RT Journal Article
SR Electronic
T1 Quantitative Characterization of Biological Age and Frailty Based on Locomotor Activity Records
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 186569
DO 10.1101/186569
A1 Timothy V. Pyrkov
A1 Evgeny Getmantsev
A1 Boris Zhurov
A1 Konstantin Avchaciov
A1 Mikhail Pyatnitskiy
A1 Leonid Menshikov
A1 Kristina Khodova
A1 Andrei V. Gudkov
A1 Peter O. Fedichev
YR 2017
UL http://biorxiv.org/content/early/2017/09/09/186569.abstract
AB Since chronological age is not a complete and accurate indicator of organism aging, the concept of biological age has emerged as a well-accepted way to quantify the aging process in humans and laboratory animals. In this study, we performed a systematic statistical evaluation of the relationships between locomotor activity and biological age, mortality risk, and frailty using human physical activity records from the 2003-2006 National Health and Nutrition Examination Survey (NHANES) and UK BioBank (UKBB) databases. These records are from subjects ranging from 5 to 85 years old and include 7-day long continuous tracks of activity provided by wearable monitors as well as data for a comprehensive set of clinical parameters, lifestyle information and death records, thus enabling quantitative assessment of frailty and mortality. We proposed a statistical description of the locomotor activity tracks and transformed the provided time series into vectors representing individual physiological states for each participant. Using this data, we performed an unsupervised multivariate analysis and observed development and aging as a continuous trajectory consisting of distinct phases, each corresponding to subsequent human life stages. Therefore, we suggest the distance measured along this trajectory as a definition of the biological age. Consistent with the Gompertz law, mortality, estimated with the help of a proportional hazard model, was found to be an exponential function of biological age as quantified herein. However, we observed that the significant contribution of clinical frailty to mortality risk can be independent of biological age. We used the biological age and mortality models to show that some lifestyle variables, such as smoking, produce a reversible increase in all-cause mortality without a significant effect on biological age. In contrast, medical conditions, such as type 2 diabetes mellitus (T2DM) or hypertension, are associated with significant aging acceleration and a corresponding increase in mortality as well. The results of this work demonstrate that significant information relevant to aging can be extracted from human locomotor activity data and highlight the opportunity provided by explosive deployment of wearable sensors to use such information to encourage lifestyle modifications and clinical development of therapeutic interventions against the aging process.