Because of shared transmission route hepatitis C virus (HCV) and HIV coinfection is frequent . However, the effect of HCV viral load and genotype on liver disease progression and mortality in HIV-HCV-coinfected individuals is not well understood . HCV infection in individuals with HIV infection differs from infection in individuals without HIV in several aspects. HIV-coinfected individuals have higher HCV viral load and lower rates of spontaneous and treatment-induced clearance . HIV coinfection is associated with rapid progression of liver-related disease . HCV viral load has been shown to predict end-stage liver disease (ESLD) and death  in one study but not in others [6–11]. In addition, it has been shown that high HCV viral load was associated with AIDS-related death . HCV genotype 1 has been associated with a higher risk of death  as well as progression to AIDS [14,15]. On the contrary, HCV genotype 3 has been associated with steatosis [16,17] and inflammation [18,19]. Recently, genetic associations between HCV outcome and single-nucleotide polymorphisms (SNPs) around the interleukin-28B (IL28B) gene have been established. HIV-HCV-coinfected individuals with certain IL28B genotypes have high plasma HCV viral load [20,21]. Generally, it is well established that HIV alters the clinical outcome of HCV. Whether inflammation, which is thought to be a consequence of chronic infection and immune activation contributes to disease progression and death in HIV-HCV-coinfected as it does in HIV-infected individuals  is not clear. Hypothetically, immune activation, dysregulation and direct viral interactions collectively drive progression of liver disease and mortality. Here, we investigated whether viral factors (HCV RNA and genotypes) and immunogenetic factors (IL28B genotypes) were associated with mortality in a HIV-HCV-coinfected cohort and we discuss whether any of these markers might provide predictive information beyond the information gained from HIV-related markers.
Material and methods
Consecutive anti-HCV-positive HIV-1-infected individuals who attended the outpatient clinic at the Department of Infectious Diseases, Copenhagen University Hospital, Hvidovre, between 1 January 1995 and 1 January 2008 were evaluated for inclusion. Inclusion criteria were chronic HCV infection defined as at least two positive HCV RNA measurements more than 6 months apart or one positive HCV RNA measurement more than 12 months after the first positive HCV antibody test. Samples used for this study were retrieved from a repository maintained at the Copenhagen University Hospital, Hvidovre, and approved by the Danish Data Protection Agency (records no. 2005-41-5520 and 2009-41-3678). Ethical permission was obtained from The Ethical Committee of The Capital Region of Denmark (record no. H-A-2010-069). Information regarding dates of first positive HIV test and start of combination antiretroviral therapy (cART), HIV exposure group, CD4 cell counts and HIV RNA were retrieved from the Danish HIV Cohort Study . Quantifications of HIV RNA were done with three different assays with detection limit of 20, 40 or 400 copies/ml during the 13-year study period, and for statistical analyses we used 400 copies/ml as the lower detection limit for all three assays. Information regarding HCV viral load, dates of first anti-HCV test, HCV treatment and hepatitis B virus (HBV) serology were retrieved by a physician from patient files. Death causes were obtained from the Danish Registry of Causes of Death (DRCD), which is a nonblinded recording of death causes performed by a physician . Causes of death during the study period were coded using ICD-10. HBV infection was defined as a positive hepatitis B surface antigen (HBsAg) test in serum or plasma. All measured values of continuous variables obtained during follow-up were included in the analyses as time-updated variables.
Serological testing has been described elsewhere . Briefly, anti-HCV was evaluated by third-generation ELISA. The assessment of HCV viral load was done by one commercially available assay according to the manufacturers’ specifications or by one of two in-house assays. HCV genotype was determined with genotype specific primers from the 5′ noncoding region of the virus by RT-PCR  or by one-step RT-PCR using C/E1 and NS5B-specific primers .
Individuals were genotyped in the IL28B SNP, rs12979860 . SNPs were genotyped from DNA extracted from blood samples (n = 167) or from plasma samples (n = 48). In order to secure that genotypings done from DNA extracted from plasma were identical with genotypings done from DNA extracted from blood samples, we included 30 samples which were previously genotyped from DNA extracted from blood. Twenty-six of 26 amplifiable samples were identical; the remaining four could not be genotyped in DNA from plasma samples. SNP genotyping was performed by Kbiosciences (Kbiosciences, Herts, UK) using a competitive allele-specific PCR .
