Environment and systemic autoimmune rheumatic diseases: an overview and future directions

被引:3
作者
Choi, May Y. [1 ,2 ]
Costenbader, Karen H. [3 ,4 ]
Fritzler, Marvin J. [1 ]
机构
[1] Univ Calgary, Cumming Sch Med, Dept Med, Calgary, AB, Canada
[2] McCaig Inst Bone & Joint Hlth, Calgary, AB, Canada
[3] Brigham & Womens Hosp, Dept Med, Div Rheumatol Inflammat & Immun, Boston, MA USA
[4] Harvard Med Sch, Med, Boston, MA USA
来源
FRONTIERS IN IMMUNOLOGY | 2024年 / 15卷
关键词
autoimmunity; autoimmune diseases; environment; autoantibodies; epigenetics; microbiome; machine learning; artificial intelligence; POSTTRAUMATIC-STRESS-DISORDER; PARTICULATE AIR-POLLUTION; LUPUS-ERYTHEMATOSUS; SLEEP-DEPRIVATION; SILICA EXPOSURE; INCREASED RISK; I INTERFERON; PORPHYROMONAS-GINGIVALIS; ARTIFICIAL-INTELLIGENCE; OCCUPATIONAL SILICA;
D O I
10.3389/fimmu.2024.1456145
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
Introduction 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.Methods Herein, we review the current state of knowledge on known environmental factors associated with the development of SARD and potential areas for future research.Results 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.Conclusion 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.
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