Short Communication: High Prevalence of Transmitted Antiretroviral Drug Resistance Among Newly HIV Type 1 Diagnosed Adults in Mombasa,Kenya

Abstract

In view of the recent antiretroviral therapy (ART) scale-up in Kenya, surveillance of transmitted HIV drug resistance (TDR) is important. A cross-sectional survey was conducted among newly HIV-1 diagnosed, antiretroviral-naive adults in Mombasa, Kenya. Surveillance drug resistance mutations (SDRMs) were identified according to the 2009 WHO list. HIV-1 subtypes were determined using REGA and SCUEAL subtyping tools. Genotypic test results were obtained for 68 of 81 participants, and SDRMs were identified in 9 samples. Resistance to nonnucleoside reverse transcriptase inhibitors (K103N) occurred in five participants, yielding a TDR prevalence of 7.4% (95% confidence interval 2.4–16.3%). Frequencies of HIV-1 subtypes were A (70.6%), C (5.9%), D (2.9%), and unique recombinant forms (20.6%). The TDR prevalence found in this survey is higher than previously reported in different regions in Kenya. These findings justify increased vigilance with respect to TDR surveillance in African regions where ART programs are scaled-up in order to inform treatment guidelines.

Introduction

With expanded access to combination antiretroviral therapy (ART) in many countries in sub-Saharan Africa, the prevalence of HIV drug resistance is likely to increase. Viral resistance can develop through selective pressure while receiving ART and can subsequently spread to newly infected individuals. The latter is referred to as primary or transmitted drug resistance (TDR).^1,2 TDR increases the risk of virological failure,^3,4 which may have a substantial impact in areas where options for first-line therapy are limited. Studies from industrialized countries have shown an increased incidence of TDR with time after the introduction of ART.^5
–7 In Africa, studies conducted during the early scale-up of ART reported low levels of TDR.^8
–10 Recently accruing evidence, however, points to increasing TDR among individuals with recent HIV infection in East Africa.^11
–13

In Kenya, national ART roll-out started in late 2003, and by 2010 an estimated 50% of adults in need of ART were receiving treatment, which is among the higher coverage rates for sub-Sahara African countries.¹⁴ Data on TDR from Kenya are limited, pointing toward levels of 0–10% in different regions and time periods.^11,15,16 Because the prevalence of transmitted resistance mutations can influence policy and guide treatment choices in the absence of individual resistance testing, the World Health Organization (WHO) has provided guidelines for TDR surveillance in developing countries.^1,9 We report the results of a survey evaluating the prevalence of TDR among newly HIV-1 diagnosed individuals attending voluntary counseling and testing (VCT) sites in Mombasa, Kenya.

Materials and Methods

Study design and population

As part of the PharmAccess African Studies to Evaluate Resistance-Surveillance (PASER-S) program, a cross-sectional survey was conducted among clients attending four VCT sites in Mombasa, Kenya: Coast Provincial General Hospital, Likoni Sub-District Hospital, Kisauni Health Centre, and Magongo Health Centre. The institutional review boards at the Kenyatta National Hospital and the Academic Medical Center of the University of Amsterdam approved the study. Eligibility criteria as defined by the WHO were used to identify individuals who were likely to have been recently infected: newly diagnosed with HIV-1 and aged between 18 and 25 years, or laboratory evidence of recent HIV-1 infection (defined as a confirmed positive antibody test with a negative antibody test in the past 12 months, or an indeterminate/negative antibody test with detectable HIV-RNA or p24 antigen).⁹ Exclusion criteria were any previous antiretroviral use, WHO clinical stage 4 event, or previous pregnancy. All participants provided written informed consent prior to enrollment. During the enrollment period of maximum 12 months, the VCT clients were screened and sequentially enrolled.

Population genotype analysis

HIV-RNA and genotypic testing was performed as previously described.¹² Final sequences were checked before inclusion using ViroScore Suite v8.1 (ABL, Paris, France). Neighbor-joining phylogenetic trees were constructed using MEGA 5.05,¹⁷ in order to rule out cross-contamination. TDR was analyzed in sequences with >1020 base pairs. Surveillance drug resistance mutations (SDRMs) were identified according to the 2009 WHO list for surveillance of TDR,¹ using the Stanford calibrated population resistance analysis tool (version 5.0).¹⁸ Subtype classification and recombinant patterns were determined using REGA¹⁹ and SCUEAL²⁰ subtyping tools.

