Prevalence and trends in transmitted and acquired antiretroviral drug resistance, Washington, DC, 1999–2014
Aldous et al. BMC Res Notes
Prevalence and trends in transmitted and acquired antiretroviral drug resistance, Washington, DC, 1999-2014
Annette M. Aldous 1
Amanda D. Castel 1
David M. Parenti 0
the DC Cohort Executive Committee
0 Division of Infectious Diseases, The George Washington University School of Medicine , 2150 Pennsylvania Avenue, NW, Washington, DC , USA
1 Department of Epidemiology and Biostatistics, The George Washington University, Milken Institute School of Public Health , Washington, DC 20037 , USA
Background: Drug resistance limits options for antiretroviral therapy (ART) and results in poorer health outcomes among HIV-infected persons. We sought to characterize resistance patterns and to identify predictors of resistance in Washington, DC. Methods: We analyzed resistance in the DC Cohort, a longitudinal study of HIV-infected persons in care in Washington, DC. We measured cumulative drug resistance (CDR) among participants with any genotype between 1999 and 2014 (n = 3411), transmitted drug resistance (TDR) in ART-naïve persons (n = 1503), and acquired drug resistance (ADR) in persons with genotypes before and after ART initiation (n = 309). Using logistic regression, we assessed associations between patient characteristics and transmitted resistance to any antiretroviral. Results: Prevalence of TDR was 20.5%, of ADR 40.5%, and of CDR 45.1% in the respective analysis groups. From 2004 to 2013, TDR prevalence decreased for nucleoside and nucleotide analogue reverse transcriptase inhibitors (15.0 to 5.5%; p = 0.0003) and increased for integrase strand transfer inhibitors (INSTIs) (0.0-1.4%; p = 0.04). In multivariable analysis, TDR was not associated with age, race/ethnicity, HIV risk group, or years from HIV diagnosis. Conclusions: In this urban cohort of HIV-infected persons, almost half of participants tested had evidence of CDR; and resistance to INSTIs was increasing. If this trend continues, inclusion of the integrase-encoding region in baseline genotype testing should be strongly considered.
HIV; Antiretroviral therapy; Drug resistance; Transmitted drug resistance; Acquired drug resistance; Cumulative drug resistance; Prevalence; Washington; DC
Since 1995, the use of combination antiretroviral therapy
(ART) has dramatically improved life expectancy and
health outcomes for people infected with HIV, but
resistance to antiretroviral drugs (ARVs) undermines their
]. Drug resistance may be acquired in
response to drug pressure (ADR) or transmitted at the
time of infection (TDR). In the United States (US),
estimates of TDR prevalence range from 4 to 27% [
Some reports indicate that resistance to nucleoside and
nucleotide analogue reverse transcriptase inhibitors
(NRTIs) has remained stable or decreased, while
resistance to nonnucleoside reverse transcriptase inhibitors
(NNRTIs) and protease inhibitors (PIs) has remained
stable or increased [
14, 19, 23, 26–28
]. Few data are
available on the prevalence of resistance to the newer ARV
classes: entry/fusion inhibitors (EIs) and integrase strand
transfer inhibitors (INSTIs). Two recent studies found no
resistance to INSTIs [
]; however, with INSTI-based
regimens featuring prominently in the latest US
Department of Health and Human Services treatment guidelines
], increasing resistance to this class is likely.
While TDR has been fairly well documented, fewer
data exist on rates of ADR and of cumulative drug
resistance (CDR), a term we use to encompass all resistance,
whether transmitted, acquired, or of unknown origin.
One study of homeless persons in San Francisco found
ADR prevalence of 36% [
]. The same study and one
other found CDR prevalence rates of 27 and 45%,
]. These categories of resistance may provide
an indication of how well a city is maintaining treatment
and adherence in its infected population. Additionally,
Tilghman et al. found that high levels of CDR at specific
gene locations predicted TDR in the same locations .
