Elevated liver stiffness is linked to increased biomarkers of inflammation and immune activation in HIV/HCV-coinfected patients

Link: https://www.ncbi.nlm.nih.gov/pubmed/29438197

Authors: Luz Maria MEDRANO 1, Pilar GARCIA-BRONCANO 1,2, Juan BERENGUER 3,4, Juan GONZÁLEZ-GARCÍA 5, Mª Ángeles JIMÉNEZ-SOUSA 1, Josep M GUARDIOLA 6, Manuel CRESPO 7, Carmen QUEREDA 8, José SANZ 9, Isabel CANOREA 1, Ana CARRERO 3,4, Victor HONTAÑÓN 5, Mª Ángeles MUÑOZ-FERNÁNDEZ 4, 10, Salvador RESINO 1, and the GESIDA 3603b Study Group

Authors’ affiliations:

(1) Unidad de Infección Viral e Inmunidad, Centro Nacional de Microbiología, Instituto de Salud Carlos III, Majadahonda, Madrid, Spain.

(2) Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA

(3) Unidad de Enfermedades Infecciosas/VIH; Hospital General Universitario “Gregorio Marañón”, Madrid, Spain.

(4) Instituto de Investigación Sanitaria del Gregorio Marañón, Madrid, Spain.

(5), Unidad de VIH; Servicio de Medicina Interna, Hospital Universitario “La Paz”, Madrid, Spain.

(6) Hospital Santa Creu i Sant Pau, Barcelona, Spain

(7) Hospital Alvaro Cunqueiro (Complejo Hospitalario Universitario de Vigo), Vigo; Pontevedra, Spain

(8) Hospital Universitario Ramón y Cajal. Madrid, Spain

(9) Hospital Universitario Príncipe de Asturias. Alcalá de Henares, Madrid, Spain

(10) Lab InmunoBiología Molecular, Servicio de Inmunología. Hospital General Universitario “Gregorio Marañón”, Madrid, Spain.

Corresponding author: Salvador Resino; Centro Nacional de Microbiología, Instituto de Salud Carlos III (Campus Majadahonda); Carretera Majadahonda- Pozuelo, Km 2.2; 28220 Majadahonda (Madrid); Phone: +34918223266. E-mail: sresino@isciii.es

Abstract

Objectives: Immune dysregulation is a hallmark of HIV and HCV infections. To evaluate the relationship between liver stiffness measure (LSM) and biomarkers of T cell activation, bacterial translocation, inflammation, endothelial dysfunction, and coagulopathy in HIV/HCV-coinfected patients.

Design: Cross-sectional study.

Methods: We studied 238 HIV/HCV-coinfected patients, 32 healthy controls and 39 HIV-monoinfected patients. Patients were stratified according to LSM into four groups: <12.5 kPa, 12.5 to 25 kPa, 25 to 40 kPa, and >40 kPa. T-cell subsets were measured using flow cytometry and plasma biomarkers using immunoassays.

Results: HIV/HCV-coinfected patients had higher biomarker levels of immune activation in peripheral blood [T cell activation (CD4+CD38+ and CD8+CD38+), bacterial translocation (sCD14), inflammation (IL-1b, IL-6, IL-8, IL-18, IP-10) endothelial dysfunction (sVCAM1, sICAM1, and sTNFR1), and coagulopathy (PAI-1)] than healthy controls and HIV-monoinfected patients. Moreover, in HIV/HCV-coinfected patients, a direct relationship between LSM and immune activation [T cell activation (CD8+CD38+ bacterial translocation (LPS), inflammation (IL-8, IP-10), endothelial dysfunction (sVCAM1, sICAM1, and sTNFR1), and coagulopathy (D-dimer)] was found. Subsequently, patients were stratified into different fibrosis stages, finding that patients with cirrhosis who had LSM>40 kPa showed higher biomarker values of immune activation [T cell activation (CD4+CD38+ and CD8+CD38+), bacterial translocation (LPS), inflammation (IL-8, IL-6, IP-10), endothelial dysfunction (sVCAM1, sICAM1 and sTNFR1), and coagulopathy (D-dimer)] than patients from the other three groups (<12.5 kPa, 12.5-25 kPa, and 25-40 kPa).

Conclusion: T cell activation, bacterial translocation, inflammation, endothelial dysfunction, and coagulopathy increased with the severity of liver fibrosis in HIV/HCV-coinfected patients, particularly in patients who had LSM>40 KPa.

Key Words

Chronic hepatitis C; HIV; cirrhosis; T cell activation; bacterial translocation; inflammation; coagulopathy

Introduction

Hepatitis C virus (HCV) infection is common among human immunodeficiency virus (HIV) -infected people due to similar routes of transmission [1]. Chronic hepatitis C (CHC) has been a leading comorbidity in HIV-infected patients since the introduction of combination antiretroviral therapy (cART) [2, 3]. The course of CHC may be accelerated in patients coinfected with HIV, resulting in higher rates of fibrosis progression, cirrhosis, and end-stage liver disease than HCV-monoinfected patients [4]. HIV infection leads to a gradual CD4+ T cell count decline and causes persistent innate and acquired immune activation, which contributes to the pathogenesis of both AIDS and non-AIDS related diseases [5, 6]. Additionally, despite suppressive cART, HIV-infected patients show abnormally high levels of plasma biomarkers related to immune activation, inflammatory and coagulation markers that may predict increased morbidity and mortality [5]. This immune activation is related to several situations, such as the persistence of HIV replication, HCV coinfection, and bacterial translocation defined as the passage of bacteria or microbial products from the intestinal lumen to mesenteric lymph nodes or other extra-intestinal sites caused by HIV itself and CHC [6, 7].

Bacterial translocation is a key factor in the pathogenesis of both HIV and HCV infection, especially in the advanced stages of disease when bacterial translocation increases (AIDS [8] and cirrhosis [9]). During HIV infection, the depletion of CD4+ T cells in gut-associated lymphoid tissue (GALT) compromises gut mucosal integrity, promoting increased intestinal permeability that leads to bacterial translocation [8]. In HCV infection, there is a pathological increase in bacterial translocation in liver cirrhosis due to changes and overgrowth of intestinal microbiota, an increase in intestinal permeability and the dysregulation of the immune response in GALT [9]. During this process, host immune cells are stimulated by bacterial pathogen-associated molecular patterns (PAMPs), such as LPS [10], which bind to the CD14/TLR4 complex activating the NF-kB pathway and inducing the synthesis of proinflammatory cytokines such as TNF-a, IL-1, IL-6, etc., and overexpressing chronic activation markers [11-13].

Moreover, during CHC the massive destruction of liver cells leads to dramatic physiological and pathophysiological changes during advanced fibrosis and cirrhosis [14]. Furthermore, cirrhosis is linked to a dysregulation in the balance between activation and homeostasis of the immune system, leading to a state characterized by immune activation and inflammation [13]. In addition, most blood proteins are produced by hepatocytes and their concentration may be altered by CHC progression, leading to an increased thrombotic risk [15].

The aim of our study was to evaluate the relationship between LSM and biomarkers of T cell activation, bacterial translocation, inflammation, endothelial dysfunction, and coagulopathy in HIV/HCV-coinfected patients.

