Chronic hepatitis C (CHC) is 1 of the leading causes of end-stage liver disease, hepatocellular carcinoma, and liver transplantation worldwide.1,2 Liver fibrosis in CHC is believed to be progressive and largely irreversible, although the progression rate is highly variable and difficult to predict. Some individuals experience fast fibrosis progression with rapid development of end-stage liver disease (ESLD), whereas other individuals experience a slow progression, which makes the development of liver decompensation very unlikely.3 This variability in fibrosis progression is probably because of multifactorial interactions between viral and host factors such as age at hepatitis C virus (HCV) infection, gender, daily alcohol intake, intravenous drug use (IDU), obesity, metabolic syndrome, HCV genotype,4–6 and coinfection by other viral pathogens such as HIV, which is common among HCV-infected patients7 and increases the rates of fibrosis progression and ESLD.8,9 Unfortunately, the determinants of liver fibrosis progression in CHC are largely unknown, and the current methods for predicting progression are not sufficiently accurate to identify which patients will progress to fibrosis/cirrhosis.3
There is increasing evidence that host genetic factors may play an important role in fibrosis progression in CHC.10 In 2007, a genomewide association study performed in HCV-monoinfected patients showed that the combination of 7 single nucleotide polymorphisms (SNPs) (rs62522600, rs4986791, rs886277, rs2290351, rs4290029, rs17740066, and rs2878771) in a cirrhosis risk score (CRS) was able to predict progression to advanced fibrosis/cirrhosis.11 Most of the genes where the 7 SNPs are located are known (AZIN1, TLR4, TRPM5, AP3S2, STXBP5L, and AQP2), except for rs4290029, which is located in an intergenic region downstream of DEGS1. However, to date, only 1 polymorphism, located in TLR4 gene, has been functionally evaluated.12,13 The genetic signature represented by the 7 polymorphisms seems to represent the best available tool for the genetic prediction of liver fibrosis in HCV-monoinfected patients so far.14–16 However, the usefulness of CRS for predicting fibrosis progression in HIV/HCV-coinfected patients remains unknown. Moreover, CRS has not been studied together with other factors associated with fibrosis such as HCV genotype, which has been shown to affect the fibrosis progression rates in HIV/HCV-coinfected patients.17 Another variable affecting liver fibrosis is the IL28B gene, which is similarly known to affect fibrosis progression in both HCV-monoinfected18 and HIV/HCV-coinfected patients.19,20
The most common HCV treatment in HCV/HIV-coinfected patients is still a combination of pegylated-interferon alpha plus ribavirin (pegIFNα/RBV),21 which has a low rate response, a high cost, and numerous side effects.22–25 However, the newer directly acting agents for HCV treatment have vastly improved the efficacy over current pegIFNα/RBV therapy, although these new directly acting agents are still costly.26 For this reason, it is desirable to identify patients who urgently need HCV treatment or conversely those who do not need to be treated.27 Thus, an accurate assessment of the risk of fibrosis development may be helpful in determining, depending on the risk of the patients, the urgent need of HCV treatment or may be helpful to identify those not needing to be treated.
The aim of our study was to assess the ability of CRS to predict hepatic fibrosis progression in HIV/HCV-coinfected patients.
We carried out a retrospective study on HIV/HCV-coinfected patients that underwent a liver biopsy at Hospital Gregorio Marañón (Madrid, Spain) between September 2000 and November 2008. All patients were of European ancestry.
Liver biopsies were performed on patients who were potential candidates for anti-HCV therapy and had not received previous interferon therapy. Selection criteria for the study were no clinical evidence of hepatic decompensation, detectable HCV RNA by polymerase chain reaction, negative hepatitis B surface antigen, CD4+ lymphocyte count higher than 200 cells per microliter, stable antiretroviral therapy, or no need for antiretroviral therapy. Patients with active opportunistic infections, active drug addiction, or unknown date of infection were excluded. Thus, from our cohort of 361 HIV/HCV-coinfected patients with liver biopsy data, 205 patients had a DNA sample collected and available CRS data, but only 190 of 205 patients had an estimate for HCV infection date.
