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JAIDS Journal of Acquired Immune Deficiency Syndromes:
Epidemiology and Social Science

Causal Pathways of the Effects of Age and the CCR5-Δ32, CCR2-64I, and SDF-1 3′A Alleles on AIDS Development

Geskus, Ronald B PhD*†; Meyer, Laurence MD, PhD‡; Hubert, Jean-Baptiste MD, MSc‡; Schuitemaker, Hanneke PhD§; Berkhout, Ben PhD∥; Rouzioux, Christine PhD¶; Theodorou, Ioannis D MD, PhD**; Delfraissy, Jean-François MD, PhD††; Prins, Maria PhD*; Coutinho, Roel A MD, PhD*

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Author Information

From the *Municipal Health Service, Amsterdam, The Netherlands; †Leiden University Medical Center, The Netherlands; ‡INSERM U569, Epidemiology Service, Hôpital de Bicêtre, Le Kremlin-Bicêtre, France; §CLB Sanquin Division, Amsterdam, The Netherlands; ∥Academic Medical Center, Amsterdam, The Netherlands; ¶Department of Virology, Hôpital Necker, Paris, France; **Laboratoire d'Immunologie Cellulaire et Tissulaire, INSERM U543, Hôpital Pitié Salpêtrière, Paris, France; and ††Department of Medicine, Hôpital de Bicêtre, Le Kremlin-Bicêtre, France.

Received for publication May 6, 2004; accepted July 26, 2004.

Supported by a grant from the Ensemble contre le Sida (Fondation pour la Recherche Médicale-Sidaction; project 23025-00-09/AO10-1) and the Dutch AIDS Fund (project 1618).

Reprints: Ronald B. Geskus, Municipal Health Service, Cluster of Infectious Diseases, Nieuwe Achtergracht 100, 1018 WT Amsterdam, Netherlands (e-mail: rgeskus@gggd.amsterdam.nl).

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Abstract

Objective: To investigate the causal pathways by which age and the CCR5-Δ32, CCR2-64I, and SDF-1 3A alleles influence progression to AIDS.

Design: Analysis of follow-up data from 2 cohort studies among homosexual men (n = 400), having >10 years of follow-up.

Methods: The effects of the 4 cofactors on the CD4 and HIV-1 RNA trajectories after seroconversion were modeled in a random-effects model. A proportional hazards model was used to investigate their effect on the risk of AIDS after correction for CD4 cell count and RNA level. This approach allows investigation as to whether they influence AIDS progression by affecting CD4 count and RNA level or by other pathways.

Results: Persons of younger age or having the CCR2-64I or SDF-1 3A mutation have significantly higher CD4 levels. Persons with the CCR5-Δ32 deletion or CCR2-64I mutation have significantly lower RNA levels. After correction for both CD4 count and RNA level, only the SDF-1 3A mutation significantly increases the AIDS risk.

Conclusions: Age and the CCR5-Δ32 deletion and CCR2-64I mutation influence AIDS progression by affecting CD4 and HIV-1 RNA. The SDF-1 3A allele increases the AIDS risk, but this effect is countered by its effect on CD4 and HIV-1 RNA level.

Several studies have investigated and confirmed the effects of the CCR5-Δ32, CCR2-64I, and the SDF-1 3A alleles on HIV-1 disease progression.1-4 A large meta-analysis found that CCR5-Δ32 heterozygotes and CCR2-64I hetero- and homozygotes exhibit a slower progression to AIDS and death.5 Whether the SDF-1 3A allele has any effect remains controversial. One study found that SDF-1 3A/3A delays the onset of AIDS,6 but results from subsequent studies were ambiguous. No effects were found in the meta-analysis.5