Patients and methods

Patients

We carried out a cross-sectional study in 238 HIV/HCV-coinfected patients, who were selected from the cohort of “Grupo de Estudio del SIDA” (GESIDA 3603b study), which is composed of patients enrolled between February 2012 and February 2016 at 19 institutions in Spain. The GESIDA 3603b study is a cohort study that has the aim of evaluating the effect of HCV eradication on the immune system and the associated clinical outcomes. We selected HIV/HCV-coinfected patients at baseline of study.

The cohort included both naïve and anti-HCV therapy experienced patients, who were candidates to receive treatment with HCV therapy (peg-IFN-a/ribavirin or peg-IFN-a/ribavirin/direct-acting antivirals (DAAs)). Anti-HCV therapy in Spain is provided by hospital pharmacies and is covered by the National Health System. The selection criteria were: 1) detectable HCV RNA and HIV RNA by polymerase chain reaction (PCR); 2) availability of a valid baseline LSM; 3) availability of a valid sample of fresh blood to carry out immunological assays; 4) CD4+ T cell count higher than 200 cells/µL; 5) stable cART for at least 6 months or no need for cART according to guidelines used in the study period. We excluded patients with acute hepatitis C, co-infection with hepatitis B virus, decompensated liver disease, or a prior diagnosis of hepatocellular carcinoma at the time of the transient elastography study.

We selected two control groups for evaluating differences of HIV/HCV-coinfected patients with respect to the normality in peripheral blood biomarkers. On the one hand, the healthy controls are negative subjects for HIV, HCV, and HBV infection. On the other hand, HIV-monoinfected patients with undetectable HIV viral load and CD4+>500 cells/mm3 represent the standard normality of HIV patient without HCV and HBV infection, and other severe comorbidities. The characteristics of both control groups are shown in Supplemental Table 1.

Supplemental Table 1. Characteristics of HIV/HCV co-infected patients.

Healthy control HIV HIV/HCV
No. * 32 39 238
Gender (male) 17 (53.1%) 24 (61.5%) 187 (78.6%)
Age (years) # 49.5 (47; 53) 51 (46; 53) 49 (46; 52)
BMI (kg/m2) # 24.9 (23.1; 27.1) 25.3 (23.5; 26.6) 24.4 (21.8; 26.9)
BMI >25 (kg/m2)* 14 (43.7%) 21 (55.3%) 98 (41.2%)
Diabetes 6 (15.8%) 20 (8.4%)
High alcohol intake 1 (3.1%) 117 (89.1%)
HIV acquired by IVDU * 186 (78.1%)
Prior AIDS * 13 (33.3%) 64 (26.9%)
Years since HIV infection # 23 (18; 26)
Years since HCV infection # 21 (16; 24)
Antiretroviral therapy *
Non treated 5 (2.1%)
PI-based# 10 (25.6%) 35 (14.7%)
2NRTI+II-based * 4 (10.2%) 59 (24.8%)
2NRTI+PI-based * 47 (19.7%)
2NRTI+NNRTI-based * 23 (64.1%) 70 (29.5%)
Others 2 (5.1%) 22 (9.2%)
HIV markers
Nadir CD4+ T-cells # 215 (107; 343) 172 (84; 254)
Nadir CD4+ T-cells<200 cells/mm3 * 14 (38.9%) 131 (55.1%)
CD4+ T-cells # 832 (685; 1036) 547 (394; 803)
CD4+ T-cells<500 cells/mm3 * 0 (0%) 100 (42%)
HIV-RNA >50 cp/mL* 0 (0%) 30 (12.6%)
Non-invasive indexes
APRI # 0.98 (0.57; 1.70)
APRI >1.5* 65 (27.3%)
FIB-4 # 2.33 (1.45; 3.59)
FIB-4 >3.25* 64 (26.9%)
Forns index # 4.94 (3.67; 6.31)
Forns index >6.9* 36 (15.1%)
HCV markers
HCV genotype (n=235)*
1 170 (72.3%)
2 5 (2.1%)
3 39 (16.6%)
4 21 (8.9%)
Log10 HCV-RNA (IU/ml) # 6.3 (5.87; 6.74)
HCV-RNA > 850,000 IU/ml * 170 (71.4%)

*Absolute number (percentage). # Mean and mean standard error. HCV: Hepatitis C virus; HCV-RNA: HCV plasma viral load; HIV-1: Human immunodeficiency virus type 1; HIV-RNA: HIV plasma viral load; IVDU: intravenous drug user; NNRTI: non-nucleoside analogue HIV reverse transcriptase inhibitor; NRTI: nucleoside analogue HIV reverse transcriptase inhibitor; PI: protease inhibitor; II: integrase inhibitor; APRI: Aminotransferase-to-platelet ratio index; FIB-4: noninvasive test for liver fibrosis based on AST/ALT ratio and platelet count; FORNS: test for liver fibrosis based on age, GGT and platelet count.

The study cohort received the approval of the ethics committees of the participating centers for analysis of anonymized routine clinical data with a view to scientific publication. This work was conducted in accordance with the Declaration of Helsinki. The Institutional Review Board and the Research Ethic Committee of the Instituto de Salud Carlos III approved the study. All patients gave their informed consent for the study.

Clinical data

All the information was recorded at each institution using a common database via an online form, which satisfied local requirements of data confidentiality. This database included all demographic, clinical, virological, and laboratory data. All the centers included in the cohort were monitored to verify that all the information in the database was consistent with the patient’s medical records.

We extracted the following baseline data from hospital records: (1) demographics; (2) HIV-related data (HIV transmission category, Centers for Disease Control and Prevention [CDC] clinical category, nadir CD4+ T cell count, the most recent CD4+ T cell count, the most recent HIV RNA load, and whether or not patients were receiving cART); (3) liver disease-related data (HCV genotype, HCV RNA load, hepatitis B surface antigen [HBsAg], and anti-HCV therapy); and (4) history of substance abuse including alcohol consumption >50 g/d.

The duration of HCV infections for patients with a history of intravenous drug use (IDU) was estimated starting from the first year they shared needles and other injection paraphernalia [16]. For non-IDU patients, we only calculated the time of infection for those patients that the initiation of HCV infection could be determined with certainty (acute hepatitis C, use of blood and blood products, needle piercing, identified sexual contact, etc.). The time of HIV infections was calculated from the date of HIV diagnosis.

Liver stiffness measurement (LSM) was assessed by transient elastography (FibroScan®, Echosens, Paris, France) using a single machine. Results were expressed in kilopascals (kPa) with a range of 2.5 to 75 kPa. Trained operators performed all FibroScan® examinations. We considered 10 acquisitions with a success rate >60% and an interquartile range <30% of the median value as representative measurements of liver stiffness [17]. Fasting was not routinely required prior to the examination.

From these values of LSM, patients were stratified according to the following clinically relevant LSM cutoffs previously used: <12.5 kPa (non-cirrhosis, [17]), 12.5 to 25 kPa (non-risk of bleeding varices, [18]), 25 to 40 kPa (risk of bleeding varices, [18]), and >40 kPa (risk of hepatic decompensation, [19]).

