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Epidemiology and Social

Effect of incident hepatitis C infection on CD4+ cell count and HIV RNA trajectories based on a multinational HIV seroconversion cohort

van Santen, Daniela K.a; van der Helm, Jannie J.a,b; Touloumi, Giotac; Pantazis, Nikosc; Muga, Robertod; Gunsenheimer-Bartmeyer, Barbarae; Gill, M. Johnf; Sanders, Eduardg,h; Kelleher, Anthonyi; Zangerle, Robertj; Porter, Kholoudk; Prins, Mariaa,l,*; Geskus, Ronald B.a,h,m,n,* on behalf of the CASCADE Collaboration within EuroCoord

Author Information
doi: 10.1097/QAD.0000000000002040

Abstract

Introduction

At the beginning of the millennium, a hepatitis C virus (HCV) epidemic emerged in HIV-positive MSM [1]. In recent years, HCV has continued to spread in this group [2,3]. HIV infection often precedes HCV infection in MSM. This differs from the main risk groups in early studies of HIV/HCV-coinfection, people who inject drugs (PWID) and haemophiliacs, in whom HCV was generally acquired before HIV infection [4,5]. It has been suggested that the order of HIV and HCV acquisition may influence the effect of HCV-coinfection on disease progression [6]. Moreover, given that the extent of excessive alcohol use and other factors associated with HCV and HIV disease progression differ between groups at risk of HCV infection, HIV/HCV-coinfected individuals are not a homogeneous population.

One recent meta-analysis concluded that among antiretroviral therapy (ART)-naive HIV-positive individuals, those coinfected with HCV had similar HIV RNA viral loads to HIV monoinfected individuals [5], whereas other studies have reported that they do have faster CD4+ T-cell count decline [7,8]. Among individuals on combination ART (cART), another meta-analysis reported that HCV-coinfection leads to significantly lower CD4+ cell counts shortly after initiating cART, but that HCV-coinfection has no effect on achieving viral suppression [4]. Most studies in both of these meta-analyses included a heterogeneous risk group population (e.g. PWID and haemophiliacs), assessed the difference in HIV RNA viral load using a single measurement in ART-naive individuals, and included individuals with a prevalent HIV and/or HCV infection [4,5]. They were therefore unable to distinguish the sequence or duration of the two viral infections. Consequently, little is known about the effect of incident HCV infection and its timing relative to duration of HIV infection on subsequent HIV disease progression among MSM. Using data from the CASCADE Collaboration with a large number of MSM with well estimated dates of HIV seroconversion (HIVsc), we are uniquely positioned to study HIV/HCV coinfection in this group. In this study, we aimed to assess the effect of HCV seroconversion (HCVsc) and its timing, relative to HIVsc, on the HIV RNA viral load and CD4+ cell count trajectories following HCVsc among MSM with newly acquired HCV while ART-naive and while on cART.

Methods

Study population

We used data from the CASCADE Collaboration within EuroCoord that includes cohorts across Europe, Australia, Canada and sub-Saharan Africa. Details of CASCADE have been previously described [9]. All cohorts include data from HIV-positive individuals with dates of HIVsc that could be reliably estimated based on the midpoint between the last HIV-negative and first HIV-positive test dates (at most 36 months apart) or, with evidence of acute HIV infection. We included 17 of the 28 participating cohorts. Eleven cohorts were excluded because they had no MSM or less than 50% of the MSM had an HCV test result (Fig. 1). We included only men who were self-reported as having acquired HIV through sex between men and whose potential HIV transmission route excluded injection drug use.

Fig. 1
Fig. 1:
Flow diagram of the study population selection for antiretroviral therapy-naive MSM and MSM on combination antiretroviral therapy from the CASCADE Collaboration.cART, combination antiretroviral therapy; HCV, hepatitis C virus; HCV−, HCV-negative MSM; HCV+, HCV-positive MSM; HCVsc, HCV seroconversion; HIVsc, HIV seroconversion; yr(s), year(s). The grey boxes depict MSM who were excluded from the analyses. aExcluded cohorts: cohorts of which more than 50% of MSM had a missing hepatitis C virus status. bOf 8604 MSM, 4502 (53.2%) MSM contributed data as antiretroviral therapy-naive as well as when on combination antiretroviral therapy. c56 MSM had ever been on combination antiretroviral therapy, but were off combination antiretroviral therapy during follow-up. dMSM without a recorded hepatitis C virus-negative test results. eExcluded due to possible hepatitis C virus treatment, defined as having ever received pegylated-interferon and/or ribavirin, and never having an hepatitis C virus-positive test result. fExcluded as the interval between hepatitis C virus seroconversion while on combination antiretroviral therapy and last visit while antiretroviral therapy-naive was less than 2 years.