Of interest are the mechanisms behind these effects. For CCR5-Δ32, the protective effect is explained by CCR5 being a major coreceptor for non-syncytium-inducing HIV-1 variants. If CCR5 expression is reduced, which is the case in Δ32 heterozygous subjects, the virus has less opportunity to enter the cell. The meta-analysis showed that the CCR5-Δ32 heterozygotes had lower HIV-1 RNA levels at baseline. The effect of the CCR5-Δ32 deletion on AIDS risk became weaker after correction for baseline HIV-1 RNA level, suggesting that host “genotypic differences in early HIV-1 RNA levels explain some of the protective effect of the CCR5-Δ32 allele.”5 A similar result was found by Meyer et al.7 Although the mechanism responsible for the protective effect of CCR2-64I remains unknown, the meta-analysis showed that the CCR2-64I mutation has an effect on early HIV-1 RNA levels similar to that of the CCR5-Δ32 deletion. Very few studies have directly modeled the ways in which the 3 genetic cofactors may influence progression to AIDS. One study found that the CCR5-Δ32 deletion influences progression to AIDS8 by affecting the CD4 trajectory. However, additional adjustment for early HIV-1 RNA level indicated that the true mechanism could operate through a reduction in early viral load, which in turn influences the CD4 trajectory and the AIDS risk.

In this paper, we present a model that separates the effects of the 3 genetic cofactors and age at seroconversion on CD4 and HIV-1 RNA evolution from their remaining effects on AIDS progression. The influence of these cofactors on the joint evolution of CD4 count and HIV-1 RNA load after sero-conversion was studied in a random-effects model. Moreover, we studied the effects of the cofactors on the risk of AIDS in a Cox model, insofar as they are not caused by their effects on CD4 and HIV-1 RNA, by correcting for the fitted CD4 and HIV-1 RNA values from the random-effects model. The presence of AIDS was judged according to the European 1993 AIDS case definition,9 which excludes cases based solely on a CD4 count <200 cells/μL.

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MATERIALS AND METHODS

We used data from 2 different sources, the Amsterdam Cohort Study (ACS) among homosexual men and the French SEROCO Cohort Study. Data was drawn from the start of each study until highly active anti-retroviral therapy (HAART) became widespread in the 2 countries. Started in 1984, the ACS has required that persons be free of AIDS-defining conditions at entry. In our analysis, we included participants who had a period between the last HIV-seronegative test and the first HIV-seropositive test of not more than 2 years (n = 132); we imputed their seroconversion date via conditional mean imputation.10 Follow-up data from hospitals were added to ACS data. Administration of HAART became widespread in the Netherlands on July 1, 1996.

The French SEROCO cohort, started in 1988, has enrolled HIV-infected, nonhemophiliac adults referred from 17 hospitals and a network of private practitioners. For reasons of homogeneity, we analyzed only homosexual men from the cohort. Like our ACS men, they had no more than 2 years between the date of last HIV-seronegative test and date of first HIV-seropositive test (n = 312). Their date of seroconversion had been imputed as described by Hubert et al.11 Data collected after February 1st, 1996 were not used.

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Laboratory Methods

All CD4 lymphocyte counts were obtained prospectively. All HIV-1 RNA levels were determined retrospectively from stored sera. In France, the 3 participating university laboratories used reverse transcriptase polymerase chain reaction (Amplicor HIV-1 Monitor assay, Roche Molecular Systems, Neuilly-sur-Seine, France; quantification threshold 200 copies/mL). In Amsterdam, one laboratory was used. Most (83.6%) of the measurements were based on the Nucleic Acid Sequence-Based Amplification (NASBA) technique (NASBA HIV-1 RNA QT; Organon Teknika, Boxtel, The Netherlands; quantification threshold 1000 copies/mL). The remaining were based on the Amplicor (2.4%) and on the Nuclisens (bioMérieux, Marcy l'Etoile, France) (14%) technique. Since the Amplicor test is known to give lower values,12 a correction factor was applied (on log HIV-RNA scale: 3.5-4.5: +0.04; 4.5-5.5: +0.22; >5.5: +0.29).

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Statistical Methods

We used a quadratic random-effects model for the joint evolution of the logarithm of HIV-1 RNA level and the cube root of CD4 count after seroconversion. The relation between CD4 and HIV-1 RNA is expressed via the random-effects covariance matrix. The fixed effect for the slope of HIV-1 RNA was allowed to change at 6 months after seroconversion. HIV-1 RNA data below the detection limit were treated as left-censored.13 We allowed both the intercept and the slope of CD4 and HIV-1 RNA to depend on age at seroconversion and the 3 genetic cofactors. For each cofactor, we compared wild-type against homozygous/heterozygous combined.

