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This paper presents a data analysis framework to uncover relationships between health conditions, age and sex for a large population of patients. We study a massive heterogeneous sample of 1.7 million patients in Brazil, containing 47 million of health records with detailed medical conditions for visits to medical facilities for a period of 17 months. The findings suggest that medical conditions can be grouped into clusters that share very distinctive densities in the ages of the patients. For each cluster, we further present the ICD-10 chapters within it. Finally, we relate the findings to comorbidity networks, uncovering the relation of the discovered clusters of age densities to comorbidity networks literature., Author summary Age and sex of a patient can be directly related to susceptibilities to certain medical conditions. We present a method to generate clusters of human phenotype, based on the age of the population. This method helps extract knowledge on age and sex from the data. The age and sex correlations with disease conditions can help in a task of predicting the susceptibility of incoming patients to conditions.