Definitions and exclusion criteria

HCV negative status throughout follow-up was based on having at least one HCV-negative test result and never being tested HCV positive. To optimize testing frequency, we performed additional HCV testing in nine cohorts that had stored specimens, as previously described [3]. HCV-positive status was based on any positive HCV test (RNA, antibodies and/or antigen). For MSM who acquired HCV during follow-up, we assumed that HCVsc occurred at the midpoint date between the last HCV-negative and first HCV-positive test. The date of HCVsc was based on a negative-antibody and a positive-antibody test results in 77.8% of cases. The remained was based on RNA tests or a combination of antibody and RNA test results. Individuals who were HCV-positive before cART initiation were excluded from the cART analyses (n = 207), and thus our analyses do not consider the effect of incident HCV while ART-naive on CD4+ cell count and HIV RNA viral load trajectories after cART initiation (Fig. 1). To determine the timing of HCVsc as precisely as possible, we excluded MSM with an HCVsc interval of more than 2 years (n = 69). Men with only HCV-positive test results throughout follow-up were excluded if the first HCV-positive test result was more than 1 year after HIVsc (n = 119). For MSM with a positive HCV test within 1 year of HIVsc but without a recorded HCV-negative one (n = 127), the date of HCVsc was estimated as the midpoint date between HIVsc and first HCV-positive test date, as HCV infection is not common among HIV-negative MSM [10,11]. MSM with an HCV-positive test before HIVsc (n = 28) were excluded.

Timing of HCVsc relative to HIVsc (hereafter referred to as ‘timing’) was calculated as the interval between the estimated dates of HIVsc and HCVsc. In those who acquired HCV while on cART, we calculated the cumulative time on cART, excluding time off cART due to a treatment interruption (hereafter referred to as ‘cumulative cART exposure’). We defined cART as a three drug ART regimen containing two different classes, or three nucleoside reverse transcriptase inhibitors, provided tenofovir or abacavir were included in the regimen.

Statistical analyses

Follow-up data

Individuals could contribute data from the first clinic/cohort visit after the estimated date of HIVsc from 1983 until 2014. For all cohorts, we used all available follow-up data, except for MSM from the French PRIMO cohort who were censored at 31 December 2005 as routine HCV test results were only recorded until that year. ART-naive MSM were censored at the start of (c)ART, or last study visit if they remained (c)ART naive. MSM on cART were censored at the moment of a treatment interruption (if off cART for more than a week) or last study visit.

Matching

We performed separate analyses for ART-naive MSM and MSM on cART. To assess the effect of incident HCV infection and its timing, each HCV-infected individual (the ‘case’) was matched to all eligible HCV-negative MSM (the ‘controls’) by HIV infection duration (For details on matching criteria see Supplementary Text 1, http://links.lww.com/QAD/B375). Hence, we could compare CD4+ cell count and HIV RNA viral load trajectories following the estimated date of HCVsc of an HCV-coinfected MSM to that of an HIV monoinfected MSM with a similar duration of HIV infection. Hereafter, we refer to ‘matched time’ of the control as the matched duration since HIVsc. The duration since HIVsc used to matched cases and controls was determined by the moment of HCVsc relative to HIVsc of the case.

Statistical models

The time origin is the estimated date of HCVsc of each case, and their control's matched time. From this time origin onwards, we modelled trends in CD4+ cell count and HIV RNA viral load over time using multilevel random-effects models including a random intercept and slope. Based on the scatterplot, we decided to use the eighth-root transformation of HIV RNA viral load, which gave a more symmetric distribution than the log10 transformation. For CD4+ cell count we used the cube root transformation. Given the small numbers of records with a detectable HIV RNA viral load among MSM on cART (8.9% of all HIV RNA viral load measurements), we assessed the effect of HCV on having a detectable HIV RNA viral load (defined as HIV RNA viral load >400 copies/ml) using a multilevel random-effects logistic regression model. In the multilevel model structure, measurements were nested within individuals (second level) and individuals were nested within case–control groups (first level).

The multivariable models included duration from HIVsc to HCVsc (i.e. ‘timing’) and the following covariables as potential confounders and/or effect modifiers: age and calendar year at matched time. For the ART-naive model we also included method of HIVsc determination [i.e. midpoint or (laboratory) evidence of acute infection]. For those on cART, we also included cumulative cART exposure. We used restricted cubic splines to model the effect of continuous variables, with four knots based on 5th, 33rd, 66th, 95th percentiles. We included interaction terms between time since HCVsc/matched time, HCV coinfection status and timing to assess whether HCV coinfection or its timing influenced CD4+ cell count and HIV RNA viral load trajectories. Furthermore, we included interaction terms to assess whether the effect of HCV coinfection and its timing on the CD4+ cell count and HIV RNA viral load trajectory differed by age or calendar year, and for those on cART, cumulative cART exposure (model details in Supplementary Text 2, http://links.lww.com/QAD/B375).