Flow cytometry

The expression of CD38 was evaluated in CD4+ and CD8+ T-cell subsets by flow cytometry in 100microL fresh anticoagulated whole blood. The cells were labeled with the following antibodies: anti-CD38-APC-Cyanine 5.5 (APC-Cy5.5, clone HIT2, Invitrogen, Frederick, MD), anti-CD4-APC-Cyanine 7 (APC-Cy7, clone OKT4, BioLegend, San Diego, CA), anti-CD8-Pacific Blue (PB, clone SK1, BioLegend, San Diego, CA), anti-CD3-Pacific Orange (PO, clone VCHT1, Invitrogen, Frederick, MD) and incubated for 20 min at room temperature in the dark. Next, the IMMUNOPREP Reagent System (Beckman Coulter, Mervue Galway, Ireland) was added to each sample using a Coulter MULTI-Q-PREP Lysing Workstation (Beckman Coulter, Miami, FL) to lyse and fixate them. Fluorescence was measured with a Gallios™ flow cytometer (Beckman Coulter, Miami, FL). The number of events was stopped at a minimum of 200,000 cells in the lymphocyte gate for each sample and flow cytometry data were analyzed using Kaluza™ acquisition software (version 1.5; Beckman Coulter, Miami, FL).

Multiplex assay and ELISA

We selected several biomarkers for each of the objectives we wanted to analyze: a) bacterial translocation [soluble CD14 (sCD14), lipopolysaccharide (LPS), fatty acid-binding protein 2 (FABP2), lipopolysaccharide binding protein (LBP)]; b) inflammation [interleukin (IL)-1b, IL-8, IL-6, IL-18, IFN-g-inducible protein 10 (IP-10); c) endothelial dysfunction: soluble vascular cell adhesion molecule 1 (sVCAM1), soluble intercellular cell adhesion molecule 1 (sICAM1), soluble tumor necrosis factor receptor 1 (sTNFR1), monocyte chemoattractant protein-1 (MCP1)]; d) coagulopathy [D-Dimer, plasminogen activator inhibitor-1 (PAI-1)].

Plasma biomarkers were measured by ProcartaPlexTM multiplex immunoassay (ThermoFisher, USA) according to the manufacturer’s specifications using a Luminex 200™ analyzer (Luminex Corporation, Austin, TX, United States) with the exception of sCD14 (Raybiotech, Georgia, USA), LPS (HycultBiotech, Uden, The Netherlands), LBP (R&D Systems, Minneapolis, USA) and FABP2 (Raybiotech, Georgia, USA), which were performed according to the manufacturer’s procedure for each specific commercial ELISA.

Statistical analysis

The statistical analysis was performed with the Statistical Package for the Social Sciences (SPSS) 21.0 (SPSS INC, Chicago, IL, USA). Statistical significance was defined as p<0.05. All p-values were two-tailed.

For the descriptive study, values were expressed as absolute number (percentage) and median [25th; 75th percentile]. Categorical data and proportions were analyzed using the chi-squared test or Fisher’s exact test as required. Kruskal-Wallis and Mann-Whitney tests were used to compare data among independent groups.

Generalized Linear Models (GLM), with a gamma distribution (log-link), was used to evaluate the association between LSM values (continuous variable and ordinal variable) and levels of biomarkers in peripheral blood. This test gives the differences between groups and the arithmetic mean ratio (AMR) or the ratio by which the arithmetic mean of the original outcome is multiplied. Each regression test was adjusted by age, gender, nadir CD4+ T cells, baseline CD4+ T cells, HIV viral load (>50 cp/mL), high alcohol intake, diabetes, log10 HCV RNA, HCV-GT1, previous HCV therapy (IFNa+ribavirin), and prior AIDS.

Results

Patients

The characteristics of the 238 HIV/HCV-coinfected patients are shown in Table 1. Overall, the median age was 49 years, 78.6% were males, 49.1% had high alcohol intake, 78.1% acquired HIV by IVDU, 26.9% had had prior aids-defining conditions, and 98% were on cART. Furthermore, the mean CD4+ T cell count was 574 cells/mm3, 12.6% had values of HIV RNA >50 copies/mL, 72.3% were HCV-GT1 and 71.4% had HCV RNA > 850,000 IU/mL. When patients were stratified by LSM values, we only found significant differences between groups in CD4+ T cells (p=0.005) and HCV-GT4 (p=0.005).

Table 1. Clinical and epidemiological characteristics of HIV/HCV-coinfected patients.

Patients stratified by LSM
All patients <12.5 kPa 12.5-25 kPa 25-40 kPa >40 kPa p
No. 238 119 73 28 18
Age (years) 49 (46; 52) 48 (45; 52) 49 (46; 51) 49.5 (46; 53) 50 (47; 52) .482
Gender (male) 187 (78.6%) 96 (80.7%) 54 (74%) 23 (82.1%) 14 (77.8%) .694
BMI (kg/m2) 24.4 (21.8; 26.9) 23.8 (21.4; 26.3) 24.6 (22.7; 28.1) 24.7 (21.9; 26.5) 24.6 (21.6; 25.9) .247
BMI >25 (kg/m2) 98 (41.2%) 46 (38.6%) 33 (45.2%) 12 (42.8%) 7 (38.9%) .693
Diabetes 20 (8.4%) 8 (6.7%) 6 (8.2%) 4 (14.3%) 2 (11.1%) .600
High alcohol intake 117 (49.1%) 56 (47.1%) 36 (49.3%) 16 (57.1%) 9 (50%) .876
HIV acquired by IVDU 186 (78.1%) 92 (77.3%) 56 (76.7%) 24(85.7%) 14 (77.7%) .773
Prior AIDS 64 (26.9%) 27 (22.7%) 23 (31.5%) 8 (28.6%) 6 (33.3%) .374
Years since HIV diagnosis 23 (18; 26) 22 (17; 26) 24 (20; 27) 21 (18; 26) 23 (19; 27) .194
Years since HCV infection 21 (16; 24) 21 (13; 24) 21 (18; 25) 19 (17; 21) 22 (15; 26) .575
Previous HCV therapy (IFNa+rib) 114 (47.9%) 38 (31.9%) 53 (72.6%) 17 (60.7%) 6 (33%) .001
Antiretroviral therapy
Non-treated 5 (2.1%) 2 (1.7%) 1 (1.4%) 2 (7.1%) 0 (0%) .242
PI-based 35 (14.7%) 19 (15.9%) 12 (16.4%) 2 (7.1%) 2 (11.8%) .627
2NRTI+II-based 59 (24.8%) 31 (26.1%) 18 (24.7%) 6 (21.4%) 4 (23.5%) .944
2NRTI+PI-based 47 (19.7%) 25 (21%) 10 (13.7%) 6 (21.4%) 6 (35.3%) .220
2NRTI+NNRTI-based 70 (29.5%) 31 (26.1%) 26 (35.6%) 10 (35.7%) 3 (17.6%) .344
Others 22 (9.2%) 11 (9.2%) 6 (8.2%) 2 (7.2%) 2 (11.8%) .274
HIV markers
Nadir CD4+ T cells 172 (84; 254) 196 (78; 277) 167 (87; 234) 160 (85; 251) 116 (95; 198) .345
Nadir CD4+ T cells<200 cells/mm3 131 (55.1%) 58 (48.7%) 42 (57.5%) 17 (60.7%) 14 (77.7%) .100
CD4+ T cells 547 (394; 803) 603 (436; 832) 511 (339; 736) 570 (395; 828) 364 (243; 520) .005
CD4+ T cells<500 cells/mm3 100 (42%) 41 (34.4%) 35 (47.9%) 13 (46.4%) 11 (61.1%) .065
HIV-RNA >50 cp/mL 30 (12.6%) 16 (13.4%) 7 (9.6%) 4 (14.3%) 3 (16.7%) .808
HCV markers
HCV genotype (n=235)
1 170 (72.3%) 78 (66.7%) 58 (80.6%) 20 (71.4%) 14 (77.8%) .205
2 5 (2.1%) 3 (2.6%) 1 (1.4%) 1 (3.6%) 0 (0%) .915
3 39 (16.6%) 18 (15.4%) 12 (16.7%) 7 (25%) 2 (11.1%) .599
4 21 (8.9%) 18 (15.4%) 1 (1.4%) 0 (0%) 2 (11.1%) .005
Log10 HCV-RNA (IU/mL) 6.3 (5.87; 6.74) 6.32 (5.83; 6.83) 6.36 (6.04; 6.69) 6.36 (5.79; 6.52) 6.16 (5.78; 6.59) .692
HCV-RNA > 500,000 IU/mL 191 (80.2%) 92 (77.3%) 63 (86.3%) 23 (82.1%) 13 (72.2%) .495
HCV-RNA > 850,000 IU/mL 170 (71.4%) 80 (97.2%) 58 (79.4%) 19 (67.9%) 13 (72.4%) .608

