Slower CD4 cell decline following cessation of a 3 month course of HAART in primary HIV infection: findings from an observational cohort
Fidler, Saraha; Fox, Juliea; Touloumi, Giotab; Pantazis, Nikosb; Porter, Kholoudc; Babiker, Abdelc; Weber, Jonathana; and the CASCADE Collaboration
From the aImperial College, St Mary's Hospital, London, UK
bAthens University Medical School, Greece
cMRC Clinical Trials Unit, London, UK.
Received 26 September, 2006
Revised 4 December, 2006
Accepted 18 January, 2007
Correspondence to Dr S. Fidler, Imperial College, St Mary's Hospital, London, UK. E-mail: email@example.com
Objective: To investigate the effect of a short course of HAART during primary HIV infection (PHI) on rate of CD4 cell and viral load change.
Methods: Data following HAART cessation from 89 individuals (seroconverting 1999–2003) who chose to take a 3 month course of HAART at PHI were compared with 179 untreated controls in CASCADE, using linear and nonlinear random effects models. Participants were non-randomized but frequency matched for age, sex, risk factor, year of seroconversion and presentation within the first 6 months of seroconversion. Time to CD4 cell count < 350 cells/μl and initiation of clinically indicated antiretroviral therapy (ART) were also compared as competing risks.
Results: The rate of CD4 cell decline following therapy cessation appeared significantly slower among treated participants than untreated controls: losses of 51 cells/μl [95% confidence interval (CI), 32–69] and 77 cells/μl (95% CI, 65–89), respectively, 3 years after seroconversion (P = 0.011). Based on extrapolated data, viral loads also differed significantly at this point (4.09 and 4.53 copies/ml, respectively). At 2 years, there was no significant difference in mean viral load levels: 4.31 copies/ml (95% CI, 4.14–4.48) and 4.47 copies/ml (95% CI, 4.28–4.66), respectively. CASCADE seroconverters were more likely to reach CD4 cell count < 350 cells/μl or initiate clinically indicated ART (hazard ratio, 1.45; 95% CI, 1.02–2.05; P = 0.039).
Conclusion: A short course of ART at PHI may delay CD4 cell decline. Confirmation of this requires a randomized clinical trial powered to address definitively the role of ART intervention in PHI (currently underway through SPARTAC).
The rate with which a host clears the initial viraemia during the early stages of HIV infection has been shown to play a key role in determining disease progression [1,2] and clinical outcome . In-vitro data of HIV-specific CD4 cell responses mounted during this stage, often referred to as primary HIV infection (PHI), have been shown to correlate with enhanced virological control [4–6] although their relevance in vivo remains unknown. These immune responses are largely lost in chronic disease and fail to regenerate upon subsequent HAART initiation and virological suppression .
It has been hypothesized that intervention with HAART in PHI may, therefore, help to boost the host's immunity to HIV-1 by preserving HIV-specific immune responses in CD4 cells and so delay disease progression . Indeed, early hopes were that intervention in early disease with the then new HAART could lead to the eradication of the virus . Such hopes were thwarted with subsequent findings that the latent reservoir represents an inaccessible source of virus for the life of an infected memory T cell . Enhancing natural control of the initially unchecked high level of plasma viraemia observed during PHI using HAART has since been explored by many groups  but the size of the treated populations and long-term clinical follow up remain limited. While there are some data to support the theoretical bases for initiating HAART in early infection, the long-term effects on disease progression are not known . Furthermore, receiving a new diagnosis of HIV infection is often not the optimal time to introduce further anxieties associated with adherence to complex HAART regimens, and the risks of development of drug toxicities in the absence of clear long-term clinical benefits.
The use of a short course of HAART in PHI has the appeal of limiting potential drug toxicity and the development of drug resistance yet delaying the rate of disease progression. The goal of such an approach is to limit the size of, rather than clearing, the latent viral reservoir . To date, however, there is scant evidence from clinical trials to support the initiation of HAART in PHI. This study compares two main surrogate markers of HIV-1 disease progression, CD4 cell count and HIV RNA, between two separate observational cohorts of persons identified during PHI.
