Ram Gouripeddi, MBBS, MS; Julio Facelli, PhD
Competition Sponsor: US National Academy of Medicine
Effects of a lifetime of environmental and lifestyle exposures on aging or age-associated diseases are not well understood. Exposures not only directly affect biological pathways but also have genetic mechanistic influences on disease and well-being. Aging leads to reduced reserve capacity to compensate for the effects of exposures due to changes at molecular and cellular levels. The challenge in generating aging exposome is the absence of readily available records for individuals over the course of their life. Instead, these would need to be assimilated from historic person reported data along with publically available data. In this proposal we explore the use of machine learning and artificial intelligence to generate exposome records and demonstrate their value in aging health studies.