HIV-1 group M viruses account for most infections internationally1 and based on genetic similarity are classified into nine different subtypes (A–D, F–H, J, and K). Variants, however, diverge 25%–35% in envelope (env), important for viral attachment to target cells during infection, and 10%–15% in the pol region, under selection pressure from common antiretrovirals (ARVs).1–3 Subtypes A and F are further divided into sub–subtypes A1, A2 and F1, F2, respectively. Although subtypes B and D are as similar as sub–subtypes, for historical reasons, they maintain separate subtype classification.1 Formerly, HIV-1 genotype assignments were based on gene fragments. Later, when gag and pol regions were genotyped, subtype E env viruses were found to include subtype A sections in the other regions of the viral genome, resulting in reclassification of subtype E as an A/E recombinant, CRF01_AE.4 Circulating recombinant forms (CRFs) result from recombination between HIV-1 genotypes within a dually infected person,1 but no complete subtype E genome has been found, leaving CRF01_AE’s recombinant status inconclusive.
Subtype B is the genotype historically common in developed countries, and nucleotide substitutions (mutations or naturally occurring polymorphisms), insertions, and deletions in the HIV-1 genome are made in reference to the earliest characterized subtype B wild-type strain, HXB2.5,6 ARVs, commonly designed on subtype B, are classified based on where the HIV-1 life cycle is interrupted. Synergistic combinations of ARVs, known as highly active antiretroviral therapy (HAART), suppress viral load (VL) thereby reducing the risk of opportunistic infections and death.7,8 However, natural drug-resistant polymorphisms may exist in patients pretherapy with higher frequencies being found in non–B subtypes.9
In vitro studies suggest differences in viral transmission characteristics between genotypes, and viral heterogeneity may have implications for disease progression. HIV-1 infection depends on the interaction of env gp120 with the target cell CD4 receptor,10 and this interaction promotes binding to a coreceptor, viral tropism being determined by the env amino acid sequence and structure. Most genotypes use R5 coreceptors during transmission and in early stages of infection with X4 using syncytium-inducing variants emerging later.11,12 Subtype C studies generally report a lack of coreceptor switching from R5 to X4, possibly affecting transmission,11 and dual tropic virus (X4/R5) found in other genotypes have not been reported in subtype D viruses.13 Where subtypes A and D cocirculate, more rapid disease progression has been found for subtype D compared with subtype A,14 although the literature suggests that subtype A infections are outpacing subtype D.15 A retrospective analysis found faster rates of CD4 decline and virologic failure in subtype D infection compared with subtypes A, B, or C, suggesting differences in HIV-1 genotypes with respect to patient response to therapy.16
In Asia, predominant genotypes are subtypes B and C, CRF01_AE, and their recombinants with country-specific epidemics featuring different group M genotypes. During 2000–2007, in India, approximately 97% of infections were from subtype C, whereas 4 Mekong River countries (Cambodia, Myanmar, Thailand, and Vietnam) reported almost 80% of infections were from CRF01_AE.17 Subtype B infections are primarily reported in Japan and the Republic of Korea (South Korea).17–20 In China’s Special Administrative Region of Hong Kong and in Malaysia, subtype B and CRF01_AE cocirculate,17,21,22 whereas in Taiwan, subtype B, CRF01_AE, and CRF07_BC have been found.23,24 Epidemic distributions differ depending on the subpopulations at risk with subtype B frequently found in injecting drug users and men who have sex with men (MSM), whereas CRF01_AE is more commonly found in heterosexual populations.25
Previously, we reported that mainly CRF01_AE and subtype B were infecting patients from Thailand, Hong Kong, and Malaysia.26 The objectives were to determine whether treatment responses (clinical deterioration, immunologic response, or virologic suppression) differed between these genotypes in treatment-naive patients initiating a first-line HAART regimen.