The study was conducted in accordance with the Declaration of Helsinki. All patients gave their written consent for the liver biopsy and genetic testing, and the Institutional Ethics Committee approved the study.
Clinical and Laboratory Data
On the date of the liver biopsy, the following information was obtained from medical records: age, gender, HIV transmission category, weight, height, alcohol intake (consumption of more than 50 g of alcohol per day for at least 12 months was considered as a high intake), Centers for Disease Control clinical category, nadir CD4+ T-cell count, current CD4+ T-cell count, plasma HIV viral load, antiretroviral therapy (if any), HCV genotype, and plasma HCV viral load.
The duration of HCV infection for patients with a history of IDU was estimated starting from the first year they shared needles and other injection paraphernalia, which are the most relevant risk practices for HCV transmission.28 For non-IDU patients, we only included those patients for whom the initiation of their HCV infection could be determined with certainty.
Cirrhosis Risk Score and IL28B Genotyping
Genomic DNA was extracted from peripheral blood by using Qiagen columns (QIAamp DNA Blood Midi/Maxi; Qiagen, Hilden, Germany). Genotyping was performed by the Spanish National Genotyping Center (CeGen; http://www.cegen.org/) using GoldenGate assay with VeraCode Technology (Illumina, Inc., San Diego, CA).
The CRS signature was performed by genotyping 7 SNPs11,15: rs62522600 (AZIN1), rs4986791 (TLR4), rs886277 (TRPM5), rs2290351 (AP3S2), rs4290029 (downstream of DEGS1), rs17740066 (STXBP5L), and rs2878771 (AQP2). From these SNPs, we calculated the CRS values using a naive Bayes formula previously described.11 The CRS value varied from 0 to 1 with a higher CRS associated with a greater risk of developing fibrosis. Two categorical cutoff points for different levels of risk have also been described in CHC patients: low risk (<0.50) versus high risk (>0.70).11
The rs12980275 polymorphism near IL28B was genotyped in a previous study.20
Liver Fibrosis Evaluation
Liver biopsies were performed as previously described,29 and liver fibrosis was estimated according to Metavir score as follows: F0, no fibrosis; F1, portal fibrosis; F2, periportal fibrosis or rare portal–portal septa; F3, fibrous septa with architectural distortion; no obvious cirrhosis (bridging fibrosis); and F4, definite cirrhosis.
HIV/HCV-coinfected patients were classified into 2 groups according to fibrosis stage developed after a minimum follow-up time of 10 years with HCV infection: (1) nonprogressors: patients with F0 from the liver biopsy and (2) progressors: patients with F1 to F4.
The statistical analysis was performed by Statistical Package for the Social Sciences (SPSS) 15.0 (SPSS, Inc., Chicago, IL). Categorical data and proportions were analyzed by using the χ2 test or Fisher exact test. Mann–Whitney U test was used to compare data among independent groups. All P values were 2 tailed. Statistical significance was defined as P < 0.05.
We performed both univariate and multivariate logistic regression analyses to investigate the association among CRS values and fibrosis stage. In each multiple logistic regression analysis, we included CRS (“Enter” algorithm) and the most significant covariables selected by the “Stepwise” algorithm. The covariables analyzed by the Stepwise algorithm were CHC clinical factors (age at HCV infection, gender, alcohol intake, IDU, HCV genotype, and IL28B genotype) and HIV clinical factors (nadir CD4+, AIDS, time on combination antiretroviral therapy [cART]). Thus, each logistic regression was always adjusted for significant covariates associated with the outcome variable.
