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

Spencer Farrell; Arnold Mitnitski; Kenneth Rockwood; Andrew Rutenberg

doi:10.1038/s41598-020-76827-3

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

Spencer Farrell, Arnold Mitnitski, Kenneth Rockwood, Andrew Rutenberg

Résultat de recherche: Article › examen 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'origine	English
Numéro d'article	19833
Journal	Scientific Reports
Volume	10
Numéro de publication	1
DOI	https://doi.org/10.1038/s41598-020-76827-3
Statut de publication	Published - 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

ODD de l’ONU

Cette action contribue à la réalisation des objectifs de développement durable suivants

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10.1038/s41598-020-76827-3

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abstract = "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.",

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

Résumé

Note bibliographique

ASJC Scopus Subject Areas

PubMed: MeSH publication types

ODD de l’ONU

Accès au document

Autres fichiers et liens

Empreinte numérique

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