HIV-1 Drug Resistance Patterns, Co-Accumulation of Resistance Classes, and Predictors of High-Level NRTI Resistance Among Patients with Virologic Failure in Kenya: A Cross-Sectional Analysis

Background: HIV-1 drug resistance (HIVDR) threatens the durability of antiretroviral therapy (ART) programmes in sub-Saharan Africa, where non-B subtypes predominate, yet national data linking mutational patterns to patient-level correlates remain scarce. Methods: We analysed 521 Kenyan patients with confirmed virologic failure referred for genotypic resistance testing (2020–2025). Resistance was interpreted with the Stanford HIVdb (v10.1), and pairwise dependencies between high-level resistance classes were quantified descriptively with mutual information (MI) normalised to marginal entropy. Multivariable logistic regression identified factors associated with high-level NRTI resistance in patients with complete covariate data (n = 299). Results: High-level resistance was present in 386 patients (74.1%, 95% CI 70.2–77.7) for NRTIs, 401 (77.0%, 73.2–80.4) for NNRTIs and 179 (34.4%, 30.4–38.5) for PIs; M184V (65.3%) and K103N (34.9%) predominated. NRTI and PI resistance co-accumulated (MI = 0.121 nats, 19% of PI entropy), whereas dependencies involving NNRTI resistance were weak (MI = 0.015, 3%). Older age was the only factor associated with high-level NRTI resistance (adjusted OR 1.05 per year, 95% CI 1.02–1.08); neither subtype nor NRTI backbone was. Conclusions: Extensive multiclass resistance is already established at virologic failure, supporting earlier failure detection, timely regimen switching and expanded access to dolutegravir-based regimens.

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Publication Details

Journal
Preprints.org
Published
2026-11-09
DOI
https://doi.org/10.20944/preprints202611.0792.v1
Primary Topic
HIV/AIDS drug development and treatment
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preprint
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preprint

HIV-1 Drug Resistance Patterns, Co-Accumulation of Resistance Classes, and Predictors of High-Level NRTI Resistance Among Patients with Virologic Failure in Kenya: A Cross-Sectional Analysis

Kamene Kimenye, Leonard Kingwara, Bhavna H. Chohan, Winnie C. Mutai et al.
Preprints.org
HIV/AIDS drug development and treatment
preprint

HIV-1 Drug Resistance Patterns, Co-Accumulation of Resistance Classes, and Predictors of High-Level NRTI Resistance Among Patients with Virologic Failure in Kenya: A Cross-Sectional Analysis

Kamene Kimenye, Leonard Kingwara, Bhavna H. Chohan, Winnie C. Mutai, James Wagude, Julius Oyugi, Charles Rombo, Seth Inzaule, Babra Mambo
preprint en

Abstract

Background: HIV-1 drug resistance (HIVDR) threatens the durability of antiretroviral therapy (ART) programmes in sub-Saharan Africa, where non-B subtypes predominate, yet national data linking mutational patterns to patient-level correlates remain scarce. Methods: We analysed 521 Kenyan patients with confirmed virologic failure referred for genotypic resistance testing (2020–2025). Resistance was interpreted with the Stanford HIVdb (v10.1), and pairwise dependencies between high-level resistance classes were quantified descriptively with mutual information (MI) normalised to marginal entropy. Multivariable logistic regression identified factors associated with high-level NRTI resistance in patients with complete covariate data (n = 299). Results: High-level resistance was present in 386 patients (74.1%, 95% CI 70.2–77.7) for NRTIs, 401 (77.0%, 73.2–80.4) for NNRTIs and 179 (34.4%, 30.4–38.5) for PIs; M184V (65.3%) and K103N (34.9%) predominated. NRTI and PI resistance co-accumulated (MI = 0.121 nats, 19% of PI entropy), whereas dependencies involving NNRTI resistance were weak (MI = 0.015, 3%). Older age was the only factor associated with high-level NRTI resistance (adjusted OR 1.05 per year, 95% CI 1.02–1.08); neither subtype nor NRTI backbone was. Conclusions: Extensive multiclass resistance is already established at virologic failure, supporting earlier failure detection, timely regimen switching and expanded access to dolutegravir-based regimens.

Preprints.org
Good health and well-being
HIV/AIDS drug development and treatment
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