UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Plasma Biomarkers to Detect Prevalent or Predict Progressive Tuberculosis Associated With Human Immunodeficiency Virus-1

Lesosky, M; Rangaka, MX; Pienaar, C; Coussens, AK; Goliath, R; Mathee, S; Mwansa-Kambafwile, J; ... Wilkinson, KA; + view all (2019) Plasma Biomarkers to Detect Prevalent or Predict Progressive Tuberculosis Associated With Human Immunodeficiency Virus-1. Clinical Infectious Diseases , 69 (2) pp. 295-305. 10.1093/cid/ciy823. Green open access

[img]
Preview
Text
Rangaka_Plasma biomarkers to detect prevalent or predict progressive tuberculosis associated with human immunodeficiency virus-1_VoR.pdf - Published version

Download (3MB) | Preview

Abstract

BACKGROUND: The risk of individuals infected with human immunodeficiency virus (HIV)-1 developing tuberculosis (TB) is high, while both prognostic and diagnostic tools remain insensitive. The potential for plasma biomarkers to predict which HIV-1-infected individuals are likely to progress to active disease is unknown. METHODS: Thirteen analytes were measured from QuantiFERON Gold in-tube (QFT) plasma samples in 421 HIV-1-infected persons recruited within the screening and enrollment phases of a randomized, controlled trial of isoniazid preventive therapy. Blood for QFT was obtained pre-randomization. Individuals were classified into prevalent TB, incident TB, and control groups. Comparisons between groups, supervised learning methods, and weighted correlation network analyses were applied utilizing the unstimulated and background-corrected plasma analyte concentrations. RESULTS: Unstimulated samples showed higher analyte concentrations in the prevalent and incident TB groups compared to the control group. The largest differences were seen for C-X-C motif chemokine 10 (CXCL10), interleukin-2 (IL-2), IL-1α, transforming growth factor-α (TGF-α). A predictive model analysis using unstimulated analytes discriminated best between the control and prevalent TB groups (area under the curve [AUC] = 0.9), reasonably well between the incident and prevalent TB groups (AUC > 0.8), and poorly between the control and incident TB groups. Unstimulated IL-2 and IFN-γ were ranked at or near the top for all comparisons, except the comparison between the control vs incident TB groups. Models using background-adjusted values performed poorly. CONCLUSIONS: Single plasma biomarkers are unlikely to distinguish between disease states in HIV-1 co-infected individuals, and combinations of biomarkers are required. The ability to detect prevalent TB is potentially important, as no blood test hitherto has been suggested as having the utility to detect prevalent TB amongst HIV-1 co-infected persons.

Type: Article
Title: Plasma Biomarkers to Detect Prevalent or Predict Progressive Tuberculosis Associated With Human Immunodeficiency Virus-1
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1093/cid/ciy823
Publisher version: https://doi.org/10.1093/cid/ciy823
Language: English
Additional information: Copyright © The Author(s) 2018. Published by Oxford University Press for the Infectious Diseases Society of America. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: HIV-1, biomarker, plasma, predictive, tuberculosis
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute for Global Health
URI: https://discovery.ucl.ac.uk/id/eprint/10082352
Downloads since deposit
13Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item