The presence of resistance-associated mutations in virus from drug-naive individuals implies transmission of resistance from treated patients and is therefore a marker of sexual risk within a population. Surveillance of THDR provides important information on the epidemiology of HIV transmission and the impact of sexual health initiatives in HIV-infected individuals. The cost-effectiveness of screening treatment-naive patients is also highly sensitive to baseline prevalence of resistance.16 A list of DRMs designed to track the transmission of resistant virus for epidemiological purposes is necessary to support these public health policies, especially as the wider use of antiretroviral drugs in resource-limited countries may lead to a rapid spread of resistance in those countries and in industrialized countries where an increasing proportion of new diagnoses are from migrants.
The majority of reports to date have defined resistance either in relation to the annually updated IAS-DRM list8 or using the Stanford genotypic resistance interpretation algorithm.9 The use of the IAS-DRM list for epidemiological purposes has a number of limitations; first, it is primarily designed as a tool for clinicians in the identification of key DRM and may therefore contain genetic determinants associated with suboptimal response to specific therapies but that are not epidemiologically relevant to the study of THDR. Second, it also excludes the “sentinel” (or revertant) mutations at RT 215, which, although not conferring resistance directly, may act as markers for the earlier presence of major DRMs at this position. Finally, the list is not always applied consistently across studies with additional substitutions at RT codons 69, 190, and 215, which are not included in the IAS-DRM list, also being considered as THDR markers. Conversely, substitutions at RT 44 and 118 have frequently been omitted, even before their removal from the IAS-DRM list in 2005.15,17,21
We compared the estimated rate of THDR in the United Kingdom using the SDRM list to define THDR with the conventional definitions using the IAS-DRM list or the Stanford interpretation algorithm. The 3 definitions did not lead to differences in overall THDR rates of more than 2%, with estimates of THDR slightly lower using the SDRM list, in agreement with other studies, mainly due to the exclusion of mutations reported as polymorphic in more than 1 subtype. Hurt et al22 identified overall THDR rates in 126 acutely infected patients of 16.7%/15.9%/12.7% by IAS-DRM 2006 list/SDRM list/Stanford algorithm, respectively, using a conservative Stanford definition of intermediate and high-level resistance only. NNRTI mutations were the most prevalent at 7.9% by SDRM and 9.5% by IAS-DRM 2006. Similarly, Liu et al23 in a larger group of primary infections identified overall rates of 15%/16%/14% by IAS-DRM 2005/SDRM/Stanford (intermediate and high-level resistance), respectively. Overall rates of THDR in our study were lower at 10.0%/9.2%/10.4% (IAS-DRM 2006/SDRM/Stanford), probably due to the inclusion of chronic and acute infections.
Overall THDR rates using IAS-DRM list and the Stanford algorithm were similar at 10.0% and 10.4%, respectively. Previous comparisons of IAS and Stanford have generally shown a high level of concordance, although this has been dependent on the choice of threshold for the Stanford algorithm. If a more conservative definition of intermediate/high-level resistance was adopted, THDR rates were reported as 5%-6% lower than those derived from the IAS list. The most commonly identified mutations responsible for this difference were substitutions at RT 118 and 215 and 33 in protease. However, some of these reports used the 2004 IAS-DRM list, which included V118I and L33I, which have been excluded from subsequent lists. If, as in this study, lower levels of (Stanford defined) resistance are also included, overall rates differ by <1%.18,23,24,25
The effect of annual updating can lead to marked alterations in apparent THDR rates; 4%-7% differences in the estimated rate of THDR were seen in this study depending on which version of the IAS-DRM list was used. Although this should not affect the pattern of temporal trends in which the entire dataset is consistently analyzed, it may compromise the validity of comparisons between cross-sectional studies from different years. Changes to the composition of the IAS list over time showed that the highest levels of resistance were predicted by the 2003 and 2004 lists, most prominently for the PIs. The major contribution to this effect was the inclusion of L33I/V (2003) or L33I alone (2004) in protease (Fig. 3), both of which were subsequently removed as major mutations due to their lack of association with PI therapy.26 NRTI resistance was also higher in pre-2005 lists, largely driven by the inclusion of V118I. This mutation does not seem to compromise NRTI-based treatment,27 is polymorphic in a number of subtypes,3 and has therefore been removed from more recent versions. Similarly, the definition of resistance using the Stanford algorithm, which is also regularly updated, has varied between studies6,18,23 influencing estimates of THDR rates and subtly affecting prescribing practices in those centers that rely on this interpretation. As new drugs and new drug classes are introduced, updates to the SDRM list will also be required. In addition, as more sequence data from non-B subtypes become available, it is likely that some mutations will be reclassified with regard to their polymorphic status, whereas other subtype-specific DRMs may be identified. A subtype-specific SDRM list would more accurately reflect the transmission of resistance, however may be impractical and difficult to implement as the number of intersubtype recombinants increases.3
Care should be taken when estimating THDR in relation to DRMs associated with recently released antiretrovirals, as very few patients will have been exposed to the new drugs, and therefore, it is very unlikely that viruses with resistance associated to the new drugs would have yet been transmitted to others. However, for this concern to have an impact on the estimates of THDR, the mutation would have to occur as a polymorphism in the absence of therapy. Such mutations, by definition, would be excluded from the SDRM list.
