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JAIDS Journal of Acquired Immune Deficiency Syndromes:
doi: 10.1097/QAI.0b013e31824a0628
Critical Review: Clinical Science

Does Genetic Diversity of HIV-1 Non-B Subtypes Differentially Impact Disease Progression in Treatment-Naive HIV-1–Infected Individuals? A Systematic Review of Evidence: 1996–2010

Pant Pai, Nitika MD, MPH, PhD*; Shivkumar, Sushmita BA, BSc; Cajas, Jorge Martinez MD

Free Access
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Author Information

*Division of Infectious Diseases and Immunodeficiency Service, MUHC, Montreal, Quebec, Canada

Division of Clinical Epidemiology, McGill University, Montreal, Quebec, Canada

Infectious Diseases Division, Queen's University, Kingston, Ontario, Canada

Correspondence to: Dr Nitika Pant Pai, MD, MPH, PhD, Division of Clinical Epidemiology, V Building, Room V2.09, McGill University Health Centre, 687, Pine Avenue West, Montreal, QC, Canada H3A 1A1 (e-mail: nitika.pai@mcgill.ca).

Supported by a Knowledge Synthesis grant from the Canadian Institutes for Health Research (CIHR) CIHR KRS 102067; CIHR New investigator Award 2010; Deanne Nesbitt Award 2011 and CIHR Operating grant CIHR HBF 103210.

This research was also presented as the following: Shivkumar S, et al. “Differential impact on disease progression in treatment-naïve HIV-1 non-B subtype infected populations: a systematic review of global evidence” at the 20th Annual Canadian Conference on HIV/AIDS Research CAHR, 2011, Toronto, Canada.

The funders had no role in the collection, analyses of data, or preparation of the article.

The authors have no conflicts of interest to disclose.

Received September 26, 2011

Accepted January 6, 2012

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Abstract

Abstract: With 88% of HIV-1–infected individuals living in areas of high prevalence of non–B subtypes and with expanded global access to antiretroviral treatment (ART), studying disease progression amongst non–B subtypes gains relevance. Optimized clinical management is a possibility with knowledge of non–B subtype profiles at baseline, which is currently not possible due to lack of subtype-specific point-of-care assays. In a systematic review, we synthesized global evidence on differential disease progression amongst non–B subtypes in ART-naive individuals. Due to lack of consistent effect measures, we avoided pooling data and inferred patterns with respect to disease progression outcomes (ie, AIDS, Death, CD4, viral load changes). Subtypes C and D were more aggressive, followed by G, AE, and AG, and A being the least aggressive of all HIV-1 subtypes. Evidence of greater rates of disease progression in globally prevalent C and D subtypes highlight the importance of expanding early HIV detection, and determining subtype profile at baseline with CD4 staging to optimize the quality of ART delivery and care in global settings.

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INTRODUCTION

Of the 33 million individuals infected with HIV-1 worldwide, approximately 29 million (88%) reside in resource-limited settings such as Africa, Asia, and Eastern Europe, where non–B subtypes of the HIV-1 virus are prevalent.1 Subtype C, for instance, is most prevalent in Southern Africa and India, whereas subtype A is present in Eastern Europe, Central Asia, West Africa, East Africa, and Central Africa; and subtype D is prevalent in the Middle East, East Africa, and Central Africa.2 Globally, subtype C accounts for 50% of infections, whereas subtype A accounts for 12%, subtype B accounts for 10%, subtype D accounts for 3%, and subtype G accounts for 6% of infections. Combined subtypes F, H, J, and K account for less than 1% of infections, and the recombinant forms, CRF01_AE and CRF02_AG, account for 5% each.2

Although North America and Western Europe have historically had a majority of subtype B infections, different non–B subtypes continue to be introduced through travel and immigration.1 Historically, evidence on the disease management of these non–B subtypes has been limited and derived primarily from the better studied B subtype.3 However, with greater availability of antiretroviral therapy (ART) in these settings,4 there is a need to understand the nature of disease progression in non-B subtypes, translating to increased research in the field. It has been postulated that variations in non–B subtype–related disease progression may have implications for the timing of ART initiation, ART switches, and the life-long management of drug resistance.