One hundred and five HIV-1 seroconverters were recruited into a non-randomized study at St Mary's Hospital (SMH), London, between November 1999 and October 2003 and offered a 3 month short course of antiretroviral therapy (SCART) to be initiated immediately after the diagnosis of PHI . PHI was defined in an HIV-infected individual if they had laboratory evidence of seroconversion in the preceding 6 months through the availability of a previous antibody negative test (n = 73), an evolving HIV-1 antibody titre (n = 26) or an ‘incident’ test on the detuned assay  (absorbance < 0.6) (n = 6). Fifteen participants declined therapy and the remaining 90 were prescribed a 3 month course of therapy: zidovudine, lamivudine and nevirapine for 29 recruited between 2000 and 2001; abacavir, zidovudine, lamivudine and efavirenz for 33 recruited between 2001 and 2002; and zidovudine, lamivudine and lopinavir for 28 recruited between 2002 and 2003.
The SMH study received ethics approval and all patients gave informed signed consent to participating in the study.
CD4 cell counts and HIV RNA data from the SMH cohort were compared with those of untreated participants in the CASCADE collaboration cohort . From the total population of 8908 participants within the CASCADE dataset, pooled in December 2004, a selected population of 179 individuals were identified using frequency matching with the SMH cohort for age, sex, HIV risk group (sex between men, sex between men and women), year of estimated seroconversion (2000 or later), and a seroconversion window interval of less than 6 months, as these factors are known to influence CD4 cell and viral load levels and rates of change. Furthermore, data from participants were used only when they were ART naive for at least 6 months following seroconversion and with at least one measurement of both CD4 cell count and plasma viral load during the ART-free period. Seroconversion date was estimated as the midpoint between negative and positive test dates for 66% and 62% of SMH and CASCADE participants, respectively; laboratory methods were used for the remaining subjects. One participant from the SMH cohort was excluded from analyses as the date of stopping SCART was not known. The analyses, therefore, compared data from 89 treated participants (SMH) with 179 untreated matched controls (CASCADE).
Plasma viral load for the SMH population was quantified using the Chiron 3.0 reverse transcriptase–polymerase chain reaction assay (Chiron, Southam, UK) with a detection range of 50 to > 500 000 copies/ml. A number of assays were used within the CASCADE cohorts: Amplicor Monitor reverse transcriptase–polymerase chain reaction (Roche Molecular Systems, Branchburg, New Jersey, USA) for 19%, Quantiplex (bDNA; Chiron) (35%), Nuclisens QT assay (bioMérieux, Boxtel, the Netherlands) (8%), and assay not recorded for 38%. CD4 cell subsets were performed using standard fluorescence-activated cell sorter analysis.
All available CD4 cell and viral load measurements for both groups were included after transformation on the square root and log10 scales, respectively. Furthermore, all marker data were excluded from analyses once ART was initiated because of disease progression in both groups.
The mean viral load set-point for both populations was crudely estimated by taking the viral load measurement closest to 1.5 years after seroconversion provided that subjects were not taking any ART. Measurements from SMH individuals were also required to be at least 6 months after the end of SCART.
CD4 cell measurements were analysed using linear and nonlinear random effects models (random intercept, random slope), after transformation, using the square root scale, and included all available marker data for both groups . For SMH data, all CD4 cell measurements taken until 6 months after the end of SCART were censored because exploratory as well as confirmatory, analysis based on non-linear modelling showed that there was a fast nonlinear fall in CD4 cell levels for about 6 months after the end of SCART, followed by a slower, almost linear decrease thereafter.
Viral load data of both populations were also modelled through nonlinear random effects models for longitudinal data after log10 transformation. Two separate models for CASCADE and SMH individuals were used: (1) a nonlinear random effects model with random intercept and random slope allowing for an initial exponential decay to model viral load data of CASCADE individuals; and (2) a similar model allowing for an exponential increase in order to capture the rise of viral load levels after the end of SCART for the SMH individuals. Viral load measurements of the SMH population taken prior to the end of SCART were censored except for the last measurement during ART, which was kept as a baseline for the post-SCART period. Simpler regression methods were also used to compare marker levels at predetermined time points.
Time to CD4 cell fall to < 350 cells/μl was analysed using survival analysis techniques allowing for the initiation of clinically indicated ART as a competing risk .
CD4 cell counts were also analysed using nonlinear random effects models. Viral load set-point was estimated by taking the average of all available log-transformed viral load measurements within a prespecified time period for each individual and then taking average over all individuals. For each individual, all available viral load measurements were kept provided they were taken (1) within the first 2.5 years after seroconversion but after the first year following seroconversion, (2) while subjects were not on any ART, and (3) at least 6 months after the end of SCART for the SMH individuals. Then, we estimated each individual's set-point as the average of the viral load measurement satisfying these criteria.
CD4 cell count data were also analysed using random effects models that allow for potential informative censoring owing to initiation of clinically indicated ART .
