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Biological Age is a Universal Marker of Aging, Stress, and Frailty

  • Timothy V. PyrkovEmail author
  • Peter O. Fedichev
Chapter
Part of the Healthy Ageing and Longevity book series (HAL, volume 10)

Abstract

We carried out a systematic investigation of supervised learning techniques for biological age modeling. The biological aging acceleration is associated with the remaining health- and life-span. Artificial Deep Neural Networks (DNN) could be used to reduce the error of chronological age predictors, though often at the expense of the ability to distinguish health conditions. Mortality and morbidity hazards models based on survival follow-up data showed the best performance. Alternatively, logistic regression trained to identify chronic diseases was shown to be a good approximation of hazards models when data on survival follow-up times were unavailable. In all models, the biological aging acceleration was associated with disease burden in persons with diagnosed chronic age-related conditions. For healthy individuals, the same quantity was associated with molecular markers of inflammation (such as C-reactive protein), smoking, current physical, and mental health (including sleeping troubles, feeling tired or little interest in doing things). The biological age thus emerged as a universal biomarker of age, frailty and stress for applications involving large scale studies of the effects of longevity drugs on risks of diseases and quality of life.

Keywords

Biological age Frailty index Blood sample Questionnaire Self-reported NHANES Inflammation Physical health Mental health 

Notes

Acknowledgements

The authors would like to thank Konstantin Avchaciov from Gero team for proof reading and thoughtful comments. The work was supported by Gero LLC.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Gero LLCMoscowRussia
  2. 2.Moscow Institute of Physics and TechnologyDolgoprudnyRussia

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