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Measuring Immunological Age: From T Cell Repertoires to Populations

  • Elena N. Naumova
  • Yuri N. Naumov
  • Jack Gorski
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Abstract

Chronological aging is an irreversible phenomenon governed by the clock and calendar. However, the aging of the immune system might accelerate or decelerate relative to chronological age. Nevertheless, there is a stage at which the immune system no longer properly responds to external or internal stressors, referred to as immunosenescence. If we understand the factors that affect the maintenance or decline of immune responses, we might learn to predict and potentially delay immunosenescence. Here we aim 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 can be achieved 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, and it can be modeled as a self-similar fractal system. Our models indicate that at a certain point the continuing exposures to influenza begin to decrease the diversity of the response, and we present cohort data that this is indeed the case. At the population level, the responses to flu are equally diverse and this diversity allows us to make an inference to “immunological kinship” and “immunological age.” Using big data, we quantified immunological age based on the probability of severe complications after influenza exposure among 36M older adults. We finish by outlining new directions for clinical, experimental, and epidemiological studies of immunosenescence.

Keywords

Influenza T cell repertoire Diversity Connectivity Immunological kinship Tipping point Immunosenescence Immunological age US elderly Hospitalization 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Elena N. Naumova
    • 1
    • 2
  • Yuri N. Naumov
    • 3
    • 4
  • Jack Gorski
    • 5
  1. 1.Friedman School of Nutrition Science and PolicyTufts UniversityBostonUSA
  2. 2.Initiative for the Forecasting and Modeling of Infectious DiseasesTufts UniversityMedfordUSA
  3. 3.Department of PathologyUniversity of Massachusetts Medical SchoolWorcesterUSA
  4. 4.Smart Diagnostics MedicaBostonUSA
  5. 5.Blood Research InstituteBloodCenter of WisconsinMilwaukeeUSA

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