The effect of the CCR5-Δ32, CCR2-64I, and SDF-1 3A alleles (heterozygous and homozygous combined) on the AIDS risk was modeled in a Cox model. We corrected for the effect of the time-updated CD4 count and HIV-1 RNA level values. We used the fitted values from the random effects model, which has been shown to reduce bias.14 So the model used was

Equation (Uncited)
Equation (Uncited)
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This approach allowed us to separate the effects of the cofactors on AIDS progression that operate through their influence on evolution of CD4 and HIV-1 RNA from effects that operate in some other way. For example, if the effect of CD4 count on the relative risk is given by

Equation (Uncited)
Equation (Uncited)
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and the effect of the CCR5-Δ32 deletion on CD4 count is given by α + β × time, then the marker-mediation effect of the CCR5-Δ32 deletion on AIDS risk is given by {gamma; × (alpha; + beta; × time)}

If, after adjustment for updated CD4 count and HIV-1 RNA level, the cofactor has an effect on AIDS risk, this indicates another effect irrespective of CD4 count and HIV-1 RNA level.

We used a Bayesian approach for estimation of the parameters, starting with noninformative priors. The principles of the model are the same as used by Faucett and Thomas,14 except that we included the evolution of HIV-1 RNA level and added the 4 cofactors to the model. Posterior distributions were obtained using Markov Chain Monte Carlo methods, using the WinBUGS program (available at: http://www.mrc-bsu.com.ac.uk/bugs/references/bugs-core-papers.shtml).15,16 Parameter estimates are the medians of the posterior distributions. The range from the 2.5% to the 97.5% quantile is used to quantify the uncertainty in the parameter estimates. This range can be interpreted as a 95% CI and will be referred to as such. If the value zero is outside this interval, the effect is seen as statistically significant.

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RESULTS

Since 42 individuals had missing SDF information, we used data from 400 persons (126 from Amsterdam and 274 from France). The median age at seroconversion was 35 (in-terquartile range [IQR] 30-40) years for the ACS men and 29 (IQR 25-35) years for the SEROCO men. Fifty-nine persons (15%) were heterozygous for the CCR5-Δ32 allele, and 1 was homozygous. Fifty-nine persons (15%) were heterozygous for the CCR2-64I allele and 5 (1%) were homozygous. A total of 120 persons (30%) were heterozygous for the SDF-1 3A allele and 20 (5%) were homozygous. These percentages were almost equal for both cohorts. The median number of CD4 measurements per person was 14 (IQR 8-23). The median number of HIV-1 RNA measurements per individual was 8 (IQR 5-13). We had 6761 CD4 records and 3807 HIV-1 RNA records. Of the latter, 9% (n = 344) was below the detection limit and 9% (n = 340) was measured in the first 6 months after seroconversion. The 400 persons contributed 2192 person-years of follow-up and 166 events of progression to AIDS.

In a (time-independent) Cox model that only included the 4 cofactors, the relative risk (95% CI) of progression to AIDS for age (per 10 years) was 1.189 (0.988-1.431); for CCR5-Δ32 0.581 (0.355-0.951), for CCR2-64I 0.710 (0.449-1.123), and for SDF-1 3A 1.074 (0.829-1.392).

Since we were interested in the effects of age and the genetic cofactors on CD4 and HIV-1 RNA evolution, and not in the CD4 and HIV-1 RNA evolution itself, we do not show the curves that show their evolution over time. The effects of age and the genetic cofactors on the intercept and slope of HIV-1 RNA level and CD4 count are given in Table 1.

Table 1
Table 1
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From the table, it is seen that older persons have a significantly steeper CD4 decline and that the CCR5-Δ32 deletion significantly decreases the HIV-1 RNA intercept. Of borderline significance are: SDF-1 3A increases the CD4 intercept, CCR2-64I weakens the CD4 decline, and CCR2-64I decreases the HIV-1 RNA intercept. The effect on evolution of CD4 count and HIV-1 RNA level is shown in Figures 1 and 2. We show the amount by which the marker values are different at each time after seroconversion for a person with the mutant allele, in comparison with a person who is wild type for the respective genotype and has equal value for the other cofactors. We do the same for each 10-year increment in age. Even if the effects on intercept and slope are nonsignificant, their combination may lead to significant effects on the marker level at later times after seroconversion. For example, if both effects are positive, the effect accumulates since the positive slope adds to the difference in level as time progresses.