Later, we analyzed the diagnostic performance of CRS for predicting fibrosis progression. We also analyzed whether the predictive accuracy could be improved by accounting for the most important clinical factors that can be determined in the first contact between clinician and patient [age at HCV infection, gender, IDU, HCV genotype, and IL28B genotype (rs12980275)]. Thereafter, several indexes were built to express the likelihood of developing fibrosis as a probability ranging from 0 to 1, through a logistic probability function.30 The area under the receiver-operating characteristic curve (AUROC) was obtained to evaluate the predictive accuracy. Criteria for levels of accuracy were as follows: 0.90–1 = excellent, 0.80–0.90 = good, 0.70–0.80 = fair, and 0.60–0.70 = poor. The diagnostic performance of CRS was evaluated according to the 2 cutoffs previously described by Huang et al11 to identify patients with low risk (CRS < 0.50) and high risk (CRS > 0.70) of developing fibrosis. The sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), and percentage of patients correctly classified (accuracy) were calculated for each cutoff point.
The clinical characteristics of the 190 HIV/HCV-coinfected patients are shown in Table 1. There were 25 nonprogressor patients with a median time of HCV infection of 25 years approximately [percentile 25th (P25th); percentile 75th (P75th): 17.1; 27.5)], and 165 progressor patients with a median time of HCV infection of 21.3 years (P25th; P75th: 17.3; 24). A history of injection drug use was significantly less frequent among nonprogressors than among progressors. Of note, nonprogressors had a longer duration of HCV infection than progressors, suggesting a high stability in the nonprogression of liver fibrosis. The median time on cART was also significantly longer in nonprogressor than in progressors.
CRS and Fibrosis Progression
CRS values were significantly lower in nonprogressors [median, 0.61 (P25th; P75th: 0.41; 0.62)] than in progressors [median, 0.67 (P25th; P75th: 0.43; 0.77)]; P = 0.043. However, among progressors, we observed similar CRS values for all fibrosis stages (F1/F2/F3/F4) (Fig. 1A). Understandably, the proportion of subjects with CRS > 0.70 was higher among progressors than among nonprogressors. However, the proportion of patients with CRS values between 0.50 and 0.70 (intermediate risk) and <0.50 (low risk) was similar for each of the fibrosis stages (P = 0.047) (Fig. 1B). Moreover, a higher proportion of CRS values above 0.70 was also observed among F ≥ 1 patients who were female, had an age at HCV infection above 18 years, HCV genotype 1/4, or rs12980275 AA genotype (Fig. 2).
Overall, logistic regression analyses showed that CRS was significantly associated with liver fibrosis progression (Table 2). For all patients, a value of CRS > 0.70 corresponded with a higher likelihood of fibrosis progression (adjusted odds ratio = 9.20; P = 0.002). Similarly, for every 10 points of CRS value, a higher likelihood of developing fibrosis was detected (adjusted odds ratio = 1.46; P = 0.008) (Table 2). Logistic regression analysis also showed a strong association of CRS values and CRS > 0.70 with liver fibrosis progression among females, patients who acquired HCV infection after turning 18 years, rs12980275 AA genotype, HCV genotype 1/4, and IDU (Table 2).
Predictive Performance of CRS
The AUROC of CRS for discriminating between nonprogressors and progressors was low but significant (0.625; P = 0.043) (Fig. 3). In this setting, the predictive performance of CRS > 0.70 had values of 48.5 Se [95% confidence interval (CI): 41 to 56.1], 84 Sp (95% CI: 65.3 to 93.6), 95.2 PPV (95% CI: 88.4 to 98.1), 19.8 NPV (95% CI: 13.3 to 28.4), and 53.2 accuracy (95% CI: 46.1 to 60.1) for identifying patients with risk of fibrosis progression.
To improve the predictive value of CRS, we analyzed the CRS in combination with clinical factors,
This worked clearly for improving it with an AUROC of 0.739 (P < 0.001) (Fig. 3). The predictive performance of CRS above 0.70 in combination with clinical variables had values of 95.1 Se (95% CI: 90.6 to 97.5), 29.2 Sp (95% CI: 14.9 to 49.2), 90.1 PPV (95% CI: 84.7 to 93.7), 46.7 NPV (95% CI: 24.8 to 69.9), and 86.6 accuracy (95% CI: 80.9 to 90.7) for identifying patients with risk of fibrosis progression.