Knowledge of differential disease progression in patients experiencing therapeutic failure is also important, in the context of limited availability, affordability, and sustainability of second-line and third-line optimized regimens in parts of the world.1 In developed countries, different non–B subtypes continue to be introduced through travel and immigration.1 Within this context, it is important to ascertain whether particular non–B subtypes are prone to result in faster immunologic deterioration than others, or whether known host, environment and subtype-related factors influence disease progression in different populations. Reports on the emergence of different drug resistance patterns in major non–B subtypes (ie, subtype C) have challenged the assumptions of similar response to ART in them.5 Knowledge about the relative virulence of HIV subtypes, infection with multiple subtypes, and host predisposition to the development of mutations is needed to optimize their clinical management. Differences in transmission and pathogenicity and impact of non–B diversity on progression require an integration of global evidence to better inform and direct policy changes worldwide.

Published narrative reviews and editorials have focused on HIV-1 genetic diversity and its impact on disease progression and drug resistance, but vary in scope and depth.1,3,6–8 They are also limited by insufficient detail with respect to hard and soft outcomes [ie, AIDS/death, AIDS-related or non–AIDS-related events, and surrogate measures CD4 and viral load (VL) changes].

In this systematic review, we exclusively focused on ART-naive individuals infected with non–B subtypes. We explored differences between the various non–B subtypes on disease progression with respect to the following outcomes, that is, AIDS/death, AIDS-related and non–AIDS-related events, if available, CD4 and VL changes over time.

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METHODS

Eleven electronic databases and conference archives were searched for studies published in the period between January 1996 and December 2010, with search limited to English language and studies in humans. Additionally, references from primary studies and review articles were searched and experts contacted for data.

Our search string included key words such as the following: Search #1: “HIV”[MeSH] OR “HIV-1”[MeSH] OR “HIV-1”[TI]Search #2: “non-b”[TIAB] OR “subtype*”[TI] OR “clade”[TI] OR “strain*”[TI] OR “variant*”[TI] OR “non-B subtype*”[TIAB]

The study selection methodology is shown in Figure 1.

Figure 1
Figure 1
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Criteria for exclusion are enumerated in Figure 1.

An all-inclusive search of all literature published on non–B subtypes yielded 10,000 citations. Of these, a few citations on genetic biochemical diversity and mutations were synthesized into separate reviews.9–12 A total of 3244 citations were deemed relevant for this review, although 1691 were excluded (Fig. 1). Of 1553 potentially relevant citations, 1514 were excluded (Fig. 1). Finally, a set of 39 full text articles and abstracts were available on disease progression, of which only 14 studies conducted in ART-naive populations were included. The remaining 25 were synthesized into another review in ART-experienced populations (in progress).

Criteria for inclusion included all study designs and studies that reported on disease progression. These included studies reporting hard (ie, time to development of AIDS and/or death, opportunistic infections) and soft (ie, change in CD4 cell counts and VL) endpoints/outcomes.

A prepiloted data abstraction form of the “Population Intervention Comparison Outcome” format was used. Variables included study author, site, country, year, patient population, eligibility criteria, sample size, comparison groups (subtypes), outcomes (ie, proportions) and measures of effect [ie, hazard ratio (HR), risk ratio (RR), odds ratio (OR)] with confidence intervals (CIs). Two reviewers (N.P.P. and S.S.) abstracted data, and a third reviewer (J.M.C.) was contacted to resolve disagreements.

Due to the lack of consistent measures of effect and use of variable subtypes as baseline comparators, a meta-analysis could not be performed. Further, measures of effect reported varied between studies, and so, funnel plots to examine publication bias could not be constructed.

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RESULTS

All 14 included studies were conducted in developing countries. Considerable heterogeneity in patient populations, settings and countries, sample sizes, durations of follow-up, with various reporting of outcomes was noted. Heterogeneity persisted in the reporting of comparisons (ie, multiple subtype comparisons, different baseline subtype as comparator), which was reflective of the prevalent subtypes at each study site. A majority of studies were cohorts. Sample sizes varied from 46 individuals to 2104 individuals and follow-up durations varied from 1 year to 13 years. Outcomes reported included soft surrogate markers such as VL and CD4 counts, and hard outcomes such as the risk of developing AIDS or mortality and opportunistic infections such as extrapulmonary cryptococcosis.13

For the purposes of reporting, we grouped studies into the following: (1) those that used subtype A as the baseline comparator, and, (2) those that used subtype B, or (3) other subtypes such as CRF02_AG as the baseline comparator.