Figure 1
Figure 1
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Figure 2
Figure 2
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In Figure 1, it is seen that persons with the CCR5-Δ32 allele have a lower HIV-1 RNA level that reaches significance over the first 7 years. This effect is already present at seroconversion and remains more or less stable over time after seroconversion at a value of about 0.35 log. The CCR2-64I allele also lowers the HIV-1 RNA level by about 0.3 log. Age and the SDF-1 3A allele have no significant effect on HIV-1 RNA development.

In Figure 2, the effects on CD4 development are depicted. Since we used the cube root of CD4 count to obtain a better fit,17 the effects of the cofactors on CD4 count depend on the actual CD4 count. We show results for 2 values of CD4 level that are reasonably far apart and represent possible levels shortly after seroconversion and close to AIDS diagnosis. Each graph plots the expected CD4 count for a person who differs only with respect to 1 specific cofactor (ie, having the mutation allele or being 10 years older) from a person who has an expected CD4 count of 800 (left y-axis) or 100 cells/μL (right y-axis). For example, we see that, compared with a person with an expected CD4 count of 800 who is wild type for CCR2, a person who is heterozygous or homozygous for the CCR2-64I allele has an expected CD4 count at seroconversion that is about 25 cells higher, but this difference increases over time after seroconversion; compared with a person with an expected CD4 count of 800 cells/μL 3 years after seroconversion, his expected CD4 level would have been 100 cells/μL higher at 900 cells/μL. Compared with a person who is wild type for CCR2 and who has an expected CD4 count of 100 cells/μL, a person who is heterozygous or homozygous for the CCR2-64I allele and has the same values for the other covariables has a predicted CD4 level of 130 cells/μL 3 years after seroconversion. Older persons on average have lower CD4 counts and this effect becomes stronger later after seroconversion. Persons with the SDF-1 3A allele tend to have higher CD4 counts, whereas no effect is visible for the CCR5-Δ32 allele.

In Figure 3, the effects of the cofactors on AIDS risk via their effects on the markers are shown. The basic pattern is the same as in Figures 1 and 2, which is explained by the direct relation via exp {γ × effect on marker} = exp{γ × (α + β × time)}, with γ the effect of the marker on AIDS risk.

Figure 3
Figure 3
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The remaining effects may be direct effects as well as effects that operate through some other independent marker that was not included in the model. The remaining effects of the cofactors (with 95% CI) on the AIDS risk are: age relative risk (RR) = 1.09 (0.87, 1.36) per 10 years older; CCR5-Δ32 RR = 0.75 (0.41, 1.28); CCR2-64I RR = 1.59 (0.91, 2.61); SDF-1 3A RR = 1.74 (1.19, 2.54). Hence, of 2 persons with the same CD4 count, HIV-1 RNA level, the same age, and the same CCR5 and CCR2 genotype, persons with the SDF-1 3A allele have a significantly higher AIDS risk. At the same time, they on average have higher CD4 counts at any time after seroconversion (Fig. 2), which decreases their AIDS risk. In Figure 3, the effect of the SDF-1 3A mutation on AIDS risk as expressed via CD4 count reaches borderline significance and has an opposite sign to the remaining effect. The HIV-1 RNA mediation effect has the same sign but does not reach significance.

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DISCUSSION AND CONCLUSIONS

We investigated the causal pathways that explain the protective effects of the CCR2-64I and the CCR5-Δ32 alleles on AIDS risk and found that both decrease HIV-1 RNA levels, confirming earlier results.5,7,8 For the CCR5-Δ32 deletion, we found no significant effect on CD4 count nor any further significant effect on the AIDS hazard after correction for CD4 and HIV-1 RNA, again confirming earlier results.8,8a The CCR2-64I allele was also significantly associated with an on average higher CD4 count.

