We have been tracking TDR in this cohort of newly infected individuals in NYC for fifteen years and have reported our past observations[1
Here, we report that the prevalence of TDR remains stable, at 14.3% overall, and 10.8% in the most recent period. Although there is no statistically significant trend in TDR, all categories of resistance peaked in 2003–2004. The distribution of mutations is very similar to that reported in the European surveillance study SPREAD[25
], with a predominance of the thymidine analog mutations, M41L and T215R/F, and L90M in the protease coding region. Specific mutations and polymorphisms, such as K103N and L33F, have increased. K103N is likely due to shifts in treatment regimens and its continued transmission could impact the use of first generation NNRTI agents as an initial treatment option in these individuals. Given current treatment recommendations this pattern supports the use of ritonavir-enhanced PI-based regimens when treating HIV-1 infection urgently and the routine use of resistance testing to inform ongoing regimen decisions[26
]. The use of integrase inhibitor based therapy could also be advocated. The transmission of M184V has steadily decreased since the 1990s. The declining trend likely may be due to more successful regimens, but possibly due to the impaired viral fitness seen as lower pre-treatment viral loads in ours and other studies[27
Furthermore, it might not be measured in population level resistance assays as we have employed, where strains comprising less than 10% of the viral quasi-species are not likely to be detected. Deep sequencing analyses of these transmitted viruses are ongoing to determine whether M184V transmissions continue but are missed by this assay or whether reversion to wild type after transmission is occurring.
The prevalence of TDR in this cohort of mostly MSM is similar to that reported in other developed countries. A 10 city survey of new diagnoses in the US revealed a 14.6% prevalence of TDR[28
]. Surveillance programs in Europe and Israel have reported a stable prevalence of 8.4%[25
]. Population studies from the United Kingdom HIV drug resistance surveillance group reported 10%, but these were mostly chronic HIV-1 infections and the data were collected through 2004 or 2006[29
]. In a more recent report of men with AHI in San Francisco, the prevalence of TDR had decreased to 15% in 2008–2009 from 24% in 2007[31
]. As treatment success improves with simpler regimens and more durable virologic suppression, we expect that TDR within the population would diminish as it had in other settings[32
]. However, the stable prevalence of TDR that remained in this sample suggests we need to better understand the drivers of TDR in the population. Differences in sampling approaches and sample characteristics (e.g. transmission risk, gender, racial/ethnic composition) as well as population parameters (e.g. circulating HIV drug resistance among treatment experienced individuals) could explain some of these variables. TDR defining mutations also have changed over time, and comparing trends must be interpreted with this understanding. Population-based surveys such as the UK HIVDR surveillance can be generalized to the source population of HIV-1 infected individuals, but acute and recent HIV infected individuals comprise such a small fraction of this sample that it is difficult to extrapolate and understand current resistance transmission trends. Data from our study were drawn from a highly selected sample and cannot be generalized to the population of HIV-1 infected individuals in NYC. Comparison of population-based and sample-based data provides insights into disparities in early HIV detection, a public health and clinical imperative, and allows some inference about the representativeness of samples. Determining AHI from single time point measurements with assays like the detuned antibody assay used in our algorithm has been discouraged because of the possibility of misclassifying individuals with low virus loads and low CD4+ T cell counts or an unknown history of ART as AHI[34
]. However such algorithms are done in the absence of clinical history and additional laboratory data. We believe that our stringent criteria of acute and early HIV-1 infection which include detailed clinical and laboratory data, high entry viral loads, exclusion of those who could enter the cohort with CD4 counts less than 200 cells/mm3
unless additional laboratory and clinical data supported the diagnosis, and conservative cut-off values of the detuned assay, reduced the likelihood of misclassification. Furthermore, in our sample we further reduced misclassification by using multiple EIA methods (i.e. documented negative EIA and western blots) in substantial numbers of individuals.
The 97% prevalence of subtype B variants is consistent with other studies in the US[28
]. Interestingly, we only identified one non-B variant prior to 2005, and 14 of the 19 subjects who had non-B subtypes were US-born. We did not identify any geographic area associated with clustering, but did find a higher prevalence of TDR in those regions of NYC with lower socio-economic status. The outer boroughs of NYC are a particular public health focus due to the higher incidence (as compared to Manhattan), and lower HIV testing and access to medical services. However, the numbers are too small to infer much from these data. Thus, identification of AHI individuals from these neighborhoods is required to better understand this pattern.
Using the strictest of three criteria for defining transmission clusters, we report that 19.3% of sequences tested during AHI clustered. Our results are similar to those from a study of multiple centers across Europe (11%)[36
]. However, the clustering observed in this study is considerably lower than that an earlier report by Brenner et al.
where 49.4% of the sequences co-clustered, as well as other samples in Europe and Australia where clustering was reported to be between 34% and 53%[5
]. Several factors may explain observed differences between transmission clusters in our study and other reports. Review of reports of other transmission networks show that there is considerable variability in both analysis and definition of transmission networks. In this study, relaxing the criteria for defining transmission clusters increased the proportion of co-clustering sequences from 19.3% to 27.7%[36
]. In addition, the samples used to determine the level of clustering among viruses within a population vary. In the UK and Montreal, these were population-based data and therefore the viral sequences represented the target population. However, in the UK, the majority of these were sequences from chronic infection where the transmission dates were unknown. Variability in the definition of AHI will also impact results. When statistical methods are used to account for this sampling time, analysis of viral sequences from chronic and AHI suggest that the level of clustering can differ by infection stage[5
]. Furthermore, the sample in this study represented a relatively small fraction of HIV infections in NYC. This low sampling fraction, and the predominance of non-Hispanic white MSM in the sample, renders it less representative of HIV-1-infected MSM or other infected populations in NYC overall. It is possible that more complete sampling would show more transmission clusters in the sample set, and/or larger clusters than those observed here. Conversely, population level sampling of NYC may show less transmission clusters than was observed in this more homogeneous cohort. It is possible that the NYC epidemic is more complex than other settings; mixing patterns may be more heterogeneous and dynamic, and in and out migration varies considerably from smaller North American cities like Montreal. These nuances in study context can produce differences and should be interpreted with caution.