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J Infect Dis. 2011 July 1; 204(1): 139–144.
PMCID: PMC3307159

Impact of Baseline HIV-1 Tropism on Viral Response and CD4 Cell Count Gains in HIV-Infected Patients Receiving First-line Antiretroviral Therapy


Background. Viral tropism influences the natural history of human immunodeficiency type 1 (HIV-1) disease: X4 viruses are associated with faster decreases in CD4 cell count. There is scarce information about the influence of viral tropism on treatment outcomes.

Methods. Baseline plasma samples from patients recruited to the ArTEN (Atazanavir/ritnoavir vs. Nevirapine on a background of Tenofovir and Emtricitabine) trial were retrospectively tested for HIV-1 tropism using the genotypic tool geno2phenoFPR=5.75%. ArTEN compared nevirapine with atazanavir-ritonavir, both along with tenofovir-emtricitabine, in drug-naïve patients.

Results. Of 569 ArTEN patients, 428 completed 48 weeks of therapy; 282 of these received nevirapine and 146 of these received atazanavir-ritonavir. Overall, non-B subtypes of HIV-1 were recognized in 96 patients (22%) and X4 viruses were detected in 55 patients (14%). At baseline, patients with X4 viruses had higher plasma HIV RNA levels (5.4 vs 5.2 log copies/mL, respectively; P = .044) and lower CD4 cell counts (145 vs 188 cells/μL, respectively; P < .001) than those with R5 strains. At week 48, virologic responses were lower in patients with X4 viruses than in patients with R5 viruses (77% vs 92%, respectively; P = .009). Multivariate analysis confirmed HIV-1 tropism as an independent predictor of virologic response at week 24 (P = .012). This association was extended to week 48 (P = .007) in clade B viruses. Conversely, CD4 cell count recovery was not influenced by baseline HIV-1 tropism.

Conclusions. HIV-1 tropism is an independent predictor of virologic response to first-line antiretroviral therapy. In contrast, it does not seem to influence CD4 cell count recovery.

Clinical Trials Registration. NCT00389207.

Human immunodeficiency virus type 1 (HIV-1) tropism for the chemokine receptors CCR5 and CXCR4 has been shown to be associated to disease progression [13]. Viruses that exclusively use the CCR5 receptor to enter the target cells (R5 viruses) are generally predominant at early stages of HIV-1 infection, whereas the emergence of CXCR4-using viruses (X4 viruses) generally occurs at later stages [46]. The presence of X4 viruses is consistently associated with low CD4+ T-cell counts and accelerated disease progression, although it is still unclear whether it is cause or consequence of disease progression [6, 7].

In the absence of antiretroviral therapy, X4 viruses are associated with faster CD4 cell count decreases regardless of baseline CD4 cell count or viral load [3, 4]. However, in the presence of antiretroviral therapy, results are controversial. Although some authors have reported that patients harboring X4 viruses display poorer immunological recovery despite comparable viral load suppression than those patients exclusively infected with R5 viruses [8, 9], other authors have reported that CD4 cell count increases and virologic response rates to antiretroviral therapy occur regardless viral tropism [4]. Disagreements might be due to the heterogeneity of study populations, mainly as result of differences in baseline CD4 cell count, viral load, antiretrovirals used, and/or time of follow-up after beginning therapy. In this regard, a prospective clinical trial comparing antiretroviral regimens could be an excellent opportunity to assess retrospectively the impact of HIV-1 tropism on treatment outcomes.


The ArTEN study was a prospective, randomized, open-label, noninferiority trial that compared nevirapine (200 mg BID or 400 mg QD) to atazanavir-ritonavir (300 or 100 mg QD), each combined with fixed-dose tenofovir (300 mg) and emtricitabine (200 mg) QD in 569 antiretroviral-naïve patients with HIV-1 infection ( ID NCT00389207). At week 48, a similar proportion of patients achieved and maintained plasma HIV RNA levels of <50 copies/mL in both the nevirapine and atazanavir-ritonavir arms (66.8% and 65.3%, respectively). The mean changes in CD4 cell counts were also similar between treatment arms (170 and 186 cells/μL, respectively) [10].

