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(http://www.niehs.nih.gov//portfolio/index.cfm?do=portfolio.grantdetail&&grant_number=R24ES036131&format=word)
| Principal Investigator: Bian, Jiang | |
|---|---|
| Institute Receiving Award | University Of Florida |
| Location | Gainesville, FL |
| Grant Number | R24ES036131 |
| Funding Organization | National Institute of Environmental Health Sciences |
| Award Funding Period | 25 Jun 2024 to 31 May 2029 |
| DESCRIPTION (provided by applicant): | ABSTRACT An individual’s phenotypes related to their health conditions are associated with the complex interplay between the individual’s genetics and their exposures to both internal and external environments. Nevertheless, genetics only account for ~10% of an individual’s health, 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 spatial and contextual exposome—social and ecological contexts in which the person lives their life (e.g., social capital, climate) or external agents to which the one is exposed (e.g., environmental pollutants), need to be systematically explored. Nevertheless, the heterogeneous definitions of the spatial and contextual exposome and the heterogeneity of their data sources require us to adopt semantic standards using an ontology-driven approach to (1) provide an 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 them. On the other hand, the rapid adoption of electronic health record (EHR) systems has made large collections of real-world data (RWD) that reflect the characteristics and outcomes of the patients being treated in real-world settings, available for research. The increasing availability of RWD combined with the advancements in analytical methods, especially artificial intelligence (AI) and machine learning (ML) offer unique opportunities to generate real-world evidence (RWE). There is also an increasing interest in spatiotemporally linking spatial and contextual exposome data, especially contextual social determinants of health, to real-world observational data including RWD to answer various questions on how exposures to environmental factors affect health status, disease development and outcomes, and health disparities and equity. However, there are key gaps in the research infrastructure to support these studies. Our long-term goal is to develop and disseminate methods and tools to advance spatial and contextual exposome research with RWD. Responding to RFA-ES-23-002, the objective of this proposal is to develop an innovative SPATIAL AND CONTEXTUAL EXPOSOME SEMANTIC DATA INTEGRATION SYSTEM (SPACESCANS) with (1) an ontology-annotated knowledge graph of existing publicly available high-quality spatial and contextual exposome data from heterogeneous sources, along with (2) a user-friendly data integration tool that can guide the users to choose (i) the appropriate spatial and contextual exposome variables, and (ii) appropriate spatiotemporal linkage methods to link exposome data with their RWD, based on their study needs; and (3) generate analysis-ready (and AI/ML-ready) RWD-exposome linked datasets for downstream analyses. |
| Science Code(s)/Area of Science(s) |
Primary: 15 - Exposure Assessment/Exposome Secondary: 03 - Carcinogenesis/Cell Transformation |
| Publications | No publications associated with this grant |
| Program Officer | Christopher Duncan |