Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests

Lauren E Chan; Elena Casiraghi; Justin Reese; Quaker E Harmon; Kevin Schaper; Harshad Hegde; Giorgio Valentini; Charles Schmitt; Alison Motsinger-Reif; Janet E Hall; Christopher J Mungall; Peter N Robinson; Melissa A Haendel

doi:10.1016/j.ijmedinf.2024.105461

Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests

Int J Med Inform. 2024 Apr 17:187:105461. doi: 10.1016/j.ijmedinf.2024.105461. Online ahead of print.

Authors

Affiliations

¹ Oregon State University, College of Public Health and Human Sciences, Corvallis, OR, USA. Electronic address: laurenchan@uchicago.edu.
² AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; European Laboratory for Learning and Intelligent Systems, ELLIS.
³ Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
⁴ National Institute of Environmental Health Sciences, Epidemiology Branch, Durham, NC, USA.
⁵ University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
⁶ AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy; European Laboratory for Learning and Intelligent Systems, ELLIS.
⁷ National Institute of Environmental Health Sciences, Office of Data Science, Durham, NC, USA.
⁸ National Institute of Environmental Health Sciences, Biostatistics & Computational Biology Branch, Durham, NC, USA.
⁹ National Institute of Environmental Health Sciences, Clinical Research Branch, Durham, NC, USA.
¹⁰ European Laboratory for Learning and Intelligent Systems, ELLIS; The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA.
¹¹ University of North Carolina, Dept. of Genetics, Chapel Hill, NC, USA.

PMID: 38643701
DOI: 10.1016/j.ijmedinf.2024.105461

Abstract

Objective: Female reproductive disorders (FRDs) are common health conditions that may present with significant symptoms. Diet and environment are potential areas for FRD interventions. We utilized a knowledge graph (KG) method to predict factors associated with common FRDs (for example, endometriosis, ovarian cyst, and uterine fibroids).

Materials and methods: We harmonized survey data from the Personalized Environment and Genes Study (PEGS) on internal and external environmental exposures and health conditions with biomedical ontology content. We merged the harmonized data and ontologies with supplemental nutrient and agricultural chemical data to create a KG. We analyzed the KG by embedding edges and applying a random forest for edge prediction to identify variables potentially associated with FRDs. We also conducted logistic regression analysis for comparison.

Results: Across 9765 PEGS respondents, the KG analysis resulted in 8535 significant or suggestive predicted links between FRDs and chemicals, phenotypes, and diseases. Amongst these links, 32 were exact matches when compared with the logistic regression results, including comorbidities, medications, foods, and occupational exposures.

Discussion: Mechanistic underpinnings of predicted links documented in the literature may support some of our findings. Our KG methods are useful for predicting possible associations in large, survey-based datasets with added information on directionality and magnitude of effect from logistic regression. These results should not be construed as causal but can support hypothesis generation.

Conclusion: This investigation enabled the generation of hypotheses on a variety of potential links between FRDs and exposures. Future investigations should prospectively evaluate the variables hypothesized to impact FRDs.

Keywords: Endometriosis; Female reproductive disorders; Knowledge graph; Ontologies; Ovarian cysts; Random forest.