Discrete data are presented as counts and percentages and mortality rate ratios (MRRs) with 95% confidence intervals (95% CIs) unless otherwise stated. Follow-up was calculated from the first date of fulfilment of our criteria of chronic HCV infection to the date of death or the end of study period (default censoring date, 1 January 2010), whichever came first. Follow-up was not calculated from the date of first positive HCV antibody test to avoid survival bias in the mortality rate estimate . Fourteen individuals were treated for HCV and were censored at the date of initiation of HCV treatment. Four individuals had default censoring date of 1 January 2009 because they moved from our clinic during follow-up. None of these four individuals died; however, we were not able to update covariates. Poisson regression with time-dependent covariables was used to model the all-cause mortality rate among individuals chronically infected with HCV. In the univariate models we included sex, race, HIV exposure group, IL28B genotypes, HCV genotype, HCV subtype, HCV viral load, HBsAg, CD4 cell count, HIV RNA, ever a diagnosis of AIDS and ever initiation of cART. The multivariate regression analysis was modeled in two ways. In the first model we adjusted for age, sex, HIV exposure group, CD4 cell count, HIV RNA and IL28B genotypes and restricted the analysis to individuals of white origin to avoid population stratification . The second model were adjusted for the same variable except IL28B genotypes and not restricted to whites. Additionally, we looked for evidence for interactions between HCV genotypes and route of HIV exposure and interactions between HCV genotypes and HCV viral load. To split person years at risk (PYR) for calculation of mortality rate we used the Stratify macro for SAS (SAS Institute Inc, Cary, North Carolina, USA) . Hardy–Weinberg equilibrium was calculated using χ2 test. SAS statistical software 9.1 was used for data analysis.
Of 2196 HIV-positive individuals, 383 were positive for HCV antibodies corresponding to a prevalence of HCV infection of 20% in this population. Two hundred and seventy-six were not tested for HCV antibodies. Seventy-one individuals had spontaneous or treatment-induced clearance and 41 lacked samples for additionally testing leaving us with 264 chronically HIV-HCV-coinfected individuals. Baseline characteristics of the study cohort at entry are presented in Table 1. Median age at the time of inclusion was 41 [(interquartile range, IQR) 35, 47] years and median follow-up was 4 (IQR 2, 6) years. Of the 17 non-white individuals, seven were of African, seven of Asian and three of Inuit origin. One hundred and eighteen deaths occurred during 1143 PYR corresponding to an overall mortality rate of 10/100 (95% CI 8, 12) PYR. Univariate analysis suggested that HCV viral load, HCV genotype, IL28B genotype, CD4 cell count, HIV RNA and AIDS-affected outcome, whereas age, sex, race, HIV exposure group, cART and HBsAg did not.
Hepatitis C viral load
For both nonsurvivors and survivors a median of 2 (range, 1–12) measurements were done. In adjusted analysis mortality rate increased with 30% per log increment in HCV viral load independently of IL28B genotypes, CD4 cell counts and HIV RNA levels (Table 2). To assess whether the distribution of HCV genotypes could confound the association of HCV viral load on mortality we performed the adjusted analysis without HCV genotypes and we found the estimates of HCV viral load unchanged (data not shown).
HCV genotypes were available for 255 individuals and the majority of individuals were infected with HCV genotype 1 or 3. Mortality rate was higher for HCV genotype 2 and 3 and lower for HCV genotype 4, compared to genotype 1 (Table 1).
In adjusted analysis, infection with HCV genotype 3 was associated with a nearly two-fold increased mortality rate compared to HCV genotype 1, irrespectively of CD4 cell counts, HIV RNA, IL28B genotypes and demographic covariates (age, sex, HIV exposure groups and race) (Table 2). Excluding the IL28B genotypes from the analysis did not alter the effect of HCV genotypes on mortality. Mortality rate associated with HCV genotypes 2 and 4 were not significantly different from rates associated with HCV genotype 1.
The distribution of HCV genotypes was constant during the study period and did not differ between HIV exposure groups (data not shown). The majority of genotypes were subtyped and we found a trend of higher mortality rate for HCV subtype 3a compared to 1a (Table 3). No significant difference was seen for subtype 1b, 2b and 4d compared to subtype 1a in the univariate analysis. Subtypes 2a, 3b and 4 h affected very few individuals and did not permit analysis (data not shown).
All genotypes were in Hardy–Weinberg disequilibrium (P = 0.04) for the 215 (81%) genotyped individuals of whom 98 died during follow-up. In adjusted analysis, the IL28B CC genotype was associated with a nearly two-fold increased mortality rate compared to the TT genotype. The TC genotype compared to the TT genotype was associated with 54% increased mortality rate, however, without statistical significance. Genotype CC vs. TC+TT was associated with aMRR of 1.65 (1.10, 2.48) (Table 2). Excluding HCV genotypes from the analysis did not alter the association of IL28B genotypes with mortality.
