Validation of molecular clock inferred HIV infection ages: Evidence for accurate estimation of infection dates.
- HIV Clinical and Translational Research
BACKGROUND: Improving HIV diagnosis, access to care and effective antiretroviral treatment provides our global strategy to reduce HIV incidence. To reach this goal we need to increase our knowledge about local epidemics. HIV infection dates would be an important information towards this goal, but they are largely unknown. To date, methods to estimate the dates of HIV infection are based mainly on laboratory or molecular methods. Our aim was to validate molecular clock inferred infection dates that were estimated by analysing sequences from 145 people living with HIV (PLHIV) with known transmission dates (clinically estimated infection dates). METHODS: All HIV sequences were obtained by Sanger sequencing and were previously found to belong to well-established molecular transmission clusters (MTCs). RESULTS: Our analysis showed that the molecular clock inferred infection dates were correlated with the clinically estimated ones (Spearman's Correlation coefficient=0.93, p<0.001) and that there was an agreement between them (Lin's concordance correlation coefficient=0.92, p<0.001). For the 61.4% of cases the molecular clock inferred preceded the clinically estimated infection dates. The median difference between clinically and molecularly estimated dates of infection was of 0.18 (IQR: -0.21, 0.89) years. The lowest differences were identified in people who inject drugs of our study population. CONCLUSIONS: The estimated time to more recent common ancestor (tMRCA) of nodes within clusters provides a reliable approximation of HIV infections for PLHIV infected within MTCs. Next-generation sequencing data and molecular clock estimates based on heterochronous sequences provide, probably, more reliable methods for inferring infection dates. However, since these data are not available in most of the HIV clinical laboratories, our approach, under specific conditions, can provide a reliable estimation of HIV infection dates and can be used for HIV public health interventions.