Statistics: Values expressed as absolute number (percentage) and median (interquartile range). P-values were calculated by Chi-square tests and Mann-Whitney tests in HIV/HCV-coinfected patients stratified by LSM (<12.5 kPa, 12.5-25 kPa, 25-40 kPa, and >40 kPa). Abbreviations: HCV, hepatitis C virus; HCV-RNA, HCV plasma viral load; HIV-1, human immunodeficiency virus type 1; LSM, liver stiffness measure; HIV-RNA, HIV plasma viral load; IVDU, intravenous drug user; AIDS, acquired immune deficiency syndrome; IFNa+rib, interferon-alpha plus ribavirin; NNRTI, non-nucleoside analogue HIV reverse transcriptase inhibitor; NRTI, nucleoside analogue HIV reverse transcriptase inhibitor; PI, protease inhibitor; II, integrase inhibitor; FIB-4, noninvasive test for liver fibrosis based on AST/ALT ratio and platelet count.

HIV/HCV-coinfected patients vs. control groups

HIV/HCV-coinfected patients had higher values of markers of T cell activation [CD4+CD38+ (p<0.001), CD8+CD38+ (p<0.001)] and plasma biomarkers of bacterial translocation [sCD14 (p<0.010)], inflammation [IL-1b (p<0.006), IL-8 (p<0.001), IL-6 (p<0.001), IL-18 (p<0.001), IP-10 (p<0.001), sVCAM1 (p<0.001), sICAM1 (p<0.001), sTNFR1 (p<0.001)], and coagulopathy [PAI-1 (p<0.001)] than healthy controls and HIV-monoinfected patients (Table 2). Additionally, HIV/HCV-coinfected patients had higher values of MCP1 than healthy controls (p=0.005).

Table 2. Summary of markers of T cell activation and plasma biomarkers of bacterial translocation, inflammation, and coagulopathy in healthy controls, HIV-monoinfected and HIV/HCV-coinfected patients.

Healthy controls (0) HIV-monoinfected (1) HIV/HCV-coinfected (2) p (0-1) p (0-2) p (1-2)
T cells (%)
CD4+CD38+ 3.7 (2.5; 5.8) 2.8 (2; 5.6) 6.8 (3.8; 14.7) .206 .000 .000
CD8+CD38+ 6.3 (5.1; 8.5) 7.1 (4.6; 10.7) 11.6 (7.1; 20) .504 .000 .001
Bacterial translocation
sCD14 (µg/mL) 3.3 (2.3; 3.9) 3.7 (1.9; 5.4) 5.1 (3.3; 7.4) .370 .000 .010
FABP2 (ng/mL) 0.5 (0.3; 0.7) 0.6 (0.4; 1.3) 0.7 (0.3; 1.6) .051 .050 .822
LPS (UE/mL) 1.3 (0.9; 4.7) 1.2 (0.9; 2.1) 1.4 (1; 1.9) .654 .765 .483
LBP (µg/mL) 0.7 (0.2; 1.4) 0.8 (0.4; 1.4) 0.9 (0.6; 1.5) .599 .088 .294
Inflammation
IL-1b (pg/mL) 0.6 (0.2; 1.5) 0.6 (0.3; 1) 1.2 (0.5; 2.3) .705 .006 .000
IL-8 (pg/mL) 1.2 (1.2; 2.8) 1.9 (1.2; 3.5) 5.2 (3.6; 11.3) .197 .000 .000
IL-6 (pg/mL) 2 (1.4; 3.6) 3.4 (2.4; 4) 5.4 (3.7; 8.1) .014 .000 .000
IL-18 (pg/mL) 87.4 (55.9; 147.3) 122.8 (87.9; 194.4) 247.7 (128.2; 507.2) .077 .000 .000
IP-10 (pg/mL) 26.6 (20.1; 50.5) 28.4 (17.5; 35.4) 203.7 (111.3; 350.6) .704 .000 .000
Endothelial dysfunction
sVCAM1 (µg/mL) 0.3 (0.2; 0.5) 0.3 (0.2; 0.6) 1.6 (0.8; 3.2) .154 .000 .000
sICAM1 (µg/mL) 0.4 (0.1; 0.9) 0.6 (0.3; 1.2) 2.1 (1.1; 3.9) .084 .000 .000
sTNFR1 (ng/mL) 1.5 (0.2; 2.2) 1.4 (0.3; 2.1) 2.3 (1.3; 3.6) .917 .000 .000
MCP1 (pg/mL) 12.5 (9.2; 21.7) 29.5 (17.7; 38.4) 20.7 (11; 41.2) .000 .005 .173
Coagulopathy
D-Dimer (ng/mL) 23.5 (10.8; 62.2) 28.2 (12.7; 50) 33.3 (12.4; 77.8) .898 .293 .253
PAI-1 (ng/mL) 4.9 (3.7; 8.6) 6.8 (5.3; 8.9) 10 (7; 12.8) .035 .000 .000

Statistics: Values expressed as median (interquartile range). P-values were calculated by Mann-Whitney tests. Abbreviations: HCV, hepatitis C virus; HIV-1, human immunodeficiency virus type 1; CDXX, cluster of differentiation; sCD14, soluble CD14; LPS, lipopolysaccharide; FABP2, fatty acid-binding protein 2; LBP, lipopolysaccharide binding protein; IL, interleukin; IP-10, IFN-g-inducible protein 10; sVCAM1, soluble vascular cell adhesion molecule 1; sICAM1, soluble intercellular cell adhesion molecule 1; sTNFR1, soluble tumor necrosis factor receptor 1; MCP1, monocyte chemoattractant protein-1, PAI-1, plasminogen activator inhibitor-1.

