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No evidence of a change in HIV-1 virulence since 1996 in France

Troude, Pénélopea; Chaix, Marie-Laurea; Tran, Laurenta; Deveau, Christianea; Seng, Rémoniea; Delfraissy, Jean-Françoisc; Rouzioux, Christineb; Goujard, Cécilec; Meyer, Laurenceafor the ANRS Primo cohort

doi: 10.1097/QAD.0b013e32832b51ef
EPIDEMIOLOGY AND SOCIAL
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Objective: We investigated temporal trends in the CD4 cell count and in plasma HIV RNA and total HIV DNA levels measured at the time of primary HIV infection, as proxies for HIV-1 virulence, taking changes in patient characteristics into account.

Design: We studied 903 patients enrolled during primary HIV infection in the French multicenter ANRS PRIMO cohort from 1996 to 2007.

Methods: Associations between the year of primary HIV infection and the values of the three markers were tested with regression models. The year of primary HIV infection was first introduced as a restricted cubic splines function in a regression model in order to explore the shape of the associations, and then as a continuous/categorical variable. The following confounders were considered in multiple regression analysis: time since infection and age (introduced as restricted cubic spline functions), sex, place of birth (Africa vs. others), symptomatic primary HIV infection, smoking, and virus-related factors (subtype B vs. non-B, and drug resistance mutations).

Results: Multivariate analysis showed no temporal trends in the CD4 cell count (square-root) or in HIV-1 RNA and DNA levels (log10) measured at the time of primary HIV infection. We observed the well described associations between the prognostic markers and the time since infection, sex, symptomatic primary HIV infection, and smoking.

Conclusion: The CD4 cell count and HIV RNA and DNA levels measured at the time of primary HIV-1 infection remained stable across 12 consecutive years (1996–2007) in the ANRS PRIMO cohort, suggesting no major change in virulence, after taking into account changes in patient characteristics.

aInserm, U822, Le Kremlin-Bicêtre; Univ Paris-Sud, Faculté de Médecine Paris-Sud; AP-HP, Hôpital Bicêtre, Service d'Epidémiologie et de Santé Publique, F-94276, France

bUniversité Paris Descartes, EA MRT 3620, AP-HP, Service de Virologie, Hôpital Necker-Enfants Malades, Paris, France

cService de Médecine Interne et de Maladies Infectieuses, Hôpital de Bicêtre, Le Kremlin-Bicêtre, France.

Received 3 December, 2008

Revised 24 February, 2009

Accepted 5 March, 2009

Correspondence to Dr Laurence Meyer, PhD, MD, Inserm, U822, Le Kremlin-Bicêtre; Univ Paris-Sud, Faculté de Médecine Paris-Sud; AP-HP, Hôpital Bicêtre, Service d'Epidémiologie et de Santé Publique, 82 rue du Général Leclerc, 94276 Le Kremlin-Bicêtre cedex, France. E-mail: laurence.meyer@inserm.fr

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Introduction

The virulence of microbial pathogens often changes during the course of epidemics [1]. The extreme variability of human immunodeficiency virus type 1 (HIV-1) has given rise to several subtypes and circulating recombinant forms (CRFs) since the beginning of the pandemic [2]. The virus is therefore still evolving. Moreover, widespread use of antiviral drugs in industrialized countries since 1996 might have applied added selective pressures, as illustrated by the occurrence of genotypic resistance to non-nucleoside reverse transcriptase inhibitors (NNRTIs) shortly following the introduction of this therapeutic class [3]. Any increase in HIV-1 virulence could have implications for the management of the AIDS pandemic, necessitating closer clinical monitoring and more stringent prevention strategies, for example.

HIV-1 virulence and pathogenicity are complex notions that are difficult to quantify [4]. The most direct measure of virulence, host mortality, has no longer been relevant to HIV-1 infection in industrialized countries since the introduction of highly active antiretroviral therapy in 1996 [5]. As the CD4 cell count and viral load are highly predictive of HIV-1 disease progression [6–9], assaying these markers shortly following infection, before any antiretroviral treatment, is an indirect way of measuring trends in virulence.

