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. 2014 Oct 15;210(8):1228-38.
doi: 10.1093/infdis/jiu238. Epub 2014 Apr 21.

Gut epithelial barrier dysfunction and innate immune activation predict mortality in treated HIV infection

Peter W Hunt  1 , Elizabeth Sinclair  1 , Benigno Rodriguez  2 , Carey Shive  2 , Brian Clagett  2 , Nicholas Funderburg  3 , Janet Robinson  2 , Yong Huang  4 , Lorrie Epling  1 , Jeffrey N Martin  5 , Steven G Deeks  1 , Curtis L Meinert  6 , Mark L Van Natta  6 , Douglas A Jabs  7 , Michael M Lederman  2
Affiliations

Gut epithelial barrier dysfunction and innate immune activation predict mortality in treated HIV infection

Peter W Hunt et al. J Infect Dis. .

Abstract

Background: While inflammation predicts mortality in treated human immunodeficiency virus (HIV) infection, the prognostic significance of gut barrier dysfunction and phenotypic T-cell markers remains unclear.

Methods: We assessed immunologic predictors of mortality in a case-control study within the Longitudinal Study of the Ocular Complications of AIDS (LSOCA), using conditional logistic regression. Sixty-four case patients who died within 12 months of treatment-mediated viral suppression were each matched to 2 control individuals (total number of controls, 128) by duration of antiretroviral therapy-mediated viral suppression, nadir CD4(+) T-cell count, age, sex, and prior cytomegalovirus (CMV) retinitis. A similar secondary analysis was conducted in the SCOPE cohort, which had participants with less advanced immunodeficiency.

Results: Plasma gut epithelial barrier integrity markers (intestinal fatty acid binding protein and zonulin-1 levels), soluble CD14 level, kynurenine/tryptophan ratio, soluble tumor necrosis factor receptor 1 level, high-sensitivity C-reactive protein level, and D-dimer level all strongly predicted mortality, even after adjustment for proximal CD4(+) T-cell count (all P ≤ .001). A higher percentage of CD38(+)HLA-DR(+) cells in the CD8(+) T-cell population was a predictor of mortality before (P = .031) but not after (P = .10) adjustment for proximal CD4(+) T-cell count. Frequencies of senescent (defined as CD28(-)CD57(+) cells), exhausted (defined as PD1(+) cells), naive, and CMV-specific T cells did not predict mortality.

Conclusions: Gut epithelial barrier dysfunction, innate immune activation, inflammation, and coagulation-but not T-cell activation, senescence, and exhaustion-independently predict mortality in individuals with treated HIV infection with a history of AIDS and are viable targets for interventions.

Keywords: CD28; CD38; CD57; D-dimer; HIV; HLA-DR; IL-6; T-cell activation; antiretroviral therapy; cytomegalovirus; gut epithelial cell barrier; hsCRP; immune activation; intestinal fatty acid binding protein (I-FABP); mortality; sCD14; zonulin-1.

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Figures

Figure 2.
Figure 2.
Relationship between plasma markers of gut epithelial barrier integrity and innate immune activation. The relationships between plasma intestinal fatty acid binding protein (I-FABP) and the plasma zonulin-1 level (A), soluble CD14 (sCD14) level (B), kynurenine/tryptophan (KT) ratio (C), interleukin 6 (IL-6) level (D), and D-dimer level (E) and the proximal CD4+ T-cell count (F) were assessed using Spearman rank order correlations.
Figure 1.
Figure 1.
Relationships between soluble immunologic markers and mortality in Longitudinal Study of the Ocular Complications of AIDS (LSOCA) and SCOPE participants. The distribution of proximal CD4+ T-cell counts (A) and plasma intestinal fatty acid binding protein (I-FABP) levels (B), zonulin-1 levels (C), soluble CD14 (sCD14) levels (D), interleukin 6 (IL-6) levels (E), kynurenine/tryptophan (KT) ratio (F), soluble tumor necrosis factor receptor-1 (sTNF-R1) levels (G), high-sensitivity C-reactive protein (hsCRP) levels (H), and cytomegalovirus (CMV) immunoglobulin G (IgG) indexes are plotted for case patients who died after confirmed antiretroviral therapy–mediated viral suppression and matched controls in the LSOCA and SCOPE cohorts. P values represent the statistical significance for each marker modeled continuously in conditional regression models (Tables 2 and 3).
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