UCSF Stanford Endometriosis Center for Innovation, Training, and Community Outreach (ENACT), University of California, San Francisco, San Francisco, California, USA.
Center for Reproductive Sciences, University of California, San Francisco, San Francisco, California, USA.
FASEB J. 2023 Sep;37(9):e23130. doi: 10.1096/fj.202300907.
Endometriosis is a common estrogen-dependent disorder wherein uterine lining tissue (endometrium) is found mainly in the pelvis where it causes inflammation, chronic pelvic pain, pain with intercourse and menses, and infertility. Recent evidence also supports a systemic inflammatory component that underlies associated co-morbidities, e.g., migraines and cardiovascular and autoimmune diseases. Genetics and environment contribute significantly to disease risk, and with the explosion of omics technologies, underlying mechanisms of symptoms are increasingly being elucidated, although novel and effective therapeutics for pain and infertility have lagged behind these advances. Moreover, there are stark disparities in diagnosis, access to care, and treatment among persons of color and transgender/nonbinary identity, socioeconomically disadvantaged populations, and adolescents, and a disturbing low awareness among health care providers, policymakers, and the lay public about endometriosis, which, if left undiagnosed and under-treated can lead to significant fibrosis, infertility, depression, and markedly diminished quality of life. This review summarizes endometriosis epidemiology, compelling evidence for its pathogenesis, mechanisms underlying its pathophysiology in the age of precision medicine, recent biomarker discovery, novel therapeutic approaches, and issues around reproductive justice for marginalized populations with this disorder spanning the past 100 years. As we enter the next revolution in health care and biomedical research, with rich molecular and clinical datasets, single-cell omics, and population-level data, endometriosis is well positioned to benefit from data-driven research leveraging computational and artificial intelligence approaches integrating data and predicting disease risk, diagnosis, response to medical and surgical therapies, and prognosis for recurrence.
子宫内膜异位症是一种常见的雌激素依赖性疾病，主要发生在骨盆中，表现为子宫内层组织（子宫内膜）的异位，导致炎症、慢性盆腔疼痛、性交和月经时疼痛以及不孕。最近的证据还支持一种全身性炎症成分，这种成分是相关合并症的基础，例如偏头痛以及心血管和自身免疫性疾病。遗传和环境对疾病风险有重要影响，随着组学技术的爆炸式发展，越来越多地阐明了症状的潜在机制，尽管针对疼痛和不孕的新疗法和有效疗法落后于这些进展。此外，在诊断、获得护理和治疗方面，有色人种和跨性别/非二元认同者、社会经济弱势群体以及青少年之间存在明显的差异，而且医疗保健提供者、政策制定者和普通公众对子宫内膜异位症的认识也令人不安地低，这种情况如果得不到诊断和治疗，可能会导致严重的纤维化、不孕、抑郁和生活质量明显下降。这篇综述总结了子宫内膜异位症的流行病学、其发病机制的有力证据、精准医学时代其病理生理学的潜在机制、最近的生物标志物发现、新的治疗方法以及该疾病边缘化人群的生殖公正问题，涵盖了过去 100 年的相关内容。随着我们进入医疗保健和生物医学研究的下一个革命时代，拥有丰富的分子和临床数据集、单细胞组学以及人群水平的数据，子宫内膜异位症将从利用计算和人工智能方法来整合数据和预测疾病风险、诊断、对医疗和手术治疗的反应以及预测复发预后的基于数据的研究中受益。
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