Statistical methods

The survey sample size was estimated from the hypothesis that TDR prevalence increased with time (from 0 to 10%). To detect such an increase with 80% power using a two-sided significance level of 0.05, the required number of HIV sequences per geographic area was 71. Assuming 20% assay failures, the target sample was 85 individuals. The proportions of sequences containing at least one SDRM were calculated by each of three drug classes: nucleoside reverse transcriptase inhibitors (NRTIs), NNRTIs, and protease inhibitors (PIs). TDR prevalence was estimated with a 95% confidence interval (CI) based on the binomial distribution. As a secondary analysis, the WHO-recommended truncated sampling technique was used to categorize TDR prevalence as low (<5%), moderate (5–15%), or high (>15%) for each of the three drug classes, based on the testing of the first 47 sequences.²¹ Categorical data were compared using the chi-square test and continuous data with the Wilcoxon rank-sum or Student's t-test. All analyses were performed using Stata version 10 (StataCorp LP, College Station, TX).

Results

Participant characteristics

Study enrollment took place from May 2009 to March 2010 at four VCT centers in Mombasa, Kenya. A total of 241 individuals were screened, of whom 93 (38.6%) were enrolled. Protocol violations were noted in 12 individuals (8 had a previous pregnancy and 4 had no laboratory evidence of recent infection). Finally, 81 participants (11 from Coast Provincial General Hospital, 51 from Likoni Sub-District Hospital, 11 from Kisauni Health Centre, and 8 from Magongo Health Centre AIC) were included in the analysis. Sixty-one participants were included based on the age criterion and 20 participants had a new confirmed HIV-1 diagnosis after a recent negative test (median 6.7 months ago, range 1.5–12 months). The median age was 23.4 years (IQR 21.6–24.9). Except for age, participant characteristics were similar for those included based on the age criterion and those with laboratory evidence of recent infection (Table 1). The median age at sexual debut was 16 years (IQR 14–18); all participants reported that they had engaged in unprotected sex in the 3 years prior to study enrolment. Among participants with a steady sexual partner, 40 (49.4%) were unaware of their partner's HIV status.

Table 1.

Participant Characteristics by Criteria to Identify Recent Infection

	Total	Age 18–24	Laboratory evidence	p
Participants	81	61	20
Sex				0.678
Female	71 (87.7)	54 (88.5)	17 (85.0)
Male	10 (12.4)	7 (11.5)	3 (15.0)
Age—median years (IQR)	23.4 (21.6–24.9)	21.4 (22.6–23.5)	28.2 (31.5–34.2)	<0.001
Kenyan nationality	79 (97.5)	60 (98.4)	19 (95.0)	0.401
Marital status				0.210
Married/cohabiting	65 (80.3)	48 (78.7)	17 (85.0)
Divorced/separated	3 (3.7)	2 (3.3)	1 (5.0)
Widowed	4 (4.9)	2 (3.3)	2 (10.0)
Never married/single	9 (11.1)	9 (14.8)	—
Education level				0.518
None/illiterate	3 (3.8)	2 (3.3)	1 (5.3)
Primary school	46 (57.5)	35 (57.4)	11 (57.9)
Secondary school	25 (31.3)	18 (29.5)	7 (36.8)
Higher education	6 (7.5)	6 (9.8)	—
Main occupation				0.860
None/at home	48 (59.3)	35 (57.4)	13 (65.0)
Employed	31 (38.3)	24 (39.3)	7 (35.0)
Student	2 (2.5)	2 (3.3)	—
Hemoglobin—median g/dl (IQR)	10.9 (8.7–12.3)	10.9 (8.7–12.3)	10.9 (9.2–12.3)	0.705
CD4 cell count—median cells/μl (IQR)	400 (239–564)	404 (246–589)	325.5 (230–550)	0.547
HIV RNA—median log₁₀ c/ml (IQR)	4.6 (4.2–5.1)	4.6 (4.3–5.1)	4.6 (3.5–5.1)	0.440

Data represent n (%) unless otherwise specified. IQR, interquartile range.