In summary, we have measured THDR rates in a large treatment-naive population using 3 definitions of transmitted resistance. The choice of definition had a minor influence on the estimated rate of THDR over an 8-year period. However, THDR rates were sensitive to the version of the IAS-DRM list used, and direct comparisons of studies using different versions of DRM lists or interpretation algorithms should be undertaken with caution. By standardizing the genotypic definition of THDR, differences seen in estimates of THDR across studies will not be due to the mutation list or algorithm used. The SDRM list is recommended for epidemiological estimates of THDR as it has been designed to include transmitted resistance-associated mutations regardless of their effect on drug susceptibility.
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UK Collaborative Group on HIV Drug Resistance Steering Committee
Jane Anderson, Homerton University Hospital, London; David Asboe and Anton Pozniak, Chelsea and Westminster Hospital, London; Sheila Burns, Royal Infirmary of Edinburgh; Sheila Cameron, Gartnavel General Hospital, Glasgow; Patricia Cane, Health Protection Agency, Porton Down; Ian Chrystie, Guy's and St Thomas' NHS Foundation Trust, London; Duncan Churchill, Brighton and Sussex University Hospitals NHS Trust; Duncan Clark, St Bartholomew's and The London NHS Trust; Valerie Delpech and Deenan Pillay, Health Protection Agency, Centre for Infections, London; Linda Lazarus, Expert Advisory Group on AIDS Secretariat, Health Protection Agency, London; David Dunn, Esther Fearnhill, Hannah Green, and Kholoud Porter, Medical Research Council Clinical Trials Unit, London; Philippa Easterbrook and Mark Zuckerman, King's College Hospital, London; Anna Maria Geretti, Royal Free NHS Trust, London; Paul Kellam, Deenan Pillay, Andrew Phillips, and Caroline Sabin, Royal Free and University College Medical School, London; David Goldberg, Health Protection Scotland, Glasgow; Mark Gompels, Southmead Hospital, Bristol; Antony Hale, Leeds Teaching Hospitals NHS Trust; Steve Kaye, St Mary's Hospital, London; Svilen Konov, Community Advisory Board; Linda Lazarus, Department of Health; Andrew Leigh-Brown, University of Edinburgh; Nicola Mackie, St Mary's Hospital, London; Chloe Orkin, St Bartholomew's Hospital, London; Erasmus Smit, Health Protection Agency, Birmingham Heartlands Hospital; Peter Tilston, Manchester Royal Infirmary; Ian Williams, Mortimer Market Centre, London; and Hongyi Zhang, Addenbrooke's Hospital, Cambridge.
Addenbooke's Hospital, Cambridge (Hongyi Zhang); Department of Virology, St Bartholomew's and The London NHS Trust (Duncan Clark, Ines Ushiro-Lumb, Tony Oliver, and David Bibby); Belfast Health and Social Care Trust (Suzanne Mitchell); HPA Birmingham Public Health Laboratory (Erasmus Smit); Chelsea and Westminster Hospital, London (Adrian Wildfire); Dulwich Hospital, London (Melvyn Smith); Royal Infirmary of Edinburgh (Jill Shepherd); West of Scotland Specialist Virology Laboratory, Gartnavel, Glasgow (Alasdair MacLean); Guy's and St Thomas' NHS Foundation Trust, London (Ian Chrystie); Leeds Teaching Hospitals NHS Trust (Diane Bennett); Specialist Virology Centre, Liverpool (Mark Hopkins) and Manchester (Peter Tilston); Department of Virology at Royal Free Hospital, London (Clare Booth and Ana Garcia-Diaz); St Mary's Hospital, London (Steve Kaye); and University College London Hospitals (Stuart Kirk).
Medical Research Council Clinical Trials Unit, London (David Dunn, Esther Fearnhill, Hannah Green, Kholoud Porter, and Kate Coughlin).