DOI: 10.3390/v18111223 ISSN: 1999-4915

A Molecular Epidemiological Study of HIV-1 Transmission in the Republic of Armenia

Svetlana V. Bayandina, Anastasia A. Karlsen, Alina A. Kirichenko, Ilya A. Lapovok, Alexey E. Lopatukhin, Olga A. Osadchaya, Anastasia V. Shlykova, Arina V. Syrkina, Mikhail A. Vinokurov, Lilit G. Petrosyan, Susanna G. Muradyan, Kristine V. Lalayan, Siranush V. Martoyan, Tamara V. Hovsepyan, Vardan G. Arzakanyan, Dmitry E. Kireev, Vasily G. Akimkin

The HIV-1 epidemic in Eastern Europe and Central Asia (EECA) remains a major public health burden, but molecular epidemiological data for several countries, including the Republic of Armenia, are limited. To address this gap, we analyzed 1549 HIV-1 pol sequences from samples collected in the Republic of Armenia between 2017 and 2024, representing 29.9% of all people living with HIV in the country. Molecular networks were constructed using a genetic distance threshold of 0.015 substitutions/site, and multivariable logistic regression was performed to identify factors associated with clustering. A total of 132 molecular clusters (449 sequences, clustering rate 29.0%) were identified. Independent predictors of clustering included male-to-male sexual contact (aOR = 2.43), having a labor migrant partner living with HIV (aOR = 1.56), and residence in Yerevan (aOR = 1.40). Conversely, lower odds of clustering were associated with virus exposure in the Russian Federation (aOR = 0.47) and with age at diagnosis between 45 and 55 years (aOR = 0.49). Large clusters (≥10 individuals) were predominantly composed of young MSM residing in Yerevan (p < 0.001 for all). These findings suggest that young MSM in Yerevan play a role in local HIV transmission. At the same time, our data lead us to hypothesize that labor migrants working in the Russian Federation may act as a “bridge” between cross-border and local HIV-1 molecular networks through their sexual partners. This study provides a detailed molecular characterization of the HIV epidemic in the Republic of Armenia based on relatively high sampling density and identifies priority populations for targeted prevention efforts.

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