Because of the longer follow up in the SMH population, their follow-up data were also censored at an arbitrary date (1 May 2004). The analyses were also repeated after first randomly excluding half of the SMH cohort's CD4 cell and HIV RNA measurements and then the 6 SMH individuals for whom seroconversion dates were based on the detuned assay.
Demographic and clinical characteristics of the 89 SMH and 179 CASCADE controls are shown in Table 1. The distribution of demographical and clinical characteristics are similar between the two groups except for follow-up time, which was significantly longer in the SMH group, and the number of CD4 cell and viral load measurements and their frequencies.
SCART was started, on average, 1.5 months [interquartile range (IQR), 0.5–2.2] after seroconversion in the SMH cohort; patients remained on therapy for a median duration of 3.3 months (IQR, 2.8–4.4). One individual remained on therapy for more than 17 months owing to changing HAART regimen to suppress transmitted multidrug-resistant HIV-1. Median CD4 cell count at SCART initiation was 500 cells/μl (IQR, 408–670) with maximum CD4 cell levels reached during SCART of 760 cells/μl (IQR, 620–930). The estimated median monthly rate of CD4 cell count increase, based on the first and last measurement, was 41 cells/μl (IQR, 11–73). However, it should be noted that for eight SMH individuals there was no evidence of an improvement in CD4 cell count from baseline, and for nine others the initial increase in their CD4 cell counts was followed by a decrease while still on SCART. Median viral load at SCART initiation was 99 421 copies/ml (IQR, 15 047–367 139). Seventy-one (80%) individuals reached viral load < 50 copies/ml during SCART; 14 (15.7%) reached 50–1000 copies/ml, and two (0.02%) had > 200 000 copies/ml.
Comparison of changes in CD4 cell count
As expected for the SMH cohort, there was a steep increase in CD4 cell count from seroconversion while on SCART. An initial steep decline was observed upon stopping treatment, followed by a more gradual decrease. Six months (SD, 1.5) after the end of SCART, the median CD4 cell count of 81 SMH individuals was 550 cells/μl (IQR, 410–650). At approximately the same time from seroconversion, the median CD4 cell count levels for 130 CASCADE individuals with available measurements at that period was 507 cells/μl (IQR, 390–667). This difference was not statistically significant (Mann–Whitney U-test, P = 0.682). CD4 cell count showed a constant rate of fall among the CASCADE group.
Subsequent rate of CD4 cell decline appears steeper for the untreated CASCADE group compared with the treated SMH group (P = 0.011; Fig. 1). This corresponded at 3 years after seroconversion to a mean annual loss of 51 cells/μl [95% confidence interval (CI), 32–69] in SMH and 77 cells/μl (CI, 65–89) in CASCADE participants (Table 2). This finding persisted even after adjustment for potential confounding effects of sex, risk group or age at seroconversion.
Comparison of changes in viral load measurements
As expected, viral load levels in the SMH cohort decreased steeply, owing to SCART initiation, from seroconversion up to a period of 6 months, coinciding with the end of SCART. Thereafter, there was a steep increase followed by a stabilization at approximately 4 log10 copies/ml. Viral load levels of CASCADE controls showed a gradual decrease during the first year after seroconversion followed by approximately stable levels thereafter. At a mean of 6 months (SD, 1.5) after the end of SCART, the median viral load of 81 SMH individuals was 4.36 log10 copies/ml (IQR, 3.60–5.00). At approximately the same time from seroconversion, the median viral load level for 130 CASCADE individuals with available measurements at that period was 4.46 log10 copies/ml (IQR, 3.92–5.00). This difference was not statistically significant (Mann–Whitney U-test, P = 0.171).
Mean estimated viral load set-point levels were 4.19 log10 copies/ml (SD, 0.095) and 4.25 log10 copies/ml (SD, 0.093) for the 81 SMH and 97 CASCADE individuals, respectively. The difference (0.06 log10 copies/ml) was not statistically significant (two-sample t-test, P = 0.684). Controlling for potential confounding effect of sex, exposure category, seroconversion calendar year and age at seroconversion, the estimated difference was 0.111 log10 copies/ml but remained not significant (P = 0.415).