Genotypic profiles

Of 76 specimens with HIV-RNA >1000 copies/ml, 72 were successfully genotyped and 4 failed to amplify or generated a poor sequence. Additionally, four sequences were excluded from analysis because they contained less than 1020 base pairs. An SDRM was identified in 9 (13.2%, 95% CI 6.2-23.6%) of the 68 valid sequences. The respective TDR prevalence was 1.5% (95% CI 0.04–7.9%) for NRTIs, 7.4% (95% CI 2.4–16.3%) for NNRTIs, and 4.4% (95% CI 0.9–12.4%) for PIs. We observed five different SDRMs: K70R and K103N in reverse transcriptase and I85V, N88D and L90M in protease. TDR was confined to a single drug class in all sequences. Table 2 summarizes the characteristics of the nine participants who harbored an SDRM. Using the WHO-recommended truncated sequential sampling technique, 5 of the first 47 sequences harbored any SDRM (moderate prevalence), of which 3 were NNRTI-associated (moderate prevalence), 2 were protease inhibitor-associated (low prevalence), and none was NRTI-associated (low prevalence). HIV-1 subtype A (n=48, 70.6%) was observed most frequently, followed by unique recombinant forms (n=14, 20.6%), C (n=4, 5.9%), and D (n=2, 2.9%).

Table 2.

Characteristics of the Participants Who Harbored a Drug Resistance Mutation

								DRMs
ID	Date of enrollment	Inclusion criterion ^a	Age (years)	Sex	CD4 count (cells/μl)	HIV-1 RNA load (log₁₀ c/ml)	Viral subtype	NRTI	NNRTI	PI
KECGP041	29-01-2010	Lab evidence	35	M	369	4.6	A1			I85V
KEKSN408	23-10-2009	Lab evidence	33	M	508	2.7	A1/D		K103N
KELKN209	25-01-2010	Age	21	F	476	4.5	A1		K103N
KELKN227	23-02-2010	Age	23	M	87	6.1	A1//C/D			N88D
KELKN252	05-03-2010	Lab evidence	30	M	564	4.9	A1/D	K70R
KELKN256	11-03-2010	Lab evidence	36	F	814	3.6	A/D			L90M
KELKN260	11-03-2010	Lab evidence	32	F	497	4.4	A1		K103N
KELKN262	12-03-2010	Age	23	F	487	5.3	A2/D		K103N
KEMGO602	02-06-2009	Age	24	F	374	4.6	A1		K103N

Study eligibility was based on age (newly diagnosed with HIV and aged between 18 and 25 years) or on laboratory evidence of recent HIV infection (defined as a confirmed positive antibody test with a negative antibody test in the past 12 months or an indeterminate/negative antibody test with detectable HIV-RNA or p24 antigen).

DRM, drug-resistance mutation; NRTI, nucleoside reverse transcriptase inhibitor; NNRTI, nonnucleoside reverse transcriptase inhibitor; PI, protease inhibitor.

This survey among 68 newly HIV diagnosed VCT clients in Mombasa, Kenya, demonstrated an estimated TDR prevalence of 7.4% for NNRTIs, 4.4% for PIs, and 1.5% for NRTIs; no multiclass resistance was detected. The TDR prevalence corresponded with the WHO-recommended truncated sampling technique, yielding “moderate” prevalence overall for the NNRTI drug class and “low” prevalence for the NRTI and PI drug classes. To our knowledge, our survey is among the first to systematically quantify TDR in the coastal region of Kenya. Our findings support the hypothesis that a rise in TDR is occurring with increasing antiretroviral drug exposure.

Few studies of primary HIV drug resistance have been conducted in Kenya. The PASER-Monitoring study among chronically infected, ARV-naive adults revealed an HIV drug resistance prevalence of 4.9% in Mombasa and 4.5% in Nairobi.¹⁶ A study conducted in Nairobi revealed reverse transcriptase mutations in 7.5% of 53 ARV-naive patients initiating ART.¹⁵ The International AIDS Vaccine Initiative cohort of newly infected individuals found a TDR prevalence of 3.1% among 64 participants tested at three sites in Kenya (Nairobi, Mtwapa, and Kilifi).¹¹ A trend toward increasing prevalence over time was found, with a prevalence of 10% in 2009, but numbers of participants per time period were too small to draw significant conclusions.

