Biological Age, derived from molecular and physiological measurements, has been proposed to better predict mortality and disease than chronological age. We have leveraged longitudinal deep phenotyping data from a real world cohort to develop a computed estimate of biological age. In our studies, biological age relative to chronological age, was elevated in the presence of chronic disease, providing evidence for the value of our approach. Since biological age is modifiable, it can be used as a metric (relative to chronological age) for healthy aging. Furthermore, repeated measurement of biological age over time can be used to determine the efficacy of interventions aimed to improve health. In fact, in our study, participation in wellness program increased the positive difference between chronological age and biological age over each year, suggesting a health-promoting effect of the program.
Recent papers describing this work:
Multi-Omic Biological Age Estimation and Its Correlation With Wellness and Disease Phenotypes: A Longitudinal Study of 3,558 Individuals. John C. Earls, Noa Rappaport, Laura Heath, Tomasz Wilmanski, Andrew T. Magis, Nicholas J. Schork, Gilbert S. Omenn, Jennifer Lovejoy, Leroy Hood, and Nathan D. Price. J Gerontol A Biol Sci Med Sci. 2019 Nov 13;74(Supplement_1):S52-S60. doi: 10.1093/gerona/glz220. PMID:31724055
Current Project Leads:
|John Earls||Nathan Price||Leroy Hood|