Average modelled viral load trends are shown in Fig. 2. Estimated mean viral load levels for the SMH and CASCADE groups, respectively, were 4.16 log10 copies/ml (95% CI, 3.99–4.33) and 4.40 log10 copies/ml (95% CI, 4.28–4.53) at 1 year from seroconversion; 4.31 log10 copies/ml (95% CI, 4.14–4.48) and 4.47 log10 copies/ml (95% CI, 4.28–4.66) at 2 years; and 4.09 log10 copies/ml (95% CI, 3.83–4.35) and 4.53 log10 copies/ml (95% CI, 4.23–4.83) at 3 years. These differences are only marginally significant. These results are in agreement with those found earlier with simpler methods and indicate that SCART may have some small effect on lowering the viral load set-point. Furthermore, the rate of change in viral load appeared to increase for the CASCADE group following the set-point being reached [mean annual rate of change, 0.064 log10 copies/ml (95% CI, −0.064 to 0.192)], but this was not significant, whereas for the treated SMH group, the viral load levels appeared to fall slowly but significantly after the initial steep increase that followed the end of SCART [mean annual rate of change, −0.246 log10 copies/ml (95% CI, −0.079 to −0.412)]. This did not result in significant differences in viral load between the two groups for at least up to 2 years after seroconversion. However, estimated viral load levels at 3 years after seroconversion differed significantly, although it should be noted that the 75th percentile of analyses follow-up time for CASCADE was 22.05 and 22.04 months for CD4 cell and HIV RNA data, respectively (Table 1). Results beyond 2 years are, therefore, largely based on extrapolated data.
Time to the initiation of clinically indicated therapy or reaching a CD4 cell count < 350 cells/μl
Clinically indicated ART was started in 26 (29.2%) SMH individuals and 51 (28.5%) CASCADE individuals. A CD4 cell count < 350 cells/μl was achieved by 51 of 89 (57.3%) SMH and 80 of 179 (44.7%) CASCADE individuals. Incidence rates for reaching this level did not appear to differ significantly between the two groups (0.308 and 0.383 events/person-year for SMH and CASCADE, respectively; P = 0.3538). However, given that the median and last CD4 cell measurements prior to ART initiation was significantly higher for the CASCADE group (330 cells/μl; IQR, 236–420) than the SMH group (225 cells/μl; IQR, 180–290) (P = 0.0014), time to CD4 cell count < 350 cells/μl with the initiation of clinically indicated ART was considered as a competing risk. In the CASCADE controls, 79 (44.1%) reached this CD4 cell count before ART initiation (or did not start ART during follow-up) and 14 (7.8%) initiated ART without reaching this CD4 cell count level. The corresponding figures for the SMH population were 51 (57.3%) and none, respectively. Hazard ratios, using competing risks methodology, could not be estimated because there were no SMH individuals initiating ART with a CD4 cell count > 350 cells/μl. The estimated hazard ratio for the combined event ‘ART initiation or CD4 < 350 cells/μl’ was 1.447 (95% CI, 1.020–2.054; P = 0.039). Figure 3 shows Kaplan–Meier curves for the cumulative probability of the combined event by group.
Average CD4 cell count trajectories for the SMH population estimated by the linear and nonlinear model almost coincided after the first year following seroconversion.
Estimating viral load set-point by utilizing only measurements taken within the first 2.5 years after seroconversion, but after the first year following seroconversion, did not significantly affect these estimates or differences between the two groups, even after adjusting for confounders. Treating clinically indicated ART as an informative censoring event and analysing CD4 cell count using models with appropriate adjustments affected mainly estimates of the rate of decline of CD4 cells. The estimated CD4 cell slopes for SMH individuals and especially CASCADE individuals (where the rate of censoring was higher, owing to higher rates of ART initiation) were steeper. As changes in the estimations of slopes were towards the same direction and more pronounced in the CASCADE population, the difference between the two groups in CD4 cell rate of decline became larger and remained significant. Adjusted rate of CD4 cell decline per year on the square root scale were 1.42 (95% CI, 0.89–1.94) and 2.66 (95% CI, 2.23–3.10) for SMH and CASCADE individuals, respectively (P < 0.001).
As the last update of the SMH cohort was almost a year after the last update of the CASCADE database, censoring SMH individuals’ follow up at an arbitrary date (1 May 2004), the total follow-up time (i.e., irrespective of initiation of clinically indicated ART) for both groups became comparable. Results regarding CD4 cell evolution and viral load set-point remained almost the same as those reported in the main analysis. These results also remained virtually unchanged when the number of CD4 cell and viral load measurements in the SMH population was randomly halved, and when those identified through the detuned assay were excluded from the analysis.
