Generating synthetic aging trajectories with a weighted network model using cross-sectional data

Spencer Farrell, Arnold Mitnitski, Kenneth Rockwood, Andrew Rutenberg

Résultat de recherche: Articleexamen par les pairs

12 Citations (Scopus)

Résumé

We develop a computational model of human aging that generates individual health trajectories with a set of observed health attributes. Our model consists of a network of interacting health attributes that stochastically damage with age to form health deficits, leading to eventual mortality. We train and test the model for two different cross-sectional observational aging studies that include simple binarized clinical indicators of health. In both studies, we find that cohorts of simulated individuals generated from the model resemble the observed cross-sectional data in both health characteristics and mortality. We can generate large numbers of synthetic individual aging trajectories with our weighted network model. Predicted average health trajectories and survival probabilities agree well with the observed data.

Langue d'origineEnglish
Numéro d'article19833
JournalScientific Reports
Volume10
Numéro de publication1
DOI
Statut de publicationPublished - déc. 2020

Note bibliographique

Funding Information:
We thank ACENET and Compute Canada for computational resources. ADR thanks the Natural Sciences and Engineering Research Council (NSERC) for an operating Grant (RGPIN 2019-05888). KR has operational funding from the Canadian Institutes of Health Research (PJT-156114) and personal support form the Dalhousie Medical Research Foundation as the Kathryn Allen Weldon Professor of Alzheimer Research.

Publisher Copyright:
© 2020, The Author(s).

ASJC Scopus Subject Areas

  • General

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't

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Citer

Farrell, S., Mitnitski, A., Rockwood, K., & Rutenberg, A. (2020). Generating synthetic aging trajectories with a weighted network model using cross-sectional data. Scientific Reports, 10(1), Article 19833. https://doi.org/10.1038/s41598-020-76827-3