In Washington, DC, which has an HIV prevalence of
], recent studies of TDR have found that 17–23%
of participants had mutations associated with
resistance to at least one drug [
] while two earlier
studies reported resistance rates of up to 17%, depending
on drug class [
]. These findings suggest resistance
is common, yet citywide prevalence is unknown. The
DC Cohort, a longitudinal observational study of
HIVinfected persons receiving outpatient care at 13
clinics throughout Washington, DC [
], affords a unique
opportunity to characterize prevalence in a major urban
area with a high burden of HIV. With 6743 people
enrolled as of December 2014, including 4969 DC
residents, the study aims to provide a representative
sample of the 16,423 people estimated to be living with HIV
in the city [
]. Additionally, the longitudinal nature
of the study makes it possible to distinguish, for some
participants, between transmitted and acquired drug
In this analysis, we aimed to describe the prevalence
of and trends in ARV drug resistance among DC Cohort
participants by category of resistance (TDR, ADR, and
CDR); specifically, to measure prevalence of individual
drug resistant mutations and to estimate resistance to
individual drugs and drug classes. We further sought to
examine associations between patient characteristics and
the presence of transmitted drug resistance.
Data source and study population
Enrollment in the DC Cohort began in January 2011.
Data on all consenting participants are electronically
exported on a monthly basis. Historical data are manually
abstracted including genotype and phenotype tests and
date of ART initiation where available [
]. For the
present analysis, we included all active participants enrolled
through December 2014 and not perinatally infected
(n = 6506). Our study population included both recently
infected individuals and people who had been living with
HIV for many years. Participants with any documented
genotype test between 1999 and 2014 were included for
the estimates of CDR (n = 3411). Those who were
documented treatment-naïve at first genotype test were
evaluated for TDR (n = 1503). Among the latter group, those
who had one or more additional genotype tests after ART
initiation were assessed for ADR (n = 309) (Fig. 1). The
DC Cohort study was approved by the George
Washington University Institutional Review Board (IRB), and all
13 sites received IRB approval to participate in the study.
Measurement of resistance
Multiple commercial assays were used for the genotype
testing, some of which occurred prior to study
enrollment. A total of 5993 genotypes were analyzed
(LabCorp: 3047; TruGene: 1279; Monogram Biosciences:
621; Quest: 467; other: 579) representing all 13 clinical
sites. Although major and minor mutations were
available for the reverse transcriptase and protease genes,
and sometimes for the envelope and integrase genes, full
sequences were not available, and we did not have
information on which specific genotypes were evaluated for
EI and INSTI resistance. We measured the prevalence
of individual drug resistance mutations (DRMs) that
were included in the WHO Surveillance Drug Resistance
Mutations list [
] or in the 2014 International Antiviral
Society-USA (IAS) HIV-1 drug mutations classification
]. From the latter, we included all bolded amino acid
substitutions and all mutations at bolded PI locations.
We then interpreted resistance to drugs and drug classes
using the IAS classification alone; however, for PIs, since
the IAS guidelines identified only major locations and
not specific amino acid substitutions, we used the 2014
Stanford HIVDB genotypic resistance interpretation
algorithm (Version 7.0), including intermediate and
highlevel resistance mutations at bolded locations [
Phenotypic data were not examined.
To determine the prevalence of TDR, we used the
first genotype test for each ART-naïve participant (1503
tests). For ADR, we assessed mutations present in tests
after ART initiation (557 tests) and absent in the initial
test (309 tests). We did not have complete data on the
drug regimen for each participant at the time of the test;
therefore specific regimen was not taken into account.
The CDR analysis group included all participants in the
TDR and ADR groups as well as many more for whom we
were not able to ascertain whether mutations were
transmitted or acquired. To estimate the prevalence of
cumulative drug resistance in this group, we included all DRMs
on every test, regardless of treatment status at the time of
testing (5993 tests). A participant with a given mutation
on any test was considered to have that mutation for the
remainder of the study period.