Changes in the virulence of HIV-1 are an ongoing concern [2,4,10]. Studies of seroconverter cohorts have given divergent results. Most studies of patients who seroconverted before 1996 showed no temporal trends in prognostic markers or the rate of HIV disease progression [11–17]. Studies including individuals who seroconverted before and after 1996 have also given conflicting results, one suggesting a loss of virulence [18], some a gain [19,20] and others no change [5,21]. The most recent study, based on the Multicenter Aids Cohort Study (MACS) cohort, showed stable CD4 cell counts and HIV RNA levels measured after seroconversion from 1984 to 2005, but the number of patients who seroconverted after 1994 was small [5].

In these studies the date of seroconversion was usually estimated to within a very large interval (to within 12 or even 24 months), whereas marker levels may change rapidly during acute infection. In addition, temporal trends in the HIV-1 DNA level at the time of seroconversion, another informative prognostic marker, were not studied [22,23]. Finally, few potential confounders such as smoking, clinical signs of primary HIV infection (PHI), the duration of PHI, and ethnicity could be taken into account in most studies.

Since November 1996, the ongoing ANRS PRIMO cohort – a multicenter cohort in France – has enrolled more than 900 patients during PHI, allowing us to examine possible temporal trends in the CD4 cell count and HIV RNA and DNA levels measured at the time of the primary infection, taking into account changes in patient characteristics.

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Patients and methods

The ANRS PRIMO cohort

The French ANRS PRIMO cohort is a prospective multicenter cohort of HIV-1 infected patients enrolled, since November 1996, during primary HIV infection [23]. The PRIMO study protocol was approved by the Paris Cochin Ethics Committee. The diagnosis of PHI is based on a western blot profile compatible with ongoing seroconversion (incomplete western blot with absence of antibodies to pol proteins) in most patients (94%), detectable plasma HIV RNA with a negative or weakly reactive ELISA (2%), or an interval of less than 6 months between a negative and a positive ELISA result (4%). Primary HIV infection is considered symptomatic if at least one symptom associated with the acute HIV syndrome is present. Patients must be antiretroviral-naive at enrollment. The date of infection is estimated as the date of symptom onset minus 15 days or, in asymptomatic patients, the date of the incomplete western blot minus 1 month, or the midpoint between a negative and a positive ELISA result. With their written informed consent, patients are enrolled if HIV infection is estimated to have occurred less than 6 months previously [median observed interval in the cohort: 47 days, interquartile range (IQR) 35–68].

Patients are seen at one of the study sites at enrollment, then at month 1, month 3, and month 6, and subsequently every 6 months. Standardized questionnaires are completed and the patients have a physical examination and laboratory tests. The questionnaires include items on demographics (age, sex), behaviors (sexual preference, injection drug use, current smoking, and number of cigarettes/day), clinical manifestations (signs of PHI and, if present, the duration of symptomatic PHI), and other medical information such as coinfection with HCV or HBV.

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Laboratory methods

Plasma and cells are collected at enrollment, every 6 months until month 24, and every year thereafter. The CD4 cell count and viral load are determined on site at each visit. The CD4 cell count is measured by flow cytometry using standard procedures. Plasma viral load is measured with the assay routinely used in each center. The Cobas Amplicor HIV-1 Monitor 1.5 assay kit (Roche Diagnostics, Meylan, France) was used in 72% of cases, the Versant HIV-1 RNA 3.0 assay (Bayer Diagnostics, Emeryville, California, USA) in 21% of cases (mainly before 2006), the NASBA QR system (Organon Technika) in 2% of cases (since 2006), and the Abbott Real-Time HIV-1 assay in 5% of cases (since 2003) [24].