Persons with the SDF-1 3A mutation allele tended to have on average higher CD4 count, while having an increased AIDS risk after correction for CD4 count, HIV-1 RNA level, and the other 3 cofactors. We did not find a net effect. A possible explanation for this phenomenon may be that the homing effects induced by SDF are reduced by the SDF-1 3A mutation. This would bring fewer CXCR4+ cells to the lymphoid tissues, leading to higher CD4 cell concentrations in circulating blood. However, if compared with persons with an equal number of CD4 cells in blood, persons with the SDF-1 3A mutation have lower total numbers of CD4 cells, which will increase their AIDS risk. We also did an analysis in which we allowed the SDF effect to be different for the 2 cohorts. The estimated effects (with 95% CI) from the ACS data were stronger, but the French data showed the same trend (ACS: 0.19 (−0.23, 0.60) and 0.12 (−0.06, 0.30) on CD4 intercept and slope; 2.57 (1.45, 4.40) remaining effect on AIDS relative risk; France: 0.20 (−0.09, 0.49) and 0.03 (−0.09, 0.16); 1.36 (0.87, 2.11) remaining effect.

Only SDF-1 3A homozygosity was reported to affect AIDS progression by Winkler et al,6 and subsequent studies have therefore compared SDF-1 3A homozygous with wild type and heterozygous combined. Since no biologic rationale supports this separation and only 20 of our sample were SDF-1 3A homozygous, we reported results of the analyses in which we combined SDF-1 3A heterozygous and homozygous persons. However, we also did the analyses comparing the SDF-1 3A/3A with heterozygous and wild type combined. The effects on CD4 count were more or less similar, but with much wider CIs (0.64 [0.08, 1.18] and −0.02 [−0.25, 0.21] for effect on CD4 intercept and slope). The remaining effect in the Cox model tended to be somewhat stronger, but again uncertainty is large (RR = 2.62 [1.20, 5.12]).

Older age was found to lower CD4 levels, and this effect appears to get stronger as time passes after seroconversion. Interestingly, the effect of age depends on the inclusion of a quadratic fixed and random effect for the development of CD4 over time. Using likelihood maximization in a linear random-effects model for CD4 development, including a linear time effect alone yields an effect of age on the slope of −0.042 (P = 0.21), whereas addition of a quadratic time trend changes this effect to −0.068 (P = 0.05). The frequent switching of HIV-1 to its syncytium-inducing phenotype among homosexual men, which causes a significant change in slope of CD4 development, makes a model that includes a quadratic term more plausible. No significant effect on HIV-1 RNA levels or a direct effect on AIDS risk could be demonstrated for age.

Recently, the protective effect of the CCR2-64I mutation allele was found to be present only in the first 5 years after seroconversion.18 Their model, applied to our data, yielded as relative risks 0.39 (0.14-1.08) for the first 4 years, 0.81 (0.46-1.43) for the next 4 years, and 1.45 (0.39-5.38) >8 years after seroconversion. This is close to their values of 0.42, 0.81, and 1.09, but our CIs were much wider. However, the CCR2-64I mutation increased average CD4 levels, and this effect tended to become stronger as time passed after seroconversion (Fig. 2), which seems to contradict these results. We combined the marker mediation effects from Figures 1 and 2 with the remaining effects of CCR2-64I on the AIDS risk, which were 0.81 (0.23-2.18) for the first 4 years, 1.60 (0.80-3.04) for the next 4 years, and 3.94 (0.77-16.1) >8 years after seroconversion. This total effect (exp{δ + γCD4 × (αCD4 + βCD4 × time) + γRNA × (αRNA + βRNA × time)}) was 0.46 (0.13-1.36) at 2 years, 0.60 (0.23-1.57) at 6 years, and 0.97 (0.12-6.47) at 10 years after seroconversion. This is again close to the results by Mulherin et al.18 Hence another unknown factor seems to make the risk of AIDS higher for persons carrying the mutation and who are >4 years after seroconversion, but CIs are too wide to draw firm conclusions. In the same paper,18 the protective effect of the CCR5-Δ32 deletion was found to be stable over time since seroconversion, which corresponds very well with our results.

