Measuring Immunological Age: From T cell Repertoires to Populations

Abstract

While an individual is aging chronologically day-by-day irreversibly, the age of immune system might accelerate or decelerate. If we know the factors that affect the decline of immune responses, we might learn to predict and potentially delay immunosenescence, the stage at which the immune system stops properly respond to internal and external stressors. Here we aims to demonstrate that it is realistic and plausible to use immune responses to seasonal influenza as a measure of immunosenescence on both individual and population levels and from immunological and epidemiological points of view. This type of questions can be explored by combining experimental research, computational modeling, and data mining of big administrative databases of medical claims. At the molecular level, we observe that a memory immune response to flu becomes diverse upon repeated exposures to the virus, yet it can be modeled as a fractal self-similar system. Although each encounter with an infectious agent is unique for every person, the commonality in responses forms an “immunological kinship” among affected individuals. Our models indicate that at a certain point the continuing exposures to influenza begin to decrease the diversity of the response. At the population level, the responses to flu are diverse as well and such diversity allows us to make an inference to “immunological kinship” and “immunological age.” Using big data we quantified immunological age based on probability of severe complications to flu among 36M older adults residing in USA. We outlined the new directions for clinical, experimental, and epidemiological studies of immunosenescence.