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验证航天飞行对人类健康风险的因果关系图:以啮齿动物骨骼数据为例

Validating Causal Diagrams of Human Health Risks for Spaceflight: An Example Using Bone Data from Rodents.

作者信息

Reynolds Robert J, Scott Ryan T, Turner Russell T, Iwaniec Urszula T, Bouxsein Mary L, Sanders Lauren M, Antonsen Erik L

机构信息

KBR Wyle Services, LLC, NASA Johnson Space Center, Houston, TX 77058, USA.

KBR, Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA 94043, USA.

出版信息

Biomedicines. 2022 Sep 5;10(9):2187. doi: 10.3390/biomedicines10092187.

Abstract

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.

摘要

作为美国国家航空航天局(NASA)人类系统风险风险管理计划的一部分,NASA人类系统风险委员会使用因果图(以有向无环图或DAG的形式)来描述从暴露于太空飞行环境到性能和健康结果的复杂事件网络。然而,以这种方式使用DAG在NASA相对较新,到目前为止,还没有阐明使用经验数据来测试其准确性的方法。在本文中,我们展示了一套这样做的程序,使用(a)一个与暴露于太空飞行后骨折风险相关的DAG;以及(b)最初为研究啮齿动物中的这一现象而生成的四个数据集。从DAG导出的预期边际相关性和条件独立性测试表明,啮齿动物数据在很大程度上与图的结构一致。一个数据集中测试与预期关系之间的不一致可能是由于数据集中关键DAG变量的表示不足所致。未来的方向包括与人类数据源(包括多组学数据)建立更紧密的联系,这可能允许对DAG变量进行更稳健的表征和测量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17e2/9496259/f8a30a2481a4/biomedicines-10-02187-g001.jpg

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