Hacking Aging: A Strategy to Use Big Data From Medical Studies to Extend Human Life

Age is the most important single risk factor for the incidence of chronic diseases and ultimately, death. The mortality rate increases exponentially with age and doubles approximately every eight years, as described by the Gompertz law of mortality. The incidence of specific diseases, such as cancer or stroke, also accelerates after the age of about forty, and doubles at a rate that mirrors the mortality rate doubling time. It is therefore entirely plausible to think that there is a single underlying process, the driving force behind the progressive reduction of the organism's fitness leading to the increasing susceptibility to diseases and death, that is aging itself. There is, however, no fundamental law of nature requiring exponential morbidity and mortality risk trajectories. The acceleration of mortality is thus the most important characteristics of aging process. It varies dramatically even among closely related mammalian species and hence appears to be a tunable phenotype. Here we follow how big data from large human medical studies, and analytical approaches borrowed from physics of complex dynamic systems can help to reverse engineer the underlying biology behind Gompertz mortality law. With this approach we hope to generate predictive models of aging and for systematic discovery of biomarkers of aging followed by identification of novel therapeutic targets for future anti-aging interventions.