For the purpose of our study, which was designed to examine the impact of baseline viral tropism on treatment outcomes, all patients enrolled in the ArTEN trial who discontinued therapy or were lost to follow-up before completing 48 weeks of therapy were excluded. In contrast, patients who stopped therapy due to virological failure according to the trial definition were included in further analyses. All baseline plasma specimens available from patients fulfilling the entry criteria were genotypically analyzed in order to determine HIV-1 tropism. The V3 region was amplified once and sequenced as described elsewhere [11]. Then V3 sequences were analyzed using Seqscape software (version 2.5; Applied Biosystems), considering nucleotide mixtures when the second highest peak in the electropherogram was >25%. Sequences with ≥9 nucleotide mixtures were excluded from the analysis.

HIV-1 tropism was inferred using geno2phenoFPR:5.75% (g2pFPR:5.75%), which is freely available on the Web (; accessed 11 April 2011), since this genotypic tool has demonstrated accurate prediction of virologic responses to the CCR5 antagonist maraviroc in antiretroviral-naïve patients, compared with the results of a phenotypic tropism test performed as a reference [12].

Statistical Analyses

All results are expressed as percentages or medians with interquartile ranges (IQRs). Univariate analyses (χ2 and Mann-Whitney tests) and multivariate analyses (linear and logistic regressions) were performed to find variables associated with virologic and immunological outcomes. Parameters included in the multivariate analyses were sex, age, hepatitis C virus (HCV) coinfection, HIV subtype, treatment arm, viral tropism, baseline CD4 cell count, and baseline viral load. The covariates were selected by backward selection, which excluded covariates with a P value of >.1, except viral tropism, which appears in all analyses. The P value displayed is derived from the last backward selection model, which included only covariates with significant or nearly significant P values (P < .1). When viral tropism was not present in this last analysis, the P value displayed derived from a model including all listed covariates. All statistical tests were 2-tailed, and only P values of <.05 were considered to be significant. All analyses were performed using SPSS software (version 17.0; SPSS).


A total of 428 patients enrolled in ArTEN fulfilled the study criteria and had stored baseline plasma samples for subsequent analyses. The samples were shipped to the Infectious Diseases Department (Laboratory of Virology) at Hospital Carlos III in Madrid, Spain, where HIV-1 tropism determination was performed. Table 1 summarizes the main characteristics of the study population. Of note, no significant differences in baseline characteristics were observed between tested and nontested patients (data not shown). A total of 96 (22.4%) of the examined individuals were infected with non-B subtypes, and 45 (10.5%) were coinfected with HCV. The distribution of non-B subtypes was as follows: A1, 13 patients; C, 21 patients; D, 1 patient; F1, 15 patients; G, 3 patients; H, 1 patient; K, 1 patient; CRF01_AE, 13 patients; CRF02_AG, 18 patients; CRF06_CPX, 3 patients; CRF12_BF, 1 patient; and CRF14_BG, 6 patients.

Table 1.
Main Baseline Characteristics of the Study Population According to Treatment Arm and Baseline Viral Tropism

The V3 region was successfully amplified in 400 (93.5%) of 428 samples (GenBank accession numbers JF283780–JF284170). Nine of them were excluded from further analysis because they harbored >8 nucleotide mixtures [13]. A higher rate of amplification was found in samples belonging to patients infected with the B subtype than in those from patients with non-B subtypes (94.9% vs 79.2%, respectively; P < .001). The proportion of X4 viruses was 14.1% (55 of 391 samples), without significant differences between treatment arms or HIV subtypes.

At baseline, patients enrolled in the atazanavir-ritonavir and nevirapine arms displayed similar plasma HIV RNA levels (median HIV RNA level, 5.19 [IQR,4.71–5.61] vs 5.17 [IQR, 4.73–5.59] log copies/mL, respectively) and CD4 cell counts (median CD4 cell count, 180 [IQR, 118–251] vs 184 [IQR, 126–258] cells/μL, respectively). However, patients with X4 viruses had higher plasma HIV RNA levels (median HIV RNA level, 5.38 [IQR, 5.03–5.69] vs 5.16 [IQR, 4.67–5.59] log copies/mL, respectively; P = .044) and lower CD4 cell counts (median CD4 cell count, 145 [IQR, 62–200] vs 188 [IQR, 134–260] cells/μL, respectively; P < .001) than patients harboring R5 viruses (Table 1).

