Evidence map›Paper›PMID 19663693›Full record

ArticleClinical infectious diseases : an official publication of the Infectious Diseases Society of America2009

Impact of select immunologic and virologic biomarkers on CD4 cell count decrease in patients with chronic HIV-1 subtype C infection: results from Sinikithemba Cohort, Durban, South Africa.

Zabrina Brumme, Bingxia Wang, Kriebashne Nair, Chanson Brumme, Chantal de Pierres, Shabashini Reddy, Boris Julg, Eshia Moodley, Christina Thobakgale, Zhigang Lu and 14 more

Abstract read
In one paragraph

Article in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 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

24 authors.

Zabrina BrummeRagon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Boston, USA.
Bingxia Wang
Kriebashne Nair
Chanson Brumme
Chantal de Pierres
Shabashini Reddy
Boris Julg
Eshia Moodley
Christina Thobakgale
Zhigang Lu
Mary van der Stok
Karen Bishop
Zenele Mncube
Fundisiwe Chonco
Yuko Yuki
Nicole Frahm
Christian Brander
Mary Carrington
Kenneth Freedberg
Photini Kiepiela
Philip Goulder
Bruce Walker
Thumbi Ndung'u
Elena Losina

Funding

The impact of HIV viral diversity and cellular immunity on HIV pathogenesisP30AI060354 · NIAID · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI ROBERT A. PARKER · 2004 to 2026
$94.4M
Effects of genetic polymorphism in MHC, KIR, and related loci on human diseaseZIABC010791 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI CARRINGTON, MARY N. · 2009 to 2025
$18.2M
Optimizing CD8+ T-cell responses against C clade HIV infection in subSaharan AfriR01AI046995 · NIAID · UNIVERSITY OF OXFORD · PI GOULDER, PHILIP J · 2000 to 2014
$6.8M
Pathogenesis of Clade C HIV InfectionR01AI067073 · NIAID · MASSACHUSETTS GENERAL HOSPITAL · PI WALKER, BRUCE D · 2006 to 2010
$2.9M
CCR NIH HHS HHSN261200800001CIntramural NIH HHSNCI NIH HHS HHSN261200800001ENIAID NIH HHS N01-AI-15422NIAID NIH HHS P30 AI060354NIAID NIH HHS R01 AI046995NIAID NIH HHS R01 AI067073NIAID NIH HHS R01-AI067073NIAID NIH HHS R01-AI46995Wellcome Trust
6 · The paper itself

Abstract

backgroundThe extent to which immunologic and clinical biomarkers influence human immunodeficiency virus type 1 (HIV-1) infection outcomes remains incompletely characterized, particularly for non-B subtypes. On the basis of data supporting in vitro HIV-1 protein-specific CD8 T lymphocyte responses as correlates of immune control in cross-sectional studies, we assessed the relationship of these responses, along with established HIV-1 biomarkers, with rates of CD4 cell count decrease in individuals infected with HIV-1 subtype C.

methodsBivariate and multivariate mixed-effects models were used to assess the relationship of baseline CD4 cell count, plasma viral load, human leukocyte antigen (HLA) class I alleles, and HIV-1 protein-specific CD8 T cell responses with the rate of CD4 cell count decrease in a longitudinal population-based cohort of 300 therapy-naive, chronically infected adults with baseline CD4 cell counts >200 cells/mm(3) and plasma viral loads >500 copies/mL over a median of 25 months of follow-up.

resultsIn bivariate analyses, baseline CD4 cell count, plasma viral load, and possession of a protective HLA allele correlated significantly with the rate of CD4 cell count decrease. No relationship was observed between HIV-1 protein-specific CD8 T cell responses and CD4 cell count decrease. Results from multivariate models incorporating baseline CD4 cell counts (201-350 vs >350 cells/mm(3)), plasma viral load (< or =100,000 vs >100,000 copies/mL), and HLA (protective vs not protective) yielded the ability to discriminate CD4 cell count decreases over a 10-fold range. The fastest decrease was observed among individuals with CD4 cell counts >350 cells/mm(3) and plasma viral loads >100,000 copies/mL with no protective HLA alleles (-59 cells/mm(3) per year), whereas the slowest decrease was observed among individuals with CD4 cell counts 201-350 cells/mm(3), plasma viral loads < or =100,000 copies/mL, and a protective HLA allele (-6 cells/mm(3) per year).

conclusionsThe combination of plasma viral load and HLA class I type, but not in vitro HIV-1 protein-specific CD8 T cell responses, differentiates rates of CD4 cell count decrease in patients with chronic subtype-C infection better than either marker alone.

Indexed as

CD4 Lymphocyte CountHIV-1HLA AntigensViral LoadAdultAllelesBiomarkersCD4-CD8 RatioCD8-Positive T-LymphocytesCohort StudiesFemaleHIV InfectionsHumansLongitudinal StudiesMaleMultivariate AnalysisBiomarkersHLA Antigens

Identifiers

PMID19663693
PMCPMC2777678

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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.