Evidence map›Paper›PMID 41057785›Full record

ArticleBMC infectious diseases2025

Prevalence of drug resistance mutations and their association with time to virological failure in people living with HIV in Uganda.

Collins Ankunda, Jude Emunyu, Sharon Namasambi, Conrad Sserunjogi, Brendah Kyomuhangi, Iving Mumbere, Jane Nakaweesi

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Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Collins AnkundaDepartment of Pharmacology and Therapeutics, Makerere University, Kampala, Uganda. ankundacollins@gmail.com.
Jude EmunyuMakerere University Walter Reed Program, Kampala, Uganda.
Sharon NamasambiUganda Public Health Fellowship Program, National Institute of Public Health, Kampala, Uganda.
Conrad SserunjogiMildmay Research Centre Uganda, Entebbe Rd, Naziba Hill, Kampala, Uganda.
Brendah KyomuhangiEntebbe Regional Referral Hospital, Entebbe, Uganda.
Iving MumbereSchool of Public Health, Makerere University, Kampala, Uganda.
Jane NakaweesiBaylor College of Medicine Children's Foundation, Kampala, Uganda.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionDespite advancements in antiretroviral therapy (ART), virological failure and drug resistance mutations (DRMs) continue to pose major challenges, especially in resource-limited settings. This study examined DRM prevalence and their association with time to virological failure, informing strategies to optimize HIV treatment outcomes.

methodsThis cohort study retrospectively reviewed medical records of 235 People living with HIV (PLHIV) who underwent HIV drug resistance testing at the Mildmay-Uganda Hospital from 2018 to 2023. Data on demographics, ART, and DRMs were analyzed. Kaplan-Meier and log-rank tests were used to compare median time to virological failure. Cox proportional hazards models assessed associations factors associated with time to virological failure, with significance set at p < 0.05.

resultsParticipants had a mean age of 28.0 years, 11.2 years on ART, and DRMs detected in 59.6%. Mutation prevalence was NNRTIs (52.8%), NRTIs (50.2%), PI major (25.5%), and INSTI (≤ 0.9%). Key drug class mutations included, NNRTIs (K103KN 25.1%, G190AS 14.0%, Y181CF/V/F 12.3%), NRTIs (M184V 45.0%, T215FY 30.2%, M41L 18.3%), PIs (M46I 15.7%, V82VA 12.3%, I54LV 9.4%), and INSTIs (E138K, T66A, G118R, E92EV all at 0.4%). DRMs were significantly associated with longer time (months) to virological failure: NRTIs (129.3 vs. 88.2, p = 0.008), NNRTIs (127.4 vs. 84.2, p = 0.003), and PIs (170.9 vs. 88.7, p = 0.000). In cox regression analysis, time to virological failure was significantly associated with age (HR: 0.987; CI: 0.976-0.998; p = 0.019, aHR: 0.976; CI: 0.959-0.993; p = 0.007), ART duration (HR: 0.828; CI: 0.800-0.856; p = 0.000, aHR: 0.827; CI: 0.792-0.863; p = 0.000), drug mutations; NRTI (HR: 0.659; CI: 0.484-0.898; p = 0.008), NNRTI (HR: 0.628; CI: 0.463-0.851; p = 0.003), and PI (HR: 0.257; CI: 0.146-0.453; p = 0.000, aHR: 0.337; CI: 0.137-0.831; p = 0.018).

conclusionDrug resistance is common among PLHIV failing ART, dominated by NNRTI and NRTI mutations. DRMs were present among PLHIV who took a longer time to virological failure. Delayed failure was independently associated with older age, longer ART duration, and PI mutations. Findings support early DRM testing and individualized ART, especially as newer ART options emerge.

Indexed as

Anti-HIV AgentsDrug Resistance, ViralHIV-1HIV InfectionsMutationAdolescentAdultFemaleHumansMaleMiddle AgedPrevalenceRetrospective StudiesTreatment FailureUgandaViral LoadAnti-HIV AgentsAntiretroviral therapyHIVHIV drug resistance mutationsVirological failure

Identifiers

PMID41057785
PMCPMC12502157

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.