This is the first report showing an apparently significant alteration in the rate of CD4 cell decline in persons treated with SCART at PHI when compared with a matched untreated population, albeit for the 3 year duration of follow up included in these analyses. Given that the comparison between the two groups was not performed within the context of a randomized clinical trial, analyses were subject to bias owing to unmeasured confounders as well as confounding by indication [19,20]. These observational data cannot, therefore, be used to infer treatment efficacy but clearly signal the need for a randomized comparison.
There are several factors that could account for the observed differences in CD4 cell decline. First, technical differences in the measurement of CD4 cell counts between the two groups could contribute to the observed differences; all SMH samples were measured in one laboratory while CASCADE included measurements from several different sites. However, all quantification of CD4 cell subsets performed during the study period were in accordance with standardized laboratory codes of practice using well-established techniques , making this explanation unlikely. Furthermore, a simulation exercise assuming a CD4 cell assay cohort effect in the CASCADE data was undertaken and found that any such assay differences did not affect differences in the two slopes. Second, the broad definition of PHI using the detuned assay as evidence of seroconversion in the SMH cohort could have influenced the observations. Only six subjects entered the SMH cohort on this criterion, and were they not true seroconverters but individuals with advanced HIV infection, their inclusion would be anticipated to increase the observed rate of CD4 cell decline in the SMH group rather than delay it. In any case, their exclusion was evaluated using sensitivity analysis and found to have virtually no effect on the main findings.
We observed an average increase of 41 cells/μl per month in the SMH group during the 3 month period on SCART, which, it may be argued, would inevitably delay the time to CD4 cell count < 350 cells/μl for that group, at least by an interval equivalent to the time spent on SCART. To acknowledge this, CD4 cell data were censored in the SMH cohort until 6 months after the end of SCART, at which time median CD4 cell counts were similar in the two groups. Importantly, we observed not only a significant difference in the overall time taken to reach CD4 cell count < 350 cells/μl between the two groups (allowing for ART initiation as a competing risk), but also that the rate of decline over the subsequent years appeared to have been delayed.
Despite the more rapid rate of CD4 cell decline observed in the CASCADE untreated population, there was no statistically significant difference in plasma viral load or viral set-point between the two groups at 2 years. The lack of effect on viral set-point concurs with previous data from other groups, who have similarly failed to demonstrate an impact on subsequent long-term viral control following ART intervention in PHI [11,22]. This finding is interesting but difficult to explain as it questions the mechanisms underlying the observed effect. The accepted paradigm of HIV pathogenesis dictates a clear relationship between plasma viraemia [23,24], CD4 cell decay and consequent disease progression. Establishment of an inaccessible latent HIV reservoir occurs early in the course of HIV infection . It is plausible, that early treatment may limit the size of the latent HIV infected reservoir in the treated patients [26–28], with consequent reduction in CD4 cell destruction but with minimal effect on plasma viral set-point. Alternatively, it may be that we failed to detect the true difference between the two groups where one exists because the number of participants in the risk set decreased over follow-up time. One other variable often cited is the correlation between the initial plasma HIV RNA levels at seroconversion and disease progression. It has been reported that plasma HIV-1 RNA level > 1 × 105 copies/ml at seroconversion is the most powerful predictor of AIDS (odds ratio, 10.8; P = 0.01) . However, there was no such difference between the baseline plasma viral loads of the two populations studied here that would account for the observed differences in CD4 cell decline.
A number of other factors may have acted as confounders for which we were unable to control in analyses. Clearance of virus is a dynamic process that involves both the adaptive and innate immune systems [4,5,29,30]; these, in turn, are influenced by genetic factors [31–33]. In particular, host chemokine genotypes  as well as HLA genotypes  are known to influence the development, and breadth of the immune responses generated . It is well established that HLA haplotype influences the rate of HIV-1 disease progression and time to AIDS [37–39], probably through immunological responses affecting the rate of HIV-1 replication . In addition, chemokine genotypes influence rates of disease progression . As neither the HLA haplotype nor chemokine receptor usage was available for the CASCADE population, we were unable to include these variables in our analyses. However, HLA data from the SMH cohort did not demonstrate an overrepresentation in White men of the HLA types known to confer enhanced outcomes nor a high prevalence of CCR5 heterozygotes (data not shown).
Despite these confounders, this is the first suggestion that even a short course of HAART implemented in PHI may have some influence on long-term clinical outcome. Given that we are unable to account for possible sources of bias, however, we cannot conclude that a 3 month short course of HAART at PHI is superior to no therapy. Our findings may at least indicate that there is a transient beneficial effect over a period longer than has hitherto been described through other studies, and they raise the question as to whether a longer course may have provided greater benefit of longer duration and an improved clinical outcome. Our research has highlighted the urgent need for a randomized clinical trial in acute and early HIV disease. This is currently underway though the SPARTAC trial, in the UK, Ireland, South Africa, Australia, Italy, Brazil and Uganda (http://www.ctu.mrc.ac.uk/studies/spartac.asp).