Patients providing data were enrolled in either the TREAT Asia Studies to Evaluate Resistance monitoring protocol (TASER-M)26 or the TREAT Asia HIV Observational Database (TAHOD).27 Data for these longitudinal cohort studies are collected prospectively. Most TASER-M sites are selected from TAHOD sites, which consist of government-based or university-based clinics and hospitals or private clinics, situated in major cities and other urban areas. Pretreatment drug resistance prevalence for the TASER-M cohort has been published elsewhere.28 Clinical interventions and testing procedures were implemented according to local practices, excepting HIV-1 genotyping in TASER-M that was collected under the protocol.
Treatment-naive patients were eligible for inclusion if they were initiating first-line HAART regimens and had HIV-1 genotype available. Eligible patients enrolled at March 2010 from 11 clinic locations in Thailand (4), Hong Kong (China) (2), Malaysia (2), Japan (1), Taiwan (1), and South Korea (1) provided prospective and retrospective data (TAHOD) for analysis. Patient covariates included demographics [age at entry to cohort, gender, HIV source exposure), hepatitis B virus and hepatitis C virus (HCV) coinfections, and baseline indices of illness severity (CD4 lymphocyte count, HIV-1 RNA, and Centers for Disease Control and Prevention (CDC) classification29]. The most severe pretherapy CDC category recorded was used as the baseline clinical status. Hepatitis B virus (HCV)–positive status was defined as having any HBsAg (HCV-Ab)-positive result before enrollment. HIV-1 genotypes were determined by Virco BVBA (Beerse, Belgium). For assessing associations between patient covariates and genotype, patients were restricted to those infected with subtype B or CRF01_AE whose sequences passed the quality control procedures of Virco. Due to small numbers, patients reporting injecting drug use exposure, receipt of blood products, perinatal transmission, or unknown exposure were collapsed into an “other” transmission category.
Patients were required to have at least 1 clinic visit or test procedure recorded post therapy initiation for inclusion. Clinical deterioration was determined as a new diagnosis of a CDC B or C (AIDS-defining) illness or death from any cause. Patient follow-up commenced at HAART initiation and ended at earliest clinical deterioration endpoint or censored at the most recent contact. Surrogate endpoints were plasma HIV-1 RNA viral suppression (<400 copies/mL) and change in CD4 cell count from baseline at 12 months post-HAART. For calculating the 12-month immunologic change, the surrogate marker value closest to the 12-month target date was chosen from windows of 9–15 months, and the CD4 count sampled within the 91 days prior and closest to therapy initiation was selected as the baseline value.
For eligible patients, baseline comparisons by country (χ2, Fisher exact or Cochrane–Armitage test for trend) were performed, as appropriate. Determinants of change in CD4 cell count and 12-month HIV-1 RNA suppression were assessed via linear regression and logistic regression, respectively. Proportional hazards models were used to evaluate predictors of time to progression to a new clinical deterioration endpoint. Analyses were based on an intention-to-continue treatment approach in that we did not take into account regimen changes or interruptions post therapy. Forward stepwise techniques were used to determine the best fitting models. Binary covariate P values and multicategorical parameter P values (from tests for trend/heterogeneity) of <0.1, in univariate analyses, were considered for inclusion in multivariate patient covariate models. Final models consisted of patient covariates remaining significant at the 0.05 level. Then, because of our a priori interest in the effect of HIV-1 genotype on outcomes, we assessed the effect of HIV-1 genotype, adjusting for any significant patient covariates, and tested for interactions between genotype and cohort. Analyses were performed using SAS version 9.2 (SAS Institute Inc, Cary, NC) and STATA version 10 (STATA Corp, College Station, TX).