Biomarkers of liver fibrosis

The relationship of liver stiffness measurements and biomarkers of activation, translocation, inflammation, and coagulopathy are shown in Table 3. In multivariate analysis, we found that the high LSM values were associated with high levels of CD8+CD38+ (aAMR=1.15; p=0.037), LPS (aAMR=1.18; p=0.009), IL-8 (aAMR=2.02; p<0.001), IP-10 (aAMR=1.27; p=0.006), sVCAM1 (aAMR=1.39; p<0.001), sICAM1 (aAMR=1.29; p=0.045), sTNFR1 (aAMR=1.48; p<0.001), and D-Dimer (aAMR=1.71; p<0.001).

Table 3. Association of liver stiffness measurements (continuous variable) with markers of T cell activation and plasma biomarkers of bacterial translocation, inflammation, and coagulopathy in HIV/HCV-coinfected patients.

AMR (95%CI) p aAMR (95%CI) p
T cells (%)
CD4+CD38+ 0.9 (0.78;1.04) .150 0.97 (0.83;1.14) .734
CD8+CD38+ 1.14 (1;1.29) .048 1.15 (1.01;1.31) .037
Bacterial translocation
sCD14 (µg/mL) 1.04 (0.89;1.22) .628 1.12 (0.93;1.3) .226
FABP2 (ng/mL) 1.09 (0.9;1.3) .376 1.14 (0.93;1.40) .205
LPS (UE/mL) 1.19 (1.06;1.34) .003 1.18 (1.04;1.34) .009
LBP (µg/mL) 1.02 (0.89;1.18) .742 1.03 (0.88;1.21) .679
Inflammation
IL-1b (pg/mL) 0.91 (0.75;1.11) .355 1.01(0.81;1.24) .975
IL-8 (pg/mL) 1.99 (1.72;2.3) .000 2.02 (1.74;2.35) .000
IL-6 (pg/mL) 1.06 (0.92;1.22) .415 1.14 (0.98;1.34) .089
IL-18 (pg/mL) 0.96 (0.78;1.2) .743 1.04 (0.83;1.29) .748
IP-10 (pg/mL) 1.34 (1.14;1.57) .000 1.27 (1.07; 1.51) .006
Endothelial dysfunction
sVCAM1 (µg/mL) 1.5 (1.29;1.75) .000 1.39 (1.18;1.65) .000
sICAM1 (µg/mL) 1.14 (0.9;1.43) .275 1.29 (1.01;1.66) .045
sTNFR1 (ng/mL) 1.51 (1.3;1.75) .000 1.48 (1.27;1.74) .000
MCP1 (pg/mL) 1.15 (0.98;1.35) .098 1.14 (0.96;1.36) .139
Coagulopathy
D-Dimer (ng/mL) 1.88 (1.54;2.3) .000 1.71 (1.38; 2.12) .000
PAI-1 (ng/mL) 1.05 (0.94;1.18) .365 1.05 (0.93;1.18) .429

Statistics: P-values were calculated by Generalized Linear Models (GLM) with a gamma distribution (log-link). Abbreviations: HCV, hepatitis C virus; HIV-1, human immunodeficiency virus type 1; AMR, arithmetic mean ratio; aAMR, adjusted AMR; CDXX, cluster of differentiation; sCD14, soluble CD14; LPS, lipopolysaccharide; FABP2, fatty acid-binding protein 2; LBP, lipopolysaccharide binding protein; IL, interleukin; IP-10, IFN-g-inducible protein 10; sVCAM1, soluble vascular cell adhesion molecule 1; sICAM1, soluble intercellular cell adhesion molecule 1; sTNFR1, soluble tumor necrosis factor receptor 1; MCP1, monocyte chemoattractant protein-1, PAI-1, plasminogen activator inhibitor-1.

Biomarkers of severe cirrhosis

Table 4 shows the univariate analysis of the biomarker values of activation, translocation, inflammation, and coagulopathy stratified by fibrosis/cirrhosis stages. Patients with >40 kPa had higher values of CD8+CD38+, IL-8, IL-6, and D-Dimer than patients with <12.5 kPa (p<0.005), 12.5-25 kPa (p<0.011), and 25-40 kPa (p<0.027). Furthermore, patients with >40 kPa had higher values of IP-10, sTNFR1, and PAI-1 than patients with <12.5 kPa (p<0.003) and 12.5-25 kPa (p<0.004). Additionally, patients with >40 kPa had higher values of sVCAM1 than patients with <12.5 kPa (p<0.001).

Table 4. Summary of T cell activation and plasma biomarkers of bacterial translocation, inflammation, and coagulopathy in HIV/HCV-coinfected patients according to fibrosis/cirrhosis stages.

<12.5 kPa (0) 12.5-25 kPa (1) 25-40 kPa (2) >40 kPa (3) p (0-3) p (1-3) p (2-3)
T cells (%)
CD4+CD38+ 7.5 (4.5; 17.6) 6 (3.3; 14.1) 5.3 (2.6; 7.6) 10.9 (5.5; 24.8) .268 .080 .015
CD8+CD38+ 11.2 (6.3; 18.7) 10.9 (7.1; 19.2) 12.7 (6.3; 17.7) 21 (10; 39.9) .005 .011 .013
Bacterial translocation
sCD14 (µg/mL) 4.8 (3.2; 7.1) 5 (3.2; 7.5) 4.9 (3.5; 6.6) 7 (4.4; 10.6) .081 .154 .100
FABP2 (ng/mL) 0.6 (0.3; 1.4) 0.6 (0.3; 1.5) 1 (0.4; 2.2) 1.2 (0.4; 2.5) .082 .149 .529
LPS (UE/mL) 1.3 (1; 1.7) 1.3 (0.9; 2) 1.7 (1.3; 2.2) 1.5 (1.1; 2.3) .121 .245 .761
LBP (µg/mL) 0.9 (0.5; 1.4) 0.9 (0.5; 1.8) 0.8 (0.5; 1.3) 1.1 (0.8; 1.4) .412 .914 .184
Inflammation
IL-1b (pg/mL) 1.3 (0.7; 2.5) 0.9 (0.5; 1.9) 1.6 (0.5; 2.8) 0.5 (0.2; 2.1) .087 .375 .192
IL-8 (pg/mL) 4.2 (2.8; 6.4) 5.6 (4.1; 10) 11.9 (6.3; 20.9) 18.8 (9.1; 31.5) .000 .000 .027
IL-6 (pg/mL) 4.6 (3.3; 7.7) 5 (3.6; 6.9) 6.7 (4.6; 12.1) 9.1 (6; 20) .000 .000 .025
IL-18 (pg/mL) 231.1 (121.2; 569.9) 301.4 (145.6; 612.4) 254.3 (129.5; 465.4) 254 (155.1; 377.7) .828 .508 .787
IP-10 (pg/mL) 171.3 (110.4; 258.3) 189.2 (79.1; 329) 336.1 (146.2; 455) 407 (203.7; 689.5) .000 .003 .283
Endothelial dysfunction
sVCAM1 (µg/mL) 1.2 (0.7; 2.1) 2.1 (0.9; 4.7) 1.4 (0.9; 4.2) 3.5 (1.1; 5.5) .000 .214 .079
sICAM1 (µg/mL) 1.9 (1; 3.3) 2.2 (1.1; 4.1) 2.4 (1.2; 4.2) 3.9 (1.1; 12.1) .070 .126 .188
sTNFRI1 (ng/mL) 1.9 (1.2; 3.3) 2 (1.2; 3.1) 2.6 (1.3; 5.4) 3.5 (2.3; 4.5) .003 .003 .498
MCP1 (pg/mL) 18.2 (10; 39.3) 23.2 (12.6; 39.2) 25.3 (11.3; 43.5) 27.9 (10.6; 52) .234 .750 .787
Coagulopathy
D-Dimer (ng/mL) 26.6 (11.4; 64.7) 32.9 (13.6; 79.8) 23.5 (8.4; 68.2) 132.2 (59.5; 280.1) .000 .000 .002
PAI-1 (ng/mL) 9.7 (7; 12.1) 8.8 (5.8; 12.8) 10.7 (7.7; 14.4) 13.2 (10.1; 15.5) .003 .004 .121