Sensitivity analyses

First, for ART-naive MSM with an HCV-positive test result but without a recorded HCV-negative test result, we applied two alternative strategies to estimate the moment of HCVsc. In the first strategy, we assumed that risk behaviour led to simultaneous infection with both HCV and HIV. In the second strategy, we assumed they became HCV infected at the time of their first HCV-positive test result. Second, we repeated the analyses restricting our population to ART-naive cases with both an HCV-negative and an HCV-positive test during follow-up and their matched controls.

Third, we examined the effect of HCV coinfection on CD4+ cell count and HIV RNA viral load trajectories using joint models [12] (except for the analysis with detectable HIV RNA viral load as outcome) to correct for informative censoring (due to cART initiation among ART-naive, and cART interruption among MSM on cART).

Results

Of 17 429 MSM included in CASCADE, 8604 MSM from 17 cohorts were eligible after applying the exclusion criteria (Fig. 1). Of these individuals, 7692 (89.4%) were ART-naive during the first visit after HIVsc and 5224 (60.7%) had available data while on cART. A total of 214 HCV-coinfected ART-naive MSM and 147 HCV-coinfected MSM on cART were included in the study, of whom 95 and 139 had well estimated dates of HCVsc, respectively. HCV-coinfected MSM were successfully matched at random to 5384 and 3954 HIV monoinfected ART-naive MSM and MSM on cART, respectively (Table 1). Median time from HIVsc to HCVsc was 0.4 years [interquartile range (IQR) = 0.1–1.0] among ART-naive and 6.2 years [IQR = 3.3–10.7] among MSM on cART. Among HCV-coinfected MSM on cART, median cumulative cART exposure at HCVsc was 3.2 years [IQR = 1.0–6.1] and 75.5% of these MSM were on their first cART regimen when they acquired HCV.

Table 1
Table 1:
General and clinical characteristics of HIV-positive MSM with and without hepatitis C virus infection from the CASCADE Collaboration by combination antiretroviral therapy use.

CD4+ cell count did HIV RNA viral load trajectories

Antiretroviral therapy-naive MSM

At the time origin (i.e. HCVsc or matched time), HIV RNA viral load was not significantly different between cases (i.e. HCV-coinfected) and controls (i.e. HIV monoinfected) (P = 0.32). The difference in HIV RNA viral load trajectory between cases and control was statistically significant (P = 0.03). HIV RNA viral load trajectories by HCV coinfection status, although not statically significant (P = 0.24), differed by the timing of HCVsc. If HCV and HIVsc occurred around the same time, both cases and controls showed a strong downward trend in HIV RNA viral load during the first year following HIV and HCVsc (Fig. 2a, first panel). In MSM who seroconverted for HCV at 1 year after HIVsc or later, we observed a downward trend in HIV RNA viral load for about 1 year following HCVsc, which was not observed in the controls. After 2 years from HCVsc, HCV-coinfected MSM appeared to have a faster increase in HIV RNA viral load, and some suggestion of a higher HIV RNA viral load later on compared with HIV monoinfected MSM (Fig. 2a, second and third panel). However, differences in actual HIV RNA viral load values at any time point were small. The effect of HCV coinfection on HIV RNA viral load trajectory did not significantly differ by age (P = 0.21).

Fig. 2
Fig. 2:
CD4+ cell counts and HIV RNA viral load trajectories from hepatitis C virus seroconversion or matched time onwards per timing of hepatitis C virus seroconversion relative to HIV seroconversion, among antiretroviral therapy-naive MSM from the CASCADE Collaboration.(a) HIV RNA viral load trajectories; (b) CD4+ cell count trajectories. HCVsc, HCV seroconversion; HIVsc, HIV seroconversion. The solid lines represent median HIV RNA viral load and CD4+ cell counts trajectories for HIV monoinfected MSM, with 95% confidence interval illustrated in grey. Dashed lines represent median HIV RNA viral load and CD4+ cell counts trajectories for HIV/HCV-coinfected MSM, with 95% confidence interval illustrated with light grey dashed lines. HIV RNA viral load and CD4+ cell counts were back-transformed from eighth root of HIV RNA viral load to 10 log HIV RNA viral load copies/ml and cube root CD4+ cell counts to CD4+ cell counts cells/μl. The first (left) panel (i.e. ‘HCV seroconversion at HIV seroconversion’, timing = 0) represents HIV RNA viral load or CD4+ cell counts trajectory for those individuals who acquired hepatitis C virus concurrently with HIV. The second (middle) panel represents MSM who seroconverted for hepatitis C virus 1 year following HIV seroconversion, and the third (last) panel represents MSM whose hepatitis C virus seroconversion took place 3 years after HIV seroconversion. All graphs are illustrated for an individual aged 35 years whose HIV seroconversion was estimated based on the midpoint date of a negative and a positive antibody test date, and seroconverted for hepatitis C virus in 2005 (or matched calendar year for HIV monoinfected).