In adjusted analysis of the effect of CD4 cell counts on mortality, reductions in mortality rate ranged from 10% per 50 cells increment for individuals with CD4 cell counts greater than 200 cells/μl to 40% per 50 cells increment in individuals with CD4 cell counts less than 200 cells/μl (Table 2).
The effect of HIV RNA ranged from three-fold increased mortality rate for undetectable virus vs. just detectable virus to a 4.6-fold increased mortality rate per 1 log increase for detectable HIV RNA. These estimates were attenuated in the adjusted analysis without IL28B genotypes (Table 2).
Causes of death
Death causes were classified as natural (n = 98, mortality rate = 9/100 PYR), accidental (n = 10, mortality rate = 0.9/100 PYR), violent (n = 1, mortality rate = 0.09/100 PYR), suicidal (n = 1, mortality rate = 0.09/100 PYR) or unknown (n = 8, mortality rate = 0.7/100 PYR). The most frequently reported underlying cause of death was HIV/AIDS (B20-24) related (56 deaths (47%)). Drug-related deaths (F10-19, K70, X42, X62, Y12, Y11) defined according to ICD-10 (World Health Organisation, 1992) accounted for 25 deaths (21%). Liver-related deaths accounted for 5 (4%) deaths.
Because the distribution of HCV genotypes among injecting drug users (IDUs) could confound the effect we observed on mortality we tested for interactions between route of HIV exposure and HCV genotype and found no statistical significant interactions (P = 0.6). Additionally no statistical significant interactions were found when we tested for interactions between HCV genotypes and HCV viral load (P = 0.7).
Our study showed that high HCV viral load, HCV genotype 3 and IL28B CC genotype were independent predictors of all-cause mortality in HIV-HCV-coinfected individuals after adjustment for important predictors of survival such as age, sex, HIV exposure group as well as HIV-related factors such as CD4 cell count and HIV RNA.
Mechanisms to explain the accelerated progression to liver disease in HIV-HCV-coinfected individuals compared to HCV-infected individuals without HIV are not well understood but may include direct viral effects  and immune dysregulation and deficiency . Considering the pathogenesis of other viral infections, it is reasonable to assume that viral burden may correlate with the severity of disease . This is certainly true in untreated HIV infection where viral load is associated to both progression to AIDS and mortality . It is generally accepted that plasma HCV viral load does not correlate directly with liver cell damage. Some studies have shown an association between intrahepatic HCV RNA levels of genotype 3 and higher degree of steatosis . Hisada et al. showed that HCV viral load was a predictor of ESLD-related death in a study of 389 IDUs of whom 168 were coinfected with HIV. In contrast, other smaller studies failed to show an association between HCV viral load and liver fibrosis progression [6,11]. It is possible that the relation to ESLD shown by Hisada et al. was influenced more by the HIV-induced immune deficiency than the higher HCV viral load itself. Our study differs in that we prospectively have followed individuals with both sexual and parenteral transmission of HCV as indicated in the HIV exposure groups, individuals infected with known HCV subtypes, and we used time-updated regression analysis to account time-varying covariates including HCV viral load. In the present study we are unable to elucidate the mechanism(s) explaining the increased all-cause mortality rate and it is likely to be a multifaceted process involving chronic infection, chronic inflammation and behavioral conditions associated with IDU [3,12]. Our data support frequent determination of HCV viral load in the management of coinfected individuals and those individuals with high HCV viral loads should be monitored closely for signs of liver damage and referred for anti-HCV therapy when appropriate.
HCV genotype 3 was associated with higher all-cause mortality than HCV genotype 1. This is in contrast to previous studies on HIV-HCV-coinfected hemophiliacs that associated an increased risk of death and progression to AIDS with HCV genotype 1 compared to all other genotypes combined [14,15]. A possible explanation may be that both studies predominantly included genotype 1 (≥70%) and only few of genotypes 2, 3 and 4 and thus a possible effect of genotype 3 may have gone undetected. Other evidence supports our finding. In a cross-sectional study of 283 HIV-HCV-coinfected individuals, genotype 3 predicted more hepatic stiffness as assessed by transient elastography than genotype 1 . Similarly, a study of 1189 mainly HCV-monoinfected individuals showed that genotype 3 was associated with histological progression of disease . If indeed, fibrosis progression is faster in HCV genotype 3 there may be a need for closer observation of predictors of fibrosis such as biochemical marker of hepatic inflammation or transient elastography. Further, earlier initiation of both antiviral and cART for individuals with genotype 3 infection may be warranted to decrease the risk of liver fibrosis and lower mortality . Pegylated interferon and ribavirin (PegIFN/RBV) remains the key treatment option for individuals infected with HCV genotype 3 as the novel protease inhibitors telaprevir and boceprevir are only active against HCV genotype 1 . Treatment of genotype 3 with pegIFN/RBV is associated with sustained virological response rates of 62–72% in HCV-infected individuals with HIV [40,41]. Of note, we were not able to analyze a possible risk associated with other subtypes than subtype 3a because all genotype 3 but one was 3a.