A total of 1105 ARV-naive patients had HIV-1 genotype information available [TASER-M: n = 922 (83.4%); Thailand: n = 675 (73.2%); Hong Kong: n = 160 (17.4%); Malaysia: n = 87 (9.4%); TAHOD: n = 183 (16.6%); Japan: n = 65 (35.5%); Hong Kong: n = 49 (26.8%); Taiwan: n = 43 (23.5%); South Korea: n = 15 (8.2%)]. Differences in ethnicity reflected population distributions within countries contributing data [TASER-M; Thai: n = 675 (73.2%), Chinese: n = 177 (19.2%), Malay: n = 37 (4.0%), Indian: n = 11 (1.2%), white: n = 5 (0.5%); and TAHOD; Thai: n = 14 (7.7%), Chinese: n = 88 (48.1%), Japanese: n = 65 (35.5%), Korean: n = 15 (8.2%), white: n = 1 (0.5%)].
Years of enrollment differed as a function of cohort recruitment, and 80% of TAHOD patients were enrolled before opening of the TASER-M cohort. Patients initiated therapy from 2003 to 2010 (TASER-M: 2007–2010; TAHOD: 2003–2010), and significant differences between cohorts were noted for covariates as shown in Table 1. All Table 1 covariates were evaluated for significance in endpoint analyses.
Most first-line regimens included lamivudine (3TC) as a nucleoside/nucleotide reverse transcriptase inhibitor backbone component [n = 1013 (91.7%)]. Regimens for the cohorts differed in the second nucleoside/nucleotide reverse transcriptase inhibitor component [TASER-M—stavudine: n = 479 (52.0%), zidovudine (AZT): n = 221 (24.0%), abacavir: n = 67 (7.3%); TAHOD—stavudine: n = 37 (20.2%), zidovudine: n = 84 (45.9%), abacavir: n = 26 (14.2%); P < 0.001]. Most regimens were based on nonnucleoside reverse transcriptase inhibitors with more TAHOD patients being prescribed efavirenz (EFV) and TASER-M patients’ regimens including higher proportions of nevirapine (NVP) [TASER—NVP: n = 484 (58.9%), EFV: n = 338 (41.1%); TAHOD—EFV: n = 77 (82.8%), NVP: n = 16 (17.2%); P < 0.001]. Of protease inhibitor regimens, most included ritonavir-boosted atazanavir (ATZ) or lopinavir (LPV), proportions of which marginally differed between cohorts [TASER-M—ATZ/r: n = 28 (38.9%), LPV/r: n = 44 (61.1%); TAHOD—ATZ/r: n = 17 (22.7%), LPV/r: n = 58 (77.3%); P = 0.048].
In East Asia, there is higher odds of CRF01_AE infecting heterosexual populations, and subtype B is more frequently found in MSM. We found differences in genotype proportions consistent with patient-reported HIV source exposures (Table 1) [TASER-M—CRF01_AE: n = 740 (86.8%), subtype B: n = 113 (13.2%); TAHOD—CRF01_AE: n = 38 (20.8%), subtype B: n = 145 (79.2)%; P < 0.001]. In TASER-M, both HIV-1 pol protease (PR) and reverse transcriptase (RT) genotypes are recorded and 59 (6.4%) of TASER-M patients were infected with discordant PR and RT genotypes, reflecting possible dual infection and/or recombination. Of discordant genotypes, 28 (47.5%) included subtype B components (assessed as including subtype B, CRF08_BC, CRF08_BC or CRF15_01B) and 23 (39.0%) were CRF01_AE recombinants (assessed including CRF01_AE or CRF15_01B). The remaining discordant genomes [n = 8 (13.6%)] included both B and AE components. Discordant genotypes and subtypes CRF02_AG (n = 1), CRF 07_BC (n = 3), C (n = 5), subtype D (n = 1) were excluded from further evaluation.