At the time origin, CD4+ cell count did not significantly differ between cases and controls (P = 0.90) (Fig. 2b). The difference in CD4+ cell count trajectory between cases and controls was highly significant (P < 0.001), but this difference did not depend on HCVsc to HIVsc timing (P = 0.78). CD4+ cell count decreased more rapidly during the first years following HCVsc in HCV-coinfected MSM, but after 3 years following HCVsc values became comparable with those of HIV monoinfected MSM. For example, when comparing MSM who seroconverted for HIV and HCV simultaneously and their controls, the difference in CD4+ cell count at 1 year following HCVsc/matched time was 43 CD4+ cells/μl (Fig. 2b, first panel). The effect of HCV-coinfection on CD4+ cell count trajectory did not significantly differ by age (P = 0.50).

MSM on combination antiretroviral therapy

For an ‘average’ individual, the probability of having a detectable HIV RNA viral load was below 2% for both cases and controls, and did not significantly differ by HCV coinfection status over time following HCVsc/matched time (P = 0.17) (Fig. 3a). However, controls had a borderline higher probability of having a HIV RNA viral load at the time origin (P = 0.05). The timing of HCVsc had no effect on HIV RNA viral load (P = 0.35). The effect of HCV coinfection on HIV RNA viral load trajectory did not significantly differ by age (P = 0.76) nor by cumulative cART exposure (P = 0.60).

Fig. 3
Fig. 3:
CD4+ cell counts trajectories and predicted probabilities of having a detectable HIV RNA viral load from hepatitis C virus seroconversion or matched time onwards per timing of hepatitis C virus seroconversion relative to HIV seroconversion, among MSM on combination antiretroviral therapy from the CASCADE Collaboration.(a) Predicted probabilities of having detectable HIV RNA viral load; (b) CD4+ cell count trajectories. HCVsc, HCV seroconversion; HIVsc, HIV seroconversion; VL, HIV RNA viral load. The solid lines represent predicted probabilities of having a detectable HIV RNA viral load and median CD4+ cell counts trajectories estimate for HIV-monoinfected MSM, with 95% confidence interval illustrated in grey. Dashed lines represent the predicted probabilities and median CD4+ cell counts trajectories estimate for HIV/HCV-coinfected MSM, with 95% confidence interval illustrated with light grey dashed lines. Cube root CD4+ cell counts were back-transformed to CD4+ cell counts cells/μl. First (left), second (middle) and third (right) panels represent MSM who seroconverted for hepatitis C virus 3, 5 and 7 years after HIV seroconversion, respectively. All graphs are illustrated for an individual aged 40 years who had been on combination antiretroviral therapy for 3 years at the matched visit and seroconverted for hepatitis C virus in 2008 (or matched calendar year for HIV-monoinfected).

At the time origin, CD4+ cell count did not significantly differ between cases and controls (P = 0.33) (Fig. 3b). Similar to ART-naive MSM, CD4+ cell count trajectories were significantly different between cases and controls (P ≤ 0.001), and did not depend on the timing of HCVsc (P = 0.69). During the first 2–3 years after HCVsc, CD4+ cell counts were significantly lower among HCV-coinfected MSM, but became comparable with HIV monoinfected MSM thereafter. For example, when comparing MSM who seroconverted for HCV 3 years after HIVsc (Fig. 3b, first panel) to their controls, the difference in CD4+ cell count at 1 year following HCVsc/matched time was 83 CD4+ cells/μl. The effect of HCV coinfection on CD4+ cell count trajectory did not significantly differ by age (P = 0.38) nor by cumulative cART exposure (P = 0.99).

Sensitivity analyses

When we assumed that HCVsc took place simultaneously with HIV or at the time of the first HCV-positive test among HCV-coinfected ART-naive MSM without HCV-negative results, comparable results with the main analyses were obtained. When analyses were restricted to ART-naive MSM with a documented HCVsc during follow-up, the difference in CD4+ cell count and HIV RNA viral load trajectory was still statistically significant (PCD4+ < 0.001; PVL = 0.04) (Supplementary Fig. 2, http://links.lww.com/QAD/B375). However, a lower HIV RNA viral load in cases than controls was no longer observable, whereas differences in HIV RNA viral load trajectory were more pronounced after 2 years from HCVsc, especially when HCVsc was closer to HIVsc (Ptiming = 0.09). The effect of timing was not significant for the CD4+ model. Furthermore, joint models yielded similar results to the main analysis, although the effect of HCV coinfection on HIV RNA viral load trajectory became borderline nonsignificant (P = 0.05).