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Subtype Comparisons With A as Baseline Comparator

Overall, 8 studies reported on variability in disease progression with subtype A as baseline, the results of which are reported14–21 in Table 1.

Table 1
Table 1
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A 2001 Ugandan study evaluated subtype D versus subtype A and reported the adjusted risk for AIDS or death as being higher in subtype D, [HR: 1.39, (95% CI: 0.66 to 2.94)]; with the risk of death alone [adjusted HR: 3.1, (95% CI: 0.89 to 10.74)], and the risk for reaching CD4 counts <200 cells per microliter in ART-naive patients higher for subtype D [adjusted HR: 1.28, (95% CI: 0.60 to 2.75)].17 This finding was corroborated by a later study, where the risk of death in subtype D versus subtype A was higher [RR: 1.29, (95% CI: 1.07 to 1.56)], and baseline CD4 counts were significantly lower (P = 0.001).16 The findings from these 2 studies were contrasted by findings from a Ugandan study that reported no difference in CD4 counts between subtypes D and A (P = 0.57), but found a statistically significant difference in VL—that is, higher VL in subtype D (P = 0.001).18 This could have been due to lack of adequate sample sizes or type of patients compared with baseline. The overall pattern suggested a greater risk of death or AIDS, and a higher VL, in subtype D compared with subtype A.

Evidence from 2 studies in Kenya that explored differences between subtype C, subtype D, and subtype A overlapped with that from Uganda. These studies found the highest rate of disease progression, advanced immune suppression, higher VL, and lowest CD4 counts (P = 0.01) in patients with subtype C and subtype D compared with subtype A.14 This finding was followed by a similar finding on mortality in a natural history study of female sex workers followed up for 10 years. In this study, 3 times the risk of mortality was reported for subtype D [adjusted HR: 2.7, (95% CI: 1, 7.2)], and 2 times the risk of mortality was reported for subtype C [adjusted HR: 1.8, (95% CI: 0.5, 6.3)] compared with subtype A.21 Overall, evidence from all these studies suggested a higher risk of mortality, disease progression, higher VL and lower CD4 counts for subtypes D and C compared with subtype A.

In another Ugandan study, CD4 declines were studied in individuals with subtype D, recombinant and multiple subtypes, and compared with baseline subtype A. In this study, infection with subtype A was associated with fewer individuals developing CD4 counts ≤250 cells per cubic millimeter (18.9%) and a significantly longer time to progress to a CD4 count ≤250 cells per cubic millimeter19 compared with those with subtype D (39.6%), recombinant subtypes (39.2%), or multiple subtypes (53.3%). This suggested that multiple recombinant and D subtypes, when untreated, could lead to faster disease progression based on CD4 counts alone. When hard outcomes such as AIDS were compared in these individuals, those infected with multiple subtypes faced significantly faster progression to AIDS than subtype A, with an HR of 4.40 (95% CI: 1.71 to 11.3); followed by subtype D [HR: 2.13, (95% CI: 1.10 to 4.11)] and those with recombinant subtypes [HR: 2.16, (95% CI: 1.05 to 4.45)].19 The risk of progression to death was higher for subtype D with an HR of 5.65 (95%CI: 1.37 to 23.4), for recombinant subtypes with an HR of 6.70 (95% CI: 1.56 to 28.8) and for multiple subtypes with an HR of 7.67 (95% CI: 1.27 to 46.3).19 Individuals infected with subtype A experienced significantly lower proportions of AIDS-associated deaths, compared with individuals infected with other subtypes.19 Recently, Kiwanuka et al19,20 characterized the CD4 cell decline in subjects infected with different subtypes of HIV-I. They found that compared with subtype A, the adjusted rate of CD4 cell decline was faster in subjects infected with subtype D [−73.7cells/μL per year (95% CI: −113.5 to −33.8)], with those infected by multiple subtypes [−63.9 cells/μL per year (95% CI: −132.3 to 4.4)], and also those infected by recombinant subtypes [−43.2cells/μL per year (95% CI: −90.2 to 3.8)].