Table 2 summarizes the results of the univariate analysis for viro-immunological outcomes in the study population following 1 year of antiretroviral treatment. A similar proportion of patients achieved viral load suppression (defined as HIV RNA level of <50 copies/mL) in the atazanavir-ritonavir and nevirapine arms, both at week 24 (77.2% vs 82.1%, respectively; P = .384) and at week 48 (88.5% vs 92.0%, respectively; P = .434). Interestingly, patients infected with X4 viruses achieved plasma HIV RNA levels of <50 copies/mL less frequently than patients infected with R5 variants, either at week 24 (60.9% vs 83.2%, respectively; P = .001) or at week 48 (76.9% vs 91.6%, respectively; P = .009). This association between baseline HIV-1 tropism and virologic outcome remained significant only for week 24 in the multivariate analyses (P = .012), although it approached significance at week 48 (P = .061).

Table 2.
Univariate Analysis of Virologic and Immunological Outcomes of Participants in ArTEN According to Baseline Viral Tropism and Treatment Arm

The extent of CD4 cell count increases in this subset of patients enrolled in the ArTEN study was similar at week 24 in both treatment arms. However, patients receiving atazanavir-ritonavir showed a greater CD4 cell count increase at week 48 than those treated with nevirapine (median CD4 cell count, 180 [IQR, 99–251] vs 152 [IQR, 78–230] cells/μL, respectively; P = .037). No influence of HIV-1 tropism on the extent of CD4 cell count increase was noticed, at neither weeks 24 nor 48 (Table 3). Multivariate analyses confirmed baseline viral load (P = .001) and CD4 cell counts (P = .024) as predictors of CD4 cell count increases at week 24 of therapy. At week 48, the independent variables significantly associated with CD4 cell count increases were baseline viral load (P < .001) and treatment arm (P = .012).

Table 3.
Multivariate Analysis of Predictors of Virologic Response and CD4 Cell Count Recovery of Participants in ArTEN

Analysis of the immunological response limited to patients who achieved undetectable viral load at week 24 (n = 275) or week 48 (n = 282) showed no significant differences in the univariate comparisons between patients infected with X4 viruses and those with R5 viruses, neither at week 24 (median CD4 cell count, 116 [IQR, 51–142] vs 116 [IQR, 59–192] cells/μL, respectively; P = .644) nor at week 48 (median CD4 cell count, 188 [IQR, 120–242] vs 157 [IQR, 82–241] cells/μL, respectively; P = .235). The multivariate analysis assessing the predictors of immunological response identified the same significant variables as those in the analysis of the whole study population, either at week 24 (baseline viral load and CD4 cell count) or at week 48 (baseline viral load and treatment arm), with only minor differences in P values. No influence of viral tropism was observed on CD4 cell count recovery under antiretroviral therapy.

Because of the lower accuracy of genotypic tools to correctly assign viral tropism in HIV-1 non-B subtypes [14, 15], multivariate analyses were additionally performed for the subset of patients infected with subtype B (Table 4). Although the new analyses confirmed the lack of impact of HIV tropism on CD4 cell count recovery, it confirmed HIV tropism as an independent predictor of virologic response at both weeks 24 and 48, along with baseline viral load.

Table 4.
Multivariate Analysis of Predictors of Virologic Response and CD4 Cell Count Recovery in Patients Infected With HIV-1 Subtype B (n = 315) in ArTEN


This study evaluated the impact of HIV-1 tropism on viro-inmunological outcomes in HIV-infected patients who initiated a first-line antiretroviral regimen based on atazanavir-ritonavir or nevirapine, in all cases along with a backbone of tenofovir-emtricitabine. All participants were part of ArTEN, a prospective, randomized trial, the results of which have already been reported elsewhere [10]. Interestingly, our analysis showed a lower rate of virologic response in patients harboring X4 variants than in those infected with R5 viruses at week 24, which was extended to week 48 in the large subset of patients infected with HIV-1 subtype B. In contrast, baseline viral tropism did not impact on the extent of CD4 cell count recovery on antiretroviral therapy, neither at week 24 nor at week 48.

As we did in our study, Waters et al [4] observed a similar CD4 cell count recovery in 289 antiretroviral-naïve patients at 24 and 48 weeks after beginning first line-therapy, regardless of viral tropism. However, in contrast with our results, they found similar rates of virological suppression in participants harboring R5 and X4 variants. However, the nonrandomized nature of the study by Waters et al [4], in which prescription of antiretroviral medications was not controlled, could explain the discordance. Moreover, nearly twice the number of patients with X4 viruses received protease inhibitors in comparison with those with R5 viruses (28.3% vs 14.2% of patients, respectively). The concordant results in both studies with respect to the influence of viral tropism on CD4 cell count increase are reassuring and strongly suggest that the potency of antiretroviral therapy in drug-naïve patients might overcome any impact of viral tropism on the extent of CD4 cell count recovery.

