Document Type
Article
Publication Date
7-1-2024
Original Citation
Chan L,
Casiraghi E,
Reese J,
Harmon Q,
Schaper K,
Hegde H,
Valentini G,
Schmitt C,
Motsinger-Reif A,
Hall J,
Mungall C,
Robinson P,
Haendel M.
Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests. Int J Med Inform. 2024;187:105461
Keywords
JGM, Humans, Female, Environmental Exposure, Genital Diseases, Female, Logistic Models, Nutritional Status, Diet, Adult, Random Forest
JAX Source
Int J Med Inform. 2024;187:105461
ISSN
1872-8243
PMID
38643701
DOI
https://doi.org/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.
Comments
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).