From Senegal, in a study with a long follow-up period of 13 years, non–A subtypes (C, D, G) were 8 times more likely to develop AIDS [HR: 8.23, (95% CI: 1.7 to 39.8)] than subtype A.15 Subtype A, the predominant subtype (68%), demonstrated a reduced ability to induce disease and a lower probability (87%) of AIDS at 5 years postinfection compared with 30% for the pooled non–A subtypes.15

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Subtype Comparisons With Subtype B as Baseline Comparator

Results from studies that either used other baseline comparators or described disease progression in one subtype only are reported in Table 2. The studies below used historical data on B subtype for baseline comparison.

Table 2
Table 2
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In a study from Thailand, the median time from seroconversion to death in those infected with subtype E was reported as 7.8 years [interquartile range (IQR): 7–9.1]. Moreover, individuals with low CD4 counts (<200 cells/μL) were 11 times more likely to die than those with CD4 counts >500 cells/μL [adjusted HR for men: 10.9, (95% CI: 7 to 17.1); adjusted HR for women: 11.4, (95% CI: 5.2, 24.7)].25 Another Thai study, where the predominant infecting subtype was subtype E, found that 83.0% of mortality was attributed to AIDS. The median time for the following: (1) to AIDS-related death was 8.4 years (95% CI: 7.5 to 9.1) (2) to development of AIDS was 7.2 years (IQR: 6.6–8.0), (3) to a drop in CD4 cell count <200 cells per microliter was 6.5 years (IQR: 6.2–7.0), and (4) to reaching the World health Organization criteria for ART was 6.3 years (95% CI: 5.9 to 6.8).26 In another study in female sex workers from Thailand infected with subtype E, the median rate of CD4 decline (3.9 cells/muL/month), median time from infection to CD4 <200 cells per microliter (6.9 years), and time to 25% mortality (6.0 years) was found to be similar to those previously reported in ART-naive HIV-1 subtype B–infected populations in the United States.22

A study from India investigated individuals infected with subtype C and compared findings with subtype B from literature published in the United States.24 Authors reported a median VL increase of 8274 RNA copies per milliliter per year (IQR: −22,212 to 78,380), and a median CD4 decline of 120 cells per year (IQR: −501 to 234) over a 2-year period associated with subtype C. Although not statistically significant, the median trajectory of increasing VL here was greater than what had been reported in the literature concerning ART-naive HIV seroconverters infected with subtype B from the United States.24

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Subtype Comparisons With Subtype AG as Baseline Comparator

One hospital-based study in Cameroon that compared the recombinant CRF_02AG with other pooled non–CRF02_AG subtypes (A, B, C, D, F, G, H, J, CRF01_AE), reported no differences in survival [HR: 1.16 (95% CI: 0.76 to 1.78)] between them, and no differences in CD4 decline or time to first AIDS-defining illness between the 2 groups.23 This was due to pooling of other non–AG subtypes in one group and inadequate sample sizes that made patterns indistinguishable.

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DISCUSSION

This review explored the differences in disease progression among ART-naive patients infected with different HIV-1 subtypes. Specifically, we focused on death, progression to AIDS, CD4, and VL changes between non–B subtypes and compared individuals infected with non–B subtypes. Comparative patterns were deduced from the reported evidence.

Subtypes C and D were found to be more aggressive, followed by G, AE, and AG, and A being the least aggressive of all HIV-1 subtypes. Evidence of greater rates of disease progression in globally prevalent C and D subtypes highlight the importance of expanding early HIV detection, subtype determinations at baseline, CD4 and VL staging at baseline with preferably point-of-care assays, use of subtype specific mutational assays to allow for optimized ART initiation protocol for HIV-1 non-B subtype predominant global settings.

Heterogeneity in reporting of outcome measures precluded pooling of results to obtain a summary estimate stratified by subtypes, and lack of consistent reporting of measures of effect prevented us from pooling data. However, we were able to infer patterns with respect to disease progression outcomes (ie, AIDS, Death, CD4, VL changes) based on their relative comparisons with subtype A. Subtype A emerged as the least aggressive of non–B subtypes.

