2018
DOI: 10.18632/aging.101603
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Abstract: We performed a systematic evaluation of the relationships between locomotor activity and signatures of frailty, morbidity, and mortality risks using physical activity records from the 2003-2006 National Health and Nutrition Examination Survey (NHANES) and UK BioBank (UKB). We proposed a statistical description of the locomotor activity tracks and transformed the provided time series into vectors representing physiological states for each participant. The Principal Component Analysis of the transformed data rev… Show more

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Cited by 30 publications
(59 citation statements)
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References 43 publications
(47 reference statements)
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“…In agreement with our findings here and in the technically similar analysis of physical activity data (Pyrkov et al 2018a), the BAA of the DNAm Phenoage was associated with smoking and, in the same time, was not different in groups of never smokers and those who quit smoking early in life. We note, that the BAA remained significantly associated with smoking even if we restrict ourselves to the chronic disease-free subjects only and hence could not be simplified only to the excess of the disease burden inflicted by smoking.…”
Section: Discussionsupporting
confidence: 93%
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“…In agreement with our findings here and in the technically similar analysis of physical activity data (Pyrkov et al 2018a), the BAA of the DNAm Phenoage was associated with smoking and, in the same time, was not different in groups of never smokers and those who quit smoking early in life. We note, that the BAA remained significantly associated with smoking even if we restrict ourselves to the chronic disease-free subjects only and hence could not be simplified only to the excess of the disease burden inflicted by smoking.…”
Section: Discussionsupporting
confidence: 93%
“…The risk models, however, require follow-up information involving the incidence of age-related diseases or death. The exponential nature of mortality and morbidity acceleration implies that a risk model could be approximated by a logistic regression (Abbott 1985, Green and Symons 1983, Pyrkov et al 2018a) to health status. This is especially useful, since morbidity data is easier to collect.…”
Section: Discussionmentioning
confidence: 99%
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“…Prediction of age and lifespan based on health parameters is an on-going challenge of medicine. So far, human biological age can be predicted from gene expression (Peters et al, 2015), DNA methylation profiles (Hannum et al, 2013), or physical activity (Pyrkov et al, 2018), but cohorts allowing for lifespan predictions are still lacking. In C. elegans, individual features such as maximum velocity (at day 9 of adulthood) or a combination of a few features such as movement, autofluorescence or body size were previously shown to correlate well with lifespan or to predict prognosis (Hahm et al, 2015;Zhang et al, 2016).…”
Section: Discussionmentioning
confidence: 99%
“…In C. elegans, the previous method based on the aggregation of 5 predictive biomarkers of biological age recorded in one strain did not assess this possibility 5 . In human, data medicine has defined molecular markers predictive of the biological age such as gene expression 24 , DNA methylation profiles 25 or functional parameters such as physical activity 26 . Based on one or few of these predictive markers it is possible to quantify the difference between the biological and the chronological age of an individual.…”
Section: Precise Representation Of Ageing By Multivariate Approachmentioning
confidence: 99%