Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
McCaig Institute for Bone and Joint Health, Calgary, AB, Canada.
Front Immunol. 2024 Sep 10;15:1456145. doi: 10.3389/fimmu.2024.1456145. eCollection 2024.
Despite progress in our understanding of disease pathogenesis for systemic autoimmune rheumatic diseases (SARD), these diseases are still associated with high morbidity, disability, and mortality. Much of the strongest evidence to date implicating environmental factors in the development of autoimmunity has been based on well-established, large, longitudinal prospective cohort studies.
Herein, we review the current state of knowledge on known environmental factors associated with the development of SARD and potential areas for future research.
The risk attributable to any particular environmental factor ranges from 10-200%, but exposures are likely synergistic in altering the immune system in a complex interplay of epigenetics, hormonal factors, and the microbiome leading to systemic inflammation and eventual organ damage. To reduce or forestall the progression of autoimmunity, a better understanding of disease pathogenesis is still needed.
Owing to the complexity and multifactorial nature of autoimmune disease, machine learning, a type of artificial intelligence, is increasingly utilized as an approach to analyzing large datasets. Future studies that identify patients who are at high risk of developing autoimmune diseases for prevention trials are needed.
尽管我们对系统性自身免疫性风湿病(SARD)的发病机制有了更多的了解,但这些疾病仍然与高发病率、残疾和死亡率有关。迄今为止,将环境因素与自身免疫的发展联系起来的最强有力的证据大部分基于既定的、大型的、前瞻性队列研究。
本文综述了目前已知的与 SARD 发展相关的环境因素的最新知识,并探讨了未来研究的潜在领域。
任何特定环境因素的风险从 10%到 200%不等,但暴露可能会协同作用,通过表观遗传学、激素因素和微生物组的复杂相互作用改变免疫系统,导致全身炎症和最终的器官损伤。为了减少或阻止自身免疫的进展,仍需要更好地了解发病机制。
由于自身免疫性疾病的复杂性和多因素性质,机器学习,一种人工智能,越来越多地被用作分析大型数据集的方法。需要进行未来的研究,以确定哪些患者有发展自身免疫性疾病的高风险,从而进行预防试验。