Discussion

We investigated in MSM with preexisting HIV infection the effect of newly acquired HCV infection, and its timing relative to HIVsc, on subsequent HIV RNA viral load and CD4+ cell count trajectories. First, in HCV-coinfected MSM, CD4+ cell counts were temporarily lower during the first 2–3 years following HCVsc compared with HIV monoinfected MSM, in both ART-naive MSM and MSM on cART. Second, we found that HCV coinfection had an effect on the HIV RNA viral load trajectories in ART-naive MSM, but we did not find a change in the probability of having a detectable HIV RNA viral load following HCVsc in MSM on cART. Third, timing of HCV acquisition relative to HIVsc was not found to affect HIV RNA viral load and CD4+ cell count trajectories, suggesting that the observed changes in these trajectories can occur at any moment after HIVsc.

Few studies have been able to assess the effect of HCV coinfection on CD4+ cell count trajectories among ART-naive HIV-positive individuals. Two studies, with relatively small sample sizes and with an unknown sequence of HIV and HCV acquisition [7,8], also reported a steeper CD4+ cell count decline in HCV-coinfected individuals when compared with HIV monoinfected individuals [8] or individuals who spontaneous cleared HCV as the control group [7]. However, the effect of HCV coinfection was not found to be statistically significant in the latter study [7]. To the best of our knowledge, only one study from the United Kingdom among ART-naive patients (i.e. PWID, MSM and heterosexuals), with known HIVsc dates, measured the effect of HCV coinfection on CD4+ cell count trajectories and also found that CD4+ cell counts were temporary lower [13].

Similar to our findings among MSM on cART, a meta-analysis and an original study among HIV-positive MSM with acute HCV also found an initial decline in CD4+ cell count among HCV-coinfected individuals [4,14]. The primary outcome in the meta-analysis however was difference in CD4+ cell count increase 3–12 months after cART initiation whereas our study examined CD4+ cell count trajectories after HCVsc. In addition, they did not account for the sequence and duration of both infections. These factors might explain why some of the individual studies in this meta-analysis did not find an effect of HCV on CD4+ cell count trajectories [4]. The temporary effect of HCV coinfection on CD4+ cell count might be mediated through a heightened state of chronic inflammation, leading to enhanced CD4+ apoptosis [15,16]. Interestingly, in our study the negative effect of HCV and convergence of CD4+ cell count trajectories between cases and controls occurred irrespective of cART use. Hence, the attenuation of the effect of HCV coinfection is probably not affected by cART use only.

The clinical short-term and long-term implications of the temporary CD4+ cell count decline warrant further research. It is unknown whether the observed temporary CD4+ cell count decline attributes to the faster liver fibrosis progression observed in HIV/HCV-coinfected individuals when compared with HCV monoinfected individuals [17] and the classical HCV-coinfected risk groups [18–20], and whether it could potentially affect HCV treatment effectiveness. However, cure rates with direct-acting antivirals among HIV-coinfected patients are similar to those in HCV monoinfected patients [21,22]. In addition, whether the temporary CD4+ cell count decline contributes to a faster HIV disease progression still needs to be elucidated. A previous study using data from the CASCADE Collaboration showed that in the cART era (>1996), HCV-coinfected MSM have a higher HIV/AIDS mortality than HIV monoinfected MSM [23]. Furthermore, ART response may be affected by HCV infection if ART initiation takes place during the temporary CD4+ cell count decline [24]. One could argue that HCV treatment shortly after an HCV infection is justifiable to prevent accelerated liver disease progression and a CD4+ cell count decline. Notwithstanding, continued follow-up after HCV infection is warranted to assess the long-term effects of HCV on liver fibrosis and HIV disease progression.

Our results on the effect of HCV on HIV RNA viral load in ART-naive individuals are not in agreement with a meta-analysis reporting no difference in HIV RNA viral load by HCV coinfection status [5]. However, the primary outcome in this meta-analysis was based on the mean HIV RNA viral load difference from a single HIV RNA viral load measurement and most of the included studies did not account for HIV and HCV infection duration [5]. Interestingly, four of the 15 individual studies in the meta-analysis reported a significantly higher HIV RNA viral load among HIV monoinfected individuals, which was also observed in our main analysis during the first year following HCVsc. However, when our analyses were restricted to those with a documented HCVsc during follow-up, we did not observe a lower HIV RNA viral load in HCV-coinfected MSM. Importantly, although HIV RNA viral load differences by HCV coinfection status in our study were small, it has been demonstrated that even small increments in HIV RNA viral load among ART-naive individuals are associated with a higher risk of heterosexual transmission and AIDS-defining event or death [25]. The bystander effect of HCV on HIV RNA viral load replication stresses the need for early HCV infection detection and could support the role of these individuals as a source of HIV transmission when left untreated for HCV. Our results of HIV RNA viral load among MSM on cART are in line with the previously described meta-analysis in which authors also reported that virological control of HIV infection after cART initiation remains unaffected by the presence of HCV [4].