Prior studies highlighted the finding that CD4 cell count increases might be lower in patients receiving antiretroviral therapy when harboring X4 versus R5 viruses. In the study by Weiser et al [8], patients harboring X4 variants experienced a lower median CD4 cell count increase than those with R5 variants (40 vs 82 cells/μL, respectively; P = .012), despite similar rates of virologic response at week 24 of treatment. Similar results were obtained by Brumme et al [9], who reported that patients infected with X4 viruses displayed poorer immunological response and earlier mortality following initiation of antiretroviral therapy, despite comparable viral load suppression rates, than patients with R5 variants. Again, differences in study design, methodology, and study population might explain the discordance with our results. Moreover, viral suppression in these 2 studies was defined as plasma HIV RNA level of <500 copies/mL instead of <50 copies/mL, as used in the ArTEN trial [10], which might result in a reduced sensitivity for detecting early or low-level virological failures. In addition, the methods used in those studies for the assignments of viral tropism (ie, the 11/25 rule, heteroduplex assay, or Trofile assay) differ from the g2pFPR:5.75% method we used in our study, which recently has been validated clinically. With different sensitivity rates for detecting X4 variants as well as criteria for considering viral load failure, any comparison with our study must be very cautious.

Viral tropism in our study was determined using the genotypic tool g2pFPR:5.75%, which recently has demonstrated a similar performance to the phenotypic assay Trofile, the most widely used tropism method. A recent post hoc reanalysis of the Maraviroc vs. Optimized Therapy in Viremic Antiretroviral Treatment-Experienced Patients (MOTIVATE) and A4001029 trials has compared the accuracy of single versus triplicate amplification for V3 genotyping to predict maraviroc response. This analysis showed minor differences (<3%) in the proportion of maraviroc responders classified as R5 when the triplicate instead of the single amplification protocol was performed using geno2phenoFPR=5.75% [16, 17]. Single instead of triplicate V3 genotyping was performed in our study.

It should be noted that 22.4% of our study population was infected with non-B subtypes, and the accuracy of genotypic tools to assess viral tropism is known to be lower with non-B subtypes than with clade B variants, especially for CRF02_AG [14, 15]. In agreement with these findings, when we limited our analyses to participants infected with clade B viruses, the strength of the impact of viral tropism on virological response was more robust than in the whole study population. Finally, a clear advantage of our study compared with previous studies examining the impact of viral tropism on treatment outcomes is that we conducted our exploration in the context of a prospective, randomized clinical trial, avoiding many of the biases of other studies.

In summary, in antiretroviral-naïve patients beginning antiretroviral therapy, baseline HIV-1 tropism seems to be an independent predictor of virologic response. This observation may have important clinical implications for the monitoring of antiretroviral therapy and interpretation of comparative trials. Moreover, in addition to viral load, CD4 cell count, and drug resistance testing, it may be worthwhile to perform baseline viral tropism testing before beginning any antiretroviral regimen.


This work was supported by Fundación Investigación y Educación en SIDA; the NEAT (European AIDS Treatment Network; grant number: LSHM-CT-2006-037570) project; the European Community's Seventh Framework Programme (FP7/2007-2013) under the project “Collaborative HIV and Anti-HIV Drug Resistance Network (CHAIN)” — grant agreement n° 223131; Red de Investigación en SIDA (grant number ISCIII-RETIC-RD06/006); Fondo de Investigación Sanitaria (grant numbers CP08/00214, CP0610284, PI06/01826, and FI09/00868 to E.S.); and Boehringer Ingelheim.


This work was presented orally at the 10th International Congress on HIV Drug Therapy, which was held in Glasgow, United Kingdom, on 7–11 November 2010 (abstract O124).

V. S. and E. P. designed the study; E. S. and M. G. performed the laboratory work; E. S., L. M. -C., and E. P. performed the data analysis; E. S., E. P., and V. S. wrote the manuscript; and H. G., V. C., M. D., and W. K. provided the samples, clinical data, and critical reading of the manuscript.