Discussion

To date, most surveys conducted in sub-Saharan Africa using WHO methodology have reported low levels of TDR.^{8
–10,22,23} By contrast, recent surveys conducted in Kampala,¹² Dar es Salaam,¹³ and Yaoundé²⁴ detected higher TDR rates (9–12%) compared to earlier estimates in the same regions, suggesting a trend of increasing TDR. Our study could unfortunately not be compared to an earlier survey in Mombasa. However, in the context of prior studies in East-Africa and the available data from chronically infected patients, our data likely represent a rise in TDR among newly infected individuals.

Subtype analysis revealed a predominance of HIV subtype A (70.6%), but also many unique recombinant forms (20.6%). This corroborates a recent study reporting high subtype recombination in a coastal town in Kenya.²⁵ Our study participants were recruited from the urban setting of Mombasa, a major port with worldwide shipping links, and therefore importation of new HIV-1 strains into the area is likely. These specific characteristics of Mombasa should also be taken into account when interpreting the observed TDR rate, as it may not be extrapolated to more rural, isolated settings in Kenya.

The presence of transmitted resistance mutations has important implications for clinical management of patients in whom ART is indicated, as their risk of virological failure is increased.^3,4 In our study, NNRTI-associated mutations were most common, which is consistent with the widespread use of this drug class for first-line ART and for prophylaxis to prevent vertical transmission. NNRTIs have a low genetic barrier for the development of resistance²⁶ and mutations may persist for a long time.²⁷ As NNRTIs are the cornerstone of standard first-line ART in sub-Saharan Africa, reduced susceptibility to this drug class is especially worrisome in view of the limited availability of alternative drug options.

Our study has the following limitations. The WHO-recommended proxy criteria for recent infection were used, which have been suggested to agree poorly with laboratory-based methods to detect recent infection.²² However, our study included a relatively high number of participants with laboratory evidence of seroconversion and their characteristics, apart from age, did not differ significantly with the participants selected by proxy criteria. Furthermore, although the study specifically selected ARV-naive participants, any unknown or undisclosed prior exposure cannot be ruled out completely.

In conclusion, 7 years after large-scale ART introduction in Kenya, this survey demonstrated a high rate of TDR among newly HIV diagnosed persons in Mombasa. These findings have important clinical consequences as TDR can impair the response to standard first-line ART. Increased vigilance is warranted and TDR surveys should be repeated at regular intervals in order to inform treatment guidelines and drug choices. Surveillance of TDR will help to avoid unnecessary costs of using less-effective first-line regimens and consequent regimen failure. The implementation of an affordable, simplified HIV drug resistance test could help to make TDR threshold surveys more routinely applicable in field conditions in African countries.

GenBank Accession Numbers

GenBank sequence accession numbers were JN628461–JN628533.

Footnotes

Acknowledgments

The authors are grateful to all survey participants and the support staff at the International Center for Reproductive Health in Mombasa, Kenya and at PharmAccess Foundation in Amsterdam, The Netherlands. PASER is part of the LAASER program (Linking African and Asian Societies for an Enhanced Response to HIV/AIDS), a partnership of Stichting Aids Fonds, The Foundation for AIDS Research (amfAR)—TREAT Asia, PharmAccess Foundation, and International Civil Society Support.

K.M., R.L.H., N.N., and T.F.R.W. designed the study. K.M. and K.C.E.S. coordinated the field work. F.O. and I.J. supervised data collection. F.L., B.M., and A.K. performed laboratory testing. K.C.E.S. analyzed the data and wrote the first draft of the manuscript. K.M., R.L.H., N.N., and T.F.R.W. critically reviewed the paper. All authors contributed to subsequent drafts and reviewed and approved the final manuscript.

PASER is an initiative of PharmAccess Foundation, supported by the Ministry of Foreign Affairs of The Netherlands through a partnership with Stichting Aids Fonds (Grant 12454). The funders had no role in the study design, data collection, data analysis, data interpretation, decision to publish, or writing of the report. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of any of the institutions mentioned above.

Author Disclosure Statement

No competing financial interests exist.

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