There are some limitations in our study. Due to a lack of systematic data on HCV treatment, we could not account for it. A study among HIV/HCV-coinfected patients comparing CD4+ cell count changes before and after pegylated-interferon/ribavirin treatment reported that CD4+ cell count decreased during the first 12 weeks of treatment, increasing thereafter and stabilizing from week 24 onwards [26]. However, HCV treatment alone could not explain the temporarily lower CD4+ cell count among HCV-coinfected MSM as we observed an effect of HCV on CD4+ cell count trajectories for at least 2–3 years following HCVsc. We also did not account for spontaneous HCV clearance. This may have led to an underestimation of the effect of chronic HCV coinfection on CD4+ cell count and HIV RNA viral load trajectories. However, around 15% of HIV-positive individuals clear HCV spontaneously [27]. Also, we did not account for other factors that could influence CD4+ cell count and HIV RNA viral load trajectories such as ART adherence, HIV super-infection and other sexually transmitted infections [28,29]. However, if these factors play an important role in the observed differences, we cannot explain why CD4+ cell count converged 3 years after HCVsc.

One of the major strengths in our study is our relatively large group of MSM with well estimated dates of HIV and HCVsc; hence, we could account for infection duration, and study the effect of the timing of HCVsc relative to HIVsc. In addition, unlike most studies, we used all available CD4+ cell count and HIV RNA viral load measurement to assess differences in trajectories by HCV coinfection status. Given the temporary effect of HCV coinfection on CD4+ cell count and HIV RNA viral load trajectory by HCV coinfection, our findings emphasize the need to account infection duration.

In conclusion, we found no difference in CD4+ cell count and HIV RNA viral load trajectories following HCVsc by its timing relative to HIVsc. Importantly, CD4+ cell counts are temporarily lower during the first 2–3 years following HCVsc among HIV-positive MSM. Even though it is expected that more MSM will start cART earlier in the coming years, reflecting changing guidelines [30], CD4+ cell counts are temporarily negatively affected following HCVsc despite cART use. Our findings would point to a consideration by clinicians to test for HCV if their HIV-positive patient's CD4+ cell count drops while on cART. HCV-coinfected ART-naive MSM appear to have a higher HIV RNA viral load trajectory 2 years after HCVsc than HIV monoinfected MSM, whereas we did not observe an effect of HCV on the probability of having a detectable HIV RNA viral load among MSM on cART. Continued HCV prevention, testing and treatment are warranted in this group. The short-term and long-term clinical implications of our findings still need to be further elucidated.

Acknowledgements

The authors wish to thank all cohort participants for their contribution and EuroCoord for funding the CASCADE Collaboration. Also, we wish to thank members from CASCADE that contributed to the design of the study: Maria Dorucci and Santiago Perez-Hoyos. We also want to thank those involved with additional HCV testing and/or data management support: Petra Blom and Margreet Bakker (AMC), Paz Sobrino Vegas, and Susana Monge (COR/MAD), Ana Avellón (CNM, ISCIII), Jamie Inshaw, Anabelle Gourlay and Ashley Olson (UKR), Stefania Carrara and Alessandro Cozzi-Lepri (ICO), Klaus Jansen (GER), Laurent Tran (PRI) and Martin Rickenbach (SWI). We thank Astrid Newsum for critically reviewing the article (GGD Amsterdam). We wish to convey special thanks to Lorraine Fradette who coordinated and provided logistical support within CASCADE.

The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under EuroCoord grant agreement no. 260694.

CASCADE Collaboration members

CASCADE Steering Committee: Julia Del Amo (Chair), Laurence Meyer (Vice Chair), Heiner C. Bucher, Geneviève Chêne, Osamah Hamouda, Deenan Pillay, M.P., Magda Rosinska, Caroline Sabin, G.T.

CASCADE Co-ordinating Centre: K.P. (Project Leader), Ashley Olson, Andrea Cartier, Lorraine Fradette, Sarah Walker, Abdel Babiker.

CASCADE Clinical Advisory Board: Heiner C. Bucher, Andrea De Luca, Martin Fisher, Roberto Muga.

