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Validating Causal Diagrams of Human Health Risks for Spaceflight: An Example Using Bone Data from Rodents.
Reynolds, Robert J; Scott, Ryan T; Turner, Russell T; Iwaniec, Urszula T; Bouxsein, Mary L; Sanders, Lauren M; Antonsen, Erik L.
Afiliação
  • Reynolds RJ; KBR Wyle Services, LLC, NASA Johnson Space Center, Houston, TX 77058, USA.
  • Scott RT; KBR, Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA 94043, USA.
  • Turner RT; Skeletal Biology Laboratory, Oregon State University, Corvallis, OR 97331, USA.
  • Iwaniec UT; Skeletal Biology Laboratory, Oregon State University, Corvallis, OR 97331, USA.
  • Bouxsein ML; Center for Advanced Orthopaedic Studies, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA.
  • Sanders LM; Department of Orthopedic Surgery, Harvard Medical School, Boston, MA 02115, USA.
  • Antonsen EL; Blue Marble Space Institute of Science, Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA 94043, USA.
Biomedicines ; 10(9)2022 Sep 05.
Article em En | MEDLINE | ID: mdl-36140288
As part of the risk management plan for human system risks at the US National Aeronautics and Space Administration (NASA), the NASA Human Systems Risk Board uses causal diagrams (in the form of directed, acyclic graphs, or DAGs) to communicate the complex web of events that leads from exposure to the spaceflight environment to performance and health outcomes. However, the use of DAGs in this way is relatively new at NASA, and thus far, no method has been articulated for testing their veracity using empirical data. In this paper, we demonstrate a set of procedures for doing so, using (a) a DAG related to the risk of bone fracture after exposure to spaceflight; and (b) four datasets originally generated to investigate this phenomenon in rodents. Tests of expected marginal correlation and conditional independencies derived from the DAG indicate that the rodent data largely agree with the structure of the diagram. Incongruencies between tests and the expected relationships in one of the datasets are likely explained by inadequate representation of a key DAG variable in the dataset. Future directions include greater tie-in with human data sources, including multiomics data, which may allow for more robust characterization and measurement of DAG variables.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Etiology_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Etiology_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article