Peripheral blood mononuclear cells (PBMCs) were isolated from fresh whole blood by centrifugation on a one-layer Ficoll Hypaque gradient. DNA was extracted from frozen PBMC and HIV DNA was quantified by real-time PCR (used from 1997 to 2007) in 76% of cases, as described elsewhere [25], and with the Amplicor HIV DNA Monitor kit (Roche Diagnostics) in the remaining 24% of cases (used from 1996 to 2001) [26]. Results were expressed as the log10 number of HIV DNA copies per 106 PBMC, and the detection limit was 70 copies per 106 PBMC (i.e. 1.84 log10 copies) [22,23].

Patient samples were further characterized for resistance genotyping and genetic subtyping. Genotypic resistance tests were performed in three centralized laboratories on plasma samples before the initiation of treatment using the consensus technique of the ANRS resistance group [26]. HIV resistance to nucleoside analog reverse transcriptase inhibitors, nonnucleoside reverse transcriptase inhibitors, and protease inhibitors, was defined according to the 2007 HIV-1 genotypic resistance interpretation algorithm of the French National Agency of Research on AIDS (ANRS) (www.hivfrenchresistance.org). The subtype and CRFs were determined by phylogenic analysis of reverse transcriptase gene.

Among the 936 patients enrolled in the cohort between November 1996 and February 2008, enrollment CD4 cell counts and HIV RNA values were available for this analysis in 903 cases (96.5%) and HIV DNA values in 775 cases (82.8%).

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Statistical analysis

Characteristics of the study population and median values of the prognostic markers were described according to the calendar year of infection. Quantile regression was used to assess temporal trends [27]. Quantile regression was introduced in econometry as an extension of linear modeling, in order to estimate the effect of covariables on various quantiles of the distribution of the response variable, and not only on its mean. As explained by Koenker and Hallock [27], the mean can be defined as the solution to the problem of minimizing a sum of squared residuals, whereas the median can be the solution to the problem of minimizing a sum of absolute residuals.

Univariate and multivariate linear regression models were used to evaluate temporal trends in the CD4 cell count and plasma HIV RNA and intracellular HIV DNA levels measured at the time of primary HIV infection, with each prognostic marker as the dependent variable. As the distribution of the CD4 cell count was right-skewed, square-root transformation was used to normalize the distribution of this marker. Log10 copy numbers of plasma HIV RNA and intracellular HIV DNA were used.

Temporal trends were first sought visually by using a linear regression model in which the year of infection was introduced as restricted cubic splines (RCS) [28]. This enabled us to graphically represent the adjusted relations and to test global associations and deviation from linearity, but not to test for trends. The year of infection was then introduced as a categorical and then continuous variable to formally test for a temporal trend. The study period was divided in six intervals (1996–1997, 1998–1999, 2000–2001, 2002–2003, 2004–2005, 2006–2007), the reference period being the most recent one.

At each step, potential confounders were considered. As the relation between the three markers and both time since infection and age at PHI diagnosis is not linear, time since infection and age were introduced as RCS functions with knots at the 5th, 25th, 50th, 75th, and 95th percentiles [29]. Indeed, our aim was not to quantify the effect of these factors on the markers but to adjust for these factors as well as possible. Other potential confounders taken into account were sex, country of birth (sub-Saharan African vs. others), symptomatic PHI, and smoking. Two virus-related factors were also considered in multivariate analysis, namely genotypic drug resistance mutations (at least one vs. none) and the HIV-1 subtype (B vs. others). We systematically verified that confounders were not modifiers before performing adjusted analyses.

Statistical analysis was performed using Stata/SE 9.0 software (Stata Press, College Station, Texas, USA).

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Results

The characteristics of the 903 patients are shown in Table 1. Mean age at HIV-1 infection was 35 years, and the mean interval between infection and the marker assays was 56 days. Most patients were infected through sexual intercourse (93.5%). Most patients were men (84%), homosexual (69% of the total population) and infected by a subtype B strain (76%), and most had symptomatic PHI (87%). Seven percent of the patients were black Africans, whereas the others were mainly from France or the French West Indies/Guyana (87% of non black Africans). Between 1996 and 2007 the frequency of symptomatic PHI increased, as did the percentage of men, mainly owing to an increase in the number of homosexual men. The percentage of individuals infected by non-B subtypes increased (mainly CRF02: 12.7% of the total population), whereas the percentage of genotypically resistant strains remained stable. There was a nonsignificant trend towards an increase in the proportion of African patients.

