Semantic standards of external exposome data. Environ Res 2021 Jun;197:111185
Date
04/27/2021Pubmed ID
33901445Pubmed Central ID
PMC8597904DOI
10.1016/j.envres.2021.111185Scopus ID
2-s2.0-85105895629 (requires institutional sign-in at Scopus site) 14 CitationsAbstract
An individual's health and conditions are associated with a complex interplay between the individual's genetics and his or her exposures to both internal and external environments. Much attention has been placed on characterizing of the genome in the past; nevertheless, genetics only account for about 10% of an individual's health conditions, while the remaining appears to be determined by environmental factors and gene-environment interactions. To comprehensively understand the causes of diseases and prevent them, environmental exposures, especially the external exposome, need to be systematically explored. However, the heterogeneity of the external exposome data sources (e.g., same exposure variables using different nomenclature in different data sources, or vice versa, two variables have the same or similar name but measure different exposures in reality) increases the difficulty of analyzing and understanding the associations between environmental exposures and health outcomes. To solve the issue, the development of semantic standards using an ontology-driven approach is inevitable because ontologies can (1) provide a unambiguous and consistent understanding of the variables in heterogeneous data sources, and (2) explicitly express and model the context of the variables and relationships between those variables. We conducted a review of existing ontology for the external exposome and found only four relevant ontologies. Further, the four existing ontologies are limited: they (1) often ignored the spatiotemporal characteristics of external exposome data, and (2) were developed in isolation from other conceptual frameworks (e.g., the socioecological model and the social determinants of health). Moving forward, the combination of multi-domain and multi-scale data (i.e., genome, phenome and exposome at different granularity) and different conceptual frameworks is the basis of health outcomes research in the future.
Author List
Zhang H, Hu H, Diller M, Hogan WR, Prosperi M, Guo Y, Bian JAuthor
William R. Hogan MD Director, Professor in the Data Science Institute department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
CausalityEnvironmental Exposure
Female
Humans
Male
Semantics
