| Literature DB >> 31185037 |
Lucía Pastor1,2,3,4, Jost Langhorst5,6, Dorit Schröder5,6, Aina Casellas1, Andreas Ruffer5,6, Jorge Carrillo1,2, Victor Urrea2, Sergio Massora4, Inacio Mandomando4, Julià Blanco2,3,7, Denise Naniche1,4.
Abstract
INTRODUCTION: Primary HIV infection (PHI) is the initial phase after HIV acquisition characterized by high viral replication, massive inflammatory response and irreversible immune-damage, particularly at the gastrointestinal level. In this study we aimed to characterize the dynamics of gastrointestinal damage biomarkers during the different phases of HIV infection and assess their association with HIV-disease markers and their accuracy to differentiate PHI from chronic HIV infection (CHI).Entities:
Mesh:
Substances:
Year: 2019 PMID: 31185037 PMCID: PMC6559643 DOI: 10.1371/journal.pone.0218000
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Clinical and demographic characteristics of study population according to HIV-status.
| 1st follow-up visit PHI (n = 40) | HIV-uninfected (n = 58) | CHI-naive (n = 26) | CHI-ART (n = 30) | P-value | |
|---|---|---|---|---|---|
Comparisons for proportions were performed by chi2 testᵡ and continuous variables by Mann and Whitney U-test* for the two group comparison and global comparison by Kruskal Wallis test**. SD, standard deviation; F, females; IQR, interquartile range; PHI, primary HIV infection; CHI, chronic HIV infection; CHI-ART, CHI on antiretroviral-treatment. Intestinal infection includes positive result for bacterial, parasite or protozoa testing in stool sample according to the methodology previously described [33].
Fig 1Dynamics of plasma and stool intestinal damage biomarkers during primary HIV-infection (PHI).
Dynamics of plasma biomarkers associated with intestinal damage (A) and dynamics of stool biomarkers associated with innate immunity (B) and intestinal permeability (C) are shown. Relative changes (Z-score) of the biomarker levels during PHI with respect to the HIV-uninfected group have been represented by a transformation of the fitted longitudinal models after subtracting the mean and dividing by the standard deviation of HIV-non infected distribution. Values were logarithmic transformed in the cases where it was required to correct non-normal distributions.
Fig 2Dynamics of highly significant biomarkers comparing chronic HIV infection (CHI) with PHI and HIV-uninfected individuals (Ctrl).
Characterization of plasma zonulin (A), stool lactoferrin (B), stool zonulin (C) and stool calprotectin (D) across different study groups and along time post-HIV-infection. M, months after HIV infection. Box as interquartile range (IQR), middle line as median, whiskers as maximum and minimum, dots as individual observations. Individual comparisons between different groups were performed using posthoc pairwise comparisons with Tukey and Kramer (Nemenyi) test (A, B and C). Individual comparisons of left-censored data were performed using Peto-Peto test with Holm adjust for multiple comparisons (D). Comparisons with PHI group were performed considering the biomarker level at 2-months post-HIV-infection (M2). Significance is indicated as *** if P < 0.001, ** if P < 0.01, and * if P < 0.05.
Fig 3Biomarkers with the best predictive ability to differentiate PHI from CHI-naïve individuals.
The ability of each biomarker's first determination to distinguish between PHI patients and CHI-naïve was assessed via logistic regression models. Receiver operating curves (ROC) curves from univariate models were compared for the best prediction. Optimal threshold was determined through maximization of the nearest to (0,1) method. Sensitivity as true positive rate and Specificity as true negative rate. AUC as area under the curve.
Fig 4Performance of univariate and multivariate biomarkers models in predicting primary HIV infection.
A) Comparison between ROC curves for plasma zonulin univariate model and multivariate model adjusted by age, plasma zonulin and plasma sCD14. AUC as area under the curve. B) Cut-off values for plasma zonulin univariate model (ng/mL) and multivariate model (score) with their respective sensitivity and specificity values. C) Prediction of plasma zonulin univariate model with a cutoff of ≥ 8.75 ng/mL was compared to that of the multivariate model with a score cutoff of ≥ 0.56.