CASCADE Collaborators: Australia PHAEDRA Cohort (Tony Kelleher, David Cooper, Pat Grey, Robert Finlayson, Mark Bloch); Sydney AIDS Prospective Study and Sydney Primary HIV Infection Cohort (Tony Kelleher, Tim Ramacciotti, Linda Gelgor, David Cooper, Don Smith); Austria Austrian HIV Cohort Study (Robert Zangerle); Canada South Alberta Clinic (M. John Gill); Estonia Tartu Ülikool (Irja Lutsar); France ANRS CO3 Aquitaine Cohort (Geneviève Chêne, Francois Dabis, Rodolphe Thiebaut), ANRS CO4 French Hospital Database (Dominique Costagliola, Marguerite Guiguet), Lyon Primary Infection Cohort (Philippe Vanhems), French ANRS CO6 PRIMO Cohort (Marie-Laure Chaix, Jade Ghosn), ANRS CO2 SEROCO Cohort (Laurence Meyer, Faroudy Boufassa); Germany German HIV-1 Seroconverter Cohort (Osamah Hamouda, Karolin Meixenberger, Norbert Bannert, Barbara Gunsenheimer-Bartmeyer); Greece AMACS (Anastasia Antoniadou, Georgios Chrysos, Georgios L. Daikos); Greek Haemophilia Cohort (Giota Touloumi, Nikos Pantazis, Olga Katsarou); Italy Italian Seroconversion Study (Giovanni Rezza, Maria Dorrucci), ICONA Cohort (Antonella d’Arminio Monforte, Andrea De Luca); The Netherlands Amsterdam Cohort Studies among homosexual men and drug users (Maria Prins, Ronald B. Geskus, Jannie J. van der Helm, Hanneke Schuitemaker); Norway, Oslo University Hospital Cohorts (Mette Sannes, Anne-Marte Bakken Kran); Poland National Institute of Hygiene (Magdalena Rosinska); Spain Badalona IDU Hospital Cohort (Roberto Muga, Jordi Tor), Barcelona IDU Cohort (Patricia Garcia de Olalla, Joan Cayla), CoRIS-scv (Julia del Amo, Santiago Moreno, Susana Monge); Madrid Cohort (Julia Del Amo, Jorge del Romero), Valencia IDU Cohort (Santiago Pérez-Hoyos); Sweden Swedish InfCare HIV Cohort, Sweden (Anders Sönnerborg); Switzerland Swiss HIV Cohort Study (Heiner C. Bucher, Huldrych Günthard, Alexandra Scherrer); Ukraine Perinatal Prevention of AIDS Initiative (Ruslan Malyuta); UK Public Health England (Gary Murphy), UK Register of HIV Seroconverters (Kholoud Porter, Anne Johnson, Andrew Phillips, Abdel Babiker), University College London (Deenan Pillay); African Cohorts: Genital Shedding Study (US: Charles Morrison; Family Health International, Robert Salata, Case Western Reserve University; Uganda: Roy Mugerwa, Makerere University; Zimbabwe: Tsungai Chipato, University of Zimbabwe); International AIDS Vaccine Initiative (IAVI) Early Infections Cohort (Kenya, Rwanda, South Africa, Uganda, Zambia: Matt A. Price, IAVI, USA; Jill Gilmour, IAVI, UK; Anatoli Kamali, IAVI, Kenya; Etienne Karita, Projet San Francisco, Rwanda).

EuroCoord Executive Board: Fiona Burns, University College London, UK; Geneviève Chêne, University of Bordeaux, France; Dominique Costagliola (Scientific Coordinator), Institut National de la Santé et de la Recherche Médicale, France; Carlo Giaquinto, Fondazione PENTA, Italy; Jesper Grarup, Region Hovedstaden, Denmark; Ole Kirk, Region Hovedstaden, Denmark; Laurence Meyer, Institut National de la Santé et de la Recherche Médicale, France; Heather Bailey, University College London, UK; Alain Volny Anne, European AIDS Treatment Group, France; Alex Panteleev, St. Petersburg City AIDS Centre, Russian Federation; Andrew Phillips, University College London, UK, Kholoud Porter, University College London, UK; Claire Thorne, University College London, UK.