An association with IL28B CC genotype and mortality was indicated in the univariate analysis but the association lost statistical significance in the adjusted model. However, the effect size was largely unchanged suggesting that the lack of statistical significance could be due to the small number of individuals with an available IL28B genotype, rather than confounding. Moreover, the IL28B genotypes were in Hardy–Weinberg disequilibrium, which could be the first indicator of a true genetic association with mortality, because the IL28B CC genotype is associated with spontaneous clearance and, therefore, might be underrepresented in this cohort of chronic HCV-infected individuals. A recent study adds support to our finding because they showed a correlation between the presence of liver fibrosis as measured by transient elastography and the IL28B CC genotype . We and others have shown an association between IL28B genotype CC and increased HCV viral load for HCV genotype 1 and 3 suggesting a possible detrimental effect of the IL28B genotype may be mediated through higher HCV viral load [20,21]. Larger studies are warranted to investigate whether there is a true association between mortality and IL28B genotype.
All-cause mortality in our cohort was 10/100 PYR and comparable to other HIV-HCV-coinfected cohorts that reported rates ranging from 6/100 PYR to 12/100 PYR [43–46]. HIV-HCV-coinfected individuals have higher rates of liver-related and all-cause mortality than HIV-infected individuals without HCV infection [47,48]. Most deaths in our cohort occurred outside of the hospital making it difficult to determine the exact underlying cause of death. Using death certificates the majority of deaths in our cohort were classified as natural deaths and the most frequent underlying cause of death was HIV/AIDS. It is well known, however, that it is more difficult to determine the cause of a non-AIDS than of an AIDS-related death . We were unable to determine whether HIV led to progression of HCV or whether it was HCV that led to progression of HIV. Regardless, HCV viral load and genotype 3, independently of HIV/AIDS progression markers, influenced the risk of all-cause mortality further suggesting that the excess mortality we observed was attributable to HCV infection. Although cART limits liver disease progression and related mortality, it does not reverse it, which implies that broader application of HCV therapies is needed to reduce excess mortality. Further, we believe that the strategy of deferring treatment of HCV in HIV-HCV-coinfected individuals is not a safe strategy and our data support that HCV therapy in this population should be considered a priority.
HIV-induced immune deficiency is reflected in decreasing CD4 cell counts as well as increasing HIV RNA and progression to AIDS. In regression analyses, AIDS, low CD4 cell counts and HIV RNA was associated with higher mortality rate whereas cART was not. This probably reflected uncontrolled HIV with progression to AIDS due to suboptimal cART because of poor adherence to cART. HIV-HCV-coinfected individuals are at risk for initiating cART significantly later than HIV infected without HCV , which also could contribute to part of the excess mortality observed. Thus, mortality rate may be lowered by an aggressive HIV treatment strategy and through programs to encourage adherence among difficult-to-treat populations.
Our study is strengthened by the consecutive inclusion of study subjects, the strict definition of chronic HCV infection and near-complete follow-up over time. Time-updated modeling permitted assessment of the most recent values prior to death as potential predictors of outcome and regression analysis was adjusted for important predictors of survival. Limitations of our study are the limited ability to detect direct associations as well as the lack of precise recording of mortality causes. Despite this we demonstrated an association between HCV viral load, genotype 3 and IL28B CC genotype and all-cause mortality in HIV-HCV-coinfected individuals independently of other disease severity markers. This may have consequences for the management of individuals who have high HCV viral load and/or infection with genotype 3.
Author contributions. Idea and design: L.N.C. and T.B.; acquisition of data: L.N.C., K.A., T.B. and K.S.; statistical analysis: L.N.C. and S.L.; drafting the manuscript: L.N.C. and T.B.; interpretation of data and revision of manuscript: L.N.C., M.V.L., S.L., K.S. and T.B.
Financial support: This study was supported by a PhD scholarship from the Faculty of Health Sciences, University of Copenhagen (L.N.C.) and research grants from Preben and Anna Simonsens Foundation (L.N.C.), Danish Medical Research Council (T.B.), the A.P. Møller Foundation for the Advancement of Medical Science (T.B.) and the Idella Foundation (T.B. and K.S.).
Conflicts of interest
There are no conflicts of interest.
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