Characteristics of the DC Cohort at enrollment were
assessed as frequencies and proportions for
categorical variables and as medians and interquartile ranges for
continuous variables. To evaluate trends in resistance
from 2004 to 2013, we used the Cochran–Armitage test
with 2-sided p values. We performed simple and
multivariable logistic regression to examine potential
associations between patient characteristics and transmitted
resistance to any drug class. In the multivariable model,
we included, a priori, age at genotype test, race/ethnicity,
transmission risk group, and years from HIV diagnosis
along with any variables that proved statistically
significant with α = 0.05 in bivariate regression. All analysis
was conducted using SAS 9.2 (SAS Institute, Cary, North
The median age of DC Cohort participants at enrollment
was 48 years. Participants were mostly male (73.8%),
non-Hispanic black (76.4%) and infected through
maleto-male sex (38.7%) or heterosexual sex (30.7%). Nearly
two-thirds had public health insurance (64.9%), and
roughly equal numbers of participants received care at
hospital-based clinics (48.2%) and community-based
clinics (51.8%). Most participants had CD4 counts above
500 cells/μl (51.5%) and viral loads below 400 copies/ml
(75.7%) at enrollment; 41.6% of participants had been
diagnosed with AIDS. The median interval between HIV
diagnosis and consent date was 9.3 years (Table 1).
Clinical characteristics at enrollment were not reflected in the
resistance results, which were based on genotype tests
that were often performed years earlier or later.
Among the 5993 genotypes analyzed, 5895 were subtype
B, 48 were C, 17 were AG, and 33 were other subtypes.
In the TDR group (ART-naïve at genotype), prevalence
of TDR to any drug class was 20.5%: 7.9% for NRTIs,
11.7% for NNRTIs, 5.7% for PIs, 1.1% for EIs, and 0.9%
for INSTIs (Table 2). In the ADR group (genotypes
before and after ART initiation), ADR prevalence was
40.5%; while in the CDR group (all participants tested),
CDR prevalence was 45.1%. In terms of specific drugs,
all three groups were most resistant to efavirenz (TDR:
10.0%; ADR: 24.6%; CDR 27.2%), and nevirapine (TDR:
10.2%; ADR: 23.9%; CDR 27.1%). The ADR and CDR
groups also had high levels of resistance to
emtricitabine and lamivudine (TDR: 3.1%; ADR: 20.4%; CDR:
24.3%), and abacavir (TDR: 3.5%; ADR: 19.1%; CDR:
24.2%). Among the protease inhibitors, the highest levels
of TDR and CDR were to nelfinavir (TDR: 1.9%; ADR:
0.0%; CDR: 7.2%), and of ADR to atazanavir (TDR: 1.8;
ADR: 3.2%; CDR: 5.3). No resistance to darunavir was
detected as TDR, ADR, or CDR. Resistance to the fusion
inhibitor enfuvirtide was found in a few participants
(TDR: 1.1%; ADR: 1.0; CDR: 1.5). Maraviroc resistance
was not assessed because tropism determination was
not available. Mutations conferring resistance to
raltegravir (TDR: 0.6%; ADR: 1.6%; CDR: 1.5%) and
elvitegravir (TDR: 0.9%; ADR: 0.6%; CDR: 1.3%) were found
in all three analysis groups; while in the CDR group only,
4 participants had evidence of resistance to dolutegravir
(TDR: 0.0%; ADR: 0.0; CDR: 0.1%). Resistance to three
or more classes was 1.2% for TDR, 1.9% for ADR, and
7.1% for CDR.
The prevalence of the K103N mutation, associated
with resistance to NNRTIs, was high for all three
analysis groups (TDR: 7.1%; ADR: 18.8%; CDR: 20.2%) (Fig. 2).
Prevalence was also high for NRTI-associated mutations
M41L (TDR: 3.0%; ADR: 1.0%; CDR: 7.3%) and M184V
(TDR: 2.8%; ADR: 17.8%; CDR: 22.9%). Among
proteaseassociated mutations, L90M (TDR: 1.5%; ADR: 0.0%;
CDR: 5.5%) was most prevalent in the TDR and CDR
NRTI, nucleoside/nucleotide analogue reverse transcriptase inhibitor; NNRTI, nonnucleoside reverse transcriptase inhibitor; PI, protease inhibitor; EI, entry/fusion
inhibitor; INSTI, integrase strand transfer inhibitor
a Interpreted using 2014 International Antiviral Society-USA (IAS) HIV-1 drug mutations classification
b Interpreted using 2014 IAS classification and 2014 Stanford HIVDB genotypic resistance interpretation algorithm
c The 2014 IAS classification did not include maraviroc
groups, and N88S (TDR: 1.3%; ADR: 2.6%; CDR: 2.6%)
in the ADR group. Integrase-associated mutations were
detected at nine sites: primarily at F121Y (TDR: 0.6%;
ADR: 0.3%; CDR: 0.8%), E92Q (TDR: 0.3%; ADR: 0.3%;
CDR: 0.2%), Q148R (TDR: 0.0%; ADR: 0.6%; CDR: 0.2%),
and N155H (TDR: 0.0%; ADR: 0.0%; CDR: 0.2%). On the
env gene, the most common mutation was N42T (TDR:
0.3%; ADR: 0.0%; CDR: 0.6%).
