Quantitative characterization of biological age and frailty based on locomotor activity records
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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 revealed a winding trajectory with distinct segments corresponding to subsequent human development stages. The extended linear phase starts from 35-40 years old and is associated with the exponential increase of mortality risks according to the Gompertz mortality law. We characterized the distance traveled along the aging trajectory as a natural measure of biological age and demonstrated its significant association with frailty and hazardous lifestyles, along with the remaining lifespan and healthspan of an individual. The biological age explained most of the variance of the log-hazard ratio that was obtained by fitting directly to mortality and the incidence of chronic diseases. Our findings highlight the intimate relationship between the supervised and unsupervised signatures of the biological age and frailty, a consequence of the low intrinsic dimensionality of the aging dynamics.
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Principal component analysis-based biological age estimators, which involve unsupervised dimensionality reduction to unbiasedly identify ageing signals, seemingly perform even better than clocks that train on chronological age.
“This concept has been demonstrated by principal component analysis-based biological age estimators, which involve unsupervised dimensionality reduction to unbiasedly identify ageing signals, seemingly even better than clocks that train on chronological age173–175.”
From Measuring biological age using omics data (2022) · cites this paper as [175]
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Cites
Regression Models and Life-Tables
Cox, D. R. (1992) · Springer Series in Statistics · doi:10.1007/978-1-4612-4380-9_37 · Cited by 26,297 · see citing works
DNA methylation age of human tissues and cell types
Horvath, S. (2013) · Genome Biology · doi:10.1186/gb-2013-14-10-r115 · Cited by 7,075 · see citing works
Early-warning signals for critical transitions
Scheffer, M., Bascompte, J., Brock, W. A., Brovkin, V., Carpenter, S. R., Dakos, V., Held, H., van Nes, E. H., Rietkerk, M., & Sugihara, G. (2009) · Nature · doi:10.1038/nature08227 · Cited by 4,854 · see citing works
Genome-wide methylation profiles reveal quantitative views of human aging rates
Hannum, G., Guinney, J., Zhao, L., Zhang, L., Hughes, G., Sadda, S., Klotzle, B., Bibikova, M., Fan, J.-B., Gao, Y., et al. (2013) · Molecular Cell · doi:10.1016/j.molcel.2012.10.016 · Cited by 4,270 · see citing works
Gompertz, B. (1825) · Philosophical Transactions of the Royal Society of London · doi:10.1098/rstl.1825.0026 · Cited by 3,816 · see citing works
Accumulation of Deficits as a Proxy Measure of Aging
Mitnitski, A. B., Mogilner, A. J., & Rockwood, K. (2001) · The Scientific World JOURNAL · doi:10.1100/tsw.2001.58 · Cited by 2,893 · cited 1× · see citing works
“Many markers of aging are shared between mice and humans and hence can be used to build a universal frailty index as a tool to quantify aging in preclinical studies [1-3].”
What is principal component analysis?
Ringnér, M. (2008) · Nature Biotechnology · doi:10.1038/nbt0308-303 · Cited by 2,407 · see citing works
De novo assembly of bacterial transcriptomes from RNA-seq data
Tjaden, B. C. (2015) · Genome biology · doi:10.1186/s13059-014-0572-2 · Cited by 1,695 · see citing works
Odamaki, T., Kato, K., Sugahara, H., Hashikura, N., Takahashi, S., Xiao, J.-Z., Abe, F., & Osawa, R. (2016) · BMC Microbiology · doi:10.1186/s12866-016-0708-5 · Cited by 1,521 · see citing works
Facing up to the global challenges of ageing
Partridge, L., Deelen, J., & Slagboom, P. E. (2018) · Nature · doi:10.1038/s41586-018-0457-8 · Cited by 1,475 · see citing works
DNA methylation age of blood predicts all-cause mortality in later life
Marioni, R. E., Shah, S., McRae, A. F., Chen, B. H., Colicino, E., Harris, S. E., Gibson, J., Henders, A. K., Redmond, P., Cox, S. R., et al. (2015) · Genome Biology · doi:10.1186/s13059-015-0584-6 · Cited by 1,261 · see citing works
