医学
重症肌无力
疾病
美罗华
临床试验
伊库利珠单抗
亚型
个性化医疗
自身抗体
生物信息学
生物标志物
免疫学
内科学
肿瘤科
抗体
生物
程序设计语言
补体系统
生物化学
计算机科学
作者
Amol K. Bhandage,Y.‐M. Huang,Tanel Punga,Anna Rostedt Punga
标识
DOI:10.1177/22143602251348753
摘要
Myasthenia Gravis (MG) is a heterogeneous neuromuscular autoimmune disorder characterized by fluctuating skeletal muscle weakness and a highly variable disease course. MG subgroups are defined by antibody type, age at onset, clinical phenotype, and thymus pathology. Given the unpredictable disease course, disease-specific objective biomarkers are needed to enable personalized treatment strategies and improve clinical trial outcomes beyond conventional clinical scales. Biomarkers are measurable indicators of physiological processes, disease states, and therapy responses. Despite significant advances in MG diagnostics and therapeutics, predictive biomarkers for personalized treatment remain underdeveloped. This review explores the progress and challenges in identifying blood-based biomarkers for MG, highlighting their potential applications in diagnosis and disease monitoring. Established diagnostic blood biomarkers include autoantibodies against acetylcholine receptors (AChR) and muscle-specific tyrosine kinase (MuSK), which confirm MG diagnosis and guide initial treatment decisions. Prognostic biomarkers, such as microRNAs (miR-150-5p and miR-30e-5p), show promise in predicting disease progression. Pharmacodynamic biomarkers, including CD20+ B cell counts, may enhance treatment precision for therapies like Rituximab. Furthermore, emerging research on metabolites, T and B-cell markers, complement factors, and proteomics offer new avenues to refine MG subtyping and identify molecular signatures predictive of treatment response to novel immunosuppressants. While the journey toward clinically useful blood biomarkers in MG remains complex, ongoing collaborative efforts within the MG research community hold the potential to revolutionize disease management. Future studies integrating multi-omics approaches, large-scale longitudinal cohorts, and disease controls will be critical to translating these biomarkers from research into routine clinical practice.
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