The authors are grateful to Dr Sarah Walker for insightful comments in analyses and interpretation of findings. We would also like to thank all study participants particularly those who took part in the SMH open study.
Sponsorhsip: The SMH study was funded by a Wellcome Trust grant. CASCADE has been funded through grants from the European Union BMH4-CT97-2550, QLK2-2000-01431, QLRT-2001-01708 and LSHP-CT-2006-018949
CASCADE Collaboration: Francois Dabis, Rodolphe Thiébaut, Geneviève Chêne, Sylvie Lawson-Ayayi (Aquitaine cohort, France); Laurence Meyer, Faroudy Boufassa (SEROCO cohort, France); Osamah Hamouda, Claudia Kücherer (German cohort, Germany); Benedetta Longo, Patrizio Pezzotti, Giovanni Rezza, Maria Dorrucci (Italian Seroconversion Study, Italy); Giota Touloumi, Angelos Hatzakis, Anastasia Karafoulidou (Greek Haemophilia cohort, Greece); Ray Brettle (Edinburgh Hospital cohort, UK); Julia Del Amo, Jorge del Romero (Madrid cohort, Spain); Liselotte van Asten, Akke van der Bij, Ronald Geskus, Maria Prins, Roel Coutinho (Amsterdam Cohort Studies among Homosexual Men and Drug Users, the Netherlands); Niels Obel, Court Pedersen, Claus Nielsen, Louise Bruun Jorgensen (Danish HIV cohort, Denmark); Ildefonso Hernández Aguado, Santiago Pérez-Hoyos (Valencia IDU cohort, Spain); Anne Eskild, Oddbjorn Brubakk, Mette Sannes (Oslo and Ulleval Hospital cohorts, Norway); Caroline Sabin, Christine Lee (Royal Free Haemophilia cohort, UK); Anne M. Johnson, Andrew N. Phillips, Abdel Babiker, Janet H. Darbyshire, Noël Gill, Kholoud Porter (UK Register of HIV Seroconverters, UK); Patrick Francioli, Heiner Bucher, Martin Rickenbach (Swiss HIV cohort, Switzerland); David Cooper, John Kaldor, Tony Kelleher (Sydney AIDS Prospective Study, Australia); David Cooper, John Kaldor, Tim Ramacciotti, Don Smith (Sydney Primary HIV Infection cohort, Australia); Roberto Muga, Jordi Tor (Badalona IDU hospital cohort, Spain); Philippe Vanhems (Lyon Primary Infection cohort, France); John Gill (South Alberta Clinic, Canada); Joan Cayla, Patricia Garcia de Olalla (Barcelona IDU cohort, Spain). Steering Committee: Valerie Beral, Roel Coutinho, Janet Darbyshire (Project Leader), Julia Del Amo, Noël Gill (Chairman), Christine Lee, Laurence Meyer, Giovanni Rezza. CASCADE Coordinating Centre: Kholoud Porter (Scientific Coordinator), Abdel Babiker, A. Sarah Walker, Janet Darbyshire, Krishnan Bhaskaran.
1. Kaufmann GR, Cunningham P, Zaunders J, Law M, Vizzard J, Carr A, et al
. Impact of early HIV-1 RNA and T-lymphocyte dynamics during primary HIV-1 infection on the subsequent course of HIV-1 RNA levels and CD4+ T-lymphocyte counts in the first year of HIV-1 infection. Sydney Primary HIV Infection Study Group. J Acquir Immune Def Syndr 1999; 22:437–444.
2. Lefrere JJ, Roudout-Thoraval F, Mariotti M, Thauvin M, Lerable J, Salpetrier J, et al
. The risk of disease progression is determined during the first year of human immunodeficiency virus type 1 infection. J Infect Dis 1998; 177:1541–1548.
3. Blattner WA, Oursler KA, Cleghorn F, Charurat M, Sill A, Bartholomew C, et al
. Rapid clearance of virus after acute HIV-1 infection: correlates of risk of AIDS. J Infect Dis 2004; 189:1793–1801.
4. Rosenberg ES, Altfeld M, Poon SH, Phillips MN, Wilkes BM, Eldridge RL, et al
. Immune control of HIV-1 after early treatment of acute infection. Nature 2000; 407:523–526.