1. Poveda E, Briz V, Quiñones-Mateu M, Soriano V. HIV tropism: diagnostic tools and implications for diseases progression and treatment with entry inhibitors. AIDS. 2006;20:1359–67. [PubMed]
2. Daar E, Kesler K, Petropoulos C, et al. Baseline HIV type 1 coreceptor tropism predicts disease progression. Clin Infect Dis. 2007;45:643–9. [PubMed]
3. Goetz M, Leduc R, Kostman J, et al. Relationship between HIV coreceptor tropism and disease progression in persons with untreated chronic HIV infection. J Acquir Immune Defic Syndr. 2009;50:259–66. [PMC free article] [PubMed]
4. Waters L, Mandalia S, Randell P, Wildfire A, Gazzard B, Moyle G. The impact of HIV tropism on decreases in CD4 cell count, clinical progression, and subsequent response to a first antiretroviral therapy regimen. Clin Infect Dis. 2008;46:1617–23. [PubMed]
5. Schuitemaker H, Koot M, Kootstra N, et al. Biological phenotype of HIV type 1 clones at different stages of infection: progression of disease is associated with a shift from mono-cytotropic to T-cell-tropic virus population. J Virol. 1992;66:1354–60. [PMC free article] [PubMed]
6. Hunt P, Harrigan R, Huang W, et al. Prevalence of CXCR4 tropism among antiretroviral-treated HIV-1 infected patients with detectable viremia. J Infect Dis. 2006;194:926–30. [PubMed]
7. Briz V, Poveda E, González MM, Martín-Carbonero L, González-González R, Soriano V. Impact of antiretroviral therapy on viral tropism in HIV-infected patients followed longitudinally for over 5 years. J Antimicrob Chemother. 2008;61:405–10. [PubMed]
8. Weiser B, Philpott S, Klimkait T, et al. HIV-1 coreceptor usage and CXCR4-specific viral load predict clinical disease progression during combination antiretroviral therapy. AIDS. 2008;22:469–79. [PubMed]
9. Brumme Z, Dong W, Yip B, et al. Clinical and immunological impact of HIV envelope V3 sequence variation after starting initial triple antiretroviral therapy. AIDS. 2004;18:F1–9. [PubMed]
10. Soriano V, Arastéh K, Migrone H, et al. Nevirapine versus atazanavir/ritonavir, each combined with tenofovir/emtricitabine, in antiretroviral-naïve HIV-1 patients: week 48 ArTEN results. Antivir Ther. [In press] [PubMed]
11. Poveda E, Seclén E, González MM, et al. Design and validation of new genotypic tools for easy and reliable estimation of HIV tropism before using CCR5 antagonists. J Antimicrob Chemother. 2009;63:1006–10. [PubMed]
12. McGovern R, Dong W, Zhong X, et al. Program and abstracts of 17th Conference on Retroviruses and Opportunistic Infections. San Francisco, CA: 2010. Population-based sequencing of the V3-loop is comparable to the enhanced sensitivity Trofile assay in predicting virologic response to maraviroc of treatment-naïve patients in the MERIT trial [abstract 92] 100.
13. Poveda E, Alcamí J, Paredes R, et al. Genotypic determination of HIV tropism—clinical and methodological recommendations to guide the therapeutic use of CCR5 antagonists. AIDS Rev. 2010;12:135–48. [PubMed]
14. Seclén E, Garrido C, González MM, et al. High sensitivity of specific genotypic tools for detection of X4 variants in antiretroviral-experienced patients suitable to be treated with CCR5 antagonists. J Antimicrob Chemother. 2010;65:1486–92. [PubMed]
15. Raymond S, Delobel P, Mavigner M, et al. Genotypic prediction of human immunodeficiency virus type 1 CRF02-AG tropism. J Clin Microbiol. 2009;47:2292–4. [PMC free article] [PubMed]
16. Swenson L, Knapp D, Harrigan PR. Program and abstracts of the 10th International Congress on Drug Therapy in HIV Infection. Glasgow, UK: 2010. Calibration and accuracy of the geno2pheno co-receptor algorithm for predicting HIV tropism for single and triplicate measurements of V3 genotype [abstract O122] 4.
17. McGovern RA, Thielen A, Mo T, et al. Population-based V3 genotypic tropism assay: a retrospective analysis using screening samples from the A4001029 and MOTIVATE studies. AIDS. 2010;24:2517–25. [PubMed]

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