ArticleFrontiers in immunology2021
Serum Biomarker Profile Including CCL1, CXCL10, VEGF, and Adenosine Deaminase Activity Distinguishes Active From Remotely Acquired Latent Tuberculosis.
Article in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it, 40 citations in OpenAlex.
- Association of Serum ADA Levels in Pulmonary Tuberculosis: A Systematic Review and Meta-Analysis.International journal of environmental research and public health · 2026Pooled it
- Diagnostic performance of biomarkers for differentiating active tuberculosis from latent tuberculosis: a systematic review and Bayesian network meta-analysis.Frontiers in microbiology · 2024Pooled it
- Detection for New Biomarkers of Tuberculosis Infection Activity Using Machine Learning Methods.Diseases (Basel, Switzerland) · 2026Review
- A Review of Adenosine Deaminase 2 (ADA2) as a Biomarker of Monocyte/Macrophage Activation.Current allergy and asthma reports · 2026Review
- The blood biomarker combination IL-8/IL-33 and IL-18/IL-33 distinguish between active tuberculosis and latent infection.Infection · 2025Article
- A bovine pulmosphere model and multiomics reveal early host response signature in tuberculosis.Communications biology · 2025Article
- Intracellular concentration of ADA2 is a marker for monocyte differentiation and activation.Frontiers of medicine · 2025Article
- Identification of inflammatory protein biomarkers for predicting the different subtype of adult with tuberculosis: an Olink proteomic study.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2025Article
- Establishment and validation of a convenient and efficient screening tool for active pulmonary tuberculosis in lung cancer patients based on common parameters.Therapeutic advances in medical oncology · 2025Article
- The value of LAM and LprG in extracellular vesicles in the diagnostic and therapeutic field of renal tuberculosis.Frontiers in tuberculosis · 2025Review
- Tuberculosis diagnosis using artificial intelligence: current trends and future prospects.Frontiers in medicine · 2025Review
- Systems genetics uncover new loci containing functional gene candidates in Mycobacterium tuberculosis-infected Diversity Outbred mice.PLoS pathogens · 2024Article
- Construction of novel multi-epitope-based diagnostic biomarker HP16118P and its application in the differential diagnosis of Mycobacterium tuberculosis latent infection.Molecular biomedicine · 2024Article
- Review
- Immune-endocrine network in diabetes-tuberculosis nexus: does latent tuberculosis infection confer protection against meta-inflammation and insulin resistance?Frontiers in endocrinology · 2024Review
- Systems genetics uncover new loci containing functional gene candidates inbioRxiv : the preprint server for biology · 2023Article
- From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning.Military Medical Research · 2023Review
- Latent Tuberculosis: Challenges in Diagnosis and Treatment, Perspectives, and the Crucial Role of Biomarkers.Current microbiology · 2023Review
- Diagnostic performance of host protein signatures as a triage test for active pulmonary TB.Journal of clinical microbiology · 2023Article
- Identification of circulating monocytes as producers of tuberculosis disease biomarker C1q.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
18 authors at 7 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: There is an urgent medical need to differentiate active tuberculosis (ATB) from latent tuberculosis infection (LTBI) and prevent undertreatment and overtreatment. The aim of this study was to identify biomarker profiles that may support the differentiation between ATB and LTBI and to validate these signatures. Materials and Methods: The discovery cohort included adult individuals classified in four groups: ATB (n = 20), LTBI without prophylaxis (untreated LTBI; n = 20), LTBI after completion of prophylaxis (treated LTBI; n = 20), and healthy controls (HC; n = 20). Their sera were analyzed for 40 cytokines/chemokines and activity of adenosine deaminase (ADA) isozymes. A prediction model was designed to differentiate ATB from untreated LTBI using sparse partial least squares (sPLS) and logistic regression analyses. Serum samples of two independent cohorts (national and international) were used for validation. Results: sPLS regression analyses identified C-C motif chemokine ligand 1 (CCL1), C-reactive protein (CRP), C-X-C motif chemokine ligand 10 (CXCL10), and vascular endothelial growth factor (VEGF) as the most discriminating biomarkers. These markers and ADA(2) activity were significantly increased in ATB compared to untreated LTBI (p ≤ 0.007). Combining CCL1, CXCL10, VEGF, and ADA2 activity yielded a sensitivity and specificity of 95% and 90%, respectively, in differentiating ATB from untreated LTBI. These findings were confirmed in the validation cohort including remotely acquired untreated LTBI participants. Conclusion: The biomarker signature of CCL1, CXCL10, VEGF, and ADA2 activity provides a promising tool for differentiating patients with ATB from non-treated LTBI individuals.
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