5. Rosenberg ES, Billingsley JM, Caliendo AM, Boswell SL, Sax PE, Kalams SA, et al
. Vigorous HIV-1 specific CD4+ T-cell responses associated with control of viraemia. Science 1997; 278:1447–1450.
6. Jansen CA, De Cuyper IM, Steingrover R, Jurriaans S, Sankatsing SU, Prins JM, et al
. Analysis of the effect of highly active antiretroviral therapy during acute HIV-1 infection on HIV-1 specific CD4 T-cell functions. AIDS 2005; 11:1145–1154.
7. Autran B, Carcelain G, Li TS, Blanc C, Mathez D, Tubiana R, et al
. Positive effects of combined antiretroviral therapy on CD4+ T cell homeostasis and function in advanced HIV disease. Science 1997; 277:112–116.
8. Ho DD, Neumann AU, Perelson AS, Chen W, Leonard JM, Markowitz M. Rapid turnover of plasma virions and CD4 lymphocytes in HIV-1 infection. Nature 1995; 373:123–126.
9. Chun TW, Stuyver L, Mizell SB, Ehler LA, Mican JA, Baseler M, et al
. Presence of an inducible HIV-1 latent reservoir during highly active antiretroviral therapy. Proc Natl Acad Sci USA 1997; 94:13193–13197.
10. Smith DE, Walker BD, Cooper DA, Rosenberg ES, Kaldor JM. Is antiretroviral treatment of primary HIV infection clinically justified on the basis of current evidence? AIDS 2004; 18:709–718.
11. Kaufmann DE, Lichterfeld M, Altfeld M, Addo MM, Johnston MN, Lee PK, et al
. Limited durability of viral control following treated acute HIV infection. PLoS Med 2004; 2:137–148.
12. Blankson JN, Siliciano J, Silician RF. The effect of early treatment on the latent reservoir of HIV-1. J Infect Dis 2005; 191:1394–1396.
13. Fidler S, Fraser C, Fox J, Tamm N, Griffin JT, Weber J. Comparative potency of three antiretroviral therapy regimes in primary HIV infection. AIDS 2006; 20:247–252.
14. Janssen RS, Satten GA, Stramer SL, Rawal BD, O'Brien TR, Weiblen BJ, et al
. New testing strategy to detect early HIV-1 infection for use in incidence estimates and for clinical and prevention purposes. JAMA 1998; 280:42–48.
15. CASCADE Collaboration. Changes in the uptake of antiretroviral therapy and survival in persons with known duration of HIV infection in Europe
. HIV Med
16. Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics 1982; 38:963–974.
17. Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 1999; 94:496–509.
18. Touloumi G, Pocock SJ, Babiker AG, Darbyshire JH. Estimation and comparison of rates of change in longitudinal studies with informative drop-outs. Stat Med 1999; 18:1215–1233.
19. Miettinen OS. The need for randomization in the study of intended effects. Stat Med 1983; 2:267–271.
20. CASCADE Collaboration. Estimating the effect of antiretroviral treatment during HIV-seroconversion: impact of confounding in observational data
. HIV Med
21. Centers for Disease Control and Prevention. 1997 revised guidelines for performing CD4 T-cell determinations in persons infected with human immunodeficiency virus (HIV)
22. Desquilbet L, Goujard C, Rouzioux C, Sinet M, Deveau C, Pellegrin I, for the PRIMO and SEROCO Study Groups. Does transient HAART during primary infection lower the virological set-point? AIDS 2004; 18:2361–2369.
23. Vlahov D, Graham N, Hoover D, Flynn C, Bartlett JG, Margolick JB, et al
. Prognostic indicators for AIDS and infectious disease death in HIV-infected injection drug users: plasma viral load and CD4+ cell count. JAMA 1998; 279:35–40.
24. Mellors JW, Kingsley LA, Rinaldo CR Jr, Todd JA, Hoo BS, Kokka RP, et al
. Quantitation of HIV-1 RNA in plasma predicts outcome after seroconversion. Ann Intern Med 1995; 122:573–579.
25. Chun TW, Engel D, Berrey MM, Shea T, Corey L, Fauci AS. Early establishment of a pool of latently infected, resting CD4(+) T cells during primary HIV-1 infection. Proc Natl Acad Sci USA 1998; 95:8869–8873.
26. Lori F, Jessen H, Lieberman J, Finzi D, Rosenberg E, Tinelli C, et al
. Treatment of human immunodeficiency virus infection with hydroxyurea, didanosine, and a protease inhibitor before seroconversion is associated with normalized immune parameters and limited viral reservoir. J Infect Dis 1999; 180:1827–1832.
