TY - JOUR
T1 - Biomarkers of progression after HIV acute/early infection
T2 - Nothing compares to CD4+ T-cell count?
AU - Turk, Gabriela
AU - Ghiglione, Yanina
AU - Hormanstorfer, Macarena
AU - Laufer, Natalia
AU - Coloccini, Romina
AU - Salido, Jimena
AU - Trifone, César
AU - Ruiz, María Julia
AU - Falivene, Juliana
AU - Holgado, María Pía
AU - Caruso, María Paula
AU - Figueroa, María Inés
AU - Salomón, Horacio
AU - Giavedoni, Luis D.
AU - Pando, María de los Ángeles
AU - Gherardi, María Magdalena
AU - Rabinovich, Roberto Daniel
AU - Pury, Pedro A.
AU - Sued, Omar
N1 - Funding Information:
Acknowledgments: Authors specially acknowledge study participants for agreeing to participate in this study and to provide blood samples. We thank Sergio Mazzini for language assistance during manuscript preparation. This work was supported by grants from the Agencia Nacional de Promoción Científica y Tecnológica and GlaxoSmithKline (PICTO-GSK, Grant # 2013/0006) and from Universidad de Buenos Aires (UBACyT 2013–2016, Grant # 20020120200263BA) to Gabriela Turk. This investigation also used resources that were supported by the Southwest National Primate Research Center grant P51 OD011133 from the Office of Research Infrastructure Programs, National Institutes of Health (NIH) to Luis D. Giavedoni. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
PY - 2018/1/13
Y1 - 2018/1/13
N2 - Progression of HIV infection is variable among individuals, and definition disease progression biomarkers is still needed. Here, we aimed to categorize the predictive potential of several variables using feature selection methods and decision trees. A total of seventy-five treatment-naïve subjects were enrolled during acute/early HIV infection. CD4+ T-cell counts (CD4TC) and viral load (VL) levels were determined at enrollment and for one year. Immune activation, HIV-specific immune response, Human Leukocyte Antigen (HLA) and C-C chemokine receptor type 5 (CCR5) genotypes, and plasma levels of 39 cytokines were determined. Data were analyzed by machine learning and non-parametric methods. Variable hierarchization was performed by Weka correlation-based feature selection and J48 decision tree. Plasma interleukin (IL)-10, interferon gamma-induced protein (IP)-10, soluble IL-2 receptor alpha (sIL-2Rα) and tumor necrosis factor alpha (TNF-α) levels correlated directly with baseline VL, whereas IL-2, TNF-α, fibroblast growth factor (FGF)-2 and macrophage inflammatory protein (MIP)-1β correlated directly with CD4+ T-cell activation (p < 0.05). However, none of these cytokines had good predictive values to distinguish “progressors” from “non-progressors”. Similarly, immune activation, HIV-specific immune responses and HLA/CCR5 genotypes had low discrimination power. Baseline CD4TC was the most potent discerning variable with a cut-off of 438 cells/μL (accuracy = 0.93, κ-Cohen = 0.85). Limited discerning power of the other factors might be related to frequency, variability and/or sampling time. Future studies based on decision trees to identify biomarkers of post-treatment control are warrantied.
AB - Progression of HIV infection is variable among individuals, and definition disease progression biomarkers is still needed. Here, we aimed to categorize the predictive potential of several variables using feature selection methods and decision trees. A total of seventy-five treatment-naïve subjects were enrolled during acute/early HIV infection. CD4+ T-cell counts (CD4TC) and viral load (VL) levels were determined at enrollment and for one year. Immune activation, HIV-specific immune response, Human Leukocyte Antigen (HLA) and C-C chemokine receptor type 5 (CCR5) genotypes, and plasma levels of 39 cytokines were determined. Data were analyzed by machine learning and non-parametric methods. Variable hierarchization was performed by Weka correlation-based feature selection and J48 decision tree. Plasma interleukin (IL)-10, interferon gamma-induced protein (IP)-10, soluble IL-2 receptor alpha (sIL-2Rα) and tumor necrosis factor alpha (TNF-α) levels correlated directly with baseline VL, whereas IL-2, TNF-α, fibroblast growth factor (FGF)-2 and macrophage inflammatory protein (MIP)-1β correlated directly with CD4+ T-cell activation (p < 0.05). However, none of these cytokines had good predictive values to distinguish “progressors” from “non-progressors”. Similarly, immune activation, HIV-specific immune responses and HLA/CCR5 genotypes had low discrimination power. Baseline CD4TC was the most potent discerning variable with a cut-off of 438 cells/μL (accuracy = 0.93, κ-Cohen = 0.85). Limited discerning power of the other factors might be related to frequency, variability and/or sampling time. Future studies based on decision trees to identify biomarkers of post-treatment control are warrantied.
KW - Acute infection
KW - Biomarkers
KW - Decision trees
KW - Disease progression
KW - HIV
KW - HLA
KW - Immune responses
KW - Soluble plasma factors
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U2 - 10.3390/v10010034
DO - 10.3390/v10010034
M3 - Article
AN - SCOPUS:85040968136
VL - 10
JO - Viruses
JF - Viruses
SN - 1999-4915
IS - 1
M1 - 34
ER -