In studies that used subtype A as the baseline comparator, subtype A was found to be associated with the lowest rates of CD4 decline and the lowest probability of developing AIDS defining illness.15–21 In Uganda, subtype D reported faster and worse disease progression compared with subtype A.16,17 But, in Kenya, where 4 subtypes (A, D, C, and G) are prevalent, subtype C demonstrated greater disease progression compared to D, A, and G.21 This finding implicating subtype C as more aggressive than the rest of the subtypes has been confirmed by other studies as well.27,28 In a few other studies, subtypes C and E were compared with reports in the literature from subtype B in the west before initiation of ART,22,24 and greater disease progression (higher VL and greater CD4 decline) was observed in subtype C compared with subtype B.24 Those infected with subtype E demonstrated a higher VL and a greater risk of developing extrapulmonary cryptococcosis than those infected with subtype B.13

Although direct comparisons of subtypes D, G, and A against subtype B were not available, evidence accumulated from studies in this review and corroborated with results from a recent meta-analysis review in women, performed by our group, that pooled mutations to single-dose nevaripine administered in the context of prevention of mother-to-child transmission. This meta-analysis confirmed the following trend of disease progression amongst subtypes in descending order: subtype C> D> AE> G >A. Moreover, rates of disease progression observed amongst non–B subtypes varied depending on which subtypes were being compared, country of origin, the objectives, and sample sizes of subtypes for each study.

So what could explain these differences between non-B subtypes in ART naïve?

In the absence of ART, differences in the rate of progression of HIV infection result from the interplay between the virulence of the infecting HIV-1 strain and the host's immune response. Among viral factors, higher exvivo replicative capacity (sometimes referred to as pathogenic fitness), CXCR4 co-receptor usage, and higher genetic diversity could explain some of the pathogenicity.29–31 Pathogenic “fitness”, generally measured as the ability of HIV-1 strains to replicate in peripheral blood mononuclear cells is variable between subtypes. Minor differences have been noted in exvivo fitness between subtypes B, D, A, E, CRF02_AG,32 with subtype A being less fit than subtypes D, B, or C.

The concept of increased genetic diversity as a factor promoting disease progression is supported by the observations that coinfection with several subtypes is associated with faster disease progression than infection with one subtype and that the genetic diversity of infecting strains increases in patients as their HIV-1 infection progresses.30,33

Host factors including immune response can be substantially affected by the socioeconomic condition of subjects (poverty and malnutrition), and their constitution, affecting their susceptibility to co-morbidities/co-infections (ie, anemia or malnutrition, tuberculosis, malaria, sexually transmitted infections). These factors could also impact differential response rates, but this fact has been hitherto unexplored in studies to date.

These results suggest that HIV-1 genetic diversity plays a role in influencing disease progression in untreated individuals. Subtypes C, B, and D are more aggressive than G, AE, and AG, with subtype A being the least aggressive of all HIV-1 subtypes. The coherence of the findings with clinical trial data in women who were administered regimens for prevention of mother-to-child transmission, and experimental data, suggest that subtype differences are plausible. They exist in transmission, in mutations, and in disease progression, and they need to be kept in mind while initiating ART and monitoring ART regimens in Central, West, and East Africa where subtype variability predominate.

The topic of HIV virulence (the disease-producing capacity of an infection, usually described as time from primary infection to immune deficiency disease or death) is highly relevant. The determinants of virulence are known to include several host factors: (1) severity of primary infection illness (or peak viremia density, the Danish cohort), (2) age at primary infection (UK hemophiliacs in the mid-90s), (3) male gender, and (4) underlying debility or comorbidity. This review focuses on the pathogen for a virologic determinant by subtypes. Differences in pathogenicity (as a virological phenotype) are plausible. This also correlates with evidence of increase in virulence of clade B (as documented by CD4 trajectory) and its evolution over a few decades in Western countries.

In sum, our review findings generate evidence to help inform public health programs in developing settings. The findings will guide management of individuals infected with non–B subtypes, with respect to the timing and type of ART initiation, and further management of ART switches in settings with prevalent HIV-1 non–B subtypes.

