Chan Lauren E, Casiraghi Elena, Putman Tim, Reese Justin, Harmon Quaker E, Schaper Kevin, Hedge Harshad, Valentini Giorgio, Schmitt Charles, Motsinger-Reif Alison, Hall Janet E, Mungall Christopher J, Robinson Peter N, Haendel Melissa A
Oregon State University, College of Public Health and Human Sciences, Corvallis, OR, USA.
AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy.
medRxiv. 2023 Jul 16:2023.07.14.23292679. doi: 10.1101/2023.07.14.23292679.
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 (e.g., endometriosis, ovarian cyst, and uterine fibroids).
We harmonized survey data from the Personalized Environment and Genes Study 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.
Across 9765 PEGS respondents, the KG analysis resulted in 8535 significant 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.
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.
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.
女性生殖系统疾病（FRDs）是常见的健康问题，可能伴有明显症状。饮食和环境是FRDs干预的潜在领域。我们利用知识图谱（KG）方法来预测与常见FRDs（如子宫内膜异位症、卵巢囊肿和子宫肌瘤）相关的因素。
我们将个性化环境与基因研究中关于内部和外部环境暴露及健康状况的调查数据与生物医学本体内容进行了整合。我们将整合后的数据和本体与补充的营养和农业化学数据合并，以创建一个KG。我们通过嵌入边并应用随机森林进行边预测来分析KG，以识别可能与FRDs相关的变量。我们还进行了逻辑回归分析以作比较。
在9765名参与个性化环境与基因研究的受访者中，KG分析得出了8535个FRDs与化学物质、表型和疾病之间的显著预测联系。在这些联系中，与逻辑回归结果相比，有32个是完全匹配的，包括合并症、药物、食物和职业暴露。
文献中记录的预测联系的机制基础可能支持我们的一些发现。我们的KG方法有助于在基于调查的大型数据集中预测可能的关联，并从逻辑回归中获得关于效应方向和大小的附加信息。这些结果不应被解释为因果关系，但可以支持假设的产生。
本研究使得能够生成关于FRDs与暴露之间各种潜在联系的假设。未来的研究应前瞻性地评估假设会影响FRDs的变量。
