks host TRIM21- and NCOA4-dependent ferritinophagy to enhance intracellular growth.The Journal of clinical investigation · 2023Article
- Review
- Development of diagnostic algorithm using machine learning for distinguishing between active tuberculosis and latent tuberculosis infection.BMC infectious diseases · 2022Article
- Diagnostic biomarkers for active tuberculosis: progress and challenges.EMBO molecular medicine · 2022Review
- Targeting ferroptosis as a vulnerability in pulmonary diseases.Cell death & disease · 2022Review
- Validation of Serotransferrin in the Serum as Candidate Biomarkers for the Diagnosis of Pulmonary Tuberculosis by Label-Free LC/MS.ACS omega · 2022Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPerturbed iron homeostasis is a risk factor for tuberculosis (TB) progression and an indicator of TB treatment failure and mortality. Few studies have evaluated iron homeostasis as a TB diagnostic biomarker.
methodsWe recruited participants with TB, latent TB infection (LTBI), cured TB (RxTB), pneumonia (PN) and healthy controls (HCs). We measured serum levels of three iron biomarkers including serum iron, ferritin and transferrin, then established and validated our prediction model.
resultsWe observed and verified that the three iron biomarker levels correlated with patient status (TB, HC, LTBI, RxTB or PN) and with the degree of lung damage and bacillary load in patients with TB. We then built a TB prediction model, neural network (NNET), incorporating the data of the three iron biomarkers. The model showed good performance for diagnosis of TB, with 83% (95% CI 77 to 87) sensitivity and 86% (95% CI 83 to 89) specificity in the training data set (n=663) and 70% (95% CI 58 to 79) sensitivity and 92% (95% CI 86 to 96) specificity in the test data set (n=220). The area under the curves (AUCs) of the NNET model to discriminate TB from HC, LTBI, RxTB and PN were all >0.83. Independent validation of the NNET model in a separate cohort (n=967) produced an AUC of 0.88 (95% CI 0.85 to 0.91) with 74% (95% CI 71 to 77) sensitivity and 92% (95% CI 87 to 96) specificity.
conclusionsThe established NNET TB prediction model discriminated TB from HC, LTBI, RxTB and PN in a large cohort of patients. This diagnostic assay may augment current TB diagnostics.
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