Generalized Additive Models (GAMs) are statistical methods that enable the modeling of variables through smooth effects. GAMs are powerful and flexible models, well-consolidated among statisticians. However, their application in medicine, especially in infectious diseases, is still limited. In this study, we applied GAMs to evaluate the dynamic of low-density lipoprotein (LDL) cholesterol in people living with HIV (PLHIV) who have switched to an antiretroviral regimen based on doravirine, rilpivirine, dolutegravir or bictegravir. Data were collected at IRCCS San Raffaele Scientific Institute and involve 2742 individuals and 54552 observations. A Generalized Additive Mixed Model (GAMM) and a quasi-Poisson GAMM were trained considering several risk factors and accounting for temporal dynamics through random effects and autoregressive covariance structures. Doravirine exhibited the most favorable effect on LDL cholesterol, with an associated average reduction in the first two years of use of \(-5.43\) mg/L per year according to the GAMM and of \(-5.41\%\) per year in the quasi-Poisson GAMM.