Statistics: Values expressed as median (interquartile range). P-values were calculated by Mann-Whitney test. Abbreviations: HCV, hepatitis C virus; HIV-1, human immunodeficiency virus type 1; kPa, kilopascal; CDXX, cluster of differentiation; sCD14, soluble CD14; LPS, lipopolysaccharide; FABP2, fatty acid-binding protein 2; LBP, lipopolysaccharide binding protein; IL, interleukin; IP-10, IFN-g-inducible protein 10; sVCAM1, soluble vascular cell adhesion molecule 1; sICAM1, soluble intercellular cell adhesion molecule 1; sTNFR1, soluble tumor necrosis factor receptor 1; MCP1, monocyte chemoattractant protein-1, PAI-1, plasminogen activator inhibitor-1.

These differences among groups were also analyzed by multivariate analysis with GLM tests, which showed significant values of aAMR >1 (patients with >40 kPa had higher values than other groups) for several biomarkers (Table 5). Patients with >40 kPa had higher values of CD8+CD38+, IL-6, sICAM1, and D-Dimer than patients with <12.5 kPa (p<0.05), 12.5-25 kPa (p<0.05), and 25-40 kPa (p<0.05). Furthermore, patients with >40 kPa had higher values of LPS, IL-8, IP-10, and sTNFR1 than patients with <12.5 kPa (p<0.05) and 12.5-25 kPa (p<0.05). Additionally, patients with >40 kPa had higher values of sVCAM1 than patients with <12.5 kPa (p<0.001) and CD4+CD38+ than patients with 25-40 kPa (p=0.038).

Table 5. Summary of values of adjusted arithmetic mean ratio (aAMR) for <12.5kPa, 12.5-25kPa and 25-40kPa, compared to HIV/HCV-coinfected patients with >40 kPa, for T-cell subsets and plasma biomarkers.

<12.5 kPa 12.5-25 kPa 25-40 kPa
T cells (%) aAMR (95%CI) p aAMR (95%CI) p aAMR (95%CI) p
CD4+CD38+ 1.35 (0.89;2.07) .159 1.55 (1;2.4) .051 1.68 (1.03;2.75) .038
CD8+CD38+ 1.66 (1.16;2.36) .005 1.49 (1.03;2.16) .036 1.62 (1.06;2.49) .026
Bacterial translocation
sCD14 (µg/mL) 1.18 (0.77;1.81) .449 1.05 (0.67;1.66) .830 1.16 (0.71;1.9) .548
FABP2 (ng/mL) 1.36 (0.82;2.26) .230 1.47 (0.86;2.52) .155 1.07 (0.6;1.91) .824
LPS (UE/mL) 1.61 (1.18;2.17) .003 1.45 (1.04;2.04) .027 1.05 (0.74;1.49) .788
LBP (µg/mL) 1.09 (0.76;1.56) .653 1.02 (0.69;1.5) .937 1.23 (0.8;1.89) .338
Inflammation
IL-1b (pg/mL) 0.74 (0.45;1.23) .245 0.91 (0.53;1.58) .745 0.52 (0.29;1.01) .073
IL-8 (pg/mL) 3.35 (2.31;4.86) .000 2.34 (1.55;3.54) .000 1.33 (0.86;2.07) .201
IL-6 (pg/mL) 1.65 (1.12;2.44) .012 2.23 (1.46;3.4) .000 1.69 (1.08;2.64) .022
IL-18 (pg/mL) 0.77 (0.48;1.24) .280 0.67 (0.4;1.13) .135 0.74 (0.42;1.29) .284
IP-10 (pg/mL) 1.78 (1.15;2.75) .010 1.72 (1.06;2.8) .029 1.04 (0.62;1.75) .885
Endothelial dysfunction
sVCAM1 (µg/mL) 2.14 (1.45;3.17) .000 1.15 (0.75;1.77) .515 1.3 (0.81;2.07) .276
sICAM1 (µg/mL) 1.84 (1;3.35) .048 2.26 (1.17;4.36) .015 2.06 (1.02;4.19) .045
sTNFR1 (ng/mL) 1.56 (1.08;2.26) .017 1.64 (1.1;2.45) .015 0.98 (0.63;1.52) .913
MCP1 (pg/mL) 1.52 (0.99;2.32) .056 1.23 (0.76;1.98) .399 1.38 (0.82;2.31) .228
Coagulopathy
D-Dimer (ng/mL) 4.06 (2.32;7.11) .000 4.34 (2.36;7.97) .000 3 (1.55;5.8) .001
PAI-1 (ng/mL) 1.23 (0.9;1.67) .189 1.31 (0.94;1.84) .112 1.13 (0.79;1.63) .503

Statistics: P-values were calculated by Generalized Linear Models (GLM) with a gamma distribution (log-link). Abbreviations: HCV, hepatitis C virus; HIV-1, human immunodeficiency virus type 1; aAMR, adjusted AMR; kPa, kilopascal; CDXX, cluster of differentiation; sCD14, soluble CD14; LPS, lipopolysaccharide; FABP2, fatty acid-binding protein 2; LBP, lipopolysaccharide binding protein; IL, interleukin; IP-10, IFN-g-inducible protein 10; sVCAM1, soluble vascular cell adhesion molecule 1; sICAM1, soluble intercellular cell adhesion molecule 1; sTNFR1, soluble tumor necrosis factor receptor 1; MCP1, monocyte chemoattractant protein-1, PAI-1, plasminogen activator inhibitor-1.

