Human aging is a complex process with several factors interacting. One of the ways to identify patterns about human aging is longitudinal population studies. In this work, we identified longevity profiles through a process of knowledge discovery. After identifying the profiles, we apply triadic rules which allow extracting rules of implication with conditions. These rules can be used to identify related factors, in the various waves, of longitudinal studies, which can better explain the conditions that favor longevity profiles.The results show that the triadic analysis is efficient to allow the analysis of the temporal evolution of clinical or environmental conditions that favor certain profiles when databases of longitudinal studies are considered.