IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1010415.html
   My bibliography  Save this article

A hierarchical process model links behavioral aging and lifespan in C. elegans

Author

Listed:
  • Natasha Oswal
  • Olivier M F Martin
  • Sofia Stroustrup
  • Monika Anna Matusiak Bruckner
  • Nicholas Stroustrup

Abstract

Aging involves a transition from youthful vigor to geriatric infirmity and death. Individuals who remain vigorous longer tend to live longer, and within isogenic populations of C. elegans the timing of age-associated vigorous movement cessation (VMC) is highly correlated with lifespan. Yet, many mutations and interventions in aging alter the proportion of lifespan spent moving vigorously, appearing to “uncouple” youthful vigor from lifespan. To clarify the relationship between vigorous movement cessation, death, and the physical declines that determine their timing, we developed a new version of the imaging platform called “The Lifespan Machine”. This technology allows us to compare behavioral aging and lifespan at an unprecedented scale. We find that behavioral aging involves a time-dependent increase in the risk of VMC, reminiscent of the risk of death. Furthermore, we find that VMC times are inversely correlated with remaining lifespan across a wide range of genotypes and environmental conditions. Measuring and modelling a variety of lifespan-altering interventions including a new RNA-polymerase II auxin-inducible degron system, we find that vigorous movement and lifespan are best described as emerging from the interplay between at least two distinct physical declines whose rates co-vary between individuals. In this way, we highlight a crucial limitation of predictors of lifespan like VMC—in organisms experiencing multiple, distinct, age-associated physical declines, correlations between mid-life biomarkers and late-life outcomes can arise from the contextual influence of confounding factors rather than a reporting by the biomarker of a robustly predictive biological age.Author summary: Aging produces a variety of outcomes—declines in various measures of health and eventually death. By studying the relationship between two outcomes of aging in the same individual, we can learn about the underlying aging processes that cause them. Here, we consider the relationship between death and an outcome often used to quantify health in C. elegans—vigorous movement cessation which describes the age-associated loss of an individuals’ ability to move long distances. We develop an automated imaging platform that allows us to precisely compare this pair of outcomes in each individual across large populations. We find that individuals who remain vigorous longer subsequently have a shorter remaining lifespan—a pattern that holds even after vigorous movement and lifespan timing are both altered by several different mutations and interventions in aging. Modelling our data using a combination of simulation and analytic studies, we demonstrate how the relative timing of vigorous movement cessation and death suggest that these two outcomes are driven by distinct aging processes. Our data and analyses demonstrate how two outcomes of aging can be correlated across individuals with the timing of one predicting the timing of the other, but nevertheless be driven by mostly distinct underlying physical declines.