Ours is the first study to look at the causal pathways behind the effects of age and the genetic cofactors on progression to AIDS in a way that includes information on the entire marker trajectory of both CD4 count and HIV-1 RNA level. Investigating causal pathways is important to gain insight into disease mechanisms and also for clinical practice. The correlation between CD4 and HIV-1 RNA development makes it difficult to establish whether the effects of a cofactor on CD4 and HIV-1 RNA are independent mechanisms or whether one is induced by the other. Still, the protective effect of the CCR5-Δ32 deletion seemed to be expressed mainly through HIV-1 RNA level, since the effect on CD4 count was much weaker. The protective effect of the CCR2-64I mutation was expressed through both CD4 count and HIV-1 RNA level. For age, the effect on CD4 was significant, whereas the effect on HIV-1 RNA was not. No significant effects were seen apart from the ones that worked via the 2 markers. Hence, once CD4 and HIV RNA values are known, knowledge of these cofactors appears not to provide extra information with respect to disease progression for persons not on HAART. For the SDF-1 3A allele, an interesting effect on instantaneous AIDS risk was seen that was not mediated by CD4 and HIV-1 RNA. However, this does not provide conclusive information on the probability of developing AIDS within a certain time span (eg, 3 years). This probability also depends on the marker development in the next 3 years. If the wild-type individual has a steeper CD4 decline, it is possible that he is more likely to develop AIDS within 3 years, even if his instantaneous AIDS risk is lower. Note that no residual effect on AIDS prognosis was seen for the genetic markers in a recent study, using a completely different approach.19

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ACKNOWLEDGMENTS

The authors thank Anneke Krol for the data management and Lucy Phillips for critically reading the manuscript. The authors thank Ronald van Rij for his helpful suggestions.

The Amsterdam Cohort Studies on HIV infection and AIDS are a collaboration between the Municipal Health Service, the Academic Medical Centre, and the Central Laboratory of the Netherlands Red Cross Blood Transfusion Service, Sanquin Division, Amsterdam, the Netherlands. The SEROCO Study Group is a collaboration funded by the Agence Nationale de Recherche sur le Sida (ANRS).

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REFERENCES

1. De Roda Husman AM, Koot M, Cornelissen M, et al. Association between CCR5 genotype and the clinical course of HIV-1 infection. Ann Intern Med. 1997;127:882-890.

2. van Rij RP, Broersen S, Goudsmit J, et al. The role of a stromal cell-derived factor-1 chemokine gene variant in the clinical course of HIV-1 infection. AIDS. 1998a;12:F85-F90.

3. van Rij RP, De Roda Husman AM, Brouwer M, et al. Role of CCR2 genotype in the clinical course of syncytium-inducing (SI) or non-SI human immunodeficiency virus type 1 infection and in the time to conversion to SI virus variants. J Infect Dis. 1998b;178:1806-1811.

4. Meyer L, Magierowska M, Hubert JB, et al., for the SEROCO Cohort, and van Rij R, Prins M, de Roda Husman AM, et al., for the Amsterdam Cohort Studies on AIDS. CC-chemokine receptor variants, SDF-1 polymorphism, and disease progression in 720 HIV-infected patients. AIDS. 1999;13:624-626.

5. Ioannidis JPA, Rosenberg PS, Goedert JJ, et al., for the International Meta-Analysis of HIV Host Genetics. Effects of CCR5-Δ32, CCR2-64I, and SDF-1 3′A alleles on HIV-1 disease progression: an international meta-analysis of individual-patient data. Ann Intern Med. 2001;135:782-795.

6. Winkler C, Modi W, Smith MW, et al. Genetic restriction of AIDS pathogenesis by an SDF-1 chemokine gene variant. Science. 1998;279:389-393.

7. Meyer L, Magierowska M, Hubert JB, et al. Early protective effect of CCR-5 Δ32 heterozygosity on HIV-1 disease progression: relationship with viral load. AIDS. 1997;11:F73-F78.

8. Taylor JMG, Wang Y, Ahdieh L, et al. Causal pathways for CCR5 genotype and HIV progression. J Acquir Immune Defic Syndr Hum Retrovirol. 2000;23:160-171.

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10. Geskus RB. On the inclusion of prevalent cases in HIV/AIDS natural history studies through a marker-based estimate of time since seroconversion. Stat Med. 2000;19:1753-1769.

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12. Agence française de securité sanitaire des produits de Santé. Annales du contrôle national de qualité, No. 17. 1999; Saint-Denis Cedex, France.

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14. Faucett CL, Thomas DC. Simultaneously modelling censored survival data and repeatedly measured covariates: a Gibbs sampling approach. Stat Med. 1996;15:1663-1685.

15. Spiegelhalter DJ, Thomas A, Best NG. WinBUGS version 1.2 User Manual. MRC Biostatistics Unit; 1999.

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Keywords:

CD4; viral load; natural history; risk factors; causal pathways

© 2005 Lippincott Williams & Wilkins, Inc.

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