HIV genetic diversity informs stage of HIV-1 infection among patients receiving antiretroviral therapy in Botswana.
Ragonnet-Cronin M., Golubchik T., Moyo S., Fraser C., Essex M., Novitsky V., Volz E., with the PANGEA Consortium None.
BACKGROUND: HIV-1 genetic diversity increases during infection and can help infer the time elapsed since infection. However the effect of antiretroviral treatment (ART) on the inference remains unknown. METHODS: Participants with estimated duration of HIV-1 infection based on repeated testing were sourced from cohorts in Botswana (n=1944). Full-length HIV genome sequencing was performed from proviral DNA. We optimized a machine learning model to classify infections as < or >1 year based on viral genetic diversity, demographic and clinical data. RESULTS: The best predictive model included variables for genetic diversity of HIV-1 gag, pol and env, viral load, age, sex and ART status. Most participants were on ART. Balanced accuracy was 90.6% (95%CI:86.7%-94.1%). We tested the algorithm among newly diagnosed participants with or without documented negative HIV tests. Among those without records, those who self-reported a negative HIV test within <1 year were more frequently classified as recent than those who reported a test >1 year previously. There was no difference in classification between those self-reporting a negative HIV test <1 year, whether or not they had a record. CONCLUSIONS: These results indicate that recency of HIV-1 infection can be inferred from viral sequence diversity even among patients on suppressive ART.