We showed for the first time that there was a relationship between CRS and liver fibrosis progression in HIV/HCV-coinfected patients. CRS helped to discriminate between nonprogressor (F0) and progressor patients (F1/F2/F3/F4), something of interest for therapeutic decision making in clinical practice. Moreover, using CRS together with clinical factors improved the performance for discriminating between nonprogressors and progressors, and its cutoff > 0.70 had an acceptable predictive performance for discriminating between nonprogressors and progressors. However, CRS had low performance for predicting advanced fibrosis/cirrhosis (data not shown), similarly as in the report of Huang et al11
The association between CRS and fibrosis progression was first established in a cross-sectional study comparing a control group of HCV-monoinfected patients without fibrosis (F0) with a case group with fibrosis/cirrhosis (F3/F4).11 Since then, 3 studies have validated the predictive value of CRS in patients infected with HCV14–16 and another in recipients of liver transplantation for HCV infection.31 Conversely, some contradictory results have been identified such as the study of Grünhage et al,32 where no association between any of the 7 SNPs and inflammation of fibrosis was found in a regression model. However, the role of CRS in predicting liver disease in HCV patients with HIV coinfection, which clearly accelerates fibrosis progression and development of ESLD,33 has not been explored so far. Our study found a weak association of CRS with the risk of developing liver fibrosis in HIV/HCV-coinfected patients; and the CRS score had an AUROC for predicting fibrosis progression of only 0.625, which is considered poor. Therefore, CRS score seems to be less useful in HIV/HCV-coinfected patients than in HCV-monoinfected patients. Because of the low accuracy, it is unlikely that CRS may be helpful in aiding clinical decision making about liver fibrosis development. However, the combination of CRS with clinical factors clearly improved the performance for discriminating between progressor and nonprogressor patients. Many environmental cofactors and common comorbidities are known to affect the course of CHC in HIV/HCV-coinfected patients,8,9 and these effects may hinder any underlying genetic predisposition affecting disease progression.
CHC is a slow disease in most cases and takes a long time to progress to advanced fibrosis.3,8 Subgroups of patients from nonprogressors to rapid progressors have been clearly described in the literature.34,35 CHC progression, such as many other complex diseases, probably involves a large number of genes, which makes difficult to define the relative contribution of each one. However, the importance of the CRS score lies in the combination of the effect of 7 polymorphisms, which have been proved to be associated with liver fibrosis progression.11 This genetic signature is made from the contribution of each single gene into a single score, which allow us to infer the probability of liver fibrosis development for each patient. Hence, the CRS remains invariable throughout the duration of HCV infection, and its determination would be necessary only once in a lifetime, unlike other noninvasive markers of liver fibrosis such as aspartate aminotransferase/platelet ratio36 or FIB4 score.37 These widely used fibrosis tests are mainly based on evaluation of serum biomarkers, which might fluctuate during concurrent illnesses or disease stage, and they identify patients with significant fibrosis only at a particular moment in time. However, genetic markers are robust and invariable between different clinical settings.
Our results suggest that CRS was associated with liver fibrosis progression in patients who were female, acquired HCV infection after turning 18 years, had HCV genotype 1/4, or carry the IL28B genotype shown to be favorable for HCV therapy response (rs12980275 AA).
A link between the favorable IL28B genotype and increased odds of liver disease severity has been reported for both HCV-monoinfected patients38,39 and for HIV/HCV-coinfected patients.19,20 The IL28B gene encodes IFN-λ3, a type III interferon cytokine with antiviral activity against HCV in the liver, via an innate immunity pathway and involving expression of inflammatory cytokines.40,41 In addition, IL28B is able to modulate adaptive immune responses, promote the Th1 immune pathway, increase T regulatory cells, and increase CD8 T-cell cytotoxicity and memory responses.40 Thus, it is reasonable to assume that IL28B could also have an influence on liver fibrosis progression in CHC. However, this controversial association and the possible mechanisms involved are still unknown.