From 2004 to 2013, TDR was fairly stable around 20%
(15.0–20.7%; p = 0.76), with a marked decrease for
NRTIs (15.0 to 5.5%; p = 0.0003) and a small increase
for INSTIs (0.0–1.4%; p = 0.04) (Fig. 3). Over the same
time period, the proportion of newly diagnosed
participants who had a genotype test within the first year
of diagnosis steadily increased [5.5–65.9%; p < 0.0001
(data not shown)]. For ADR, the number of participants
tested prior to 2008 was too small to permit
meaningful analysis (fewer than 10 per year). The prevalence of
ADR decreased from 66.7% in 2008 to 41.6% in 2013
(p = 0.003). ADR also decreased significantly for NRTIs
(47.2 to 24.1%; p = 0.0004) and NNRTIs (47.2 to 26.9%;
p = 0.002). Resistance to PIs rose slightly (5.6–6.3%;
p = 0.25), but the difference was not significant and
as noted above, no resistance was found to
darunavir, which has perhaps the highest barrier to resistance.
CDR prevalence to any drug class declined significantly
from 70.6% in 2004 to 45.0% in 2013 (p < 0.0001). The
trend was also significant for NRTIs (63.9 to 29.9%;
p < 0.0001), NNRTIs (43.6 to 29.1%; p < 0.0001), PIs
(32.4 to 14.8%; p < 0.0001), and to any 2 (33.6 to 17.0%;
p < 0.0001) or 3 (17.9 to 6.9%; p < 0.0001) drug classes,
while resistance increased for EIs (0.0–1.5%; p < 0.0001),
INSTIs (0.0–1.8%; p < 0.0001), and any four drug classes
(0.0–0.4%; p < 0.0001).
Logistic regression analysis
We decided a priori to include age at test,
race/ethnicity, transmission risk group and years from HIV
diagnosis in the multivariable regression model. Based on a
statistically significant association at the α = 0.05 level
in bivariate regression analysis, we added clinic type to
the model. In multivariable analysis, TDR was not
predicted by time between HIV diagnosis and genotype, age
at genotype or race/ethnicity. Although not statistically
significant, individuals infected through injection drug
use (OR 1.53; 95% CI 0.79–2.97) and those receiving
HIV care at community-based clinics (OR 1.27; 95% CI
0.95–1.72) were more likely to have transmitted
resistance than individuals infected through male-to-male
sexual contact and participants cared for at
hospitalbased clinics, respectively.
To our knowledge, we are the first to report positive
findings of mutations associated with transmitted resistance
to INSTIs (0.9%) and EIs (1.1%). We also found evidence
of resistance to these classes among participants analyzed
for ADR and CDR as well as significantly increasing trends
for cumulative resistance to both classes and
transmitted resistance to INSTIs. Unfortunately we were not able
to determine which genotypes included the INSTI and EI
encoding regions, and so we report prevalence among all
genotypes assessed; thus, our rates are underestimates of
prevalence for these classes. The emergence of resistance
to INSTIs is likely due to their increasingly widespread
use in clinical practice as well as their earlier use in clinical
trials. Several DC sites participated in registration trials
for all three INSTIs, the first of which (raltegravir) was
FDA-approved in 2007; and by the end of 2014,
approximately 10% of DC Cohort participants were on
INSTIbased regimens. Surprisingly, resistance to elvitegravir—as
interpreted using the IAS guidelines—was higher than to
raltegravir, although the latter was introduced earlier. This
was mainly attributable to the presence of E92Q and T66I
mutations. In the most recent US Department of Health
and Human Services (DHHS) treatment guidelines, four
of the five recommended regimens for ART-naïve patients
were INSTI-based, while inclusion of the integrase region
in routine genotype testing was still optional [
the emergence of INSTI resistance in the Washington, DC
area, baseline resistance testing for integrase inhibitors
should be strongly considered.