Table 1

Table 1

Table 2 shows the median values of the three prognostic markers and their IQRs according to the calendar period of infection. The median CD4 cell count was 519/μl (IQR 372; 690), the median HIV-1 RNA level was 5.09 log10 copies/ml (IQR 4.45; 5.69), and the median HIV DNA level was 3.33 log10 copies/106 PBMC (IQR 2.93; 3.67). Quantile regression showed no temporal trend in the median CD4 cell count or the median HIV RNA level (P = 0.14 and 0.31, respectively), whereas the median HIV DNA level increased significantly over the years in the crude analysis (P = 0.01).

Table 2

Table 2

When the year of infection was introduced as a RCS function in a regression model, we found no graphical evidence of deviation from linearity for any of the markers. Moreover, the test for deviation from linearity was nonsignificant. This enabled us to introduce the year of infection in 2-year classes, and thus formally to test for a temporal trend. The associations between the year of infection and the CD4 cell count (√CD4 /μl), HIV RNA level (log10 copies/ml), and HIV DNA level (log10 copies/106 PBMC) in crude and multiple linear regression models are shown in Table 3. We observed no calendar trend in the CD4 cell count or HIV RNA level. Furthermore, the significant trend in the HIV DNA level observed in the crude analysis disappeared in the adjusted model.

Table 3

Table 3

To improve the consistency of our results, we performed several sensitivity analyses. The trend results remained unchanged when we replaced sex with sexual preference divided into three classes (homosexual men, heterosexual men, and women). As most patients were infected with subtype B strains, the HIV-1 subtype was entered as a binary variable (subtype B vs. others). However, when the analyses were restricted to patients infected by subtype B strains, the results remained unchanged. Finally, we also verified that similar trend results were obtained when we adjusted for host factors alone.

As expected, all three markers were associated with the time since infection. Other well described associations were also found, including those with sex and with symptomatic PHI. Women had higher CD4 cell counts and lower HIV RNA and DNA levels than men. Patients with symptomatic PHI had lower CD4 cell counts and higher levels of HIV RNA and DNA than patients with asymptomatic PHI. The CD4 cell count was influenced by ethnicity and smoking, black African patients having lower counts than patients born in other countries, and smokers having higher counts than nonsmokers. Concerning viral factors, patients infected by subtype B strains had higher CD4 cell counts and lower HIV DNA levels than patients infected by other subtypes. Patients infected by resistant strains had lower HIV RNA levels than patients infected by wild-type strains, and the same results were obtained when the analyses were adjusted for sexual preference instead of sex.

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Discussion

To our knowledge this is the first study of calendar trends in three major prognostic markers of HIV-1 disease progression measured at the time of primary HIV infection. Moreover, contrary to most previous studies, the analyses were adjusted for most known confounding factors, namely sex, smoking, symptomatic PHI, and the HIV-1 subtype. We found that initial CD4 cell counts and HIV RNA and DNA levels were stable across 12 consecutive years (1996–2007) in the French ANRS PRIMO cohort, suggesting that HIV-1 virulence has remained stable.

