Khan Umair, Oskotsky Tomiko T, Yilmaz Bahar D, Roger Jacquelyn, Gjoni Ketrin, Irwin Juan C, Opoku-Anane Jessica, Elhadad Noémie, Giudice Linda C, Sirota Marina
Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA; Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA, USA.
Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA; Division of Clinical Informatics and Digital Transformation, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
Cell Rep Med. 2025 Aug 19;6(8):102245. doi: 10.1016/j.xcrm.2025.102245. Epub 2025 Jul 31.
Endometriosis is a prevalent, complex, inflammatory condition associated with a diverse range of symptoms and comorbidities. Despite its substantial burden on patients, population-level studies that explore its comorbid patterns and heterogeneity are limited. In this retrospective case-control study, we analyze comorbidities from over forty thousand endometriosis patients across six University of California medical centers using de-identified electronic health record (EHR) data. We find hundreds of conditions significantly associated with endometriosis, including genitourinary disorders, neoplasms, and autoimmune diseases, with strong replication across datasets. Clustering analyses identify patient subpopulations with distinct comorbidity patterns, including psychiatric and autoimmune conditions. This study provides a comprehensive analysis of endometriosis comorbidities and highlights the heterogeneity within the patient population. Our findings demonstrate the utility of EHR data in uncovering clinically meaningful patterns and suggest pathways for personalized disease management and future research on biological mechanisms underlying endometriosis.
子宫内膜异位症是一种常见、复杂的炎症性疾病，伴有多种症状和合并症。尽管它给患者带来了沉重负担，但探索其合并症模式和异质性的人群水平研究却很有限。在这项回顾性病例对照研究中，我们使用去识别化的电子健康记录（EHR）数据，分析了加州大学六个医学中心的四万多名子宫内膜异位症患者的合并症。我们发现数百种疾病与子宫内膜异位症显著相关，包括泌尿生殖系统疾病、肿瘤和自身免疫性疾病，且在各数据集间有很强的重复性。聚类分析确定了具有不同合并症模式的患者亚群，包括精神疾病和自身免疫性疾病。本研究对子宫内膜异位症的合并症进行了全面分析，突出了患者群体中的异质性。我们的研究结果证明了EHR数据在揭示具有临床意义的模式方面的实用性，并为个性化疾病管理以及子宫内膜异位症潜在生物学机制的未来研究提供了途径。