Suggested Citation

  • Natasha Oswal & Olivier M F Martin & Sofia Stroustrup & Monika Anna Matusiak Bruckner & Nicholas Stroustrup, 2022. "A hierarchical process model links behavioral aging and lifespan in C. elegans," PLOS Computational Biology, Public Library of Science, vol. 18(9), pages 1-28, September.
  • Handle: RePEc:plo:pcbi00:1010415
    DOI: 10.1371/journal.pcbi.1010415
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010415
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1010415&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1010415?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Laura A. Herndon & Peter J. Schmeissner & Justyna M. Dudaronek & Paula A. Brown & Kristin M. Listner & Yuko Sakano & Marie C. Paupard & David H. Hall & Monica Driscoll, 2002. "Stochastic and genetic factors influence tissue-specific decline in ageing C. elegans," Nature, Nature, vol. 419(6909), pages 808-814, October.
    2. Omer Karin & Amit Agrawal & Ziv Porat & Valery Krizhanovsky & Uri Alon, 2019. "Senescent cell turnover slows with age providing an explanation for the Gompertz law," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    3. James Vaupel & Kenneth Manton & Eric Stallard, 1979. "The impact of heterogeneity in individual frailty on the dynamics of mortality," Demography, Springer;Population Association of America (PAA), vol. 16(3), pages 439-454, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bagdonavicius, Vilijandas & Nikulin, Mikhail, 2000. "On goodness-of-fit for the linear transformation and frailty models," Statistics & Probability Letters, Elsevier, vol. 47(2), pages 177-188, April.
    2. Feehan, Dennis & Wrigley-Field, Elizabeth, 2020. "How do populations aggregate?," SocArXiv 2fkw3, Center for Open Science.
    3. Filipe Costa Souza & Wilton Bernardino & Silvio C. Patricio, 2024. "How life-table right-censoring affected the Brazilian social security factor: an application of the gamma-Gompertz-Makeham model," Journal of Population Research, Springer, vol. 41(3), pages 1-38, September.
    4. K. Motarjem & M. Mohammadzadeh & A. Abyar, 2020. "Geostatistical survival model with Gaussian random effect," Statistical Papers, Springer, vol. 61(1), pages 85-107, February.
    5. Xu, Linzhi & Zhang, Jiajia, 2010. "An EM-like algorithm for the semiparametric accelerated failure time gamma frailty model," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1467-1474, June.
    6. Annamaria Olivieri & Ermanno Pitacco, 2016. "Frailty and Risk Classification for Life Annuity Portfolios," Risks, MDPI, vol. 4(4), pages 1-23, October.
    7. James W. Vaupel, 2002. "Post-Darwinian longevity," MPIDR Working Papers WP-2002-043, Max Planck Institute for Demographic Research, Rostock, Germany.
    8. Maxim S. Finkelstein, 2005. "Shocks in homogeneous and heterogeneous populations," MPIDR Working Papers WP-2005-024, Max Planck Institute for Demographic Research, Rostock, Germany.
    9. Luping Zhao & Timothy E. Hanson, 2011. "Spatially Dependent Polya Tree Modeling for Survival Data," Biometrics, The International Biometric Society, vol. 67(2), pages 391-403, June.
    10. Yeo, Keng Leong & Valdez, Emiliano A., 2006. "Claim dependence with common effects in credibility models," Insurance: Mathematics and Economics, Elsevier, vol. 38(3), pages 609-629, June.
    11. Hui Zheng, 2014. "Aging in the Context of Cohort Evolution and Mortality Selection," Demography, Springer;Population Association of America (PAA), vol. 51(4), pages 1295-1317, August.
    12. Graziella Caselli & Franco Peracchi & Elisabetta Barbi & Rosa Maria Lipsi, 2003. "Differential Mortality and the Design of the Italian System of Public Pensions," LABOUR, CEIS, vol. 17(s1), pages 45-78, August.
    13. Enrique Acosta & Alain Gagnon & Nadine Ouellette & Robert R. Bourbeau & Marilia R. Nepomuceno & Alyson A. van Raalte, 2020. "The boomer penalty: excess mortality among baby boomers in Canada and the United States," MPIDR Working Papers WP-2020-003, Max Planck Institute for Demographic Research, Rostock, Germany.
    14. Zhang, Zhehao, 2018. "Renewal sums under mixtures of exponentials," Applied Mathematics and Computation, Elsevier, vol. 337(C), pages 281-301.
    15. Hess Wolfgang & Tutz Gerhard & Gertheiss Jan, 2016. "A Flexible Link Function for Discrete-Time Duration Models," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 236(4), pages 455-481, August.
    16. Bas Klaauw & Limin Wang, 2011. "Child mortality in rural India," Journal of Population Economics, Springer;European Society for Population Economics, vol. 24(2), pages 601-628, April.
    17. Xian Liu, 2000. "Development of a Structural Hazard Rate Model in Sociological Research," Sociological Methods & Research, , vol. 29(1), pages 77-117, August.
    18. Hsieh Fushing, 2012. "Semiparametric efficient inferences for lifetime regression model with time-dependent covariates," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(1), pages 1-25, February.
    19. M S Finkelstein, 2008. "Reliability modelling for biological ageing," Journal of Risk and Reliability, , vol. 222(1), pages 1-6, March.
    20. Cizek, P. & Lei, J. & Ligthart, J.E., 2012. "The Determinants of VAT Introduction : A Spatial Duration Analysis," Other publications TiSEM 835efbcb-4537-4dab-aaa3-c, Tilburg University, School of Economics and Management.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1010415. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.