Evidence map›Paper›PMID 30256919›Full record

ArticleClinical infectious diseases : an official publication of the Infectious Diseases Society of America2019

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

Maia Lesosky, Molebogeng X Rangaka, Cara Pienaar, Anna K Coussens, Rene Goliath, Shaheed Mathee, Judith Mwansa-Kambafwile, Gary Maartens, Robert J Wilkinson, Katalin Andrea Wilkinson

Abstract read
In one paragraph

Article in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Maia LesoskyDivision of Epidemiology & Biostatistics, School of Public Health and Family Medicine.
Molebogeng X RangakaWellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Diseases and Molecular Medicine, Observatory, South Africa.
Cara PienaarDivision of Epidemiology & Biostatistics, School of Public Health and Family Medicine.
Anna K CoussensWellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Diseases and Molecular Medicine, Observatory, South Africa.
Rene GoliathWellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Diseases and Molecular Medicine, Observatory, South Africa.
Shaheed MatheeSite B Khayelitsha Community Health Centre, Western Cape Department of Health, South Africa.
Judith Mwansa-KambafwileWellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Diseases and Molecular Medicine, Observatory, South Africa.
Gary MaartensDepartment of Medicine, Faculty of Health Sciences, University of Cape Town, Observatory, South Africa.
Robert J WilkinsonWellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Diseases and Molecular Medicine, Observatory, South Africa.
Katalin Andrea WilkinsonWellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Diseases and Molecular Medicine, Observatory, South Africa.

Funding

Validation and Application of a Model for Human-like TB Latency in RabbitsU19AI111276 · NIAID · RBHS-NEW JERSEY MEDICAL SCHOOL · PI ALLAND, DAVID, HORSBURGH, CHARLES ROBERT · 2014 to 2020
$23.3M
Medical Research Council MC_U117588499NIAID NIH HHS U19 AI111276Wellcome TrustWellcome Trust 084670Wellcome Trust 104803Wellcome Trust 203135
6 · The paper itself

Abstract

backgroundThe 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.

methodsThirteen 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.

resultsUnstimulated 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.

conclusionsSingle 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.

Indexed as

AdultBiomarkersClinical Decision RulesDiagnostic Tests, RoutineFemaleHIV InfectionsHumansMalePlasmaTuberculosisBiomarkersbiomarkerHIV-1plasmapredictivetuberculosis

Identifiers

PMID30256919
PMCPMC6603269

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.