The Efficiency of Cox's Likelihood Function for Censored Data
Efron, B. (1977) · Journal of the American Statistical Association · doi:10.1080/01621459.1977.10480613 · Cited by 1,240 · see citing works
Large-scale physical activity data reveal worldwide activity inequality
Althoff, T., Sosič, R., Hicks, J. L., King, A. C., Delp, S. L., & Leskovec, J. (2017) · Nature · doi:10.1038/nature23018 · Cited by 1,000 · see citing works
Levine, M. E. (2012) · The Journals of Gerontology Series A · doi:10.1093/gerona/gls233 · Cited by 897 · see citing works
Obesity accelerates epigenetic aging of human liver
Horvath, S., Erhart, W., Brosch, M., Ammerpohl, O., Schönfels, W. V., Ahrens, M., Heits, N., Bell, J. T., Tsai, P.-C., Spector, T. D., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1412759111 · Cited by 760 · see citing works
Liu, Z., Kuo, P.-L., Horvath, S., Crimmins, E., Ferrucci, L., & Levine, M. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002718 · Cited by 754 · see citing works
Kirkwood, T. B. L. (2015) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2014.0379 · Cited by 610 · see citing works
A database of vertebrate longevity records and their relation to other life‐history traits
de Magalhães, J. P., & Costa, J. S. (2009) · Journal of Evolutionary Biology · doi:10.1111/j.1420-9101.2009.01783.x · Cited by 577 · see citing works
Stochastic modelling for quantitative description of heterogeneous biological systems
Wilkinson, D. J. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2509 · Cited by 573 · see citing works
HIV-1 infection accelerates age according to the epigenetic clock
Horvath, S., & Levine, A. J. (2015) · Journal of Infectious Diseases · doi:10.1093/infdis/jiv277 · Cited by 556 · see citing works
Benefits of Smoking Cessation for Longevity
Taylor, D. H., Hasselblad, V., Henley, S. J., Thun, M. J., & Sloan, F. A. (2002) · American Journal of Public Health · doi:10.2105/ajph.92.6.990 · Cited by 523 · see citing works
Kochanek, K. D., Murphy, S. L., Xu, J., & Tejada‐Vera, B. (2016) · PubMed · Cited by 520 · see citing works
Accelerated epigenetic aging in Down syndrome
Horvath, S., Garagnani, P., Bacalini, M. G., Pirazzini, C., Salvioli, S., Gentilini, D., Blasio, A. M. D., Giuliani, C., Tung, S., Vinters, H. V., et al. (2015) · Aging Cell · doi:10.1111/acel.12325 · Cited by 488 · see citing works
Glycans Are a Novel Biomarker of Chronological and Biological Ages
Krištić, J., Vučković, F., Menni, C., Klarić, L., Keser, T., Beceheli, I., Pučić-Baković, M., Novokmet, M., Mangino, M., Thaqi, K., et al. (2014) · The Journals of Gerontology: Series A · doi:10.1093/gerona/glt190 · Cited by 400 · see citing works
DNA methylation signatures in peripheral blood strongly predict all-cause mortality
Zhang, Y., Wilson, R., Heiss, J., Breitling, L. P., Saum, K.-U., Schöttker, B., Holleczek, B., Waldenberger, M., Peters, A., & Brenner, H. (2017) · Nature Communications · doi:10.1038/ncomms14617 · Cited by 382 · see citing works
Hahm, J., Kim, S., DiLoreto, R., Shi, C., Lee, S., Murphy, C. T., & Nam, H. G. (2015) · Nature Communications · doi:10.1038/ncomms9919 · Cited by 237 · see citing works
A Frailty Index Based On Deficit Accumulation Quantifies Mortality Risk in Humans and in Mice
Rockwood, K. J., Blodgett, J. M., Theou, O., Sun, M., Feridooni, H. A., Mitnitski, A., Rose, R. A., Godin, J., Gregson, E., & Howlett, S. E. (2017) · Scientific Reports · doi:10.1038/srep43068 · Cited by 237 · see citing works
The Efficiency of Cox's Likelihood Function for Censored Data
Efron, B. (1977) · Journal of the American Statistical Association · doi:10.2307/2286217 · Cited by 218 · see citing works
Hu, K., Someren, E. J. W. V., Shea, S. A., & Scheer, F. A. J. L. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0806087106 · Cited by 171 · see citing works
Identification of 12 genetic loci associated with human healthspan
Zenin, A., Tsepilov, Y., Sharapov, S., Getmantsev, E., Menshikov, L. I., Fedichev, P. O., & Aulchenko, Y. (2019) · Communications Biology · doi:10.1038/s42003-019-0290-0 · Cited by 170 · see citing works
