ABSTRACT
Advances and availability of antiretroviral treatment has enabled a longer life expectancy for HIV population. However, with its chronicity, premature aging challenges the management of people living with HIV. This study, conducted between 2018-2020, aimed to identify an association between risk factors and premature aging, using the biological age estimated by artificial intelligence (AI) based on deep learning (Aging 3.0). This was a cross-sectional, analytical study, involving older people living with HIV (OPLHIV), 66.1% of whom were men. Premature aging was identified in 67.8%. The presence of cannabis and diabetes were significant (p = 0.045 and p = 0.042, respectively). For current and nadir CD4 + cell counts, participants were divided into groups comparing biological age (BA) and chronological age (CA). Just one group presented no premature aging, whereas the group with premature aging was subdivided into BA > CA up to 4 years and BA > CA in 5 or more years. In conclusion, premature aging was present in most of the OPLHIV. The use of cannabis was self-reported in those with higher BAs and those with a lower BA presented a higher prevalence of diabetes. Factors directly linked to HIV infection, lower current and nadir CD4 + counts were associated with premature aging.