The TDR prevalence of 20.5% found in this analysis was
comparable to rates reported throughout the US and in
Washington, DC. We were surprised to find that 18
participants analyzed for TDR had mutations associated with
resistance to three drug classes. Review of the medical
records for these 18 participants is warranted to confirm
that the recorded dates are accurate. Our findings of no
significant association between TDR and sex,
race/ethnicity, or transmission risk group support those of most
11, 14, 21, 28
], but not all  previous studies. However,
resistance appeared to be higher among injection drug
users (IDUs), and although the difference was of
borderline significance, it is plausible given that IDUs may have
more barriers to adherence and retention in care [
Cumulative drug resistance may serve as a measure of a
community’s burden of ARV resistance and, like
community viral load, may reflect the success of treatment and
adherence in that community [
]; however, these results
should be interpreted with caution. First, the decrease
in CDR observed between 2006 and 2010 was probably
due in part to the dilution effect of increased resistance
testing among newly diagnosed individuals following the
2007 DHHS recommendations [
]. Second, because
patients on treatment generally have genotype testing
performed when treatment fails, the prevalence of
resistance in tested individuals may be higher than in the
overall population of persons infected with HIV. To avoid
this overestimation of resistance, others have included
all treatment-experienced patients in the
] or used modeling to extend resistance estimates
from tested to untested individuals [
]. By assuming
that untested individuals do not have resistance, the
former approach underestimates prevalence. Using this
method, we found that resistance appeared to increase as
the proportion of participants tested increased
dramatically over the time period (results not shown). That is,
the degree of underestimation decreased over time, and
the resulting apparent increase in resistance was
misleading. In future analysis, we hope to model resistance
in the overall Cohort taking into account the rate of
testing and other secular trends. Third, our measurements
were based on genotype tests that do not detect
minority or archived HIV strains and thus, may underestimate
the true prevalence of resistance. Furthermore, some
transmitted archived strains may not have been detected
until after treatment was initiated, resulting in
misclassification of TDR as ADR. However, since our estimates
for ADR and CDR were cumulative, we maximized our
ability to include archived strains within the limitations
of the tests.
Other strengths of this study include the large size and
representative, citywide composition of the DC Cohort,
together with the availability of genotypic, demographic,
and clinical data. The long-term use of INSTIs in the
study population provided early evidence of resistance
to this drug class, while the longitudinal data allowed us
to assess acquired and cumulative resistance in a large
In this urban cohort of HIV-infected persons in care,
almost half of participants tested had evidence of CDR,
and resistance to INSTIs was increasing. If this trend
continues, inclusion of the integrase-encoding region in
routine genotype testing may become advisable. With
new treatment guidelines recommending immediate
initiation of ART for most people, innovations to promote
adherence, such as co-formulations and longer-acting
regimens, will be more critical than ever. Continued close
surveillance of transmitted and acquired resistance will
measure the success of these efforts and inform future
testing and treatment guidelines.
ADR: acquired drug resistance; ART: antiretroviral therapy; ARV: antiretroviral;
CDR: cumulative drug resistance; DRM: drug resistance mutation; EI: entry/
fusion inhibitor; IAS: International Antiviral Society; IDU: injection drug user;
INSTI: integrase strand transfer inhibitor; IRB: institutional review board; MMS:
male-to-male sex; NNRTI: nonnucleoside reverse transcriptase inhibitor; NRTI:
nucleoside/nucleotide analogue reverse transcriptase inhibitor; PI: protease
inhibitor; TDR: transmitted drug resistance.
AMA contributed to study conception and design, analyzed and interpreted
data, and drafted the manuscript. ADC and DMP contributed to study
conception and design and data acquisition, and revised the manuscript. The DC
Cohort Executive Board contributed to data acquisition and reviewed the
manuscript. All authors read and approved the final manuscript.