Self-citationUniversal Scaling Law in Human Behavioral Organization
Nakamura, T., Kiyono, K., Yoshiuchi, K., Nakahara, R., Struzik, Z. R., & Yamamoto, Y. (2007) · Physical Review Letters · doi:10.1103/physrevlett.99.138103 · Cited by 157 · see citing works
Kim, S., Myers, L., Wyckoff, J., Cherry, K. E., & Jazwinski, S. M. (2017) · GeroScience · doi:10.1007/s11357-017-9960-3 · Cited by 133 · see citing works
A Method for Identifying Biomarkers of Aging and Constructing an Index of Biological Age in Humans
Nakamura, E., & Miyao, K. (2007) · The Journals of Gerontology Series A · doi:10.1093/gerona/62.10.1096 · Cited by 117 · see citing works
Krotov, D., Dubuis, J. O., Gregor, T., & Bialek, W. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1324186111 · Cited by 115 · see citing works
Extracting biological age from biomedical data via deep learning: too much of a good thing?
Pyrkov, T. V., Slipensky, K., Barg, M., Kondrashin, A., Zhurov, B., Zenin, A., Pyatnitskiy, M., Menshikov, L., Markov, S., & Fedichev, P. O. (2018) · Scientific Reports · doi:10.1038/s41598-018-23534-9 · Cited by 111 · see citing works
Self-citationAge-Dependent Changes in the Cerebrospinal Fluid Proteome by Slow Off-Rate Modified Aptamer Array
Baird, G. S., Nelson, S. K., Keeney, T. R., Stewart, A., Williams, S., Kraemer, S., Peskind, E. R., & Montine, T. J. (2012) · The American Journal of Pathology · doi:10.1016/j.ajpath.2011.10.024 · Cited by 110 · see citing works
Of Mice and Men — Universality and Breakdown of Behavioral Organization
Nakamura, T., Takumi, T., Takano, A., Aoyagi, N., Yoshiuchi, K., Struzik, Z. R., & Yamamoto, Y. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002050 · Cited by 101 · see citing works
Epigenome-wide differential DNA methylation between HIV-infected and uninfected individuals
Zhang, X., Justice, A. C., Hu, Y., Wang, Z., Zhao, H., Wang, G., Johnson, E. O., Emu, B., Sutton, R. E., Krystal, J. H., et al. (2016) · Epigenetics · doi:10.1080/15592294.2016.1221569 · Cited by 98 · see citing works
Park, J., Cho, B., Kwon, H., et al. (2008) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2008.04.003 · Cited by 74 · see citing works
Age‐related behavioral changes in Drosophila
Iliadi, K. G., & Boulianne, G. L. (2010) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2009.05372.x · Cited by 71 · see citing works
Antoch, M. P., Wrobel, M. M., Kuropatwinski, K. K., et al. (2017) · Aging · doi:10.18632/aging.101206 · Cited by 66 · see citing works
Gao, X., Zhang, Y., Saum, K.-U., Schöttker, B., Breitling, L. P., & Brenner, H. (2016) · Epigenetics · doi:10.1080/15592294.2016.1271855 · Cited by 64 · see citing works
Gu, C., Coomans, C. P., Hu, K., Scheer, F. A. J. L., Stanley, E., & Meijer, J. H. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1424706112 · Cited by 51 · see citing works
Scaling Behavior of Human Locomotor Activity Amplitude: Association with Bipolar Disorder
Indic, P., Salvatore, P., Maggini, C., Ghidini, S., Ferraro, G., Baldessarini, R. J., & Murray, G. (2011) · PLoS ONE · doi:10.1371/journal.pone.0020650 · Cited by 50 · see citing works
Evaluation of Biological Aging Process – A Population-Based Study of Healthy People in China
Bai, X., Han, L., Liu, Q., et al. (2009) · Gerontology · doi:10.1159/000262449 · Cited by 46 · see citing works
Jee, H., Jeon, B. H., Kim, Y. H., Kim, H., Choe, J., Park, J., & Jin, Y. (2012) · Gerontology · doi:10.1159/000335738 · Cited by 45 · see citing works
Zhang, W. G., Bai, X., Sun, X., Cai, G., Bai, X.-Y., Zhu, S.-Y., Zhang, M., & Chen, X.-M. (2013) · The journal of nutrition health & aging · doi:10.1007/s12603-013-0345-8 · Cited by 34 · see citing works
Metabolic and Genetic Markers of Biological Age
Jazwinski, S. M., & Kim, S. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00064 · Cited by 30 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [6]Liu Z, Kuo PL, Horvath S, Crimmins E, Ferrucci L, Levine M. Phenotypic age: a novel signature of mortality and morbidity risk. bioRxiv. 2018 363291 https://www.biorxiv.org/content/early/2018/07/05/363291
- [12]Feldman RS. Development across the life span. Prentice Hall, 2003.
