García-Peña C, et al. - Researchers used data from the Mexican Health and Aging Study (main data 2012, mortality 2015) in order to generate in vivo evidence that frailty works as a complex network, given frailty is still a challenge in the aging research area with a number of gaps in knowledge yet to be filled. They operationalized frailty with a 35-deficit frailty index (FI). Deficits plus death constituted the analyzed nodes. Via structural learning, they acquired edges linking those nodes and derived an undirected graph related to a discrete probabilistic graphical model (Markov network). They derived graph structure using two algorithms, hill-climbing and Peter and Clark. This study included 10,983 adults aged 50 or older. Experts concluded that modeling of frailty phenomenon is possible using a Bayesian network. Using the whole sample, self-report of health (most connected node) and difficulty walking a block were identified as the most central nodes, and interconnection of all deficits associated with mobility was seen. Taking frailty levels into account, variation in the most connected nodes was seen, however, these were associated with vitality, principally at lower frailty levels. Experts concluded that not all deficits were equally associated as clusters of very associated deficits and non-connected deficits were obtained, which might be taken into account in the development of the FI score.