Discussion

In this study, HIV/HCV-coinfected patients were found to have higher biomarker levels of immune activation in peripheral blood [T cell activation (CD4+CD38+ and CD8+CD38+), bacterial translocation (sCD14), inflammation (IL-1b, IL-6, IL-8, IL-18, IP-10), endothelial dysfunction (sVCAM1, sICAM1, and sTNFR1), and coagulopathy (PAI-1)] than healthy controls and HIV-monoinfected patients. Moreover, in HIV/HCV-coinfected patients, we found a direct relationship between LSM values and biomarker values of immune activation [T cell activation (CD8+CD38+), bacterial translocation (LPS), inflammation (IL-8, IP-10), endothelial dysfunction (sVCAM1, sICAM1, and sTNFR1) and coagulopathy (D-dimer)]. Subsequently, patients were stratified at different fibrosis stages according to their LSM values. The resulting analysis found that patients with cirrhosis who had LSM>40 kPa showed higher biomarker values of immune activation [T cell activation (CD4+CD38+ and CD8+CD38+), bacterial translocation (LPS), inflammation (IL-8, IL-6, IP-10), endothelial dysfunction (sVCAM1, sICAM1 and sTNFR1), and coagulopathy (D-dimer)] than patients from other groups (<12.5 kPa, 12.5-25 kPa, and 25-40 kPa). We also stratified by established cut-off points of LSM, such as <7.1 kPa (F0-F1), 7.1-9.4 kPa (F2; significant fibrosis), 9.5-12.4 kPa (F3; advanced fibrosis), and>12.5 kPa (F4; cirrhosis) [20]. However, although some significant differences were found, we did not find a clear trend in the relationship of LSM stages (ordinal variable) with markers of T cell activation and plasma biomarkers of bacterial translocation, inflammation, and coagulopathy in our HIV/HCV-coinfected patients (data not shown). To our knowledge, this is the first time that very high levels of these biomarkers of immune activation have been found in HIV/HCV-coinfected patients with compensated cirrhosis. Our results could shed light on the importance of hyperactivation of the immune system in the physiopathology of CHC in HIV/HCV-coinfected patients with compensated cirrhosis. However, longitudinal studies are necessary to confirm this hypothesis.

In our study, HIV/HCV-coinfected patients had higher plasma sCD14 values than control groups (healthy controls and HIV-monoinfected patients), which may indicate increased bacterial translocation in these subjects, which is usually accompanied by increased inflammation and liver disease severity [21-24], increasing the risk of developing non-AIDS-related complications and death [25]. However, we did not find any association between sCD14 values and LSM in HIV/HCV-coinfected patients. It is possible that the characteristics of our cohort may have played a part in this lack of association, resulting in the loss of a linear relationship between sCD14 and LSM. Moreover, plasma LPS levels were similar in HIV/HCV-coinfected patients and both control groups, although elevated LPS levels have been previously observed in HIV/HCV-coinfected patients [26]. It is possible that the absence of significant differences may be influenced by the highest sCD14 values in our HIV/HCV-coinfected patients, which may capture and remove LPS from systemic circulation [27], reducing the plasma LPS concentration. However, LPS levels were related to LSM values and cirrhotic patients with LSM>25 kPa had the highest LPS values. In accordance with our data, an association between plasma LPS levels and advanced stages of liver disease has been found in CHC patients [21, 22]. Thus, LPS may indicate liver disease severity and short-term survival of cirrhotic patients [28, 29]. However, there are also studies that did not find any such association [30, 31].

Regarding T cell immune activation, HIV/HCV-coinfected patients had higher percentages of CD4+CD38+ and CD8+CD38+ than healthy controls and HIV-monoinfected patients. Previous reports have shown that HCV infection may increase the percentage of CD8+CD38+ to above levels of healthy controls [32, 33], with a return to normal values after HCV elimination [33]. In addition, HIV/HCV-coinfected patients have higher levels of immune activation than HIV-monoinfected patients [34]. This immune activation may accelerate CHC [35, 36], and it has been linked to AIDS progression, onset of comorbidities and death in HIV-infected patients [25]. Moreover, high LSM values were linked to high CD8+CD38+ percentages, and cirrhotic patients with LSM>40 kPa had the highest CD8+CD38+ percentages. The activation of the immune system is one of the main pathogenic mechanisms of liver disease, including CHC [9, 13]. Thus, the increased CD38 expression in T cells may indicate an increased risk of CHC progression in HIV/HCV-coinfected patients, as well as an increased risk of progression to AIDS.

Inflammation is characteristic of both HIV and HCV infection, and it plays a key role in the pathogenesis of liver disease in HIV/HCV-coinfected patients [25, 35, 36]. Additionally, inflammation is directly related to endothelial dysfunction, which is implicated in the development of liver diseases and increased cardiovascular risk [37]. In our study, among all biomarkers analyzed, the most relevant were IL-6, IL-8, IP-10, sVCAM1, sICAM1, and sTNFR1. These biomarkers showed higher values in HIV/HCV-coinfected patients and increased as liver stiffness increased, particularly in patients with advanced cirrhosis (LSM>40 kPa). Overall, our results are in accordance with previous results described in the literature in HCV-infected patients. Firstly, plasma IL-6 levels are higher in patients with HCV infection, particularly in patients with advanced fibrosis or cirrhosis [23, 24, 38, 39]. Secondly, increased circulating IL-8 levels have been linked to CHC progression [40-45]. Thirdly, increased plasma IP-10 levels are related to liver disease severity in HCV-infected patients [26, 44, 46-49]. Fourthly, plasma sVCAM1 and sICAM1 are linked to liver disease severity in HCV-infected patients [46, 50, 51] and HIV/HCV-coinfected patients [52, 53]. Fifthly, plasma sTNFR1 levels are increased in CHC patients with cirrhosis [54], are related to the influx of portal endotoxin [55] and liver disease severity [56-58], and predict death in patients with cirrhosis [59]. However, the excessive increase of inflammatory biomarkers in our compensated cirrhotic patients with LSM>40 kPa should be highlighted, because this could indicate an increased risk of AIDS progression [25], development of decompensated cirrhosis and death [12, 35, 36].

Coagulopathy has been linked to an increased risk of progression and death in HIV-infected people [60] and CHC patients [61]. In our study, HIV/HCV-coinfected patients had higher PAI-1 values than healthy controls and HIV-monoinfected patients; whereas high LSM values were directly related to increased levels of D-Dimer with cirrhotic patients with LSM>40 kPa having the highest D-Dimer levels. Previous studies have reported plasma PAI-1 values to be associated with liver disease severity in patients with non-alcoholic fatty liver disease (NAFLD) [62, 63], and D-Dimer levels are elevated in cirrhotic patients and gradually increase with increasing hepatic dysfunction [64, 65]. Additionally, D-Dimer is related to the development of portal venous thrombosis [66] and bleeding of esophageal varices [67, 68]. D-dimer also predicts in-hospital mortality in patients with hepatic cirrhosis [64]. Thus, coagulopathy biomarkers were also altered in HIV/HCV-coinfected patients, indicating a risk of clinical progression and death, particularly in cirrhotic patients with LSM>40 kPa.

Limitations of study

Several aspects must be taken into consideration for the correct interpretation of our results. Firstly, this report has a cross-sectional design, which may entail a lack of uniformity, and the study has a limited number of patients in some of the study groups, which could limit the possibility of finding significance. Secondly, all selected patients met a set of criteria for starting HCV treatment (e.g., no alcohol abuse, CD4+ cell counts >200 cells/mm3, controlled HIV replication, and good treatment adherence), and this may have introduced a selection bias. Thirdly, we did not have a control group of HCV-monoinfected patients to provide information of possible differential biomarkers from HIV/HCV-coinfected patients. We also did not have any patients with decompensated cirrhosis to study the biomarker profile in end-stage liver disease. Fourthly, our results were not adjusted by multiple comparisons. In this regard, when clinical-orientated studies are not a random search of a meaningful result, it is not recommended adjusting the “p-value” after multiple tests because it can penalize significantly relevant results [67, 68]. Note that our hypothesis is supported by theory and previous reports in patients infected with HIV, HCV, or both, as discussed previously. Additionally, our data had a clear interpretation since always pointed out in the same direction and the analyzed biomarkers cannot be considered completely independent. Fifthly, we have not used a fixable Live/Dead dye in our freshly whole blood samples, which may influence the results of flow cytometry. However, it is unlikely that there was a bias with respect to a group, since all the samples were processed in the same way. Finally, another limitation of the study is that TE was not always performed in the fasting state, as is currently recommended, since food intake increases liver stiffness in patients with HCV infection [71].

