Diagnostic accuracy of combinations of serological biomarkers for identifying clinical tuberculosis

Zaida Araujo, Noe Macias-Segura, Juan E. López-Ramos, Jacobus Henri de Waard, Magnolia Vanegas, Manuel Alfonso Patarroyo, Antonio Salgado, José Antonio Enciso-Moreno

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5 Scopus citations


Introduction: Confirmation of tuberculosis (TB) cases in endemic TB settings is a challenge; obtaining fast and cheap, though accurate, diagnostic tools such as biomarkers is thus urgently needed to enable the early detection of TB. This paper evaluates the diagnostic accuracy of combinations of host serological biomarkers for identifying TB. Methodology: Enzyme-linked immunosorbent assays (ELISA) were used on 70 Venezuelan Creole individuals for evaluating host biomarkers (i.e. CXCL9, sCD14, MMP9 and uPAR proteins) and anti-synthetic peptides covering certain Mycobacterium tuberculosis (Mtb) ESAT-6 (P-12033, P-12034 and P-12037) and Ag85A (P-29878) antigen sequences. The target population consisted of adults having active TB (ATB, n = 28), the tuberculin skin test positive (TST+) or individuals with latent TB infection (LTB, n = 28) and TST- or control subjects (CTRL, n = 14). Results: Receiver operator curve (ROC) analysis revealed good biosignature discriminative ability for 5 serological biomarkers; the accuracy of 3 combinations had a good discriminative ability for diagnosing TB. Anti-P-12034/uPAR detected TB with 96.7% sensitivity and 86.0% specificity, followed by anti-P-12033/uPAR having 96.7% sensitivity and 81.4% specificity. Anti-P-29878/MMP9 had the highest sensitivity (100%), but low specificity (52.17%). Biomarker combinations did not prove efficacious for identifying incipient subclinical TST+TB subjects at high-risk for TB. Conclusions: The anti-P-12034/uPAR combination could be useful for identifying clinical TB patients. Such an approach holds promise for further validation.

Original languageEnglish (US)
Pages (from-to)429-441
Number of pages13
JournalJournal of Infection in Developing Countries
Issue number6
StatePublished - Jun 2018

All Science Journal Classification (ASJC) codes

  • Parasitology
  • Microbiology
  • Infectious Diseases
  • Virology


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