EuroCoord Council of Partners: Jean-Pierre Aboulker, Institut National de la Santé et de la Recherche Médicale, France; Jan Albert, Karolinska Institute, Sweden; Silvia Asandi, Romanian Angel Appeal Foundation, Romania; Geneviève Chêne, University of Bordeaux, France; Dominique Costagliola (Chair), INSERM, France; Antonella d’Arminio Monforte, ICoNA Foundation, Italy; Stéphane De Wit, St. Pierre University Hospital, Belgium; Peter Reiss, Stichting HIV Monitoring, The Netherlands; Julia Del Amo, Instituto de Salud Carlos III, Spain; José Gatell, Fundació Privada Clínic per a la Recerca Bíomèdica, Spain; Carlo Giaquinto, Fondazione PENTA, Italy; Osamah Hamouda, Robert Koch Institut, Germany; Igor Karpov, University of Minsk, Belarus; Bruno Ledergerber, University of Zurich, Switzerland; Jens Lundgren, Region Hovedstaden, Denmark; Ruslan Malyuta, Perinatal Prevention of AIDS Initiative, Ukraine; Claus Møller, Cadpeople A/S, Denmark; Kholoud Porter, University College London, UK; Maria Prins, Academic Medical Centre, The Netherlands; Aza Rakhmanova, St. Petersburg City AIDS Centre, Russian Federation; Jürgen Rockstroh, University of Bonn, Germany; Magda Rosinska, National Institute of Public Health, National Institute of Hygiene, Poland; Manjinder Sandhu, Genome Research Limited; Claire Thorne, University College London, UK; Giota Touloumi, National and Kapodistrian University of Athens, Greece; Alain Volny Anne, European AIDS Treatment Group, France.

EuroCoord External Advisory Board: David Cooper, University of New South Wales, Australia; Nikos Dedes, Positive Voice, Greece; Kevin Fenton, Public Health England, USA; David Pizzuti, Gilead Sciences, USA; Marco Vitoria, World Health Organisation, Switzerland.

EuroCoord Secretariat: Silvia Faggion, Fondazione PENTA, Italy; Lorraine Fradette, University College London, UK; Richard Frost, University College London, UK; Andrea Cartier, University College London, UK; Dorthe Raben, Region Hovedstaden, Denmark; Christine Schwimmer, University of Bordeaux, France; Martin Scott, UCL European Research & Innovation Office, UK.

Ethics committees: Ethics approval has been granted by the following committees: Austrian HIV Cohort Study: Ethik-Kommission der Medizinischen Universität Wien, Medizinische Universität Graz – Ethikkommission, Ethikkommission der Medizinischen Universität Innsbruck, Ethikkommission des Landes Oberösterreich, Ethikkommission für das Bundesland Salzburg; PHAEDRA Cohort: St Vincent's Hospital, Human Research Ethics Committee; Southern Alberta Clinic Cohort: Conjoint Health Research Ethics Board of the Faculties of Medicine, Nursing and Kinesiology, University of Calgary; Aquitaine Cohort: Commission Nationale de l’Informatique et des Libertés; French PRIMO Cohort: Comite Consultatif de Protection des Personnes dans la Recherché Biomedicale; German HIV-1 Seroconverter Study: Charité, University Medicine Berlin; AMACS: Bioethics & Deontology Committee of Athens University Medical School and the National Organization of Medicines; Greek Haemophilia Cohort: Bioethics & Deontology Committee of Athens University Medical School and the National Organization of Medicines; ICoNA Cohort: San Paolo Hospital Ethic Committee; Amsterdam Cohort Studies in Homosexual Men: Academic Medical Centre, University of Amsterdam; Oslo University Hospital Cohorts: Regional komite for medisinsk forskningsetikk – Øst- Norge (REK 1); CoRIS-scv: Comité Ético de Investigación Clínica de La Rioja; Madrid Cohort: Ethics Committee of Universidad Miguel Hernandez de Elche; Swiss HIV Cohort Study: Kantonale Ethikkommission, spezialisierte Unterkommission Innere Medizin, Ethikkommission beider Basel, Kantonale Ethikkommission Bern, Comité départemental d’éthique de médecine et médecine communautaire, Commission d’éthique de la recherche clinique, Université de Lausanne, Comitato etico cantonale, Ethikkommission des Kantons St. Gallen; UK Register of HIV Seroconverters: South Birmigham REC; Early Infection Cohorts: Kenya Medical Research Institute, Kenyatta National Hospital.

Conflicts of interest

K.P. has served on ViiV healthcare advisory boards. M.J.G. has been an ad-hoc member of National HIV Advisory Boards to Gilead, ViiV and Merck. M.P.'s institute received speaker's fee and unrestricted grants from Gilead, Roche, MSD and Abbvie for research projects other than the current study. G.T.'s institute has received grants from Gilead Sciences Europe unrelated to this study.

The other authors who have taken part in this study declared that they do not have anything to disclose regarding funding or conflict of interest with respect to this article.

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* Maria Prins and Ronald B. Geskus contributed equally to the article as senior coauthors.

Keywords:

CD4+ cell count; hepatitis C virus; HIV; HIV RNA; MSM

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