Human aging is associated with a decline of physical and cognitive function and high susceptibility to chronic diseases, which is influenced by genetics, epigenetics, environmental, and socio-economic status. In order to identify the factors that modulate the aging process, established measures of aging mechanisms are required, that are both robust and feasible in humans. It is also necessary to connect these measures to the phenotypes of aging and their functional consequences. In this review, we focus on how this has been addressed from an epidemiologic perspective using proteomics. The key aspects of epidemiological models of aging can be incorporated into proteomics and other omics which can provide critical detailed information on the molecular and biological processes that change with age, thus unveiling underlying mechanisms that drive multiple chronic conditions and frailty, and ideally facilitating the identification of new effective approaches for prevention and treatment.
Keywords: aging models; biological pathways; epidemiological models; longitudinal cross-sectional; machine learning and artificial intelligence; phenotypes; proteomics; resilience.
Copyright © 2021 Ubaida-Mohien, Moaddel, Moore, Kuo, Faghri, Tharakan, Tanaka, Nalls and Ferrucci.