Quantitative Characterization of Biological Age and Frailty Based on Locomotor Activity Records

Abstract

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.

Universal transcriptomic signature of age reveals temporal scaling ofCaenorhabditis elegansaging trajectories

“…Recently, we observed evidence of critical dynamics in association with aging in a subset of the large-scale human 2003-2006 National Health and Nutrition Examination Survey (NHANES) dataset representing physical activity metrics [65]. Although aging dynamics in humans appears on a relatively slow (sub-exponential) time course, agerelated changes are responsible for most of the variance in the signal, and progress along the aging trajectory explains most of the variation in mortality.…”

Longitudinal analysis of blood markers reveals progressive loss of resilience and predicts ultimate human lifespan limit

“…Morbidity and mortality rates increase exponentially with age and a log-linear risk predictor model is a good starting point for characterization of the functional state of an organism and quantification of the aging process [15,25]. Accordingly, we employed Cox proportional hazards model [28] and trained it using the death register of the NHANES study using log-transformed CBC measurements and sex variable (but not age) as covariates.…”

“…Notably and in agreement with the dynamic nature of DOSI, the effect of smoking appeared to be reversible: while the age-and sex-adjusted DOSI means were higher in current smokers compared to non-smokers, they were indistinguishable between groups of individuals who never smoked and who quit smoking (c.f. [15,34]).…”

“…In [15] we observed, that the variability of physical activity (namely, the logarithm of the average physical activity), that is another hallmark of aging and is associated with age and risks of death or major deceases, also increases with age and hence maybe used as an organism state indicator. The autocorrelation function of the physical activity levels shows already familiar exponential profile and signs of the loss of resilience in subsequent age-matched cohorts as shown in Fig.…”

“…[3][4][5]) and diseasespecific mortality [6]. Much of our current understanding of the relationship between aging and changes in physiological variables over an organism's lifespan originates from large cross-sectional studies [7][8][9] and led to development of increasingly reliable "biological clocks" or "biological age" estimations reflecting age-related variations in blood markers [10], DNA methylation (DNAm) states [11,12] or patterns of locomotor activity [13][14][15] (see [16] for a review of biological age predictors). All-cause mortality in humans [17,18] and the incidence of chronic agerelated diseases increase exponentially and double every eight years [3].…”