27. Strain MC, Little SJ, Daar ES, Havlir DV, Gunthard HF, Lam RY, et al
. Effect of treatment, during primary infection, on establishment and clearance of cellular reservoirs of HIV-1. J Infect Dis 2005; 191:1410–1418.
28. Pires A, Hardy G, Gazzard B, Gotch F, Imami N. Initiation of antiretroviral therapy during recent HIV-1 infection results in lower residual viral reservoirs. J Acquir Immune Defic Syndr 2004; 36:783–790.
29. Little SJ, McLean AR, Spina CA, Richman DD, Havlir DV. Viral dynamics of acute HIV-1 infection. J Exp Med 1999; 190:841–850.
30. Harari A, Cellarai C, Pantaleo G. Role of HIV-1 specific CD4 T-cells. Curr Opin HIV AIDS 2006; 1:22–27.
31. Moore CB, John M, James IR, Christiansen FT, Witt CS, Mallal SA. Evidence of HIV-1 adaptation to HLA-restricted immune responses at a population level. Science 2002; 296:1439–1443.
32. Nolan D, Gaudieri S, John M, Mallal S. Impact of host genetics on HIV disease progression and treatment: new conflicts on an ancient battleground. AIDS 2004; 18:1231–1240.
33. Tang J, Wilson CM, Meleth S, Myracle A, Lobashevsky E, Mulligan MJ, for the REACH Study Group. Host genetic profiles predict virological and immunological control of HIV-1 infection in adolescents. AIDS 2002; 16:2275–2284.
34. Smith MW, Dean M, Carrington M, Price DA, Taylor JH, Beilman GJ, et al
. Contrasting genetic influence of CCR2 and CCR5 variants on HIV-1 infection and disease progression. Haemophilia growth and development study, Multicentre AIDS cohort study (MACS) Multicentre Haemophilia cohort study (MHCS) San Francisco City Cohort (SFCC) ALIVE study. Science 1997; 277:959–965.
35. Kiepiela P, Leslie AJ, Honeyborne I, Reay E, Verhoeven D, Shacklett BL, et al
. Dominant influence of HLA-B in mediating the potential evolution of HIV and HLA. Nature 2004; 7018:769–775.
36. Leslie A, Kavanagh D, Honeyborne I, Pfafferott K, Edwards C, Pillay T, et al
. Transmission and accumulation of CTL escape variants drive negative associations between HIV polymorphisms and HLA. J Exp Med 2005; 201:891–902.
37. Saah AJ, Hoover DR, Weng S, Carrington M, Mellors J, Rinaldo CR Jr, et al
. Association of HLA profiles with early plasma viral load, CD4+ cell count and rate of progression to AIDS following acute HIV-1 infection. Multicenter AIDS Cohort Study. AIDS 1998; 12:2107–2113.
38. Gao X, Bashirova A, Iversen AK, Phair J, Goedert JJ, Buchbinder S, et al
. AIDS restriction HLA allotypes target distinct intervals of HIV-1 pathogenesis. Nat Med 2005; 11:1290–1292.
39. Gaudieri S, DeSantis D, McKinnon E, Moore C, Nolan D, Witt CS, et al
. Killer immunoglobulin-like receptors and HLA act both independently and synergistically to modify HIV disease progression. Genes Immun 2005; 6:683–690.
40. Lyles RH, Munoz A, Yamashita TE, Bazmi H, Detels R, Rinaldo CR, et al
. Natural history of human immunodeficiency virus type 1 viraemia after seroconversion and proximal to AIDS in a large cohort of homosexual men. Multicentre AIDS cohort study (MACS). J Infect Dis 2000; 181:872–880.
41. Altfeld M, Addo MM, Rosenberg ES, Hecht FM, Lee PK, Vogel M, et al
. Influence of HLA-B57 on clinical presentation and viral control during acute HIV-1 infection. AIDS 2003; 17:2581–2591.
© 2007 Lippincott Williams & Wilkins, Inc.
What does "Remember me" mean?
By checking this box, you'll stay logged in until you logout. You'll get easier access to your articles, collections,
media, and all your other content, even if you close your browser or shut down your
To protect your most sensitive data and activities (like changing your password),
we'll ask you to re-enter your password when you access these services.
What if I'm on a computer that I share with others?
If you're using a public computer or you share this computer with others, we recommend
that you uncheck the "Remember me" box.
Data is temporarily unavailable. Please try again soon.