Progression to CDC B or CDC C (AIDS-Defining) Illness or Death
A total of 1036 patients (93.8%) infected with CRF01_AE or subtype B were eligible for inclusion in clinical deterioration analyses (Table 2) and contributed 1546.7 person-years of retrospective and prospective follow-up [median: 413 days, interquartile range (IQR): 169–672 days]. During this time, there were a total of 104 events (22 CDC B diagnoses, 63 AIDS diagnoses, and 19 deaths) giving an event rate of 6.7 per 100 person-years (95% confidence interval: 5.5 to 8.1). Clinical deterioration endpoints were recorded between 2003 and 2010 (TASER: n = 76, range: 2007–2010; TAHOD: n = 28, range: 2003–2009). Significant univariate associations were found with age group, baseline CD4 count, and HIV-1 RNA VL. After adjustment for Table 1 covariates, patients older than 40 years had higher risk of clinical deterioration (hazard ratio = 2.17; P = 0.008), whereas patients having baseline CD4 cell counts greater than 200 cells per microliter had lower risk of clinical deterioration (hazard ratio = 0.373; P < 0.003). A total of 450 (43.4%) patients contributing to the clinical deterioration analyses were also included in immunological and virological analyses.
Change in CD4 Cell Count at 12 Months After HAART
For immunologic analyses, 532 patients (48.1% of eligible) had CD4 counts available at baseline and at 12 months, with a median increase of 187.2 cells per microliter over the period (Table 3). To calculate the change in CD4 over the period, baseline CD4 was subtracted from the 12-month result. In unadjusted analyses, smaller increases in CD4 counts were associated with age older than 40 years, whereas larger improvements were associated with being infected with subtype B. Excluding patients with unknown baseline VL, compared with patients with less than 10,000 copies per milliliter, patients with higher VLs evidenced larger increases. These associations were maintained after adjustment for other covariates (age > 40 years, P = 0.002; subtype B; P = 0.024; HIV-1 RNA ≥ 10,000 copies/mL, P = 0.024). There was no interaction between HIV-1 genotype and cohort membership (change in CD4: TASER-M—median: 168 cells/μL, IQR: 100–252 cells/μL; TAHOD—median: 166 cells/μL, IQR: 101–250 cells/μL; interaction; P < 0.402). As shown in Table 3, 459 (86.3%) patients had greater than 10,000 copies per milliliter at study entry. Median CD4 count increases for these patients, in all age categories, were higher for subtype B–infected patients (age < 30 years; subtype B—median: 185 cells/μL, IQR: 138–289; CRF01_AE—median: 178.5 cells/μL, IQR: 120–276; age: 30–40 years; subtype B—median: 251 cells/μL, IQR: 165–299; CRF01_AE—median: 176 cells/μL, IQR: 99–250; age > 40 years; subtype B—median: 157.5 cells/μL, IQR: 101.5-217; CRF01_AE—median: 152.5 cells/μL, IQR: 76–218). Most patients infected with CRF01_AE came from Thailand [Thailand: n = 288 (75.0%), Hong Kong: n = 68 (17.7%), Malaysia: n = 27 (7.0%), Taiwan: n = 1 (0.3%)], whereas the majority of subtype B patients came from high-income economies [Hong Kong: n = 74 (50.0%), Taiwan: n = 32 (21.6%), Japan: n = 13 (8.8%) South Korea: n = 11(7.4%) vs. Thailand: n = 15 (10.1%), Malaysia: n = 3 (2.0%)]. TAHOD patients from Japan and South Korea were only infected with subtype B, but excluding these patients from analyses did not impact upon interpretations.
HIV RNA at 12 Months After HAART
Due to the heterogeneity of virology assays and associated dynamic ranges across sites, we defined the lower limit of detection as 400 copies per milliliter. Analyses included 530 patients (48.0% of eligible) who had an HIV-1 RNA result available at 12 months and 92.6% of patients were virologically suppressed below the lower limit of detection [TASER: n = 383 (94.3%), TAHOD n = 108 (87.1%)]. Multivariate analyses showed no associations between the patient characteristics shown in Table 1 and the virologic outcome.
