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Aging is a complex, multifactorial process, where different life stages reflect changes in metabolic processes, immune capacities, and genetic/epigenetic repertoires. With accumulating exposure to environmental stresses and deterioration of physiological functions, body systems become more prone to low-grade chronic inflammation and an increasing range of pathologies. We hypothesized that differential susceptibility to diseases across life span reflects phased changes in an organism’s physiological capacity that may highlight when interventions may be appropriately used. Furthermore, the number of life stages may vary between species and be impacted by signalment such as breed. We tested this hypothesis using disease diagnoses data from veterinary electronic medical records containing almost 2 million cats and over 4 million dogs. Bi-clustering (on rates of disease diagnoses) and adaptive branch pruning were used to identify age clusters that could be used to define adult life stages. Clustering among diagnoses were then interpreted within the context of each defined life stage. The analyses identified 5 age clusters in cats and 4 age clusters within each of the 4 canine breed size categories used. This study, using population scale data for two species, one with differential size and life expectancies, is the first to our knowledge to use disease diagnosis data to define adult life stages. The life stages presented here are a result of a data-driven approach to age and disease stratification and are intended to support conversations between clinicians and clients about appropriate health care recommendations.