- [24]Podolskiy D, Molodtcov I, Zenin A, Kogan V, Menshikov LI, Gladyshev V, Robert J Shmookler Reis RS, Fedichev PO. Critical dynamics of gene networks is a mechanism behind ageing and gompertz law. arXiv. 2015; 1502.04307.
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Measuring biological age using omics data
Rutledge, J., Oh, H., & Wyss-Coray, T. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00511-7 · Cited by 570 · see citing works
Pyrkov, T. V., Avchaciov, K., Tarkhov, A. E., Menshikov, L. I., Gudkov, A. V., & Fedichev, P. O. (2021) · Nature Communications · doi:10.1038/s41467-021-23014-1 · Cited by 139 · see citing works
Self-citationExtracting biological age from biomedical data via deep learning: too much of a good thing?
Pyrkov, T. V., Slipensky, K., Barg, M., Kondrashin, A., Zhurov, B., Zenin, A., Pyatnitskiy, M., Menshikov, L., Markov, S., & Fedichev, P. O. (2018) · Scientific Reports · doi:10.1038/s41598-018-23534-9 · Cited by 111 · see citing works
Self-citation
- Authors
- Pyrkov, T. V., Getmantsev, E., Zhurov, B., Avchaciov, K., Pyatnitskiy, M., Menshikov, L., Khodova, K., Gudkov, A. V., & Fedichev, P. O.
- Affiliations
- Gero LLC, Moscow, 1015064, Russia
- Roswell Park Comprehensive Cancer Center (US)
- Moscow Institute of Physics and Technology (RU)
- Venue
- Aging
- Publisher
- Impact Journals, LLC
- Published
- 2018-10-26
- Volume
- 10
- Issue
- 10
- Pages
- 2973-2990
- ISSN
- 1945-4589 (ISSN-L)
- PMC
- PMC6224248
- OpenAlex
- W2951064640
- Cited by
- 55
- References
- 48
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 13 · 2023: 1 · 2022: 7 · 2021: 7 · 2020: 9 · 2019: 7 · 2018: 2
- FWCI
- 3.46
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Aging (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Medical Research Council (MC_PC_17228)
- Topics
- Health Sciences › Medicine › Geriatrics and Gerontology › Frailty in Older Adults
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Social Sciences › Demography › Insurance, Mortality, Demography, Risk Management
- Keywords
- biological age, frailty, locomotor activity, aging trajectories, Gompertz mortality law, mortality risk, healthspan, chronic disease incidence, hazard ratio, stages of human development
- MeSH terms
- Frailty, Adolescent, Adult, Age Factors, Aged, Aged, 80 and over, Aging, Child, Child, Preschool, Female, United Kingdom, Humans, Locomotion, Male, Middle Aged, Models, Biological, Nutrition Surveys, Predictive Value of Tests, Risk Factors, Time Factors, United States, Models, Statistical, Geriatric Assessment, Frail Elderly, Risk Assessment, Young Adult, Actigraphy
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