Some studies have suggested an increase in HIV-1 virulence [19,20], whereas others have suggested the contrary [18]. Our results conflict somewhat with those of the Italian HIV-seroconversion study conducted between 1985 and 2002 [19], but they are compatible with the Cascade results for similar calendar periods [18,20]. Our results are also consistent with a Swiss report of stable virulence [21] and with the results of a recent American study of patients enrolled in the MACS cohort [5]. Muller et al.[21] found no time trends in virulence markers between 1984 and 2002 among white male and female northwest European patients, using as proxies the slopes of the declines in the CD4 cell count and in the CD4: CD8 ratio, and the ‘viral set-point’. Herbeck et al.[5] used three prognostic markers of disease progression as proxies for virulence, namely the plasma HIV RNA level and the CD4 cell count at ‘set-point’ (measured between 9 and 15 months after seroconversion) and the rate of the CD4 cell count decline during the first 3 years after seroconversion. They found no significant trend between 1985 and 2005 in this population of men who have sex with men (MSM), but only 18 of the 357 participants seroconverted during the most recent period, from 1995 to 2005. Moreover, smoking, symptomatic PHI, and the HIV-1 subtype were not taken into account in these analyses.

We chose to study the prognostic markers at the time of primary HIV infection, as we did in a recent genomewide association study [30]. An alternative would have been to measure the markers further from infection, for instance at the ‘viral set-point’, when markers are supposed to be more stable [5]. However, this would have meant restricting the analysis to those patients who had not yet started antiretroviral treatment. Moreover, in the PRIMO cohort, the proportion of early-treated patients fell from 88% in 1996–1997 to 29% in 2006–2007, early treatment being increasingly reserved for seriously ill patients. Finally, our decision to focus on PHI did not prevent us from observing the following well described associations. The CD4 cell count varied according to age at seroconversion, sex, and ethnicity, as described in the literature [18,31–36]. We also found that the mean CD4 cell count was about 100/μl higher in smokers than in nonsmokers [37,38]. HIV RNA levels varied according to sex and the symptomatic nature of PHI [39–41]. These associations underline the need, when assessing temporal trends in prognostic markers, to take into account population characteristics, which may also change over the years. Virus-related factors such as drug resistance mutations and the HIV-1 subtype might also influence prognostic marker values, as observed here [42–44]. In our study the proportions of injecting drug users and HCV-coinfected patients were very small (0.6 and 3.2%, respectively). We cannot exclude the possibility that diurnal variations might have influenced the CD4 cell count in some patients, but it should be noted that most samples were drawn in the morning at hospital; moreover, this did not prevent us from observing the well known influence of sex and smoking on the CD4 cell count. In keeping with French national HIV surveillance data, we attribute the increasing frequency of symptomatic PHI to a higher frequency of moderately symptomatic acute HIV-1 infections diagnosed in recent years, whereas such cases previously went unnoticed and were diagnosed only during the chronic phase [45].

One potential limitation of our study is that we did not take into account the different HIV RNA and DNA assay techniques used in the participating centers. Indeed, various types of assays were used to quantify HIV RNA and HIV DNA, but measuring assays were linked to the calendar period, making adjustment difficult. Furthermore, a given assay may evolve over time. These concerns have been raised in several studies [5,19,20], and can be offset by investigating temporal trends in several markers simultaneously, as was the case here.

Finally, the use of prognostic markers studied around the time of seroconversion as a proxy for virulence assumes that any changes in virulence would be observable early after infection. Furthermore, we cannot rule out the possibility that HIV-1 virulence is evolving too slowly for changes to have been observed during our 12-year study period.

In conclusion, after controlling for known confounding factors, we observed no temporal trends in the values of three major predictors of HIV-1 disease progression between 1996 and 2007, as measured during primary HIV-1 infection among patients enrolled in the French ANRS PRIMO cohort. Further studies of large cohorts of seroconverters will be necessary to detect possible future changes in HIV-1 virulence.

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Acknowledgements

We thank M.O. Wehr, A. Talamani, and Y. Zittoun for data monitoring, and D. Young for editing the manuscript. We also thank all the patients and the participating physicians.

The PRIMO cohort is funded by ANRS (Agence Nationale de Recherches sur le SIDA et les hépatites virales) and P.T. received a grant for this study from Fondation pour la Recherche Médicale.

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Keywords:

CD4 cell count; epidemiology; HIV-1; intracellular HIV DNA; seroconversion; temporal trend; viral load

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