Subtype B and CRF01_AE have been circulating in Asia for more than 10 years,30 and we report on the first evaluation of treatment responses in these genotypes in ARV-naive patients. Patients initiated therapy from 2003 to 2010, and findings from adjusted analyses demonstrated that patients infected with subtype B had increased immunological response to therapy, compared with CRF01_AE. A retrospective cross-sectional study of mainly treated patients also found lower immunologic response in CRF01_AE patients compared with subtype B.9 However, our finding in treatment-naive patients is uncomplicated by genomic variation attributable to drug selection pressures. A study from Singapore found increased CD4+ T-cell loss in predominantly Chinese males infected with CRF01_AE31 and, as mentioned previously, studies in other cohorts have reported differences in HIV-1 transmission and disease progression. Several in vitro studies have suggested structural reasons for these differences.
Patients older than 40 years had reduced immunologic response at 12 months, whereas baseline HIV-1 RNA greater than 10,000 copies per milliliter at study entry was predictive of larger CD4 counts increases, compared with patients with lower viral burdens. Older patients with low CD4 counts pretherapy had increased risk of clinical deterioration, consistent with the literature.32 Comparisons of virological suppression in other genotypes have yielded mixed results16,33 but no differences were found in virologic suppression post-HAART, and approximately 90% of patients achieved virologic suppression at 12 months post therapy.
Patients being followed under protocol at funded study sites or with HIV-1 genotype recorded in observational data suggest that site clinicians have diagnostic technologies available to guide patient treatment. Consequently, treatment outcomes for our patients may be better than those experienced in general clinic populations. Adherence information was not available, and limited follow-up for TASER-M patients may have contributed to our nonsignificant finding in relation to clinical deterioration. Country differences were not specifically controlled although cohort membership may serve as a surrogate for these effects. Separate PR and RT genotypes are not reported in TAHOD, and 6.4% of discordant genotypes was noted among the TASER-M patients. Therefore, a small proportion of TAHOD patients with discordant genotypes may have been misclassified.
ARVs are commonly designed on subtype B. If immunologic response in the year after HAART affects patient prognosis, our findings of a reduced response for patients infected with CRF01_AE may possibly translate to a higher burden on country health systems, for these patients than for their subtype-B–infected counterparts. Studies of longer duration in representative patient populations, including socioeconomic information and in vitro studies are required to investigate this hypothesis. Patients starting therapy with low CD4 counts have been infected for some time and are commencing therapy later than recommended by international guidelines.34,35 Late therapy initiation for patients from developing economies generally reflects resource issues. However, for patients from high-income Asian economies, this may be due to ignorance of HIV-positive status. Increased testing to alert of HIV infection, before CD4 counts decrease substantially, should be encouraged, particularly in high-risk groups.
Our finding of no differences in virologic response to treatment suggests that with appropriate diagnostic testing, all patients have opportunities to suppress circulating virus to nondetectable levels, thereby potentially increasing disease-free survival. In addition to being a welcome outcome for individual patients, levels of onward transmission are reduced in virologically suppressed individuals.36
The HIV pandemic is of increasing complexity and where genotypes cocirculate, individuals coinfected with multiple variants provide HIV-1 opportunities for recombination, augmenting viral diversity.37 We found discordant PR and RT genotypes in 6.4% of our patients, reflecting possible dual infection and/or recombination. Strategies such as serosorting, where same HIV status partners are sought for unprotected sex, have been reported in MSM, as have higher frequencies of multivariant transmission.38 Serosorting is not supported as a risk-reduction strategy and increases opportunities for recombination,39 further complicating vaccine initiatives that seek to target transmitted virus.
Assays which evaluate patient circulating viral sequence for the presence of drug-resistant mutations also determine the circulating viral genotype. Although phylogenetic investigations cannot determine the direction of HIV evolution, and, consequently the direction of transmission in humans,40 mechanisms to capture genotypes resulting from HIVDR testing at country-level may contribute to monitoring and quantification of HIV-1 diversity and genotypic proliferation in at-risk population networks.41 Genotyping sequencing is expensive, but there have been recent improvements in dried blood spot methodologies, a less expensive alternative for specimen collection.42 Increased availability of low-cost genotyping may contribute to local surveillance efforts.