With respect to HCV genotype, which does not seem to exert any influence on the progression of liver disease; however, some reports have identified an increased rate of fibrosis progression in both HCV-monoinfected patients and HIV/HCV-coinfected patients infected with HCV genotype 3.17,42,43 In our study, we observed that CRS is able to predict fibrosis progression in patients with HCV genotype 1/4. However, no significant results were obtained for HCV genotype 2/3, which may be because of the reduced number of HCV genotype 2/3 patients that do not have liver fibrosis (data not shown).
Another factor that is associated with a higher likelihood of progression to fibrosis is age at the time of HCV infection. The chronicity rate in HCV infection seems to be lower in younger individuals, which has been widely reported in follow-up studies with children with posttransfusion or vertical transmission and patients younger than 20 years.44 Our results indicate a high predictive power of CRS in patients who were older than 18 years when they acquired HCV but not for those who were younger.
Similarly, fibrosis progression seems to be lower in women, particularly in younger women.44 On examining these variables, CRS seems to better predict fibrosis progression in females in our cohort. Therefore, in addition to a lower fibrosis progression in females, those that do finally develop a fibrosis stage are more likely genetically predisposed to it. Regarding men, CRS showed a reduced predictive value, which is probably because of confounding variables for different epidemiological history such as a higher alcohol intake and a greater likelihood of being IDUs than female patients.
The AUROC for predicting the risk of fibrosis progression reported by Huang et al11 was 0.75, a value high enough to conclude that the CRS is a useful tool for identifying CHC patients with HCV monoinfection that are at high risk for developing fibrosis. In our study, the performance of the CRS alone was low indicating that the genetic signature alone is not sufficient for predicting liver fibrosis among HIV/HCV-coinfected individuals, perhaps because HIV infection markedly influences fibrosis progression in CHC. However, the combination of the CRS score with readily available clinical parameters (age at HCV infection, IDU, gender, IL28B genotype, and HCV genotype) improved the diagnostic ability of CRS to identify likely progressors (AUROC, 0.73). Thus, those patients with a cutoff value above 0.70 have significant odds of developing liver fibrosis. However, CRS was unable to correctly classify HCV/HIV-coinfected patients with values less than 0.7. Then, the logistic function with CRS plus clinical factors may be of help for decision making in clinical management of HIV/HCV-coinfected patients, by identifying those patients who do not need to be treated. Unfortunately, CRS plus clinical factors are unable to distinguish between who will develop advanced fibrosis or cirrhosis (F3/F4) from those who will have low or mild fibrosis (F0/F1).
Some clarifications need to be made to properly interpret our results. First, this is a cross-sectional study and the strategy for monitoring of our cohort was not designed to develop a model for predicting different stages of fibrosis progression. Ideally, the design would be longitudinal with serial biopsies from all patients, but only a small percentage of patients had repeated biopsies before any HCV therapy, and based on current practice, the time interval between the 2 biopsies is generally short (3–5 years). Second, the patients selected for our study met a set of criteria for starting HCV treatment (eg, low alcohol abuse, high CD4 cell counts, controlled HIV replication, and good treatment adherence), and it is possible that this may have introduced some selection bias. In addition, because the IDU patients may well be more likely to die from IDU-related causes, the nonprogressors may be enriched for those not injecting drugs; and, therefore, have a longer follow-up.
In conclusions, the CRS itself seems not to be a good marker for identifying HIV/HCV-coinfected patients who are at high risk of developing liver fibrosis. However, CRS score coupled with clinical factors (age at HCV infection, IDU, gender, IL28B, and HCV genotype) might help to distinguish between nonprogressors and progressors patients.
The authors thank the Spanish National Genotyping Center (CeGen) for providing the genotyping services (http://www.cegen.org). They also acknowledge the patients in this study for their participation.
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Keywords:© 2013 by Lippincott Williams & Wilkins
AIDS; chronic hepatitis C; genetic polymorphisms; liver fibrosis; predictive genetic markers