Alzheimer’s Disease (AD) is a complex, multifactorial and comorbid condition. The asymptomatic behavior in early stages of the disease is a paramount obstacle to formulate a preclinical and predictive model of AD. Not surprisingly, the AD drug approval rate is one of the lowest in the industry, an exiguous 0.4%. The identification of risk factors, preferably obtained by the subject herself, is sorely needed given that the incidence of Alzheimer’s disease grows exponentially with age [Ferri et al., 2005], [Ganguli and Rodriguez, 2011].During the last 7 years, researchers at Proyecto Vallecas have collected information about the project’s volunteers, aged 70 or more. The Proyecto Vallecas dataset includes information about a wide range of factors including magnetic resonance imaging, genetic, demographic, socioeconomic, cognitive performance, subjective memory complaints, neuropsychiatric disorders, cardiovascular, sleep, diet, physical exercise and self assessed quality of life. The subjects in each visit were diagnosed as healthy, mild cognitive impairment (MCI) or dementia.In this study we perform Exploratory Data Analysis to summarize the main characteristics of this unique longitudinal dataset. The objective is to characterize the evolution of the collected features over time and most importantly, how their dynamics are related to cognitive decline. We show that the longitudinal dataset of Proyecto Vallecas, if conveniently exploited, holds promise to identifying either factors promoting healthy aging and risk factors related to cognitive decline.