Conclusions

In conclusion, biomarker levels of T cell activation, bacterial translocation, inflammation, endothelial dysfunction, and coagulopathy increased with the severity of liver fibrosis in HIV/HCV-coinfected patients, particularly in patients who had LSM>40 KPa. Further studies are needed to confirm our findings and evaluate whether these biomarkers in HIV/HCV-coinfected patients with compensated cirrhosis can serve as prognostic factors.

List of abbreviations

Human immunodeficiency virus (HIV)

Hepatitis C virus (HCV)

Liver stiffness measures (LSM)

Chronic hepatitis C (CHC)

Combination antiretroviral therapy (cART)

Gut associated lymphoid tissue (GALT)

Direct-acting antivirals (DAAs)

Soluble CD14 (sCD14)

Lipopolysaccharide (LPS)

Fatty acid-binding protein 2 (FABP2)

Lipopolysaccharide binding protein (LBP)

Interleukin (IL)

IFN-g-inducible protein 10 (IP-10)

Soluble vascular cell adhesion molecule 1 (sVCAM1)

Soluble intercellular cell adhesion molecule 1 (sICAM1)

Soluble tumor necrosis factor receptor 1 (sTNFR1)

Monocyte chemoattractant protein-1 (MCP1)

Plasminogen activator inhibitor-1 (PAI-1)

Generalized Linear Models (GLM)

Arithmetic mean ratio (AMR)

Non-alcoholic fatty liver disease (NAFLD)

Acquired immune deficiency syndrome (AIDS)

Non-nucleoside analogue HIV reverse transcriptase inhibitor (NNRTI)

Nucleoside analogue HIV reverse transcriptase inhibitor (NRTI)

Protease inhibitor (PI)

Integrase inhibitor (II)

Declarations

Ethics approval and consent to participate

The study was conducted in accordance with the Declaration of Helsinki and patients gave their written consent. The Institutional Review Board and the Research Ethic Committee of the Instituto de Salud Carlos III (ISCIII) approved the study.

Consent for publication

Not applicable

Availability of data and materials

The datasets used and/or analyzed during the current study may be available from the corresponding author upon reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This study was supported by grants from Instituto de Salud Carlos III (ISCII; grant numbers PI14/01094 and PI17/00657 to JB, and PI14CIII/00011 and PI17CIII/00003 to SR) and Ministerio de Sanidad, Servicios Sociales e Igualdad (grant number EC11-241). The study was also funded by the RD16CIII/0002/0002 and RD16/0025/0017 projects as part of the Plan Nacional R + D + I and cofunded by ISCIII- Subdirección General de Evaluación and the Fondo Europeo de Desarrollo Regional (FEDER). JB is an investigator from the Programa de Intensificación de la Actividad Investigadora en el Sistema Nacional de Salud (I3SNS), Refs INT15/00079 and INT16/00100.

Author contributions

Conceptualization: SR, JB, and JGG.

Data curation: JB, JGG, JMG, MC, CQ, JS.

Formal analysis: SR, LMM.

Funding acquisition: JB, JGG, and SR.

Investigation and methodology: LMM, PGB, and IC.

Project Administration: JB.

Supervision and visualization: SR.

Writing – original draft preparation: LMM, PGB, and SR.

Writing – Review & Editing: MAJS, JB.

Acknowledgements

We want to particularly acknowledge the patients in this study for their participation and to the Spanish HIV HGM BioBank integrated in the Spanish AIDS Research Network (RIS) and collaborating Centers for the generous gifts of clinical samples used in this work. The HIV BioBank, integrated in the Spanish AIDS Research Network, is supported by the Institute of Health Carlos III, ISCIII, Spanish Health Ministry (Grant nº RD06/0006/0035 and RD12/0017/0037) as part of the State Plan for Scientific and Technical Research and Innovation and cofinanced by ISCIII- Sub-Directorate General for Research Assessment and Promotion and European Regional Development Fund (ERDF) and Foundation for Research and Prevention of AIDS in Spain (FIPSE). This study would not have been possible without the collaboration of all the patients, medical and nursing staff and data managers who have taken part in the project (See Text, Supplemental Digital Content 1, which show all collaborators). The RIS Cohort (CoRIS) is funded by the ISCIII through the Spanish AIDS Research Network (RIS C03/173 and RD12/0017/0018) as part of the State Plan for Scientific and Technical Research and Innovation and cofinanced by ISCIII- Sub-Directorate General for Research Assessment and Promotion and European Regional Development Fund (ERDF).

Authors’ information

Not applicable

The GESIDA 3603b Cohort Study Group

Hospital General Universitario Gregorio Marañón, Madrid: A Carrero, P Miralles, JC López, F Parras, B Padilla, T Aldamiz-Echevarría, F Tejerina, C Díez, L Pérez-Latorre, C Fanciulli, I Gutiérrez, M Ramírez, S Carretero, JM Bellón, and J Berenguer.

Hospital Universitario La Paz/IdiPAZ, Madrid: JR Arribas, F Arnalich, I Bernardino, V Hontañón, C Busca, A Delgado, L Martín-Carbonero, R Micán, R Montejano, ML Montes, V Moreno, I Pérez-Valero, E Valencia and J González-García.

Hospital de la Santa Creu i Sant Pau, Barcelona: JM Guardiola, P Domingo, I Garcia

Hospital Universitari Vall d’Hebron, Barcelona: E Van den Eynde, M Pérez, E Ribera, M Crespo.

Hospital Universitario Ramón y Cajal, Madrid: JL Casado, F Dronda, A Moreno, MJ Pérez-Elías, MA Sanfrutos, S Moreno, C Quereda.

Hospital Universitario Príncipe de Asturias, Alcalá de Henares: A Arranz, E Casas, J de Miguel, V. Victor, J Sanz.

Hospital Universitario de La Princesa, Madrid: J Sanz-Sanz, I de los Santos, A Gómez-Berrocal y L García-Fraile

Hospital Donostia, San Sebastián: MJ Bustinduy, JA Iribarren, F Rodríguez-Arrondo, MA Von-Wichmann.

Hospital Clínico San Carlos, Madrid: J Vergas, MJ Téllez.

Hospital Universitario San Cecilio, Granada:

Hospital Clínico Universitario, Valencia: A Ferrer, MJ Galindo.

Hospital General Universitario, Valencia: M. Garcia Deltoro, E Ortega.

Hospital Universitario La Fe, Valencia: M Montero, M Blanes, S Cuellar, J Lacruz, M Salavert, J López-Aldeguer.

Hospital del Campus de la Salud, J Hernández-Quero

Fundación SEIMC-GESIDA, Madrid: M Yllescas, E Aznar, H Esteban

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