Plasma viral load and CD4+ lymphocytes as prognostic markers of HIV-1 infection.
ABSTRACT The rate of disease progression among persons infected with human immunodeficiency virus type 1 (HIV-1) varies widely, and the relative prognostic value of markers of disease activity has not been defined.
To compare clinical, serologic, cellular, and virologic markers for their ability to predict progression to the acquired immunodeficiency syndrome (AIDS) and death during a 10-year period.
Prospective, multicenter cohort study.
Four university-based clinical centers participating in the Multicenter AIDS Cohort Study.
1604 men infected with HIV-1.
The markers compared were oral candidiasis (thrush) or fever; serum neopterin levels; serum beta 2-microglobulin levels; number and percentage of CD3+, CD4+, and CD8+ lymphocytes; and plasma viral load, which was measured as the concentration of HIV-1 RNA found using a sensitive branched-DNA signal-amplification assay.
Plasma viral load was the single best predictor of progression to AIDS and death, followed (in order of predictive strength) by CD4+ lymphocyte count and serum neopterin levels, serum beta 2-microglobulin levels, and thrush or fever. Plasma viral load discriminated risk at all levels of CD4+ lymphocyte counts and predicted their subsequent rate of decline. Five risk categories were defined by plasma HIV-1 RNA concentrations: 500 copies/mL or less, 501 to 3000 copies/mL, 3001 to 10000 copies/mL, 10001 to 30000 copies/mL, and more than 30000 copies/mL. Highly significant (P < 0.001) differences in the percentages of participants who progressed to AIDS within 6 years were seen in the five risk categories: 5.4%, 16.6%, 31.7%, 55.2%, and 80.0%, respectively. Highly significant (P < 0.001) differences in the percentages of participants who died of AIDS within 6 years were also seen in the five risk categories: 0.9%, 6.3%, 18.1%, 34.9%, and 69.5%, respectively. A regression tree incorporating both HIV-1 RNA measurements and CD4+ lymphocyte counts provided better discrimination of outcome than did either marker alone; use of both variables defined categories of risk for AIDS within 6 years that ranged from less than 2% to 98%.
Plasma viral load strongly predicts the rate of decrease in CD4+ lymphocyte count and progression to AIDS and death, but the prognosis of HIV-infected persons is more accurately defined by combined measurement of plasma HIV-1 RNA and CD4+ lymphocytes.
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ABSTRACT: Plasma HIV-1 RNA concentration, or viral load, is an indication of the magnitude of virus replication and largely correlates with disease progression in an infected person. It is a very useful guide for initiation of therapy and monitoring of response to antiretroviral drugs. Although the majority of patients who are not on antiretroviral therapy (ART) have a high viral load, a small proportion of ART naive patients are known to maintain low levels or even undetectable viral load levels. In this study, we determined the rate of undetectable HIV-1 RNA among ART naive HIV positive patients who presented for treatment at the University College Hospital (UCH), Ibadan, Nigeria from 2005 to 2011. Baseline viral load and CD4 lymphocyte cell counts of 14,662 HIV positive drug naive individuals were determined using the Roche Amplicor version 1.5 and Partec easy count kit, respectively. The detection limits of the viral load assay are 400 copies/mL and 750,000 copies/mL for lower and upper levels, respectively. A total of 1,399 of the 14,662 (9.5%) HIV-1 positive drug naive individuals had undetectable viral load during the study period. In addition, the rate of non-detectable viral load increased over the years. The mean CD4 counts among HIV-1 infected individuals with detectable viral load (266 cells/μL; range = 1 to 2,699 cells/μL) was lower than in patients with undetectable viral load (557 cells/μL; range = 1 to 3,102 cells/μL). About 10% of HIV-1 infected persons in our study population had undetectable viral load using the Roche Amplicor version 1.5.Virology: Research and Treatment 01/2013; 4:35-40.
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ABSTRACT: Flow cytometry (FC) is a single-cell profiling platform for measuring the phenotypes of individual cells from millions of cells in biological samples. FC employs high-throughput technologies and generates high-dimensional data, and hence algorithms for analyzing the data represent a bottleneck. This dissertation addresses several computational challenges arising in modern cytometry while mining information from high-dimensional and high-content biological data. A collection of combinatorial and statistical algorithms for locating, matching, prototyping, and classifying cellular populations from multi-parametric FC data is developed. The algorithmic pipeline, flowMatch, developed in this dissertation consists of five well-defined algorithmic modules to (1) transform data to stabilize within-population variance, (2) identify cell populations by robust clustering algorithms, (3) register cell populations across samples, (4) encapsulate a class of samples with templates, and (5) classify samples based on their similarity with the templates. Components of flowMatch can work independently or collaborate with each other to perform the complete data analysis. flowMatch is made available as an open-source R package in Bioconductor. We have employed flowMatch for classifying leukemia samples, evaluating the phosphorylation effects on T cells, classifying healthy immune profiles, and classifying the vaccination status of HIV patients. In these analyses, the pipeline is able to reach biologically meaningful conclusions quickly and efficiently with the automated algorithms. The algorithms included in flowMatch can also be applied to problems outside of flow cytometry such as in microarray data analysis and image recognition. Therefore, this dissertation contributes to the solution of fundamental problems in computational cytometry and related domains.01/2015;
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ABSTRACT: It has been suggested that HIV-1 has evolved its set-point virus load to be optimized for transmission. Previous epidemiological models and studies into the heritability of set-point virus load confirm that this mode of adaptation within the human population is feasible. However, during the many cycles of replication between infection of a host and transmission to the next host, HIV-1 is under selection for escape from immune responses, and not transmission. Here we investigate with computational and mathematical models how these two levels of selection, within-host and between-host, are intertwined. We find that when the rate of immune escape is comparable to what has been observed in patients, immune selection within hosts is dominant over selection for transmission. Surprisingly, we do find high values for set-point virus load heritability, and argue that high heritability estimates can be caused by the 'footprints' left by differing hosts' immune systems on the virus.PLoS Computational Biology 12/2014; 10(12):e1003899. · 4.87 Impact Factor