In summary, our finding of reduced immunological response in CRF01_AE-infected patients, compared with subtype B, suggests that genotypic diversity impacts upon patient response to treatment. Evidence of dual infection and recombination in our patients may suggest a need for regional epidemic surveillance. Tracking of local variants may help to identify increasing incidence of HIV-1 genotypes in at-risk groups and contribute to monitoring HIV-1 diversity and proliferation in the region.
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TAHOD–TASER STUDY MEMBERS
A. Kamarulzaman (current Steering Committee Chairs), A. Kajindran, and L.Y. Ong, University Malaya Medical Center, Kuala Lumpur, Malaysia; C. K. C. Lee, R. David, and B LH Sim, Hospital Sungai Buloh, Kuala Lumpur, Malaysia; C. V. Mean, V. Saphonn, and K. Vohith, National Center for HIV/AIDS, Dermatology and STDs, Phnom Penh, Cambodia; E. Yunihastuti, Working Group on AIDS Faculty of Medicine, University of Indonesia/Ciptomangunkusumo Hospital, Jakarta, Indonesia; F. J. Zhang, H. X. Zhao, and N. Han, Beijing Ditan Hospital, Capital Medical University, Beijing, China; J. Y. Choi, S. H. Han, and J. M. Kim, Division of Infectious Diseases, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea; M. Mustafa and N. Nordin, Hospital Raja Perempuan Zainab II, Kota Bharu, Malaysia; N. Kumarasamy, S. Saghayam, and C. Ezhilarasi, Y. R. G. Centre for AIDS Research and Education, Chennai, India; O. T. Ng, A. Chua, L. S. Lee, and A. Loh, Tan Tock Seng Hospital, Singapore; P. C. K. Li (Current Steering Committee Chairs) and M. P. Lee, Queen Elizabeth Hospital and K. H. Wong, Integrated Treatment Centre, Hong Kong, China; P. Kantipong and P. Kambua, Chiang Rai Prachanukroh Hospital, Chiang Rai, Thailand; P. Phanuphak, K. Ruxrungtham, M. Khongphattanayothin, and S. Sirivichayakul, HIV-NAT/Thai Red Cross AIDS Research Centre, Bangkok, Thailand; R. Ditangco, E. Uy, and R. Bantique, Research Institute for Tropical Medicine, Manila, Philippines; R. Kantor, Brown University, Rhode Island; S. Oka, J. Tanuma, and T. Nishijima, National Center for Global Health and Medicine, Tokyo, Japan; S. Pujari, K. Joshi, and A. Makane, Institute of Infectious Diseases, Pune, India; S. Sungkanuparph, S. Kiertiburanakul (co-Chairs), L. Chumla, and N. Sanmeema, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand; T. P. Merati (co-Chairs), D. N. Wirawan, and F. Yuliana, Faculty of Medicine, Udayana University and Sanglah Hospital, Bali, Indonesia; T Sirisanthana, R Chaiwarith, W Kotarathititum, and J Praparattanapan, Research Institute for Health Sciences, Chiang Mai University, Chiang Mai, Thailand; T. T. Pham, D. D. Cuong, and H. L. Ha, Bach Mai Hospital, Hanoi, Vietnam; V. K. Nguyen, V. H. Bui, and T. T. Cao, National Hospital for Tropical Diseases, Hanoi, Vietnam; W. Ratanasuwan and R. Sriondee, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand; Y. M. A. Chen, W. W. Wong, Y. J. Chen, L. H. Kuo, and Y. T. Lin, Taipei Veterans General Hospital and AIDS Prevention and Research Centre, National Yang-Ming University, Taipei, Taiwan; A. H. Sohn, N. Durier, B. Petersen, and T. Singtoroj, TREAT Asia, amfAR—The Foundation for AIDS Research, Bangkok, Thailand; D. A. Cooper, M. G. Law, and A. Jiamsakul, The Kirby Institute, University of New South Wales, Sydney, Australia. Cited Here...