The review findings suggest that HIV-1 subtype should be considered a confounder, influencing response to naive HIV infection in patients from African and Asian settings. Therefore, physicians in these settings should be aware of the non–B subtype at baseline although profiling patients before initiating them on ART. Perhaps point-of-care assays that enable subtype determinations could be developed to aid in this. These assays could first be focused to detect subtypes C, D, and A that globally constitute the majority of non–B subtypes. Point-of-care assays that monitor mutations (preferably subtype specific mutations) to ART response could also be developed. Currently, B subtype–specific assays are used to detect non–B subtype–specific mutations to ART. Although CD4, CD8, and VL assays are claimed by manufacturers to work well for B and non–B subtypes, ideally, subtype-specific point-of-care VL RNA assays will aid clinicians in studying the trajectory of VL in their patients. This will facilitate quality clinical management of patients in such settings.

More studies are needed to inform treatment protocols in Kenya or the subtype rich central African belt. A vast majority of cohorts had limited data on subtypes, therefore, subtype assays to detect subtype at baseline before ART initiation should be made available.

HIV-1 subtype tests, mutation (phenotypic and genotypic) assays for key subtypes C, A, and D, and recombinants AE and AG will cover a majority of the globally representative non–B subtypes.

Future studies in this field should report one measure of effect, either a RR or HR, preferably with subtype A as the baseline comparator. This will make it easier for evidence generators to compare across non–B subtypes. Consistent reporting of one measure, RRs, ORs, or HRs would facilitate pooling of studies and should be encouraged. Future studies may also focus on understanding treatment protocols specific for non–B subtypes.

We call for increased research funding for the key C, A, and D subtypes and key recombinants AE and AG that form the majority of non–B subtypes. Combination of new data and new and improved diagnostics will help inform physicians on what regimens to follow and which mutations to detect, which will, in turn, directly inform choices on ART regimen sequencing.

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Strengths and Limitations

We followed a prespecified protocol, conducted a thorough search, and contacted authors for original data. We attempted to reduce publication bias by including full text articles, abstracts, and brief reports. However, our review also had some limitations, restricting our search to English language studies raised the possibility of reporting (ie, language) bias. Despite our exhaustive search, there remains the possibility that we missed some studies. Finally, we were limited by the quality of reporting of individual studies to the extent that they reported meaningful outcomes such as HRs that could be pooled and the completeness of their reporting. Subtype comparisons were obtained from single studies. General trends of differences between subtypes were observed, but effect measures and 95% CIs were not consistently reported across studies, limiting our ability to make inferences about longer durations of follow-up and across non-B (C vs. B, D vs. B, AE vs. C) subtypes; we were only able to infer patterns.

We were limited by the data in exploring the role of virulence factors of the host (ie, age, gender, time from primary infection or seroconversion) to observation for outcomes and racial differences in host populations. These potential confounding factors were expected to reduce or even out over time. Host factors that influence virulence through immunity may also confound comparisons of factors that influence virulence through pathogenicity, but this could not be evaluated in our current analysis.

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CONCLUSIONS

In conclusion, HIV-1 genetic diversity does seem to affect the rate of disease progression in ART-naive patient populations. Some subtypes, such as A, are associated with lower rates of disease progression, whereas others (C, D) are associated with a higher rate. Coinfection with several subtypes and recombinants is associated with higher rates of disease progression in patients not yet on ART. Experimental data correlate with these findings.34,30 Evidence of greater rates of disease progression in C and D subtypes that constitute the majority of non–B subtypes in Sub Saharan Africa and Asia, compared with the B subtype in North America and Europe, highlight the importance of expanding early HIV diagnosis, and early initiation of therapy with subtype determinations at baseline in these settings.

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Plos One
Effect of HIV-1 Subtypes on Disease Progression in Rural Uganda: A Prospective Clinical Cohort Study
Ssemwanga, D; Nsubuga, RN; Mayanja, BN; Lyagoba, F; Magambo, B; Yirrell, D; Van der Paal, L; Grosskurth, H; Kaleebu, P
Plos One, 8(8): -.
ARTN e71768
CrossRef
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Keywords:

disease progression; differential response; HIV-1 non–B subtypes; ART naive

© 2012 Lippincott Williams & Wilkins, Inc.

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