Data in this manuscript were collected by the DC Cohort investigators and
research staff located at: Cerner Corporation; Children’s National Medical
Center Adolescent and Pediatric Clinics; Family and Medical Counseling
Service; Georgetown University; George Washington Medical Faculty Associates;
George Washington University Department of Epidemiology and Biostatistics;
Howard University; La Clinica Del Pueblo; MetroHealth; Unity Health Care;
Veterans Affairs Medical Center; Washington Hospital Center; and
WhitmanWalker Health. The authors would also like to acknowledge members of the
DC Cohort Data and Statistics Coordinating Center including Dr. Naji Younes,
Dr. James Peterson, Ms. Maria Jaurretche and Ms. Lindsey Powers Happ, of the
George Washington University Milken Institute School of Public Health as well
as Dr. Jeffrey Binkley, Mr. Harlen Hays, Ms. Thilakavathy Subramanian, Ms. Dana
Franklin, and Ms. Bonnie Dean of Cerner Corporation, for their work on DC
Cohort data collection and processing. They would also like to acknowledge
Dr. Mary Young, Dr. Carl Dieffenbach, and Dr. Deborah Goldstein for their
assistance with this study as well as the research assistants at all of the
participating sites and the DC Cohort Community Advisory Board. Abstract presented at
ID Week 2015, San Diego, CA, October 7–11, 2015.
DC Cohort Executive Committee: Alan E. Greenberg, MD, MPH, George
Washington University Milken Institute School of Public Health; Debra Benator,
MD, Veterans Affairs Medical Center; Princy Kumar, MD, Georgetown
University; Richard Elion, MD, Whitman-Walker Health; Maria Elena Ruiz, MD
Washington Hospital Center; Angela Wood, MSW, Family and Medical Counseling
Service; Lawrence D’Angelo, MD, MPH, Burgess Adolescent Clinic, Children’s
National Medical Center; Natella Rakhmanina, MD, Ph.D., Special Immunology
Service Pediatric Clinic, Children’s National Medical Center; Sohail Rana, MD,
Pediatric Clinic, Howard University Hospital; Maya Bryant, MD, Saumil Doshi,
MD, Adult Infectious Disease Clinic Howard University Hospital; Annick Hebou,
MD, MetroHealth; Ricardo Fernandez, MD, La Clinica Del Pueblo; Stephen
Abbott, MD, Unity Health Care; Rachel Hart, MS, Cerner Corporation; Michael
Kharfen, BA, HIV/AIDS, Hepatitis, Sexually Transmitted Diseases, Tuberculosis
Administration, DC Department of Health; Henry Masur, MD, National
Institutes of Health.
The authors declare that they have no competing interests.
Availability of data and materials
The DC Cohort database contains numerous individual-level variables and
is therefore not publicly available in keeping with the IRB approval and our
informed consent process. However, a de-identified limited dataset
supporting the conclusions of this article is available by request. Interested parties
may contact Dr. Amanda Castel, the DC Cohort Principal Investigator, at
to arrange access to such.
Consent for publication
All participants have consented to publication of collective data during the
informed consent process.
Ethics approval and consent to participate
Written informed consent was obtained from all participants prior to
enrollment in the DC Cohort. The DC Cohort study was approved by the George
Washington University Institutional Review Board (IRB), which served as the
IRB of record for Whitman-Walker Health, La Clinica del Pueblo, Family and
Medical Counseling Service, Unity Health Care, The GW Medical Faculty
Associates, MetroHealth, and Children’s National Health System (pediatric and
adolescent clinics). The study was independently approved by the IRBs of Howard
University Hospital (adult and pediatric clinics), MedStar Washington Hospital
Center, Georgetown University, and the Veterans Affairs Medical Center.
The DC Cohort is sponsored by the National Institute of Allergy and
Infectious Diseases of the National Institutes of Health (UO1 AI69503-03S2).
Additionally, this publication resulted (in part) from research supported by
the District of Columbia Center for AIDS Research, an NIH funded program
(P30AI117970), which is supported by the following NIH Co-Funding and
Participating Institutes and Centers: NIAID, NCI, NICHD, NHLBI, NIDA, NIMH,
NIA, FIC, NIGMS, NIDDK, and OAR. The content is solely the responsibility of
the authors and does not necessarily represent the official views of the NIH.
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
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